From d435fcadaf9365130e10371a1e19735345794b38 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Mon, 22 May 2017 13:24:10 -0700 Subject: [PATCH 01/40] fixed broken link in description --- DESCRIPTION.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION.rst b/DESCRIPTION.rst index 16dfc4933..415a93a97 100644 --- a/DESCRIPTION.rst +++ b/DESCRIPTION.rst @@ -4,7 +4,7 @@ .. |Visualizers| image:: http://www.scikit-yb.org/en/latest/_images/visualizers.png :width: 800 px -.. _Visualizers: http://scikit-yb.org/ +.. _Visualizers: http://www.scikit-yb.org/ Yellowbrick =========== From 173f1f7cf3daf8067e1822124da0b5a8b864b847 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 22 May 2017 14:29:11 -0700 Subject: [PATCH 02/40] added great example of PR from Carlos to the contributors documentation --- docs/contributing.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/contributing.rst b/docs/contributing.rst index 8023211fc..3997af713 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -75,7 +75,7 @@ Once forked, use the following steps to get your development environment set up $ git fetch $ git checkout develop -At this point you're ready to get started writing code. If you're going to take on a specific task, we'd strongly encourage you to check out the issue on `Waffle `_ and create a `pull request `_ *before you start coding* to better foster communication with other contributors. +At this point you're ready to get started writing code. If you're going to take on a specific task, we'd strongly encourage you to check out the issue on `Waffle `_ and create a `pull request `_ *before you start coding* to better foster communication with other contributors. For a great example of a pull request for a new feature visualizer, check out `this one `_ by `Carlo Morales `_. The next section is about managing GitHub branches for contributions, but if you're ready jump straight to `Developing Visualizers`_! From 0afee680bab0639a9171447609addf1bcdc2b604 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 23 May 2017 11:20:49 -0700 Subject: [PATCH 03/40] modify code headers in files --- tests/base.py | 2 +- tests/checks.py | 2 +- tests/dataset.py | 2 +- tests/test_base.py | 2 +- tests/test_cluster/__init__.py | 2 +- tests/test_cluster/test_base.py | 2 +- tests/test_cluster/test_elbow.py | 2 +- tests/test_cluster/test_silhouette.py | 2 +- tests/test_features/__init__.py | 2 +- tests/test_features/test_base.py | 2 +- tests/test_features/test_jointplot.py | 2 +- tests/test_features/test_pcoords.py | 2 +- tests/test_features/test_radviz.py | 2 +- tests/test_features/test_scatter.py | 2 +- tests/test_pipeline.py | 2 +- tests/test_regressor/__init__.py | 2 +- tests/test_regressor/test_alphas.py | 2 +- tests/test_regressor/test_residuals.py | 2 +- tests/test_style/__init__.py | 2 +- tests/test_style/test_colors.py | 2 +- tests/test_style/test_palettes.py | 2 +- tests/test_style/test_rcmod.py | 2 +- tests/test_text/test_base.py | 2 +- tests/test_text/test_freqdist.py | 2 +- tests/test_text/test_postag.py | 2 +- tests/test_text/test_tsne.py | 2 +- tests/test_utils/__init__.py | 2 +- tests/test_utils/test_decorators.py | 2 +- tests/test_utils/test_helpers.py | 2 +- tests/test_utils/test_types.py | 2 +- tests/test_utils/test_wrapper.py | 2 +- yellowbrick/classifier/__init__.py | 17 ++++++++-- yellowbrick/classifier/base.py | 32 +++++++++++++++++-- yellowbrick/classifier/class_balance.py | 26 +++++++++++++-- .../classifier/classification_report.py | 24 ++++++++++++-- yellowbrick/classifier/confusion_matrix.py | 18 +++++++++++ yellowbrick/classifier/rocauc.py | 27 +++++++++++++--- yellowbrick/cluster/__init__.py | 2 +- yellowbrick/cluster/base.py | 2 +- yellowbrick/cluster/elbow.py | 2 +- yellowbrick/cluster/silhouette.py | 2 +- yellowbrick/features/__init__.py | 2 +- yellowbrick/features/base.py | 2 +- yellowbrick/features/jointplot.py | 2 +- yellowbrick/features/pcoords.py | 2 +- yellowbrick/features/radviz.py | 2 +- yellowbrick/features/rankd.py | 2 +- yellowbrick/features/scatter.py | 2 +- yellowbrick/pipeline.py | 2 +- yellowbrick/regressor/__init__.py | 2 +- yellowbrick/regressor/alphas.py | 2 +- yellowbrick/regressor/base.py | 2 +- yellowbrick/regressor/residuals.py | 2 +- yellowbrick/style/__init__.py | 2 +- yellowbrick/style/colors.py | 2 +- yellowbrick/style/rcmod.py | 2 +- yellowbrick/text/__init__.py | 2 +- yellowbrick/text/base.py | 2 +- yellowbrick/text/freqdist.py | 2 +- yellowbrick/text/postag.py | 2 +- yellowbrick/text/tsne.py | 2 +- yellowbrick/utils/__init__.py | 2 +- yellowbrick/utils/decorators.py | 2 +- yellowbrick/utils/helpers.py | 2 +- yellowbrick/utils/types.py | 2 +- yellowbrick/utils/wrapper.py | 2 +- 66 files changed, 190 insertions(+), 74 deletions(-) diff --git a/tests/base.py b/tests/base.py index c4fb7e406..d2622ba85 100644 --- a/tests/base.py +++ b/tests/base.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: base.py [] benjamin@bengfort.com $ +# ID: base.py [b8e3318] benjamin@bengfort.com $ """ Helper functions and cases for making assertions on visualizations. diff --git a/tests/checks.py b/tests/checks.py index cd4b54751..851509e25 100644 --- a/tests/checks.py +++ b/tests/checks.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: checks.py [] benjamin@bengfort.com $ +# ID: checks.py [4131cb1] benjamin@bengfort.com $ """ Performs checking that visualizers adhere to Yellowbrick conventions. diff --git a/tests/dataset.py b/tests/dataset.py index eddb66ba4..dfec69d3b 100644 --- a/tests/dataset.py +++ b/tests/dataset.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: dataset.py [] benjamin@bengfort.com $ +# ID: dataset.py [8f4de77] benjamin@bengfort.com $ """ Helper functions for tests that utilize downloadable datasets. diff --git a/tests/test_base.py b/tests/test_base.py index 282456280..9c5fbc68e 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -8,7 +8,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: tests.test_base.py.py [] benjamin@bengfort.com $ +# ID: test_base.py [83131ef] benjamin@bengfort.com $ """ Assertions for the base classes and abstract hierarchy. diff --git a/tests/test_cluster/__init__.py b/tests/test_cluster/__init__.py index ec8386ffb..2ff63587b 100644 --- a/tests/test_cluster/__init__.py +++ b/tests/test_cluster/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [241edca] benjamin@bengfort.com $ """ Tests for the cluster visualizers. diff --git a/tests/test_cluster/test_base.py b/tests/test_cluster/test_base.py index 27afd26ad..32734db5f 100644 --- a/tests/test_cluster/test_base.py +++ b/tests/test_cluster/test_base.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: tests.test_cluster.test_base.py [] benjamin@bengfort.com $ +# ID: test_base.py [241edca] benjamin@bengfort.com $ """ Test the cluster base visualizers. diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index 67bcb2426..10206d718 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_elbow.py [] benjamin@bengfort.com $ +# ID: test_elbow.py [5a370c8] benjamin@bengfort.com $ """ Tests for the KElbowVisualizer diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py index 1d01db777..5dcaac792 100644 --- a/tests/test_cluster/test_silhouette.py +++ b/tests/test_cluster/test_silhouette.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_silhouette.py [] benjamin@bengfort.com $ +# ID: test_silhouette.py [57b563b] benjamin@bengfort.com $ """ Tests for the SilhouetteVisualizer diff --git a/tests/test_features/__init__.py b/tests/test_features/__init__.py index 12623b509..d8573ef26 100644 --- a/tests/test_features/__init__.py +++ b/tests/test_features/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [1d407ab] benjamin@bengfort.com $ """ Tests for the feature visualizers diff --git a/tests/test_features/test_base.py b/tests/test_features/test_base.py index 09a2558d2..75f3023f4 100644 --- a/tests/test_features/test_base.py +++ b/tests/test_features/test_base.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_base.py [] benjamin@bengfort.com $ +# ID: test_base.py [2e898a6] benjamin@bengfort.com $ """ Tests for the feature selection and analysis base classes diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py index 1f5689bd5..9bad72d5c 100644 --- a/tests/test_features/test_jointplot.py +++ b/tests/test_features/test_jointplot.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_jointplot.py [] pdamo24@gmail.com $ +# ID: test_jointplot.py [9e008b0] pdamodaran@users.noreply.github.com $ """ Test the JointPlotVisualizer. diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py index ae3cd30e0..13b7cc802 100644 --- a/tests/test_features/test_pcoords.py +++ b/tests/test_features/test_pcoords.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_pcoords.py [] benjamin@bengfort.com $ +# ID: test_pcoords.py [1d407ab] benjamin@bengfort.com $ """ Testing for the parallel coordinates feature visualizers diff --git a/tests/test_features/test_radviz.py b/tests/test_features/test_radviz.py index d2be595e1..4b4d9fbc0 100644 --- a/tests/test_features/test_radviz.py +++ b/tests/test_features/test_radviz.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_radviz.py [] benjamin@bengfort.com $ +# ID: test_radviz.py [01d5996] benjamin@bengfort.com $ """ Test the RadViz feature analysis visualizers diff --git a/tests/test_features/test_scatter.py b/tests/test_features/test_scatter.py index dfe9e3ac2..f712d52cd 100644 --- a/tests/test_features/test_scatter.py +++ b/tests/test_features/test_scatter.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_scatter.py [] nathan.danielsen@gmail.com $ +# ID: test_scatter.py [fc94ec4] ndanielsen@users.noreply.github.com $ """ Test the ScatterViz feature analysis visualizers """ diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index cf5981695..e7ea1c616 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_pipeline.py [] benjamin@bengfort.com $ +# ID: test_pipeline.py [1efae1f] benjamin@bengfort.com $ """ Tests to ensure that the visual pipeline works as expected. diff --git a/tests/test_regressor/__init__.py b/tests/test_regressor/__init__.py index 1fac4fdd9..891dc2116 100644 --- a/tests/test_regressor/__init__.py +++ b/tests/test_regressor/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [7d3f5e6] benjamin@bengfort.com $ """ Tests for the regressor visualizers. diff --git a/tests/test_regressor/test_alphas.py b/tests/test_regressor/test_alphas.py index 51738be86..af0844465 100644 --- a/tests/test_regressor/test_alphas.py +++ b/tests/test_regressor/test_alphas.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: tests.test_regressor.test_alphas.py [] benjamin@bengfort.com $ +# ID: test_alphas.py [7d3f5e6] benjamin@bengfort.com $ """ Tests for the alpha selection visualizations. diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index 87a71b92e..aaaf13398 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_residuals.py [] benjamin@bengfort.com $ +# ID: test_residuals.py [7d3f5e6] benjamin@bengfort.com $ """ Ensure that the regressor residuals visualizations work. diff --git a/tests/test_style/__init__.py b/tests/test_style/__init__.py index 3a4b286a0..cdf542fd3 100644 --- a/tests/test_style/__init__.py +++ b/tests/test_style/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [c6aff34] benjamin@bengfort.com $ """ Tests for the style handling module of yellowbrick. diff --git a/tests/test_style/test_colors.py b/tests/test_style/test_colors.py index 86e87971a..01121ac74 100644 --- a/tests/test_style/test_colors.py +++ b/tests/test_style/test_colors.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_colors.py [] benjamin@bengfort.com $ +# ID: test_colors.py [c6aff34] benjamin@bengfort.com $ """ Tests for the color utilities and helpers module diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index acfb045eb..60f1d7cc8 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_palettes.py [] benjamin@bengfort.com $ +# ID: test_palettes.py [c6aff34] benjamin@bengfort.com $ """ Tests the palettes module of the yellowbrick library. diff --git a/tests/test_style/test_rcmod.py b/tests/test_style/test_rcmod.py index 83aeecd9d..5c48f9b8e 100644 --- a/tests/test_style/test_rcmod.py +++ b/tests/test_style/test_rcmod.py @@ -8,7 +8,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_rcmod.py [] benjamin@bengfort.com $ +# ID: test_rcmod.py [c6aff34] benjamin@bengfort.com $ """ Testing the matplotlib configuration modifications for aesthetic. diff --git a/tests/test_text/test_base.py b/tests/test_text/test_base.py index abc7e2f46..aa496cbf7 100644 --- a/tests/test_text/test_base.py +++ b/tests/test_text/test_base.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: test_base.py [] benjamin@bengfort.com $ +# ID: test_base.py [6aa9198] benjamin@bengfort.com $ """ Tests for the text visualization base classes diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py index d8c118194..b4f0e00d7 100644 --- a/tests/test_text/test_freqdist.py +++ b/tests/test_text/test_freqdist.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_freqdist.py [] rbilbro@districtdatalabs.com $ +# ID: test_freqdist.py [bd9cbb9] rebecca.bilbro@bytecubed.com $ """ Tests for the frequency distribution text visualization diff --git a/tests/test_text/test_postag.py b/tests/test_text/test_postag.py index 462994c5d..15f22f01d 100644 --- a/tests/test_text/test_postag.py +++ b/tests/test_text/test_postag.py @@ -8,7 +8,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_postag.py [] rbilbro@districtdatalabs.com $ +# ID: test_postag.py [bd9cbb9] rebecca.bilbro@bytecubed.com $ """ Tests for the part-of-speech tagging visualization diff --git a/tests/test_text/test_tsne.py b/tests/test_text/test_tsne.py index ee71d4a03..8d9044e91 100644 --- a/tests/test_text/test_tsne.py +++ b/tests/test_text/test_tsne.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 Bengfort.com # For license information, see LICENSE.txt # -# ID: test_tsne.py [] benjamin@bengfort.com $ +# ID: test_tsne.py [6aa9198] benjamin@bengfort.com $ """ Tests for the TSNE visual corpus embedding mechanism. diff --git a/tests/test_utils/__init__.py b/tests/test_utils/__init__.py index adf429c8c..bd1ef00e9 100644 --- a/tests/test_utils/__init__.py +++ b/tests/test_utils/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [79cd8cf] benjamin@bengfort.com $ """ Tests for Yellowbrick utilities diff --git a/tests/test_utils/test_decorators.py b/tests/test_utils/test_decorators.py index b2c0dfa9a..99e6a477f 100644 --- a/tests/test_utils/test_decorators.py +++ b/tests/test_utils/test_decorators.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_decorators.py [] benjamin@bengfort.com $ +# ID: test_decorators.py [79cd8cf] benjamin@bengfort.com $ """ Tests for the decorators module in Yellowbrick utils. diff --git a/tests/test_utils/test_helpers.py b/tests/test_utils/test_helpers.py index 24a32dff0..5768c754f 100644 --- a/tests/test_utils/test_helpers.py +++ b/tests/test_utils/test_helpers.py @@ -8,7 +8,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_helpers.py [] benjamin@bengfort.com $ +# ID: test_helpers.py [79cd8cf] benjamin@bengfort.com $ """ Tests for the stand alone helper functions in Yellowbrick utils. diff --git a/tests/test_utils/test_types.py b/tests/test_utils/test_types.py index 81a21215b..de4b10045 100644 --- a/tests/test_utils/test_types.py +++ b/tests/test_utils/test_types.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_types.py [] benjamin@bengfort.com $ +# ID: test_types.py [79cd8cf] benjamin@bengfort.com $ """ Very difficult test library for type detection and flexibility. diff --git a/tests/test_utils/test_wrapper.py b/tests/test_utils/test_wrapper.py index 4dd77f2d9..35e70ab3d 100644 --- a/tests/test_utils/test_wrapper.py +++ b/tests/test_utils/test_wrapper.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: test_wrapper.py [] benjamin@bengfort.com $ +# ID: test_wrapper.py [b2ecd50] benjamin@bengfort.com $ """ Testing for the wrapping utility. diff --git a/yellowbrick/classifier/__init__.py b/yellowbrick/classifier/__init__.py index 1ad7c6ed8..d4905b48b 100644 --- a/yellowbrick/classifier/__init__.py +++ b/yellowbrick/classifier/__init__.py @@ -1,5 +1,19 @@ # yellowbrick.classifier -# Visualizers for Classification analysis and diagnostics +# Visualizations related to evaluating Scikit-Learn classification models +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [5eee25b] benjamin@bengfort.com $ + +""" +Visualizations related to evaluating Scikit-Learn classification models. +""" ########################################################################## ## Imports @@ -12,4 +26,3 @@ from .classification_report import ClassificationReport, classification_report from .confusion_matrix import ConfusionMatrix from .rocauc import ROCAUC, roc_auc - diff --git a/yellowbrick/classifier/base.py b/yellowbrick/classifier/base.py index d88df28a0..ead9bf6ca 100644 --- a/yellowbrick/classifier/base.py +++ b/yellowbrick/classifier/base.py @@ -1,10 +1,35 @@ +# yellowbrick.classifier.base +# API for classification visualizer hierarchy. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: base.py [5388065] neal@nhumphrey.com $ + +""" +API for classification visualizer hierarchy. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + from ..exceptions import YellowbrickTypeError from ..utils import isclassifier from ..base import ScoreVisualizer -import numpy as np - +########################################################################## +## Base Classification Visualizer +########################################################################## + class ClassificationScoreVisualizer(ScoreVisualizer): def __init__(self, model, ax=None, **kwargs): @@ -14,7 +39,8 @@ def __init__(self, model, ax=None, **kwargs): """ if not isclassifier(model): raise YellowbrickTypeError( - "This estimator is not a classifier; try a regression or clustering score visualizer instead!" + "This estimator is not a classifier; " + "try a regression or clustering score visualizer instead!" ) super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_balance.py index 64f4e5583..a83458f3f 100644 --- a/yellowbrick/classifier/class_balance.py +++ b/yellowbrick/classifier/class_balance.py @@ -1,10 +1,30 @@ +# yellowbrick.classifier.class_balance +# Class balance visualizer for showing per-class support. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: class_balance.py [5388065] neal@nhumphrey.com $ + +""" +Class balance visualizer for showing per-class support. +""" -from .base import ClassificationScoreVisualizer -from ..style.palettes import color_palette +########################################################################## +## Imports +########################################################################## import numpy as np import matplotlib.pyplot as plt +from .base import ClassificationScoreVisualizer +from ..style.palettes import color_palette + from sklearn.model_selection import train_test_split from sklearn.metrics import precision_recall_fscore_support @@ -124,7 +144,6 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ - # Set the title self.set_title('Class Balance for {}'.format(self.name)) @@ -136,6 +155,7 @@ def finalize(self, **kwargs): cmax, cmin = max(self.support.values()), min(self.support.values()) self.ax.set_ylim(0, cmax + cmax* 0.1) + def class_balance(model, X, y=None, ax=None, classes=None, **kwargs): """Quick method: diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py index 1fb7772e0..774af392d 100644 --- a/yellowbrick/classifier/classification_report.py +++ b/yellowbrick/classifier/classification_report.py @@ -1,5 +1,23 @@ -from .base import ClassificationScoreVisualizer +# yellowbrick.classifier.classification_report +# Visual classification report for classifier scoring. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: classification_report.py [5388065] neal@nhumphrey.com $ + +""" +Visual classification report for classifier scoring. +""" +########################################################################## +## Imports +########################################################################## import numpy as np import matplotlib.pyplot as plt @@ -8,8 +26,10 @@ from sklearn.metrics import precision_recall_fscore_support from ..utils import get_model_name -from ..style.palettes import color_sequence from ..style import find_text_color +from ..style.palettes import color_sequence +from .base import ClassificationScoreVisualizer + ########################################################################## ## Classification Report diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index 522bf75f3..e46cf439f 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -1,3 +1,21 @@ +# yellowbrick.classifier.confusion_matrix +# Visual confusion matrix for classifier scoring. +# +# Author: Neal Humphrey +# Created: Tue May 03 11:05:11 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: confusion_matrix.py [5388065] neal@nhumphrey.com $ + +""" +Visual confusion matrix for classifier scoring. +""" + +########################################################################## +## Imports +########################################################################## from .base import ClassificationScoreVisualizer diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index b9d956af8..377d4e76a 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -1,16 +1,35 @@ +# yellowbrick.classifier.rocauc +# Implements visual ROC/AUC curves for classification evaluation. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: rocauc.py [5388065] neal@nhumphrey.com $ + +""" +Implements visual ROC/AUC curves for classification evaluation. +""" -from .base import ClassificationScoreVisualizer -from ..utils import get_model_name -from ..style.palettes import LINE_COLOR +########################################################################## +## Imports +########################################################################## import numpy as np import matplotlib.pyplot as plt +from .base import ClassificationScoreVisualizer +from ..utils import get_model_name +from ..style.palettes import LINE_COLOR + from sklearn.model_selection import train_test_split from sklearn.metrics import auc, roc_auc_score, roc_curve - ########################################################################## ## Receiver Operating Characteristics ########################################################################## diff --git a/yellowbrick/cluster/__init__.py b/yellowbrick/cluster/__init__.py index 7f01709eb..4dab8c2e3 100644 --- a/yellowbrick/cluster/__init__.py +++ b/yellowbrick/cluster/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [241edca] benjamin@bengfort.com $ """ Visualizers for Cluster analysis and diagnostics, particularly visualizations diff --git a/yellowbrick/cluster/base.py b/yellowbrick/cluster/base.py index 3a5b3772f..1067a3b1c 100644 --- a/yellowbrick/cluster/base.py +++ b/yellowbrick/cluster/base.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: base.py [] benjamin@bengfort.com $ +# ID: base.py [241edca] benjamin@bengfort.com $ """ Base class for cluster visualizers. diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index 6092aea52..12d01e502 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: elbow.py [] benjamin@bengfort.com $ +# ID: elbow.py [5a370c8] benjamin@bengfort.com $ """ Implements the elbow method for determining the optimal number of clusters. diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py index f5a57917b..380cabedf 100644 --- a/yellowbrick/cluster/silhouette.py +++ b/yellowbrick/cluster/silhouette.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: silhouette.py [] benjamin@bengfort.com $ +# ID: silhouette.py [57b563b] benjamin@bengfort.com $ """ Implements visualizers that use the silhouette metric for cluster evaluation. diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py index 109d1a89d..bde805f67 100644 --- a/yellowbrick/features/__init__.py +++ b/yellowbrick/features/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [0f4b236] benjamin@bengfort.com $ """ Visualizers for feature analysis and diagnostics. diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index c0e5e6bd7..9add7ca58 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -8,7 +8,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: base.py [] benjamin@bengfort.com $ +# ID: base.py [2e898a6] benjamin@bengfort.com $ """ Base classes for feature visualizers and feature selection tools. diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index 0d576669f..3b5f1d006 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -8,7 +8,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: jointplot.py [] pdamo24@gmail.com $ +# ID: jointplot.py [7f47800] pdamodaran@users.noreply.github.com $ ########################################################################## ## Imports diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 77c7bcc44..877b0c172 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: pcoords.py [] benjamin@bengfort.com $ +# ID: pcoords.py [0f4b236] benjamin@bengfort.com $ """ Implementations of parallel coordinates for multi-dimensional feature diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index bf40f9d01..41e80c802 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: radviz.py [] benjamin@bengfort.com $ +# ID: radviz.py [0f4b236] benjamin@bengfort.com $ """ Implements radviz for feature analysis. diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 4186d811d..dbe8a6c60 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: rankd.py [] benjamin@bengfort.com $ +# ID: rankd.py [ee754dc] benjamin@bengfort.com $ """ Implements 1D (histograms) and 2D (joint plot) feature rankings. diff --git a/yellowbrick/features/scatter.py b/yellowbrick/features/scatter.py index 51a00e2fa..2076b1f6d 100644 --- a/yellowbrick/features/scatter.py +++ b/yellowbrick/features/scatter.py @@ -6,7 +6,7 @@ # # For license information, see LICENSE.txt # -# ID: scatter.py [] nathan.danielsen@gmail.com $ +# ID: scatter.py [fc94ec4] ndanielsen@users.noreply.github.com $ """ Implements a 2D scatter plot for feature analysis. """ diff --git a/yellowbrick/pipeline.py b/yellowbrick/pipeline.py index 0eb81592f..8541b3ea4 100644 --- a/yellowbrick/pipeline.py +++ b/yellowbrick/pipeline.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: pipeline.py [] benjamin@bengfort.com $ +# ID: pipeline.py [1efae1f] benjamin@bengfort.com $ """ Implements a visual pipeline that subclasses Scikit-Learn pipelines. diff --git a/yellowbrick/regressor/__init__.py b/yellowbrick/regressor/__init__.py index 44853d793..9270f3405 100644 --- a/yellowbrick/regressor/__init__.py +++ b/yellowbrick/regressor/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [7d3f5e6] benjamin@bengfort.com $ """ Visualizers for Regression analysis and diagnostics, particularly diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py index 4715da4ed..915cb166c 100644 --- a/yellowbrick/regressor/alphas.py +++ b/yellowbrick/regressor/alphas.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: alphas.py [] benjamin@bengfort.com $ +# ID: alphas.py [7d3f5e6] benjamin@bengfort.com $ """ Implements alpha selection visualizers for regularization diff --git a/yellowbrick/regressor/base.py b/yellowbrick/regressor/base.py index 3890cc6cf..b92225fd1 100644 --- a/yellowbrick/regressor/base.py +++ b/yellowbrick/regressor/base.py @@ -8,7 +8,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: base.py [] benjamin@bengfort.com $ +# ID: base.py [7d3f5e6] benjamin@bengfort.com $ """ Base classes for regressor Visualizers. diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 1d38424da..7072e2b4f 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -8,7 +8,7 @@ # Copyright (C) 2016 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: residuals.py [] benjamin@bengfort.com $ +# ID: residuals.py [7d3f5e6] benjamin@bengfort.com $ """ Regressor visualizers that score residuals: prediction vs. actual data. diff --git a/yellowbrick/style/__init__.py b/yellowbrick/style/__init__.py index 6be218913..0b2045e71 100644 --- a/yellowbrick/style/__init__.py +++ b/yellowbrick/style/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] benjamin@bengfort.com $ +# ID: __init__.py [c6aff34] benjamin@bengfort.com $ """ Manage the style and aesthetic of the yellowbrick library. diff --git a/yellowbrick/style/colors.py b/yellowbrick/style/colors.py index 96be185e4..12eca0f04 100644 --- a/yellowbrick/style/colors.py +++ b/yellowbrick/style/colors.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: colors.py [] benjamin@bengfort.com $ +# ID: colors.py [c6aff34] benjamin@bengfort.com $ """ Colors and color helpers brought in from an alternate library. diff --git a/yellowbrick/style/rcmod.py b/yellowbrick/style/rcmod.py index 4e726f71b..b59b13be9 100644 --- a/yellowbrick/style/rcmod.py +++ b/yellowbrick/style/rcmod.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: rcmod.py [] benjamin@bengfort.com $ +# ID: rcmod.py [c6aff34] benjamin@bengfort.com $ """ Modifies the matplotlib rcParams in order to make yellowbrick more appealing. diff --git a/yellowbrick/text/__init__.py b/yellowbrick/text/__init__.py index a3eb52cbb..123e8ce39 100644 --- a/yellowbrick/text/__init__.py +++ b/yellowbrick/text/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: __init__.py [] rbilbro@districtdatalabs.com $ +# ID: __init__.py [75d9b20] rebecca.bilbro@bytecubed.com $ """ Visualizers for text feature analysis and diagnostics. diff --git a/yellowbrick/text/base.py b/yellowbrick/text/base.py index dc8d8b7fb..688eda292 100644 --- a/yellowbrick/text/base.py +++ b/yellowbrick/text/base.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: base.py [] rbilbro@districtdatalabs.com $ +# ID: base.py [75d9b20] rebecca.bilbro@bytecubed.com $ """ Base classes for text feature visualizers and text feature selection tools. diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py index dad332afb..9de090b77 100644 --- a/yellowbrick/text/freqdist.py +++ b/yellowbrick/text/freqdist.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: fredist.py [] rbilbro@districtdatalabs.com $ +# ID: freqdist.py [67b2740] rebecca.bilbro@bytecubed.com $ """ Implementations of frequency distributions for text visualization diff --git a/yellowbrick/text/postag.py b/yellowbrick/text/postag.py index 7500d77ce..7bfdb7756 100644 --- a/yellowbrick/text/postag.py +++ b/yellowbrick/text/postag.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: postag.py [] rbilbro@districtdatalabs.com $ +# ID: postag.py [849f5a8] rebecca.bilbro@bytecubed.com $ """ Implementation of part-of-speech visualization for text, diff --git a/yellowbrick/text/tsne.py b/yellowbrick/text/tsne.py index ef3c0165c..63570c0cd 100644 --- a/yellowbrick/text/tsne.py +++ b/yellowbrick/text/tsne.py @@ -7,7 +7,7 @@ # Copyright (C) 2016 Bengfort.com # For license information, see LICENSE.txt # -# ID: tsne.py [] benjamin@bengfort.com $ +# ID: tsne.py [6aa9198] benjamin@bengfort.com $ """ Implements TSNE visualizations of documents in 2D space. diff --git a/yellowbrick/utils/__init__.py b/yellowbrick/utils/__init__.py index e9e6acaee..864802bf3 100644 --- a/yellowbrick/utils/__init__.py +++ b/yellowbrick/utils/__init__.py @@ -10,7 +10,7 @@ # Copyright (C) 2016 District Data LAbs # For license information, see LICENSE.txt # -# ID: __init__.py [] jason.s.keung@gmail.com $ +# ID: __init__.py [79cd8cf] benjamin@bengfort.com $ """ Utility functions and helpers for the Yellowbrick library. diff --git a/yellowbrick/utils/decorators.py b/yellowbrick/utils/decorators.py index 9e04dce2b..81315fed9 100644 --- a/yellowbrick/utils/decorators.py +++ b/yellowbrick/utils/decorators.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: decorators.py [] benjamin@bengfort.com $ +# ID: decorators.py [79cd8cf] benjamin@bengfort.com $ """ Decorators and descriptors for annotating yellowbrick library functions. diff --git a/yellowbrick/utils/helpers.py b/yellowbrick/utils/helpers.py index 28bd2775b..1b5a9cc9b 100644 --- a/yellowbrick/utils/helpers.py +++ b/yellowbrick/utils/helpers.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: helpers.py [] benjamin@bengfort.com $ +# ID: helpers.py [79cd8cf] benjamin@bengfort.com $ """ Helper functions and generic utilities for use in Yellowbrick code. diff --git a/yellowbrick/utils/types.py b/yellowbrick/utils/types.py index 7e3808a51..51e9a2d21 100644 --- a/yellowbrick/utils/types.py +++ b/yellowbrick/utils/types.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: types.py [] benjamin@bengfort.com $ +# ID: types.py [79cd8cf] benjamin@bengfort.com $ """ Detection utilities for Scikit-Learn and Numpy types for flexibility diff --git a/yellowbrick/utils/wrapper.py b/yellowbrick/utils/wrapper.py index 7dc93b4db..1c8c77106 100644 --- a/yellowbrick/utils/wrapper.py +++ b/yellowbrick/utils/wrapper.py @@ -7,7 +7,7 @@ # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt # -# ID: wrapper.py [] benjamin@bengfort.com $ +# ID: wrapper.py [b2ecd50] benjamin@bengfort.com $ """ Utility package that provides a wrapper for new style classes. From e6d98505f32268fd42808ada63bc0e65e129f89e Mon Sep 17 00:00:00 2001 From: Nathan Date: Tue, 23 May 2017 11:31:41 -0700 Subject: [PATCH 04/40] Update CONTRIBUTING.md (#222) --- CONTRIBUTING.md | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index aaba59f2e..dd3bb4b0d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -95,14 +95,13 @@ The Yellowbrick repository is set up in a typical production/release/development You can work directly in your fork and create a pull request from your fork's develop branch into ours. We also recommend setting up an `upstream` remote so that you can easily pull the latest development changes from the main Yellowbrick repository (see [configuring a remote for a fork](https://help.github.com/articles/configuring-a-remote-for-a-fork/)). You can do that as follows: -`` -$ git remote add upstream https://github.com/DistrictDataLabs/yellowbrick.git -$ git remote -v -origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (fetch) -origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (push) -upstream https://github.com/DistrictDataLabs/yellowbrick.git (fetch) -upstream https://github.com/DistrictDataLabs/yellowbrick.git (push) -`` +`$ git remote add upstream https://github.com/DistrictDataLabs/yellowbrick.git` +`$ git remote -v` +> origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (fetch) +> origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (push) +> upstream https://github.com/DistrictDataLabs/yellowbrick.git (fetch) +> upstream https://github.com/DistrictDataLabs/yellowbrick.git (push) + When you're ready, request a code review for your pull request. Then, when reviewed and approved, you can merge your fork into our main branch. Make sure to use the "Squash and Merge" option in order to create a Git history that is understandable. @@ -216,12 +215,18 @@ class MyVisualizerTests(VisualTestCase, DatasetMixin): self.fail("my visualizer didn't work") ``` -Tests can be run as follows:: +The entire test suite can be run as follows:: ``` $ make test ``` +You can also run your own test file as follows:: + +``` +$ nosetests tests/test_your_visualizer.py +``` + The Makefile uses the nosetest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The `DatasetMixin` also requires requests.py to fetch data from our Amazon S3 account. ### Documentation From c38ffb81afee3bb56f571dc51e2516a198a53c0b Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 23 May 2017 11:46:32 -0700 Subject: [PATCH 05/40] Updated quickstart download link for yb bikeshare data. Fixed #240 --- docs/quickstart.rst | 6 +++++- examples/download.py | 2 +- tests/dataset.py | 2 +- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 8348a4e53..c1494b0f1 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -105,7 +105,11 @@ These quick functions give you slightly less control over the machine learning w Walkthrough ----------- -Consider a regression analysis as a simple example of the use of visualizers in the machine learning workflow. Using a `bike sharing dataset `_, we would like to predict the number of bikes rented in a given hour based on features like the season, weather, or if it's a holiday. We can load our data as follows: +Consider a regression analysis as a simple example of the use of visualizers in the machine learning workflow. Using a `bike sharing dataset `_ based upon the one uploaded to the `UCI Machine Learning Repository `_, we would like to predict the number of bikes rented in a given hour based on features like the season, weather, or if it's a holiday. + +.. note:: We have updated the dataset from the UCI ML repository to make it a bit easier to load into Pandas; make sure you download the `Yellowbrick version of the dataset `_. + +After downloading the dataset and unzipping it in your current working directory, we can load our data as follows: .. code-block:: python diff --git a/examples/download.py b/examples/download.py index 7aa9f85e1..c9085efe4 100644 --- a/examples/download.py +++ b/examples/download.py @@ -69,7 +69,7 @@ }, 'bikeshare': { 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/bikeshare.zip', - 'signature': '7eb79d0be41f9d9f6373b1fa3f4789e7a9ef880720dc8ea29ec752b914d4b525', + 'signature': 'a9b440f65549746dff680c92ff8bdca3c7265f09db1cf09e708e6e26fc8aba44', }, } diff --git a/tests/dataset.py b/tests/dataset.py index dfec69d3b..6a260fb00 100644 --- a/tests/dataset.py +++ b/tests/dataset.py @@ -74,7 +74,7 @@ }, 'bikeshare': { 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/bikeshare.zip', - 'signature': '7eb79d0be41f9d9f6373b1fa3f4789e7a9ef880720dc8ea29ec752b914d4b525', + 'signature': 'a9b440f65549746dff680c92ff8bdca3c7265f09db1cf09e708e6e26fc8aba44', 'type': 'numpy', }, } From 6e6c7664b066908b84dfb9874161cc7b15fc219b Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Tue, 23 May 2017 14:43:11 -0700 Subject: [PATCH 06/40] playing with pipelines prototype and running into some funny looking plots --- examples/rebeccabilbro/pipelines.ipynb | 256 +++++++++++++++++++++++++ 1 file changed, 256 insertions(+) create mode 100644 examples/rebeccabilbro/pipelines.ipynb diff --git a/examples/rebeccabilbro/pipelines.ipynb b/examples/rebeccabilbro/pipelines.ipynb new file mode 100644 index 000000000..de7139e95 --- /dev/null +++ b/examples/rebeccabilbro/pipelines.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chained Visualizations with Yellowbrick Pipelines\n", + "\n", + "In Yellowbrick, `VisualPipelines` are modeled on Scikit-Learn `Pipelines`, which allow us to chain estimators together in a sane way and use them as a single estimator. This is very useful for models that require a series of extraction, normalization, and transformation steps in advance of prediction. For more about Scikit-Learn Pipelines, check out [this post](http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html) by Zac Stewart.\n", + "\n", + "`VisualPipelines` sequentially apply a list of transforms, visualizers, and a final estimator which may be evaluated by additional visualizers. Intermediate steps of the pipeline must be kinds of 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`.\n", + "\n", + "Any step that implements draw or poof methods can be called sequentially directly from the VisualPipeline, allowing multiple visual diagnostics to be generated, displayed, and saved on demand. If `draw` or `poof` is not called, the visual pipeline should be equivalent to the simple pipeline to ensure no reduction in performance.\n", + "\n", + "The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. These steps can be visually diagnosed by visualizers at every point in the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"/Users/rebeccabilbro/Desktop/waves/stuff/yellowbrick\")\n", + "\n", + "import requests\n", + "import numpy as np \n", + "import pandas as pd\n", + "import yellowbrick as yb \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fetching the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + "}\n", + "\n", + "\n", + "def load_data(name, download=False):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " \n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the classification data set\n", + "data = load_data('credit') " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest\n", + "features = [\n", + " 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n", + " 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n", + " 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n", + " 'jul_pay', 'aug_pay', 'sep_pay',\n", + " ]\n", + "\n", + "classes = ['default', 'paid']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.default.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + 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HH37AhAkTcPr0aRQWFsLPzw9FRUUIDAyUXAfniRARyUAlY3D67LPPcPXqVYwePRqzZ8/G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\ngdCoSE0fbQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.rankd import Rank2D \n", + "\n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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u7oSGhiKTyfD09KSsrAwHBwdmzJiBi4sL165dQ6/XA+VLtUy8vb3x9vaudMyz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\n4vSuDQoK6tTcy5Ytg1KpxPbt280VrlVhP0wrolQq8Z///Ac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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.radviz import RadViz\n", + "\n", + "visualizer = RadViz(classes=classes, features=features)\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ok now try with `VisualPipeline`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from yellowbrick.pipeline import VisualPipeline\n", + "\n", + "from yellowbrick.features.rankd import Rank2D \n", + "from yellowbrick.features.radviz import RadViz\n", + "\n", + "\n", + "multivisualizer = VisualPipeline([\n", + " ('rank2d', Rank2D(features=features, algorithm='covariance')), \n", + " ('radviz', RadViz(classes=classes, features=features)),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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agaJ5SSEXQgijUvR70bpRNC81r0oIIYQoJ6RHLoQQBqXqw1t6UTUvKeRCCGFU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\nQ7Vq1UhKSuKll15i4cKFrFq1iueff56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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multivisualizer.fit(X, y)\n", + "multivisualizer.transform(X)\n", + "multivisualizer.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 4148cbc5e44317e602c858386c9d81c72fd91440 Mon Sep 17 00:00:00 2001 From: Nathan Date: Wed, 24 May 2017 08:04:22 -0700 Subject: [PATCH 07/40] Visual Unit Tests (#241) * Visual Unit Tests (#241) --- .gitignore | 4 + tests/base.py | 142 +++++++++++++++++- .../test_class_balance/test_class_report.png | Bin 0 -> 4233 bytes .../test_class_report.png | Bin 0 -> 11350 bytes .../test_scatter/test_scatter_image.png | Bin 0 -> 5122 bytes .../test_scatter/test_scatter_image_fail.png | Bin 0 -> 20653 bytes tests/requirements.txt | 4 +- tests/test_classifier/test_class_balance.py | 2 +- .../test_classification_report.py | 2 +- tests/test_features/test_scatter.py | 40 ++++- tests/test_style/test_rcmod.py | 3 +- 11 files changed, 188 insertions(+), 9 deletions(-) create mode 100644 tests/baseline_images/test_classifier/test_class_balance/test_class_report.png create mode 100644 tests/baseline_images/test_classifier/test_classification_report/test_class_report.png create mode 100644 tests/baseline_images/test_features/test_scatter/test_scatter_image.png create mode 100644 tests/baseline_images/test_features/test_scatter/test_scatter_image_fail.png diff --git a/.gitignore b/.gitignore index 244cca5b6..ddcb967bc 100644 --- a/.gitignore +++ b/.gitignore @@ -123,3 +123,7 @@ fabric.properties # *.ipr .idea + + +# VisualTestCase Outputs +/tests/actual_images/* diff --git a/tests/base.py b/tests/base.py index d2622ba85..3557c310f 100644 --- a/tests/base.py +++ b/tests/base.py @@ -17,8 +17,17 @@ ## Imports ########################################################################## +import inspect +import os + import unittest import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib import ticker +from matplotlib import rcParams + +from matplotlib.testing.compare import compare_images +from matplotlib.testing.exceptions import ImageComparisonFailure ########################################################################## @@ -37,9 +46,140 @@ def setUpClass(klass): Note: """ klass._backend = mpl.get_backend() + super(VisualTestCase, klass).setUpClass() def setUp(self): """ - Assert tthat the backend is 'Agg' + Assert tthat the backend is 'Agg' and close all previous plots """ + plt.close("all") # close all existing plots + rcParams['font.family'] = 'DejaVu Sans' # Travis-CI does not have san-sarif self.assertEqual(self._backend, 'agg') + super(VisualTestCase, self).setUp() + + def _setup_imagetest(self, inspect_obj=None): + """Parses the module path and test function name from an inspect call obj + that is triggered in the unittest specific "assert_images_similar" + """ + if inspect_obj is not None: + full_path = inspect_obj[1][1][:-3] + self._module_path = full_path.split('yellowbrick')[1].split('/')[2:] + self._test_func_name = inspect_obj[1][3] + return self._module_path, self._test_func_name + + def _actual_img_path(self, extension='.png'): + """Determines the correct outpath for drawing a matplotlib image that + corresponds to the unittest module path. + """ + module_path, test_func_name = self._setup_imagetest() + module_path = os.path.join(*module_path) + actual_images = os.path.join('tests', 'actual_images', module_path) + + if not os.path.exists(actual_images): + mpl.cbook.mkdirs(actual_images) + + self._test_img_outpath = os.path.join(actual_images, test_func_name + extension) + return self._test_img_outpath + + def _base_img_path(self, extension='.png'): + """Gets the baseline_image path for comparison that corresponds to the + unittest module path. + """ + + module_path, test_func_name = self._setup_imagetest() + module_path = os.path.join(*module_path) + base_results = os.path.join('tests', 'baseline_images', module_path) + if not os.path.exists(base_results): + mpl.cbook.mkdirs(base_results) + base_img = os.path.join(base_results, test_func_name + extension) + return base_img + + def assert_images_similar(self, visualizer, tol=0.01): + """Accessible testing method for testing generation of a Visualizer. + + Requires the placement of a baseline image for comparison in the + tests/baseline_images folder that corresponds to the module path of the + VisualTestCase being evaluated. The name of the image corresponds to + the unittest function where "self.assert_images_similar" is called. + + For example, calling "assert_images_similar" in the unittest + "test_class_report" in tests.test_classifier.test_class_balance would + require placement a baseline image at: + + baseline_images/test_classifier/test_class_balance/test_class_report.png + + The easiest way to generate a baseline image is to first run the test that + calls "assert_images_similar", and then copy the actual test generated + image from: + + actual_images/ + + visualizer : yellowbrick visualizer + An instantiated yellowbrick visualizer that has been fitted, + transformed and had all operations except for poof called on it. + + tol : float + The tolerance (a color value difference, where 255 is the + maximal difference). The test fails if the average pixel + difference is greater than this value. + + """ + # inspect is used to locate and organize the baseline images and actual + # test generated images for comparison + inspect_obj = inspect.stack() + module_path, test_func_name = self._setup_imagetest(inspect_obj=inspect_obj) + + # clean and remove the textual/ formatting elements from the visualizer + remove_ticks_and_titles(visualizer.ax) + + plt.savefig(self._actual_img_path()) + base_image = self._base_img_path() + test_img = self._actual_img_path() + # test it! + yb_compare_images(base_image, test_img, tol) + + +def remove_ticks_and_titles(ax): + """Removes tickets and formatting on sub ax object that is useful for the + assert_images_similar as different OS having varying font styles and other + system level differences + """ + null_formatter = ticker.NullFormatter() + ax.set_title("") + ax.xaxis.set_major_formatter(null_formatter) + ax.xaxis.set_minor_formatter(null_formatter) + ax.yaxis.set_major_formatter(null_formatter) + ax.yaxis.set_minor_formatter(null_formatter) + try: + ax.zaxis.set_major_formatter(null_formatter) + ax.zaxis.set_minor_formatter(null_formatter) + except AttributeError: + pass + +def yb_compare_images(expected, actual, tol): + """ Compares a baseline image and test generated actual image + using a matplotlib's built-in imagine comparison function + + expected : string, imagepath + The image filepath to the baseline image + + actual : string, imagepath + The image filepath to the actual test generated image + + tol : float + The tolerance (a color value difference, where 255 is the + maximal difference). The test fails if the average pixel + difference is greater than this value. + """ + __tracebackhide__ = True + + if not os.path.exists(expected): + raise ImageComparisonFailure('image does not exist: %s' % expected) + + # method from matplotlib.testing.compare + err = compare_images(expected, actual, tol, in_decorator=True) + + if err: + raise ImageComparisonFailure( + 'images not close (RMS %(rms).3f):\n\t%(actual)s\n\t%(expected)s ' + % err) diff --git a/tests/baseline_images/test_classifier/test_class_balance/test_class_report.png b/tests/baseline_images/test_classifier/test_class_balance/test_class_report.png new file mode 100644 index 0000000000000000000000000000000000000000..99aa71a1337df530630030b341ac6c7c77caaeec GIT binary patch literal 4233 zcmeHKdrXs86u+(L5QWN|lSx-m6O0ihGL=UAU_K@+3U0U|6DSO5M{2QEgqA)e#@VT8 zH|NT`b;el}fmT?lNTC5TtE;1Coq!;8wQ9w-R4DWzZP~5!pZ&Xa#%0``?{#x?zWbeX ze!uT`PV7$J5gM{21OO14l(_9<0A@u2fQ$~F181&ejdsFI(4j3!dxGKM1m}GPpXbsO 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Testing Requirements +# Testing Requirements nose>=1.3.7 coverage>=4.1 requests>=2.10.0 @@ -13,6 +13,6 @@ requests>=2.10.0 # Python 2 Testing Requirements mock>=2.0.0 -# Optional Testing Dependencies +# Optional Testing Dependencies nltk>=3.0 pandas>=0.18 diff --git a/tests/test_classifier/test_class_balance.py b/tests/test_classifier/test_class_balance.py index e7bdb49c6..c58d2f1e9 100644 --- a/tests/test_classifier/test_class_balance.py +++ b/tests/test_classifier/test_class_balance.py @@ -32,4 +32,4 @@ def test_class_report(self): model.fit(X,y) visualizer = ClassBalance(model, classes=["A", "B"]) visualizer.score(X,y) - + self.assert_images_similar(visualizer) diff --git a/tests/test_classifier/test_classification_report.py b/tests/test_classifier/test_classification_report.py index c63916685..c93994bec 100644 --- a/tests/test_classifier/test_classification_report.py +++ b/tests/test_classifier/test_classification_report.py @@ -33,4 +33,4 @@ def test_class_report(self): model.fit(X,y) visualizer = ClassificationReport(model, classes=["A", "B"]) visualizer.score(X,y) - + self.assert_images_similar(visualizer) diff --git a/tests/test_features/test_scatter.py b/tests/test_features/test_scatter.py index f712d52cd..a5486405b 100644 --- a/tests/test_features/test_scatter.py +++ b/tests/test_features/test_scatter.py @@ -21,11 +21,14 @@ import numpy.testing as npt import matplotlib as mptl -from tests.dataset import DatasetMixin from yellowbrick.features.scatter import * from yellowbrick.exceptions import YellowbrickValueError from yellowbrick.style import palettes +from tests.dataset import DatasetMixin +from tests.base import VisualTestCase +from matplotlib.testing.exceptions import ImageComparisonFailure + try: import pandas except ImportError: @@ -36,7 +39,7 @@ ########################################################################## -class ScatterVizTests(unittest.TestCase, DatasetMixin): +class ScatterVizTests(VisualTestCase, DatasetMixin): # yapf: disable X = np.array([ @@ -48,13 +51,15 @@ class ScatterVizTests(unittest.TestCase, DatasetMixin): [2.110, 3.620, 4.470, 8.210, 5.612, ] ]) # yapf: enable - y = np.array([1, 1, 0, 1, 0, 0]) + y = np.array([1, 0, 1, 0, 1, 0]) def setUp(self): self.occupancy = self.load_data('occupancy') + super(ScatterVizTests, self).setUp() def tearDown(self): self.occupancy = None + super(ScatterVizTests, self).tearDown() def test_init_alias(self): features = ["temperature", "relative_humidity"] @@ -209,3 +214,32 @@ def test_integrated_scatter_numpy_arrays_no_names(self): visualizer = ScatterViz(features=[1, 2]) visualizer.fit_transform_poof(self.X, self.y) self.assertEquals(visualizer.features_, [1, 2]) + + def test_scatter_image(self): + """ + Assert no errors occur during scatter visualizer integration + """ + # self.setUp_ImageTest() + + X_two_cols = self.X[:, :2] + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features) + visualizer.fit(X_two_cols, self.y) + visualizer.draw(X_two_cols, self.y) + + self.assert_images_similar(visualizer) + + + def test_scatter_image_fail(self): + """ + Assert no errors occur during scatter visualizer integration + """ + + X_two_cols = self.X[:, :2] + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features) + visualizer.fit(X_two_cols, self.y) + visualizer.draw(X_two_cols, self.y) + + with self.assertRaises(ImageComparisonFailure): + self.assert_images_similar(visualizer) diff --git a/tests/test_style/test_rcmod.py b/tests/test_style/test_rcmod.py index 5c48f9b8e..d62ca2820 100644 --- a/tests/test_style/test_rcmod.py +++ b/tests/test_style/test_rcmod.py @@ -39,7 +39,8 @@ class RCParamTester(VisualTestCase): excluded_params = { "backend", # This cannot be changed by manipulating rc - "svg.embed_char_paths" # This param causes test issues and is deprecated anyway + "svg.embed_char_paths", # This param causes test issues and is deprecated anyway + "font.family", # breaks the visualtest case } def flatten_list(self, orig_list): From 7fd786d8ff89e82ee4b1bb9f6078c5878c684650 Mon Sep 17 00:00:00 2001 From: Carlo Morales Date: Thu, 25 May 2017 13:26:41 -0600 Subject: [PATCH 08/40] ROCAUC needs X/Y axis labels (#248) * Added x and y axis to ROC curve. Updated example in doc string * Update rocauc.py --- yellowbrick/classifier/rocauc.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index 377d4e76a..08df850f9 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -68,9 +68,14 @@ class ROCAUC(ClassificationScoreVisualizer): Examples -------- - + >>> from sklearn.datasets import load_breast_cancer >>> from yellowbrick.classifier import ROCAUC >>> from sklearn.linear_model import LogisticRegression + >>> from sklearn.model_selection import train_test_split + >>> data = load_breast_cancer() + >>> X = data['data'] + >>> y = data['target'] + >>> X_train, X_test, y_train, y_test = train_test_split(X, y) >>> logistic = LogisticRegression() >>> viz = ROCAUC(logistic) >>> viz.fit(X_train, y_train) @@ -165,6 +170,10 @@ def finalize(self, **kwargs): # Set the limits for the ROC/AUC (always between 0 and 1) self.ax.set_xlim([-0.02, 1.0]) self.ax.set_ylim([ 0.00, 1.1]) + + # Set x and y axis + self.ax.set_ylabel('True Postive Rate') + self.ax.set_xlabel('False Positive Rate') def roc_auc(model, X, y=None, ax=None, **kwargs): From 5ded9ecf51f8c69b32b9cb055be35c7919de4fde Mon Sep 17 00:00:00 2001 From: JimStearns206 Date: Thu, 25 May 2017 12:27:45 -0700 Subject: [PATCH 09/40] JtPlotVis: Only warn of matplotlib maj ver 1 if that is so (fix #243) (#246) * JtPlotVisualization: Only warn of matplotlib maj ver 1 if that is so (fix #243) * Filter unit test to check for this particular warning message. Fixes Travis failure of this UT due to font-missing warnings. * Try#2 to filter out user warnings not related to matplotlib version. * Try#3 to filter out user warnings not related to matplotlib version. * Try#4 to filter out user warnings not related to matplotlib version. --- tests/test_features/test_jointplot.py | 31 ++++++++++++++++++++++++--- yellowbrick/features/jointplot.py | 9 ++++---- 2 files changed, 33 insertions(+), 7 deletions(-) diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py index 9bad72d5c..05ba757d4 100644 --- a/tests/test_features/test_jointplot.py +++ b/tests/test_features/test_jointplot.py @@ -68,12 +68,13 @@ def test_warning(self): self.assertEqual(len(w), 1) self.assertEqual( str(w[-1].message), - "JointPlotVisualizer requires Matplotlib version 2.0.0.Please upgrade to continue." + "JointPlotVisualizer requires matplotlib major version 2 " + "or greater. Please upgrade." ) @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") - def test_jointplot(self): + def test_jointplot_has_no_errors(self): """ Assert no errors occur during jointplot visualizer integration """ @@ -84,7 +85,7 @@ def test_jointplot(self): @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") - def test_jointplot_integrated(self): + def test_jointplot_integrated_has_no_errors(self): """ Test jointplot on the concrete data set """ @@ -99,3 +100,27 @@ def test_jointplot_integrated(self): visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot="hex") visualizer.fit(X, y) # Fit the data to the visualizer g = visualizer.poof() + + + @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + def test_jointplot_no_matplotlib2_warning(self): + """ + Assert no UserWarning occurs if matplotlib major version >= 2 + (and not exactly 2.0.0). + """ + with warnings.catch_warnings(record=True) as ws: + # Filter on UserWarnings + warnings.filterwarnings("always", category=UserWarning) + visualizer = JointPlotVisualizer() + visualizer.fit(self.X, self.y) + visualizer.poof() + + # Filter out user warnings not related to matplotlib version + ver_warn_msg = "requires matplotlib major version 2 or greater" + mpl_ver_cnt = 0 + for w in ws: + if w and w.message and ver_warn_msg in str(w.message): + mpl_ver_cnt += 1 + self.assertEqual(0, mpl_ver_cnt, ws[-1].message \ + if ws else "No error") + diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index 3b5f1d006..e7a63574a 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -129,13 +129,14 @@ def __init__(self, ax=None, feature=None, target=None, xy_plot='hist', xy_args=None, size=6, ratio=5, space=.2, **kwargs): - #check matplotlib version - needs to be version 2.0.0 - if mpl.__version__ == "2.0.0": + # Check matplotlib version - needs to be version 2.0.0 or greater. + mpl_vers_maj = int(mpl.__version__.split(".")[0]) + if mpl_vers_maj >= 2: pass else: warnings.warn(( - "{} requires Matplotlib version 2.0.0." - "Please upgrade to continue." + "{} requires matplotlib major version 2 or greater. " + "Please upgrade." ).format(self.__class__.__name__)) super(JointPlotVisualizer, self).__init__(ax, **kwargs) From f557e1dc07470544342e1a4b35bebde115497676 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 25 May 2017 15:38:59 -0400 Subject: [PATCH 10/40] skipping pcoords test due to timeout see #230 --- tests/test_features/test_pcoords.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py index 13b7cc802..7492bf59d 100644 --- a/tests/test_features/test_pcoords.py +++ b/tests/test_features/test_pcoords.py @@ -48,6 +48,7 @@ def test_parallel_coords(self): visualizer = ParallelCoordinates() visualizer.fit_transform(self.X, self.y) + @unittest.skip("takes too long with matplotlib 2.0.2; see #230") def test_integrated_pcoords(self): """ Test parallel coordinates on a real, occupancy data set From d9546bcdfbb3b486358710a67b6aba4a372b16a8 Mon Sep 17 00:00:00 2001 From: Nathan Date: Fri, 26 May 2017 04:41:44 -0700 Subject: [PATCH 11/40] 235 Neural Paint color palette (#250) * add neural paint palette * update tests --- tests/test_style/test_palettes.py | 10 ++++++++++ yellowbrick/style/palettes.py | 6 ++++++ 2 files changed, 16 insertions(+) diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index 60f1d7cc8..69d8e8e5e 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -181,6 +181,16 @@ def test_seaborn_palettes(self): pal_out = color_palette(name) self.assertEqual(len(pal_out), 6) + def test_other_palettes(self): + """ + Test that the other palettes exist + """ + pals = ["flatui", "paired", "neural_paint", "set1"] + for name in pals: + pal_out = color_palette(name) + self.assertTrue(pal_out) + + def test_bad_palette_name(self): """ Test that a bad palette name raises an exception diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 7008a9bd9..af845699b 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -70,6 +70,11 @@ "set1": ["#377eb8", "#4daf4a", "#e41a1c", "#984ea3", "#ffff33", "#ff7f00", "#a65628", "#f781bf", "#999999"], + + # colors extracted from this blog post during pycon2017: + # http://lewisandquark.tumblr.com/ + "neural_paint": ["#167192", "#6e7548", "#c5a2ab", "#00ccff", "#de78ae", "#ffcc99", + "#3d3f42", "#ffffcc"], } @@ -471,6 +476,7 @@ def color_palette(palette=None, n_colors=None): * :py:const:`sns_bright` * :py:const:`sns_dark` * :py:const:`flatui` + * :py:const:`neural_paint` n_colors : None or int Number of colors in the palette. If ``None``, the default will depend From 898149bd93ceb86a615a626ad667397fa554ec9b Mon Sep 17 00:00:00 2001 From: Carlo Morales Date: Fri, 26 May 2017 06:55:45 -0600 Subject: [PATCH 12/40] WIP pca_feature_visualization (#247) * trying to resolve error while running tests * updated test cases to not be dependent of pytest library * rename test_pca.py to test_x_pca.py --- examples/cjmorale/PCA_Examples.ipynb | 144 +++++++++++ .../test_classifier/test_confusion_matrix.py | 2 +- tests/test_features/test_x_pca.py | 228 ++++++++++++++++++ yellowbrick/features/pca.py | 177 ++++++++++++++ 4 files changed, 550 insertions(+), 1 deletion(-) create mode 100644 examples/cjmorale/PCA_Examples.ipynb create mode 100644 tests/test_features/test_x_pca.py create mode 100644 yellowbrick/features/pca.py diff --git a/examples/cjmorale/PCA_Examples.ipynb b/examples/cjmorale/PCA_Examples.ipynb new file mode 100644 index 000000000..231c30c28 --- /dev/null +++ b/examples/cjmorale/PCA_Examples.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCA Plotting Exmaples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YhJPXHuJLXBIc7Kzh09gdG+b+ABsNPRIFkdaknZCQgPv37yMxMVFlBDnDMJg4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\nREREHuL/A4ohsW0Pn1fKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.pca import PCADecomposition\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "params = {'scale': True, 'color': y}\n", + "visualizer = PCADecomposition(**params)\n", + "visualizer.fit(X)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xWDQTSp7nUVtbi8HBQTG5W0vUWJiCIGBgYAAOhwNmsxnNzc1x0aCJyFW6SjQa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\ngoJCNVACpoKCgoKCQjX4/1IygZfGVANhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.pca import PCADecomposition\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "params = {'scale': True, 'color': y, 'proj_dim':3}\n", + "visualizer = PCADecomposition(**params)\n", + "visualizer.fit(X)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SjB8/Hh07djRkCIQQQojJMmjSPnr0KOzt7bF8+XIkJyejZ8+elLQJIYSQAjJo0u7atSu6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\n2kRERB6CSZuIiMhD/D+Ga7/3ps+KvAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.pca import pca_decomposition\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "pca_decomposition(X, color=y, colormap='RdBu_r')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gpqYGfX19YnK3lqixMAkh6O3thd1uh9lsRmNjY1w0aCJyla4SiUTQ3t4+wAWc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\nQUFBQaEKKAFTQUFBQUGhCvwfvC9HXExUDWkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.features.pca import pca_decomposition\n", + "from sklearn import datasets\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "pca_decomposition(X, color=y, proj_dim=3, colormap='RdBu_r')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py index 13a667b49..a2c4eabd6 100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -60,4 +60,4 @@ def test_one_class(self): model = LogisticRegression() cm = ConfusionMatrix(model, classes=[0]) cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) \ No newline at end of file + cm.score(self.X_test, self.y_test) diff --git a/tests/test_features/test_x_pca.py b/tests/test_features/test_x_pca.py new file mode 100644 index 000000000..81c94848a --- /dev/null +++ b/tests/test_features/test_x_pca.py @@ -0,0 +1,228 @@ +########################################################################## +## Imports +########################################################################## + +import unittest +import numpy as np +import numpy.testing as npt +import yellowbrick + +from yellowbrick.features.pca import * +########################################################################## +##PCA Tests +########################################################################## + +class PCADecompositionTests(unittest.TestCase): + """ + Test the PCADecomposition visualizer (scaled or non-scaled) for 2 and 3 dimensions. + """ + def test_pca_decomposition(self): + """ + Test the quick method PCADecomposition visualizer 2 dimensions scaled. + """ + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + pca_decomposition(X=X, color=y, roj_dim=2, scale=True) + + def test_scale_true_2d(self): + """ + Test the PCADecomposition visualizer 2 dimensions scaled. + """ + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + params = {'scale': True, 'proj_dim': 2, 'col': y} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + X_pca_decomp = np.array( + [[-2.13928666, -0.07820484], + [-2.0162836, 0.38910195], + [-2.21597319, -0.05875371], + [1.70792744, -0.6411635], + [1.95978109, -0.71265712], + [2.70383492, 1.10167722]] + ) + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + + params = {'scale': True, 'proj_dim': 2} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + def test_scale_false_2d(self): + """ + Test the PCADecomposition visualizer 2 dimensions non-scaled. + """ + X = np.array( + [[2.318, 2.727, 4.260, 7.212, 4.792], + [2.315, 2.726, 4.295, 7.140, 4.783, ], + [2.315, 2.724, 4.260, 7.135, 4.779, ], + [2.110, 3.609, 4.330, 7.985, 5.595, ], + [2.110, 3.626, 4.330, 8.203, 5.621, ], + [2.110, 3.620, 4.470, 8.210, 5.612, ]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + params = {'scale': False, 'proj_dim': 2, 'col': y} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + X_pca_decomp = np.array( + [[-0.75173446, -0.02639709], + [-0.79893433, -0.0028735], + [-0.80765629, 0.01702425], + [0.67843399, 0.11408186], + [0.83702734, -0.00802634], + [0.84286375, -0.09380918]] + ) + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + params = {'scale': False, 'proj_dim': 2} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + def test_scale_true_3d(self): + """ + Test the PCADecomposition visualizer 3 dimensions scaled. + """ + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + params = {'scale': True, 'proj_dim': 3, 'col': y} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + X_pca_decomp = np.array( + [[-2.13928666, -0.07820484, -0.11005612], + [-2.0162836, 0.38910195, 0.06538246], + [-2.21597319, -0.05875371, 0.03015729], + [1.70792744, -0.6411635, 0.20001772], + [1.95978109, -0.71265712, -0.16553243], + [2.70383492, 1.10167722, -0.01996893]] + ) + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + params = {'scale': True, 'proj_dim': 3} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + def test_scale_false_3d(self): + """ + Test the PCADecomposition visualizer 3 dimensions non-scaled. + """ + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + params = {'scale': False, 'proj_dim': 3, 'col': y} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + X_pca_decomp = np.array( + [[ -7.51734458e-01, -2.63970949e-02, 3.23270821e-02], + [ -7.98934328e-01, -2.87350350e-03, -2.86110098e-02], + [ -8.07656292e-01, 1.70242492e-02, -4.98720042e-04], + [ 6.78433990e-01, 1.14081863e-01, -2.51825210e-02], + [ 8.37027339e-01, -8.02633755e-03, 6.65986453e-02], + [ 8.42863750e-01, -9.38091760e-02, -4.46334766e-02]] + ) + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + + params = {'scale': False, 'proj_dim': 3} + visualizer = PCADecomposition(**params) + visualizer.fit(X) + pca_array = visualizer.transform(X) + visualizer.poof() + + + npt.assert_array_almost_equal(pca_array, X_pca_decomp) + + def test_scale_true_4d_execption(self): + """ + Test the PCADecomposition visualizer 4 dimensions scaled (catch YellowbrickError). + """ + X = np.array( + [[2.318, 2.727, 4.260, 7.212, 4.792], + [2.315, 2.726, 4.295, 7.140, 4.783, ], + [2.315, 2.724, 4.260, 7.135, 4.779, ], + [2.110, 3.609, 4.330, 7.985, 5.595, ], + [2.110, 3.626, 4.330, 8.203, 5.621, ], + [2.110, 3.620, 4.470, 8.210, 5.612, ]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + params = {'scale': True, 'center': False, 'proj_dim': 4, 'col': y} + with self.assertRaisesRegexp(yellowbrick.exceptions.YellowbrickError, "proj_dim object is not 2 or 3"): + PCADecomposition(**params) + + def test_scale_true_3d_execption(self): + """ + Test the PCADecomposition visualizer 3 dims scaled on 2 dim data set (catch ValueError). + """ + X = np.array( + [[2.318, 2.727], + [2.315, 2.726], + [2.315, 2.724], + [2.110, 3.609], + [2.110, 3.626], + [2.110, 3.620]] + ) + + y = np.array([1, 0]) + + params = {'scale': True, 'center': False, 'proj_dim': 3, 'col': y} + + + with self.assertRaisesRegexp(ValueError, "n_components=3 must be between 0 and n_features"): + pca = PCADecomposition(**params) + pca.fit(X) diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py new file mode 100644 index 000000000..38314fda9 --- /dev/null +++ b/yellowbrick/features/pca.py @@ -0,0 +1,177 @@ +########################################################################## +## Imports +########################################################################## +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +from yellowbrick.features.base import DataVisualizer +from yellowbrick.style import palettes +from yellowbrick.exceptions import YellowbrickError + +from sklearn.pipeline import Pipeline +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler +########################################################################## +## Quick Methods +########################################################################## +def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, + colormap=palettes.DEFAULT_SEQUENCE, color=None, **kwargs): + """Produce a two or three dimensional principal component (PC) plot of a data set + projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted + data set before applying a PC decomposition. There are scale and center arguments + that can be used to control centering anc scaling of an inputted data set. Therefore + this class is a one stop shop for easily getting a PC plot. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + ax : matplotlib Axes, default: None + The axes to plot the figure on. + + scale : bool, default: True + Boolean that indicates if the values of X should be scaled. + + proj_dim : int, default: 2 + The dimension of the PCA project for visualizer. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + color : list or tuple of colors, default: None + Specify the colors for each individual class. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + >>> from sklearn import datasets + >>> iris = datasets.load_iris() + >>> X = iris.data + >>> y = iris.target + >>> pca_decomposition(X, color=y, proj_dim=3, colormap='RdBu_r') + + """ + # Instantiate the visualizer + visualizer = PCADecomposition(X=X, y=y, ax=ax, scale=scale, proj_dim=proj_dim, + colormap= colormap, color=color) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y, **kwargs) + visualizer.transform(X) + + # Return the axes object on the visualizer + return visualizer.poof() +########################################################################## +##2D and #3D PCA Visualizer +########################################################################## +class PCADecomposition(DataVisualizer): + """ + Produce a two or three dimensional principal component (PC) plot of a data set + projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted + data set before applying a PC decomposition. There are scale and center arguments + that can be used to control centering anc scaling of an inputted data set. Therefore + this class is a one stop shop for easily getting a PC plot. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + scale : bool, default: True + Boolean that indicates if user wants to scale data. + + proj_dim : int, default: 2 + The dimension of the PCA project for visualizer. + + color : list or tuple of colors, default: None + Specify the colors for each individual class. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + >>> from sklearn import datasets + >>> iris = datasets.load_iris() + >>> X = iris.data + >>> y = iris.target + >>> params = {'scale': True, 'center': False, 'col': y} + >>> visualizer = PCADecomposition(**params) + >>> visualizer.fit(X) + >>> visualizer.transform(X) + >>> visualizer.poof() + + """ + def __init__(self, X=None, y=None, ax=None, scale=True, color=None, proj_dim=2, + colormap=palettes.DEFAULT_SEQUENCE, **kwargs): + super(PCADecomposition, self).__init__(ax=ax, **kwargs) + # Data Parameters + if proj_dim not in (2, 3): + raise YellowbrickError("proj_dim object is not 2 or 3.") + + self.color = color + self.pca_features_ = None + self.scale = scale + self.proj_dim = proj_dim + self.pca_transformer = Pipeline([('scale', StandardScaler(with_std=self.scale)), + ('pca', PCA(self.proj_dim, )) + ]) + # Visual Parameters + self.colormap = colormap + + def fit(self, X, y=None, **kwargs): + self.pca_transformer.fit(X) + return self + + def transform(self, X, y=None, **kwargs): + self.pca_features_ = self.pca_transformer.transform(X) + self.draw() + return self.pca_features_ + + def draw(self, **kwargs): + X = self.pca_features_ + + if self.proj_dim == 2: + self.ax.scatter(X[:, 0], X[:, 1], c=self.color, cmap=self.colormap) + if self.proj_dim == 3: + self.fig = plt.figure() + self.fig = self.fig.add_subplot(111, projection='3d') + + self.ax = self.fig.scatter(X[:, 0], X[:, 1], X[:, 2], c=self.color, + cmap=self.colormap) + return self.ax + + def finalize(self, **kwargs): + # Set the title + if self.proj_dim == 2: + self.set_title('Principal Component Plot') + self.ax.set_ylabel('Principal Component 2') + self.ax.set_xlabel('Principal Component 1') + + else: + self.fig.set_title('Principal Component Plot') + self.fig.set_xlabel('Principal Component 1') + self.fig.set_ylabel('Principal Component 2') + self.fig.set_zlabel('Principal Component 3') From 384918f7bf7123c6c9ec140def6cf9b9342644f2 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 26 May 2017 13:56:30 -0400 Subject: [PATCH 13/40] New and improved ROCAUC curves as well as code clean up Micro and macro averages and per-class curves closes #153 Image based testing for ROCAUC --- .../test_rocauc/test_multiclass_rocauc.png | Bin 0 -> 54157 bytes .../test_rocauc/test_rocauc_no_classes.png | Bin 0 -> 34942 bytes .../test_rocauc/test_rocauc_no_curves.png | Bin 0 -> 3669 bytes .../test_rocauc/test_rocauc_no_macro.png | Bin 0 -> 34647 bytes .../test_rocauc_no_macro_no_micro.png | Bin 0 -> 26944 bytes .../test_rocauc/test_rocauc_no_micro.png | Bin 0 -> 35383 bytes tests/test_classifier/test_rocauc.py | 345 +++++++++++++++- tests/test_features/test_radviz.py | 4 +- yellowbrick/classifier/base.py | 64 ++- yellowbrick/classifier/class_balance.py | 38 +- .../classifier/classification_report.py | 29 +- yellowbrick/classifier/confusion_matrix.py | 81 +--- yellowbrick/classifier/rocauc.py | 380 ++++++++++++++---- yellowbrick/cluster/silhouette.py | 1 - yellowbrick/regressor/alphas.py | 2 +- yellowbrick/regressor/residuals.py | 2 + yellowbrick/style/palettes.py | 1 - yellowbrick/text/postag.py | 3 +- 18 files changed, 730 insertions(+), 220 deletions(-) create mode 100644 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zP!RvfQ5i4((%dL%!(TTH+wp;B63IsG5b>8`2l5rW!RBxc`O`t1X+D}#v(5i!58Z7h Z +# Author: Rebecca Bilbro +# Created: Tue May 23 13:41:55 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_rocauc.py [] benjamin@bengfort.com $ + +""" +Testing for the ROCAUC visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import unittest +import numpy as np +import numpy.testing as npt -from yellowbrick.classifier.rocauc import * from tests.base import VisualTestCase -from sklearn.svm import LinearSVC +from tests.dataset import DatasetMixin +from yellowbrick.classifier.rocauc import * +from yellowbrick.exceptions import ModelError + +from sklearn.svm import LinearSVC, SVC +from sklearn.naive_bayes import MultinomialNB +from sklearn.tree import DecisionTreeClassifier +from sklearn.datasets import load_breast_cancer +from sklearn.linear_model import LogisticRegression +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.model_selection import train_test_split as tts + ########################################################################## ## Data @@ -16,19 +49,313 @@ [ 2.110, 3.620, 4.470, 8.210, 5.612,]] ) -y = np.array([1, 1, 0, 1, 0, 0]) +yb = np.array([1, 1, 0, 1, 0, 0]) +ym = np.array([1, 0, 2, 1, 2, 0]) + + +########################################################################## +## Fixtures +########################################################################## + +class FakeClassifier(BaseEstimator, ClassifierMixin): + """ + A fake classifier for testing noops on the visualizer. + """ + pass + ########################################################################## ## Tests ########################################################################## -class ROCAUCTests(VisualTestCase): +class ROCAUCTests(VisualTestCase, DatasetMixin): + + def load_binary_data(self): + """ + Returns the binary test data set. + """ + # Load the Data + data = self.load_data("occupancy") + + X = data[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + + y = data['occupancy'].astype(int) + + # Convert X to an ndarray + X = np.array(X.tolist()) + + # Return train/test splits + return tts(X, y, test_size=0.2, random_state=42) + + def load_multiclass_data(self): + """ + Returns the multiclass test data set. + """ + raise NotImplementedError("Need to add multiclass data soon!") + + @unittest.skip("binary classifiers don't currently work as expected") + def test_binary_rocauc(self): + """ + Test ROCAUC with a binary classifier + """ + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(LinearSVC()) + visualizer.fit(X_train, y_train) + + # Score the visualizer + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.93230159261495249) + + # Check the scores + self.assertEqual(len(visualizer.fpr.keys()), 4) + self.assertEqual(len(visualizer.tpr.keys()), 4) + self.assertEqual(len(visualizer.roc_auc.keys()), 4) + + for k in (0, 1, "micro", "macro"): + self.assertIn(k, visualizer.fpr) + self.assertIn(k, visualizer.tpr) + self.assertIn(k, visualizer.roc_auc) + self.assertEqual(len(visualizer.fpr[k]), len(visualizer.tpr[k])) + self.assertGreater(visualizer.roc_auc[k], 0.0) + self.assertLess(visualizer.roc_auc[k], 1.0) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_multiclass_rocauc(self): + """ + Test ROCAUC with a multiclass classifier + """ + # Load the Data + # TODO: Switch to a true multi-class dataset + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(MultinomialNB()) + visualizer.fit(X_train, y_train) + + # Score the visualizer + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.93230159261495249) + + # Check the scores + self.assertEqual(len(visualizer.fpr.keys()), 4) + self.assertEqual(len(visualizer.tpr.keys()), 4) + self.assertEqual(len(visualizer.roc_auc.keys()), 4) + + for k in (0, 1, "micro", "macro"): + self.assertIn(k, visualizer.fpr) + self.assertIn(k, visualizer.tpr) + self.assertIn(k, visualizer.roc_auc) + self.assertEqual(len(visualizer.fpr[k]), len(visualizer.tpr[k])) + self.assertGreater(visualizer.roc_auc[k], 0.0) + self.assertLess(visualizer.roc_auc[k], 1.0) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_rocauc_quickmethod(self): + """ + Test the ROCAUC quick method + """ + data = load_breast_cancer() + model = DecisionTreeClassifier() + + # TODO: impage comparison of the quick method + ax = roc_auc(model, data.data, data.target) + + def test_rocauc_no_micro(self): + """ + Test ROCAUC without a micro average + """ + # Load the Data + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), micro=False) + visualizer.fit(X_train, y_train) - def test_roc_auc(self): + # Score the visualizer (should be the macro average) + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.99578564759755916) + + # Assert that there is no micro score + self.assertNotIn("micro", visualizer.fpr) + self.assertNotIn("micro", visualizer.tpr) + self.assertNotIn("micro", visualizer.roc_auc) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_rocauc_no_macro(self): + """ + Test ROCAUC without a macro average + """ + # Load the Data + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), macro=False) + visualizer.fit(X_train, y_train) + + # Score the visualizer (should be the micro average) + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.99766508576965574) + + # Assert that there is no macro score + self.assertNotIn("macro", visualizer.fpr) + self.assertNotIn("macro", visualizer.tpr) + self.assertNotIn("macro", visualizer.roc_auc) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_rocauc_no_macro_no_micro(self): + """ + Test ROCAUC without a macro or micro average + """ + # Load the Data + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), macro=False, micro=False) + visualizer.fit(X_train, y_train) + + # Score the visualizer (should be the F1 score) + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.98978599221789887) + + # Assert that there is no macro score + self.assertNotIn("macro", visualizer.fpr) + self.assertNotIn("macro", visualizer.tpr) + self.assertNotIn("macro", visualizer.roc_auc) + + # Assert that there is no micro score + self.assertNotIn("micro", visualizer.fpr) + self.assertNotIn("micro", visualizer.tpr) + self.assertNotIn("micro", visualizer.roc_auc) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_rocauc_no_classes(self): + """ + Test ROCAUC without per-class curves + """ + # Load the Data + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), per_class=False) + visualizer.fit(X_train, y_train) + + # Score the visualizer (should be the micro average) + s = visualizer.score(X_test, y_test) + self.assertAlmostEqual(s, 0.99766508576965574) + + # Assert that there still are per-class scores + for c in (0, 1): + self.assertIn(c, visualizer.fpr) + self.assertIn(c, visualizer.tpr) + self.assertIn(c, visualizer.roc_auc) + + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_rocauc_no_curves(self): """ - Assert no errors occur during ROC-AUC integration + Test ROCAUC with no curves specified at all """ + # Load the Data + X_train, X_test, y_train, y_test = self.load_binary_data() + + # Create and fit the visualizer + visualizer = ROCAUC( + LogisticRegression(), per_class=False, macro=False, micro=False + ) + visualizer.fit(X_train, y_train) + + # TODO: Raise an exception in this case. + + # Compare the images - should be blank + visualizer.poof() + self.assert_images_similar(visualizer) + + @unittest.skip("Not implemented yet") + def test_rocauc_label_encoded(self): + """ + Test ROCAUC with label encoding before scoring + """ + pass + + @unittest.skip("Not implemented yet") + def test_rocauc_not_label_encoded(self): + """ + Test ROCAUC without label encoding before scoring + """ + pass + + def test_decision_function_rocauc(self): + """ + Test ROCAUC with classifiers that have a decision function + """ + # Load the model and assert there is no predict_proba method. model = LinearSVC() - model.fit(X,y) - visualizer = ROCAUC(model, classes=["A", "B"]) - visualizer.score(X,y) + with self.assertRaises(AttributeError): + model.predict_proba + + # Fit model and visualizer + visualizer = ROCAUC(model) + visualizer.fit(X, yb) + + expected = np.asarray([ + 0.204348, 0.228593, 0.219908, -0.211756, -0.26155 , -0.221405 + ]) + + # Get the predict_proba scores and evaluate + y_scores = visualizer._get_y_scores(X) + npt.assert_array_almost_equal(y_scores, expected, decimal=1) + + def test_predict_proba_rocauc(self): + """ + Test ROCAUC with classifiers that utilize predict_proba + """ + # Load the model and assert there is no decision_function method. + model = MultinomialNB() + with self.assertRaises(AttributeError): + model.decision_function + + # Fit model and visualizer + visualizer = ROCAUC(model) + visualizer.fit(X, yb) + + expected = np.asarray([ + [0.493788, 0.506212], + [0.493375, 0.506625], + [0.493572, 0.506428], + [0.511063, 0.488936], + [0.511887, 0.488112], + [0.510841, 0.489158], + ]) + + # Get the predict_proba scores and evaluate + y_scores = visualizer._get_y_scores(X) + npt.assert_array_almost_equal(y_scores, expected) + + def test_no_scoring_function(self): + """ + Test ROCAUC with classifiers that have no scoring method + """ + visualizer = ROCAUC(FakeClassifier()) + with self.assertRaises(ModelError): + visualizer._get_y_scores(X) diff --git a/tests/test_features/test_radviz.py b/tests/test_features/test_radviz.py index 4b4d9fbc0..0770d4ac9 100644 --- a/tests/test_features/test_radviz.py +++ b/tests/test_features/test_radviz.py @@ -21,14 +21,16 @@ import numpy as np import numpy.testing as npt +from tests.base import VisualTestCase from tests.dataset import DatasetMixin from yellowbrick.features.radviz import * + ########################################################################## ## RadViz Base Tests ########################################################################## -class RadVizTests(unittest.TestCase, DatasetMixin): +class RadVizTests(VisualTestCase, DatasetMixin): X = np.array( [[ 2.318, 2.727, 4.260, 7.212, 4.792], diff --git a/yellowbrick/classifier/base.py b/yellowbrick/classifier/base.py index ead9bf6ca..584be83ec 100644 --- a/yellowbrick/classifier/base.py +++ b/yellowbrick/classifier/base.py @@ -21,9 +21,10 @@ import numpy as np -from ..exceptions import YellowbrickTypeError from ..utils import isclassifier from ..base import ScoreVisualizer +from ..style.palettes import color_palette +from ..exceptions import YellowbrickTypeError ########################################################################## @@ -32,19 +33,78 @@ class ClassificationScoreVisualizer(ScoreVisualizer): - def __init__(self, model, ax=None, **kwargs): + def __init__(self, model, ax=None, classes=None, **kwargs): """ Check to see if model is an instance of a classifer. Should return an error if it isn't. + + .. todo:: document this class. + .. tood:: accept as input classes as all visualizers need this. """ + # A bit of type checking if not isclassifier(model): raise YellowbrickTypeError( "This estimator is not a classifier; " "try a regression or clustering score visualizer instead!" ) + # Initialize the super method. super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) + # Convert to array if necessary to match estimator.classes_ + if classes is not None: + classes = np.array(classes) + + # Set up classifier score visualization properties + self.colors = color_palette(kwargs.pop('colors', None)) + self.classes_ = classes + + @property + def classes_(self): + """ + Proxy property to smartly access the classes from the estimator or + stored locally on the score visualizer for visualization. + """ + if self.__classes is None: + try: + return self.estimator.classes_ + except AttributeError: + return None + return self.__classes + + @classes_.setter + def classes_(self, value): + self.__classes = value + + def fit(self, X, y=None, **kwargs): + """ + Parameters + ---------- + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs: keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self : instance + Returns the instance of the classification score visualizer + + """ + # Fit the inner estimator + self.estimator.fit(X, y) + + # Extract the classes from the estimator + if self.classes_ is None: + self.classes_ = self.estimator.classes_ + + # Always return self from fit + return self + #TODO during refactoring this can be used to generalize ClassBalance def class_counts(self, y): unique, counts = np.unique(y, return_counts=True) diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_balance.py index a83458f3f..6ce67ba6d 100644 --- a/yellowbrick/classifier/class_balance.py +++ b/yellowbrick/classifier/class_balance.py @@ -20,10 +20,8 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt from .base import ClassificationScoreVisualizer -from ..style.palettes import color_palette from sklearn.model_selection import train_test_split from sklearn.metrics import precision_recall_fscore_support @@ -57,39 +55,11 @@ class ClassBalance(ClassificationScoreVisualizer): Keyword arguments passed to the super class. Here, used to colorize the bars in the histogram. + Notes + ----- These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - def __init__(self, model, ax=None, classes=None, **kwargs): - - super(ClassBalance, self).__init__(model, ax=ax, **kwargs) - - self.colors = color_palette(kwargs.pop('colors', None)) - self.classes_ = classes - - def fit(self, X, y=None, **kwargs): - """ - Parameters - ---------- - - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs: keyword arguments passed to Scikit-Learn API. - - Returns - ------- - self : instance - Returns the instance of the classification score visualizer - - """ - super(ClassBalance, self).fit(X, y, **kwargs) - if self.classes_ is None: - self.classes_ = self.estimator.classes_ - return self def score(self, X, y=None, **kwargs): """ @@ -148,8 +118,8 @@ def finalize(self, **kwargs): self.set_title('Class Balance for {}'.format(self.name)) # Set the x ticks with the class names - # TODO: change to the self.ax method rather than plt.xticks - plt.xticks(np.arange(len(self.support)), self.support.keys()) + self.ax.set_xticks(np.arange(len(self.support))) + self.ax.set_xticklabels(self.support.keys()) # Compute the ceiling for the y limit cmax, cmin = max(self.support.values()), min(self.support.values()) diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py index 774af392d..bdaec3431 100644 --- a/yellowbrick/classifier/classification_report.py +++ b/yellowbrick/classifier/classification_report.py @@ -25,7 +25,6 @@ from sklearn.model_selection import train_test_split from sklearn.metrics import precision_recall_fscore_support -from ..utils import get_model_name from ..style import find_text_color from ..style.palettes import color_sequence from .base import ClassificationScoreVisualizer @@ -70,33 +69,11 @@ class ClassificationReport(ClassificationScoreVisualizer): """ def __init__(self, model, ax=None, classes=None, **kwargs): - - super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) - - ## hoisted to ScoreVisualizer base class - self.estimator = model - self.name = get_model_name(self.estimator) + super(ClassificationReport, self).__init__( + model, ax=ax, classes=classes, **kwargs + ) self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) - self.classes_ = classes - - def fit(self, X, y=None, **kwargs): - """ - Parameters - ---------- - - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs: keyword arguments passed to Scikit-Learn API. - """ - super(ClassificationReport, self).fit(X, y, **kwargs) - if self.classes_ is None: - self.classes_ = self.estimator.classes_ - return self def score(self, X, y=None, **kwargs): """ diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index e46cf439f..868098462 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -17,15 +17,15 @@ ## Imports ########################################################################## -from .base import ClassificationScoreVisualizer - import numpy as np import matplotlib.pyplot as plt + from sklearn.metrics import confusion_matrix -from ..style.palettes import color_sequence -from ..style import find_text_color from ..utils import div_safe +from ..style import find_text_color +from ..style.palettes import color_sequence +from .base import ClassificationScoreVisualizer ########################################################################## @@ -69,15 +69,9 @@ class ConfusionMatrix(ClassificationScoreVisualizer): def __init__(self, model, ax=None, classes=None, **kwargs): - - super(ConfusionMatrix, self).__init__(model, ax=ax, classes=None,**kwargs) - #Parameters provided by super (for reference during development only): - #self.ax - #self.size - #self.color - #self.title - #self.estimator - #self.name + super(ConfusionMatrix, self).__init__( + model, ax=ax, classes=classes, **kwargs + ) #Initialize all the other attributes we'll use (for coder clarity) self.confusion_matrix = None @@ -87,49 +81,6 @@ def __init__(self, model, ax=None, classes=None, **kwargs): self.cmap.set_over(color='#2a7d4f') self.edgecolors=[] #used to draw diagonal line for predicted class = true class - - #Convert list to array if necessary, since estimator.classes_ returns nparray - self._classes = None if classes == None else np.array(classes) - - #TODO hoist this to shared confusion matrix / classification report heatmap class - @property - def classes(self): - ''' - Returns a numpy array of the classes in y - Matches the user provided list if provided by the user in __init__ - If no list provided, tries to obtain it from the fitted estimator - ''' - if self._classes is None: - try: - print("trying") - return self.estimator.classes_ - except AttributeError: - return None - return self._classes - - @classes.setter - def classes(self, value): - self._classes = value - - #todo hoist - def fit(self, X, y=None, **kwargs): - """ - Parameters - ---------- - - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs: keyword arguments passed to Scikit-Learn API. - """ - super(ConfusionMatrix, self).fit(X, y, **kwargs) - if self._classes is None: - self.classes = self.estimator.classes_ - return self - def score(self, X, y, sample_weight=None, percent=True): """ Generates the Scikit-Learn confusion_matrix and applies this to the appropriate axis @@ -152,14 +103,16 @@ def score(self, X, y, sample_weight=None, percent=True): """ y_pred = self.predict(X) - self.confusion_matrix = confusion_matrix(y_true = y, y_pred = y_pred, labels=self.classes, sample_weight=sample_weight) + self.confusion_matrix = confusion_matrix( + y, y_pred, labels=self.classes_, sample_weight=sample_weight + ) self._class_counts = self.class_counts(y) #Make array of only the classes actually being used. #Needed because sklearn confusion_matrix only returns counts for selected classes #but percent should be calculated based on all classes selected_class_counts = [] - for c in self.classes: + for c in self.classes_: try: selected_class_counts.append(self._class_counts[c]) except KeyError: @@ -198,16 +151,16 @@ def draw(self, percent=True): self.max = self._confusion_matrix_plottable.max() #Set up the dimensions of the pcolormesh - X = np.linspace(start=0, stop=len(self.classes), num=len(self.classes)+1) - Y = np.linspace(start=0, stop=len(self.classes), num=len(self.classes)+1) + X = np.linspace(start=0, stop=len(self.classes_), num=len(self.classes_)+1) + Y = np.linspace(start=0, stop=len(self.classes_), num=len(self.classes_)+1) self.ax.set_ylim(bottom=0, top=self._confusion_matrix_plottable.shape[0]) self.ax.set_xlim(left=0, right=self._confusion_matrix_plottable.shape[1]) #Put in custom axis labels - self.xticklabels = self.classes - self.yticklabels = self.classes[::-1] - self.xticks = np.arange(0, len(self.classes), 1) + .5 - self.yticks = np.arange(0, len(self.classes), 1) + .5 + self.xticklabels = self.classes_ + self.yticklabels = self.classes_[::-1] + self.xticks = np.arange(0, len(self.classes_), 1) + .5 + self.yticks = np.arange(0, len(self.classes_), 1) + .5 self.ax.set(xticks=self.xticks, yticks=self.yticks) self.ax.set_xticklabels(self.xticklabels, rotation="vertical", fontsize=8) self.ax.set_yticklabels(self.yticklabels, fontsize=8) diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index 08df850f9..65b8b0558 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -20,51 +20,94 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt -from .base import ClassificationScoreVisualizer -from ..utils import get_model_name +from ..exceptions import ModelError from ..style.palettes import LINE_COLOR +from .base import ClassificationScoreVisualizer +from scipy import interp +from sklearn.preprocessing import label_binarize from sklearn.model_selection import train_test_split -from sklearn.metrics import auc, roc_auc_score, roc_curve +from sklearn.metrics import auc, roc_curve + + +# Dictionary keys for ROCAUC +MACRO = "macro" +MICRO = "micro" ########################################################################## -## Receiver Operating Characteristics +## ROCAUC Visualizer ########################################################################## class ROCAUC(ClassificationScoreVisualizer): """ - Plot the ROC to visualize the tradeoff between the classifier's - sensitivity and specificity. + Receiver Operating Characteristic (ROC) curves are a measure of a + classifier's predictive quality that compares and visualizes the tradeoff + between the models' sensitivity and specificity. The ROC curve displays + the true positive rate on the Y axis and the false positive rate on the + X axis on both a global average and per-class basis. The ideal point is + therefore the top-left corner of the plot: false positives are zero and + true positives are one. + + This leads to another metric, area under the curve (AUC), a computation + of the relationship between false positives and true positives. The higher + the AUC, the better the model generally is. However, it is also important + to inspect the "steepness" of the curve, as this describes the + maximization of the true positive rate while minimizing the false positive + rate. Generalizing "steepness" usually leads to discussions about + convexity, which we do not get into here. Parameters ---------- - ax : the axis to plot the figure on. model : the Scikit-Learn estimator Should be an instance of a classifier, else the __init__ will return an error. - roc_color : color of the ROC curve - Specify the color as a matplotlib color: you can specify colors in - many weird and wonderful ways, including full names ('green'), hex - strings ('#008000'), RGB or RGBA tuples ((0,1,0,1)) or grayscale - intensities as a string ('0.8'). + classes : list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + Note that the curves must be computed based on what is in the target + vector passed to the ``score()`` method. Class names are used for + labeling only and must be in the correct order to prevent confusion. - diagonal_color : color of the diagonal - Specify the color as a matplotlib color. + micro : bool, default = True + Plot the micro-averages ROC curve, computed from the sum of all true + positives and false positives across all classes. + + macro : bool, default = True + Plot the macro-averages ROC curve, which simply takes the average of + curves across all classes. + + per_class : bool, default = True + Plot the ROC curves for each individual class. Primarily this is set + to false if only the macro or micro average curves are required. kwargs : keyword arguments passed to the super class. Currently passing in hard-coded colors for the Receiver Operating Characteristic curve and the diagonal. These will be refactored to a default Yellowbrick style. - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. - + Notes + ----- + ROC curves are typically used in binary classification, and in fact the + Scikit-Learn ``roc_curve`` metric is only able to perform metrics for + binary classifiers. As a result it is necessary to binarize the output or + to use one-vs-rest or one-vs-all strategies of classification. The + visualizer does its best to handle multiple situations, but exceptions can + arise from unexpected models or outputs. + + Another important point is the relationship of class labels specified on + initialization to those drawn on the curves. The classes are not used to + constrain ordering or filter curves; the ROC computation happens on the + unique values specified in the target vector to the ``score`` method. To + ensure the best quality visualization, do not use a LabelEncoder for this + and do not pass in class labels. + + .. sealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html + .. todo:: Allow the class list to filter the curves on the visualization. Examples -------- @@ -76,27 +119,20 @@ class ROCAUC(ClassificationScoreVisualizer): >>> X = data['data'] >>> y = data['target'] >>> X_train, X_test, y_train, y_test = train_test_split(X, y) - >>> logistic = LogisticRegression() - >>> viz = ROCAUC(logistic) + >>> viz = ROCAUC(LogisticRegression()) >>> viz.fit(X_train, y_train) >>> viz.score(X_test, y_test) >>> viz.poof() - """ - def __init__(self, model, ax=None, **kwargs): - super(ROCAUC, self).__init__(model, ax=ax, **kwargs) + def __init__(self, model, ax=None, classes=None, + micro=True, macro=True, per_class=True, **kwargs): + super(ROCAUC, self).__init__(model, ax=ax, classes=classes, **kwargs) - ## hoisted to ScoreVisualizer base class - self.name = get_model_name(self.estimator) - - # Color map defaults as follows: - # ROC color is the current color in the cycle - # Diagonal color is the default LINE_COLOR - self.colors = { - 'roc': kwargs.pop('roc_color', None), - 'diagonal': kwargs.pop('diagonal_color', LINE_COLOR), - } + # Set the visual parameters for ROCAUC + self.micro = micro + self.macro = macro + self.per_class = per_class def score(self, X, y=None, **kwargs): """ @@ -105,7 +141,6 @@ def score(self, X, y=None, **kwargs): Parameters ---------- - X : ndarray or DataFrame of shape n x m A matrix of n instances with m features @@ -113,44 +148,95 @@ def score(self, X, y=None, **kwargs): An array or series of target or class values Returns - ------ + ------- + score : float + The micro-average area under the curve of all classes. + """ - ax : the axis with the plotted figure + # Compute the predictions for the test data + y_pred = self._get_y_scores(X) - """ - y_pred = self.predict(X) - self.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred) - self.roc_auc = auc(self.fpr, self.tpr) - return self.draw(y, y_pred) + # # Classes may be label encoded so only use what's in y to compute. + # # The self.classes_ attribute will be used as names for labels. + classes = np.unique(y) + n_classes = len(classes) - def draw(self, y, y_pred): - """ - Renders ROC-AUC plot. - Called internally by score, possibly more than once + # Store the false positive rate, true positive rate and curve info. + self.fpr = dict() + self.tpr = dict() + self.roc_auc = dict() - Parameters - ---------- + # Compute ROC curve and ROC area for each class + for i, c in enumerate(classes): + self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c) + self.roc_auc[i] = auc(self.fpr[i], self.tpr[i]) - y : ndarray or Series of length n - An array or series of target or class values + # Compute micro average + if self.micro: + self._score_micro_average(y, y_pred, classes, n_classes) + + # Compute macro average + if self.macro: + self._score_macro_average(n_classes) + + # Draw the Curves + self.draw() + + # Return micro average if specified + if self.micro: + return self.roc_auc[MICRO] + + # Return macro average if not micro + if self.macro: + return self.roc_auc[MACRO] + + # Return the base score if neither macro nor micro + return self.estimator.score(X, y) - y_pred : ndarray or Series of length n - An array or series of predicted target values + def draw(self): + """ + Renders ROC-AUC plot. + Called internally by score, possibly more than once Returns ------ - ax : the axis with the plotted figure - """ - self.ax.plot( - self.fpr, self.tpr, c=self.colors['roc'], - label='AUC = {:0.2f}'.format(self.roc_auc) - ) + colors = self.colors[0:len(self.classes_)] + n_classes = len(colors) + + # Plot the ROC curves for each class + if self.per_class: + for i, color in zip(range(n_classes), colors): + self.ax.plot( + self.fpr[i], self.tpr[i], color=color, + label='ROC of class {}, AUC = {:0.2f}'.format( + self.classes_[i], self.roc_auc[i], + ) + ) + + # Plot the ROC curve for the micro average + if self.micro: + self.ax.plot( + self.fpr[MICRO], self.tpr[MICRO], linestyle="--", + color= self.colors[len(self.classes_)], + label='micro-average ROC curve, AUC = {:0.2f}'.format( + self.roc_auc["micro"], + ) + ) + + # Plot the ROC curve for the macro average + if self.macro: + self.ax.plot( + self.fpr[MACRO], self.tpr[MACRO], linestyle="--", + color= self.colors[len(self.classes_)+1], + label='macro-average ROC curve, AUC = {:0.2f}'.format( + self.roc_auc["macro"], + ) + ) # Plot the line of no discrimination to compare the curve to. - self.ax.plot([0,1],[0,1],'m--',c=self.colors['diagonal']) - + self.ax.plot([0,1], [0,1], linestyle=':', c=LINE_COLOR) return self.ax def finalize(self, **kwargs): @@ -164,39 +250,174 @@ def finalize(self, **kwargs): """ # Set the title and add the legend - self.set_title('ROC for {}'.format(self.name)) - self.ax.legend(loc='lower right') + self.set_title('ROC Curves for {}'.format(self.name)) + self.ax.legend(loc='lower right', frameon=True) # Set the limits for the ROC/AUC (always between 0 and 1) - self.ax.set_xlim([-0.02, 1.0]) - self.ax.set_ylim([ 0.00, 1.1]) - - # Set x and y axis + self.ax.set_xlim([0.0, 1.0]) + self.ax.set_ylim([0.0, 1.0]) + + # Set x and y axis labels self.ax.set_ylabel('True Postive Rate') - self.ax.set_xlabel('False Positive Rate') + self.ax.set_xlabel('False Positive Rate') + + def _get_y_scores(self, X): + """ + The ``roc_curve`` metric requires target scores that can either be the + probability estimates of the positive class, confidence values or non- + thresholded measure of decisions (as returned by "decision_function"). + This method computes the scores by resolving the estimator methods + that retreive these values. -def roc_auc(model, X, y=None, ax=None, **kwargs): - """Quick method: + .. todo:: implement confidence values metric. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features -- generally the test data + that is associated with y_true values. + """ + # The resolution order of scoring functions + attrs = ( + 'predict_proba', + 'decision_function', + ) + + # Return the first resolved function + for attr in attrs: + try: + method = getattr(self.estimator, attr, None) + if method: + return method(X) + except AttributeError: + # Some Scikit-Learn estimators have both probability and + # decision functions but override __getattr__ and raise an + # AttributeError on access. + continue + + # If we've gotten this far, raise an error + raise ModelError( + "ROCAUC requires estimators with predict_proba or " + "decision_function methods." + ) + + def _score_micro_average(self, y, y_pred, classes, n_classes): + """ + Compute the micro average scores for the ROCAUC curves. + """ + # Convert y to binarized array for micro and macro scores + y = label_binarize(y, classes=classes) + if n_classes == 2: + y = np.hstack((1-y, y)) + + # Compute micro-average + self.fpr[MICRO], self.tpr[MICRO], _ = roc_curve(y.ravel(), y_pred.ravel()) + self.roc_auc[MICRO] = auc(self.fpr[MICRO], self.tpr[MICRO]) + + def _score_macro_average(self, n_classes): + """ + Compute the macro average scores for the ROCAUC curves. + """ + # Gather all FPRs + all_fpr = np.unique(np.concatenate([self.fpr[i] for i in range(n_classes)])) + avg_tpr = np.zeros_like(all_fpr) + + # Compute the averages per class + for i in range(n_classes): + avg_tpr += interp(all_fpr, self.fpr[i], self.tpr[i]) - Displays the tradeoff between the classifier's - sensitivity and specificity. + # Finalize the average + avg_tpr /= n_classes - This helper function is a quick wrapper to utilize the ROCAUC - ScoreVisualizer for one-off analysis. + # Store the macro averages + self.fpr[MACRO] = all_fpr + self.tpr[MACRO] = avg_tpr + self.roc_auc[MACRO] = auc(self.fpr[MACRO], self.tpr[MACRO]) + + +########################################################################## +## Quick method for ROCAUC +########################################################################## + +def roc_auc(model, X, y=None, ax=None, **kwargs): + """ROCAUC Quick method: + + Receiver Operating Characteristic (ROC) curves are a measure of a + classifier's predictive quality that compares and visualizes the tradeoff + between the models' sensitivity and specificity. The ROC curve displays + the true positive rate on the Y axis and the false positive rate on the + X axis on both a global average and per-class basis. The ideal point is + therefore the top-left corner of the plot: false positives are zero and + true positives are one. + + This leads to another metric, area under the curve (AUC), a computation + of the relationship between false positives and true positives. The higher + the AUC, the better the model generally is. However, it is also important + to inspect the "steepness" of the curve, as this describes the + maximization of the true positive rate while minimizing the false positive + rate. Generalizing "steepness" usually leads to discussions about + convexity, which we do not get into here. Parameters ---------- - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. + model : the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. - y : ndarray or Series of length n - An array or series of target or class values. + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features - ax : matplotlib axes - The axes to plot the figure on. + y : ndarray or Series of length n + An array or series of target or class values + + ax : the axis to plot the figure on. - model : the Scikit-Learn estimator (should be a classifier) + classes : list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + Note that the curves must be computed based on what is in the target + vector passed to the ``score()`` method. Class names are used for + labeling only and must be in the correct order to prevent confusion. + + micro : bool, default = True + Plot the micro-averages ROC curve, computed from the sum of all true + positives and false positives across all classes. + + macro : bool, default = True + Plot the macro-averages ROC curve, which simply takes the average of + curves across all classes. + + per_class : bool, default = True + Plot the ROC curves for each individual class. Primarily this is set + to false if only the macro or micro average curves are required. + + Notes + ----- + ROC curves are typically used in binary classification, and in fact the + Scikit-Learn ``roc_curve`` metric is only able to perform metrics for + binary classifiers. As a result it is necessary to binarize the output or + to use one-vs-rest or one-vs-all strategies of classification. The + visualizer does its best to handle multiple situations, but exceptions can + arise from unexpected models or outputs. + + Another important point is the relationship of class labels specified on + initialization to those drawn on the curves. The classes are not used to + constrain ordering or filter curves; the ROC computation happens on the + unique values specified in the target vector to the ``score`` method. To + ensure the best quality visualization, do not use a LabelEncoder for this + and do not pass in class labels. + + .. sealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html + .. todo:: Allow the class list to filter the curves on the visualization. + + Examples + -------- + >>> from sklearn.datasets import load_breast_cancer + >>> from yellowbrick.classifier import roc_auc + >>> from sklearn.linear_model import LogisticRegression + >>> data = load_breast_cancer() + >>> roc_auc(LogisticRegression(), data.data, data.target) Returns ------- @@ -212,6 +433,7 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) visualizer.score(X_test, y_test) + visualizer.finalize() # Return the axes object on the visualizer return visualizer.ax diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py index 380cabedf..97ba87561 100644 --- a/yellowbrick/cluster/silhouette.py +++ b/yellowbrick/cluster/silhouette.py @@ -94,7 +94,6 @@ def draw(self, labels): """ # Track the positions of the lines being drawn - y_upper = 10 # the top of the silhouette y_lower = 10 # The bottom of the silhouette # Get the colors from the various properties diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py index 915cb166c..11fba7c94 100644 --- a/yellowbrick/regressor/alphas.py +++ b/yellowbrick/regressor/alphas.py @@ -302,7 +302,7 @@ def __init__(self, model, ax=None, alphas=None, ).format(name)) # Call super to initialize the class - super(AlphaSelection, self).__init__(model, ax=ax, **kwargs) + super(ManualAlphaSelection, self).__init__(model, ax=ax, **kwargs) # Set manual alpha selection parameters self.alphas = alphas or np.logspace(-10, -2, 200) diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 7072e2b4f..90b074633 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -193,6 +193,7 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) visualizer.score(X_test, y_test) + visualizer.finalize() # Return the axes object on the visualizer return visualizer.ax @@ -404,6 +405,7 @@ def residuals_plot(model, X, y=None, ax=None, **kwargs): # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) visualizer.score(X_test, y_test) + visualizer.finalize() # Return the axes object on the visualizer return visualizer.ax diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 7008a9bd9..feee21b56 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -503,7 +503,6 @@ def color_palette(palette=None, n_colors=None): n_colors = len(palette) elif not isinstance(palette, string_types): - palette = palette if n_colors is None: n_colors = len(palette) diff --git a/yellowbrick/text/postag.py b/yellowbrick/text/postag.py index 7bfdb7756..034eea138 100644 --- a/yellowbrick/text/postag.py +++ b/yellowbrick/text/postag.py @@ -93,7 +93,6 @@ def __init__(self, ax=None, **kwargs): 'IN' : 'darkwhite', 'POS' : 'darkyellow', 'PRP$' : 'magenta', - 'PRP$' : 'magenta', 'DT' : 'black', 'CC' : 'black', 'CD' : 'black', @@ -140,6 +139,6 @@ def transform(self, tagged_tuples): self.tagged = [ (self.TAGS.get(tag),tok) for tok, tag in tagged_tuples ] - # + # # print(' '.join((colorize(token, color) for color, token in self.tagged))) # print('\n') From 0cbfdebcd5157330483dbdf9a171c487d5209abc Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 26 May 2017 16:16:04 -0400 Subject: [PATCH 14/40] documentation update to bikeshare headers fixes #242 --- docs/quickstart.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/quickstart.rst b/docs/quickstart.rst index c1494b0f1..8500e8d6f 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -118,7 +118,7 @@ After downloading the dataset and unzipping it in your current working directory data = pd.read_csv('bikeshare.csv') X = data[[ "season", "month", "hour", "holiday", "weekday", "workingday", - "weather", "temp", "atemp", "hum", "windspeed" + "weather", "temp", "feelslike", "humidity", "windspeed" ]] y = data["riders"] From 5539f6027e2d8936c7e4cf50a26d5607ca66d175 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 26 May 2017 18:41:21 -0400 Subject: [PATCH 15/40] fixed radviz and pca test dependency fixed #251 --- .gitignore | 1 - .../{test_x_pca.py => test_pca.py} | 25 +++++++-- tests/test_features/test_scatter.py | 27 ++++++---- yellowbrick/classifier/rocauc.py | 6 +-- yellowbrick/features/pca.py | 51 +++++++++++++------ 5 files changed, 75 insertions(+), 35 deletions(-) rename tests/test_features/{test_x_pca.py => test_pca.py} (94%) diff --git a/.gitignore b/.gitignore index ddcb967bc..314ebf661 100644 --- a/.gitignore +++ b/.gitignore @@ -124,6 +124,5 @@ fabric.properties .idea - # VisualTestCase Outputs /tests/actual_images/* diff --git a/tests/test_features/test_x_pca.py b/tests/test_features/test_pca.py similarity index 94% rename from tests/test_features/test_x_pca.py rename to tests/test_features/test_pca.py index 81c94848a..7b67fab82 100644 --- a/tests/test_features/test_x_pca.py +++ b/tests/test_features/test_pca.py @@ -1,18 +1,35 @@ +# tests.test_features.test_pca +# Tests for the PCA based feature visualizer. +# +# Author: Carlo Morales <@cjmorale> +# Created: Tue May 23 18:34:27 2017 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_pca.py [] cmorales@pacificmetrics.com $ + +""" +Tests for the PCA based feature visualizer. +""" + ########################################################################## ## Imports ########################################################################## import unittest +import yellowbrick import numpy as np import numpy.testing as npt -import yellowbrick -from yellowbrick.features.pca import * +from tests.base import VisualTestCase +from yellowbrick.features.pca import * + ########################################################################## ##PCA Tests ########################################################################## -class PCADecompositionTests(unittest.TestCase): +class PCADecompositionTests(VisualTestCase): """ Test the PCADecomposition visualizer (scaled or non-scaled) for 2 and 3 dimensions. """ @@ -145,7 +162,7 @@ def test_scale_true_3d(self): visualizer.poof() npt.assert_array_almost_equal(pca_array, X_pca_decomp) - + def test_scale_false_3d(self): """ Test the PCADecomposition visualizer 3 dimensions non-scaled. diff --git a/tests/test_features/test_scatter.py b/tests/test_features/test_scatter.py index a5486405b..cfcfc5436 100644 --- a/tests/test_features/test_scatter.py +++ b/tests/test_features/test_scatter.py @@ -86,8 +86,8 @@ def test_color_builds(self): visualizer.fit_transform(X_two_cols, self.y) def test_scatter_no_features(self): - """Assert no errors occur during scatter visualizer integration - with no featues + """ + Assert no errors during scatter visualizer integration - no features """ X_two_cols = self.X[:, :2] visualizer = ScatterViz() @@ -96,7 +96,7 @@ def test_scatter_no_features(self): def test_scatter_only_two_features_allowed_init(self): """ - Assert that only two features are allowed for this visualizer in init + Assert that only two features are allowed for scatter visualizer init """ features = ["temperature", "relative_humidity", "light"] @@ -106,7 +106,7 @@ def test_scatter_only_two_features_allowed_init(self): def test_scatter_xy_and_features_raise_error(self): """ - Assert that x,y and features will raise error + Assert that x,y and features will raise scatterviz error """ features = ["temperature", "relative_humidity", "light"] @@ -115,15 +115,14 @@ def test_scatter_xy_and_features_raise_error(self): def test_scatter_xy_changes_to_features(self): """ - Assert that x,y and features will raise error + Assert that x,y with no features will not raise scatterviz error """ visualizer = ScatterViz(x='one', y='two') self.assertEquals(visualizer.features_, ['one', 'two']) def test_scatter_requires_two_features_in_numpy_matrix(self): """ - Assert that only two features are allowed for this visualizer if not - set in the init + Assert only two features allowed for scatter visualizer if not in init """ visualizer = ScatterViz() with self.assertRaises(YellowbrickValueError) as context: @@ -151,7 +150,7 @@ def test_integrated_scatter(self): def test_scatter_quick_method(self): """ - Test scatter on the real, occupancy data set + Test scatter quick method on the real, occupancy data set """ # Load the data from the fixture X = self.occupancy[[ @@ -173,7 +172,7 @@ def test_scatter_quick_method(self): "Pandas is not installed, could not run test.") def test_integrated_scatter_with_pandas(self): """ - Test scatter on the real, occupancy data set with a pandas dataframe + Test scatterviz on the real, occupancy data set with pandas """ # Load the data from the fixture X = self.occupancy[[ @@ -193,6 +192,9 @@ def test_integrated_scatter_with_pandas(self): visualizer.fit_transform_poof(X, y) def test_integrated_scatter_numpy_named_arrays(self): + """ + Test scatterviz on numpy named arrays + """ dt = np.dtype({ 'names': ['one', 'two', 'three', 'four', "five"], 'formats': [ @@ -211,13 +213,16 @@ def test_integrated_scatter_numpy_named_arrays(self): def test_integrated_scatter_numpy_arrays_no_names(self): + """ + Test scaterviz on regular numpy arrays + """ visualizer = ScatterViz(features=[1, 2]) visualizer.fit_transform_poof(self.X, self.y) self.assertEquals(visualizer.features_, [1, 2]) def test_scatter_image(self): """ - Assert no errors occur during scatter visualizer integration + Test the scatterviz image similarity """ # self.setUp_ImageTest() @@ -232,7 +237,7 @@ def test_scatter_image(self): def test_scatter_image_fail(self): """ - Assert no errors occur during scatter visualizer integration + Assert bad image similarity on scatterviz errors """ X_two_cols = self.X[:, :2] diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index 65b8b0558..7b71c2712 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -106,7 +106,7 @@ class ROCAUC(ClassificationScoreVisualizer): ensure the best quality visualization, do not use a LabelEncoder for this and do not pass in class labels. - .. sealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html + .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html .. todo:: Allow the class list to filter the curves on the visualization. Examples @@ -199,7 +199,7 @@ def draw(self): Called internally by score, possibly more than once Returns - ------ + ------- ax : the axis with the plotted figure """ colors = self.colors[0:len(self.classes_)] @@ -408,7 +408,7 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): ensure the best quality visualization, do not use a LabelEncoder for this and do not pass in class labels. - .. sealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html + .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html .. todo:: Allow the class list to filter the curves on the visualization. Examples diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py index 38314fda9..90752fd5f 100644 --- a/yellowbrick/features/pca.py +++ b/yellowbrick/features/pca.py @@ -1,32 +1,51 @@ +# yellowbrick.features.pca +# Decomposition based feature visualization with PCA. +# +# Author: Carlo Morales <@cjmorale> +# Created: Tue May 23 18:34:27 2017 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: pca.py [] cmorales@pacificmetrics.com $ + +""" +Decomposition based feature visualization with PCA. +""" + ########################################################################## ## Imports ########################################################################## + import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from yellowbrick.features.base import DataVisualizer from yellowbrick.style import palettes -from yellowbrick.exceptions import YellowbrickError +from yellowbrick.exceptions import YellowbrickValueError from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler + + ########################################################################## ## Quick Methods ########################################################################## + def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, colormap=palettes.DEFAULT_SEQUENCE, color=None, **kwargs): - """Produce a two or three dimensional principal component (PC) plot of a data set + """Produce a two or three dimensional principal component (PC) plot of a data set projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted data set before applying a PC decomposition. There are scale and center arguments - that can be used to control centering anc scaling of an inputted data set. Therefore + that can be used to control centering anc scaling of an inputted data set. Therefore this class is a one stop shop for easily getting a PC plot. - + Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features - + y : ndarray or Series of length n An array or series of target or class values @@ -38,12 +57,12 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, proj_dim : int, default: 2 The dimension of the PCA project for visualizer. - + colormap : string or cmap, default: None optional string or matplotlib cmap to colorize lines Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. - + color : list or tuple of colors, default: None Specify the colors for each individual class. @@ -58,7 +77,7 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, >>> X = iris.data >>> y = iris.target >>> pca_decomposition(X, color=y, proj_dim=3, colormap='RdBu_r') - + """ # Instantiate the visualizer visualizer = PCADecomposition(X=X, y=y, ax=ax, scale=scale, proj_dim=proj_dim, @@ -75,30 +94,30 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, ########################################################################## class PCADecomposition(DataVisualizer): """ - Produce a two or three dimensional principal component (PC) plot of a data set + Produce a two or three dimensional principal component (PC) plot of a data set projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted data set before applying a PC decomposition. There are scale and center arguments - that can be used to control centering anc scaling of an inputted data set. Therefore + that can be used to control centering anc scaling of an inputted data set. Therefore this class is a one stop shop for easily getting a PC plot. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features - + y : ndarray or Series of length n An array or series of target or class values ax : matplotlib Axes, default: None The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). - + scale : bool, default: True Boolean that indicates if user wants to scale data. - + proj_dim : int, default: 2 The dimension of the PCA project for visualizer. - + color : list or tuple of colors, default: None Specify the colors for each individual class. @@ -106,7 +125,7 @@ class PCADecomposition(DataVisualizer): optional string or matplotlib cmap to colorize lines Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. - + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -129,7 +148,7 @@ def __init__(self, X=None, y=None, ax=None, scale=True, color=None, proj_dim=2, super(PCADecomposition, self).__init__(ax=ax, **kwargs) # Data Parameters if proj_dim not in (2, 3): - raise YellowbrickError("proj_dim object is not 2 or 3.") + raise YellowbrickValueError("proj_dim object is not 2 or 3.") self.color = color self.pca_features_ = None From 277edd38fa845b454e2d15c9191e936ef3cd60b2 Mon Sep 17 00:00:00 2001 From: JimStearns206 Date: Tue, 30 May 2017 08:44:50 -0700 Subject: [PATCH 16/40] Refactor of test of matplotlib version. Suggested by @bbengfort. (#254) --- yellowbrick/features/jointplot.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index e7a63574a..d2946e070 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -131,9 +131,7 @@ def __init__(self, ax=None, feature=None, target=None, # Check matplotlib version - needs to be version 2.0.0 or greater. mpl_vers_maj = int(mpl.__version__.split(".")[0]) - if mpl_vers_maj >= 2: - pass - else: + if mpl_vers_maj < 2: warnings.warn(( "{} requires matplotlib major version 2 or greater. " "Please upgrade." From ff10ca06758af08c1dea8cefbb78474c7c5a8f39 Mon Sep 17 00:00:00 2001 From: Carlo Morales Date: Mon, 5 Jun 2017 16:44:23 -0600 Subject: [PATCH 17/40] initial coding to resolve issue 244 (#253) * inital coding to resolve issue 244 * Fixed typo in docstring * change assertRaisesRegex to assertRaisesRegexp * label_encoder argument added to __init__ method * changes from code review * updates after code review --- ...onfusionMatrix label_encoder example.ipynb | 113 ++++++++++++++++++ .../test_classifier/test_confusion_matrix.py | 34 +++++- yellowbrick/classifier/confusion_matrix.py | 35 ++++-- 3 files changed, 171 insertions(+), 11 deletions(-) create mode 100644 examples/cjmorale/ConfusionMatrix label_encoder example.ipynb diff --git a/examples/cjmorale/ConfusionMatrix label_encoder example.ipynb b/examples/cjmorale/ConfusionMatrix label_encoder example.ipynb new file mode 100644 index 000000000..d617c6d2b --- /dev/null +++ b/examples/cjmorale/ConfusionMatrix label_encoder example.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from yellowbrick.classifier.confusion_matrix import *\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import LabelEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAIARoQAAAIwIBQAAYEQoAAAAI0IBAAAYEQoAAMCIUAAAAEaEAgAAMCIUAACAEaEAAACM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\nEaEAAACMCAUAAGD0/wAwrDQlVlGeQgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv('examples/data/occupancy/occupancy.csv')\n", + "\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "target = \"occupancy\"\n", + "\n", + "X = df[features]\n", + "y = df[target]\n", + "classes = [\"unoccupied\", \"occupied\"]\n", + "le = LabelEncoder()\n", + "le.fit(classes)\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)\n", + "\n", + "viz = ConfusionMatrix(LogisticRegression(), classes=classes, label_encoder =le)\n", + "viz.fit(X_train, y_train)\n", + "viz.score(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2uJSsLXuplphAQdb+gC11gIyMDOLi4ggPDyc5OZmIiAh8Ph8hISGkp6dTo0YNsyOeEivM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\nrFu3zq/F7vV6cblcADgcDmJiYggODiY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "digits = load_digits()\n", + "X = digits.data\n", + "y = digits.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + "X_train = X_train\n", + "X_test = X_test\n", + "y_train = y_train\n", + "y_test = y_test\n", + "\n", + "model = LogisticRegression()\n", + "classes = ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']\n", + "mapping = {'zero': '0', 'one': '1', 'two': '2', 'three': '3', 'four': '4', 'five': '5',\n", + " 'six': '6', 'seven': '7', 'eight': '8', 'nine': '9'}\n", + "cm = ConfusionMatrix(model, classes=classes, label_encoder = mapping)\n", + "cm.fit(X_train, y_train)\n", + "cm.score(X_test, y_test)\n", + "cm.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py index a2c4eabd6..75e5c3aea 100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -1,13 +1,12 @@ - +import yellowbrick from yellowbrick.classifier.confusion_matrix import * from tests.base import VisualTestCase - +from sklearn.preprocessing import LabelEncoder from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression - - +from sklearn.linear_model import PassiveAggressiveRegressor class ConfusionMatrixTests(VisualTestCase): @@ -61,3 +60,30 @@ def test_one_class(self): cm = ConfusionMatrix(model, classes=[0]) cm.fit(self.X_train, self.y_train) cm.score(self.X_test, self.y_test) + + def test_defined_mapping(self): + model = LogisticRegression() + classes = ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] + mapping = {0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five', + 6: 'six', 7: 'seven', 8: 'eight', 9: 'nine'} + cm = ConfusionMatrix(model, classes=classes, label_encoder = mapping) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + + def test_inverse_mapping(self): + model = LogisticRegression() + le = LabelEncoder() + classes = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] + le.fit(['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']) + cm = ConfusionMatrix(model, classes=classes, label_encoder=le) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + + def test_isclassifier(self): + model = PassiveAggressiveRegressor() + message = 'This estimator is not a classifier; try a regression or clustering score visualizer instead!' + classes = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] + + with self.assertRaisesRegexp(yellowbrick.exceptions.YellowbrickError, message): + ConfusionMatrix(model, classes=classes) + diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index 868098462..b2fef338b 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -32,6 +32,9 @@ ## ConfusionMatrix ########################################################################## +CMAP_OVERCOLOR = '#2a7d4f' + + class ConfusionMatrix(ClassificationScoreVisualizer): """ Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion @@ -50,11 +53,18 @@ class ConfusionMatrix(ClassificationScoreVisualizer): ax : the matplotlib axis to plot the figure on (if None, a new axis will be created) - classes : a list of class names to use in the confusion_matrix. - This is passed to the 'labels' parameter of sklearn.metrics.confusion_matrix(), and follows the behaviour + classes : list, default: None + a list of class names to use in the confusion_matrix. This is passed to the 'labels' + parameter of sklearn.metrics.confusion_matrix(), and follows the behaviour indicated by that function. It may be used to reorder or select a subset of labels. If None, values that appear at least once in y_true or y_pred are used in sorted order. - Default: None + + label_encoder : dict or LabelEncoder, default: None + When specifying the ``classes`` argument, the input to ``fit()`` and ``score()`` must match the + expected labels. If the ``X`` and ``y`` datasets have been encoded prior to training and the + labels must be preserved for the visualization, use this argument to provide a mapping from the + encoded class to the correct label. Because typically a Scikit-Learn ``LabelEncoder`` is used to + perform this operation, you may provide it directly to the class to utilize its fitted encoding. Examples -------- @@ -68,7 +78,7 @@ class ConfusionMatrix(ClassificationScoreVisualizer): """ - def __init__(self, model, ax=None, classes=None, **kwargs): + def __init__(self, model, ax=None, classes=None, label_encoder=None, **kwargs): super(ConfusionMatrix, self).__init__( model, ax=ax, classes=classes, **kwargs ) @@ -77,9 +87,10 @@ def __init__(self, model, ax=None, classes=None, **kwargs): self.confusion_matrix = None self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) - self.cmap.set_under(color='w') - self.cmap.set_over(color='#2a7d4f') - self.edgecolors=[] #used to draw diagonal line for predicted class = true class + self.cmap.set_under(color = 'w') + self.cmap.set_over(color=CMAP_OVERCOLOR) + self.edgecolors = [] #used to draw diagonal line for predicted class = true class + self.label_encoder = label_encoder def score(self, X, y, sample_weight=None, percent=True): """ @@ -103,6 +114,16 @@ def score(self, X, y, sample_weight=None, percent=True): """ y_pred = self.predict(X) + + if self.label_encoder: + try : + y = self.label_encoder.inverse_transform(y) + y_pred = self.label_encoder.inverse_transform(y_pred) + except AttributeError: + # if a mapping is passed to class apply it here. + y = [self.label_encoder[x] for x in y] + y_pred = [self.label_encoder[x] for x in y_pred] + self.confusion_matrix = confusion_matrix( y, y_pred, labels=self.classes_, sample_weight=sample_weight ) From bd2bf1e39929f218e548d016e86c47b64d7f7a81 Mon Sep 17 00:00:00 2001 From: Carlo Morales Date: Thu, 8 Jun 2017 09:21:00 -0600 Subject: [PATCH 18/40] updating docstrings in pca.py file (#256) --- yellowbrick/features/pca.py | 38 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py index 90752fd5f..e255e79df 100644 --- a/yellowbrick/features/pca.py +++ b/yellowbrick/features/pca.py @@ -35,19 +35,18 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, colormap=palettes.DEFAULT_SEQUENCE, color=None, **kwargs): - """Produce a two or three dimensional principal component (PC) plot of a data set - projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted - data set before applying a PC decomposition. There are scale and center arguments - that can be used to control centering anc scaling of an inputted data set. Therefore - this class is a one stop shop for easily getting a PC plot. - + """Produce a two or three dimensional principal component plot of the data array ``X`` + projected onto it's largest sequential principal components. It is common practice to scale the + data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using + the ``scale`` argument. + Parameters ---------- X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features + A matrix of n instances with m features. y : ndarray or Series of length n - An array or series of target or class values + An array or series of target or class values. ax : matplotlib Axes, default: None The axes to plot the figure on. @@ -56,10 +55,10 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, Boolean that indicates if the values of X should be scaled. proj_dim : int, default: 2 - The dimension of the PCA project for visualizer. + Dimension of the PCA visualizer. colormap : string or cmap, default: None - optional string or matplotlib cmap to colorize lines + Optional string or matplotlib cmap to colorize lines. Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. @@ -94,35 +93,34 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, ########################################################################## class PCADecomposition(DataVisualizer): """ - Produce a two or three dimensional principal component (PC) plot of a data set - projected onto it first 2 or 3 PC. It is best practices to center and scale the inputted - data set before applying a PC decomposition. There are scale and center arguments - that can be used to control centering anc scaling of an inputted data set. Therefore - this class is a one stop shop for easily getting a PC plot. + Produce a two or three dimensional principal component plot of the data array ``X`` + projected onto it's largest sequential principal components. It is common practice to scale the + data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using + the ``scale`` argument. Parameters ---------- X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features + A matrix of n instances with m features. y : ndarray or Series of length n - An array or series of target or class values + An array or series of target or class values. ax : matplotlib Axes, default: None - The axes to plot the figure on. If None is passed in the current axes + The axes to plot the figure on. If None is passed in the current axes. will be used (or generated if required). scale : bool, default: True Boolean that indicates if user wants to scale data. proj_dim : int, default: 2 - The dimension of the PCA project for visualizer. + Dimension of the PCA visualizer. color : list or tuple of colors, default: None Specify the colors for each individual class. colormap : string or cmap, default: None - optional string or matplotlib cmap to colorize lines + Optional string or matplotlib cmap to colorize lines. Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. From ee91da75a55036c540e64c321c8875e08c2f0262 Mon Sep 17 00:00:00 2001 From: Nathan Date: Mon, 19 Jun 2017 17:58:39 -0700 Subject: [PATCH 19/40] Decision Boundary Visualizer (#196) * added a scatterviz with tests in features * added scatter plot example notebook and changes to init method * rename examples folder * adding in feature selection logic to fit method * added tests for the pandas addition plus a few other minor areas that were missed before * added one more test case * adding color support to scatter viz * pep8ing * added BivariateFeatureMixin with tests * knn works * remove neighbors folder and just put in neighbors.py * test files using KnnDecisionBoundariesVisualizer * working examples in notebook * Implementing usage of KnnDecisionBoundariesVisualizer using CollegeScore dataset * minor fix to make the colormap load default palettes * committed changes to notebook to fix path issues * updated data and notebook * working implemetnation * adding in other types of models and running throw the visualizer * add glass notebook with multi models * user study using balance scale data * minor cleanup * cleaning up and adding tests * tests pass * adding scatter alpha param to init * removing workspace junk * remove more junk * add python2 test comapt * cleanup and removing unused imports * add short name * add title to visualizer * add example notebook * fix test * testing and evaluating DecisionBoundariesVisualizer * fixed color assignment bug in DecisionBoundariesVisualizer * fixed color assignment bug in DecisionBoundariesVisualizer * reformatted doc string * add is_structured_array util and tests * commented out is_structure_array logic * general cleanup * Added x,y to init with controlling logic, named array and selection by array column index * adding stronger logic for handling X with multiple columns such as a dataframe, structured array, narray * work in progress * named arrays work in tests * add visual unit tests * make select feature columns private method * add quick method to decision boundaries * fix spacing * minor changes to alphas * update notebook * fix requirements merge conflict * fix pandas not found testing error * fix test file * moved to classifiervisualizer * fixed doc strings * made changes requested in code review --- examples/balavenkatesan/balance-scale.csv | 626 ++++++++++++++++++ examples/balavenkatesan/testing.ipynb | 468 ------------- examples/balavenkatesan/testing.py | 84 +++ ...Decision Boundaries Example Notebook.ipynb | 293 ++++++++ examples/ndanielsen/Untitled.ipynb | 385 +++++++++++ examples/ndanielsen/Untitled1.ipynb | 212 ++++++ requirements.txt | 2 - ...est_integrated_plot_numpy_named_arrays.png | Bin 0 -> 11487 bytes .../test_real_data_set_viz.png | Bin 0 -> 37490 bytes tests/test_classifier/test_boundaries.py | 330 +++++++++ yellowbrick/classifier/__init__.py | 1 + yellowbrick/classifier/boundaries.py | 436 ++++++++++++ 12 files changed, 2367 insertions(+), 470 deletions(-) create mode 100644 examples/balavenkatesan/balance-scale.csv delete mode 100644 examples/balavenkatesan/testing.ipynb create mode 100644 examples/balavenkatesan/testing.py create mode 100644 examples/ndanielsen/Decision Boundaries Example Notebook.ipynb create mode 100644 examples/ndanielsen/Untitled.ipynb create mode 100644 examples/ndanielsen/Untitled1.ipynb create mode 100644 tests/baseline_images/test_classifier/test_boundaries/test_integrated_plot_numpy_named_arrays.png create mode 100644 tests/baseline_images/test_classifier/test_boundaries/test_real_data_set_viz.png create mode 100644 tests/test_classifier/test_boundaries.py create mode 100644 yellowbrick/classifier/boundaries.py diff --git a/examples/balavenkatesan/balance-scale.csv b/examples/balavenkatesan/balance-scale.csv new file mode 100644 index 000000000..fab8d04a2 --- /dev/null +++ b/examples/balavenkatesan/balance-scale.csv @@ -0,0 +1,626 @@ +CLASS-NAME,LEFT-WEIGHT,LEFT-DISTANCE,RIGHT-WEIGHT,RIGHT-DISTANCE +B,1,1,1,1 +R,1,1,1,2 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a/examples/balavenkatesan/testing.ipynb +++ /dev/null @@ -1,468 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 148, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "import os\n", - "import sys \n", - "\n", - "# Modify the path \n", - "sys.path.append(\"..\")\n", - "\n", - "import yellowbrick as yb \n", - "import matplotlib.pyplot as plt\n", - "from sklearn.datasets import load_boston\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Using Yellowbrick to Boston Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "This is a user study to test YellowBrick library by exploring the Boston Dataset in sklearn. " - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "#loading the dataset, extracting the data, targets and feature names\n", - "boston = load_boston()\n", - "X = boston.data[:, None, 0]\n", - "\n", - "# target contains the price\n", - "y = boston.target\n", - "# Use only one feature\n", - "features = boston.feature_names" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Boston House Prices dataset\n", - "===========================\n", - "\n", - "Notes\n", - "------\n", - "Data Set Characteristics: \n", - "\n", - " :Number of Instances: 506 \n", - "\n", - " :Number of Attributes: 13 numeric/categorical predictive\n", - " \n", - " :Median Value (attribute 14) is usually the target\n", - "\n", - " :Attribute Information (in order):\n", - " - CRIM per capita crime rate by town\n", - " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", - " - INDUS proportion of non-retail business acres per town\n", - " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", - " - NOX nitric oxides concentration (parts per 10 million)\n", - " - RM average number of rooms per dwelling\n", - " - AGE proportion of owner-occupied units built prior to 1940\n", - " - DIS weighted distances to five Boston employment centres\n", - " - RAD index of accessibility to radial highways\n", - " - TAX full-value property-tax rate per $10,000\n", - " - PTRATIO pupil-teacher ratio by town\n", - " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", - " - LSTAT % lower status of the population\n", - " - MEDV Median value of owner-occupied homes in $1000's\n", - "\n", - " :Missing Attribute Values: None\n", - "\n", - " :Creator: Harrison, D. and Rubinfeld, D.L.\n", - "\n", - "This is a copy of UCI ML housing dataset.\n", - "http://archive.ics.uci.edu/ml/datasets/Housing\n", - "\n", - "\n", - "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", - "\n", - "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", - "prices and the demand for clean air', J. Environ. Economics & Management,\n", - "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", - "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", - "pages 244-261 of the latter.\n", - "\n", - "The Boston house-price data has been used in many machine learning papers that address regression\n", - "problems. \n", - " \n", - "**References**\n", - "\n", - " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", - " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", - " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", - "\n" - ] - } - ], - "source": [ - "print (boston.DESCR)" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(506,)" - ] - }, - "execution_count": 152, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "### Creating a LinearRegression model by splitting the data and targets into train & test datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "## from sklearn.model_selection import train_test_split as tts\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", - "from yellowbrick.regressor import ResidualsPlot\n", - "\n", - "model = LinearRegression()\n", - "\n", - "# Split the data into training/testing sets\n", - "X_train = X[:-20]\n", - "X_test = X[-20:]\n", - "\n", - "# Split the targets into training/testing sets\n", - "y_train = y[:-20]\n", - "y_test = y[-20:]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(486, 1)" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients: \n", - " [-0.42249408]\n", - "Variance score: -1.39\n" - ] - }, - { - "data": { - "image/png": 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BwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFC\nFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgKNLQdcqOCqWCnLIT5TQA\nwMzOKP7QYqmosckxTc1OqVKtqCfZo5ODJzU6PKozmTNRTAkATJiHbrFU1LmL5zRzd+bJ5xariyo4\nBU0vTGv87Lhy2Zz1tADAhPnywtjk2FOBu97M3RldmLxgPCMAsGMauqVySVOzUy3HXJm9whovgI5l\nGrpO2VGlWmk5plKtyLlD6ALoTKahm0lnlEqmWo5JJVPK9GaMZgQAtkxDN5vO6tTgqZZjTg+eViZN\n6ALoTOYbaaPDoxo6MNTwa0MHhnR++LzxjADAjnnonsmc0fjZceUz+SdLDalkSvlMXhPfn6BOF0BH\ni+RwRC6bUy6bk1N25NxxlOnNsKQAIBYiCd26TJqwBRAvNLwBAEORhS7NbgDEUSS9F2h2AyCuTEOX\nZjcA4s50eYFmNwDizix0aXYDAIahS7MbADAMXZrdAIBh6NLsBgCMN9JodgMg7kxDl2Y3AOLO/HAE\nzW4AxFlkDW9odgMgjmh4AwCGCF0AMNRqeaFLkpaXl42mAgDb37rM7Gr09Vahe1iSbty4EfCUACAW\nDkv6eOMnW4XulKRhSTclrYQ0KQDoNF1yA7dhs5lErVaznQ4AxBgbaQBgiNAFAEOELgAYInQBwND/\nA1p4c6CIUAJvAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Train the model using the training sets\n", - "model.fit(X_train, y_train)\n", - "\n", - "# The coefficients\n", - "print('Coefficients: \\n', model.coef_)\n", - "\n", - "# Explained variance score: 1 is perfect prediction\n", - "print('Variance score: %.2f' % model.score(X_test, y_test))\n", - "\n", - "# Plot outputs\n", - "plt.scatter(X_test, y_test, color='green')\n", - "plt.plot(X_test, model.predict(X_test), color='blue', linewidth=3)\n", - "\n", - "plt.xticks(())\n", - "plt.yticks(())\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import cross_val_predict\n", - "y_pred = cross_val_predict(model, X, y, cv=10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(506,)" - ] - }, - "execution_count": 157, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(506,)" - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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VLV9J8TFY4uLq53GE2sdp1Dwu4vGVeXztY3HJrG4p5K4tCHittOTmL8cSSuAY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\nrfUnQFP6JSoBz06HJKA82AtYLJY2/0W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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.scatter(y, y_pred)\n", - "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n", - "ax.set_xlabel('Measured')\n", - "ax.set_ylabel('Predicted')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "image/png": 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1s7xmvSt0UBjfvyufXncaG289h7umDeevn3/PsrxiyqqNoNm6XXOCB+SmrGVd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\nliFoYNBa9zd1luabBTwL7MKoQrpDa/1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from yellowbrick.regressor import PredictionError\n", - "from yellowbrick.regressor import RegressionScoreVisualizer\n", - "y = y.reshape(-1, 1)\n", - "sviz = PredictionError(model)\n", - "sviz.score(y, y_pred)\n", - "sviz.poof() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "from sklearn.neighbors import KNeighborsRegressor\n", - "knn = KNeighborsRegressor()\n", - "knn.fit(X_train, y_train)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "sviz = PredictionError(knn)\n", - "sviz.score(y, y_pred)\n", - "sviz.poof() " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/balavenkatesan/testing.py b/examples/balavenkatesan/testing.py new file mode 100644 index 000000000..0793d55ed --- /dev/null +++ b/examples/balavenkatesan/testing.py @@ -0,0 +1,84 @@ +import pandas as pd +import csv +from sklearn import neighbors +from sklearn import datasets +import numpy as np +import yellowbrick as yb +from yellowbrick.neighbors import KnnDecisionBoundariesVisualizer +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap + + +def load_adm_sat_school_data(return_X_y=False): + + with open("./merged_adm_sat_data.csv") as csv_file: + data_file = csv.reader(csv_file) + temp = next(data_file) + n_samples = int(temp[0]) + n_features = int(temp[1]) + target_names = np.array(temp[2:]) + + + df = pd.read_csv("./merged_adm_sat_data.csv", sep=",", usecols=(0, 1, 2, 3), skiprows=0) + data = np.empty((n_samples, n_features), dtype=int) + target = np.ma.empty((n_samples,), dtype=int) + + for index, row in df.iterrows(): + data[index] = np.asarray([df.iloc[index][0], df.iloc[index][1], df.iloc[index][2]], dtype=np.float) + target[index] = np.asarray(df.iloc[index][3], dtype=np.int) + + feature_names = np.array(['ACT_AVG','SAT_AVG','GRAD_DEBT','REGION']) + + if return_X_y: + return data, target + + return datasets.base.Bunch(data=data, target=target, + target_names=target_names, + DESCR='School Data set', + feature_names=feature_names) + +def show_plot(X, y, n_neighbors=10, h=0.2): + # Create color maps + cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#AAAAFF']) + cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000',]) + + for weights in ['uniform', 'distance']: + # we create an instance of Neighbours Classifier and fit the data. + clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights) + clf.fit(X, y) + clf.n_neighbors = n_neighbors + + # Plot the decision boundary. For that, we will assign a color to each + # point in the mesh [x_min, x_max]x[y_min, y_max]. + x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 + y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), + np.arange(y_min, y_max, h)) + Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) + + # Put the result into a color plot + Z = Z.reshape(xx.shape) + plt.figure() + plt.pcolormesh(xx, yy, Z, cmap=cmap_light) + + # Plot also the training points + plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) + plt.xlim(xx.min(), xx.max()) + plt.ylim(yy.min(), yy.max()) + plt.title("3-Class classification (k = %i, weights = '%s')" + % (n_neighbors, weights)) + + plt.show() + + +if __name__ == '__main__': + school = load_adm_sat_school_data() + X = school.data[:, :2] # we only take the first two features. + y = school.target + #show_plot(X,y,3) + model = neighbors.KNeighborsClassifier(10) + model.fit(X,y) + model.predict(X) + #visualizer = KnnDecisionBoundariesVisualizer(model, classes=school.target_names, features=school.feature_names[:2]) + visualizer = KnnDecisionBoundariesVisualizer(model) + visualizer.fit_draw_poof(X, y) \ No newline at end of file diff --git a/examples/ndanielsen/Decision Boundaries Example Notebook.ipynb b/examples/ndanielsen/Decision Boundaries Example Notebook.ipynb new file mode 100644 index 000000000..54748f3b7 --- /dev/null +++ b/examples/ndanielsen/Decision Boundaries Example Notebook.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"./../..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# example adapted from: http://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py\n", + "%reload_ext yellowbrick\n", + "from yellowbrick.classifier import DecisionViz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.datasets import make_moons, make_circles, make_classification\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.gaussian_process import GaussianProcessClassifier\n", + "from sklearn.gaussian_process.kernels import RBF\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", + "\n", + "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n", + " \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n", + " \"Naive Bayes\", \"QDA\"]\n", + "\n", + "classifiers = [\n", + " KNeighborsClassifier(3),\n", + " SVC(kernel=\"linear\", C=0.025),\n", + " SVC(gamma=2, C=1),\n", + " GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),\n", + " DecisionTreeClassifier(max_depth=5),\n", + " RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n", + " MLPClassifier(alpha=1),\n", + " AdaBoostClassifier(),\n", + " GaussianNB(),\n", + " QuadraticDiscriminantAnalysis()]\n", + "\n", + "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n", + " random_state=1, n_clusters_per_class=1)\n", + "rng = np.random.RandomState(2)\n", + "X += 2 * rng.uniform(size=X.shape)\n", + "linearly_separable = (X, y)\n", + "\n", + "data_set = make_moons(noise=0.3, random_state=0)\n", + "\n", + "X, y = data_set\n", + "X = StandardScaler().fit_transform(X)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LRKiuGQaQUb2RGptzmDRrGJeXre/We6qeNZSmQDzXSSYZnFKBLfiUavF8A7aJ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\nY4wxLf4/Cq0nQ8e+ZbAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = classifiers[0]\n", + "title = names[0]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ALcAR0bKZZjbF3deFHaOIlKPSmGeQqXzUFJYC3wIejDs+EFji7h8CmNkM4CvAo+GGJ+ko\n6lE8WmSvrDWNJho/clnRrE+US6H/pbr7n82sX4KiLsDGmOebgK6hBCWZK9ZRPFp+o4yVZ59BWwrp\n9u1joHPM887AR3mKRdJRhKN4tPxGOQrqhWoqSqyQksIiYH8z6wZ8QtB0dHN+QxIpDEXdPFcwVDNI\nRd5/k8zsVKCTu99tZhcDfyPYO/oed1+d3+hECkSxNs8VhPKaZ5CpvCQFd18OHBV9/MeY41OBqfmI\nSXbSXWmBKsLmufxSzSAd+uuWXaV7V6pRPFIgaiobmTBKo4nSoaQgibX3rlSjeKQgBE1Fqh2kT0lB\nskKjeCS/1FSULUoKJUx9AyVGzXO7qCBCTVWkZDa4KQQpfSuY2cHAyOj509x9fi6DkizRiJXSoea5\nOEHN4LYTSnNPg3xqMymY2enAFcBkoAJ43Myudvd7chybZEO6I1Z0V1pQ1DwHpbj1ZSFKpabwX8CR\n7v4BgJldC0wDlBRKle5KpaBoBnJ7zZu+mDUrN9CzrhuDhg9o12tTSQpVTQkBwN3Xm1lje4OU4qG7\nUikUGlrafpPvncGQkQcwaPgAVtevZ/K9Mxhz1rCUX59KUnjdzG4FJkWfnwO8nkasIiIp0miidMx5\ncTFDRh5A7/57A0T/ewDzpi9OucaQSlI4l6BP4R6C5SeeB36UTsCSB+obkKISJINxI+rVVJSGdas2\nMGREyy//3v33Zv7Md2B4au+RSlL4MXCvu1/a7gglv0LqG+i4chY9Gpaztrofm+uGZu19pZyoZpAN\nPeu6sbp+fXNNAWB1/Xp69v1cyu+RUp8CcKeZdSdYrO5JgmGpDe2MV0IWRt/AoJX3c9LofvTufzCr\n69fzxJT7mVd3Rk6vKaVEi9Vl06DhA5h87wwgaEJaXb+eOdPezm6fgrvfANxgZl2AU4H7CPY60AY4\nZa7jyleiCWFn++VJo8GnzFKNQdqgmkGujDlrGPOmL2b+zHfo2fdz7UoIkNo8hVOAEQQtUjuARwj6\nFaTM9WhYQe/+B7c41rv/3vTYtoClKCnIrrT1ZTgGDR+Qch9CvFSaj26Jnncr8Bd3X5zepaTUrK3u\nl7D9cm1tv/wFJQWrAtUMikFlWye4ex+CmsJG4Goze83M/pDzyKTgba4byhNTlrO6fj1AtE9huZqO\nJEaEoKmokTtGL1JCKAKprohWBdQAu0f/bclZRFJU5tWdgU+ZRY9tC1hb24/N6mQWQH0GxSuVPoXV\nwArgKeAKd5+X86ikqGyuG6o+BInSkhTFLmlSMLMz3P1+4FB3Xx9iTCJSdFQzKBWt1RQuAu7PdkIw\ns0rgDuBQYCvwfXdfElM+ERgGbIoeGuPuG7MZQ65pHwMpJxVEuOqYpRpNVCLy8e10EtDB3Y82s6OA\nXwJjYsoHA8cVde1E+xhIWdCeBqWotaRwkJktS3C8Aoi4+xfSvOYw4BkAd3/FzIY0FURrEfsDd0dn\nUE8q2n0b0t3HQKSgBV/+aioqXa0lhSXAN3NwzS4Ew1ub7DCz6uiyGR2B24FfEYx4esHM5rj7GzmI\nQ0TaJaIO5DLQWlLY5u4rcnDNjwmWyWhSGbOO0hZgortvATCz5wn6HpQURPJCNYNy01pSmJmja84E\nTgQeifYpvBlTNgB42MwOJ5hYNwy4P0dxiEgrgg5kLVSXikx2Ois0SZOCu/8kR9d8HPiamb1E0D9x\nlpldDCxx9ylm9iDwCrAdeMDd38pRHLmlfQykiNVUNqoDOUWZ7nRWaEIffeTujcAP4w6/HVN+E3BT\nqEFlm/Y4lqIUjCYKFqxbqoSQgmzsdFZoNGA+B7THsRQbNRWlJxs7nRWalJKCmX0Z+BJwLzDU3f+R\n06hEJAQ7awYTj1dTUTqysdNZoUll7aOLCCac9QYeBe4ys0nufnOugxORXNCSFNmSjZ3OCk0qNYUz\ngaHALHf/wMyOAGYDSgoiRUUzkHMh053OCk0qSWGHu28zs6bnnxHswCYiBS978wwatjWw6LWVCcsG\nHl5HdW35dlFmstNZoUnl/+KLZnYz0NHMTgJ+ADyX27BEJFPZXqhu0WsrueWlvgnX9BrLCr40NN2V\nb6SQpJIUfgacC7wOfA/4K3BnLoMSkUxFGD8qB/sgJ1vTi1wsflD4SmnSWpNUksIz7v514K5cByMi\nmWg5z0CdyLlVapPWmqSSFHY3s33d/Z85j0ZE0qJ5BuEqxUlrTVJJCvsAy83sPeBTMl86W0SyQvMM\n8qUUJ601SSUpHJfzKETKXPt268vjPAOt6QWU5qS1JqkkhRFJjj+QzUBEylqKu/XVVDYybkR9XvY0\nGHh4HWNZQaJO5YGH14UeTz6V4qS1JqkkhVExj2sIKkf/QElBJLta3a0v/7OQq2urNew0RqlNWmvS\nZlJw97Nin5tZN+DhnEUkIi1UV0S4Up3IBamUJq01qUzjNZ8A/bIch4gkceHRK5UQJDSpLIj3AjT3\nLVUAXyCYwCYiIiUmlT6FK2IeR4D17p5kmISIpE0je6QApJIUvu3uF8QeMLP73f2MHMUkUjZqKhup\nqIBxp9eycek7aGSP5FvSpGBmvydoKhpiZgfFFNUAXXMdmEhpSzCaaG+N7JH8a62mcA1Bh/JE4MqY\n4w3AonQvaGaVwB3AocBW4PvuviSm/FzgvOh1rnH3J9O9lkjhCRqIJoxalpe5BiJtSZoU3H05sBw4\nNDoMtSNBR3MVcBjwfJrXPAno4O5Hm9lRwC+BMQBm1gO4EBgCdABmmNn/uXuWl3oUCVv+5xmIpCKV\n0Ue/AH5M0Gz0AdALmEOwG1s6hgHPALj7K2Y2JKbsSGBmNAlsNbMlwCHAq2leSyTPIlSAFquTopHK\nPIXvAPsSTFgbCRwLZDIkoguwMeb5DjOrTlK2CfVfSFGKUFPZyNXHLOH3Jy9UQpCikUpSWOPuHwML\ngEPd/QWgewbX/BjoHBuDuzckKesMfJTBtURCFySDpdw5ZpGSgRSdVIakbjSz04G5wAVm9i6wVwbX\nnAmcCDwS7VN4M6ZsNnCtmXUAdgMGEiQjkSIQNBWp30DSUSi7uKVSUzgH+Ly7TyPoeL4LuDyDaz4O\nfGZmLwG3AGPN7GIzG+3ua4HbgOkEHdk/d3cN0ZCs67hyFvste5iOK2dl4d3UVCSZmXzvDLr36cbx\n3z2K7n26RVdgzY+KSKTtjTnMrCOwH8Fd++7uvjnXgaVi7ty5/YD6MZPfYc3m7fkOR4rEoJX3c9Lo\nfs1LHj8xZTnz6to7F1NbX0p2zHlxMT3ruu2yN8O6VRtyVmP4zhFXsmDBAoD+gwcPXh5b1mZNwcyO\nAV4HJhP0JdSb2ddzEKdIznVc+UpzQoBgt6yTRvdrR40hAkSYMGoZd45ZxG9Hq99AMrNu1YYWCQGC\n38s1Kz7ISzypNB/9gmAY6UfuvoZgBNJNuQxKJFd6NKxI+AfYY9vyNl65s4lo0skLNfFMsqZpF7dY\nre3iNm/6Yp76wyvMm744J/GkkhQqo239AGgxPClma6v7JfwDXFvbL+lrKohoNJHkzKDhA5gz7e3m\n38umXdwSNR2F0feQyuijVWZ2AhAxsz0JJrKtzHokIu3QceUsejQsZ211PzbXpT6PcnPdUJ6Ycj8n\njaZFn8LmBH0KNZWNTBi1jF5dNKFeciuVXdzmvLiYISMPaNH0CQcwb/rirPY9pJIUziNY/2hfYCnB\nqKAfZC0CkXba2VF8cPRL/f52dRTPqzsDnzKLHtsWsLa2X4KEoCUpJHxt7eK2btUGhoxo+eXfu//e\nzJ/5TlZ3f2ttldTe7r7a3d8jmNUsKWhsaIBVSVrY+hxIZXUqeViSSdxRDD5lVrtrDEt3WalFS1JI\n4Wrqe4gfpZSs7yFdrfUpTG16YGb/ldWrlrJVC6kAKrru0/JftEwyk35HcWs0z0AKX3v6HjLR2m1r\nRczj7xKsZiqp6LoPFXv1bHEoAuCzaVRtISNNHcXxd0utdRQnU0GEmqqI5hlI0Uil7yFTrX07xc5q\nq0h6lrTPqoXQ75B8R1G02tNRnFzQZ3DbCW9TW9X25E2RQtJW30OmUr1l1V9ONnTUgq/Z0HZHcWJN\nW1+OH6kRRSLJtJYUDjKzZdHHvWMeVwARd9fegZI3iTuKk6tAo4lEUtFaUsjfMn3FbuP7u1atNmay\nBYWkJ/i/oKYikdS1th3nijADKRl9DiQy5ynYtAE6d2tZ1mM/2PxhfuIqK5pnIJIuDYPJssrqahp7\n9A965rvu07IwUQ1CsihIBuNG1GttIpE0KSnkQp8DiaxamLjJqM+B4cdT8lQzEMkWJYUcqKyu1rDT\nkFQQ4apjlmo0kUiWKClIkdJcA0ldw7YGFr2WeB3PgYfXUV2rr8Im+iSkiOwcTaSmImmPRa+t5JaX\n+ibs5xvLCr40VCPsmygpSFEImom0UJ1kINnyM2igZaxQk4KZ7Q48BHwe2ASc4e7vx50zGdgb2A58\n6u7fCDNGKSSaZyAStrBrCucDb7r7FWb2n8DlwEVx5+wPHOTu+gYoWxpNJJIvYSeFYcCN0cdPA+Nj\nC82sO7AnMDW6y9v17v5kuCFKvk0YtUzzDETyJGdJwczOAcbGHV4HbIw+3gTErxBXS7BE90SgGzDT\nzGZHN/qRkhbUDioqoGdnDS+VHNDyMynJWVJw90nApNhjZvYXoHP0aWfgo7iXrQXudPcG4D0zew0w\nQEmhZKmpSHJv4OF1jGUFiTqVBx5eF35ABSzs5qOZwDeB2cA3gOlx5ccCFwDfNLNOwMHAolAjlJBo\nnoGEp7q2WsNOUxR2UvgtcL+ZzQC2AacCmNmNwGPu/rSZHWdmrwCNwGXuvj7kGCVnNM9ApNCFmhTc\nfQtwSoLjl8Q8/mmYMUlYImXbgTxv+mLWrNxAz7puWd9PVyTbKvMdgJS+mspGrj5maVkmhMn3zqB7\nn24c/92j6N6nG5PvnZHvkERapaQgORKhprKR2qrGaFNR+Y0omvPiYoaMPIDe/fcGgj2lh4w8gHnT\nF+c5MpHktMyF5ED5NhXFWrdqA0NGtGwu6t1/b+bPfCenG6+LZEI1BcmSnTWDq49ZUvYJAaBnXTdW\n17ccJ7G6fj09+34uTxGJtE01BcmYFqtLbNDwAdE+hKAJaXX9euZMe5sxZw3Ld2iSpnIYNKCagqRp\nZ83gjtGLlBCSGHPWMNat2sBTD73MulUblBCKWLkMGlBNQdpJM5Dba9DwAepDKHKJBg1AMGig1GoM\nSgqSsprKRsaNqFd/gZSdcho0oOYjSUHQVDRhVHnONRApp0EDqilIEjtXLR0/Uk1FUt7KadCAkoIk\noHkGIvHGnDWMedMXM3/mO/Ts+7mSTAigpCDNVDMQaUs5DBpQUhDNMxCRZkoKZaymspGKCph4vPY0\nEJGAkkJZ0lwDkWIT1mxqJYWyEqEC1FSUQ+WwDIKEb/K9Mxgy8gAGDR/A6vr1TL53Rs46ujVPoSxE\nonsaLOH3Jy9UQsiRclkGQcIV9hLsSgolbWcyuHOM1ifKJe2dILmybtWG5t+rJr37782aFR/k5HpK\nCiUpSAa/Hb1IySAkYf/hSvkIezZ1XvoUzOxk4BR3PzVB2bnAeUADcI27Pxl2fMUsWI5iWVnudJZP\nTX+4sYmhVJdBaI36VLIv7NnUodcUzGwicF2ia5tZD+BC4MvAccB1ZrZbuBEWpwoiZb31Zb4NGj6A\nOdPebr6ja/rDLacvRvWp5E6YS7Dno6bwEvAEQW0g3pHATHffCmw1syXAIcCrIcZXRIK5BTWVEW47\nQXMN8q1clkFIpJyWls6XsGZT5ywpmNk5wNi4w2e5+8NmNjLJy7oAG2OebwK65iC8Iqd5BoWqHJZB\nSKSclpYudTlLCu4+CZjUzpd9DHSOed4Z+ChrQRW9oCagxeqk0KhPpXQU2uS12cC1ZtYB2A0YCCzI\nb0iFQDUDKWzlsLR0uXSiF0RSMLOLgSXuPsXMbgOmE3RE/9zdy/qWOFisTp3HUvhKuU8lzBnF+VYR\niRRv5+TcuXP7AfVjJr/Dms3b8x1OlgW1A21/KZJfc15cTM+6brs0ja1btaFoawzfOeJKFixYANB/\n8ODBy2PLCqKmILHUVCRSSMqtE11JoWBosTqRQlRunehKCnmnmoFIISuHTvRYSgp5oa0vRYpJKXei\nx1NSCF3XBD3HAAAJ/UlEQVRE8wxEilC5TExUUghJ09aX40dqsToRKVxKCjmnPgMRKR5KCjmjeQYi\nUnyUFLJONQMpTOWyTINkRjuvZU2ECiLa+lIKkvY6kFSpppAx1QyksGmvA2kPJYU0VRChpiqi0URS\n8MptmQbJjJJCWiKM11wDKRLltkyDZEZ9CimLEDQVNXL1MUuUEKRoaP9oaQ/VFFIQ7GmgheqkeJXT\nMg2SGSWFpIJ9JmoqI9x2wtvUVhXvvhMiUD7LNEhmlBR2odFEIm3RnIfSpT6FGDWVjUwYtUzzDERa\noTkPpU01BUC1g/KlO9720ZyH0peXpGBmJwOnuPupCcomAsOATdFDY9x9Y24iUTIoZ+W0GXu2aM5D\n6Qs9KUS/9I8D5ic5ZTBwnLuvz10UEXUglznd8aZHcx5KXz76FF4Czk9UYGaVwP7A3WY208zOzt5l\nW84zuHPMIiWEMrZu1YYWX2wQJIY1Kz7IU0TFQXMeSl/Oagpmdg4wNu7wWe7+sJmNTPKyjsDtwK+A\nKuAFM5vj7m9kEovmGUg83fGmT3MeSlvOkoK7TwImtfNlW4CJ7r4FwMyeBw4F0k4KNZWNaiaSXZTb\nZuzZpjkPxalpYEWn9yfTv26/hOcU2pDUAcBMM6sysxqCDud57X+boJmotqqRCaOWKiFIQmPOGsa6\nVRt46qGXWbdqgxKClLTYocQrt76c9LyCGJJqZhcDS9x9ipk9CLwCbAcecPe32vNeaiqS9tAdr5SD\n+IEV3ffdK+m5eUkK7j4NmBbz/Fcxj28CbmrfOwY1g4oKmHi8mopERGIlGkqcTEHUFDJ18b8sp1un\nHE1lEJGSUM4TFRMNrEim0PoU0tK98/Z8hyAiBazcl+aIH0q87p8fJj23JJKCiEgyiSYqDhkZTFQs\nJ7EDK+p2OzrpeUoKIlLSNFFxp0HDB3D8aUdz4jfHJD1HSUFESlpTe3osTVRMTklBREqaluZon2If\nfVQFUFOxe77jEJGQLZhdz/vvfsQ+vfbk4CP7t3rut8/+Ogtm1zPj9bfZp/defPvsr4cUZWHatq15\nHldVfFmxJ4WeAPvt9tV8xyEiITvgK7k9v5QtXtzcyd4TWBpbVuxJ4VWC+ahrgB15jkVEpFhUESSE\nV+MLKiIRzf4VEZGAOppFRKSZkoKIiDRTUhARkWZKCiIi0kxJQUREmhXtkFQz6wo8BHQBaoGL3f3l\nuHPOBc4DGoBr3P3J0ANtGc/JwCnufmqCsokEO81tih4a4+55WQ+8jTgL4jM1s90J/v9/nuAzO8Pd\n3487ZzKwN8GGTZ+6+zdCjK8SuINgO9mtwPfdfUlMeaF8jm3FWTC/l9F4hgI3uPvIuOMnAhMIPs97\n3P13eQivhVZiHQt8H2j6fT3P3T3k8JIq2qQAXAw85+63mpkB/wsMaio0sx7AhcAQoAMww8z+z923\n5iPY6B/XccD8JKcMBo5z9/VJykPRWpwF9pmeD7zp7leY2X8ClwMXxZ2zP3CQu+dj3PVJQAd3P9rM\njgJ+CYyBgvsck8YZVRC/lwBmdglwOrA57ngNcAtwRLRspplNcfd14UfZHFPCWKMGA99z97nhRpWa\nYm4+ugW4K/q4GvgsrvxIYKa7b43e2SwBDgkxvngvEXyR7SJ6t7Y/cLeZzTSzs0ONrKWkcVJYn+kw\n4Jno46eBY2MLzaw7sCcw1cxmmNkJ+YrP3V8hSABNCvJzjI+zwH4vIZh5+60ExwcSbOf7obtvA2YA\n+Z6/nCxWCJLCuOjv5bgQY0pJUdQUzOwcYGzc4bPc/dXoXddDwE/jyrsAsdXcTUDX3EUZaCXWh81s\nZJKXdQRuB35FMNPwBTOb4+5vFFichfSZrouJJVEctQR3vROBbgR3j7Pd/b1cxhoj/rPaYWbV7t6Q\noCyUzzGJ1uIM/feyNe7+ZzPrl6CokD5PoNVYAf4E/Ab4GHjczE7Id9N2rKJICu4+CZgUf9zMvkTw\nAf+3u78YV/wx0DnmeWfgo5wFGZUs1jZsASa6+xYAM3ueoI03Z398acZZMJ+pmf0lJpZEcawF7ox+\nub1nZq8BBoSVFOI/q8poLInKQvkck2gtztB/L9NUSJ9nq8ysAri1qV/GzJ4CDgcKJikUbfORmR0I\nPAqc6u5PJzhlNjDczDpEO6UHAgvCjLEdBhDcyVZF20eHAfPyHFMihfSZzgS+GX38DWB6XPmxBL8f\nmFkn4GBgUWjRxcQXbat/M6asID/HBHEWy+/lImB/M+tmZrUETUcvt/GafOkCLDCzTtEE8VWgoPoW\niqKmkMR1BJ10E4N+Zja6+xgzu5igfXGKmd1G8GVRCfzc3eP7HfIqLtYHgVcIRso84O5v5Te6nQr0\nM/0tcL+ZzQC2AadGY70ReMzdnzaz48zsFaARuCzkztLHga+Z2UtABXBWgX6ObcVZyL+XpwKd3P3u\naMx/I/g873H31fmNrqW4WC8DXiAY7fWcu/81v9G1pAXxRESkWdE2H4mISPYpKYiISDMlBRERaaak\nICIizZQURESkWTEPSRVpU3RW6WJgYVzRie7+z3a+V3/gcnc/J0vhxb53J+AGgnWnNhNMyLrC3Z/L\n9rVEWqOkIOXgXXc/LAvv0xfYLwvv00J0EtNUgkUID3T3bWZ2OPCUmZ3q7tOyfU2RZJQUpGxFF827\nC9iXYILbOHf/u5n1JlhWY0+gJ/C/7v7/gNuAL5jZbwhmS1/RtCyymd0HTIv+ewZYT7BI43HATcBI\ngvWD7nP3W+JCGUGQcL7atKKru79mZtcA44FpZjaN6ExoYB/ggugEvYQ/Q9Y+JCk76lOQctDLzObH\n/PtZ9PhEgtmvg4HRwF1m1hn4DkEiOIpg9dIfmdneBMtdz3H3H7dxPQNOc/djgXMB3H0QweqoY8xs\neNz5R0TfN34m6T+iZU1q3f1ogsUBr2njZxBJi2oKUg6SNR8dCxxgZldFn9cA+7n7zWY2ysz+m2DN\npFqCFUNT9Z67L4+5xmFm9tXo807Al2i5VlOExH+LtXHPm5YKX0Cw8mvSn4Hk+3aItEpJQcpZFUGT\nzQYAM+sFrDOzXwJfAP4IPEHwxVsR99pI3LGamMefxl3jEnf/S/Qae7PrxiuzgAvNrMbdt8ccPxp4\nNeZ50/pIsddO+DO09kOLtEbNR1LOngd+BM2r7r4B7AF8DbjJ3R8laKvvTfDl28DOG6n1BP0LHcys\nG0Fbf7JrnGtmNdERRjOAobEnuPt04C3g1uhqpJjZYILd5K5O82cQSYtqClLOLiDYVewNgjvv0919\nk5ldBzxoZh8R3HXPAfoDrwF7mtmD7n56dC38t4Dl7Lp0d5M7CXYve43g7+3eJKOJvgVcS7Cs8g5g\nA0G/RKJz2/wZUvrpRRLQKqkiItJMzUciItJMSUFERJopKYiISDMlBRERaaakICIizZQURESkmZKC\niIg0+/93XsT9n54NXgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = classifiers[1]\n", + "title = names[1]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[2]\n", + "title = names[2]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[3]\n", + "title = names[3]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[4]\n", + "title = names[4]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[5]\n", + "title = names[5]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature Alpha', 'Feature Beta'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[6]\n", + "title = names[6]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[7]\n", + "title = names[7]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[8]\n", + "title = names[8]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = classifiers[9]\n", + "title = names[9]\n", + " \n", + "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n", + "viz.fit(X_train, y_train)\n", + "viz.draw(X_test, y_test)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/Untitled.ipynb b/examples/ndanielsen/Untitled.ipynb new file mode 100644 index 000000000..e14329518 --- /dev/null +++ b/examples/ndanielsen/Untitled.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "from sklearn import neighbors\n", + "from sklearn import naive_bayes" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data = load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(150,)" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.to_records(index=False).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = np.array([(1,2.,'Hello'), (2,3.,\"World\")], dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2], dtype=int32)" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x['foo']" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.318, 2.727, 4.26 , 7.212, 4.792],\n", + " [ 2.315, 2.726, 4.295, 7.14 , 4.783],\n", + " [ 2.315, 2.724, 4.26 , 7.135, 4.779],\n", + " [ 2.11 , 3.609, 4.33 , 7.985, 5.595],\n", + " [ 2.11 , 3.626, 4.33 , 8.203, 5.621],\n", + " [ 2.11 , 3.62 , 4.47 , 8.21 , 5.612]])" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X['one']" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[( 2.318,), ( 2.727,), ( 4.26 ,), ( 7.212,), ( 4.792,)],\n", + " [( 2.315,), ( 2.726,), ( 4.295,), ( 7.14 ,), ( 4.783,)],\n", + " [( 2.315,), ( 2.724,), ( 4.26 ,), ( 7.135,), ( 4.779,)],\n", + " [( 2.11 ,), ( 3.609,), ( 4.33 ,), ( 7.985,), ( 5.595,)],\n", + " [( 2.11 ,), ( 3.626,), ( 4.33 ,), ( 8.203,), ( 5.621,)],\n", + " [( 2.11 ,), ( 3.62 ,), ( 4.47 ,), ( 8.21 ,), ( 5.612,)]], \n", + " dtype=[('one', '\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# raise Exception(self.yy.ravel().shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mPredicted\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \"\"\"\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0mjll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_joint_log_likelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36m_joint_log_likelihood\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m return (safe_sparse_dot(X, self.feature_log_prob_.T) +\n\u001b[0m\u001b[1;32m 708\u001b[0m self.class_log_prior_)\n\u001b[1;32m 709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfast_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: shapes (160000,2) and (8,3) not aligned: 2 (dim 1) != 8 (dim 0)" + ] + } + ], + "source": [ + "X = X_two_cols\n", + "model = naive_bayes.MultinomialNB()\n", + "model.fit(X, y)\n", + "\n", + "# Plot the decision boundary. For that, we will assign a color to each\n", + "# point in the mesh [x_min, x_max]x[y_min, y_max].\n", + "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + "\n", + "# set the step increment for drawing the boundary graph\n", + "x_step = (x_max - x_min) * step_size\n", + "y_step = (y_max - y_min) * step_size\n", + "\n", + "xx, yy = np.meshgrid(\n", + " np.arange(x_min, x_max, x_step), np.arange(y_min, y_max, y_step))\n", + "\n", + "# raise Exception(self.yy.ravel().shape)\n", + "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/Untitled1.ipynb b/examples/ndanielsen/Untitled1.ipynb new file mode 100644 index 000000000..669d625b0 --- /dev/null +++ b/examples/ndanielsen/Untitled1.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.array([\n", + " (1.1, 9.52, 1.23, 0.86, 7.89, 0.13), \n", + " (3.4, 2.84, 8.65, 0.45, 7.43, 0.16),\n", + " (1.2, 3.22, 6.56, 0.24, 3.45, 0.17),\n", + " (3.8, 6.18, 2.45, 0.28, 2.53, 0.13), \n", + " (5.1, 9.12, 1.06, 0.19, 1.43, 0.13),\n", + " (4.4, 8.84, 4.97, 0.98, 1.35, 0.13),\n", + " (3.2, 3.22, 5.03, 0.68, 3.53, 0.32),\n", + " (7.8, 2.18, 6.87, 0.35, 3.25, 0.38), \n", + " ], dtype=[('a','=0.10.0 # Build Requirements (uncomment for deployment) #wheel>=0.29.0 - - diff --git a/tests/baseline_images/test_classifier/test_boundaries/test_integrated_plot_numpy_named_arrays.png b/tests/baseline_images/test_classifier/test_boundaries/test_integrated_plot_numpy_named_arrays.png new file mode 100644 index 0000000000000000000000000000000000000000..94f8df77d016463244f9b75f3e2285e64785fd01 GIT binary patch literal 11487 zcmeHtcT`j9*X{{8z$l3G*$|{C4vf-8L8Oifh^XkG^rkc+6s3~@iM=2qC}5;23L+(l z6OfXCAR+_^NE2x)ElP<52n15@dlF&vx4iex_s6|!-LKm*B^WztEmnJ_=_ zNn-=BjCc>>^vf4wKe%f3y}RVV%KiU;{@;(lYZMVd8hutLW1ib_$TpGK51tFs z#?J0gz7;t&w7+{SUsPG_psW|c4*VtdgWR+J@Z4^6mt5o{Wpav>9m zRyN(R`tg$_FIBbQ&Fq5}$Krz`#JY>nagiRM9Snlzi&JdQ49)zdY4-My=#3pQMF-e%z1Bvtj8q`#{Xr-N8fDGCP$`m3{;t 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zLHp^{>S|I@1r0_++ur+AA-9u>S5|_FUUQ)TR+}%8eq7Pv=*S52^{voeEfNGiJVjDM zu&}Vq-o9;#<21%2^k@x$dbOj?Z?z$Kve%!{u&w|D_xCa~2A$pR%+$So_Kv(`!Lqol zRiTuBoodtC>JuJro>CeCg?+hl5k}e~4gW)InzYJuUUkOelyo+lWt^h0KJUdBh3wY0QoHN1DqY!s@_xQxbowmk`++1z@hMSuk9@5Ys zy}hP}hSX%d=9=a8MMV)+RaM~K*=x$%Rbj{#2?AmfD)@WkO_`lwVn}GHyJJmTTiZQ@ z@?@~0fl-Z@m)AWF>2mXlNbt(E>G1s5TCL0RX4Xq#r~Ma^?5B}en@ctf_wU=>T%9XE zeY&>T7ThzMnU(eHV5vhQ^X6&8B4 z4Iar3HHkHjdztEaD+8%g@$vEUc|W_sRXjY}7_N0awP-tV*qcoR8@XSeWT@w5M4l;Awk(ag2$DWol!?rKXlKV{tnY^6c5OoZJWDlwy6? zm#5r3JW;v1E)oZ{y)U_GX=&lN^O|HKNyOC#tTpxpcnS^C+y?mJWfe~CINWZ51rOAnqbyj@5Yl!B+J09OC~EHvS>@ Yj%&K0CoOad94bU!MoGFr@`d;R0kQ*NeE +# Created: Sun Mar 19 13:01:29 2017 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_boundaries.py [] nathan.danielsen@gmail.com $ +""" +Ensure that the Decision Boundary visualizations work. +""" + +########################################################################## +# Imports +########################################################################## + +try: + from unittest import mock +except ImportError: + import mock + +from collections import OrderedDict +import numpy as np + +try: + import pandas as pd +except ImportError: + pd = None + +import unittest +from tests.base import VisualTestCase +from yellowbrick.classifier import * +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.exceptions import YellowbrickValueError + +from sklearn import datasets +from sklearn import neighbors +from sklearn import naive_bayes +########################################################################## +# Data +########################################################################## + +X = np.array([ + [2.318, 2.727, 4.260, 7.212, 4.792, ], + [2.315, 2.726, 4.295, 7.140, 4.783, ], + [2.315, 2.724, 4.260, 7.135, 4.779, ], + [2.110, 3.609, 4.330, 7.985, 5.595, ], + [2.110, 3.626, 4.330, 8.203, 5.621, ], + [2.110, 3.620, 4.470, 8.210, 5.612, ], + [2.318, 2.727, 4.260, 7.212, 4.792, ], + [2.315, 2.726, 4.295, 7.140, 4.783, ], + [2.315, 2.724, 4.260, 7.135, 4.779, ], + [2.110, 3.609, 4.330, 7.985, 5.595, ], + [2.110, 3.626, 4.330, 8.203, 5.621, ], + [2.110, 3.620, 4.470, 8.210, 5.612, ] + ]) + +y = np.array([1, 2, 1, 2, 1, 0, 0, 1, 3, 1, 3, 2]) + +X_two_cols = X[:, :2] + +########################################################################## +# Residuals Plots test case +########################################################################## + + +class DecisionBoundariesVisualizerTest(VisualTestCase): + """Testcases for the DecisionBoundariesVisualizers """ + + def test_decision_bounardies(self): + """Assert no errors occur during KnnDecisionBoundariesVisualizer integration + """ + model = neighbors.KNeighborsClassifier(3) + viz = DecisionViz(model) + viz.fit_draw_poof(X_two_cols, y=y) + + def test_init(self): + """ + Testing the init method + """ + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model) + + self.assertEquals(viz.step_size, 0.0025) + self.assertEqual(viz.name, 'KNeighborsClassifier') + self.assertEqual(viz.estimator, model) + + self.assertIsNone(viz.classes_) + self.assertIsNone(viz.features_) + self.assertIsNotNone(viz.markers) + self.assertIsNotNone(viz.scatter_alpha) + self.assertTrue(viz.show_scatter) + + self.assertIsNone(viz.Z) + self.assertIsNone(viz.xx) + self.assertIsNone(viz.yy) + self.assertIsNone(viz.class_labels) + self.assertIsNone(viz.title) + self.assertIsNone(viz.x) + self.assertIsNone(viz.y) + + + def test_scatter_xy_and_features_raise_error(self): + """ + Assert that x,y and features will raise error + """ + model = neighbors.KNeighborsClassifier(3) + features = ["temperature", "relative_humidity", "light"] + + with self.assertRaises(YellowbrickValueError) as context: + visualizer = DecisionBoundariesVisualizer(model, features=features, x='one', y='two') + + def test_scatter_xy_changes_to_features(self): + """ + Assert that x,y and features will raise error + """ + model = neighbors.KNeighborsClassifier(3) + visualizer = DecisionBoundariesVisualizer(model, x='one', y='two') + self.assertEquals(visualizer.features_, ['one', 'two']) + + + def test_fit(self): + """ + Testing the fit method + """ + model = neighbors.KNeighborsClassifier(3) + model.fit = mock.MagicMock() + model.predict = mock.MagicMock() + + viz = DecisionBoundariesVisualizer(model) + fitted_viz = viz.fit(X_two_cols, y=y) + + # assert that classes and labels are established + self.assertEqual(fitted_viz.classes_, {0: '0', 1: '1', 2: '2', 3: '3'}) + self.assertEqual(fitted_viz.features_, ['Feature One', 'Feature Two']) + + # assert that the fit method is called + model.fit.assert_called_once_with(X_two_cols, y) + # mock object is called twice in predict and reshape + self.assertEqual(len(model.predict.mock_calls), 2) + + # test that attrs are set + self.assertIsNotNone(fitted_viz.ax) + self.assertIsNotNone(fitted_viz.Z_shape) + + def test_fit_class_labels(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, classes=['one', 'two', 'three', 'four']) + fitted_viz = viz.fit(X_two_cols, y=y) + self.assertEquals(fitted_viz.classes_, + {'three': '2', + 'four': '3', + 'two': '1', + 'one': '0'}) + + def test_fit_class_labels_class_names_edge_case(self): + """ Edge case that more class labels are defined than in datatset""" + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, classes=['one', 'two', 'three', 'four', 'five']) + self.assertRaises(YellowbrickTypeError, viz.fit, X_two_cols, y=y) + + def test_fit_features_assignment_None(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model) + self.assertIsNone(viz.features_) + fitted_viz = viz.fit(X_two_cols, y=y) + self.assertEquals(fitted_viz.features_, ['Feature One', 'Feature Two']) + + def test_fit_features_assignment(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) + fitted_viz = viz.fit(X_two_cols, y=y) + self.assertEquals(fitted_viz.features_, ['one', 'two']) + + @mock.patch("yellowbrick.classifier.boundaries.OrderedDict") + def test_draw_ordereddict_calls(self, mock_odict): + mock_odict.return_value = {} + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) + self.assertRaises(KeyError, viz.fit_draw, X_two_cols, y=y) + self.assertEquals(len(mock_odict.mock_calls), 2) + + @mock.patch("yellowbrick.classifier.boundaries.resolve_colors") + def test_draw_ordereddict_calls_one(self, mock_resolve_colors): + mock_resolve_colors.return_value = [] + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) + self.assertRaises(StopIteration, viz.fit_draw, X_two_cols, y=y) + self.assertEquals(len(mock_resolve_colors.mock_calls), 1) + + def test_draw_ax_show_scatter_true(self): + """Test that the matplotlib functions are being called """ + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) + fitted_viz = viz.fit(X_two_cols, y=y) + fitted_viz.ax = mock.Mock() + fitted_viz.ax.pcolormesh = mock.MagicMock() + fitted_viz.ax.scatter = mock.MagicMock() + fitted_viz.ax.legend = mock.MagicMock() + + fitted_viz.draw(X_two_cols, y=y) + self.assertEquals(len(fitted_viz.ax.pcolormesh.mock_calls), 1) + self.assertEquals(len(fitted_viz.ax.scatter.mock_calls), 4) + self.assertEquals(len(fitted_viz.ax.legend.mock_calls), 0) + + def test_draw_ax_show_scatter_False(self): + """Test that the matplotlib functions are being called when the + scatter plot isn't drawn + """ + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, features=['one', 'two'], show_scatter=False) + fitted_viz = viz.fit(X_two_cols, y=y) + fitted_viz.ax = mock.Mock() + fitted_viz.ax.pcolormesh = mock.MagicMock() + fitted_viz.ax.scatter = mock.MagicMock() + fitted_viz.ax.legend = mock.MagicMock() + + fitted_viz.draw(X_two_cols, y=y) + self.assertEquals(len(fitted_viz.ax.pcolormesh.mock_calls), 1) + self.assertEquals(len(fitted_viz.ax.scatter.mock_calls), 0) + self.assertEquals(len(fitted_viz.ax.legend.mock_calls), 1) + + def test_finalize(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, features=['one', 'two'], show_scatter=False) + fitted_viz = viz.fit(X_two_cols, y=y) + fitted_viz.draw(X_two_cols, y=y) + + fitted_viz.ax = mock.Mock() + fitted_viz.ax.legend = mock.MagicMock() + fitted_viz.ax.set_xlabel = mock.MagicMock() + fitted_viz.ax.set_ylabel = mock.MagicMock() + + fitted_viz.poof() + + fitted_viz.ax.legend.assert_called_once_with(loc='best', frameon=True) + fitted_viz.ax.set_xlabel.assert_called_once_with('one') + fitted_viz.ax.set_ylabel.assert_called_once_with('two') + + def test_fit_draw(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, features=['one', 'two'], show_scatter=False) + + viz.fit = mock.Mock() + viz.draw = mock.Mock() + + viz.fit_draw(X_two_cols, y=y) + + viz.fit.assert_called_once_with(X_two_cols, y) + viz.draw.assert_called_once_with(X_two_cols, y) + + def test_fit_draw_poof(self): + model = neighbors.KNeighborsClassifier(3) + viz = DecisionBoundariesVisualizer( + model, features=['one', 'two'], show_scatter=False) + + viz.fit = mock.Mock() + viz.draw = mock.Mock() + viz.poof = mock.Mock() + + viz.fit_draw_poof(X_two_cols, y=y) + + viz.fit.assert_called_once_with(X_two_cols, y) + viz.draw.assert_called_once_with(X_two_cols, y) + viz.poof.assert_called_once_with() + + + def test_integrated_plot_numpy_named_arrays(self): + model = naive_bayes.MultinomialNB() + + X = np.array([ + (1.1, 9.52, 1.23, 0.86, 7.89, 0.13), + (3.4, 2.84, 8.65, 0.45, 7.43, 0.16), + (1.2, 3.22, 6.56, 0.24, 3.45, 0.17), + (3.8, 6.18, 2.45, 0.28, 2.53, 0.13), + (5.1, 9.12, 1.06, 0.19, 1.43, 0.13), + (4.4, 8.84, 4.97, 0.98, 1.35, 0.13), + (3.2, 3.22, 5.03, 0.68, 3.53, 0.32), + (7.8, 2.18, 6.87, 0.35, 3.25, 0.38), + ], dtype=[('a',' +# Created: Sat Mar 12 14:17:29 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +from collections import OrderedDict +import itertools +import numpy as np + +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap +from matplotlib.patches import Patch + +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.classifier.base import ClassificationScoreVisualizer +from yellowbrick.utils import get_model_name +from yellowbrick.style.colors import resolve_colors +from yellowbrick.utils import is_dataframe, is_structured_array, has_ndarray_int_columns +from yellowbrick.style.palettes import PALETTES + + + +########################################################################## +# Quick Methods +########################################################################## + +def decisionviz(model, + X, + y, + colors=None, + classes=None, + features=None, + show_scatter=True, + step_size=0.0025, + markers=None, + pcolormesh_alpha=0.8, + scatter_alpha=1.0, + title=None, + **kwargs): + """DecisionBoundariesVisualizer is a bivariate data visualization algorithm + that plots the decision boundaries of each class. + + This helper function is a quick wrapper to utilize the + DecisionBoundariesVisualizers for one-off analysis. + + Parameters + ---------- + model : the Scikit-Learn estimator, required + Should be an instance of a classifier, else the __init__ will + return an error. + + x : matrix, required + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the x-axis + + y : array, required + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the y-axis + + classes : a list of class names for the legend, default: None + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + features : list of strings, default: None + The names of the features or columns + + show_scatter : boolean, default: True + If boolean is True, then a scatter plot with points will be drawn + on top of the decision boundary graph + + step_size : float percentage, default: 0.0025 + Determines the step size for creating the numpy meshgrid that will + later become the foundation of the decision boundary graph. The + default value of 0.0025 means that the step size for constructing + the meshgrid will be 0.25%% of differenes of the max and min of x + and y for each feature. + + markers : iterable of strings, default: ,od*vh+ + Matplotlib style markers for points on the scatter plot points + + pcolormesh_alpha : float, default: 0.8 + Sets the alpha transparency for the meshgrid of model boundaries + + scatter_alpha : float, default: 1.0 + Sets the alpha transparency for the scatter plot points + + title : string, default: stringified feature_one and feature_two + Sets the title of the visualization + + kwargs : keyword arguments passed to the super class. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the decision boundaries graph were drawn on. + """ + # Instantiate the visualizer + visualizer = DecisionBoundariesVisualizer(model, + X, + y, + colors=colors, + classes=classes, + features=features, + show_scatter=show_scatter, + step_size=step_size, + markers=markers, + pcolormesh_alpha=pcolormesh_alpha, + scatter_alpha=scatter_alpha, + title=title, + **kwargs) + + # Fit, draw and poof the visualizer + visualizer.fit_draw_poof(X, y, **kwargs) + + # Return the axes object on the visualizer + return visualizer.ax + +########################################################################## +# Static ScatterVisualizer Visualizer +########################################################################## + +class DecisionBoundariesVisualizer(ClassificationScoreVisualizer): + """ + DecisionBoundariesVisualizer is a bivariate data visualization algorithm + that plots the decision boundaries of each class. + + Parameters + ---------- + + model : the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + x : string, default: None + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the x-axis + + y : string, default: None + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the y-axis + + classes : a list of class names for the legend, default: None + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + features : list of strings, default: None + The names of the features or columns + + show_scatter : boolean, default: True + If boolean is True, then a scatter plot with points will be drawn + on top of the decision boundary graph + + step_size : float percentage, default: 0.0025 + Determines the step size for creating the numpy meshgrid that will + later become the foundation of the decision boundary graph. The + default value of 0.0025 means that the step size for constructing + the meshgrid will be 0.25%% of differenes of the max and min of x + and y for each feature. + + markers : iterable of strings, default: ,od*vh+ + Matplotlib style markers for points on the scatter plot points + + pcolormesh_alpha : float, default: 0.8 + Sets the alpha transparency for the meshgrid of model boundaries + + scatter_alpha : float, default: 1.0 + Sets the alpha transparency for the scatter plot points + + title : string, default: stringified feature_one and feature_two + Sets the title of the visualization + + kwargs : keyword arguments passed to the super class. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + + """ + + def __init__(self, + model, + x=None, + y=None, + features=None, + show_scatter=True, + step_size=0.0025, + markers=None, + pcolormesh_alpha=0.8, + scatter_alpha=1.0, + # title=None, + *args, + **kwargs): + """ + Pass in a unfitted model to generate a decision boundaries + visualization. + """ + super(DecisionBoundariesVisualizer, self).__init__(model, *args, **kwargs) + + self.x = x + self.y = y + self.features_ = features + self.estimator = model + self.show_scatter = show_scatter + self.step_size = step_size + self.markers = itertools.cycle( + kwargs.pop('markers', (',', 'o', 'd', '*', 'v', 'h', '+'))) + self.pcolormesh_alpha = pcolormesh_alpha + self.scatter_alpha = scatter_alpha + + # these are set later + self.Z = None + self.Z_shape = None + self.xx = None + self.yy = None + self.class_labels = None + + if self.x is not None and self.y is not None and self.features_ is not None: + raise YellowbrickValueError( + 'Please specify x,y or features, not both.') + + if self.x is not None and self.y is not None and self.features_ is None: + self.features_ = [self.x, self.y] + + # Ensure with init that features doesn't have more than two features + if features is not None: + if len(features) != 2: + raise YellowbrickValueError( + 'DecisionBoundariesVisualizer only accepts two features.') + + def _select_feature_columns(self, X): + """ """ + + if len(X.shape) == 1: + X_flat = X.view(np.float64).reshape(len(X), -1) + else: + X_flat = X + + _, ncols = X_flat.shape + + if ncols == 2: + X_two_cols = X + if self.features_ is None: + self.features_ = ["Feature One", "Feature Two"] + + # Handle the feature names if they're None. + elif self.features_ is not None and is_dataframe(X): + X_two_cols = X[self.features_].as_matrix() + + # handle numpy named/ structured array + elif self.features_ is not None and is_structured_array(X): + X_selected = X[self.features_] + X_two_cols = X_selected.view(np.float64).reshape(len(X_selected), -1) + + # handle features that are numeric columns in ndarray matrix + elif self.features_ is not None and has_ndarray_int_columns(self.features_, X): + f_one, f_two = self.features_ + X_two_cols = X[:, [int(f_one), int(f_two)]] + + else: + raise YellowbrickValueError(""" + ScatterVisualizer only accepts two features, please + explicitly set these two features in the init kwargs or + pass a matrix/ dataframe in with only two columns.""") + + return X_two_cols + + def fit(self, X, y=None, **kwargs): + """ + The fit method is the primary drawing input for the decision boundaries + visualization since it has both the X and y data required for the + viz and the transform method does not. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs : dict + Pass generic arguments to the drawing method + + Returns + ------- + self : instance + Returns the instance of the visualizer + """ + X = self._select_feature_columns(X) + + # Assign each class a unique number for drawing + if self.classes_ is None: + self.classes_ = { + label: str(kls_num) + for kls_num, label in enumerate(np.unique(y)) + } + self.class_labels = None + elif len(set(y)) == len(self.classes_): + self.classes_ = { + label: str(kls_num) + for kls_num, label in enumerate(self.classes_) + } + self.class_labels = dict(zip(set(y), self.classes_)) + else: + raise YellowbrickTypeError( + """Number of classes must be the same length of number of + target y""" + ) + + # ensure that only + self.estimator.fit(X, y) + + # Plot the decision boundary. For that, we will assign a color to each + # point in the mesh [x_min, x_max]x[y_min, y_max]. + x_min, x_max = X[:, 0].min() - (X[:, 0].min() * .01), X[:, 0].max() + (X[:, 0].max() * .01) + y_min, y_max = X[:, 1].min() - (X[:, 1].min() * .01), X[:, 1].max() + (X[:, 1].max() * .01) + + self.ax.set_xlim([x_min, x_max]) + self.ax.set_ylim([y_min, y_max]) + # set the step increment for drawing the boundary graph + x_step = (x_max - x_min) * self.step_size + y_step = (y_max - y_min) * self.step_size + + self.xx, self.yy = np.meshgrid( + np.arange(x_min, x_max, x_step), np.arange(y_min, y_max, y_step)) + + # raise Exception(self.yy.ravel().shape) + Z = self.estimator.predict(np.c_[self.xx.ravel(), self.yy.ravel()]) + self.Z_shape = Z.reshape(self.xx.shape) + return self + + def draw(self, X, y=None, **kwargs): + """ + Called from the fit method, this method creates a decision boundary + plot, and if self.scatter is True, it will scatter plot that draws + each instance as a class or target colored point, whose location + is determined by the feature data set. + """ + # ensure that if someone is passing in another X such as X_test, that + # features will be properly handled + X = self._select_feature_columns(X) + + color_cycle = iter( + resolve_colors(color=self.colors, num_colors=len(self.classes_))) + colors = OrderedDict([(c, next(color_cycle)) + for c in self.classes_.keys()]) + + self.ax.pcolormesh( + self.xx, + self.yy, + self.Z_shape, + alpha=self.pcolormesh_alpha, + cmap=ListedColormap(colors.values())) + + # Create a data structure to hold the scatter plot representations + to_plot = OrderedDict() + for index in self.classes_.values(): + to_plot[index] = [[], []] + + # Add each row of the data set to to_plot for plotting + for i, row in enumerate(X): + row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1) + x_, y_ = row_[0], row_[1] + # look up the y class name if given in init + if self.class_labels is not None: + target = self.class_labels[y[i]] + else: + target = y[i] + index = self.classes_[target] + to_plot[index][0].append(x_) + to_plot[index][1].append(y_) + + # Add the scatter plots from the to_plot function + # TODO: store these plots to add more instances to later + # TODO: make this a separate function + + if self.show_scatter: + for kls, index in self.classes_.items(): + self.ax.scatter( + to_plot[index][0], + to_plot[index][1], + marker=next(self.markers), + color=colors[kls], + alpha=self.scatter_alpha, + s=30, + edgecolors='black', + label=str(kls), + **kwargs) + else: + labels = [ + Patch(color=colors[kls], label=kls) + for kls in self.classes_.keys() + ] + self.ax.legend(handles=labels) + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + # Divide out the two features + feature_one, feature_two = self.features_ + + self.set_title(self.title) + # Add the legend + self.ax.legend(loc='best', frameon=True) + self.ax.set_xlabel(feature_one) + self.ax.set_ylabel(feature_two) + + def fit_draw(self, X, y=None, **kwargs): + """ + Fits a transformer to X and y then returns + visualization of features or fitted model. + """ + self.fit(X, y, **kwargs) + self.draw(X, y, **kwargs) + + def fit_draw_poof(self, X, y=None, **kwargs): + """ + Fits a transformer to X and y then returns + visualization of features or fitted model. + Then calls poof to finalize. + """ + self.fit_draw(X, y, **kwargs) + self.poof(**kwargs) + + +DecisionViz = DecisionBoundariesVisualizer From 3d06fd6723da5adf062109c65e7d403701dfa69a Mon Sep 17 00:00:00 2001 From: Phillip Schafer Date: Thu, 22 Jun 2017 13:46:22 -0400 Subject: [PATCH 20/40] Rank 1D (WIP) (#258) * refactor of rankd.py and add Rank1D * add Rank1D to __init__.py * move tick labeling to 'draw', make rank variable names consistent * fix handling of dataframe inputs * remove outdated TODO * various changes for PR review * comments and cleanup --- examples/pbs929/rankd.ipynb | 1107 ++++++++++++++++++++++++++++++ yellowbrick/features/__init__.py | 2 +- yellowbrick/features/rankd.py | 380 ++++++++-- 3 files changed, 1424 insertions(+), 65 deletions(-) create mode 100644 examples/pbs929/rankd.ipynb diff --git a/examples/pbs929/rankd.ipynb b/examples/pbs929/rankd.ipynb new file mode 100644 index 000000000..de5767d4d --- /dev/null +++ b/examples/pbs929/rankd.ipynb @@ -0,0 +1,1107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking `examples/examples.ipynb` as a starting point. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "sys.path.append(\"../..\")\n", + "\n", + "import numpy as np \n", + "import pandas as pd\n", + "import yellowbrick as yb" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.features.rankd import Rank1D, Rank2D, rank1d, rank2d" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# !pip install pandas requests nose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# %run download.py" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from download import download_all \n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + "}\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " limit sex edu married age apr_delay may_delay jun_delay jul_delay \\\n", + "0 20000 2 2 1 24 2 2 -1 -1 \n", + "1 120000 2 2 2 26 -1 2 0 0 \n", + "2 90000 2 2 2 34 0 0 0 0 \n", + "3 50000 2 2 1 37 0 0 0 0 \n", + "4 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " aug_delay ... jul_bill aug_bill sep_bill apr_pay may_pay \\\n", + "0 -2 ... 0 0 0 0 689 \n", + "1 0 ... 3272 3455 3261 0 1000 \n", + "2 0 ... 14331 14948 15549 1518 1500 \n", + "3 0 ... 28314 28959 29547 2000 2019 \n", + "4 0 ... 20940 19146 19131 2000 36681 \n", + "\n", + " jun_pay jul_pay aug_pay sep_pay default \n", + "0 0 0 0 0 1 \n", + "1 1000 1000 0 2000 1 \n", + "2 1000 1000 1000 5000 0 \n", + "3 1200 1100 1069 1000 0 \n", + "4 10000 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the classification data set\n", + "data = load_data('credit') \n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify the features of interest\n", + "features = [\n", + " 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n", + " 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n", + " 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n", + " 'jul_pay', 'aug_pay', 'sep_pay',\n", + " ]\n", + "\n", + "X = data[features]\n", + "y = data.default" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rank1D \n", + "New visualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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RGRlJkyZNOHLkCL6+vnh5edGkSRMOHz7MunXrePfddy01NBERkXrvlgMCZ2dn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\ntm3bKCkpYceOHfj7+ysYEBERqaRqe8v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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "visualizer = Rank1D(algorithm='shapiro')\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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RGRlJkyZNOHLkCL6+vnh5edGkSRMOHz7MunXrePfddy01NBERkXrvlgMCZ2dn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\ntm3bKCkpYceOHfj7+ysYEBERqaRqe8v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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Raw numpy version\n", + "visualizer = Rank1D(algorithm='shapiro', features=features)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "visualizer = Rank1D(algorithm='shapiro')\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "visualizer = Rank1D(algorithm='shapiro', show_feature_names=False)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### vertical orient" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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Qs2fPcm80ERGRJSjzTDg+Ph5dunQBALRu3RoXL16UHnN0dETt2rWRnZ2N7OxsaDSa8msp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\nKxwdHaHVatGnTx+kpqbKOt6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "visualizer = Rank1D(algorithm='shapiro', orient='v')\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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Qs2fPcm80ERGRJSjzTDg+Ph5dunQBALRu3RoXL16UHnN0dETt2rWRnZ2N7OxsaDSa8msp\nERGRhSnzTPjBgwdwcnKSPra2toZWq4WNTcGhtWrVQt++fZGfn49x48YZ9U0LB7lI8fHxqtd8fudl\nRcd9P6y5yi0xjVqvjRp1zKktxdUR/TMvj99jInoyygxhJycnZGZmSh/rdDopgGNjY5GamooTJ04A\nAEaPHg0vLy+0atWq1JotW7aEvb29Ke3+H4VvgADg7e2tThsKU9iecmkLoE571HqN1XptLLGOuf0e\nE5EqcnJySj3xLPNytJeXF2JjYwEACQkJ8PT0lB5zcXGBg4MD7OzsYG9vD2dnZ9y7d0+FZhMREVm+\nMs+Ee/bsibi4OAwZMgR6vR7Lli1DREQE3N3d0aNHD5w9exZBQUGwsrKCl5cXOnXq9CTaTUREVOGV\nGcJWVlYICwsr8jkPDw/p/1OmTMGUKVPUbxkREZGF445ZREREgjCEiYiIBGEIExERCcIQJiIiEoQh\nTEREJAhDmIiISBCGMBERkSAMYSIiIkEYwkRERIIwhImIiARhCBMREQnCECYiIhKEIUxERCQIQ5iI\niEgQhjAREZEgDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiIShCFMREQkCEOYiIhIEIYwERGR\nIAxhIiIiQRjCREREgjCEiYiIBLER3QAiUo91aKSi4/LXBKvcEiIyBkOYiB7DMCd6Mng5moiISBCG\nMBERkSAMYSIiIkE4JmymOCZHlkCt32P+PZClYggT0X+C0iAHGOZUfhjCREQy8Kyc1MQxYSIiIkEY\nwkRERIIwhImIiARhCBMREQnCECYiIhKEIUxERCQIQ5iIiEgQhjAREZEgDGEiIiJBGMJERESCMISJ\niIgEYQgTEREJwhAmIiIShCFMREQkCEOYiIhIEN5P+P/wHqFERPSk8UyYiIhIEIYwERGRIGVejtbp\ndFi0aBF+++032NnZYcmSJahfv770+DfffIMNGzZAr9ejRYsWWLhwITQaTbk2moiIyBKUeSYcExOD\n3Nxc7NmzB6GhoQgPD5cee/DgAVatWoXNmzdj3759qFOnDtLT08u1wURERJaizBCOj49Hly5dAACt\nW7fGxYsXpcd++ukneHp6YsWKFRg2bBiqVq0KNze38mstERGRBSnzcvSDBw/g5OQkfWxtbQ2tVgsb\nGxukp6fj3LlzOHDgAJ566ikMHz4crVu3RsOGDUutWTjIRYqPjzeLGqxT/jVYp/xrsE751yDLU2YI\nOzk5ITMzU/pYp9PBxqbgMFdXVzz77LOoVq0aAKBt27b49ddfywzhli1bwt7e3pR2/8/Oy4oP9fb2\nNrlOkRqWWoevcfnX4Wtc/nXM7TWm/4ScnJxSTzzLvBzt5eWF2NhYAEBCQgI8PT2lx1q0aIHExESk\npaVBq9Xi559/RuPGjVVoNhERkeUr80y4Z8+eiIuLw5AhQ6DX67Fs2TJERETA3d0dPXr0QGhoKMaM\nGQMA6N27d5GQJiIiopKVGcJWVlYICwsr8jkPDw/p/3379kXfvn3VbxkREZGF42YdREREgjCEiYiI\nBGEIExERCcIQJiIiEoQhTEREJAhDmIiISBCGMBERkSAMYSIiIkEYwkRERIIwhImIiARhCBMREQnC\nECYiIhKEIUxERCQIQ5iIiEgQhjAREZEgDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiIShCFM\nREQkCEOYiIhIEIYwERGRIAxhIiIiQRjCREREgjCEiYiIBGEIExERCcIQJiIiEoQhTEREJAhDmIiI\nSBCGMBERkSAMYSIiIkEYwkRERIIwhImIiARhCBMREQnCECYiIhKEIUxERCQIQ5iIiEgQhjAREZEg\nDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiIShCFMREQkCEOYiIhIEIYwERGRIAxhIiIiQRjC\nREREgjCEiYiIBGEIExERCcIQJiIiEqTMENbpdFiwYAEGDx6M4OBgJCcnF/s1Y8aMwa5du8qlkURE\nRJaozBCOiYlBbm4u9uzZg9DQUISHhz/2NevWrcO9e/fKpYFERESWqswQjo+PR5cuXQAArVu3xsWL\nF4s8/tVXX0Gj0UhfQ0RERMaxKesLHjx4ACcnJ+lja2traLVa2NjYIDExEV988QXef/99bNiwwehv\n+miQixIfH28WNVin/GuwTvnXYJ3yr0GWp8wQdnJyQmZmpvSxTqeDjU3BYQcOHEBKSgpGjhyJW7du\nwdbWFnXq1MGLL75Yas2WLVvC3t7exKb/n52XFR/q7e1tcp0iNSy1Dl/j8q/D17j865jba0z/CTk5\nOaWeeJYZwl5eXjh16hR8fHyQkJAAT09P6bFZs2ZJ/1+/fj2qVq1aZgATERFRgTJDuGfPnoiLi8OQ\nIUOg1+uxbNkyREREwN3dHT169HgSbSQiIrJIZYawlZUVwsLCinzOw8Pjsa+bPHmyeq0iIiL6D+Bm\nHURERIIwhImIiARhCBMREQnCECYiIhKEIUxERCQIQ5iIiEgQhjAREZEgDGEiIiJBGMJERESCMISJ\niIgEYQgTEREJwhAmIiIShCFMREQkCEOYiIhIEIYwERGRIAxhIiIiQRjCREREgjCEiYiIBGEIExER\nCcIQJiIiEoQhTEREJAhDmIiISBCGMBERkSAMYSIiIkEYwkRERIIwhImIiARhCBMREQnCECYiIhKE\nIUxERCQIQ5iIiEgQhjAREZEgDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiISxEZ0A4iI/ous\nQyMVHZe/Jljllpif/9JrwzNhIiIiQXgmTEREqlB6BgtUzLNYNfBMmIiISBCeCRMRkUWqCGPLPBMm\nIiIShCFMREQkCEOYiIhIEIYwERGRIAxhIiIiQTg7moiIKsRMYkvEM2EiIiJBGMJERESCMISJiIgE\nYQgTEREJwhAmIiISpMzZ0TqdDosWLcJvv/0GOzs7LFmyBPXr15ce3759Ow4fPgwA6Nq1KyZNmlR+\nrSUiIrIgZZ4Jx8TEIDc3F3v27EFoaCjCw8Olx27evIlDhw5h9+7d2Lt3L86cOYMrV66Ua4OJiIgs\nRZlnwvHx8ejSpQsAoHXr1rh48aL0WM2aNfH//t//g7W1NQBAq9XC3t6+nJpKRERkWcoM4QcPHsDJ\nyUn62NraGlqtFjY2NrC1tYWbmxv0ej1WrlyJ5s2bo2HDhmV+08JBLlJ8fLxZ1GCd8q/BOuVfg3XK\nv4aaddRiTs/LnNpirDJD2MnJCZmZmdLHOp0ONjb/OywnJwfz5s1DpUqVsHDhQqO+acuWLdU7Y955\nWfGh3t7eJtcpUsNS6/A1Lv86fI3Lv46lvsZq4Wtcch0T5OTklHriWeaYsJeXF2JjYwEACQkJ8PT0\nlB7T6/WYMGECmjZtirCwMOmyNBEREZWtzDPhnj17Ii4uDkOGDIFer8eyZcsQEREBd3d36HQ6fP/9\n98jNzcXp06cBACEhIWjTpk25N5yIiKiiKzOEraysEBYWVuRzHh4e0v8vXLigfquIiIj+A7hZBxER\nkSAMYSIiIkEYwkRERIKUOSZMRETmyzo0UtFx+WuCVW4JKcEzYSIiIkEYwkRERIIwhImIiARhCBMR\nEQnCECYiIhKEIUxERCQIQ5iIiEgQhjAREZEgDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiIS\nhCFMREQkCEOYiIhIEIYwERGRIAxhIiIiQRjCREREgjCEiYiIBGEIExERCcIQJiIiEoQhTEREJAhD\nmIiISBCGMBERkSAMYSIiIkEYwkRERIIwhImIiARhCBMREQnCECYiIhKEIUxERCQIQ5iIiEgQhjAR\nEZEgDGEiIiJBGMJERESCMISJiIgEYQgTEREJwhAmIiIShCFMREQkCEOYiIhIEIYwERGRIAxhIiIi\nQRjCREREgjCEiYiIBGEIExERCcIQJiIiEoQhTEREJAhDmIiISBCGMBERkSBlhrBOp8OCBQswePBg\nBAcHIzk5ucjje/fuhb+/P4KCgnDq1KlyaygREZGlsSnrC2JiYpCbm4s9e/YgISEB4eHh2LRpEwDg\nzp07iIyMxP79+5GTk4Nhw4ahU6dOsLOzK/eGExERVXRlhnB8fDy6dOkCAGjdujUuXrwoPfbLL7+g\nTZs2sLOzg52dHdzd3XHlyhW0atWq2Fp6vR4AkJubq0bbAQC1KtkqPjYnJ8fkOoVrWGodvsblX4ev\ncfnX4Wtc/nUs9TU2hSHvDPn3KI2+pEf+z1tvvYVXXnkFXbt2BQC89NJLiImJgY2NDQ4ePIjExETM\nnDkTADBr1iz4+fmhY8eOxda6f/8+EhMTFT8ZIiKiisjT0xPOzs6Pfb7MM2EnJydkZmZKH+t0OtjY\n2BT7WGZmZrHfxKBSpUrw9PSEra0tNBqNrCdARERU0ej1euTl5aFSpUrFPl5mCHt5eeHUqVPw8fFB\nQkICPD09pcdatWqFdevWIScnB7m5uUhKSiry+KOsrKxKDWkiIiJL4+DgUOJjZV6O1ul0WLRoERIT\nE6HX67Fs2TLExsbC3d0dPXr0wN69e7Fnzx7o9XqMGzcOvXr1Uv0JEBERWaIyQ5iIiIjKBzfrICIi\nEoQhTEREJAhDmIiIilBzLwcqnfWiRYsWiW4EkVL+/v7Izs5GgwYNSp2BWJatW7eiQYMGcHR0VLF1\npKYzZ87gxo0bxf5zd3cX3TyT5ebmwtraWnQzAAB+fn64du0aatasiSpVqiiqERYWhurVq6NatWoq\nt86yWOTErLCwMCxYsED6eNasWVi5cqWsGikpKahRo4b08aVLl9CiRQvV2misAwcOlPiYn5+foprX\nr19HcnIymjZtiho1ashes+3v74/+/fvDz88Prq6uitoAAF999RVefvllad25Evfu3cPnn3+Ozz//\nHLVq1UJgYGCJm8WUZteuXTh06BCqVauGgIAAvPjii4rXsl+4cAHPPvusomMNtm7dioEDB8LNzc2k\nOqa6du1aiY81bNjQqBohISElvpZr1qwxui1z584t8bHly5cbXae0szw5W+6eOXOmxMc6d+5sdB0D\nX19ftG/fHoGBgaUu9SyLGr87Op0Op0+fxv79+5Geno7+/fvDx8enxLWuxYmNjcX+/fuRkpKC/v37\no3///nByclLUHjXeK4CCbAgMDMQzzzxjUh01WVQIR0VFYdOmTbh7964UDnq9Ho0bN8bHH38sq1a/\nfv0wZ84cdO7cGdu2bcOhQ4dKDcSSGP4Y9Xo9MjIyUK9ePXz55ZdGH294k0pISICjoyPatGmDCxcu\nQKvVYsuWLbLbs2PHDhw/fhwZGRnw8/PDjRs3inRYjKFW8K1evRqxsbHo1KkTBg0aBA8PD9k1DJKS\nkrBx40acPXsWdevWxRtvvIGePXvKrvP7779j8+bNiI+PR0BAAF599VW4uLjIqjF9+nTcunVLeuOp\nXLmy7HaY2ilQK/iCg4OL/bxGo8Enn3xiVI3vv/++xMeef/55o9uiVnh2794dGo3msW0ENRoNTpw4\nYXQdtToFBmoEH6Beh1Kv1yM2NhaffvopkpOT8dRTT6Ffv34YMWKErDppaWlYunQpTp48iV69emHC\nhAmyr1yo9V6hZsdALRYVwgabN2/G+PHjTarx77//YubMmUhLS0Pbtm0xa9Ysk29McevWLXzwwQeK\n/kBHjx6NrVu3Sh+//vrr2LZtm+w6Q4cORVRUFEaOHInIyEgEBARg//79susA6gSfTqeT/jDu3LmD\noKAg+Pr6wtbWuD1fo6KicPDgQTg5OWHQoEHo2bMntFotgoKC8Pnnnxvdjnv37uHw4cM4ePAgnJ2d\nERQUhPz8fGzfvh27d++W9ZwAICMjA1988QViYmLg5uaGoKAgvPDCC7LrKO0UqBV8atizZ0+Jjw0e\nPNjoOobwLEyv18sOT7Wo1SkoTK3gA0zrUK5cuRInTpzA888/j8DAQLRq1Qo6nQ7+/v5Gn4wkJSUh\nOjoap06dwvPPP4+goCBotVosWrQI0dHRsp+Pqe8VhanRMVCLaef2ZubUqVPo1q0bXF1dH/vDl/PH\nDgBXrlzBnTt34OXlhV9//RW3b982+YdUp04dXL16VdGxaWlpuHfvHipXroz09HTcvXtXUR3Dm5bh\nzUzJm8Xu/rpQAAAbC0lEQVSjwRceHi4Fn5wQ1uv1OHPmDA4cOCCdOaanp2P8+PFFOhylSU1NxZo1\na1CvXj3pc7a2tggLC5P1nAYNGoT+/ftj7dq1qF27tvT5X3/9VVYdg3/++Qd//fUX0tPT4eHhgaNH\nj2Lfvn1YvXq1Ucc/2il46623kJ+fj3HjxhnVKSjtMrKcEC7tsmppl2MLu3PnjtHfrzQnT55Upc7g\nwYNLPDOU0+Hq3bu3qp2CwsE3duzYIsEnJ4RN/d0BgAYNGiA6OrrIWbiVlRU++OADo9vx9ttvIygo\nCJMmTSoy1yIgIMDoGgZqvFcAj3cMoqKioNVqMW3aNEUdAzVYVAgbgumff/4xudb69evx4Ycfonbt\n2khISMDEiRNlnVkZFL4smJqaqniSw/jx4+Hn5wcXFxfcv38f8+fPV1Snb9++GD58OP766y+MHTsW\nL7/8suwaagXfK6+8grZt2yI4OBje3t7S5//44w+ja7z22muIi4tDfHw89Ho9UlNTMW7cOLRp00ZW\nW44ePVrkDTU1NRXVq1fH9OnTZdUBgMDAQDg4OCAwMBBTp06VOjqjR482uoapnQK1gs/YoC3NoEGD\nULNmzVI7BsYwzPUoLkTlhOfatWtNaoeBWp0CAzWCD1CnQ/n8889jx44dyMvLA1Dw9xAWFoa6desa\nXWPXrl1ITU1Feno60tLSkJqaijZt2mD48OHGP5n/o8Z7BaBux0AtFnk5WqvV4o8//ihyuaik2yuW\nJD8/H9nZ2fjzzz/h7u4OnU6naOyg8GVBe3t7tGzZUvEMSK1Wizt37qBq1aqKLsEYJCUlITExEY0a\nNULTpk1lH5+eno64uDhotdoiwSfXgwcPirymeXl5sp/XiBEj0KhRIyQmJsLe3h6Ojo7YvHmz7La8\n99572LVrF/Ly8vDw4UM0aNAAhw8fll0HKJj41qBBA0XHGhjOqAwMnQJj3b59u8TgM3ZCFQBs3LgR\nEyZMKHaM2dix5eXLl2Pu3LkIDg6Wahien7HjykBB57pq1aq4devWY4/VqVPH6Dr79u1DYGAg1qxZ\n89hzCgkJMbqOWp0Cg+vXr+Po0aOPBZ9cpv7uAJCGds6dO4fq1asjKysL77//vqwa8+bNQ0JCArKz\ns5GdnQ13d3fs3btXVg0DNd4rDFJTU4u8d8ntsKvNos6EDcaNG4fc3FxpQoxGo5Hdm4yJicGmTZuQ\nn58vXXaaMGGC0ceXNG5y7do1RbOaz58/j3feeUdqT+3atREYGCi7TuHJJLGxsbC1tUXNmjUxfPhw\no8eLJk+e/FjwKfHFF18gIiJC+oOwsbHBsWPHZNXQ6/UICwvD3LlzsXTpUgwbNkxRW06ePInY2Fgs\nW7YMo0aNwjvvvKOoDlDQyVm8eDHy8vKg1+tx9+5d2VdR3n//fZM6BREREZg7dy4WLFhgUvB1794d\nADBkyBBZ7S/M8DsXGRmJtLQ03Lp1C/Xr15c9Ya1q1aoACsYGV65cievXr6NJkybSrVSNVbNmTQBA\no0aNZB33KMP7gVpn1jNmzEDPnj3x448/SsGnhKm/OwDw1FNPYdy4cbh+/TqWL1+u6O/qypUrOHz4\nMBYsWIDp06dj6tSpsmsYqPFeAajbMVCLRW7WkZOTg8jISGzYsAEbNmyQHcBAwZvY3r174erqigkT\nJiAmJkbW8UlJSdL4w5EjR/D333/j2LFjOHLkiOy2AMC6deuwY8cOVK1aFePHj8euXbsU1cnJyUH1\n6tXh4+ODOnXqICUlBbm5uZg9e7bRNQzB17BhQ0RERCgen46KikJkZCRefPFFLF++HI0bN5Zdw9ra\nGjk5OcjOzoZGo0F+fr6itlSrVg12dnbIzMxE/fr1pbMRJdatW4dJkyahVq1aGDhwoKKrDYZOga+v\nL44cOVJkuZwxCgffunXrMHPmTGzYsEFWAANAs2bNAABNmjTByZMnsW3bNpw+fVrREo/9+/dj2LBh\n2Lx5MwYPHqz4b2HevHkYNGgQdu7ciX79+mHevHmyju/SpQsAwMfHBw8ePMDFixeRk5OD/v37y6pT\nuFMQHh6O8ePHY82aNbCyUva2agi+GjVqIDw8XPGwmqm/O0DBicudO3eQmZmJrKwsRR2Cp59+GhqN\nBllZWSYvtVPjvQL4X8egc+fOOHLkCOzt7U1qlxosMoTbtm2L06dP46+//pL+yWVtbQ07OztpEpPc\ns73Q0FCEhobC1tYWW7ZswZtvvomNGzdCq9XKbgtQMDbk6uoKjUYDe3t72csWDNLS0jB9+nR06dIF\nkyZNQl5eHqZNm4b79+8bXUOt4KtevTqqV6+OzMxMvPDCC7LaYDB8+HBs374dnTp1QteuXWWNWRVW\ns2ZNfPrpp3B0dMSaNWtw7949RXWAgudluMTl7++PlJQU2TXU6hSoFXyzZ8+Gu7s7pk2bhho1asjq\ntBns2rULBw8exIYNG7B//35EREQoaou1tTW6du0KZ2dndO/eHTqdTlGdOXPmICUlBR06dEBycrLs\nMDcwtVNgoEbwAer87kyaNAnHjx/HgAED8PLLL6NDhw6ya7Ro0QJbt26V5lY8fPhQdg0DNd4rAHU7\nBmqxyMvR//77L5YtW1bkcrTcMRpvb2+EhoYiJSUFCxYsULz5glqzmt3d3bFmzRrcvXsXW7ZsKTLh\nQo4HDx4gKSkJHh4eSEpKQmZmJtLT02X9wT8afIUnSsjh7OyMmJgY6eej5LUpfOvMPn36KF7zFxYW\nhr///hu9e/fGZ599ZtIlRltbW5w/fx5arRanT59Genq67BpqdQoMwWdvb4+srCyMHDkSPj4+suvk\n5ORIlySbNWuGo0ePyq7h6uoqbbbg4OAg+3K0YZKYo6MjPvroI7Rr1w6//PKLdEYq1z///IN3330X\nAPDyyy8rWgYE/K9TABRcvpe7J4HBo8E3YMAARXXU+N1p164dPDw8cPPmTRw5ckTRpjwhISHIzMyE\ng4MDvvnmG9nzcgpT470CULdjoBaLnJg1fPhwREVFmVTj9u3biImJQUZGBqKjo7F+/Xo0b95cdp1j\nx44hPDxcukQ1f/586Q9WDq1Wi3379iExMREeHh4ICgpStLzol19+waJFi5CamopatWph/vz5uHDh\nAqpWraroXtCPTpiQe+yNGzdQpUoVREREoFu3bkavpVVrmYlaa1gLS0lJwdWrV1GtWjW899576N27\nN/r27Surhk6nw99//w0XFxd89tln6Nixo6INCsaMGYMPP/wQ1tbW0Ol0GDt2rKwlHYaJXe+99x56\n9eqFtm3b4pdffkFMTIzR690Nk7quXbuG/Px8PPfcc7h8+TIcHBywY8cOo9ui9o5ZCxcuxNChQ9Gq\nVStcuXIFO3bswJIlS4yuY+gUREVFwcvLS+oU/Pzzz7I2RCksLS0NN2/eRP369RXvRqfT6XD79m1U\nrlwZn332GTp06CD78m1UVBQ+/vhjNGnSBH/88QcmTJggu1Nw7dq1ImP3s2fPljWBrrAHDx7g5s2b\ncHNzk/1e8ahHOwZKO3Fqscgz4aZNmyIhIaFIaMoNrBkzZmDSpEnYuXMnQkJCsHz5ckRGRspui6ur\nKxwdHaHVatGnTx+kpqbKOt6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Raw numpy version\n", + "visualizer = Rank1D(algorithm='shapiro', features=features, orient='v')\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "visualizer = Rank1D(algorithm='shapiro', orient='v')\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "visualizer = Rank1D(algorithm='shapiro', show_feature_names=False, orient='v')\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### quick methods" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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0fNVRREREbozNC4L8/HwGDx7MwIEDKSwsJCYmBl9fX4KDg0lISGDNmjUsW7aM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\nMTFV5iB4e3szY8YMTp48SVF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "rank1d(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Raw numpy version\n", + "rank1d(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "rank1d(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "rank1d(X.values, y.values, show_feature_names=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### quick methods, vertical" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "rank1d(X, y, orient='v');" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Raw numpy version\n", + "rank1d(X.values, y.values, orient='v');" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "rank1d(X.values, y.values, orient='v');" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.virtualenvs/yellowbrick/lib/python3.6/site-packages/scipy/stats/morestats.py:1326: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "image/png": 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BrMHBwZieno6IiJmZmSiVSvVtW7ZsiXK5HPPz8/Hbb7/Fd999t2Q7AHBiDd8TrlarsXfv\n3pidnY1arRbj4+MxPT0d/f39sW3btjhw4EBMTk5GrVaLXbt2xfXXX79SaweANa1hhAGA1nCzDgBI\nIsIAkESEASCJCANAEhEGgCQiDABJRBgAkvwH5RBBfsF8tfkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "rank1d(X.values, y.values, show_feature_names=False, orient='v');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rank2D \n", + "Fixing order of the tick labels, using the feature names to label." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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mhBCVVKW9FJmQkMDevXvJzs7m5s2bDB48mD179nD27FkmTJjAjRs32LlzJw8f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\nQx+GAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "visualizer = Rank2D()\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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mhBCVVKW9FJmQkMDevXvJzs7m5s2bDB48mD179nD27FkmTJjAjRs32LlzJw8f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\noe9Pz549YWdnh8zMTBw7doyX8UBgYCCzqOW5c+c4Y+1qhH4XAPEiSrB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hQ1jbiFlNXswU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\nQx+GAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# raw numpy version\n", + "visualizer = Rank2D(features=features)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ix44dQ8uWLREWFoZjx47h6tWrpZ0XEZFtseKboZ+FrCJSs2ZNfP3111i6dCmc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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "visualizer = Rank2D()\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "visualizer = Rank2D(show_feature_names=False)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Quick method" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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5R82KE8JWWbMIsu7YbrNjlY8/bcGRWFaNL/cXQgghLElqRQohRC0lLxoVQghh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\nAoFAaFcQYSMQCARCu+L/ATYXXgd7BdD9AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get features from column names...\n", + "rank2d(X);" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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5R82KE8JWWbMIsu7YbrNjlY8/bcGRWFaNL/cXQgghLElqRQohRC0lLxoVQghh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\nAoFAaFcQYSMQCARCu+L/ATYXXgd7BdD9AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# raw numpy version\n", + "rank2d(X.values, features=features);" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tyjRgdBFPP73imPr3+yuOObP/LL5s9qDiuJ7H9iiOISIFOBMhIiJpLCJERCRLrXess4gQ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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy version, no feature names\n", + "rank2d(X.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# disable tick labels\n", + "rank2d(X, show_feature_names=False);" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py index bde805f67..0b0661a2b 100644 --- a/yellowbrick/features/__init__.py +++ b/yellowbrick/features/__init__.py @@ -20,6 +20,6 @@ ## Hoist visualizers into the features namespace from .pcoords import ParallelCoordinates, parallel_coordinates from .radviz import RadialVisualizer, RadViz, radviz -from .rankd import Rank2D, rank2d +from .rankd import Rank1D, rank1d, Rank2D, rank2d from .scatter import ScatterViz, ScatterVisualizer, scatterviz from .jointplot import JointPlotVisualizer diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index dbe8a6c60..2cb0024dc 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -19,6 +19,7 @@ import numpy as np import matplotlib.pyplot as plt +from scipy.stats import shapiro from yellowbrick.utils import is_dataframe from yellowbrick.features.base import FeatureVisualizer @@ -26,12 +27,65 @@ from yellowbrick.style.colors import resolve_colors, get_color_cycle +__all__ = ["rank1d", "rank2d", "Rank1D", "Rank2D"] + + ########################################################################## ## Quick Methods ########################################################################## +def rank1d(X, y=None, ax=None, algorithm='shapiro', features=None, + orient='h', show_feature_names=True, **kwargs): + """Scores each feature with the algorithm and ranks them in a bar plot. + + This helper function is a quick wrapper to utilize the Rank1D Visualizer + (Transformer) for one-off analysis. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + ax : matplotlib axes + the axis to plot the figure on. + + algorithm : one of {'shapiro', }, default: 'shapiro' + The ranking algorithm to use, default is 'Shapiro-Wilk. + + features : list + A list of feature names to use. + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + orient : 'h' or 'v' + Specifies a horizontal or vertical bar chart. + + show_feature_names : boolean, default: True + If True, the feature names are used to label the axis ticks in the + plot. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the parallel coordinates were drawn on. + + """ + # Instantiate the visualizer + visualizer = Rank1D(ax, algorithm, features, orient, show_feature_names, + **kwargs) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y, **kwargs) + visualizer.transform(X) + + # Return the axes object on the visualizer + return visualizer.ax + def rank2d(X, y=None, ax=None, algorithm='pearson', features=None, - colormap='RdBu_r', **kwargs): + show_feature_names=True, colormap='RdBu_r', **kwargs): """Displays pairwise comparisons of features with the algorithm and ranks them in a lower-left triangle heatmap plot. @@ -53,10 +107,14 @@ def rank2d(X, y=None, ax=None, algorithm='pearson', features=None, the ranking algorithm to use, default is Pearson correlation. features : list - a list of feature names to use + A list of feature names to use. If a DataFrame is passed to fit and features is None, feature names are selected as the columns of the DataFrame. + show_feature_names : boolean, default: True + If True, the feature names are used to label the axis ticks in the + plot. + colormap : string or cmap optional string or matplotlib cmap to colorize lines Use either color to colorize the lines on a per class basis or @@ -69,7 +127,8 @@ def rank2d(X, y=None, ax=None, algorithm='pearson', features=None, """ # Instantiate the visualizer - visualizer = Rank2D(ax, algorithm, features, colormap, **kwargs) + visualizer = Rank2D(ax, algorithm, features, colormap, show_feature_names, + **kwargs) # Fit and transform the visualizer (calls draw) visualizer.fit(X, y, **kwargs) @@ -80,14 +139,12 @@ def rank2d(X, y=None, ax=None, algorithm='pearson', features=None, ########################################################################## -## Rank 2D Feature Visualizer +## Base Feature Visualizer ########################################################################## -class Rank2D(FeatureVisualizer): +class RankDBase(FeatureVisualizer): """ - Rank2D performs pairwise comparisons of each feature in the data set with - a specific metric or algorithm (e.g. Pearson correlation) then returns - them ranked as a lower left triangle diagram. + Base visualizer for Rank1D and Rank2D Parameters ---------- @@ -95,23 +152,29 @@ class Rank2D(FeatureVisualizer): The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required). - algorithm : one of {'pearson', 'covariance'}, default: 'pearson' - The ranking algorithm to use, default is Pearson correlation. + algorithm : string + The ranking algorithm to use; options and defaults vary by subclass features : list - a list of feature names to use + A list of feature names to use. If a DataFrame is passed to fit and features is None, feature names are selected as the columns of the DataFrame. - colormap : string or cmap, default: 'RdBu_r' - optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. + show_feature_names : boolean, default: True + If True, the feature names are used to label the axis ticks in the + plot. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. + Attributes + ---------- + ranks_ : ndarray + An n-dimensional, symmetric array of rank scores, where n is the + number of features. E.g. for 1D ranking, it is (n,), for a + 2D ranking it is (n,n) and so forth. + Examples -------- @@ -126,25 +189,21 @@ class Rank2D(FeatureVisualizer): process, but can and should be set as early as possible. """ - ranking_methods = { - 'pearson': lambda X: np.corrcoef(X.transpose()), - 'covariance': lambda X: np.cov(X.transpose()), - } + ranking_methods = {} - def __init__(self, ax=None, algorithm='pearson', features=None, - colormap='RdBu_r', **kwargs): + def __init__(self, ax=None, algorithm=None, features=None, + show_feature_names=True, **kwargs): """ - Initialize the Rank2D class with the options required to rank and + Initialize the class with the options required to rank and order features as well as visualize the result. """ - super(Rank2D, self).__init__(ax=ax, **kwargs) + super(RankDBase, self).__init__(ax=ax, **kwargs) # Data Parameters - self.ranking_ = algorithm + self.ranking_ = algorithm self.features_ = features - # Visual Parameters - self.colormap = colormap + self.show_feature_names_ = show_feature_names def fit(self, X, y=None, **kwargs): """ @@ -166,11 +225,6 @@ def fit(self, X, y=None, **kwargs): self : instance Returns the instance of the transformer/visualizer """ - - # TODO: This class is identical to the Parallel Coordinates version, - # so hoist this functionality to a higher level class that is extended - # by both RadViz and ParallelCoordinates. - # Get the shape of the data nrows, ncols = X.shape @@ -190,7 +244,6 @@ def fit(self, X, y=None, **kwargs): # Fit always returns self. return self - def transform(self, X, **kwargs): """ The transform method is the primary drawing hook for ranking classes. @@ -205,19 +258,21 @@ def transform(self, X, **kwargs): Returns ------- - Xp : ndarray - The transformed matrix, X' + X : ndarray + Typically a transformed matrix, X' is returned. However, this + method performs no transformation on the original data, instead + simply ranking the features that are in the input data and returns + the original data, unmodified. """ - # Rank and draw the input matrix - Xp = self.rank(X) - self.draw(Xp, **kwargs) + self.ranks_ = self.rank(X) + self.draw(**kwargs) # Return the X matrix, unchanged return X def rank(self, X, algorithm=None): """ - Returns the ranking of each pair of columns as an m by m matrix. + Returns the feature ranking. Parameters ---------- @@ -229,8 +284,10 @@ def rank(self, X, algorithm=None): Returns ------- - R : ndarray - The mxm ranking matrix of the variables + ranks : ndarray + An n-dimensional, symmetric array of rank scores, where n is the + number of features. E.g. for 1D ranking, it is (n,), for a + 2D ranking it is (n,n) and so forth. """ algorithm = algorithm or self.ranking_ algorithm = algorithm.lower() @@ -240,9 +297,213 @@ def rank(self, X, algorithm=None): "'{}' is unrecognized ranking method".format(algorithm) ) + # Extract matrix from dataframe if necessary + if is_dataframe(X): + X = X.as_matrix() + return self.ranking_methods[algorithm](X) - def draw(self, X, **kwargs): + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: dict + generic keyword arguments + + """ + # Set the title + self.set_title( + "{} Ranking of {} Features".format( + self.ranking_.title(), len(self.features_) + ) + ) + + +########################################################################## +## Rank 1D Feature Visualizer +########################################################################## + +class Rank1D(RankDBase): + """ + Rank1D computes a score for each feature in the data set with a specific + metric or algorithm (e.g. Shapiro-Wilk) then returns the features ranked + as a bar plot. + + Parameters + ---------- + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + algorithm : one of {'shapiro', }, default: 'shapiro' + The ranking algorithm to use, default is 'Shapiro-Wilk. + + features : list + A list of feature names to use. + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + orient : 'h' or 'v' + Specifies a horizontal or vertical bar chart. + + show_feature_names : boolean, default: True + If True, the feature names are used to label the x and y ticks in the + plot. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + ranks_ : ndarray + An array of rank scores with shape (n,), where n is the + number of features. It is computed during `fit`. + + Examples + -------- + + >>> visualizer = Rank2D() + >>> visualizer.fit(X, y) + >>> visualizer.transform(X) + >>> visualizer.poof() + """ + + ranking_methods = { + 'shapiro': lambda X: np.array([shapiro(x)[0] for x in X.T]), + } + + def __init__(self, ax=None, algorithm='shapiro', features=None, + orient='h', show_feature_names=True, **kwargs): + """ + Initialize the class with the options required to rank and + order features as well as visualize the result. + """ + super(Rank1D, self).__init__( + ax=None, algorithm=algorithm, features=features, + show_feature_names=show_feature_names, **kwargs + ) + self.orientation_ = orient + + def draw(self, **kwargs): + """ + Draws the bar plot of the ranking array of features. + """ + if self.orientation_ == 'h': + # Make the plot + self.ax.barh(np.arange(len(self.ranks_)), self.ranks_, color='b') + + # Add ticks and tick labels + self.ax.set_yticks(np.arange(len(self.ranks_))) + if self.show_feature_names_: + self.ax.set_yticklabels(self.features_) + else: + self.ax.set_yticklabels([]) + + # Order the features from top to bottom on the y axis + self.ax.invert_yaxis() + + # Turn off y grid lines + self.ax.yaxis.grid(False) + + elif self.orientation_ == 'v': + # Make the plot + self.ax.bar(np.arange(len(self.ranks_)), self.ranks_, color='b') + + # Add ticks and tick labels + self.ax.set_xticks(np.arange(len(self.ranks_))) + if self.show_feature_names_: + self.ax.set_xticklabels(self.features_, rotation=90) + else: + self.ax.set_xticklabels([]) + + # Turn off x grid lines + self.ax.xaxis.grid(False) + + else: + raise YellowbrickValueError( + "Orientation must be 'h' or 'v'" + ) + + +########################################################################## +## Rank 2D Feature Visualizer +########################################################################## + +class Rank2D(RankDBase): + """ + Rank2D performs pairwise comparisons of each feature in the data set with + a specific metric or algorithm (e.g. Pearson correlation) then returns + them ranked as a lower left triangle diagram. + + Parameters + ---------- + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + algorithm : one of {'pearson', 'covariance'}, default: 'pearson' + The ranking algorithm to use, default is Pearson correlation. + + features : list + A list of feature names to use. + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + colormap : string or cmap, default: 'RdBu_r' + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + show_feature_names : boolean, default: True + If True, the feature names are used to label the axis ticks in the + plot. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + ranks_ : ndarray + An array of rank scores with shape (n,n), where n is the + number of features. It is computed during `fit`. + + Examples + -------- + + >>> visualizer = Rank2D() + >>> visualizer.fit(X, y) + >>> visualizer.transform(X) + >>> visualizer.poof() + + Notes + ----- + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + + ranking_methods = { + 'pearson': lambda X: np.corrcoef(X.transpose()), + 'covariance': lambda X: np.cov(X.transpose()), + } + + def __init__(self, ax=None, algorithm='pearson', features=None, + colormap='RdBu_r', show_feature_names=True, **kwargs): + """ + Initialize the class with the options required to rank and + order features as well as visualize the result. + """ + super(Rank2D, self).__init__( + ax=None, algorithm=algorithm, features=features, + show_feature_names=show_feature_names, **kwargs + ) + self.colormap=colormap + + def draw(self, **kwargs): """ Draws the heatmap of the ranking matrix of variables. """ @@ -250,16 +511,12 @@ def draw(self, X, **kwargs): self.ax.set_aspect("equal") # Generate a mask for the upper triangle - mask = np.zeros_like(X, dtype=np.bool) + mask = np.zeros_like(self.ranks_, dtype=np.bool) mask[np.triu_indices_from(mask)] = True - # Reverse the rows to get the lower left triangle - X = X[::-1] - mask = mask[::-1] - # Draw the heatmap # TODO: Move mesh to a property so the colorbar can be finalized - data = np.ma.masked_where(mask, X) + data = np.ma.masked_where(mask, self.ranks_) mesh = self.ax.pcolormesh(data, cmap=self.colormap, vmin=-1, vmax=1) # Set the Axis limits @@ -271,20 +528,15 @@ def draw(self, X, **kwargs): cb = self.ax.figure.colorbar(mesh, None, self.ax) cb.outline.set_linewidth(0) - def finalize(self, **kwargs): - """ - Finalize executes any subclass-specific axes finalization steps. - The user calls poof and poof calls finalize. - - Parameters - ---------- - kwargs: dict - generic keyword arguments - - """ - # Set the title - self.set_title( - "{} Ranking of {} Features".format( - self.ranking_.title(), len(self.features_) - ) - ) + # Reverse the rows to get the lower left triangle + self.ax.invert_yaxis() + + # Add ticks and tick labels + self.ax.set_xticks(np.arange(len(self.ranks_)) + 0.5) + self.ax.set_yticks(np.arange(len(self.ranks_)) + 0.5) + if self.show_feature_names_: + self.ax.set_xticklabels(self.features_, rotation=90) + self.ax.set_yticklabels(self.features_) + else: + self.ax.set_xticklabels([]) + self.ax.set_yticklabels([]) From a03411c8d520ab765041d55a61ee5b3ff8685ce3 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 22 Jun 2017 13:56:26 -0400 Subject: [PATCH 21/40] residuals plot doc copy and paste error fixes #262 --- yellowbrick/regressor/residuals.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 90b074633..29e3d5dbb 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -193,7 +193,7 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) visualizer.score(X_test, y_test) - visualizer.finalize() + visualizer.finalize() # Return the axes object on the visualizer return visualizer.ax @@ -246,7 +246,7 @@ class ResidualsPlot(RegressionScoreVisualizer): >>> from yellowbrick.regressor import ResidualsPlot >>> from sklearn.linear_model import Ridge - >>> model = PredictionError(Ridge()) + >>> model = ResidualsPlot(Ridge()) >>> model.fit(X_train, y_train) >>> model.score(X_test, y_test) >>> model.poof() From 0c576e4a3223d8f0d97748dbc8774c2c44d041ce Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 22 Jun 2017 14:20:45 -0400 Subject: [PATCH 22/40] shared limits on prediction error ref #263 --- yellowbrick/regressor/residuals.py | 48 +++++++++++++++++++++++++----- 1 file changed, 41 insertions(+), 7 deletions(-) diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 29e3d5dbb..8fb04ed13 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -55,6 +55,14 @@ class PredictionError(RegressionScoreVisualizer): The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). + shared_limits : bool, default: True + If shared_limits is True, the range of the X and Y axis limits will + be identical, creating a square graphic with a true 45 degree line. + In this form, it is easier to diagnose under- or over- prediction, + though the figure will become more sparse. To localize points, set + shared_limits to False, but note that this will distort the figure + and should be accounted for during analysis. + point_color : color Defines the color of the error points; can be any matplotlib color. @@ -82,15 +90,19 @@ class PredictionError(RegressionScoreVisualizer): its primary entry point is the `score()` method. """ - def __init__(self, model, ax=None, **kwargs): - + def __init__(self, model, ax=None, shared_limits=True, **kwargs): + # Initialize the visualizer super(PredictionError, self).__init__(model, ax=ax, **kwargs) + # Visual arguments self.colors = { 'point': kwargs.pop('point_color', None), 'line': kwargs.pop('line_color', LINE_COLOR), } + # Drawing arguments + self.shared_limits = shared_limits + def score(self, X, y=None, **kwargs): """ The score function is the hook for visual interaction. Pass in test @@ -135,6 +147,9 @@ def draw(self, y, y_pred): # Ideally we'd want the best fit line to be drawn only once draw_best_fit(y, y_pred, self.ax, 'linear', ls='--', lw=2, c=self.colors['line']) + # Set the axes limits based on the range of X and Y data + # NOTE: shared_limits will be accounted for in finalize() + # TODO: do better than add one for really small residuals self.ax.set_xlim(y.min()-1, y.max()+1) self.ax.set_ylim(y_pred.min()-1, y_pred.max()+1) @@ -152,6 +167,25 @@ def finalize(self, **kwargs): # Set the title on the plot self.set_title('Prediction Error for {}'.format(self.name)) + # Square the axes to ensure a 45 degree line + if self.shared_limits: + # Get the current limits + ylim = self.ax.get_ylim() + xlim = self.ax.get_xlim() + + # Find the range that captures all data + bounds = ( + min(ylim[0], xlim[0]), + max(ylim[1], xlim[1]), + ) + + # Reset the limits + self.ax.set_xlim(bounds) + self.ax.set_ylim(bounds) + + # Ensure the aspect ratio is square + self.ax.set_aspect('equal', adjustable='box') + # Set the axes labels self.ax.set_ylabel('Predicted') self.ax.set_xlabel('Measured') @@ -205,12 +239,12 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): class ResidualsPlot(RegressionScoreVisualizer): """ - A residual plot shows the residuals on the vertical axis - and the independent variable on the horizontal axis. + A residual plot shows the residuals on the vertical axis and the + independent variable on the horizontal axis. - If the points are randomly dispersed around the horizontal axis, - a linear regression model is appropriate for the data; - otherwise, a non-linear model is more appropriate. + If the points are randomly dispersed around the horizontal axis, a linear + regression model is appropriate for the data; otherwise, a non-linear + model is more appropriate. Parameters ---------- From 1001b6f6dccef82511fb5d9bef7cc8b3a957d848 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 22 Jun 2017 16:25:31 -0400 Subject: [PATCH 23/40] identity line and added to pe plot ref #263 --- yellowbrick/bestfit.py | 70 ++++++++++++++++++++++++++++++ yellowbrick/regressor/residuals.py | 19 ++++++-- 2 files changed, 86 insertions(+), 3 deletions(-) diff --git a/yellowbrick/bestfit.py b/yellowbrick/bestfit.py index f98c0b899..d704caa5d 100644 --- a/yellowbrick/bestfit.py +++ b/yellowbrick/bestfit.py @@ -18,6 +18,7 @@ ########################################################################## import numpy as np +import matplotlib.pyplot as plt from sklearn import linear_model from sklearn.preprocessing import PolynomialFeatures @@ -173,6 +174,75 @@ def fit_log(X, y): raise NotImplementedError("Logrithmic best fit lines are not implemented") +########################################################################## +## Draw 45 Degree Line +########################################################################## + +def draw_identity_line(ax=None, dynamic=True, **kwargs): + """ + Draws a 45 degree identity line such that y=x for all points within the + given axes x and y limits. This function also registeres a callback so + that as the figure is modified, the axes are updated and the line remains + drawn correctly. + + Parameters + ---------- + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + dynamic : bool, default : True + If the plot is dynamic, callbacks will be registered to update the + identiy line as axes are changed. + + kwargs : dict + Keyword arguments to pass to the matplotlib plot function to style the + identity line. + + + Returns + ------- + + ax : matplotlib Axes + The axes with the line drawn on it. + """ + + # Get the current working axes + ax = ax or plt.gca() + + # Define the standard line color + if 'c' not in kwargs and 'color' not in kwargs: + kwargs['color'] = LINE_COLOR + + # Define the standard opacity + if 'alpha' not in kwargs: + kwargs['alpha'] = 0.5 + + # Draw the identity line + identity, = ax.plot([],[], **kwargs) + + # Define the callback + def callback(ax): + # Get the x and y limits on the axes + xlim = ax.get_xlim() + ylim = ax.get_ylim() + + # Set the bounding range of the line + data = ( + max(xlim[0], ylim[0]), min(xlim[1], ylim[1]) + ) + identity.set_data(data, data) + + # Register the callback and return + callback(ax) + + if dynamic: + ax.callbacks.connect('xlim_changed', callback) + ax.callbacks.connect('ylim_changed', callback) + + return ax + if __name__ == '__main__': import os diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 8fb04ed13..a91e70d26 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -22,10 +22,11 @@ from sklearn.model_selection import train_test_split -from ..bestfit import draw_best_fit from ..style.palettes import LINE_COLOR from .base import RegressionScoreVisualizer from ..exceptions import YellowbrickTypeError +from ..bestfit import draw_best_fit, draw_identity_line + ## Packages for export __all__ = [ @@ -145,7 +146,10 @@ def draw(self, y, y_pred): # TODO If score is happening inside a loop, draw would get called multiple times. # Ideally we'd want the best fit line to be drawn only once - draw_best_fit(y, y_pred, self.ax, 'linear', ls='--', lw=2, c=self.colors['line']) + draw_best_fit( + y, y_pred, self.ax, 'linear', ls='--', lw=2, + c=self.colors['line'], label='best fit' + ) # Set the axes limits based on the range of X and Y data # NOTE: shared_limits will be accounted for in finalize() @@ -167,6 +171,12 @@ def finalize(self, **kwargs): # Set the title on the plot self.set_title('Prediction Error for {}'.format(self.name)) + # Draw the 45 degree line + draw_identity_line( + ax=self.ax, ls='--', lw=2, c=self.colors['line'], + alpha=0.5, label="identity" + ) + # Square the axes to ensure a 45 degree line if self.shared_limits: # Get the current limits @@ -183,13 +193,16 @@ def finalize(self, **kwargs): self.ax.set_xlim(bounds) self.ax.set_ylim(bounds) - # Ensure the aspect ratio is square + # Ensure the aspect ratio is square self.ax.set_aspect('equal', adjustable='box') # Set the axes labels self.ax.set_ylabel('Predicted') self.ax.set_xlabel('Measured') + # Set the legend + self.ax.legend(loc='best', frameon=True) + def prediction_error(model, X, y=None, ax=None, **kwargs): """Quick method: From 0271d384741c32b3fe53bc7bce7b155059a55704 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sun, 25 Jun 2017 08:50:06 -0400 Subject: [PATCH 24/40] identity line in residuals, conditional freq dist --- yellowbrick/bestfit.py | 47 ++++++- yellowbrick/features/rankd.py | 8 +- yellowbrick/regressor/residuals.py | 67 ++++++++-- yellowbrick/text/freqdist.py | 190 +++++++++++++++++++++-------- 4 files changed, 241 insertions(+), 71 deletions(-) diff --git a/yellowbrick/bestfit.py b/yellowbrick/bestfit.py index d704caa5d..af7c24edd 100644 --- a/yellowbrick/bestfit.py +++ b/yellowbrick/bestfit.py @@ -54,14 +54,43 @@ def draw_best_fit(X, y, ax, estimator='linear', **kwargs): The estimator function can be one of the following: - 'linear': Uses OLS to fit the regression - 'quadratic': Uses OLS with Polynomial order 2 - 'exponential': Not implemented yet - 'log': Not implemented yet - 'select_best': Selects the best fit via MSE + - ``'linear'``: Uses OLS to fit the regression + - ``'quadratic'``: Uses OLS with Polynomial order 2 + - ``'exponential'``: Not implemented yet + - ``'log'``: Not implemented yet + - ``'select_best'``: Selects the best fit via MSE The remaining keyword arguments are passed to ax.plot to define and describe the line of best fit. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + estimator : string, default: 'linear' + The name of the estimator function used to draw the best fit line. + The estimator can currently be one of linear, quadratic, exponential, + log, or select_best. The select best method uses the minimum MSE to + select the best fit line. + + kwargs : dict + Keyword arguments to pass to the matplotlib plot function to style and + label the line of best fit. By default, the standard line color is + used unless the color keyword argument is passed in. + + Returns + ------- + + ax : matplotlib Axes + The axes with the line drawn on it. """ # Estimators are the types of best fit lines that can be drawn. @@ -118,6 +147,9 @@ def draw_best_fit(X, y, ax, estimator='linear', **kwargs): if 'c' not in kwargs and 'color' not in kwargs: kwargs['color'] = LINE_COLOR + # Get the current working axes + ax = ax or plt.gca() + # Plot line of best fit onto the axes that were passed in. # TODO: determine if xlim or X.min(), X.max() are better params xr = np.linspace(*ax.get_xlim(), num=100) @@ -206,6 +238,11 @@ def draw_identity_line(ax=None, dynamic=True, **kwargs): ax : matplotlib Axes The axes with the line drawn on it. + + Notes + ----- + + .. seealso:: `StackOverflow discussion: Does matplotlib have a function for drawing diagonal lines in axis coordinates? `_ """ # Get the current working axes diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 2cb0024dc..0a8ab19e9 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -359,14 +359,14 @@ class Rank1D(RankDBase): Attributes ---------- - ranks_ : ndarray + ``ranks_`` : ndarray An array of rank scores with shape (n,), where n is the number of features. It is computed during `fit`. Examples -------- - >>> visualizer = Rank2D() + >>> visualizer = Rank1D() >>> visualizer.fit(X, y) >>> visualizer.transform(X) >>> visualizer.poof() @@ -468,9 +468,9 @@ class Rank2D(RankDBase): Attributes ---------- - ranks_ : ndarray + ``ranks_`` : ndarray An array of rank scores with shape (n,n), where n is the - number of features. It is computed during `fit`. + number of features. It is computed during ``fit``. Examples -------- diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index a91e70d26..716c61033 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -64,6 +64,17 @@ class PredictionError(RegressionScoreVisualizer): shared_limits to False, but note that this will distort the figure and should be accounted for during analysis. + besfit : bool, default: True + Draw a linear best fit line to estimate the correlation between the + predicted and measured value of the target variable. The color of + the bestfit line is determined by the ``line_color`` argument. + + identity: bool, default: True + Draw the 45 degree identity line, y=x in order to better show the + relationship or pattern of the residuals. E.g. to estimate if the + model is over- or under- estimating the given values. The color of the + identity line is a muted version of the ``line_color`` argument. + point_color : color Defines the color of the error points; can be any matplotlib color. @@ -91,7 +102,8 @@ class PredictionError(RegressionScoreVisualizer): its primary entry point is the `score()` method. """ - def __init__(self, model, ax=None, shared_limits=True, **kwargs): + def __init__(self, model, ax=None, shared_limits=True, + bestfit=True, identity=True, **kwargs): # Initialize the visualizer super(PredictionError, self).__init__(model, ax=ax, **kwargs) @@ -103,6 +115,8 @@ def __init__(self, model, ax=None, shared_limits=True, **kwargs): # Drawing arguments self.shared_limits = shared_limits + self.bestfit = bestfit + self.identity = identity def score(self, X, y=None, **kwargs): """ @@ -146,10 +160,11 @@ def draw(self, y, y_pred): # TODO If score is happening inside a loop, draw would get called multiple times. # Ideally we'd want the best fit line to be drawn only once - draw_best_fit( - y, y_pred, self.ax, 'linear', ls='--', lw=2, - c=self.colors['line'], label='best fit' - ) + if self.bestfit: + draw_best_fit( + y, y_pred, self.ax, 'linear', ls='--', lw=2, + c=self.colors['line'], label='best fit' + ) # Set the axes limits based on the range of X and Y data # NOTE: shared_limits will be accounted for in finalize() @@ -171,12 +186,6 @@ def finalize(self, **kwargs): # Set the title on the plot self.set_title('Prediction Error for {}'.format(self.name)) - # Draw the 45 degree line - draw_identity_line( - ax=self.ax, ls='--', lw=2, c=self.colors['line'], - alpha=0.5, label="identity" - ) - # Square the axes to ensure a 45 degree line if self.shared_limits: # Get the current limits @@ -196,6 +205,13 @@ def finalize(self, **kwargs): # Ensure the aspect ratio is square self.ax.set_aspect('equal', adjustable='box') + # Draw the 45 degree line + if self.identity: + draw_identity_line( + ax=self.ax, ls='--', lw=2, c=self.colors['line'], + alpha=0.5, label="identity" + ) + # Set the axes labels self.ax.set_ylabel('Predicted') self.ax.set_xlabel('Measured') @@ -226,6 +242,35 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): ax : matplotlib Axes The axes to plot the figure on. + shared_limits : bool, default: True + If shared_limits is True, the range of the X and Y axis limits will + be identical, creating a square graphic with a true 45 degree line. + In this form, it is easier to diagnose under- or over- prediction, + though the figure will become more sparse. To localize points, set + shared_limits to False, but note that this will distort the figure + and should be accounted for during analysis. + + besfit : bool, default: True + Draw a linear best fit line to estimate the correlation between the + predicted and measured value of the target variable. The color of + the bestfit line is determined by the ``line_color`` argument. + + identity: bool, default: True + Draw the 45 degree identity line, y=x in order to better show the + relationship or pattern of the residuals. E.g. to estimate if the + model is over- or under- estimating the given values. The color of the + identity line is a muted version of the ``line_color`` argument. + + point_color : color + Defines the color of the error points; can be any matplotlib color. + + line_color : color + Defines the color of the best fit line; can be any matplotlib color. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + Returns ------- ax : matplotlib Axes diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py index 9de090b77..6745aa2be 100644 --- a/yellowbrick/text/freqdist.py +++ b/yellowbrick/text/freqdist.py @@ -19,6 +19,7 @@ import numpy as np import matplotlib.pyplot as plt +import matplotlib.patches as mpatches from operator import itemgetter @@ -77,7 +78,7 @@ def freqdist(X, y=None, ax=None, color=None, N=50, **kwargs): return visualizer.ax -class FreqDistVisualizer(TextVisualizer): +class FrequencyVisualizer(TextVisualizer): """ A frequency distribution tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable @@ -88,33 +89,50 @@ class FreqDistVisualizer(TextVisualizer): Parameters ---------- - ax : matplotlib axes + features : list, default: None + The list of feature names from the vectorizer, ordered by index. E.g. + a lexicon that specifies the unique vocabulary of the corpus. This + can be typically fetched using the ``get_feature_names()`` method of + the transformer in Scikit-Learn. + + ax : matplotlib axes, default: None The axes to plot the figure on. + + n: integer, default: 50 + Top N tokens to be plotted. + + orient : 'h' or 'v', default: 'h' + Specifies a horizontal or vertical bar chart. + color : list or tuple of colors Specify color for bars - N: integer - Top N tokens to be plotted. + kwargs : dict Pass any additional keyword arguments to the super class. These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - def __init__(self, ax=None, color=None, N=50, **kwargs): - """ - Initializes the base frequency distributions with many - of the options required in order to make this - visualization work. - """ + + def __init__(self, features, ax=None, n=50, orient='h', color=None, **kwargs): super(FreqDistVisualizer, self).__init__(ax=ax, **kwargs) + # Check that the orient is correct + orient = orient.lower().strip() + if orient not in {'h', 'v'}: + raise YellowbrickValueError( + "Orientation must be 'h' or 'v'" + ) + # Visualizer parameters - self.N = 50 + self.N = n + self.features = features - # Visual Parameters + # Visual arguments self.color = color + self.orient = orient - def freq_dist(self): + def count(self, X): """ Called from the fit method, this method gets all the words from the corpus and their corresponding frequency @@ -122,28 +140,24 @@ def freq_dist(self): Parameters ---------- - kwargs: generic keyword arguments. - """ - counts = np.asarray(self.docs.sum(axis=0)).ravel().tolist() - self.word_freq = list(zip(self.features, counts)) + X : ndarray or masked ndarray + Pass in the matrix of vectorized documents, can be masked in + order to sum the word frequencies for only a subset of documents. - def get_counts(self): - """ - Called from the fit method, this method sorts the words - from the corpus with their corresponding frequency - counts in reverse order. + Returns + ------- - Parameters - ---------- - kwargs: generic keyword arguments. + counts : array + A vector containing the counts of all words in X (columns) """ - sorted_word_freq = sorted(self.word_freq, - key=itemgetter(1), reverse=True) - self.words, self.counts = list(zip(*sorted_word_freq)) + # Sum on axis 0 (by columns), each column is a word + # Convert the matrix to an array + # Squeeze to remove the 1 dimension objects (like ravel) + return np.squeeze(np.asarray(X.sum(axis=0))) - def fit(self, docs, features): + def fit(self, X, y=None): """ The fit method is the primary drawing input for the frequency distribution visualization. It requires vectorized lists of @@ -152,22 +166,41 @@ def fit(self, docs, features): Parameters ---------- - docs : ndarray or DataFrame of shape n x m - A matrix of n instances with m features representing the corpus of - vectorized documents. + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features representing the corpus + of frequency vectorized documents. - features : list - List of corpus vocabulary words + y : ndarray or DataFrame of shape n + Labels for the documents for conditional frequency distribution. - Text documents must be vectorized before passing to fit() + .. note:: Text documents must be vectorized before ``fit()``. """ - self.docs = docs - self.features = features - - self.freq_dist() - self.get_counts() + # Compute the conditional word frequency + if y is not None: + # Fit the frequencies + self.conditional_freqdist_ = {} + + # Conditional frequency distribution + self.classes_ = [str(label) for label in set(y)] + for label in self.classes_: + self.conditional_freqdist_[label] = self.count(X[y == label]) + else: + # No conditional frequencies + self.conditional_freqdist_ = None + + # Frequency distribution of entire corpus. + self.freqdist_ = self.count(X) + self.sorted_ = self.freqdist_.argsort()[::-1] # Descending order + + # Compute the number of words, vocab, and hapaxes + self.vocab_ = self.freqdist_.shape[0] + self.words_ = self.freqdist_.sum() + self.hapaxes_ = sum(1 for c in self.freqdist_ if c == 1) + + # Draw and ensure that we return self self.draw() + return self def draw(self, **kwargs): """ @@ -179,12 +212,58 @@ def draw(self, **kwargs): kwargs: generic keyword arguments. """ - # Plot the top 50 most frequent words - y_pos = np.arange(self.N) - self.ax.bar(y_pos, self.counts[:self.N], align='center', alpha=0.5) - - # Set the tick marks - self.ax.set_xticks(y_pos) + # Prepare the data + bins = np.arange(self.N) + words = [self.features[i] for i in self.sorted_[:self.N]] + freqs = {} + + # Set up the bar plots + if self.conditional_freqdist_: + for label, values in sorted(self.conditional_freqdist_.items(), key=itemgetter(0)): + freqs[label] = [ + values[i] for i in self.sorted_[:self.N] + ] + else: + freqs['corpus'] = [ + self.freqdist_[i] for i in self.sorted_[:self.N] + ] + + # Draw a horizontal barplot + if self.orient == 'h': + # Add the barchart, stacking if necessary + for label, freq in freqs.items(): + self.ax.barh(bins, freq, label=label, align='center') + + # Set the y ticks to the words + self.ax.set_yticks(bins) + self.ax.set_yticklabels(words) + + # Order the features from top to bottom on the y axis + self.ax.invert_yaxis() + + # Turn off y grid lines and turn on x grid lines + self.ax.yaxis.grid(False) + self.ax.xaxis.grid(True) + + # Draw a vertical barplot + elif self.orient == 'v': + # Add the barchart, stacking if necessary + for label, freq in freqs.items(): + self.ax.bar(bins, freq, label=label, align='edge') + + # Set the y ticks to the words + self.ax.set_xticks(bins) + self.ax.set_xticklabels(words, rotation=90) + + # Turn off x grid lines and turn on y grid lines + self.ax.yaxis.grid(True) + self.ax.xaxis.grid(False) + + # Unknown state + else: + raise YellowbrickValueError( + "Orientation must be 'h' or 'v'" + ) def finalize(self, **kwargs): """ @@ -198,12 +277,21 @@ def finalize(self, **kwargs): """ # Set the title self.set_title( - 'Frequency distribution for top {} tokens'.format(self.N) + 'Frequency Distribution of Top {} tokens'.format(self.N) + ) + + # Create the vocab, count, and hapaxes labels + infolabel = "vocab: {:,}\nwords: {:,}\nhapax: {:,}".format( + self.vocab_, self.words_, self.hapaxes_ ) - # Rotate tick marks to make words legible - self.ax.set_xticklabels(self.words[:self.N], rotation=90) + self.ax.text(0.68, 0.97, infolabel, transform=self.ax.transAxes, + fontsize=9, verticalalignment='top', + bbox={'boxstyle':'round', 'facecolor':'white', 'alpha':.8}) # Set the legend and the grid - self.ax.legend(loc='best') - self.ax.grid() + self.ax.legend(loc='upper right', frameon=True) + + +# Backwards compatibility alias +FreqDistVisualizer = FrequencyVisualizer From 695514cd6a695c17f8ec87ce5c3301f6b089fd4a Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Wed, 19 Jul 2017 19:12:21 -0400 Subject: [PATCH 25/40] fixed freqdist test error --- tests/test_text/test_freqdist.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py index b4f0e00d7..793928bca 100644 --- a/tests/test_text/test_freqdist.py +++ b/tests/test_text/test_freqdist.py @@ -30,6 +30,7 @@ class FreqDistTests(unittest.TestCase, DatasetMixin): + def test_integrated_freqdist(self): """ Assert no errors occur during freqdist integration @@ -40,5 +41,5 @@ def test_integrated_freqdist(self): docs = vectorizer.fit_transform(corpus.data) features = vectorizer.get_feature_names() - visualizer = FreqDistVisualizer() - visualizer.fit(docs, features) + visualizer = FreqDistVisualizer(features) + visualizer.fit(docs) From e2916b003d343126a14b04af9682b5b63177071d Mon Sep 17 00:00:00 2001 From: Phillip Schafer Date: Wed, 19 Jul 2017 19:26:05 -0400 Subject: [PATCH 26/40] Normalized pcoords and pcoords improvements (#259) * normalized pcoords and pcoords improvements * make 'normalize' an option to ParallelCoordinates * add 'sample' argument to ParallelCoordinates * PR fixes * move param validation to constructor, add tests --- examples/pbs929/pcoords.ipynb | 722 ++++++++++++++++++++++++++++ tests/test_features/test_pcoords.py | 57 ++- yellowbrick/features/pcoords.py | 126 ++++- 3 files changed, 881 insertions(+), 24 deletions(-) create mode 100644 examples/pbs929/pcoords.ipynb diff --git a/examples/pbs929/pcoords.ipynb b/examples/pbs929/pcoords.ipynb new file mode 100644 index 000000000..3045c037b --- /dev/null +++ b/examples/pbs929/pcoords.ipynb @@ -0,0 +1,722 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking `examples/examples.ipynb` as a starting point. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "sys.path.append(\"../..\")\n", + "\n", + "import numpy as np \n", + "import pandas as pd\n", + "import yellowbrick as yb" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.features import (ParallelCoordinates,\n", + " parallel_coordinates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from download import download_all \n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + "}\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20560\n" + ] + }, + { + "data": { + "text/html": [ + "
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datetimetemperaturerelative humiditylightC02humidityoccupancy
02015-02-04 17:51:0023.1827.2720426.0721.250.0047931
12015-02-04 17:51:5923.1527.2675429.5714.000.0047831
22015-02-04 17:53:0023.1527.2450426.0713.500.0047791
32015-02-04 17:54:0023.1527.2000426.0708.250.0047721
42015-02-04 17:55:0023.1027.2000426.0704.500.0047571
\n", + "
" + ], + "text/plain": [ + " datetime temperature relative humidity light C02 \\\n", + "0 2015-02-04 17:51:00 23.18 27.2720 426.0 721.25 \n", + "1 2015-02-04 17:51:59 23.15 27.2675 429.5 714.00 \n", + "2 2015-02-04 17:53:00 23.15 27.2450 426.0 713.50 \n", + "3 2015-02-04 17:54:00 23.15 27.2000 426.0 708.25 \n", + "4 2015-02-04 17:55:00 23.10 27.2000 426.0 704.50 \n", + "\n", + " humidity occupancy \n", + "0 0.004793 1 \n", + "1 0.004783 1 \n", + "2 0.004779 1 \n", + "3 0.004772 1 \n", + "4 0.004757 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the classification data set\n", + "data = load_data('occupancy') \n", + "print(len(data))\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data.head(1000)[features]\n", + "y = data.head(1000).occupancy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parallel Coordinates\n", + "* add dataframe compatibility" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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2fRQq28I+Jp1oJGnlENR2wdNVk5LfjxdWcM0MYezXpuglalP0LM1GVSDrhDQk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy inputs\n", + "visualizer = ParallelCoordinates(features=features, classes=classes)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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qSdkbxA2qJM0sQeTFU7wS8RQvS7NRFcg5AQ2JGut7E3Lw2UFEBrYkSdJbpFjp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\nXOQGpTW1WiN1g7GG2E/aoBALGrm5os94Ye8LOMJlVU1oVJt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m9GOCJqTR7Z65I3x8NyT2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\nEZEDMLCJiIgcgIFNRETkAP8HoqQ5xlMp59AAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# numpy inputs, no labels\n", + "visualizer = ParallelCoordinates(classes=classes)\n", + "visualizer.fit_transform_poof(X.values, y.values);" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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2fRQq28I+Jp1oJGnlENR2wdNVk5LfjxdWcM0MYezXpuglalP0LM1GVSDrhDQk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# dataframe inputs\n", + "visualizer = ParallelCoordinates(classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HVFX1ZoEdr0OvsYSUFrvxKmWVE3kdutER8io5WMOX0kLruk2u1hq0Rmldz/Ld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\nQpDslZKAoekUZRso5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/SzZRKdXROyB/jpf/VxLk0rBJDMW9NBCNoryF4CSl1bspfAr7z7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\nM2v7M1RiiIcN40sJEkEQM5hWf4hdcmbDfErxmyfvqm/j3wArfMuE4JV1T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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# quick method\n", + "parallel_coordinates(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `normalize` argument" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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aLtdhRpdDmEjWTqbZ91pphAGJSmPzRdthsGCycmJL18NV4w1C32Nzu85+6wz9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KJG9DCFDaBqNEnzDC+aczy7Raeyy07rEkfT6yz88RI7HM6I0/CRIl6VDbZaF5cWPX9Eb36b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\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xY48Lu23yzMfWRHRZZWssMwzBViWqhs7m0MWPY0xVRZZElhqFBfuHnruMSM5kSWXWgE+t\n9FBFgQwoKTKhnxAI7le7nL+h+JZ62iw29vGR59f4ncd3EAWVn7hnP5MVkz94+iKSkHDXYpm75iV+\n++GHKVkxVRuqmswHvudWqrrLpd0OL2z08eKEg80y48DhU2evcXprwDiENJNoWjp3Lc1xdKpB0zZZ\n7Y44386ZKrX4gVsXKGkCv/vkBR671GbXc9GlmFjIaMgvKczjNEUyRCxV5NbZMu+5ZZHVocMfP3uN\nsRchiDJvPNCi5/j8zuk2XyinT9kyuiozdjwCERRyJOAnX3eYz1/vIImw3LT542fXGfigSBpHJ5s8\ned0BweIHjy/yyw9dJM7Nl3U+m4bCP733CH/y+Stc7XtkgCpCy9YRBQFBgI2BR5aLqELG8IuObRgK\nYZLgfJnNrS7CUqPEUt3k2p6y9Z59Lc4ASzWHqh4yDEzOt196KFuaz3I1QJFTVgcGPUeEXEaXE8bR\ndb7/poBhmNF2NRByZishFS0nA5xQ4NSWjRPLTJgReZYTZBq6lNEPZLJcuDFOuNovNAZNKyx8xpWY\nmZKPKGQESTGnn2bFoqikZwh5ziiUWB/qdPw6dd1FklJsKWGx6hMkIm1XQ5VzkixDFsENJcJEJEnb\nZJnBbQu3Ib2M3XycFg54sqRiaX8/HfD+SyEIIpW9hLuh36Zpz37dhDtLL3Fk6gTPXXsCP/ZRxISh\nt4siFVGxumozWzuMFw7xohEiKSIKSVaIWcPEZ+jvUjYmaFjzRX59NGAcWFTMSRr2DFHisTteZ3u0\nhiqbNEoz5OT40ZiaPUM6TnCjHjdNTnFyc4PtkYMmbbPUmMJUy/jxmL67Td2e5sDkHVzeeZaBv7u3\ns5+nO94kSMZkERhqca8LgCYbBIlHlPo4IZh5BXPPsyGMHdxwSJYnNOxZBr7Brzy4yZW+Tl3POT4p\ncO/+JsvNBh9/cYtnrg8RkLlnqYIh+5zfdeh7MjdNz5MLGRfamxhKzGJVQVVyBn7MyJdpuyGqFLO/\nXoyl5ZSLdMphhboRMV0KaFoKCltsj3wmLANTrdHzBXadiJohMF0OSFIJJ9LoeQINI2SqJDMIc7IM\ndEVnOBBIE4HZSsBsOQdhjaP162RpjSRf4ELbJ8k1IkWnG7hYcsaBRooXRVzomBxolnl1XaKk9tgY\n9eh6Oh0nYxwGNIyEkqoyCg2EXGa2bLFYt0nSFFtX6QcRDcsiSXOeWHUIIxlRlKlpKZnkUNYFDFkn\nzW2uDTz8OMFUFGRJ5ECzxDuOTvKhz11lHKbMlJtkocuntnuokoggZDR0Gd+PCUVw07+HYrxPXzjF\nB58+w1w5480Hp7hpMuc/njqLJEQcbBi8aV+JDz15im0vIclEFEXm/7r/bo5Mtei4ES9seYyjEsen\na5DnfOT583zmik+QSshizlxF462HJ7l52qKkx1ztXOda32WxqvP2IyVEhvzeU6s8vdpmGAYIpHiJ\nQJKIiIJAuifrl0WZiqrynUfnuXd/i+fWO3zsxeskaULFlHn9couT612e3RremD1fKBv4qcDuIKAX\nSySZCJnAz7/9dh663Eagxj1zVd738AVGgYqpSrxmrsHn18coksqP3X2Qn/vrk1/RuvYr4XDD5jsO\nT/CBR1/EiYqye0mVqBoqeZYTpglRkiGIIqYEW1+UllgSwYsT/ORL0+4MEQ5NVpmvmlztOUiiwLtv\nXeK933aCfwvsq4V0fZnL3RoChWK4rMYcanpUjJwrXY1dV2PHVVgqiZR0j9fODxiHKa6rkOawrxpQ\n1ov3GyTw3HYZN5ZpWoUroBeLe1MCMmkuIgoZkgDbY50ogznLRVUy/Fjg5tkAU0kJ0yKVjhz8VEYW\nU2qKiBeLDHyNz12zqFghi9UUUYCqFpKS0/YMZClHEHKSVESScjqeSkVLqBoJw8DkyNTBr/tZ5HnO\nyC9ywctG42XFsb6CAn+XhLs7lm7h0u55+t42cZbixQ5+NEISJfrOFtPVA0xVl1nrvliEEYkCKRm6\nZBCnAW44RJUMTK1EszRf9OudLVTZwtbKtMqLxGnEwNtha3gZda9HH8QOQTSmac+zM7qKfSOUJwAA\nIABJREFUG3U5PjPHyY1rrPUHKJLEXE3AUMp40bAge2uG/ZO3cXn3eUZeG002i/Q6bwMvdPDDvBD0\nUSx8DMUmSFyCyC1adXlxj7bH14gSn6rZwlLn+Jm/fIzVQYKEwGxF5i1H9/HqpUXObXt88vwWAgnH\nJivMlC1W+y4dJ+BA02CxAp9ZHTP0bW6emkCSM4ZBnzjxGQQBcZIToaBJ0LJzBGGIKAjIok6aGyjK\nJLqacXl3jSyLON5KyYh5fktGFFTSXEGXRCQ1QBI9epmOrDQIEsgYkmUqbgh+EqFKOhN5hSDJGUe7\nVI2I+xa7tF2HNG1wfVRjGAo4kULTMtGVmCR32d9QOTFTZaI0yfpokzDuowgDgqTw6zBUjYqWYCgu\nJb3MQt0iSTMsTWEcxFQ0BVWEx1babI1jgljFVBTGoYcoZEyYKbYWsTHsUNZkJMFEkiX2N0q87eAk\nf/T5a7hRzGLNIvY9Pn4tQMCgaiQcaeT4fkwgFJs8XfrbiaLfDHxLEf2/fvwKQ1/k1QtTvOnQAf79\n51a4PpCpGiXuP3GEf/PIGR5cAVnUKGsif/juuzkx36A99vjs6jZOEHKwaSLkXf7D81c5v9WmbEAF\naNoqr1+uc2zaRJdhtetybRgxW67xzpsXMBWRf/fkJR663Gd3nOwJ0SQUMkwN8qyY4dYViYap8iN3\nLjNRMnn4UjG/70UZE7bFWw7P84dPXeFyPyTJCgOY402bK32BrusxjIqbVpPgV++/gwcvbiOJCq+d\nq/D+Ry4wChJMVeKWySrnOiM0ReYfHGvx3r8++bLOoQi8dl+DpqHyfz+9WnjwAy1LQxUhTAqDBwBL\nU8iyhE3nJef6sgQhEH45yQtw63ydpqlzpeegiAL/5J7D/OhrDrLR3QLgrUfm+cBjAVGeM1cx2HYc\njk06lLWE6yOJILXYdWQgB3HEOw500bWUvi8R5BIL5YCKXpBtnMJzmxZeKFE3UrIsJ0ckSCSyXMBP\nRAQKku/5Cn4sMVXyUeUMLxA5PjXG0iLiFEa+RpQJuIlCGAvoekKeQ5jorPSrOKlMSx5jqhKaGNGw\nYnYdBTfMMVXwEhFdyhgGMkkmsr/uECYinzgn8E9fHzGnfu0dvR87hbBKsdHkl1eNeQUvwd7zdy8S\n7mwU6SukNH0RZEnllvlX8cSVh3AiH0XKGHoddNli6LXRVZsJe4GR36XrbJDnxShqmPjoik2UBAyD\nNo3SHCWtRmi69NwtOuNrKNIBdNViqry0tyjoszm4XIwFGnW8cESYeEyUFtkZXSWIu5yYXeL59RWu\ndLooksxURcRSK7jRkJ67RcOeYbl5CyudFxj5XXTVomHNAVv44QA/StGVou0oCiKmUsbPxwSxc2PR\nGMQesqSgKxV+97OPcKXjo4oSE5bCdx+f4K7FadKswm88dp7LHZXjk1VOzNbpOCPWB2MalsrBRoXT\n2z2yLGC5blAxFLxYZdcV2OzFRGlCVU9RJLBUmTBVcMMQQ4kolRIMNWOqZPDiTsKzGwZzJZWqkTAO\nfI42U7xYo+9XCVMNN8yBjP2NBFsL2HYUhp5JTY/o+R5RqnKgrrBQsXhuo02clJgsp0xaLnXL43VL\nPtvjPk9cqyMJZUxF42Ino6yXuWXG5MiEwdZ4lx1HoDO2SfKQkuKi1STcsMK2o7FcFzk0IZPSRZJL\njKMcS5OxNYXHVrbZGnn4cTGSOAoShqFCTZXQZBgGI3Qp5HAzxU3A0prcMd/i/3luFS/JONgss9Xr\ncq5XPFtzBGzF5uxGgG7mVPUEQcj5ibv3fVPvnRv3xH+V3/IysT2WWGw0+W/vOsJHnrvC7nhMwxT5\n4dvrPHrxNCuDTZbrIIvw/rffyv4Jic1Bm2eudRiFMQcaZZqWwR88dYVPnuvjpDJCDq2Swb3Lc5yY\nraNKAivdEZujgIWqwduONBHx+NDTqzy1uo0bxShSgiR8QcQu4keQCzKqJFM3y7z3LcfJBYU/evoq\nT1/zCFKTW2fqvP3oLP/8Pz/DKMqB4uF/10KD3XFA33UZ7jnTmjL8ynfewcOXt5FEgfvmK7zv4QsM\n/BhdkTjWqrA2dNEVie8+Nsn7Hrvyss6fIsD3HJvjxfaQJ652i1I9MFMxCJIMN86AFFEUmbAMOq5P\nx3uJ5G0RRnv9eEnIkcQMXc6oqhl3LdYw1IC20+FoM+d7T8xy+9yQj37+E3x+vfBrv3ffbQyjMb/z\nmfM4YcRrF0NMOcZPBHYcndW+RN3QEIUxr9vXR9cjBBS8SGDCDqmbMYpUkPzJDQMn1rG1lFxIERDo\nexKanDP2C3czSxUZBALDUKaiR1S0FD8SOdgY07KLefkoVfBiETeWCWOBqlm48nU9hTBt0A9KtOwh\nchaSZjBXDxgHKlsjDUtPSTIBWchIMoGBL9OwQspazErf4GRb4eJGm7nqVx//yvKUcdBFFETKxisC\nvL8LREGibDTpu9uFPa4183XHEo/NHObM5hk64w3iNCMSI0Zhl5o0Rd/dQldsZmuH8KMRXjRGRkFE\nIU59JEEjSkIGzjYTpXlq1jRh7O7F4W4wUV7E0MpMVZdZ715gGLTZHurMSgqmWi5aAmJM056jPb5G\nkvQ4MbPMC+uXubi7jSLLNC3xxmt77hYNa4al5glWO6cZ+x0M1aZhTdNDwIsGe/nzxTipKIjYWg0n\n6uNGA6C4zhr2Ep9b6/LpS2N0JeemyYzXLGrcvVhlttLkp//yFFc6Y1q2ztuO7qMXJHx+w8VQShxu\nWawORgz9EFOWmCrrCESMAoeeE7IyEIgSi6ZpMV9NqEkxfhyQIxClOqos0yqJeOEuQeyxVNUw1Bov\nbEUoYs6UHWGrGRNWl3Gostq3SDEoaSpJHmLJDrKlcnY7wwl1DjZz7lxo8fhqGy8SqOgqYSxydkem\naYVM2T4z5THvPOLT8Vweu1pBkSyOtJqcmJtlczxmZ+QQRB5uHNBz1aKiUolRpQGtkkXTnsZPZCw1\nJM1GVDUFS6vx2Mo2G8OC5BVRxIsTxmGKJQkoqsGLOwFkOtMVlUk74fAELFVjHrpyCtA5NtnkzNoW\n68FL1+N8Wac7CvAQGLkaw0Dht759gQs9n4NT3/xW3rcU0d88Bf/wzhafvnCBjUGPiibxPcdnuNJ2\n+OipbZJMIs8Ffuudd/Cmw0uMgpjPr28zCDQOT0ywVDf53cfP8umLmyDlVJWcSUvlzYfrHJtUAZeV\njsOuE7JUN3nroWl0Web3n1nlgQs9tkcC40gkSXWiVCBHJE5AlkQqusJtyy3+5ZuPseME/OZnznO1\nMybJBd50cIrX75/iv/vwEzfEbApwfLbGzjhg6LkM9kjelkV++R23FyQvFCT//kcKkheAm1plNkYe\nhirzhsUWv/4ySd6URb7vYI2PX96mHyTkQF2XqZgaQy8iyTJURUCXZRaqOmu9LpKQslDJUKUcW80Q\nhAxNylGlDFnKEYUcTYTDExXifMDAj5gty7zj6AwHmgafOr/OxbZPlhf9+H/1yHX+z++4he2xzxNX\nTlEzQmQknt/WWB/qpLlImHp8x6EBS9WgsL51BJpmzpQVoYiFwv58W6cXGahiiiTm5LlIz1cw1YyO\nJ5PlIEsCTpTjRRq6lNKyI9I8o2kmzFYKI50wkuh5MlmuMAxlamaMLEGQiIwijfOdEk1LQMgTRDGn\nbriQi7RdA1tPIYc0FZAk6PoKuSCwvx4QJCKntkuAyC88+CK37ZumZn7lGXoneEWA942ArlgYqo0f\nOXjRCEv72kZDkihx99I9PHDuLxlFPooo44UjTKU4buDulfAry6x1z5KlGYgRcq4iiIXrYZQEDLwd\nKuYUDXuBMAlx9ubrK9YktlqnVVlkq3+ZnreFKptMVfehKxZB7KLJJnVrhq6zjiaOuHlmmVMbK5zb\n2uDErETNFDHVEl40pudtUbemWWwc41rnLKOgW5C9PYPoCLjRgCj2KZQrOYIgUdIbN4jeUGw2+l0+\nfr6oUIiCyoGmyt1LTZbqNr//1DOsdXdo2SrvOLpIkuWc2R4QpnDb3AyjAF7c8VBllaOTRdWp74c4\nQUTbGaPLGSVVQhR1wtTgysBHyMc0zRBTzZkp6ySJyIs7Hpqcs1iNCOI2Vb2oqK0Na8yVE0TRQxJD\njkyEiGKJQVBh4At7lsEuFT3CVExevXSA5zeHXBtEGIrOhKXS8UYIgkzHFdkYSeyrBjTthFm5x/1H\nRgzDFksTC4z8kLV+RMcz2R1HkGfoSo6lSKwNBOYqGot1EVnokmY248hGlzKqesbp7VW8MCZKVWRR\nJkhTxmGKIYGpq3ScAD9KMFSdni8xVTZoWDJPX79G1Yg4Minx2MUrtGONL7iezpd1dkbBjbhxRYDf\n/q5X8+ClXWqaAVPfjDvmS/Et9eT5R3ec4OnNAQ+vBIi5zQ/fvkSQ5vzqYxdRRBFVzPmlt93E6w7U\n2Bnv8NRah74XsVy3Wahk/OlzZ3j08hpIIGQCVd3gjQcXuGV2giSFK12P7bHOwYlp3nP7flRJ5P1/\nc4Y/PzVgtZOQiwJJptwYWBMAVRapWxrvvm2RH7p9P+fafT7w6AX6boAgiLzrxBxTePzIh5+48Xc0\nDJHZSpmeFzH0ffph8f2KKvHzb7+FR1a2UQSB+xaKnXzPKxRvJ6YqbI8DDFni1qkKH3x25WWdt5al\n8IblGh+/tIUgpEyXMvZVVQTRJ4oHzJUyDBkqhkhVl1kfbnK09dLxklDMqec5ZLlAnIk4oYgsShyb\nm2TbzdkcRdh6g3/xuluZLpX5tUcusD6sUjcn+Wevv4l/A6wPPd73N2f556+fhlRkvQ+XeiZRZBCm\noIgZd8702V8fI+Q5HU9GlWGq5KHLxXu4NlDxfQORHEPJSVKBQShhqyk9XyFKJAwlJUdgy9GQ8pz5\nWoAsQJQIHGz4mFJGmMAglAgzkbGvoAoJ5BBn4EYa53aqDMKE26YLHYZIiKXEbI0McmRMJWXgiShy\nRpIVzntTVoilplzsGGw5RQvm6WtdNkf+VyT6VwR431iU9AZh4uMEPTTFRBa/dstk38QszfUFtgcr\n+JmIkhbE3SovMPSLEn7Tni9K+O4GCCpZnhKnIZpikWQhXjRGlU0svcxEuejXd91NNMXG0ErUzBnC\nOKAzXqM9voYqG0yU5lBlgzDxMNQyVXOKvreNpSocmVzg3PYaZzevc2JOompUMRQbP3bouzvUrSny\nPON67xzjoIeplIqAH1fADQfEcQj5S2MwNWMagMnqcf73j3+GXSenbqbMV1Lu2VfhQKPKyQ2fBy7s\nIggZ9+3TaVkjLnU6eEHCkYkWTUPjgYtbBInM0cl5gkwgTlz8KGJt4OGECYYsoMoZDRvyPKDjpmR5\nmSQXmK3KqErIhZ1d0iwlx8CLIEg8WlbKhJmQkuJGJud2y1SNgIVqgiZFtKxNFEFnfaTTdVMUSeDu\nBQNV6PDC5hgnsrl1SubaaESWqjTMIl9DFFQ2HIO2FzJdKix1ZytdEJ5m3WnihBNsDmJ23AxJsJg0\nM+LUp2XLtEoWaSYiSiGyNKIq+Rhag8+uRvS8GFGImK/E9D2FridiSCKGLtN1C5LXFQVNljg0UeFg\ns8SfndpCEivcu6hwdmOdViXFMiOuDTRUucr1fsAXPjFdgl+7/w4+dXEbEDgx2wRefgjZ3xXfUkS/\nMuzyxNXr1PSMNx2YpKY7/OLfnKG1Vxn9J3ft49tvmiKIUz63NqbniSw3Jrl9tsGHnr7Kv358h35U\nBMTMlQ3efvQAr15s4sUpFztjdh2BY5Mt3n3bEpIIv/zAaf7shVXW+x6ZAGn2EsFD0Y+frZj89BuO\ncutMgydWtvng01fwwhhLV/je4wukos/PfGLtxt9wtGkiSgrjKGbgeQzCotfdNGR+7ttO8JmrbRRR\n5L75Mr/88MUbJH9sskzbi7BUmdmyxp+d2UAgRxEzNHnvS8pR5SJCVd37/0JVwVIFNkbb3D6bIwJT\nJYNxNCKIEmRdRJFELFVHEiVObbn4iUqYinvq8aLfHSYSYVqM6OUINE2Zd92yxIsdh62Rz2K9xq+/\n8w66Xsh7P3EGL0o5OlnjZ9945Eb5f3/TZq27yx88c4W3H67x4ecV/DjDSyUmjZiZ6pA75kYoUsaO\noyDmAss178asfNuRudizEXKoGjF+IuFGEraaMfRl3EjGUFMQBHZGKgIwWQ4wlSKu9lWzI6p6QpTB\nOFQIEpFxoOAmMFPOycgY+DI7Tokdz2S6FODGKVVdYMKM6HoKV/sa+5sZXiQiSylJBiNPRgCW6j5e\nJHJy+yXSToHf++wFfvWdd6DKX2rqckOAp78iwPtGQBLlGx7uI79D3Zr+mq8XBIF797+W/3xyAzf0\nsGSDJAtxwyG2XqXvbqMrFjO1g3jRCD9ykORiIiVOiiCYJI0Z+R0a0hwlrU5gOAy8bXbHq8zIB9Fk\nk1Z5nij1Gbg77IxWUCWNemmaLE/xoxElvU6SRYz9LjWjyXJjliuddc5srXHbrIit19D37HgH3i4N\ne4Ysz9jsX2AcDDAUm7o1jSAIOMGAOE0gHEGWIYnFdfWBR87zwCUNULhpMuOtkxbLzRr9wOGT51cx\n5JTJco399Rpbw4C2M2KppnNkwudz19vkOexv1pBliSTN6QcKZzZEth2FkpqjKTllXUWVoOf6WArI\nMizWKkyWmzyx0ma1P2KhLGMqGV03IMkUZEGmZubocoAmhSiiQJKVGQY1JNFBEzMsJWChMkaTNASx\nwULN5tOXNqnpGfsbJpsjiVGg0yoJbDsSEjnTleK5OooUkuEEZV1AlMbEyZCmPiarbNIZVRhKNTRR\nZdtNqBs1bE3DVBJycuJUJRMl6mbOtd410ixnHOtkqYGphoiix8G6iJdYrA/DPa2EgqFKHGyWWK4b\nPHBxizjLuWOuxR88u46llpgrB0zaEa9diLk23MEJjULEq8r8b2+9hU9fKqq5bz40jZR980kevsWI\n/jMrm6QZ3Lk4xdGpaf77Dz+Jl6okmcBP3XOEn3zDnYRJzl+/uMG2a3K4VeG+5RYffPIiv/HwJYZx\n8aCdK+v86F0HuXNxglEYcXF3TM8PuWW6xg/ctoRAxvseOM1/eP4qGwOfjGI3WfjEFf+UtGLF9ovf\nfpySqvOxs9f489PXCZOE2YrFO2+a5cxGnz984SWSf+O+Oltugh8nDL3gBslPWSr/7E0389jKLooo\n8oaFEv/q0XMkaUDLyjgyYRCkXWZKApaSsTF2uW+pSJwTha+uytxXtdkah2yOMoJUQUagVSrx3GbE\n0JdRRBlLMymZJrIv8fjVDhnVG8dLwFeya5graXz/rYuc3BzQ9UJumqzw6991Bw9d3uEvTl8D4P6b\nZvnB2/ZxdmeIFxVE/6N3LvDhZ1foukOeXFP4ubfcw49+9AL9YExFc3jj8hBLSWl7ClEqcKjhY2s5\naQ7DQOBqt4RATlkvDD68WESVc9xIYhjJGEqKQE7PU4kzkYYZYqsZXVfg1ukxE2ZESk6QSLiRyCBQ\n6fkis6WILAcvkuk4Gqd2LMpqjCZHkEfMlX2CRObaQMdUEpwARElHV2DoC4xikfmKh6lknG+bdLwv\n3b3/0TOX+Z/fcIzF+kvxtF40fkmAp7wiwPtGwVRLBNGYMPbwozHG10m4m6rUma4eZq17inEiIIsi\nTtDb87l3CyOdyn4mq/u43nmROAmRJRlZ1EjzBBBI0oi+t8WEPU/dmiZMPNxwQHe8Qau8iCJpTFWW\nSZIIN+yzPbqyF4rTwstT3GhAw54pFg1Bl7naNFHa4np/l9Nba5yYFimbdTTFJIhdhn6bidI8WZ7C\n4DJOMEDXStStaURBYhx0ibOYLHLI9mysVvtrzJY1JFHnroUGr953iJsmZ/mZv3yCzaHAXEXljlkF\nN9qh7YXYqsH+ZpVzuw5R4jFXlZktuaR5TCcUuN6LWR96RIlIlpewdRVRhN3xkDQT0BWBKVvmUFPm\nWr/NSq9NkpoEucULWwMMNaehxRiaSJJLDMMcgZCGmaHLLn4SsuuotJMqeTqipCXMVjIONF1e2LxM\nkMiUdB1RSDAVH1GU2RmrJGnEfMXAjSWyyKWiwWzVomRa7DoW5D1UcURFHXPfkkfHH/PcZgNJrjJf\nt6kYFqMwRRF9TDWgrGlc3O3T82IUKWZfJaLjKqwNFUy5cPhLsjETZs5YMslFiQPNErNlg0dX2iRZ\nzmsW6/zpyXUAnEjmQsdCECr0gz5NM+WWqTFZpvOGQ7fy+MoOuiLxHUdn+T8ePI0XhPynd379yZ3/\nv/iWIvqtUYmliSpvPbyP+z/4IIOoUNf+6B3LvO/+1xAmKZ88v8H60ONAs8zr97f48LNX+eVPPcdw\nb957rqzz4685zKsWGgy8iIvtEcMw5taZOu+6ZZEkzXj/g6f50+evsjX0SeFGGI4ACCJUdYV7llq8\n9403EeYCv/fURZ682ibJc45P1Xjr0Vn++KlLfHajf+O9v/vENCe3hshCip86qHLKUjVjtiTzrltb\nnG+fYqmccaSh8slLpzjaKsixaarEaYAii2RZRtePsRSBaK98HqVf+BIIkr1deCJyV8vmj0+G+EnR\nHz82YRIKMg+tOKSZRlWXmKuVyRGQBZHHr7a/xAznC+X6L8f+msl3HZ/nmes9xmHMq+aa/PxbTvDv\nnrrEqc0+hiLxk689zHKzxLPrPR44v85Hni8WO+e2XuS+fSaPrYS8sAXymTH//t338CN/8kkO1dtU\n9YiuK+HHIouVgIpevCM/hmc2SkiCwISWMEgEokSiqooMopy+L6OJGYIA41DGiSRsJaZmFCOA85WI\nVilEkjKCWGIUKPixzK4jM1Uuxqe8GKJM5bmNEn6asVT2UMWcuhGTZAleUkKVJSTRw40kmnZGlgmM\nI4VcyFmsBTjRF3rzX4pBnPH4xS0W7jqAIAhkeYoT9BAEgZLxSgTtNxo37HGDLppsIopf2x73dftf\nzfboKl44wpZKSOKQod+hbk0x8jsYss1EaR7H7tEdb4AgkuQRcq4hSzJpFpMkEQN/l6o5SdOeJ0pC\nnKCPLlvUrCkMxSrEeb1zDIMuyugqkijvec33cIIBrfIiW8PL9N1tDk7ME6YJu6Mu53ZWOTZdiDXz\nPMOPCjX9VGUfeZ4CV3HCIaZaomK2AIFx0CXNYrZHhfvFvqrLvopPxTS4fbbCQlXmtx47w+euhyjS\nBG89MoebBKz01xGFjIPNlLG/hRfEyKLObLlMkqcEkUOaRHihQ0nLiWUdU1OoGiYbw5C+b2KrGYuG\nxJGpEm4Y8/zGDpaaMmFp9H2HXScDSjiWyEI1pqz6JGlIgoyBQpInKKLPpBWghzkbQ4XrQZkjrTLr\ngy6qGHDnLARxxoWOjCSJWEJCrofEqUqU1dgcDrE1jfmqwWxFo+8FOHFGx7UJI5HpkkPDiJi2R3zb\nfp9x7JJiESQpSQ6SYGOoNU5vb7HrxiSJREkrdEol3eOwIjMITK50RBRRoGFD3Ypo2QaaLPHUWpc0\nh9csNPjIyes3rjWRwiH0XNsHSlT1mLtmE15/uMzO6DzzlRLHW/v42b96lrYTMVv6e+h1P18z+Yd3\n7ueH/vARBlHBQvcfmeZ33/Naoj2Sv9Z32dco8ZZDU3zs9HV+7mOfuyF0mynp/I+vO8wtM3V23YAr\nXQcnjHnVbJ3vu2WBKM74pQdO8afPX2V3HJB80e8WAFEUmLA1vufmBf6bO/fTd31++/FzrHVGqHLK\nG5eq3Llg8ftPPkE/CrllKkOVMl63YLPhXOL2mZyhHxLsbZObpsJrlmqs9jexFYEDVYu/ONem5wtE\nqUrNMLnSLcaBel7K+jgpiHyvfP6VoElw00SVv1kfkAKyAN92cIrT2wPazhhJLM7DkVaFth+RBy5P\nt4Mvsa9VxKJX/eU43irz9qMzPL7aIUpT7ltu8Y/uPMCvPHSaHSdgtmzwv77pGF6ccWF3xK8/dIZn\nN/pFXB7w8fMv8raDk7z9phN88JkhD17cwZIzfuLumLNbwd7CRWHC9Jm0EgQKkn9ho1JoMKSEcSoh\n5hJpnuOlKVJuIZAgSRAmIv1AQZcyJksxQg6KkLNcL/ryUSoxDkWiTGLHVbG1FJGcJMsJE5WzuyYp\nJtNll1zIsZSUpukxDBW6nkndcBgGInGaM/JSJEUHAZarIxQx52LHohd8ZdHdLz34Am+7eZ6mrd+I\nEy3p9a/bR34F/+WQJQVbrzHyu4yCLlWz9TVfX7Us9k0c58Lmk4zCCG2vfx4kHrpo7RnpWEyXl/eM\ndFx02SSnaN2ookqSxQTRGFcq5vqbpTl2R1fpuhvoaglDtbG1KpPlJTb6lxh4O6iSjiTK2FqdcdDF\ni0ZMlw+wMbhA113n+PQ+nk1juu6Qi7vXODwpUDFb5HmOF44QBZHp6gGyPIPRddxogKGUqJgTCIhs\njjp8/FzxnNhxdPbVcg43ZUraiEcuPsFm32N/Q+PE9AKGmnF6M2N92OCOOYuchKv9dWQpYX8tJs46\n5JlKmMicb/u4UURFEzA0n5Yl4UTpXniWjKGaHJpqISDziQsXcCKJ2XLh0ueFDk1LLPw5FIV+YHGl\nK1MxJJZrObKY4YUCcSYjCTGaFLNUj5kqyYz8gGc3JBqmwXw1I81H3DwJHcdgbahiKBItW2AU9nAi\nDVuvc2jCpucHjPwQN4rxwpA4lVgbVBmGPk0roG4k1M0BUXaSQdBkGC1RNaqc2hxwvp0QRDp1UyLN\nRfI8pKplWGqGJIzIU5FBZNILdE5My9T0lNXeBhVdZaFU5SOnXiJ5CZgwJNaG/t53BGbsOgempnlh\nq818JedgLeNj556goio4ss0P3rrwTbpLvhTfUkT/D25b4qc++lmujwv12t1zNf7iH7+FJM345PlN\nrnYdFmoWbz8yw8OXtvif/uOTdPdS1mZKOv/LG45yZLLK1jhkpTMiSDNeNd/ge47PE8UZ/+Ljn+ej\nZ9bpjkNSCs97Q84w5AxLhZmKwvcdr3ColXB55zkeuHidihJx60zOsakq89WADz+NHfXmAAAgAElE\nQVT3WSwNrL3n/avm6qx0PXJk1nsRw1AjSgWmLJPbFvbz5LUxolDj3vkmv/DwRdpOsSNcqBrsuDkl\nTWFr5DKOBL4wkvfVUNFkTEXihe3BnhmNxHcfm+dj59dxwgRNErmpVaJlW2w7Pnka8mw7+Fs/5yuR\n/Ktnqtx7YIrHVtrkwNuPzHLnfINf+fRZwjTlzoUmP/7qg1wZOLy40+E3H75Ax/ExlJw3Hyzxe0Ac\nh/zJSZcfv8vkf7h3jt989Axntz6L2BoxU7I45afYasxirZgwiDJ4bsMiRCyEd5mIGwtkWY6tZGy5\nRY/f1jKSTKTtKohCxqQdoogZfU/ingWHip4Q5eBEEKUiu45ClIq07JgszxmFIm1X5cUdg/lqhCYk\nSGLCXMXDT0Sudm1KRoChqjhRgiLHRHlGFhbz8/PVgHEkcXbb/tsnbg9X+gGbI5+KIe4J8JSvqwx/\nBX93mGoFP3aK8v3LaI/cu3wrq+1zeGEPP65hqdGNzPsw8em720xX9zNZ3se17ovEqY8saUiCQJon\niKJMkhWVGlUyKOt1wriws90ZrTBXO4wi61StacLYZ3e8theKYyBJMrZWxQkHhKnLVGWZzcFluu4a\nd8wf4KnVC2yPxqjyBsuCQMVo4YR9nGCAgMRM9SBZniGMwY1H6LJNSo2PnXPpBkUp008s5utllpoV\n/Djh5OYqthqzv5EwYW/QdndJUombJxvMllUeXvHxogmOTuoMQw9LccnzgFEwxlQShFwiSg0qmrnn\nBlqUrzVZZblpMWsr/PX5Hc61M5pGFT+WuNLtocsqpgINI8NUQrpuiCDkxGmVQaAzCMeo4ghNiuiH\nAkKuMleRMJWMnrfL4QmRNLO5sKNgqFA3ImqmR83yGQU2HU9AEjIONlVun9PwEo31UYgbSPRcCUMW\n0BUQEWi7FqJYwlJTRMZIYkDT3GLCHHKl32C1Z+AGIEkSHVfETwSmLQGBDCX2UaSEybJIMwuxdYM4\nNfnc+piqIXK8lfPE9etUdYVBICMLAjVVYNt/qRl692yVm+earPZcpkpNTGWaDzz+PLOlnIPNkNft\nUzm1cZUfmD/yTb1X4FuM6D/09CWe3x4DcLhp8cRPfydplvGpC5usdMfMVkzecXSO59e7/OMPf4bd\nva3zTEnnZ994E4cmq2wOXK72euR5zO0zNvftE1nvXeFDT53l7PYuB6oRhxsZigRfiIRRJJGWpfGm\ngw0qRsL13jZPrHZwIxBQuXNhkgMTdd77V6cIEoswFRESkdcenOM/nXUw1QaXdvu4e2X026ZKfPvx\n/Ty51kWVDN6wUOcXHjpP2ylKD3NVgyDJKWkSq70x0Vcg3i9HGYjSlGFY7IQPNixumqzw0dPXSfKM\nsqbwhv2TjMKEbdcnyRNe2PZuHC/wJZk3X4L7lurcsdji8ZU2siTwjkPTWLrCHzxzCVXK+aHbZrlz\nvs7J7TUevHCNRy6v09AjDtREXrfcQFeLhdmub3OpI/ELD17k1+6/gx++NePsZo+eH9G0JjnWynGD\n1UIbkMPpbZMwU5DEjDwHN5RJcwFLSxm6CuNQpmqkkBeOVlAk2hlKStcVuW1mRN2MyMiJYhk3kuj5\nGruuzKFmSJrleLGEFys8u1nC0sBQQ2qGQN0IyPKMrlcmzkUkISROdQyFPRGfQJRmHGx6iAJc7ZmM\nYxVZhOSrfF7/8uOf59++6/CezqD5igDvmwhBEL7IHrdDU55D/BrnW1cUbpq5g5PXHmIcOdh6jSQv\nyLRk1BkFXXTXplmaoxb26I43iymNNESRDYQ8hzwjy1IGfjFf/8X9+vb4OpPlZWRJZqK0WIjzvB3a\no+uokkHdnroxHiipMpOVJbYHK/Tca9y5eJQnr57mWn+IIsoIdZGqOcHQ79zwYJitHirGYhxoO33+\n32cdTm/LmGqxo79jzuTo5ASt0iS/8egFtkY2++rs5al7xMmYg3WBhhWw1t/BklWm7SqqZCAIdXb8\nCte7HbbHARUtpWKk1IwAS4X1YdESUySF2YrO0ZbEpd0VNoc9pi2VVrnMubaDF+mYks5sVQAhZxhE\nxLnHhCXSsnLGUUTHkcnzOn40omb6zJZFJiyTS50ReS5QMSDPx6iNnJ6rs9o3sNWIlp3RsjzKusso\nLLFYNzDllOvDXbww42InR8glLN2kKWaIYsikLdMwTNxIwkPDVj1sxSPJxzSMIa9b1Diz2+Rqv4QT\nZWiiRCcose14lNSUhiXTshMWaxICDlf7DlVDpSKV+KuLPepmzqQd0TQTPE9lM3iphfSW/U1mqiU2\nRx776jZJnPBrj54nTnU6I5V79on0vBFL1W9xZ7wsy/jFX/xFLly4gKqqvP/972dxcfFvvebHfuzH\nePOb38x73vOer/szH1opcronLYUXf+57ybKcBy5scak9omXLvOVgjbOba/zURx9FU0OOGDmTtsR7\nbmvQsDZZ712i6wbYCsxXTY40czYHQ/7q7DVe3BkgiynIAlFaCL3iVEAUZI62Gnz/LfuRVYOnrvb4\n81MBbtxkqmTwzmNzzEsh3/dH5/lCYvw+W+bAYpOTWw41Q+XMVv9Guf7VM1W++9YlnlnrokrwpsU6\nv/TwedpOQYYzZY0gSVHiiBXnK4vhvhyTKrQjyJLCNvEdR6dZH/p84vwmCALzFYO3Hpnj7PYAN4qJ\n4oTTO+Mbx381khfI+fbDDfY3bZ67vs6EJfCG/Q167i6Xuw7zFZHvPjaLpox5bm2dT11apz32mCpl\nTJV0jk9V0WSBpl0scF6zdIQXd3ZY7bn89qMP8q6bR7QsWB/pXOl6LFa76CrECaz2DcLURBISFCmn\n7ytkOZS0lJEvse0rzJkJbgZtT0EVJWw1pKIlOKHI/kbIVClCEjPCRMKJRJxYYa2vstSIyDKBIBFI\ncomTWyVGocLBmo+SJyhiwqQd03YVrvZUZqsRaQr9OKFuqRhpxI4jIgsRE5bLOFQ4t1siy+FraCN5\nanWdreE0y83mKwK8/wpQJA1Lq+AEA5yg93UT7u5cPMLF7bOMgk1GgVw4v0UjDNVGFYo4W12xmCwv\n44UjgthFV2ySNEKRVCRJKUJkMpGBu0PVmi769XGAGwwYKW1q1hSyLDNTO0Cchjhhn53RVWRJpWZO\noso6fuRgaVWa9ixtZx0n2OCuxeM8uXqKlW6nyDLf8/kf+LsM/TaCIDJbO0Tf83nw0lXidISpGFhq\nce/dMttk/8QsH3pqha2Rg6moHG1N4sQCa70eqiQzWRIZBj4SI5ZrIqbq4MU2QWoTBrDSc+l5Jtui\nzXxNoG6K9L0huhQwWxGwVZObJxu0nYQn1gYYSsZMQ2Tg71CSE5RcQ1EMJMli18loO0Ms1aQ1qRDm\nOTCmqsOOk9MPJaKszmsWm5zZXifKBGxNxY0EsjxEk3Kmyh41U2AUGvRDk8yLaZgxN7UiKnqXjZFL\nEEmM/AhDgSSVSVOdbddn2rapmiKylBEkKbJokeYW296QIOpjqwENM+CehU2WKhonN+vseGWcMCSM\nIUhtMgGmyxLjyCdKHRoWTAspz2w4KJLKtYHGpJ0xocZIdsCMItF2Ve4/MoeqKrSdkP+PvTeNsi0t\n6zx/756HM5+Yxxt3zptz3iQHSDIFRFOUcsIuVEq7LdtV1S6s1auV7ta216rqKhd2ddkWNKKylFKp\nLoFSSUQGZTQzIZOcbt68Q94x4t4Yz4k48z573vvtDzu8gJDAooB2rcrnS0wn3ogz7PN/3+f5DzfP\nVrnU6vPRCztkuWTW0aiVHU63EiqGzT131r7ua/bbVd8y0H/yk58kjmPe//73c+rUKd7+9rfz7ne/\n+ytu89u//dsMh8Nves2ZUkjTjfnoL9zL2u4ZTm222RwMaZgKJyaqnNm8zu9+/kVqTkLNKbzbf+im\nGVwroxcktEcZiuJwuFnnvpVZslThHY9e4JFzgm2vTJwqZLLY9SsCmo7Bm25b4ifuWEFK+E/PXeXR\nq22SDI5P13j4+DyeF/JTH33xxv/42uUGuaZxteMxXbJ4Zn2P/ekBDyw1ePjEEk9d72Cogn908yK/\n/JdP0xoVvu8zJZMok2h5zGb0jR8PFSib0Nq/raUK/tkrFvnguTZtL0QRgvuXG9w60+DZrS5BnOJH\nOS92CpAXyBsGOIoi0dVCrqerEkOR3L9cZdLNuNzdZK6icOdMla3BDmEas1wz+b5jUwzCIVf3PP76\n4jZBnIJQuG1uirlamZrtYKsaH3x+E4CV5jw/fZfLX517geXyJtuDlNlqk8WqzTBYI8uKWXtGjX5k\noSsRlgrbY5UsVyiZKX6ssDM2KBsZYwlBqBGmKo6eMFtKiDKBY2QcqAc4Wn5j0zZOdTYGFnUnRSEn\nyxWyXGW1Z7M+tJkuRQg1R1czFipjwkzl/I5LxY3Js4w01zB1SZTmpJmJlBnLjRFZLlnvOyiKhaak\n5HnBi/h7DsEoQlJzYj53eZPb5o9+06/5l+u/rEpmnTDxv6mEO11VuHPpPj5/5cMMox4VcxrENqOg\nS700S5pF9Px9Fn7lANc754nTAF2zyWWGIlVUoZHnKWHqMw57uFaVicoiu8NVOt4GluFi62V01WKu\ndoTrncL8RhuuogqVujtDlmeMoz4Ve4I0T+iNd1CVNq9Yupkn117gQmsbXdVRhELNmaI/bjMI2uSy\nyr/5VJ+9UUrdFty9mFGxbB4DXnnwJI+c2eGxNQ9T07hzfhKEynZvSJDBUm2aUaJytdOhagbUbEiy\nGEvrYii79MhYKOuUDBNDLTFdqnGtp9DywDVclus5h5oWGQMutDvU7RxbKzMKczYHMa4pmCwnVC2B\nguRSJ8WLVSbKFYahxjgeoYqMLItARizXBLfN1rnQ8Xlu26Bq6hyqpyS5h6lqRElxrdp6zlItZBwF\n9CMbTa1TNk364RBT7WJrGbpiUDVMNAV6UYqllag4DrnMGEQ+FTPBVFN6fs7VngDp0nQ1SnpCxQxZ\nrIyZKkWsdQc8fq2CxMU1NBZqFXa8jGEgma4oTDoee8GY5ZqkZKQ0bYOhZ3B2bDLlJpTNjB87Uaaf\nRHTGklcsN/nE+U2e3ihI20cm3EKuGyTUHZ0oSXjH5/d43Q/Xv+PXybcM9M888wyvfvWrAbjjjjs4\nc+bMV/z84x//OEKIG7f5Zupo0+fXvv9W+uMWl3ZHbA19XNPi7sUZMqnwm586xaWeRpQYlCyTtz14\nO7VSidYwZbXnY+kqd883efj4HH6U8kuPfJH3P7VH+Pfuqq7AfMXhF+4/wj3Lk0RxzP/z+Ytcao/I\nkTywMskrV6Z57MIW73vhS2SL//G+FZ7cHtHq+6w0XP72cot9ziCvXZngtccWeHajg6Eq/Mhti/zK\nI0/dAPkp1yBKM0wytr96bP5VZWvFrLofFSfyqZLBT906xx8+u4GfpBiqys+94gB+mvD8dgtBikLI\nMPZZrkl0pZDn6XwNOwYpeXClgWXorPZGNG3BckNnrd8llzk3z1a5c67Cnh/w1LUeT17vEaYCSy/z\nusNzVGyHpXqVp6/1+dPnN/D2ZYR/dvoaP3LzDBN3BURJTHes0A0ijk4MccyELINhaHBs6ghe3Gaj\nn9ALFAwhEFpKlgt2PAtLK5z5xrFKN9YwlZRJNyaT4MWCVy76VI2UREKYCrxYYXtkEmUKi9WUKCnm\nbYNI42yrhGuklM0MR89YrAcIIbjScbBNlZIREmUqQhEEiSiSCXGo2yOmSxGDUGd9VC9yq+OMGPk1\nT/VNJ0FTJO/5wg4/fU/G9NdXfb1c36YSQqFiT9D1thgGuzS/QcLdLXPznN5cZjC+Sj+KmXQrRNmQ\nIB5iGxW8sEdfb9EsLdAo9el4m2R5ioJEKhqgIhDIPMeLeuiaRcVq7mvgW+wMVlls3ISu6DhGlanK\nCpu9iwyD3RvkvKozxXC/Ld9w50jyGC/oUnV0bl88znPXz3NuZx1d1W6A/bXOdd7x6KN84doYLyzx\n/UcNDtQ1jkwU72vPb23woRdWGYYqdy3OUXVrXOvusevDwUYFyzA5t9MBoVK2JtkLoGxkyHzIntfb\nj5iNOKQWznVhGjOMBXXbQFNt5qqzuLbL37x4hbansFARVKyA7WHITEkhyiwMxcRUNPrRmLKRslg1\nqNoGLS+h5Smkqc0gktQthcOTLlJKul6bubJC2Sxxbk8BWaFuh5TMmJIh0DWVYRihKXCgHtFwxgzC\nIs56HGZIEpZqATkxWwOT2ZJDzYGSmdMNFMpmhVymDIMBLW+MQkaUaaz3FRxdZcJWKBsJJStmpTlk\nthpwvVdmEC0yDFNafoSjW6x3JE9vWCxWJJOlmNlywoydsKalqIFBa2zws7fP4aVjFMY8tKLz/z53\ngbPtGCEEN09V6IcpSZax1HC53B6wF2TMuv/AWfee51EqfYmcpKoqaZqiaRoXL17kIx/5CO94xzt4\n17ve9U2v+S+/9yGmmzM8da3P2nBMzbR5053LICUPveOjnGrpgM6ka/D2174C29JoeRFXe2NcQ+MV\ny5O8/sgMfpTy1g99kfc/vcrfPzibquDoZIW3vfYmpisleuOQ3370xYI0ogjeeHye2xeavPuz53hy\nu3/j9/6vN9zM+09v0/Ejbpmt8dGzmzdY+z96Ypa7lqZ4dqODrir86G1LvO3DT7E1LEB+wjGI0xwU\nyXbwjWcyOkWojNw/kT+wXKfmqnzw7GWqVsqBmsY/vWeBs60d9ryAqinp+zGjOKZs/l2glSBNVfpp\nEdihigL8LT3j+4/WkWT0gzHzZahYGntegqboPHBwgblqhdYg4r3PtLm8F5JkLkem6jx8fJmKZTFp\n2/zbz5zhxfYARai8amWS68BT19sca77ITRNjxqpFy5NUzAFJGqOqoKoaO/1pRhvbnFxs4CeCPT/F\nNIp5+vrAREqJrWcgNbq+hqbkTLgJlibZHWrcszigsa+XjxMVL1HpBRY7Q4Obp8dEmYKfFZ2bs60y\ncaZwoBJgaRlls5DktTyDa32Nw82EKIFYCqpGRi4UBoGGUHIO1ANyqXCp49D3oWRITEMli4pAHMmX\nxi6GmlO3EuJMsNYTvLjRZvqm5Zd6el+ub3OZmo1jVvCj4b4ZzkufkFRF4f6D9/PJs5t4YY+as4ym\nFB0BW3eRUtxo4U+Vl/CjAWHiYRhVkjTE1F2KXlmORGEQtGmWFpgozRfa/mRUmOlUDiMUSaM0Q5z6\ntIbX6PstdNVE2ZfdDYI2g3CX6fIKaRYz2N+onJg9wpnNC5zZWuX2BYVxLPnNz2xwdnuPSVuyWJlg\ntn6IW+dCptziXeg/nzpHnqfcMVfi5HzGrrfH1iCm7kwzValxanOLMDGZr2j4WdEpTDLJek/nWr+E\nrsbMuClTlsTWMhTaHK4L4sykZE5ydDLg9GaXMztjhGjgGDbndjuUjZyGnTFTjnFNGIaCUSBB6DRd\niyQLMJSEuRJc62dIqVGxJ5kqT/DxC1fRhMliVSVOR0w4OWGqszMyUMcWi1VJkvnoiqThKJRNjThN\nUUUPW8sYSBUvthCKRBBzsBmgKzmK0BklCVXLRldNuoHgyq5ECoWqkWIbKVkEXmThJzo1K8T0FZql\nhIqZccfsCD+5xOmdGlHWpDUM2PZzQOPcbom5MGO5PMa1E5arIZNOwk/cMsHGKGSUuNy/aPHnZy4R\nZwkrNZWp8gTXBglCSg5WDL54vXvjgPjdqm8Z6EulEuPx+MbXeZ6jacVyH/rQh2i1Wvzsz/4sm5ub\n6LrO/Pw8Dz744Nddc2nmME9vDXihFVG1HH7stiVMVeU17/wrTrWKHNVJ2+CdP3wPqq7QGgas9jzK\nps69yxO87ugcXhDzCx94kr84vfZVJ1lXV7hveYp/8cARTNPgSrvP7z9xBT9OMXWFH7t5iVvm6/zi\nBz9P68vCXj78c9/D//7xUwzDhPsWJ/jA89dugPxP3rrIsbkaz210MbQC5H/lkafYGITkFLnucZbh\nRdlXzeMFxanbUIt2uqZIGnpOSNFmN1XJG26a4cx2jwutoDCHqNq8+c4VPn15jz0vIZUG1zoeLV+Q\nZiaKktPYv1htM2GilGKoEk2VaEJycr5BkKX0/BRbMwkzk95Ap2ravOXuY2RC44trPd7x2DVGocTQ\nLN54YpHjM3UWaw4X2gP+5SeeJ0gyarbB//Kam7FNnQ8Ar1rqYIoOvUBQtxss13zK5hApwY9hqnKc\nA/WQjf6YZzaGvO7IIYLkCsMw5PrAJkgETSclzRS2PQ1LA9dIKZkZA1/h+PS40MtTSOn8RGUQmlzq\nmBxuBqRSECYKilA4v+eyPjQ5VI8wlBxDyTjcDPBijRd2StStnCTL0TUNmUlGsYKjaYxTnarp0XAi\n2p7Gxd0ip9q1dEq6IIxTIslXiB+nSxFCQHtsIBG87WOn+dTBOUrmy9K671aVrQZR4uNFPSzdRfs6\nCXeHJho8UzrC7ugM/XGP2fIUUbrNKOxRsSf3DXJazFQOMlU5wHrnPFEyxjQckizGUE0QKnleEGP7\nfoumO8dEaZGt/kXGYZ++1qZeKkzMp6sHibOI3niLzngbU7f3Xf4aDIMOo6jDXO0I693zdL0t5qor\nROkKl1qrPHv9Mo+vpnx+dcwwMjg5L7h11uI1x+a5Y7bBzrAYK4aJx4G6yu1zOn4yYs+LaTg6J6Zj\n1robjIKEsl0jFw4qMUEa0/H6rPaKTpqq6ASpS5CatDwPSy3a03U7Z74yZOT36QcRBxs2NbvCtX5E\nPxDsejVGZY2DGiB8gnjAbBWmS4IwDWmNJEmu4oXFZntxGl572OLjF6+z60HNbLI2SElTKBsRhpYy\nX0nRVJsg1dkYuMzXFFaaGolMCGKPMElIs4ypUsYsKZ2xQZgZlAwV1xDkslO4GpIRxBHXuhGDSKIK\ngyRRsMwQV8/J1Zh+pHKtZ1C1dVQ1RQFsI8RQI+6e32Wh0uHRaxV2wzJprlAyNFojwdaowkwp4mDT\n53sP1fCSNk3b4aaJBr/35DatocpMSXLrrEU36DHt6lSNCp9Z++ZH2d/O+paB/q677uIzn/kMb3jD\nGzh16hRHj35pLvm2t73txufvfOc7mZiY+IYgD/DcRpenNnqUTJ0fu22Jkqnzg7/7cb6wUZhCNG2d\n3/tv7iXOYdcLudrzqFkG9x+Y5HsOz+AFMf/9B57gL05fI/mydQVQtTV+/NYlfvz2FVQBT6y2+cDz\n14jSnKmSyQ/fvMDJpSl+8Hf/5kYXwBTwe2++n//tY8/hRRmvPzrLe564fMN45udOLrPYrHB6q4+p\nKfzIbUu87ZGnb4B8RRdImZLmKSWzAPMvn5Nryldu60wBkQRLgqGq/OCxRT52oU17nJHlFvcuT/ND\nxxf4j89tMQg0arbCeqeNpmUcbRataUeVSEXe8K6PM4UoUxiECg8eXGBtqLLr5diGSdsXGJrOLTM1\nfu6eFTaGEf/hyUt8/MUtMimZKln84zuWmaq4zFVNfu/xSzy90UEIwT2LTf7ZfUc53RrQ3imen9cd\njhn4GVsDnW4w5lCjS5ZBkMELOxVm/S63zdZJcri8p/CZy+u85lCdD59XiFLBQjVgHBeACQJTS2k6\nCWEiqNspB2ohlpqTSoU4FQxjnasdmyk3wdZyRpGCBNojnYsdl4aToqkZppYXG4FMcHHXQVehbKWk\nOUSpKDLtlYxuqJHnOXMVjzAR7Ixq6LpBIjO8MMHVVSxdI4+KbV4KlIwUR8/xYpVxXFxOz2722RoG\nHJ18Gei/W/XlCXeDb5BwJ4TgVQdfwUdeWGW0Pyt3NZcg8TB1F0NzGIVdLM2lUZqj7k7T8bfJsgQh\nBJnM0IROjiSXOXEaMAo7lOwaE/kSu8M19rx1TMPFMSsIJHO1Q8RZiBd2aQ91NMXYl2DWGEd9/HjI\nfP0oG93ztIZrHJk8Rt/3+fNTz7M9GuBqFhNOk6NTU7zqoMPBeoymKvzl+eI11w1K3D7vksuEvr8P\nqrUyw6DHYBzRcG1mKj7jyEdTbbxI4/FrAj8ycAyVOV2hatt0/IgwVhkrZVJUDky6xHLE1qBDzcqY\nLyeEmUcYgy5sFGFTslzy3OXxazkSldumNeI8Js19Jp0MP8kJYrCEzT2Lc5ze7hAnI440VQw1Z7WX\n48UWrbGBo8bMVSQTboahjjlQ15iv1xFKmY3BkCBOiLMiu6OCRIqMZinEUFNUpcwo1tBVgaXm6KJD\nN5I4mgaOhh9p+KkgkRaBklAyYmw1Rrd0UmkSZhq7QYgV57i6gcqYipXwA0e73Db0OL1d5/yeTb5v\nlL7rWfzgiWNc7LSZKUlmShmnti8w4xhYwqDqNjnbTpkpSZYqgnO720w4Oh1ff0mflO9UfctA//rX\nv57HH3+cN7/5zUgp+Y3f+A3e+973srS0xOte97pvac3nNju4psGP37ZExdL5iT/4JH99pWDiN2yN\nP3zzK/HijH4Qs9b1aDgGr1qZ4oGD0wyCmH/6p4/xkTObXwHyCjBdNvmlV9/EzbN1VAX+4vR1Pnu5\nRZrnHJms8H3H5njw0BQnf+ujN35vwlb59dffyf/56XNEScYD0xa//8TlG+z1X7zvIBMVh3M7HUqG\n4AdumuQ3PvE4SR6wUM2pGKCqEH4NLZaUkOZin/mvkOQCmQmCvFACrNRLHJtu8J6nd/ATQd0y+Mnb\npzg4ofHo6vPMuCGH65LrPY/FxpfWNKTCjq8xijW8WMOLFeJMReaCn7xjmWvD4rFTAD9WKFs6P3B8\nlocOz/L8Tpe3f/IMqx0PIQR3zTd4+MQCUyWL1sDnVx45Sz+McQ2Vf/7Ko0yUHB67vkeQpNw6sZ+5\nLMeU7QrXhwmHywXIqyr4wRSSlNZozAsITi4eJEy6DMIBz2wJfurO23nvU6fpBwpRYpBKsLW0kM5J\niBLBybkxrpGSU4TX9COV7aFFkgtmKglepBHnBfP2+VYJATTtBEvLmHIjSkbG+tBkN9CYKRXRuYoE\nKTJ0RTAIFUaRwnzZp2omdHyDflzH1jKCOGccZbiGhqMrhAlEedGRmSrFSBGgBNsAACAASURBVAlt\n70snyBz49589y7//sXvR1Jcldt+tsnT3hmd8EI9wvk6Q0Hy9xEz1VjZ6T9Dzd6g3Z0nlGn40wNJt\nkNDzdzB1l8nyMn4yJEx8XL1KnIZouoaiqOR5VnjQR30MzaJmTxDGHoOgRWtwlaXGCVRVR9dM5muH\nud45xyjs0B7pqIpBzZm6kXanKTqztSNs9C6wuvsij7zg8+x2QtkQHJ/OONCw+J5jC5yYruBHXf7w\niSf589MF8XapcRhNq3Bpb49B2OXEtIoiYvbGAyoWNB0YxQFV0ybNJdc7AypGhCpUVGFhqDV2POiO\nMzTVZrKksNIsjKy+eN1nZ+gwWwZDjUmzkNmyZKGaoIgIKVKudguFjW24qEqZs62MODVw1DGKMmbS\nkdw0oxAn22wNAhAWlm7RCzxqtqRmRgxjQSZNxlmZnZZPwza4dU5nvqLRGe8xijLWOioCm0nXJMkT\nLDWisa+dl3jULUhzhzDV6fohORkL1YhxrNHRDKxYoxdqRKmCF+rU7JyaLalakkGY0fV1ynqNHTnA\nUi1cPaXupBxoBMyUYg41bJ7YrNEfm/zkyYOs90Oq9iR74xqfu3qRCVvhQC3Cnhas97topRJ93+Bs\n22fSFTSdhKqVsjs2GEZf39Hx21nfMtArisK/+lf/6iu+d+jQoa+63Vvf+tZvek1TU/lHty5Rd0x+\n9n2f5S/O7wBQN3X+6M3304tSRlHCWtej6Zo8cHCa+w9MMghi/smffI6PX9j5iva4KuDIRIn/+TW3\nUC9ZqDLjdx67yIutAZmE+5YnuHd5iuNW9BUgf3K6wg/dfoDffeICMs95eMngfee3aTjFnPvn71nE\n0gOu9VpMugoPrkzyu49/kSiLKJsFO34UQxILkkwjyQVJVqTCJZkgzb88OqcoTSniYn/oeIUoDdno\nXuTEZMykq3D/coNcdDi746GKDNsweWZjzDixGEUqXqwxaRpcHuRftVPUBfzUnYv0opzOKCAFyqbO\nRMng515xkIpt8OEzV3jPFy4RpQlNW/DwTXMcniwz40Q8cnaNU5t76GrOq5ds3nR7nc3hRTY6HjNO\nzvVul6fXCyeocawihMUd013EvsXuRq/Gcr1JzpC1rs/pnYQw3eG+ZZeLuzZXeg5/u7rOj94yz588\ns0MiJQ0zwTVidCWnNTZ45UKfmp0gpSTOFLxYpT22WB/o3DE3JogV/FjB0OD0jkMvMDhQCzHUHEvN\nWKqFDEONF3cdykaOAqiqJEgUkAJDhUzqqCJjrlrE517tleiNc2qORZjmeHHKKE4o6xqmrpJGGZNu\nQcDb83WS/CsB/Y+fusyvft/tzFdfltl9N6tiN2+csE3decloYCEErz58G//51EWCuEvXh7pdx486\neGEx58/yhL6/w0z1IFPlZTZ6FwlTD9soE2Uhlu4iFEkuU1RFZxDsFfP68jxx6uMnI3ZGa8xVDwOF\nyc9M9SAb3RcZBXvoiomqKNTcGTKZ4sdDylaTqrXIHz7xOa50Bqzuuty5qNB0FA5PpByoZWiKwQdP\n7fH5tU2qZnG9H5uqsj302RxJlmoLOGaZpzZ2UKTFgUaOF4+pWwpSBGwP9oCYkqHgGAYNR6dme1zr\nJSS5IBdlJkpNZisuX7y2xfndnIrukuQq59oeqqJStzKabkbVzIiyHouVjBlXx9KhFyS4WgZSZ7Wn\nMk7q3LNUQhEqZ/bWabopdQv2/DGK1PBjhTgXVExJ04VR1EVBpe7UmK/PcLkzYBTGjMKAuhMj0Agz\nnW5fZ6Hm0lQE4zhAFSGWnmGoPnGWkEuVKNHIMoGmZixWxgxDFUvT2BtrhBik0kAIjTANUZWYCUfj\nbDslx6RsqMyUFOLcwNIiqlbG8akxK80QU1vkbLvHVLnBzsDjc1d3yfIyB2oON00nkAQcbga0Rj6j\nyEBXTVa7Fg0npWEnzJYjapaCIr87YP8PyjDn4eNzTLgWb/3go7zvuYLtXjFV3veWV9Eex4zjhOu9\nMROuyUOHp3nFUgHyP/VHn+UTl1p/z+ZV8KqVKd764FEEKmmS8VuPX6A9CkAovPGmWU7M1Fhv9Xjb\nF85T2W+t/8zt8ximyt9efoH5cs4rZ23e/+IGM/ss6jffvoChw9XOCFUxeOjgPG//9HlWu5I4t1CF\nwiiGvw/kRRUbBdfIMNUcW88oGxmukeFoOScXSmz2N0jIabqSqmVx79IsLU9yrh3RHZepWiX++nIH\nRZQQAjQhWaqYbHohNbsg3SlCIgSYquTho9MEaZ80CXHNjKajs1B1+KGbJ/GTNu/9whqntvo0bEnD\nMXjNoSmaTkiW9/jo+U00GfHggZyjkzZlY0xruI2QhXPd2p5PkuXYevHIX+5k3DpVROUCrA8MtkYq\nab7HoWYdP3FpeR47ox4vbCfcvXCCUdJi1xtyflfhzXfezO8/eZ48L8IqrvcMbp31mCoVc/lMqgRJ\nYYpzftfhxJS/z8TXMDTJ+sBire8yXUow1BxNSTk66ROmCmdaJQwVqlZGlhfmPIqaYwpJ19eIUpUJ\nJ6BkJnTHJqtdF8sAVaFg3CcZQZTjGODoKjJPqFgJSSbo+l/dovczePTCNm++56s3vy/Xd65URaNs\nNxj4uwyDPeruS4d9N0sWy427uNr6NN1gg7pzBEP38RMPU3PQVINx1Gfgt2mU5qjZU/T8LaI0RFO0\nIs5Ws0lzyPIUVWj0/Z39ef0S24NL+/P6Fs3yPLnMqLnThOmY1mCVfrCLrhXkvJo9zSBvs+e1eddj\n23zuSsh0KeXVh1OmK0c5POmhiITVvWs8eX2VR84GOLrOyQWTTwFe2GVnGFGzLQ42y5xvDfBCyUxl\nkm6g4BgwzmJawxY7wyGayCmbOVVL0HRitkc+ulKoe+arOncvwNXOgM9dHRHnZWoTFud2R6SZgavr\nCBRsUyHJYsbxCEfP9xMieyiKxNY0dkYqeU2japW4f7nCh87s0vbqLNcFg8DH0ArOTS2TpJmKIkr0\ngow4hfmaysl5g46/Qz+QnNuBMC0xXcqZKkXoasySq1KzSwxClVQaNG2XLPaJkiG5lDTtGGnH9EOd\nKC0OWZaeMavFVCyDMJEgDMZJzihUqVoqO2OPplO8pwwijTRVKVkJTcdgGOVMORmLDY083+KBpT7P\nbe3w+JpGLjUWahYJCs/v5Byu2bRGXcpWyuHmmLqd0B6bbA5NrvZsJt2YipkxYX0TOutvQ/2DAvqm\na/Grf/EEv/PEGlDkt//pWx5gcxQRpinrfZ8J1+I1R2a4a6HJIIj5ifd+hk9daX/FOq6m8qY7FnnT\n7SvEacreeMifPH2RKI2ZcuH7j06x3Mx59NLzfHatw9K+U+n/dO8Rzg3GPLPRRxEK9y7Uec+zLZJM\nJ8kEv/zgTURS57mtEabm8KM3LfK2jzzLajdDomOoxRu8QGKoBZibeo6tFfPzspl9WcRsjqpKBAJy\nWKyXWet4DGJBJnUmnAoPHFzkfHvIRn9EkmbMlwXP7Wywsk8q1gXULI3dYMj0vgDi78xxdAEPHZ7E\nj2O6fkgiJZOuzS2zDR46MMXlvR6PnL1EkIyZL2ccmjA50FAoGVus9z22BgGOJnFNhWMTFfwkZ8/P\ncTSDjeGY1ighyzUUJWdSK4Bu0h5jahIpIcwcJsuzBGmXPT9nEIXcOTePImI2+hGPraaMohYPrDh8\ncSPmXCtHyj73L7tcbA8YBCpHJiIWKsXJPJMKYSrohTovtl0WKgmukdPxNRRgGGmcbZcx1ZyKmWJp\nGfOVCF2VrHZtRonOpBMTZQJdAUWRxKkAFfxEARFzoB4SpQpn2y66ZpJkkq4f4eoqkakxDBO8KKVs\nqMxWU1SgtU/A+1r1qx99mh+4dZGq/dLEsJfr21+OUdknbY0JEw9Lf2nr4lcdPML17jmieIs9b8B8\nbZqhv0GQDqnoUyAl3f0W/kR5gSAZFcQ8zSLJItJMQ9V00iRCKpIkDRmFXcp2nThf3PfD38AySrhm\nlVxmzFYPEqcB3fEOvXELQ7NRhIqp1/mdRx/nsdUt1roGk67GrbMady0Ibpu/j8evPMPHX1xnc5DQ\ndEocaC5y86wNQMfrUDIkR6cEe94W/SCi6bq4ukaUZRiawd445/FVjUFYx9IyDjZhoabQj8ZIIuqW\noGbDzbMqA7/DsxtdJt2c6bLL7sijNUpJszJ1R6XmaPhpxnqvD7LMSl2lHyVkmY+hxjhGwkIVFmsa\nyw2TczsXKZuChm0S5zqXuzpRUsfSE2YqIYtViYqHqecYqsVKs0knyOn7KXvjEWUro6mqIE1e3NOY\nL1eYLgtUJSLLx1RMm3GssjFIyHKHshlTMyNMNafhxKSpYBgp+LGKUFTqVo5ZivDiDC3U8TG52I3R\nhF4QgI2UmiHY9jX8sUU/yDjcSKk5Fu1xwrSbMwpHLNUH/ONbdLZGk5zb08lzmHRUPntNoqtVlmsB\ns+WQhl207Bt2xObQYntk0Q8kywv/FZ7o3/XZM/zmY5cAKOkK/+mfvIrrw4g4zdgYBEy4Jt97dI7b\n5uoMgpgf+YNP8flrbWztSyS3pqvy3929wOFJizDeYLUz4jOXdzDVnEnX4IEDEyw3bd796AUudiOS\nXCXJFD72k3fx757e48x2gq5VeMPROv/usVUUoaEIyb/+3tvoxCFr3T2qluDh4xP8208/BvmYYxNF\nXryh5phahr2fG6/vM901taBvpFKQ5UXrPkoVvEDFUnUM3eb0VsoosSibGnNVh1cfmOb5zR5bo4Ak\nAz3NeXQnJpcquRRUDI0Qwdm9DCkNcimQEjJZzJ7feMs81zoB/XCIY2SsNDTuX9ZpuiOeur7KuZ0u\nDVtilgXLDZuqBYKYM1se4yQFFBZqJaZch26UIvfHDae2ByS5RCIQQjBbroEogH65Lvfn1TBOqzRL\nEUv1Kme2Qq4PQBHrnJiy8aIpXtwNuLS3jWs0efXBY/zVuXVe2N5mpSG5ba7JM+sDVuo9SnqKpHDT\n64UqG30LKSRz1YiBr5JkRev9fLuEF6scqAcYarG5Wqgk7AUa5/dcalaKouTYqqQfauQSXCOjF+r4\nqcLBaoBrpGx7JmtdG8dOEFIhjiVlw8BUMxQhiOKcKVfSsCQ7oyI+96Xq2ihmc+C/DPT/P1TVnmTP\nW2cYFDbUL5VwV7Z0js+8gjMbf0VvvEnTvQNT7xPEQwzFwzbLZFm2H2e7wkRpic3BBYLYwzFrJKmP\nqmj7rnkJmmrgRX101aJmTxbzer/NzuAqS80TaKpBLnMWasdI0sI5r+tpIDX+4zOX+JPnOlSNjNtm\nDQ5NHeaO+ZyZcsbA3+RzVzW2BgkVI+HOWYVjMxpJXmxiWmOHW2dt4jRmZ9hn0lFputAPPaZKZeI0\n45n1LoMoJZUCQ3dwjAqXOpK2Z2DrIUvVnJUJCykTXmzv4egRU45FnI+Is5i5soapp7hGiYplcHor\nZM93OVDXiHOV1d6YJNXRlAhL86nZGQfrOkHco2zF3DKpE+c6mwOVxYrGOFHxYpVBUGMcaeRyzHIt\n4eiEgakOidOEYZjihwIUE1MV+FnAkabOhGuRSourPYPZcpU4HbE3HhCnKXGmECQW/cCkZsWUjRBL\nS6nZGWUzJ05VcqyC1KcnWErIjhwz7er0fB0/UXAUEEbGpJPgJyqmYlN1yux4Ics1jRe2h6SZTt1J\nWazlTJVbLNYGrHUm+NRaEUoWZ4U8t+0ZLNYCpp2YKTehYqbMlGM2ejZndr92SNa3u/5BAf1vPV6A\nvKMJ3v8zD3Kt75NkKTsjj+mSxoMrLsu1lLXdNf6HD3yOnWHA4YZECHmDdPfTdy1h6QJByNPrXZ7d\n7JNkMFd2uXt5iltmG7z1z55gnOQoAiwt57P//EH+zSdfYNcbc6Chcu9ijfc9d57lfXfCX7z3MP14\ni+54zJQjuXuxzgee/dti12jJfQZ9jipACEmaF/P4vyPbjWMFP1EJ06L1HKYKWV5Y14a5IIozotSk\n4VoslaqcXJrib6502B7oSHQGgc/FTgYUrl8HaxZbo7hIk0JSt4u5j2tmNO2Mu5dc0uwCYyPkkCpo\nODpHJkoIEXBqvc/uOEFTBbamsVSvUDJNdkYhq12PXIKtm9w6VydMJK1xiqGoXGxHXOmm+JkNstiZ\nrzTKoOocagQ3nsNRCJd7FXRlTI5J3bE4PrNItrNOkCQ8t61x++w8OTusdWM+cXFMlG1z66zL0+tt\neoFkpTnDK5dahd2olESZwijW2PFsVgcG9yyM8WOFUaJRNjIu7rlcH1jMVSI0RaKKjGOTPn6icGqr\nTNnIKZsZMhd4uYKiSBQEfqoyjlXKRsZyI8BPFC7ulZAUhDuLlFzX6UcJNUMlylSGYYKlBwgUeqF1\nI974payMf/lDT/Khn/9eDO27R7x5ufYT7vZT475Rwt3dS/NcaS8zjq7QHm1xeGKeNIvw0wGGbqEI\nFT8e0A92abqz1OJpeuNtoniEodnEaYBjlvfT7hI0oTMMd2m680yU54ukvMRjZ7jKfO0YIFBVnbna\nUa51ztAd7/H41S6fvZyQ5zqaWuHkkstDh1zuWT7E6t5ZPnjqFKt7gkHcYLEaULUSwqTHxrDIszjQ\nbGLqFs9udJCyQcPV2R2PmasoqErAhU4fQy0IYWAyUymhqwrrgxF+oqCrdWbrU5StEo9fvcqVjsZM\nGUxNMPDGTJcKZYquCqp2xu64i6uHuDWDhYrD1kiwPbDQVY0wUSlZJifnXAZhzuZgm6qlUrFzomTM\ngXoRuT2MVILURlMF7aEkkSamPk0iy7QHHXp+GyEjlhoCQ03pjBXKuoNruriGQi8YMl8xiZKcM3sq\nXc+i6YRUrBRFgTyH7aHOjqJSMWIm3JiqJak7OakMGIeCOFXwk5yqlRVud05C39fpRxpJomCoOctV\nhamyRi/KmDJLfOziHramMuFmRFmJjUFM3U6ZLQfY+jq2afH0ZpVtzwYEg0jHa2vsuRGz5ZgpN2LW\njagYyd+Znnznr4fvyl/5JmuuXJAt/o+Hb+Z69zw5KaMwYNbVOTFbxdFTrrRj/u9PnyaUCdMlQbZ/\nSl6ul/jx21eIU4GuqHziQotr3TGZhBPTFY5P1zi5UOPn//QxJODoYAj4k7c8xK9/7DTbo5C6bXLf\nYpk/fvYamgK6IvkXrzrI5sijNQowdcErFhv82fOrtLwEQcGYj1JBkqukefECDlOVMFEIsyI/Ppdf\n2doVwHLFYC9QkDJHCJ1m2eTOuTpHpys8emWPrZGPpiis7vbZDWKqRkbNSbmlljPKuxyeyCgbKbae\nowqJKiSGKlmoW8TpHkkqKRsqTdfh0ESNYZDx5MYALxIowmC+4rDcKKMJlWc2euz5EbnUWKlXODzZ\noDWWpKkgSGO+sNrGTySKomKqcHSySs2KWK6tYyoepIXOIZfgZTPoqk+WS651MzaHOccnPW6fc3l+\ny+firkqYbHDfUoUgMdkYjvnbq5sEscmJaYfVjuB65zyL1YAgkUSJRpwq7PkmZ3dcbp0tvBu6gYat\n5+z6Bhc6LlWrkLmZas6BeogiJBe7NpkssuvTDBwjZxhqpLmgYmZ0A500F0yWIiwtY2tkcrlrYWvF\nACRVFHQgSxIS3cLSVOxSCHlOe6yjqwaaiL+u+cUnL7XZGYUs1d3vyDXzcr10uWb1BgPfNkqY2tcm\nRjqGxi3zJ3l6dZNBuEM3nKVi1RkEu4TxGNeqgcjpj1tYukuzNEcQj4jSMYawyKUkTiMMzSFKxuRk\nKEKhvz/bnywtsj24hB/26XlbTFQWyfIE16wwWV7msxefYX0wYsKxMPQ6x6aneO3hWQ5PKGz1Wrz7\n8z5hFDBXyzlZnWa6vERndJVeZ4skL+aO0+Wc8zsdkixnruLSCzLq9gRS0Xh+q83F3RxFKDh6znQJ\nZqsRG/0+tgaObnJ0wuXm6Spntnt8bjVAFXUszeJMa4CpKEyXUxpOzqQjyOQQkYdMOCrTZY0wGWGI\nhJmyYMeTjBKNutugUZrgExfXUEWDuYpASo+y4VO1kqI1biUoImcQBbiahqFXWaqHtEYBV7sR6z0b\nobgcqEpsfUSzJKlZEWUDtseCilXGjwQ73pAwCXFNST806EUFW97RQww1I5M549ggyS3iHEq6j2sm\nheV2AggIUhUhJE2rOHHXIo0936BqWkyULTKZMetEPLXRZRCqRIqOpdooIsQ1NDaHCopIqdsxxyd9\nFisRFzoOT29WGUQGmSxcP4eRziDUaNgx06WY++b+KzzRH24EvOnOJdpeCykFoyinapncNtdkrlIi\niHP+1788zepAJZNFCIpA8H1HZ3jjrSsMghRNUfgPz67RHmbkWDy0MsORqRonnIzvefdzSGmhKZKl\nqsZ73/Iqfu1jzzIMQ+YrBicaZf7o2esICm31Lz1wiPVhxO4oxNQV7l2a4g+eXOV6X5JLi2gfyMP0\nSx+/kT6yqoFtmUQIVAGGruMYGq9emcQxJGe3VpH5kOPNjM54wCuXU0pGsZuedhSGcYqmgKZIJIVM\nL84Usgwmy1U2+znDSME1DG6eqXCgUebFtscXrw+IUw1NVbhrvsF0pURrFPH5ax3CRMXVXb7n8CQA\nm8MQW5Vc7g7Y7AeUrJRjzZClWkTTzTDUza953y7ulZmv6izUHK7uxWx7BpoypmF7qEqF49MH6QY7\njGKfR9cUXrm8zDhusz3cY2PoM1ma5e65jD3fYxwluKZBnOWMYpMrnQrL9ZCymdEa6mgCskxwruWS\n5oK5coKh5FSMhGk3ZsczuNJxqDsZmiKxNFm43gmBYxS690GkUrdSlqshQapwplVGQUEIQZrmpDmY\nMiFRNLwoZdIVOHpGPxC0PJ2yIdFUhXRfQvm1Qu0y4I+feJFfe/iul9R1v1zfmbqRcDfeZLjPiH+p\nhLvb5ic5u32EcfAC7eEazZmj6KpHkIzQVAPbcMnzjN54h+nqChPlBTb7FwkTv/DbTwt5nKFZREmA\nEBpJHjEKO5TtJnEW0hqu7efXlyjbdaIk5qPnA56+nrFYy1ispVQtlXtWKpxcmmOju8N7njjDpd0I\nZJM33JQyWx7j5zU6QZ00C2jYhQFLZ7SLlAHLVRNEgmbqVG2Ttc6IMzs+o0AlxWGuYlNzHK71Bvhx\nRtmAuark/mXY9dZ54lobS5XMVWtsDkN2RwJdr+CnGqpqoqkxO8M2uqIwX9XR1YQ486jbglEENUth\noWrx0CGDz1y+ziCUOHqN87sZUVq0wCtWzJQTs9zIsPWIspUwXSru+ygOyPKcsqawUtNRNYu+r9IL\nGuTCLroJfo8pBzLZxQsTBkFOmmqkuYJjCgQZfizp+QZCKDQdScOVOLoopLKxgzKK0fSQSado59t6\nQQIMMhWBpOnELFUEKBp+mpJLhTOtAZoiWa5kSKXEKIIks9lIA6ZKGTkq44FNyUxpOil3zo44UAs4\n1y7x3E6FLNXwkyJ/YxBpLFbrvNDO+P657/y18A8K6H/mnge4MojJpUJ3nNJwLb7v+CIrzQp9P+K1\n73qE66MvhVY4qsLbXnszJ5cmGIQJcZbyzicuMQxyLM3kjTfPcHDCwdFC3vLhUyxUirn5ydkq/+19\nR/jXf/0FwjjhUNPiUNnh/efWMFRQkPz6/8fem8Zall33fb+9z3zOnYc3vxq7qrqKPbFbJJujKFJk\nmIhioMhOIkUJEgP5kMBxDFgZLGQALMtJZAcWZMAKksBOAoGUHCvRQDUki6RIiqLYYs/d1V3dVV3D\nm+c7nnmfvfPhvKYospuJZJEikP5/vDjAu/fdc+7aa63/8OErvHicszer8OyQx8+t8Pe/8Co3jyFT\nAfn/h6L+rVgIbQSafpgxCDVtr6Tla64uegh2mOcp51r1KTcuSoZh3SqqShC6NgdzTVo5dTiPAoX1\njcPOtcUO1/dzsgq6vsd7zi3QcSVPvLLH1mSGNtALPd61NiBwBc9s7bM/T4lcw5VBwOVBi3GRoqsK\no0YcJiMudkveuazw7ToE6Js/rdZQVjDPYVTUe8KT1EIzY7Xdod9oszeb04sSDuZw88jw6LriA+ca\nPLVV8dohzLIdHllx8CzJUSx5cuOA3uWMtq9JSsUsEwyCIbdPPAK34Fy34CC2yLWg7Rte2ovYjz3O\ndLJ6dSI1V4YJ88Lm2d0m7aB+gGvfgjqUuNLgYUiVhQSGUY5rabanPtszDwmkpcaTUGpDLCWBlGit\n8O2yfq+JRV4abFnhOxZlpSm/Q1f/D7/8Cv/Rhx6gH31vTu9v40/g2B6h2ybOx8yzEa2g/6bXebbF\nu84+zJdfvcu8OOFgnrLQXGI03yJXMY7lIqVNWsyYJkd0o0V64RLH8x2SYorvhGQqIXRa2JaLMjmu\n9InzCY706IRDsnLOJD1kb3IbS1zl117c5jeu3+U4bdDyNFcXK9Y6gtV2ya39DX7hD/Y5micMQnhw\nZY2ldkCa3+UgvkmmFmi5K3h2HZhyMDug4dZTp7SqWGqGJMUee9MxgV1RuJKBF3LfsMn2TLE7sdGm\nzWrL5aHVAanK+aO72whSri4GlOoISU4vcrCET7/hE7kBz2wVHCVt1tsBgSfIyzE2CmkphFCcaUse\nWG6wcbKL0TkXeh5SVGxNFLmxOEg8DpOAQjscZQUWCed7incsQVklGJPQ8RR+V1Jpj1hlNByPwA3p\nBg67U/CdVeZTRZwdUumEQaggyqmMzSS1OYwFWoNjaSxZ8wdmuUVRFQR2iSpzSgOy9Bmnmk5QMAwV\noavxtCFT9ZTDscEWCTYp9yYGS1iEXt0sFTojkJKbIzDGYVzY9IOShahgmtcR221PMWyUvPfMhMv9\nhOf3m7y418DC4mx7wFNbMWfa35vD//dVod+c2lSVzTgrGTRCPnltjbO9BgeTmIf+wa9zmPxJzzQI\nHH7+U4/R9Gym2Yyd0ZTPvnIXTyquDCQfuDBgrVNxY/suv/LSNpFbj5Z/8qF1Hl4b8ot/cIOkUFwe\nNlmMPH75hS1AYgz83Eev8cxhxcbYxrEafPTCGf7L33qG22NN7UT/FaR/UwAAIABJREFUnWFR0fQr\n2r6i7StanmI5MkRuQeRoIk8iRH3oWGh4FGpKUmgMUFQ1mS2rPOa5hTKSvu+xOy/Rp2Q7txJkok5M\nixx4cClicxrjOYaVlsuHLvSYFQn//KUDsrLCs+FCr8nFYYN5lvK1W2MyVTvzvWu1gW/lpOU9WjJF\niwzpGmyrNht6w9fdmLpjzSubeyOPw8QFXC70+5wZ1IevTqAZJYKNSc61hS7vOpuxN5Fc3/eYlwWN\n/W0eWOlxbeECx8k283LGreOKR1c6vIxiGG4yz3MsIQgdn6PC4rUTm49cukbj3pOMMou08Gj5OTtT\nn9fHEYOoltJZEi4PEpQWvHoYIoXEsRTaQMevOIwdjIGmXzHNbKaZxTAqWG9lZJXgpf0IkH/SlUuJ\nRKONwZgS1zVYskQTIIwEqSg0RDZ/qqt/s3o/LgxP3zng4w+s/zmfjLfxL4OG3yVXMUkxIXAaOPab\nH7guL3R4busa4/kfczB7nYXmo/juiLSYUVYZgdVGoxile/hOSCdcIimm5GWKNhoQ5ComcNvosqrJ\nedJhmh3Tj1YYNtfJy4SkmPPE9af4P5/LScqK5YZDM7jIWntG6CZsHG/wxI2Kg7mmqFp85IzPcstw\nksDuNMIVMVcHExbb19ga15/FGIPSOQdxyiB0mSQZGycZaVHiWHCm7bDeUTjWCcfzAt+ShF7Ae84u\n4jsRT7yyzTNbNsuNDvMCDuYTAlfTszWtoGCllbM5mZOpgpbnsdAM2J4UHMcRWvukVcJSQ/H+cwGz\nvOA4nrEYge9qDueKYSQJHIt+JHHtAK0Fe3NJYA+47LS5O62YxIeo6oiOn9EPKxwro6kzVlsZtqyY\n5jGrzRClU7YmFa+fGGZ5C9/RLDYKen5Ow63tbVMlmeQuRru4UqK1Ji4c9qclFS4tT+FYGt8STDOX\nWe7ScEoGkWIhAiErdAWJMsyLimFoWIgspjmnv51wVNSvTbI6c+ModhlnNivNgtCpOE4dxrnFMFQM\nGgUfCkY8tBCzEy/y4t6cVAmEeGtTp79IfF8V+qrSzArNIPL51ANrrHUiDiYxF37216ikoeHWzPYr\nw4Cf/qH7SIpDVAXXd054amuEKw0rLZ9H1xe5b9Dmn37tNZ7aGSGo9dC/8KMPMs4N//OTr5GpiodW\n+1ha8ktP7pIpj1xJPv3jj/HV/YzNcUzoNvnY5SV++jefZmOSftv7bbgFHb+i5SnaXkHT03S8goZn\ncOzqG3v+wBYIqcFIpHBIKwHGo+s32Z1a7M8rMiU5mOWM83rfLUUdRtPxLfbiEt/W+Lam5RgyA5Wu\nO/mVVoPNaYJjSVaaER88v8CzOxOe3hpRVOBaHu9aH7LY8Hl2c5+4GNEPMpabmmFYYcQ+4g0q2alt\nrhGnHbsBbSSl7lJWTa4fajZHmqoSLLZ9PnS+R+hB062L3IVel6/OSnanktDe5P6FiHa4iGPlDN05\n4xx+++WEj1wueWy1yWuHMZVWfG0z52MXU5JCYaiY5hYNz6ETDXj5qI218wLvWGrx+7dKml5CoR1e\nOWpgCUPHV7jS0A8z2r5ie+Jxd+LRDzW2MISOYZLZGCNwLE1ZSVJl4dmahSjHtgw744C9OPjG9yqB\nQmlcC1RlSIHz3RKlDbtTSRS4JKWiLDWFMPiOhfp/6er/1me/ztevrOA7b5PyvteQ30i42/1GcMyb\nrVEcS/LeC9f43eu3SIsTtsYHXBysUarXSYoZlnTwnJBKK0bxHguts/SiVXYnt8hPR/h5lVJWKb4T\nkRYz0AYhBZP0gF5jlX5jnWdf+zqv7B/T8X0s0eGR1Q4fu7LOua7g5Z3nuHG4ga5cBmGXR9ZaDBt9\nTpJ99mYTjpMG14YrLLdi0uIOrx3V3A/buY9JPqEbVhRVxtFkzjiryakdX9D2JJGr2ZvNwWgWmg5X\nF0POdue8snfA3eMTup5F4PncOEhJipDIlSxEkrN9l1mWM0lq97vzPZtcj1BViWvb7E0rNB6Rv4iW\nbX7jlXu0HMGZrmYex0ihabgVLbfEsmwcKdmPMy50bc5062nd5onihX0bVfZZaAoWGiU9f8pKW9H2\nNdpMGYSCSsccJxaBY1hv2eTKIa9cxonLqwcevlWw0MjpR4qzHYVrxeTKZpQ57I5LlLCxpSEprVry\n7CoCRyClAQxtHTDNoeEXVOQYKhwpsW2JMYaFKKVQkr1Y0vEtskpjS02kKk5Sl6KS3B37NFzFaitH\nGsne3OMwtjnX1ZzpFnSjbXqBx+50mWuL3/2IWvg+K/RJWdINHP7V+7u0/ZxXtnb41P/2BdZ7tQEM\nwOPrPX78kfMcxRkt3+eJl/d45WCO0TbXlls8vNLh4ZUW/+0TX2cvLvBPP+Gnf+whnjwSfOaZTaaF\nyw9dXGeSFvzTpzYADwv4wn/8MX7jpS22JgmBU+/Nf+aJPyavplzqV7T9ko6v6PiKpqvwbF1HwUqD\nJQ1S1B13UdVszqyUxMZmK7dxnAC0S6oqmq7N2V6T3VnBKEnQlaqtaa2KYUPjCIMF2Jag0AWOJdCV\nwBUWu7EgrwQCyVor4qWDEt+JeHRhwHvP9PnlZ+9x9yTGtyRXFjQPLwLiHsezMff16+7XtwWCWgon\nqCcdxkCpIS0tJqnLrGwTuQso4ZEVJS/vj7BlRScwXFtssdp1adhzdqcxz03qwCHP7fD+i22CzVtU\nuuKp7YzF5pD3nJFsTgqu70OqKr50a4NHVwOuLHi8cmAzCEZMspiOD3llEedwY2q4b7jGu9YmnMQZ\nrx45fOjikK9v7PDVzZBxanG2k9ckRFtxXy9jnls8vdOkF9QHQqjNg2ruhCFwNOPMYZ5LlhoFy82C\nXFm8ePCnddZvdPV5BRYwiEq0KZmmHvOsotMQBK7FNK+IC0M7rL+r8ltD6r8JLx/G7EwTLvTfzq/9\ny4Bn13KypHgj4a7zpted7UYMGg9xMP0y4/ge89Z7aHgdptkJhUqxhIO0JEk5Z5ad0AkX6YaLnMx3\nScoZgRORVymWdHGdgLxM8URAWeWMkwN+7+aMJ64XLLU0l/oFgevz4cvnuTz0uHWU8Hs3DW2vYqGZ\nc23JIvINe7MpuzMLWygeXNKsd8+yN7nDznSXxahuQCpt0Q6HrHcjXt494dndAzBzQlex2ICmLzic\njymVoeEJBpHFmbbD3mTGCzuHdALDUsPlYHZAy9M0HRfL8hk2W8S5xdPbOUpFXFvyyCvDOJ3TcDSW\nTFlvG1pByAfPhfzmy9vMc0Fgt3lmWzEvbBpuQT8oGDYqhoFFplIWI+iGPt0w5ShJmRcZy00QeGBs\nbp04DMMe0rI5Tqd0vCnNIIMqwbcTVpuwGEri0iEtHfbmFpFjMcslR2nAUeoyDA2DKCdyUgKZsN6D\nWW4xTh2y0kJrSVLaOLKiE5SsN0M0EoXi9rFCCouma+gEUJlatVNUppb3NhVpJZllFi1PMskcXEsz\nyR2muc28cHj9yKYb1ZK60HE5SAT7c5vFZsHVYc4PrB3R8UPgzHf9/v++KvS9YMq7zg3x7Rl39uf8\n25/+IywJmZIUleQ/+IH7eP+FZfZmKZHj8plnbjNOY5puxTvXepzpRrxrvcVf+8xXGeeSTDmUSnLw\ncz/FP/jCK3z6mbuUlctfffgMtw8nfOaFLQA8Kn7rP3yc333lOfJiwvl2znrb8LVbX+Jfu1QXc/u0\nmAsMIMgrSVHVZitZaZEoySxzSJSFqgSuFPiOhW0LLvQ94qxEWzlrkUs/sknLKXmZYQnFKK+14toI\nssLGKEFxahCTKomNoO1aHOW6lr9ZhuV2wL2pou35fPjSkKaX8tvXv8ZKmHG1r+iHhsCDShUYNP1T\nwrEUADWRrzSSOJMcxhZ3xgH7swApfa4stBhEAiEUB9NDDmf15KIXWLxjsUXgWwjgyY0Zs7ykrOou\n9YlX5vzgRXj32SbPb0te2HMYpfs8tOyx3umzPSnQ6RwhSnYmCQ8sL/LeM4ZSHWDLkqzS+JbHUeVy\n+zjklYM7/PhDknbY5vaxYWtywsOrl/jyRsbF7hiERgrD/YOEshK8uNcgcGozHK0Nveh0ZA+EjiYu\nLKa5RdPTLDdzLGnYGnscJ2+9O/ddQ8spSErB8UwiHJt5XhLYFpmqKBWUZYXnSHJVvSkh7w38nd95\nln/yEx9EyrdJeX8ZaPo9cpUwz09OE+6+fQ1Xx9he4LdefI2k3GZntMG1lbOkZUxR5bh2iS0iDIpx\nso/nhLSDBZJiRqFS1KlDXlbOafg9qkpRVBmu9Pnizdf5lWdGHCc2TS/kwrLgvoHivoHNjf05v/zM\nTTbHLg8vdXiwV2DZMaNkl3ESUagmlxZXWG6V3Dna5pldh8v9JsOwzplwrRnne4tsTRJe2pvUI+Yy\nZMkO6IRtNqYzZumYwC4YRLDUsNmaHHPzcIahYhD6TNKMWVbSdC08RzFsKAbhjJcPEjxLsxRFWCLk\n5nHOPA8RFChtWG5afOh8hxf3toiclMHQJSlKjitNqeCgcJnmAUoEbE4zGk7C2S70I5eszJhnCf1Q\nI7HRRjPKch5oeHRCh6yyGMVNpvmA8d6EeX7IWjthGCqafkHfKSiqnLYnmJUWaemQlBpjFHHpsjvz\nuHMsaASKpUYtg2t5GdrAOLOZZg7T3OFiv8eoNHREydbkBInAEg6OdDmKFaFb4EiDkBWOAY2pE/6a\nmqSsHfcKZeEnNS9olDqUleRw7hHaDdBzHLukEg6zImK94yKYkGQbEP7/rNC/79wqS90OGydz3v9L\nz6KNhy0htDU/94n76TYcdqf7SKP51ed2SYoCW9q87/wZllptPnH/We77e5+l1HVVa7qC8X/3U/zn\nv/kUT7yygScV//67F7l1+DL7swP+jWuKrqf44Us9rm8/QdtV9DyN71hsjmKavqhDZyqY5rWRwjyX\nzIua4am0RBsDSKQ4jZa1NR3PELg2rgWrHZ+9SYoGhmFI03c5jktOEovdict+IqiMe8qeF7wx2NWA\nJTU9X9B2JPtphZTgiZKzbZtOcMwjS4qLCxZ5fpc0zrk6fMM4qG7V1Tel+xgpkVgU2mKee7x2ZHF3\n5DLLJZ4jaHqCMz2Ls52AypTkecXGdE6hFL5TdzoXB008C+6NYm4eZYwSi0L5XF6o/99FlfHa4ZRz\n3TYPr95PUu5yHB/z6oHGsZo8utbiznGK0hlpKfjCzWN+7GpMO6rIywqlJPuppN9cQSE4193l+W3J\nI6v3sdaZsD2RHKYBP/lol8/dmDHLBN0wIXA0G2OfvdinH5an3AVNXEoKLWvTIglxUafk9YKShUZO\nUdXWuG8FA3T8HCFhf+KSI/HRlMoQBB6hYzOuFElpaDkC1xJk1Vt39f/suXv8/KfexUIzeMtr3sZ3\nD1JatII+o3j/NOFu+U1H+KvtkNX2Q2weHzJONzmO1+lGddeeljMQAs8OqE5Z+AutswwaK2yPb5Gr\nlIbXoVApWTkj8JrM0zF/eGebX3/pgMApaHotFlsD7hsWdH3D0/eu889frBilJetth4vDq0j7Hllx\nRF4qXBse7rZYaHd4eW+HvekJl/ptBs2LTJNbAFzpT0jLMeM4I3LrQ23HD7mvHzEvNYexISubuK7P\nI2eW8O2SL926w9Fc0AssjCkoq4J2KLCFhe8Ker5ke5KQFhkrTZvVlmJaHBLZGgvJ7lwjRMj9i2s8\nv5twfd8wCD2k0JQ6ZaFhGESCyliEbkRSlpwkQNjHd/vcHaXcPd7HtUq6oSZwoahSzndtQge00JhM\nEjUcjuMJO7OCeRayNWvT9io6fsIgmNM/1aQvhDUhsDIFuap/2+6N64jbWW5xbxxw8xi6gWLxtOgP\nAsVqJyJXJWlp88XNHEsGRK7iQtcwLwpsYZgXLr6jCB2BaxnkacNnjCZwoOFqMlXzsaa5xb7jcpK4\nNL0W47TgIPZZbAru61ec64QcJoZx2qDtSOh99+/976tCv9jq8+LdHX7yn32VlVaFJSC0Jf/VDz9E\nrjXjVDHLJP/3SwfMcofIbfEj19Y502vyUKQ4+3efwLcMg0bCQ0PJ//DJy/yd3/5fqPIx//qVivN9\nn5P4Fh0v591rYAu4f7HD/nxGrgRKOyA9PndzzixvkVU2marH8AZwZM3ktKVBSoMn9amO36BNzUIX\n2JTCoeeEdHyPl/fnQMS5XgNp22xOcg5mFXfHJfNCo42FFOBYho6jKfkTQlfbtel5JdKe885uwSAo\n6EUGV1aErsG3QJVVXchOmxPHBV1BUQlKJSm1JLBbpGVInHncmyiOkoK8rHBtw6ILCMEwDOiGLklp\nSIqUkyRGCkG/IXhgsUXghhTK4it3xkxSTakl7cBjqdmgH9XF8lK/YpIbPndLc//iiA+cb/DqfsVv\n3KgI7TFCCM50bOLS4dkdw/vPTClMil+B57iME9ie+fz+XcmPPVCxPZa8vO8yzjf4yMU+o3SRVw5L\nWn7OB853+fpWzrl2zkFs89Ruk0FQIjFYGHxHszN1kcLQcCvmhcVJatMNKtZaGQLYnHiMsrfu5iNX\nEboVs9wiKex6ea8qlLRISkVgSxILitOu3nckeVW9KSEP6sS7f3F9i596/NJfxOPyNv4c8J0GvlPb\n46bljND9djKUEIL3nFtld3qGQt1iZ/Qaw7V34Doj8iKhqhRa1k6JWZkwy47pBEt0w6V6hF9MCd0W\nZZVRlBlPbcb8ziubpKWm4Tr84EWPD168wHqr4tnNF3l5fw9HegzCBd53roclDfdGA3w5o+FlLPgl\nvlPy3NZtbp+4XFto0wsFW+MT5sUyAFnV4+5ojyRP6QeKpUjQDhQNT3HnRONISTMKePd6l8VGyB/f\nO+SlfYkvhwjpsns4xrMyemFFy9d0fZhmY9KiIHIEg8hmXhSME4VjGYqqYq0F9y/4NN0RT22O8Cyb\nQoc8u5NgNPiOoulrFiOJa2dIoRhGDud7DZSOeWFvzsHcxZM+S5WFUjEr7Yp2ALZlyMqMYSiY5jOq\nSrEUWpShi4VikgleP7F5qegROYqlVsn5Xs5iVND0Shw3R0rFVUcSK5tZZjHKXGa5zTS3uH0SYhnD\nu86GGJPjyYSTMmW9I5gXDoKQWycGR1i4bskwKAgrQaHqjj1wBNbpXl8YAUITWhA6ioZj0Q9KMgWb\nkwnzwqUXhiw0fPpRg83JCUqPMdgcxt+bEvx9Vei/+Orz/O1/cYPIrQtV1w/47z/1XramCmMcXtob\n8bmbu+hKcGUo+cH7bBYbm8SzMf/jS/f46++u8B3NMLK5NGjw+VtfwZEVCw1YbEbsTxO2ZxXzwiMv\nbP7NR9/Bc7sJo6Qmw03Sghf2ZkATSxiEfGNXbtBGUGpBVtUSOUuemhoJ6i8ag23ZND2fq4ttxlnB\nC/sZtgw432tRYbExSpinBduzBNBE3+SM6mpBrjWrzYyFRsFqq6IXlAihvpFs51i1j76k9mhPT3fC\nAoElLaSwOZ7DNK0Zpw0/Yhg1mSjJKC64O0pIS0gKUMYl1xaRLbk0cAk8hdAx0yylVCWebdELQx5Y\nHlJV8NphymuHM+Z5LSc52+vTCSMeWl3g2lLEfwMstQJGBy6zrGRrtI/RPg8uX+JHzJivb26TFgXH\nCay3B3zk/AFtP8cSmrwS2Bh8Z8DGtEk/GLM1SlnurLA7nSFFwmeey/i33rlEobfZHs+50A9533rO\nztThxn6LtqcRwqAxdALFOLWpjMS1KrSBoqr106FTu18VleT6/lvvywWGhaiOoN2PXYQErQ0JENk1\ny75ybELHoahK0tLQtOquPv8OXf1/8cTT/Ng7zxF5b2fV/2WhFQzqhLv0BM9+84S7hWbAxeGDvLq7\nwzQ/YHt6lrXWOnvqNkWVYNsunhVidME4OcCzI1rBgPSNEb7KkZbF1+5u8ulnDykqGISCc/0e7znT\n4ky74tmdks+/rugFivv6gsV2gDYeW5MR41TTC1ZY9g+xmHPveIMkb/HQ4gDXHbA13kVKxYPLNdfg\npX2Pp7cbZIVF6FZc7ku6gc1RMiN0Cq4OJEtNw+XhmN3JmJf2Tmg4gkEYcW+cMcktAruFxqEXhUzz\nkrvjA1xhWGpJhCgoqpLQhbQwCCwGUchaO+KP7tV7/bYPJ8khw7AiVzZpZZGqiHHuMx3HdEKHC30f\n1y7ZPjmh4ZREHYHrRIySCiEiUhVxnArujhOWopJZPmOS1az7YavCFoqsFBgMlrRRlSCrXKZlxAt7\nbdqBxmVG6CUsNopT5VNJxytZahZkSjLPLWa5SyfqUZSCV4587pwYej6stQrW2gZj5iS5YXtuczDx\n2Jx4DMJaPjeMcgpdETka1xKnbqgGSwhsarfVhqcp9ZSmZ7OeNwicFrYbsDlK2E8EVhVSMWUQvJWn\n5l8svq8K/c/8ziYbM59cSf6VS33++ocu8PrBBp4dc/NoB6FmfOpKSS+gjlj0bA4mGcdxwblebR6z\n1myy1uvy5EbMwSwiNz4fvXSe33xxn6d3Ukot8aXkH/2Vx/n8zR1O4op5XnLrcMS8LL8xylNGICqD\nkQaj6teEqMfplZYU1Z+M/LQBicNKp80DwzbXD6ccxoqe73KxH2DbJUezOZM0Y3+ekleCUtWSkJVm\nzlJU0o4UTbfClhrPrsfvGoPWfyJvKyvwHBulIc00pRaoyqEf1T7vG2NFXNbM/ov9Jkb4HKZwYz9m\ne1yRKo+GZ2h6FUuhouNXDCILS2akpWFvqkgrQalDHllZYNhscZJqvvT6iOO4Nq7phg0u9Jtc6Ls8\nturSD2NOZvcAuLp0jtxoQmeP/XnGCztwZ7TDD13oonWTrckhR3ObSh/xvrMZooIKgapgY2xhZIPH\n19vM8yOOYou74xkfuBDwR3cEN498/vFXX+Q/fd86L5mcUbzD2bbFob2AsSyaVkyqase7UgumuY1n\nG0KnYlbYHCc2w6hkpZkBhjvjkGnx1sW2H5Y4luE4ccgriYBTu1tBUanaVKkyBI7ELyWZ0hRK4dkW\nefXWD+9eXPLq9gGPXlj9l31c3safE5a0afp9Jukh0/SYbrT4bdcIIXhsfYG7h/dRqJfYn9xiuf0u\nQq9Nko8oihiMwXMiqqpglOyx0DhLv7FSs/BVyo0dxWevbyGFRlcBF/sB7znb5GJ/yJMb9/jsy3Mm\nqcMgbLPeNiizz/ak5Di1WW7arLcXqLA4mt3GlTnnez6FztmZHCJEh0cWK2xRW+Be3x+T5IpUSdpB\nk27U47m9OcexJHQrrgws7hs2mecJtw4PWIwUl/ohR/M5HU/T9DzAZdgK6YQ+T28kHMQhK60+Sely\nbzLGIsWzcoQsGTY0F/vw8v4WhorAdhklxalpmcH1S4aOphdKRkmCCG0Wm00MLZ7enNXOlpZmqW1T\nVSmLTUEvMISeZG+q8ZyAV48t7hwrXMumHyqWmwpbFlQmp+ND1y/RWJRVVZPljM3+tOReYlNWHXxH\nMwhzFhs5w6ig4ylCp5Y9N11BVR1xlBnmuWSl4aC1z9Y0hEmFb6e0gvpwAJAryUni8PJhA+8kYLWV\ns9TI6QUFkVvhWqdZI5qaCKUNjlUROrDaismrLfZmIaOsQZoLdmcFSvvMG9+bA//3VaH/0NljkHMe\nXPRohgdsH7+IBRzOChq2ptGA0HXxHI9+2OTr96bcGrkcxxEHicPf/5F3c3m4xN/67PNsT3Iansff\n+MAV/uEXX+DlI4VnWQwC+PkffZgvv36PSZKye3TC3bkCcUpUE7VwXIpTs5pvzhk39aQhV/WuV1MH\nyLQCyUcv9VlqOjy3vU9WKlZaNusdH20Em+OUcTwhcuc8tpzRj0o6foVr1Vp6iUGeTgiEqG+SeWEw\nxkKI+i1BXeRPYkleGXJt4VoBw1aTW0e1KU1a+jT8Fhd7XU4Kl1sHMRvjEZFTsNLMGDYFllCnBdCp\nzSRK2Jy6HMSGvPLohS0+fvkMqrL44q0xz+9OMUaz1FA8tmqx0JxwpjNnsekhgO1x9Y3DUSsY8viZ\nKTe8ECFsbp8olJ7xpdsT3rFo89jakC++PuNC95CyLIhcEFpyGEvuTiJe2s/4iYc2WYxC7o0cbBnz\n3JbigZWrXD8YY4sx/8czt/ipRxbZn8VszTzW2ld4eHmD1w9zbFHSdCt2Z/WoxLOr04ALC882BE7d\nPeXK4pXDt97NO1LTC0qUFhyfRtAawJZQaUNlwDGGUlbYlYXnWiRKkyloWnVy4HeS2v3N33yG3/8b\ny1jyzV3a3sZ3H4HbJC1nZOWcrIzeNOGuG3rcv3I/L2zcY5adsHG8z5WFdbaKGcoojDFoXSClRV6m\nzPIT2uGQTrDEk3dv8Ds3DjmMBd1Q8OBSgw9cWGepqfjqvW2+eHNEYBd0ewtcGK6Qqy2SMsYSFWvt\nM6y2G8yLOS/sWKy1OyyFU0ozIy40nWDAlYUG4HD76C4ADfuY3IOFRsClvs9RknIQ55SVS9cJeXBl\nFYXm87fusj32OduGuKjIVcZKy2BLhe9qllsut44mWCJnveWz0pJsT3NGqYMxFqPMoxNYfOJKlzuj\nY8ZJQtMTCJGhdEU/hLySaOMQ2g7jpCYan2nBaltzGO9iyPEsQeCG7EwkCFht13v5aZqy0JBM0xij\ncnqBoNQuqWrw/K4hr1Iatke/kdP2a8l1J6hwZMY0K+k3JK7rnFqRS44Sl4O5h2trul7JekdxuQ9K\nlxQ6x5GKSwMB2OQq5SRx2J7YHGcOG1MHz64P/f2wYLlVR8rGhcXezOP2SchCVLDeSVlqZPSDurhr\nU5ucWdLDtw2GEksULDZibDnixf0AbSKEqH+bvhf4vir0F/opw2YdOpJXFoIGLx8oTpKIeRlwdWGd\nljvgh6+e5cO/+Ltsz/1vuNMVf/8nuLl/xH/y619jmmec7dr8e48t8r8++TWO0oSFCDqexX/98Yf4\n4u099icpN4/HHCcKKcypH734xoK80G8U9NOibgRSGDzb4NuaKKw5BC3P4hNXl6m04at3TpjlgsWG\nxeVeiW9vYzFldTnHkRXWN8nwjK4nAUobqkpQGVO7bwlIlUK9p4rsAAAgAElEQVSbmtmOsXFsC0tY\nbE4NSSkolc1apwfC5fdeTTmMmyjt8861AYNQsD874SS5R89LWVo1hLYBIUiUpFAOQjcoy4hxIrhx\nWDDNBBUWn7iywkMrTfYmR1zf28IRCY+vKfqRoOW7dAKX1XaDbtgiUx6IJgudHue69Qh8nicYnfCO\nxSG+E6H0PTbHx0DOvZHkysKQH3vHiFmuMKYiVwIhXKDLSzsej63POEpK9qYLPLTssT1JeX4v4MnN\nMf/Oo00+99qcJM94fvc6Dy8PuL01ZC/e5T3rPZJCkxTHHMYWcWkRuRWONMwLm3Fms9QoWG0WCGF4\nfRQQf4fUuYVGgRCwP/vTEbSFruV22kBlgWUgKQragU9il/Wuvjp1yyveuqv/w80xB/OM5dabe6+/\nje8+3rDHPZpv1Ql3p1Gx34qHV/rc2rufpPg6x7ObzDsfpBMuMU73KKsUS1r4tocWFZP0AN+JeG4n\n57MvH1Hpgk7ocl+/y+PnWpzthnzp9T2e3tojVxZnew0uDyzGucUkiQjtmMVIEfkJ+3OXu6MZw8ih\n4d3HvLyJJY7p+TYLLcjKMa8eWtw5qt9nomof+jNdQcObMx+nNBxNGIW8/1ybyJN85c4J1/cLIqfN\nXmKzO53hCIteBIPQcK4RMElnVNWc9fbpXj4f0/Rqg52DBPqhw2NrQyaZ5Olti9AeMs1hnM6I3Lze\nUbt11r0mPiUpuzS9gL1Zwb1xjkCz1LSQIiN0NO3Ap+F5bM8rHNHmME45nqc4lqHlGSK3JFM5kook\nl8TK4vi4TcMR9EPDsKGo1AzLlni2JnRzzGlTNiss0tImVxbGeBjR59axYZyPcaTFICzph+DbJZat\n8Fo5g0iSnh4SDucOo8xlbxrhOZpBVNIPFJGbA4JpJrm+H7E377IUzVhppgwbimZQc6gyJSkU2FaJ\nMIpBpHjfmZxL/YTnd5v0gu+N3Pb7qtA/fuHDbEw8ZoXHPDf86vN3SUtFJ/T40aurLLVCfvQdq1z6\nu78KUrPQ0ESW5it/8+N85dYL/OIf3KDSJfcNAv7qQ2v8wpdfZmNcE6/6gcPPfPwBvnR7n81xwks7\nY0oDxghSZZGfdupvjOQtAb5Tm9R0fM03k3ONAVVJ2mHApx5YJM6POZju8s7ljIWoHg/VAgyNMZrK\n1L7slYY0gwIQOJSVwbI0EknoWFgWTNKKSjtoI9DGJnQ8DmKL/Vl900gZ8QNLqzy5k/HifkJg2Tyw\nLHhoucAStxkncyxZy+tKbeHIkNfHkknmonRIL2ri2h7bo5iDOMG3Kx5cNHzscoQxW2wcTRinGb0A\nQNIJAlyryXp3mQdXVjH4THOF55YsNW16gUSIOmimG+QcxxItlri2VJCpAIFDUhTcGzn0glfp+Rnd\nyCbNNUkB98YuSbXEJ69NKauCe2OPuIiJvJSFxjKZsrCtmM/fTPjYlQVu7t8kV5rfeKXkkw8u8vTG\nFk9vz/jIpTZ/fG/O9lTg2SWhXZGWFtPcpulVhE5J2yvISosbh28dMNNwFQ23Ii4s5t9yGDDUaohv\nOAaqCmNJClURug6Fqgk4kVs/WOo73Ov/+Esv87M/+gN/xifkbfxFwrZcGl6XWXbCLDuhHQy/7Zqm\n7/Dg+n187fYd5vkRd47u8cjaGebFiFJllKoe7bp2iNIFT969wT/6yjHT3OaBBcWVbshjZ5ZYabl8\n/ubrfO5mgmdLLg1d1tpd4jJhHB8yygKGgyUi74RJesDOJKMTDBlEhpN0RlEscLFvaDgp0+yY7UnM\n0cxmc1rfo7uzJpcGIaEbcG80Is4reqHg2qLLWidja3TC3eNj+oGk7QXcOomZ5dBwwtp3vdNkL9bc\nOCjABFzoeZRVRaZiWh6kUrMkJcsth4u9mC/fPmGpIfDskHujgklqc5A4OEKw3LKJi5LKpHQDTS+0\nQMzqQ7EHUjhoLZnmmn7k0fIc4nLO0BfEZUxeZhggLr3aXjZTtTOhrWn7mrZV4coSg4fB4blNmFUt\nIlfTDUoGYe1M51oVg1BhjCK0LUKvSVqm3DnJGaUSWwSkqsFBovFICf2CyNVEdr1GbXkpZzspaWlx\nnDjsz11OUpftiUfk1kW/7ZWstSWlTjlMPOZll9JIznYmGDPDmALbMiQ5KGMTOIrAMZzpZCw1C07m\nGrj/u3+vf9f/wp8B29M+qRbcPRnzu6/uYCi5PAz48MUui82S95+VfOAX/3cWT4mybU/yf/21j/L0\nxiH/01dvEhclF/tN/soj5/l7v/csB3GBJWExcvnpjz7A7792yNdvH/DqRJArl0JJxGmX7tl1lKln\nfXtRz6vTwBqlWGyVnO+k3D80nO0dY8wtIqFYCg2WrF24VAW5gkRBnEsMDlqDFgYbjW0JpIDAEQhh\n12QgIdgZVySVR5xLND59P+TlI8PBXDJJPS4N+1wbuvzxzh2aXsInL5csNG1C1yYpFJszxUlscRg3\nqaoGWoZsTBSOgIWGzbm+h82cSbrNSrPgvn7F+a7PmV6Dw/kJrx+njDLJSdIgsFsMWl0aQYPHz/bo\nBILjZI4xM9qBy2o7xLdtbMtFnHZCHd9lpXOR14/GbIxOsGXFlaHPSdogco7oBwlZqZBAYDscxA43\njloodcIPXkyxZI/nd0sWmiWvHQvS3PDRS32e34mJi4KnNjb44HmLp7d8nt70iItX+PGHz/L05gmv\nHx3w6No6m5MR02wCQKkFcSFZbuasNAuEFNw8DMjUm9/230zAO4jfPENeUx8CtYZCgicEWamIfA/f\nrgu9qipsAd/BP4df/MNX+M8+9hAt/+2s+r9MvJFwl+RTfKeBZ3+79PHqYpvru1eZJ3/IKL7NQXyG\nfrTK/vQOhgoQVEaxMUp44pV7CCwkEcNml4dXJcNI8/lbJ1zfPSGwYak1ZL1riIuYg7nAkRn3L7Ro\nuh32pnOUmXC2LdGyy+5M4sqKK0s9QjtkkrxGWhwSZyGTLMA7DelZbgVcHna5fTxnc2zh221WOy0u\nLw4YxVO+em8EFJzv+hzFYwZhyWKjjsFeaPg0PZsnN8cczByWWy3GhcXxfI4AXKuk0BXLDZsHl3ye\n3t6vI10jl6N4RD9UdHxBUdm4doDvSLZnDq4V0I0a5Npw42CfwKLOn7ArKlPSCWqHymmWUxmHSVm7\ngxoMLQ8aXkValpi8pKwko9QGaRHa4ISSpqM5nk9Z7MBAC+JSMk5tdqYurmVoeYpBVLLaNHRCm6pK\nKKuCxUYdvoMISArFeA4FLlZSS6Kj0z1+6CgaXkVoV0TtivV2RlFJjk6L/nHskRURR3FJP1Sc6cBq\nWyCF5MZhh3kZ0HfH9BtzQkfgCEOlBdNMEFgVgas51/12x9XvBr6vCr02c57f3uPG/gmLDc3FQYOr\nCwHnepJlK+GHfunraCMQwP29iH/y736QL7y2zWeeuU2mKh5cafPJa+v87O88w+asZn4uNkL+9scf\n59NP3+Vzrx2TKYnvaKKg3pF/a1F/Y7eTq4phqFjvpJxt5yw1Ctp+hWPXznKOlFRGkysoKwGVgxEu\naSGYZ4oCg+RUiifAdTT2qa3tG9G6ubIR0kWXHi/sFuTGQlWSwG1hCZ+vbWREjuJMt+RT1wSFeo04\nzznfA9eqWfFJ7vKVHclL+4I4swh8h/eud7gzmiDMlKuDitV2vScsVco4LYlccCybR1ZWgIDfey3h\n1siQZB7NwOLywGeh4fCO5YgrwyapyjlJDJ4dcL7Xod8IwRiULnlmc4dfefZFADrhIoHnsdgQvH4U\nk5cxFwY9LgqXzeNNSqVBKIpKME59ljvnGR5btJy7HMWak7TNBy54bIyOeXG/ydYkw7Y2eM/ZLs9v\nH7DSnPDqgcNa81HWuvcYpzmfeXaHn3y4wUsHDs/ulHziSpev3om5N4GTxKIbKCJH0fIVaWHx6nfY\nzfdOCXgnqfMNlv6b4Y0CLgFdVWgpKaraKjNDk1cQOBK71G/Z1c9LeOrOAR+5uvZnekbexl8shJC0\ngiHH822m6SGDxhriWxLuQtfmnetn+PJrt0nLLTaOX+Xd5x4kdI9JiillVVtm/9rze5RasdrS9BoN\nPnLxHL3GnC/f3uT67pxcWVweNlhsehzNNbmKsYTFcrOH56TcOFCkqsHFrsKRJdN8G89a5v6FIUIU\n3B1JJkmEb81w5ZSGoxjLev3z2IrDYTzlIM7+H/bePMay7L7v+5xz9+Xtr9au6n169o0arhqRImxJ\ntB2Kgi1LshMrQeLIQWxJNvKXDDiIIDjxAtiOjQSIBUEGFTmUY8mybIqSLIrkkBxyhkNOc/aeme6u\n7qquvd56393POfnj1pBSuMiWSHoAzfevBhp4/YB+9/6274JlSda7IY9fXCWv4GPXEq4fu6y32rw+\nypllFS3PIXBgKRKcH2iuH+/gy4JLA5elKOD2LGeUgiRgklusxjb3XF7ime1DTjJJ5HqNZbUucS2w\nbYFlNbnus2LGhZ6gH8Z0vIqndxJmuYfnRBRaktcFK1HjVFnUJY5VIXVOQU3Hh1o7COlytDAkRZPW\nGTmK2Be4QuM6NkoJXj1S1NhIYQgdTdtTtL3mZFYqcWqQ1aHtRxzsJlQmoeVoYlfTCwSIlNAyhK4k\nKSVZJamVZKZtktJCCpfQbQp96Cg6nsJ3FOutgrPtHIPNSeowLUKM6RD7HWZFQVJMweSUdc1rC5sX\njnucaeVc6BW0vRpLamojmBaCrvMnMI/+81u3eH00x8LigdUlzg1iHl7v88Ube/z0U9dxLRDC8KF7\nVvmp9z/Ibzx/k1997g5JKXnnuXV+6KFL/OUPf5b9xMazLR5e8fmZ77+ff/XsVZ65fUjbhzdUs9pA\n9pV4WYtQVlwaLjjXyTjTbswUHEdjAUI2BvBKN25JRrscpYLjrMZoh9CxiHxJUTYSFGkZQtFI8Gxp\nTskZkkRJFqVkXtp4tovntLCMwzO7MxZlSFVL7loKid0ERx7zA1ea6Mal0GV/0RjEjLOIwOmy1Frj\nye2U146nYCpiR/HYGZvVlmJvdoNLvRrXgnbgI5HcmZYczm0WVcC5XpfHz6/x3OGcq7vHVHWNKzWb\nywGrbZ+NbsijZ5ZoeR6zoj6NbbTohxVFfYftUcXOOOE3Xtxmd5o2MkPgx3/5Kv/gg1c4mI8I7JLV\nuIO0OqTZ87T9iryCStlMcsFrI4/bNxQ/9EDBPHP51E0LSyRMM0PPW2OWG1ZbOTtzRX2z5gNXavYT\nh+cOIpJyjz97ZchHXzmhrGd8emvGOzYv88ydBbfGBY9tDJnkUw6EIrArVloFQsC145BSf33yiyM1\ng1MC3vHimzNh33g0tYYckEpjY/Ac+zT9ylBrzR9Gtfsf/vXnePFv/wUc6y1S3n9OuLZP5HVYFFOS\nYkzL/9qEuytLbV7YvZfR/JBZtsPt0QXOdDfYObnGwSzld64doNHUyuXKks93bXicHcR85Nl9juZT\nHCk4tzRgue0wy2YcpQ6hE3KhZ9BacOOkwJIpq/ESuZZUao/IqzgX1ZRKszVOOZon3DgJ2OgELEcz\nWl7GuV6zERIipdILlkNNy/d59/k+jpXyxPURt0YJ/cCh1obduUJpjxobpMPdcZvXj+fcPGk83892\nJYtySssuidswygWR6/DOc0NePdI8syuJ3TbzoiLJU1zbwpEG6dgMfJusbu7jg8hjKYIbJ0d0/Zrl\nsAnROVpoBoGP54QcZwKlFMoUzNMZjp3T8jT9QFObBY5QRI4gry2S0oVagONgSU1SpLzhO/XGgJZV\njXGZEBA4mkt9wTDUjLMTJoVmnjvs4tH1bUIrJ3QLPEfhWYpBqBAIag3zwiavG5JgVlnklcUkh5Os\nScjseop+UBM4ivVWUzdip2Rezrg9sdgZO+wk1imRDyJXMSk8nj9waHklm53ylMsgMOI7s9F7UxX6\n3WmFMQ7vudBjqeXx9s0uH/78K/zujSPcU2LD//L+R/nhd1zh//rcdX7pmQlKhXzogRU+cO8Kf+1X\nPo5nV1weGC73A/7bd53n5z/1NC9PKvLKOiVGSCxZc66b8vBKykanZBhWeL+/qNPc1MtKMq0k07yx\n4B1EbWJXMC0KaqXwbYvIbSxXszKnVjWu3ZgoFLUkqwVJ0bgyLWqbrBBU2mK95WNbAXVt+PLBlI5X\nc7GXsdGR1HpMrQwGSSeIyKqA33695iSNcaTL28/1kLri6p3bKJNzqVvTDeDepZD9xYLdWU2tQciI\nlttid2J47Tglqx1iX/Jf3LtCFFj87utbHCUptoDAt1hvRwwjh7uHEWf7EVk95WRRE9iwFPu4lqSs\nLQ7mBb9y9Q4v7KckhUSZLo9urAIwLyb8nd98ku++0OWxzS53r57l5d2rlPUUrRWuJRGWRbVo8/pJ\nh0E4YnuUM4xXefSMw/Zkl1sTmztTyfff1eLa0QFJCbY1Y3eWs9RaY54HuPaM37tR8YG7l3lx/wZb\nI8nObMQHr7R4bbxgbyF5+MwG2myjVNOJJ4XF9dE3vs2/QcA7/P8R8P4wGJrApNo0cjtfCgoMpWrY\n99J8/Zx6gOuTnP1ZxmbvG3+vt/CdQez3yauURTHFd2Ic6w8aKXm2xWNn1/nda2fJq+vcmbzERv/d\nTPOQT964QVo21rdXlnzetrFENxB85EvP8fR2ScsN+a4NQdvTHCeaedFIhFdayxRqxt70BEXIcmRR\nqITDhc+5zgp9f8KiPGRnUnD9xCWrc4RUvHDQ5dG1ms1OQTtoGtftScUkk7R8hwdWO/RCzdbJHoez\nY853DaHnsT2uCCxwHA/btrh7qcs4VXx5ryAtfC4OY3anmnGW4NgCTEHL1dw1dGm7c149GnGuIxHC\nY2tUMs5sjHGJPcH5vsM4L1Fa0wscBqHPziRlUVVIYxF5LlldsBJZtPwSKMmqhix7MCkZZxaGLiux\nw+4sReuMyK+JbE3LV/SDhvhY65LDhWF26k5q4CsE6eC0PzcG2q5H6LrsTHIWVY1nGTodg2dpkrzk\npLDYnoeAIXI1g7Ci5dX4dk03qJsMEAxpYVOcnnhrLbClQ1I6JKWh5dmstCRtqUmrjFrPWQ5rYhdW\n2xaHC4/bUw9jPDpeTSeoqI1PUjhcHgpcu6Tt+Xwn8KYq9N1A8M7VAb0o4v2XNviv/+/P8fxRRlEH\nVFrw6l9/N2c21/h7/+FLfOrGDkuh5gfuWeGxzYC/9WsfR6MRQnCl3+fH3n43P/XrX2acaM72FI+t\nTbnYL+gGFYGr/kBR11pQ1ILpKelivBBNx4sgcGu6rmCt4xE4FWlZYYzCkhA5Fq6lyaqS2hhKLZim\nTSZyWjWTuzZNoIqgcda7PAhpuQJHzDlczHl4rfGdDxyHw0QzL1wqHXDX8hJf2MmYFc1N+54ll0t9\nm8Nkl3Ga0Q2aSeBMJ2a9G/H0rYRxZlMrh+VWSMdz2BrNScqSbmi4r+3xvktDXj2e8PTtOWWtsSzD\nSuyxHLuNPeMwxrMd0qrEthw2uwNWWm0cy2d7WvEPP/46T7x+TFbbuHaXxy8s87//4NtZ6rp8+L+C\nu5YcjuY5z945YFZIhtE2VT3CkZpCN45xsb3KXSsXmFYT0CeMc8FzBznvPqd4eG3AL1+t2UtqwpND\nHlhrs3Uy4mI/5XBh8+9fEfzwwzFX7+Rszwxf3tvmkdUBv3GtQJYzPrud8Z4Lqzy3m+E5iofWWhzN\nR0jglaOIUn/9yTk6JeC98X/2H4smNud0lV9rKmnwLIlvS7JagwDbNOTLb4Sf+Xdf4Jf+yvu+rhXr\nW/jOQQpJJxgyWpwm3EVfm3B3oR+z0rqb/fEeSXbEJ165zi99aZelUBO5htWWzyPrPQZhxO+9fouj\neYbvxNy3sk7oJUzyhEVZ0PZDlmKXpJhzfWRYjW1i1zDLHaRccFc/ou2vMUlLFuUxlcrIVYfDxCZw\nFP3IxohzrLTHFNUEgEFwyCBwWOt0OdvzOEoUn7uVkOQWq22XSZpii4qNjo1jZay3BW1vxnP7CdJU\nbHQjXFuyPc6otE2VSkptcbEfstzu8ukbO0gsuqFgvEgYhDW9ABAOoeujNNye2ARuwCDyuHmSMckN\noSvoeA5FXeEIC99xqZQgqwxQk9czYlfRDSw6PhwvCiZZTVK47M19bFvQdhXDyBB5JbWq6Hianl83\nAWL6dEWfW9TaQmA403FZigVH8wW50kjZJBMKBPNUUcmGzd8+7eXyWrI3d7kz87CEph82wWWBo4i8\nmtgDEFhYFLUAAe3AZhCFRI7k1qRkkkvyqiCwc9pecyrsBhX3Ly8YZTaHC5fbE5fYcbgwcEDArcmC\n3XHNY9+BpFphjPnOHAm+CYqi4IUXXuDzM0O31eUH79/krr/7r8hVje9oPEvz9N/8PrRR/NMnXubq\n7ggpBH/23k3etrnGX/rwk4xyyCvFD10JeN+9kld2X2cprIi8U4tY2dBmMIK8FqRlY4CwO3PIlItE\nE3mK2D1NpLNMk/RmWYSuizCSTBVIUWPJ5kZeK8EoM8wKi0UhyZVkVggsKbFOU+0MIIyh7RvWWi5t\nX5BV5alhgiCvbAQeRwvBvHJYbUVsdmy2p2Ns0RguXOj7GCG4fpyRVzXGCGLP422bSxwnJS/sT5FC\nE9hwth9QKcPWOKOswRKCR88MWO8FfO7mMSeLAmUMnuWw3IroRyFXhl0uDIbUxkYIh7V2i8vDPpa0\neHF/xD954hU+eX2PoizxbMGjG23+5z91PxeWY7RWVKpgpXOeX/vSb/P5rdu8fJBRKnj8/JTLfQul\nCoSUCKtL4F0i8s9wPHueO5MJT94KUTphuaWw5BnWumf56IsvNxGYFvzpSwXjLOeTWzGjzOVMS/P+\ny+d5aX8Xx54zSSMeWNnk2f1bWKJkGHd4aO0Styd3iOwMzCG7M/iNVwZ8vWW6wHC+l+FIw9Yk+Ka3\n+a8Hi6bgN5HEAmEMvt0k2ymaqb40Xz+n/g0c/9yP0Au/sRXvtxtCCN4Er4E3BSbpIVk5px0MiLyv\nTbi7NUr42EtfoMif5+XDio9e63JlkPP4Bc2DK0PavscTt465eVwRe4rzgy5SLnOUZDjiiLYf0A7a\nTLOco0WB77QJHBttJtTK5VyvjS1zdmYuu9OUwNpFUDLOXLYmHTwn4JEzARd6y+xM95Fmm5/94E/y\nkx/5x3QCh/P9DgrBS/tzjhOF7wScpIa9maE2Lq5tsxJ7XB6GbI3G7E4zQtdiOfI4yWqmebONHGWG\nlhfwgXvO8cnrhxwnOS3PYpIVZEWG7za37uXYpu1LxlmFKwWDOCKrJK8dFxS1oBsGWELh2zWrLYFn\nGfJKUSjNcVoxThtH0aXIpagL8qo4NSADIyTaWGhjk1c2t2cKWxoCu24c7zyFbytcu3mylRG4tgvG\n5fZEkZQN6bkfCAJbkddf5cwY3vBLaXhTjbz69KRbWafSXwgddaqjB89SuFYzyffCAGMsRllBXtYc\nJjU1hlo121/XUqxGJaGnCO1m5R+7FogAY0KevFkyKiVnYpe//92P8sADD+B53753wJtqoj/bc3nn\nBZ93/eNfpH+6zfQk/OZf+z6UUvzc717jy7sphhZ/9Z33cf+axc999N/yZ+7KWI5LlqOmSBel5q4h\nX7mr57XD7kyyO/XZTSWetIhcQ8evGcY1UuZYQmPJZko2NKz5wHaIPYGUFUWtMAgKZePhsJ9abE8M\ni1LgOgLfbua7XtBI6oRoIlJ9R9N2IA58IkcySmuujwXTLGJW2NjCwrVqQhfes2KRqhnjrCJ2DL3A\nYbPrszPNOU6axsC1JZeHXc53A768P+ZgnmNb0PE8NvstXj1IOFxUCGHR9T1+8N5zvHg44yNfPKbQ\nBqUCllotBnGLM50O7zy/RuTZVErR8gQX+gG2NFzd3uJXrr7GM9tH5FXFWiw414348ccu8chGH9Ck\nxQyAtDg1kqgSfvjhi+zOLZ668VlsUbA3q+lFDrEVcXbpLhAX2Rk/jyUKLg/vojIzDuYp1w4lW5Oa\ne5Z3+ZH7l/l31w4ZBBMmeYm0BgjRpheMmRSCX3/xFv/lIzHP7yt25zaww+Nne3zm1oiXDwoKdYfH\nzngczg9wpWSa9+j4LuP8a6lx/bDC/Y8g4H0jvKGWrzRY2iAsgREC1xaktaEyzUNWfZPP+Ndfvs5/\n/+77/pP/7bfwrUfbH1DUKfN8hGd/bcLdRjcEVnhm5yq9MONs12ezv8bD6wLPKvjEjUMOk4LQtbg4\n7OJahsP5iGnucq63TCdIOEknHMwdhpGDZ5ccLQQtJ+TSQFBo2JtWHCcTbo9dXCtgo1PQDkrucWo2\n+ku0PItrRwccJA6eaMZB1/E40w2BmjvjhKouWYkF2lQY3XB9BBaWDFhv22xPp9wclRjjMogC9uY1\nWaUIHIM2BRd6No9tuuxOtxAmY73lMC1hXlQUyqIoHBQOfumyO8/xbIuNjoOFYZzNGASatu+iSUgr\nSexGJKXDflGjTUGpMrSu6YUW/UAwLzNGqSYtbQRgWZK2p2l7GmVKDBl3DTg9iUomucvuXGAJCGzF\nMKhZa4MrNaWac6YNSguEtFgUNvtzQWGaz27Ms1TjZwJgDFJoBE3D7kpN7TbvAqUFSR4wyQQtX3Ku\nCy3fptYlSZFQVjVJqXAdgQdo21Cq5li3l3hkU0nsaK4MLTqWAXKm2RGPbjYhW5bpfEd+12+qQr9u\nz3jf//EkpZIUStLzPD7/P/0wRQY/8WsfR+vbPL654L4VcMxLPH19wQ/c00winpTkCkYLyThz2Z56\ndLxltiY1yqT0g4p2UPNIu34jd+i0sIPSkoUCtEXsa3xbsRRZuFaTKFeUglnhkdYOrhVysqibyErL\nsBQbBJqkaghdrm1O3ZCaDtG3HFwnRpiQL9zOuTWD0IG2WzPo5ri2xrclZzo+42yKhSZyJcMgxhjJ\nl3YXlLUBLAahz3dtLpGV8JvXRswKjTYel3ptfNfmE69PKRVYwuXh9SHvPL/Cv3zmNnfmBZWy6fou\nl4cRax2P+1ZanO25aHNEVsJqKyB2HV49POa3XrrDM/nUhx4AACAASURBVDsnzPISg8Vau8WPPnKJ\n77k0pDIwzRVlrVkUFTdHc26OmvXhShSw0l0jLa7y6HozkQhLcJQo9gj47ruvMFrsEdgpuRhg2xH3\nLmfE7oDdmU9RLziYj3nilscP3jfgYHbMztTikzcc3n3ekJQBX9qtOdfN+ezWIW87ey/H2QmVKnh2\nb8RjZ5b59ZfmHM5OeM21ONOuOEhs4mCde4YFLx7NmBVfNbKxfx8B7w0HvD8qBM2K3iiDpMZ1bIq6\nRnE6NXyTqf5v//ur/Pjb78azvzMuWW/hG0NKi7Y/YJIeMsuO6Mfrf+Dvrx/N+WefeZWkCPlzV1Ie\nP1fwwOYFhq2S33rpGSZZjms7XBrG1NphkiYIMi7213Ftn93ZDGUKVmILhMcsrxlGivX2CvPiiKPF\nMTdPACo8q2Q/8YicNleWMlYiw7iY8vyuhzIVVZ2ykzSFoh/dg+cKbh4fsjXO8G0XW0NeFjiWIbIU\nlmUYhgWVOsSTJfcvCwLHJ69zyrohEh7MwZY+V5a6THPBzZMFHc8gUORVydmOOd2MOXQDh5Msp6gl\nkReghc8TWxMsGbAUSpwaME3D4VoFaZkgXMNJqhmnCqVDVmOP3XlJURV4liYMAdH4xyttszuTjHOF\na0sCuwmU8WxN12+a9lo34V2OE7M7txilOZZw6Pk1/Uhjo4i9nNhr5LaVkiSlxSS3Uacmaf7p51qn\nz6kUBs9SWEIQOlAog2+7DCKHfhhxZ1pzlBqKygYMgdPEYAuaoDPPbvxZAkexKi06YYjveHzm5oJc\neQwCSS+oWe8Ilvw/gaz7H/nVW9yahTiU/KWHBP/NOz0+evXn2Zmc8D3nK6QQBI5ECsnxoiIpJePM\nAeXz2R2Xw0VAJ6hZCUv+1AWP7eSIe5crlG58iC1pKFVjXVvVkrI2dH1N6Cna0iCEQmlB6NhAwP5c\ncrCwWRTQCQQtV5MUCySKwAbfbtLsDArfbbq/N1ibSWUR2yF+y8eSgr3ZiMgreWjVnHaOzcTvSRtp\n29welySlg+d4bLYCro8LjhY1de3iWDZvP7vE3ctdnt8b86U7YzCG2HO4d6XLrVHK8wcztJHErsOf\nf/gCt0ZTfvGpl6mUohdI1lsBq52A9bbH3UttQs9FG0E/iumHIdvjBb/8xW2e3NpnvKgwAgZRxPsv\nD3nXuQGgeWH/CICsKjmYp+zPMkpVY4nmCn1zLDHmZfLyCCEUSy2HeaG4eeJxdd/i2Z1P89+9w6Ht\nR5yPL/La4TbKVNy1fAnX8Wl5r/HKYcHxYsHO5IDNXofnj1p4bsHWeEbH7/LYmZxKzTlYuPzC53f4\nyw+G3E5sDpOKT95M+MDdPZ7fW1BUE+YZzIs1aiT3rvfIasVrJwmLqum4V6KGgHeUuF9Z3f1xoAFX\nNFO+xmCdaunL01XkN/LKG+WKV+8c8uC5tT/2d3gLf3wEbou8WpBXC9JyTug27mWvH874sV96gq1R\ngiBmUdVcWa4oim3+6Wc0FharscVGt4XWhqxMSCqbM20PSyS8Pq7BBKy3FNoo5kXFSsunGzgcJhO2\nxoayytBKcbCwGEaKjY5hubPOcmvBLD8iLfeo6h47icPAh4sDxaeAM/1Ntscpv/3agqSwOdeRTPMZ\nlsgZRoaWq1jrOihjmGYLhKgYhhaulTEvFRf6grIWbNaSlh+xErl8fjsFI8lqn/1ZRlFbuLah6wuW\nW5Jap/R8xdmOTd83vHQ0oeVZWMIB47Iz0yxFMaNcUtUZSmtqVaJ04z63FBrm5ZzZoiYtLbS2AEnk\nGzq+QlIQuBC4zR19cZpAqQ0Ep1N5x9WsdkDplEQolmKolUVtAl491BRAaCvaXk3Lb0h5y1HJclRS\n6SaZdF7aTHOLWksknDYSGscWpBUMIouV2KHtS0b5hFleM15U5FpQ1y4IcC1Nx2uakNCpcaRhJbLo\nhw5lVXB7MqPlS9xaUmuXvB7w7J2MgS/4vtVv/2/6TVXo/8ojO3jOnGEoCF2LrRPDoqybF6Zy6Ic9\nBq1lfva3DtlZhESO5qFVTalSLg1KHllP8W3DpWGPVw+njSe8kqAFykCumoSltlPjBI38TRvQptkg\njFMHiIjcNpM8RYoa16ppt5tIwrwqCWxFxzO4tqRShkI10rl5KShqi1o3pLuLPYt+qDAm4TjNsawK\nh0bKV9ZNdOLAb3G7UE3spZFsdCNsKXjqzpxaaSwpuLIU8thGF0sIPn1ji71ZgWfBUhwyiEO+uD1h\nUdVYUnLvUovvPT/k117aYXdaUGlB7EVsdtssxwGXltqstTwKbSgzje8IXtod8cWda1w7mjDPS8Cw\n3rW5f6XFYxt9Yt9G6QQpIK8qxmnGNK/QRrEcS9ZaHiutNn8XqHXCrZMtXCtHUmFJwUZnjZXOeZ7e\nO6TtH/I7ryoeWf8uBnHKUmRY1Gsgh1wYLIi9NXxbsCi2Keqaz25pLg036Ae7fHlXcX2U8I6NGt9u\ncXUf2kHNE7dGvPd8h7Sy2Z0bXj484KHVgKyesD13GOcBd694HC9KvuvckErDjVGCFCWx1xDwZsUf\n/zF4oy+vDaDAQuHZkrrSjckOXyXvfT38xK9+gSf/1gffIuW9SdAKBqcJdyd4dsCdccqP/dKnePVo\nhmNZvPtsn7XuJpV6nlcPX+XG0ZCNbsRm10WbinEuEAg2Oy7KGA6ThMDSuE6HeSnwrQlnux62HbI7\nnXO4SNmdWhSVTcsvaHkWjhXz0KqDtDxeOSyJHYkma1ji/oD1zpB7li1+EZinx3z2xiHzvGK1HXJz\nnDHNPUI34jAVnO8H9GKXa0fHTFJBP6xZiSXjLEOIJhfCtWp6gcVGp2J7ssPlvkEIm6MEQhuS0iKr\nfVzHZ5pbTLKa2HMIbIft6YLQUfQCTWgr0jplzW3yMOaZYZrDOLOZ5RrXdrnQ97k9SSiUxpGGXtBM\n6I4tkViczA2ptpEYbKvRyfuhZkDDqk8rC3Wajvf6iaamxJGCrq/o+pqiznA7X5XezSuHo9Q9Xd1r\nOn7DofAsw2pcsBJDdbpJLiqLRemSFJJh6BF7PpZsJI5JVpJUFYFriDAoGiJ3qSSj3AYDke1wfuDh\n24LrJxNqo3CloetWdDyPXClmxQm+LbHln0AL3LV2iedIIjdEG48v7yheP4kxcshffcclAqb8r5+6\nyma/4r61E1quRp0SIJSGyHUYhG2+uD2j0I1DXcvVdMKqSVSyTGNfawSVsjhOLY5Tl9GiYWzeveSw\nFClmxTE9X+PZNYEDSisqZfCsNzLMLA4TWFQ2dS2w7Saa0PEUEliNXSLPZlpIdqc1u3OHpAyQQhDZ\nmtAVDCOXRb7AleA6Nhd6DjuzGZOyInIkXuDwznOrDKKIw3nBEzdPWBQ1CI8rwy6TvOZ3rs2ptYVn\nufzoI+fYn+X8vU/torTGtSUb3YZRPwwV650ayYS9mcaRoJTixmjGrdGCpCzRBlZbkkvDFo9fWGKj\nG+BYkroumZc1x4uCaaYolKEf+gzjkF4QkFY2+0lTvpbDQ+Z5TqUKbAtcq8V6/y42+/cwDK7yH149\n4ot3Qr68/zq3Zx4fuv8u7u5d4fYkZ29a0A88vvdSzO2R4Nqxz6dvxNy1dIMH19o8uL7BzeObLMqS\n3VmHB/ouh8UMaRk+fnPOg2tnuDyYkVcVk3xCP3D53FHM4WJGN/DpRz5HScljm30qo5A6RRs4SL61\nBJg3inoNoDWOgPyUeW/xjaf6p+9MOV4ULMXfGbnNW/jmsKVD7PeZZce8fnCbH/9/XuKVwxm2FLzv\nwpC/8b77eGlvwidelVzslzy4nnCm+yCz8gjMIa606EUhlaq4MxO0XUnsFYyLEmMczvf7KJ2zNz1m\nZ6qY5RWSnKTyCJyAC33BRrdPWuZMkkOOU49DHXC2U9INai4vSVpBxCBqDHOe27uD0gn3rzrMsxzP\nqhhGNghJyw+5POxy7SjhhQOJJXooQrYmKaXK6Xk1iCb7/ZEzAVuTOXkJgQ3K5PQCRccTICSOzDEi\n5WghWI5cfDtie5ozygSu5SNwSEpFP4DAgbJe4NiNkY4tavqhTcePOE5S9hJDWnrYAlzH0PENHQxK\nlXiewTsVpioDWdVI6YQAWxj6fsMpyqo5cwvS0qJUNnszn7xSOI4mdBTBqY154Gg41d03HvgOx6eF\n35GablARuZqWo+n7GikrHMslcgWVErxwYDhKKuaFhe9IQkfhWAZbNp8fuxUCCB2XXhRTlobPbC2o\njUfgaFoubHYtTvIcm4quZ9jsupyN/wTq6O/rvY0HL7+TL24f8c8+8yyClEtDeNc5RV08y6++vseV\npabzymvBSdp41NsCzrYtAsdjazyi4zcJba6lT4l1AmUa68KThc2ksClraPuatleztFSxFEliNyOt\nK5ZO8+cRNrOCUw1+s5Iq68YQxcgmdc5zDQJDoQQGizPtNkY43J7mJFlOqhS9APpBDQg8KRHSafyc\nhWQYhhgj+fx2zqKy0MbjkfUh77lwBo3g6e0JT7w+RxmH2A24f8nn2d05SVHg2nC+5/LdZ3s8ufU6\n07xgGBli12I5dok9xXJLM4wEtqgRElwh2J0teP14RlKWGDTLLYvNbot3n1vhnuUOUtrMC80oq5lm\nASepplBtur7PRhTS9kNO0pob44ppXhFbTTee5NNGtmg167OJHnCSDnCt2yjmfPCB+8GqeWX/Bi/v\nZ3xh+zb/6ENXWG0pLBFzezTBmF3O9PrMyjM8dOaYWTbh01sp9y5r3n8p4IkteO1EcKmf8+iyx3NH\nOYtK8tvXjvnQgwGTRBA4FbenPqutM4zyKZ/bOuJ9F1fwPZtxXvM95yJeP5ly/UT+kQh4fxgUIBRg\ng/v7pvrT2KRvONX/o088z//2wbd/y7/PW/ijIXTb7IwO+ZmPPs3WqMaWDo+fH/KT77uPUhk+8uwW\nd2YtzrQXXOmX3J6PKJTDZttnGAlmuSEpC3q+BSIkrzO6XsIw3mRWFMzTMQeLilkmKI1GG8FqC9Za\nQy4MDIfzMfsLF2lKMCVFHWGEzbluhudkdIIc65SMd3VX4MsAS2iSMiN2m4HCdwouDTyS4pDjZEFg\nG/pBzKKsOUoMtvQ4nDv4TsS5/hIvHOS8eiQZhi0cK6eoMgJH0fZr2p4kcKDWGRttgWMVVGpO2zPk\nygECJrmk1gESn8O5YZZDXisqpYk9mzNth6RY4NkVG23AQKFstLFQyua1E02pQxzZ3M2D04Idul91\no/AtgWM5jLOaSjcnskFYY0lFVudkp2v+eWlztGie78A5/bzTe/wbN35o7vxJ6VCUkpGQtFzBWsfQ\nDyVFXVHrhMhWyJagFzQbwFlhU6rGpbUhXCs2ug6rLZe96YTSKLph0yRElo2RLi8fNM1DJ/S5EgdM\n0il1OeN9Xxux8C3Hm6rQLw2W+NhLn+SpW3e42KtYiS0uDDq4JuUjzx9R1A4K0XRRTjOh98OK9dhF\ni4KiShiGUOtmHT/LGyvDRSWb+5KlaQeKpbhqkuREQ8hr+zZSwEFiyJWH1iCMxbiArDJ4FgwiGyE0\nQpR4bpM7XGtBrQVaN5anZzohQihGWUGS18yqJgKlqCVK29jSZbcQZJWNES73LsV8aitltCixZcRm\nz+cH71vBdyQ7ozHP3DlmlKYsx5p+5BA6NdvTY1qeYRjCvctttDF85tYtqhoCW7Dc8liJPYaRy7le\nTMu3SMsax5bcmWY8s58wWlQkpca1As722rz/8ibvvbyGFC7Hac3RvGZRavZnJQhB7NssO01k7iRX\n3DiZYlHgy4SDyQ43ij0AKlUhrQoLSSc4w7jc4JXDI3Ynd9johKx1L/Kjj5zw/O6cf/FMyosHhh//\nlx/jp77nLt57eY3J4oistAicTd596SydUPPZm/ClnYx5dsSdmcPbNy5xnOyS1yXbieD+YchHXzWs\ntgu+tJ3xrg1FrS1ePgzJ9Jx3bAx5aueYJ24e8r5Ly7i2QZNwadDhxqgGim/Lb1kDdQ1SaGwJuf7G\nxjlv4B988hX+zg88Sui+qR7LP7E4mC74if/3FaZ5wmpLcra7wk9/7/3Mi5q//3svcrQoWIo6LCpD\nrvbx5Q6Ge4g8ybzYJ69zHBlgCUOmcjqeRzeUTNJjtiaSo0TScUuEhLIIWI4tLg9CpHR4cb/EtUrK\nqiAtXZbikntWAlbiNTreMUpPKKp9bh41XulpJQmCiGsnOVkhGMY2NZq1TkCtYX8+wrMr7oodPCvh\ncFGyHFvMC0HoOty93CZwHD67dYIjArZr2JtZaO0Re4Z+COe6EshwRE0/VMSeolIlvm3ohSW1KlmO\nJI6ck1aScWYxTiUnmSCrPFpBxPa4ZpRCbRxCxxC6TVCNJTRJVXDBb96reS3JK0lSWBwvXBrSWzNt\nByGUdY3GnMpaG/Lzom5ogqFT49uaARX6dM2/KC3mhc1JKoFTgx37q83ESqQwGCJX0AtcWn7I3qxi\ntDAcp5qWLwidZjPQ9nKUaYp4Xkvq2qETdViUko+9nGA5PqGj6fpNPkBRF1hyzrBl0fd8lLB5bq8A\nPDZb3xlZ7ZvqjfLF7Re5uj9CGcmw1eXK0pBQ1PzCs9cJHU3sGxxL48saR0LHB9e2yOuCrDBUprmX\nZJVFVsvT6NdGpmEH6ivUZ2OaKT+rLTpuyATBJCuQAmwBnjQUumQ51HgO+I7NolTMM02uBcY0a6RK\nNYE3rnQZxl2OUjhINLvTmoPEsKgkaWnhC0EcSmzR5M+vtj1cqXnl6ICup1iL4e6lmDNdidYHjJOK\n109mSBTDULDaijhcFNweV0gp6Ps+77045OntE44XTaHqhg6bnZB24LPWjliOA9KqCXo4Sgq+sDPl\nYF4xziws6XOh3+FPXznLX3jwLFoIdmcZk6wkrzQnaY0x4FkSpXOKakqSpZRqQWgvKIsRJ4s5WVVg\naMgnAFm5wHdtatnFMutcWVlh++Q50iLn1dEqUh7S8hc8fOYi//zsJv/jv/0ch/Mx//CTr/DS3k2+\n95LkXH8VzT2Msz2uLLUJ3GVa3stMsxnXjm2Kasqff7DPkzf3mOWGZxPFe85F3JnnhHZNVmckRYxl\nDVB1yZcPxjy2PuALuyd86vohH7ovbO6GZciDazZlPeJg8a0v9prm4VICXCmxtEbxzdf3AJ94ZZc/\n99DZb/n3eQv/aTiapvzFD3+aq3dmDCKP914I+NG3bXK0KPjHn3qJk6Tk3CDiodUOz92xCO0TVtsF\n02LG7WmMJz0GoUEbTalhObSwnYjRYsIombI39UjKht/TDwzLscW57oBZWTJdjJgWDmlus9opaPkW\nV4brdIOSTmBjWCXJUm6PRhzOmwCn+5dS9uYVLccwDD20VizFMbHb4bn9GdePffqhT+g6jNI5ljBE\nTo0jNcttwf3LJU/eusnAByNctsYleWWQ0qI0FlIGvHqiSQuHXmgxLGCcpXS8kqWWjS1yWn5N19dU\nqsYSBYPQ0PIMm11B4DhM8ykHrqDnvzGAOeS1Q1oJpkVzq/dtje9oYlcRu199UkolEKbxn98aG5LC\nJnQakqFjaRxHIYU4lcs163ho7HA9SxG7EmiK8+K08E8Lm2nuEDkWE9uw0pKseg6xbzhezJjnGbmG\nyIO0spmVjU+GJTSxp/Btw0os6PqaUXbErIC1rmRWOAhtMc1DticlkRuy1ra40LM5ThNssaAXGvpB\nwOVh7zvye35TFfqnbtfkyubuJZeNrqGo7/DkzgnnuwrHamQP4jQzvuV71EYzLSApYHHa/VXa+opZ\njQS0MVRaoI0BI9ECnFMDnbMdiRQFk6Km5TYsem0MWdWEXRjhUNQ2h4nFcdoQt3IlySuLrLJIa4uV\nMOTupQ7P3ClZVCWHkwVaNvKMtl/RcQVSNlOdYwn6ocUsS6hRrLUEHd/lobUhQkpsabE/y3jleIGq\nJZ7tcr4X8NL+jMpoPMviwTN9Qtfl37x4SFEbHOmw0Q1ZbrXohyHr3TZCND/i40XF52+N2Z/njFOQ\n0uPu5RbvOrfEhx7YxHdsXj1JyKuSrFqwKBYUVUqtM/I6wxIl2uS4sqasc0aLhJt5TaUUxjRyFVu6\nDMLToBgBBzPYnodcWQvIq5doOSWrnQvsJhYvH9yhHzo8fukeelGbf/4jj/J/fvoan7p+i5PFIb/x\nYsDffO/30o4k1w4tZkXIuW5NeDni2Z0hL+4bPHvOU9tz3rE54PM7c27uWYTOjLuXIrSaU2nBZ7d9\n1iKLsBWwN8944WjC21YHvHJ8yAv7h9y/OiSvXVxbcu9Kh2pvzCj7Zkr3PxoUzXZJGo1nQaq+eZEH\n+Ov/5vP8mQc2kfItUt5/LoyTgh/58BNcvTMGKbh3eY2/+MiQg9kxP//UmOOF4q6lmAfX+hwmBUoI\nTrJVluI72OIOx8lFNjodNBWYmmHUR5Aznp9wawLa1MRuyTSPMKbFWrtiGPnszhakVc041WhT49gh\nK1HERhf6UYwUFUpNuTlx2To2tLzyK2qRtMzBKHqhhStLeqHLxYFmd7pDVWecaUt6QchxWjLOHAQ+\ni0qx1pbcuzzk6t4RgophJBnlU9ZbijoUKOHQckMqrZgXJZHj4Fkur5ykSO1T0WFcGCypWI4FtycF\n2mQIClxZ0A00K7FA6RrPqtnsGDY7spE1I0mr5v06LWySsrELH2c2pZKNXO3U3rbjKALPkFcpqy0Q\ncbO1nZeSSWmRp00pe4ONHziNpwqAJTS+o5AIhGOI3ZrSlwgjMMKmqB18O2AQtsiV5Klbc/anAstp\n7uuBrfDtPxiAVtcWoR9iFFzdTfFciW8bOr5mo12yKA2FyhmEFqETM84Fn7mlG9VFp82DQ4ft2ZTx\n4pjvX9r8tv+m31SF/mx3RsvXtANBVS946WBO04g1sjWEILAEju1zksKscJkWUNYaIRsHOt9S2LbB\nFo30TYjmz7bUGMSpBE7QCx0wglGqqLREG8ne3GacSyplsRzHFMpib1pxlCqUEWgjMEYjT7Pq710S\nrLRqZvkubVcjdUE0BEucWt5aDTNfCIvAaUgXtycVWktc2+P9l5Y504sbhj2aL94ZceskoTKStVZI\nqRRPbc8RQhC7AT9w93k+fv2ArdGEUrm03ICLq336UcggDhhEfmPDm+U8u3PC/ixjkpcY4MpShwfW\nIh6/0GUYwa2T19A6p9YFZZWzqBRZWVKpEscqsS0NSnM0LxmlOaWqUKeicEu6DCOHC72A2Lcp62aq\nsKTHqycRLxxaHM5f4t3nNSroUouYs+0ZxwvFYdrnN1+e8vazKastj7/x3nu5f2XEZ28mPL3j84Ff\n+DQ/+/0bvP/KWZIy4sW9F+n4Nu8+fzdGzHl+7wZ5VfDUzozLS+vUekxWZhzNJ9y7DK8ceZykLifp\njLv6EatxwP4845XjMe/caLgTv3VtweMXO1RK0Q9drizFvHTwBzX23woYThPuBNhSYGtDbb75VL89\nKzhMclbbXxuX+ha+/ZimJT/8Lz7JMzsjEPCezSE//b77uX5ywsdeuo0tNPcur3JlqcfuNGVe1jy0\n2mdrbLM/P2IlzhgGIxblGq7V4Wx3QVkv2J2ZxlXuNPDIswXne7DW7eLZmoPkmKzSHCQukWNouxYX\n+x2W4jaxN8UwJy1jXj7Y53hRcWfqsNZqcWmQAtDyZsSuRAoLy3ZZb3sk+ZSjJMWzDV3fI69TAqei\n5QrSWrFuOzy6tsz144SdqcEWIbt5I1t2bWj7huUQQqdgmi+4eyjxLM3OLCWyJZ7lIwWUNfTdgKOF\nYpIa0kpS1i4tv8X5nsONUYnSCxyrpudXdH1F5ClcqbA8RdsFc/qOL+vmBj7JbeaFzby0qSqbceFx\nZwZG/H/svXmwZddV5vnbe5/5zveNmS/fy1FSKjVYkiXbsmzZxjZFU7ioLjMZbLopqttQQFFQTUMN\nf9ARVJebgo4AmgKiqcIFBS4jNyBjYxs8yEbGki1ZY6aUqZynN9/5nnnv3X+cmynZVWCiWzgcqFZE\nxnuR97xzz7tvn7P2+ta3vk9Rcw01zyCloREYrvHWX+o+uj110VbgCEPoVsnfU5UPiacsLU/jOQJX\nGXxX0PJL4qLPxX5OPzWUQpHkDpOZfrWgEj+LHM1CTXBwzqGfpOwkBseBrFRIDcK69OOc0LW0PKgF\nkrTsMxdJuoFivlZna6L5xJmUpFDsqb8CoftuGBAFhqwwHN+coKRASktpoCglNcclKUOuTix5KZjm\nBm1LQreqoGuexpEgqEbTrK3MYaDyJS/tzJwmCrk6UlwaGDIj8YSkn0rSsqq6D7Qj+klJnE9BapYa\ns36+uIYqwHLDx1WWUZZiDPTigtyoSmCnFDhCIaTAV4o9DZf1cUppDK5yuHG+zltvWCbW9rrc45+9\nsM3OtKQwHofbNZ7aTOklGkSNu1YWWGmE/PIX1kmyEs8JuXGhycG5OqHrsNAI6IQe/WnGia0BW6MJ\nhY7xVME9q4p9TZcDnQmL9Qxd9tgcVolnmheM0pxxlmJsiSdLPE+xM0qZpAlpWYLV5AYQkrm6w0rL\noxN5eMKQ6TGTNEebqhqueSu89cgReukFFhs9zvfg0rjJ227YJM0T5mvLzNX3cr7f5+GzGYfn2+yp\nj9jTULz7zjt5cjMmdLb5xc8+xxcuGH76rWss1w27SYdGOM+bDmvmo0UevbDNpWHJZ17oc/9BxTAL\nccWIUWbZmnSJZtXzC70pq82APY2IuBiwOS2YDzuc6RV8+oVN3nRokUxbluoRaSF4YWdwfcb+5QoD\n5AaUtdfZ+F9rO/E/f+CzfPi93/KyXsd/i68dkzTnO9//Gb54aRewvH51np94yzGe3+zzH754Hk9K\nXrXHpxO5XBrGJFpzdKFJWhrSQnOmN083uko36tFLl9jTWmSUXqQfj7k6rKpegabQIXsagn1tl61J\nwYVRgbUCJUq6oaQRtDjQrTEfgecAYo4LvXOc291hY+RQ8zPWuj7z0QqTfKO6eFHB5L4q6EYCRw6Y\npDmLUSXehM0ZpYbQqRw0PUeyv1PHigk70wlLCReupAAAIABJREFUkaC0FePddUBbhRIBwnpcHWUo\noeiEimmR0g4sgSsJHM20gMjxmRSaODZM8pK0sLiOQ9MPuDQwbI5LClsnmsHyDbeCvqUsqXsl7aCg\nEVRSsb6jWfZK9jQq0RlrK1e6QeowzBymmSLRLjuxQ1o6GCNwVZWEA+clDPtZaFup6Y0zh0JXQjx1\nx1IE0JKCmq+YjxRxMZ1J+8LBjrre2r3W349nLnYtL8AYxSdPJyjlVYx6V7NUl5S2pNAFke8wF3oM\nkpLR2CBFwFLdJXQtW9MBShkOdCpp9TeufR2YeHyDJfpGIJgWMWd7MQhJP1X0YwclfRYbLS5OLDvT\nnKZf4KqMTpgTuZX+sSsrpnxpBdpIslyAUJRasRsLJoWkMILlWsCZnmCUFFgh8FXFCq15mrlaRWYb\nJROkqM5byehWvfjCSIpCcGy+wST3GKQlSZqxk5YY/JmikiVyKxJWpASep1gfxwgBndDjW4+u0QhD\nEq2IXJ8rg4T/55l1BqlL06uxUPf42AsDjBU0g4h33rKfP7+wySMXdjHWstAIuX2phes41D042LUk\n+Q5ntodMsphApuzvaBquQyuqKu+Vlofv+DjSxRGWfhqzNR6TlQnWauo+VVU8yZnmJYU25Bo8BXVf\nsi/ymY88ap7F2pykiInRlKWlMIairBTdNiYLzNU9vu8uj8uDgI+ddCn1lC9e6HOo2yI1Ad2w4IY5\nj0tDy5mddTYHl9nfWeD2A6/jI/8o4d9+8lEeeLbHg8fP0Y9f4MfvO8Zr9t/Ic5t98hJuWW4SeR4f\neW5KVvY4vWvY1zDMR/D0ZsC5PuxphQzinFGuuTRKsbbk1mXJ1hTOD0oOd+q80J/wuXNb3H9okcLA\nvnZIZgrO7VRGGC9nCGbjdvZF1r3kLyfnffTUNuO0oBH8/1Pr+2/x149plvMd7/8sX7i4i8Xy2rV5\nfvwtt/DlS7v83hPniYuSVy0v0Y1ShlkfY5vcNNdmnBVsTTLaUcAg6bIxHnJ4bsJCtMPp3b3sjKHl\npvjSsjEN2NsUrLYlrbDNznREXuywOw2RwmVfS7O3FTJfq9EKPBAO03zCY1cSeuMSz4kJ3IjFxjxz\nYc5zW0MuDK5B93VybVmseRjhcnk4ZZhC6Dq4SpAVOc2wknldqFnqvs98bcLZ3QkHW1Vi3401JpRo\nLVCORzsw9JMpNdcSuYq0zCk0BEriSUVWWLq+oLAZipJ2oAkdi8ah4QWMspydSUZhJQpBphXaKLJC\ncH40kwh3XoTGQ7ek6WsaXkknLGl6Gs8piTxD3S/YZyuxs0xXXiXDzGWSO4xmm4BR5pAV4rqjXTVa\np4lcQzRL/q4ErMRRPr4TIoTDc5sJu1NwXK/yN3EMnVCzIAsKI8hKiTUK3w3ZGmsujaBEUmqFMopY\nRDy7VeIrn72Nys1yfZzjKUPdd1lrBZzdnXCp0Ahc6r7iNWstWqFbTVR8HeIbKtHnZcgfHJ+yPZ1j\nkDpMc8V9a11Cr2Sa7VL3Jqw2CzxVaY8ZBLmuzAySUlLM/OVzIym1wOAxTA0GidYwH3lMcsukKBHS\nIqwkLqpeT831qSmX8zuwnTjsTCy2MiqCWTVfd+ENh+foTRO0TjDkJFZTm6EvAhBWUGiHVuBzdVKS\nDgBR457VBf772w8xSAoKC4c7Ib/7xEUevbhDrg37Ipdebnjs8gBXwbGlFjfO1/n9Z89hTMpCzXDj\nfMByoyByN9nTVEih2Z3kjNOCXBukrciJkRdRD0JuXOjQDl0kJdZO2ZluM4hjMq1xhEZSMkxLrg5L\nkrJCQnxlCR3BcrOymqz7hkCV5DpmmleJPS8tce4yLQKsqBM4VUJ6bgf2F8+zt1mwWF/jf7irw+fO\nnSAtNJ84maPUJt95R8Ik81is+0zSLaaZ4sRuBz/ocaCT80/fcgd3raX8xucfojeN+ZEHT/Iv377E\nnXvhglVY63PDYoN3uot88XzO81t9lBwxKSSaZRyVsz3OaIYOZaGJLRgm7E7BUx2ysuTyOOZQu8bZ\nwZQ/P7vF6w8sYCwcaNfRheb8ML2uV/1yhKWqLBwBLpVS3tc6++88+jz/+E23vWzX8N/iL484K/ju\n//g5/uL8NtZa7l6d4yfefIwvnN/iD56+SFoa7trboRv5XBiWdIKMG+YMVycZw6Qg8CSR41Bowzhf\nIdNnEXaT4+uGJBfMRR7ztZzVNiw1OnhSszkekxSVWNZclOM5TQ7OzTFfi3FkSWlCLg37nNmJGaQw\nTBQ3LrRYrBu2ppYvXYqBFDsDri+PW+xpuIR+xNn+hEu9HCUdOqHDxijDUBGAtSiYDxX7O3XO94cI\nIPQhzlK6kWWuVrUca15JnMe0fYu1itxIpjkI4eJKh3FhqfkuhYG0qFzlhDA0A8lcWAnmpHnJ/q5A\n66o6LnU1/dTPq0FTYwVxUVXL18KRhsgxND2D52gip6QWaFpeScMvCb0SRxraYeUyZ6zAzKr2aaEq\nS/DcpZ9WG4DduPKwkMLSCQyRK5iLJEtNB0XOxmjEuCgRqkJ9x7aaLpIzlv81pn870EyLActNSacm\nyAtFqX12U0lcWHwh6NZCtuKK9O1Kjxu7PrtxyrleTOBUGvl37Glx5+ocha4cUNtfpwmbbyj3uvd8\n/AQXRznL9ZRbFxK++QZFXE6xNq9G26j6OElZ9XLSmcHBJHVICoeklCAkUriAoJdUkra5Fuxt1ejH\ngo1JzjiTWCr3OSUFtyw1cKRlnKWMs5Rcazz1leSLhqs4ttxic5qRFbAxyumltqrydaWMJK1EWmhE\nHsO0crnb0wh577034XuKQhtaoUfXd/g3nz7OxijFYjjQrvPsVg9HlMxF8JZDHdbHPcbZtGKMBpVC\nk6MkzaBiie7GBf3EkpVQaIEUinoQELqKW5Z8FuuVgYLWOb0kZ5SmaFOitWGcaca5ptAlvlOlHM8R\n1FyXucih7lcQn6cEaQGxNhSlYJLBIPMxuASOT+jUuGFpnlftWeDQ0jH+9cd/H+xm9fmK/Rzs5tTc\nlOc2XT74dELNzai5cNueOe5YSYmcKY1ghc14P6XeZr7u8HeO3o61E85vnOL//Px5Pn3aZb6W8+3H\n5vihN9xIP9HsxE3ibIeN0ZBHzp+l1Fuc3I64OFrm1SttPnN2G62rKsQXKc1aSlJI+knEobkWlwZT\nlJQsRQEXhlMcCa9bm0coSWkMz6/3uTj6mxm7c6mq+8pv4S+fqZdA8vPfh6Ne/jn/r45Xsntdkhd8\nz29/js+c3sQYy91rXf6XN9/CZ89s8scnLpOXhnvW5ohch51pSs1zWetkxPmUnamHFHU8t0pUBzt1\nTmwMQJ9ipdnj6jjg0UvzHOgW3L6cstL02Yp9dDkhLQsGacBCvWC57jPf2EsnCtFmSppPeX4rYWME\niS6oey7dqIkQPruTq4yynN044lDX4DqSX/+e/5G3/MofcGSuzSjTPHFlQFIYFiKX0pZkZU7ds2S6\npB0KXre/y4XBmJ1xhichLjIyXeBLQ92vJF9dqbG2wHUEEktpLI4UeI4iLw2urMTDxrllklnSQmCE\nInQ8klLQj3MQVQUtZu1OYySTmYrcJK8IzWlZQfPaVuvcAZSYjafaqjceKoPrmkpe1qlIznXPUPdK\nmkGBryyuNDiyUqu4xqfKSsk4V5WYTqkY5yHIiHZQY5Jp1gcjMqoxu8CpTM1eGmUpqXmS0sC01LjS\nUPcNTccQBRasprQOjnAZZQ69xGGau3SiOp4SnNyeYKxFScmx5SbfeesaKI0UOUma8MTVdcpC88/v\nOvrKcq/73tuu4LqTCnJxFbk2KAFpKUhKhzh32JwoRrkkKVyS0iGZLZyylAgkjhOihMPJ3ZxCVzDO\nq5brnNnJGGYJQlTjdr5jqLmCowtNdpMpcV6iTV6R99QMqp8l8G4QMt9o8eWrGf1JQS+1JPorFY08\nJQg9QV7CKCsJPcXfuWkPb79xhVFWgIU7Vro8eXmLX/7s81ibcmQOIrdgkm1y76qmHbnsbYSc613A\n2oJ2KKh5AYHn4SiPVuDTTw2ndjTG+hhTTQ90QkvN0xzolKy0BEIkxFnKMImZ5AWl1ozygqzQGGsA\nU1WXriJwPOZrinooiRTUPA+BwyTPGeaQ5pLduOIeOMrFVQELzQ537F3i4FwDY+X1Hfkb90vO9UNO\nbC0wzYec702Zi1ocXFjlZ9+ueODp5znXSzm9c5mWH7OnsUhmVtnX0vRjyfbE5T8/cZZbF8csNQJ+\n/bvewb/6k2d49MILfObMVU7tTviFb30jS8s1nr6yScv3ONw1XBqEXB52mOQ5X74y4E0HF/mL89sk\nRcmebklNwfm+R6Ytp7aH3LjQ4uJgwsY0YaUZcnkY88WLO9y12q0MSRba5LrHxvTlZeILwArwgOxr\nWNca4IWrW9y8+nUQwn6FRl5q3v2f/pzPnN5EG8vdax1+8s238PGT63zy1FVKbXjN2jy+o9iapnQC\nl2bgcqlvidwJnbAgKSyulBzo1NiJM66OYi72Ir7p0IDFWsyBTsFyY4FOlDNItyiKlCtjl/maYF9L\ns9hcYDEyuM4EbUJOb2t2pwPGmaawDivNkMgL2YmnnN0ZMc0VN81L5iLBtIgwVETYY0sljhiyPp6w\nWMsIHBdNTj/WhI5klIMjfFbbC5zdtTy1kVBzFXmRM8k0jlLUfFCOYjtWjJIcKcFXYExB5EEjkIik\npBZYrLWUZY61hoZvaQUC39EYWzDJK5lwYyWlqRKv1oLSVqNqQlTuMXlZwfmFnlX2mcO0UEzySiZc\nIFBCMNUK9ItVvxQWfwb5h46m4WtqXknNMzS9krpfoqQlcDR1v0RQMew9J0eIhEHaY31YQsthN3EZ\nZi67sUtpIHDsdQGc+RBGRYmhmsiyVlAUHpu5QaZQk5pmTWKoUJmVlqLheVwcDNmOFXORS91v8vdv\nO0inFmKNpdTw0AsjTu2MyEvFauPro4T5DVXRPzb4E7TI0MZjeyK5OHTZmgQU1PCEx1ObGclMSKEw\nauY+ZImkpdtQtH1JK3Q42xviqWrm/lAnYJIbBnFezTGLyqO97vnsb9e4MCgY5IZhbOilhkLL2Xmr\nONTyWW7X6cU529OEUVJSvOQTu6Z2FrqCUoOjBAe7dX7o3sMoUZCZhG4IRzqKB54+zaXhAG0sLU8w\nykuMrVyS9jVr9BPNxqQg1QJhPbq1kLof0I5c0tyyGxdVwpUl4WxnW3MNSw2H1ZYLIicrCsZZQZyV\njHJNWpSURlcCP1aAFdR9j8W6pOFLAgcizyfyXNKyZJxCUkpGqWJaFBRa4KoIJescXujy6pUFlppN\nktJhN4Z+YijKMW8/egN/+OXf4qal28hKl4fPnmB3GnO23yBwOxxbgm4YcXJrh+H0OVINX7y8wI0L\ne3jzkYBuVCP05rkyPIcwfZbbq3zLTXcQF1t8+uR5fv0LpxhllmHW4hffcYA7Vto8dPoFLvcusjWd\no5/t4dELW2yMUyJPcWyxxcX+FpEbM8598szjajJDLyTcvNTiYr+q7Nu+y8YkIXAUd6x08V2HYZJx\nYr1P72Vm4sNXVvV/VRxbiHjmZ975sr//V8crsaLPS817fudzfOzkOtoY7lzp8FNvuYU/fu4Knzuz\niTWWe9bmEFLSm+YsNnw8pRgmOXmpaYeahjcl8GrUvAUuD6ec2BixNZ7STwpumhvyurUhjmowzG9i\nkg0JnG2UKDDWZ6kesK/t0QoihDTEWcrJ7Yz1kWJaZKy1CzphjVHusTEasxMbHBQ1v14Jt3gThmlA\n4AT81ru/i5/+owdYH07ZjhNcIfCUYlJqrLGzqSLB3kad5WbEk1d7YAVpCVvjgkRXuvyRF9D0fXan\nGWCo+4LCltQcSyt0EKLy1HCEIM4zkjxHSovvwHwgcBxLnGcwG192ZFU0CWupvN2qCt3YynvEmY1A\n2xm2ZREzvXlBNkv4u4lLP1UME4/S/uXo1jUp2kqOtqQZlNQcTSvQdAJLOxIEypLrjKw0lFZgZ5yu\nrBSkZWVn208dktxjkjlsp9X7BY6h6Rpq/kxt1VhqvkLPNFmkkuypKaZ5QWFSfMfS8CW3LDdZatTR\nxqU0Po9dynn8akqSC6ywHJ6vc7QZ8oM3119ZFX3du50zcZcPP7PD2V6KEoK1bp1F3/LpCz085eBK\nSzOoDBhCZeiEAqSk5hoavsvJnR5Nv/oDLNVD1iewOa4sGEsjUNLlULdJLQh45PKQuFSkuWWUC6rB\npxfj9uUGjnK4ujtkOzVk2lSmJbMQQCgNkV9WanU1y5sPdTi25JDoUyhhOdDyMEbzO49fYZwVOFTi\nEVfGJWmpcGTIaqvBo1emxLnF2IhuGNIMIwI3IC4E65sxoZPSDjL2NrLKv961NH3LQt0DDJM8Jck1\n/aSSpZ3kZubaJzA4tHyXvQ1BJ3TwFfiuT+BIXCVJCsHVsSUrPQZxzrSwGOsgZZt2VONVK4u8ZnUF\n1w3ZnRpO7aYkecnGYJ0/PXkCY3YA2JiGBENBqHZ4zVqD3nSVzGiGSZ8nL+fsa5esdUa4nS4PnZVc\nGgYIcZl+4nLv2s2sdndZjHJGWcTFgc/vfvkE96y63Hd4iZuXOvzkh0/Riye875NP8cYjS9yyMMbi\n0ant53WHFjg8X+PBZy9zemfM8c0er12VXB0ItibVMl+uKzYm1RTB8Y0hKwGMjMMgK5iv+exMM566\n0uPW5S7tyOfQQpNyY8CofHmToKXabGRfo1F/YjtmkOS0w6+PHvYrJQpt+IEPPHw9yb9qb4efeNNR\nHnj6Io9eqNby3avzWAu9acbeRoCQkt1pSmksdc+lMC6tsKpiz+xu8cTVjFGSE5eGmu/SzxawGAIn\n5lz/Euf6DVaaEYfnJiw3PBZrS3juFGNTzm0LevGArCzIbY3VToOmV7A9mbA9HTNKfeZrLpHrEBeG\n7Vix5gbs71jErEd/tudysS9RIqAVKAZZgTbgSYG2moW6y+F5h+MbW9TcCuLOdcZcrYK5A9ehERQM\nk4TFusVRgrQUuEbguT6TXFEal8CpxMWmmQThU/MEC0rRywzbvRyDwpOVBryvbJX0lcERBimrQssR\nBqUqzRIBSDmD7KXBmenCq3plsGVmiEBpKiG0ceYwzBTD1GWQKuLcIdOV89wkl0xy2Ik9HCwNH7qR\nYqUpGGQlk3SEEYq6p2fjdgZPGRqeoeFr5sKSw8JWKLGpkMpxrpikLr2px27sgwHHkYwLg+8aliLw\nlOXCIKtaFcrnULfJq9e6GFNgTM6lfo8royllabhlQRC6EVr7PH51k8s7Pj948w1/42v+G6qif6gP\nv/PEGUZJQuBabt/TxBU5j18d/Bc/0/QqV7rMOkS+R9MPeOTi8HrPfKUeMS00OzMhFFdCw3d53VqX\nnWnJxmhKXGqKUjPMv/KJK4C7VhrkWjCcxGxMS6Q0BG5Jza2kHyNX0/QqYQbfgaWGz90rHZSy5MbS\n9HxW2i2e3Zjwied36KcgZ4Y6vdhiUKx16owSw4VBTlIKGp7HvlbEctMSuQlKjKh7GZFbafcrcU14\nR9EJPXzlkBlBLy7ZHGWMskrG0dgKZmqFgsWay3LLwUXgOx5SWkLPQ1jFOLOMCkuW5+wmME49pKrh\nqpD93S73Hljhtr1LDBLN1VHM9njAJNvki+fOsDXeojAFWIuS8O/f8zPc8m9+g1ctS7795jq12iI3\nLh4hkJZPnb7AsxtTsmKL5VqPejBH3X81F3obfPrMea6OFKlx+bs3WO5ardEK11Cqxdb4EqA5PNfk\nrn1LLDT38i8+/AkeOrNBy485PFdwoHuUuw/ehaMk06zgua0hHz1+mavDK7R8jRAttoaGjaRECkHL\nl2xMKyU/BaxF0DcOSkpqrqIXZ0Sew7HFNvXQ5WJvyvn++GWfsXeYMfG/Rrz3ngP8u+9548v63l8d\nr6SKvtSGH/zAw/zRs5cpjOH2PR1+8s1H+dBTl/jy5V2UlNy1r0tRGiZFyWo7Ii0Mo7TAAnXfQSBY\n60RkZc7u+BJXhikndzxyLehGHgc6NXxHEWdXOLa4Tlo4nOytcaAbcOceTTvIUMplkruc3d5kmhds\nxYqVhqUTOlwehqyPMprBBCUlvlPDWsm4sESuJHRqBF6d1eYIR0n+yVveyS3ve4BhomkH/qxdV1Lz\nJJkpWIgU9x9e4NmNXaZpiqdgmKYkpUVhaQSS+ZpDVmqKssRRM90SIPQUAkVhwJMO/aRkkhVoC0oq\nGp5LaSVXBhkl1T1lZ6JkCIurKntwgcW91ktXtkr28pqCnUBQcaKUsIiZH70SVQXtO9U/V1aCaRow\nM4Qyn/G0prnDaEbEK7WkNC5K+bR9n2lpeWEroYTZGJ6m6ZW0wup57juG+kwHH6Hx1Is24maGIhgj\nMFaRlIpJ7hKXAUbXeKGnSQtL6AluXgz41qOLeI7BUnJuZ8Kp7SFZUaCUZi5SdALNbpyQlBaBZCmq\n8d5jd/6NV/TfUIn+f/vSCa6OE1wled3aAsMs5c/PDch1NRp3rWd+QytkO5ekpWUx8llqhjx0Zus6\nFLoQOZQa+tnM+lDCSiPk3kPzPHVlyDjNmRQaXWhG5Vcm+UAZXrcvAFnN0QubE3glkasr2F9W+vjV\nQhYo4XJ0aY6Dc10mebVbW2k3Wa7V+KWHX+BLl4ZMCkPDCbgwzMg01FyXg3MR53bHaFPQ9kuOLUn2\ntkoClSBExSx1pCVwBMYILBIhFK0woBYETOKSK6OEYZpT6Er0RxtLNxQsNRTLDZfQ9fCUg3IAJIEj\n0dpjmOWkhaafSHYSxSQPqHl1Ajfk9r3LvPXGfTR8j8v9ARf7VxkmG1zYucr5fo9xVmKMQdvKCCLy\n6rzh0Bo/fP+3cM+//TVWmglCCNrRGu969S14Kmat3WJrPOSxC4/QTyxPb66wUG9xdKGk7gb8xy8P\nGCTrrLVi0rLOHWvHODLn0A4KenFCqQXNaIn7D3bwnYRPPLfOZ08/wjgTPHppD7/yD+7jLTfu4fjG\ngElWcmZnm8cvVp/9pWFAJwoIFJwfpJWqoqfYnm0ABXCwBn3tIqXAV4JhWlDzHG5eatLwXM7sTrgw\nmBK/zDP2fxUZ76WR/B/fi+eor33g/9freIUk+lIb3vvBz/Ohpy9RGMOty21+/E1H+f0nL/Ds+gBf\nKW5f6ZAWmqw07Gn4JKVmkpWVaJXvVm22VsQgyXl2s884HhB6MXHuIVWbo4stXCk52xtzqT/m9Wvr\nrLUzcr2XY8uvIvLHlOVlrg4TzuwKCp3gu4ZOEKKUoDedMkhhnHk0A4elekacS8a5z2Jd4Kga8zWf\nmlfDWCjKDf7Xb/5e3v4rv47vOqSlYJSCQjIpqmR8z+oiW9OC0ztTrBXsTnP6aeW+1goke5ouwhrS\nIsNTFqWq8eDQUbhKVBrwjmCa5yRlMYPdJe3AAQHb44LUgDYCY2VlKz6DtcuZRbh4yWqvkvmLX11Z\nWdF60qBmjnBKwEvvjmvgvhLgOZX8rD+ryK8hBVDZjmvjInAQ0mM31uzEFWE5K1WlblpWeirlDPGM\nVCW+E3qaVlDp5EeuwZXVBiNU1fUpCUIKAilJykrauNASJX1uWFiiFc5TmojTuyVfvLjLNE2QqmQu\nEizWBFeGCanWuMKw1LDctlwjdBTfuvwKI+NtTwTTMuIfHD3El15Y59NXMizR9dcV8JZDizy7PcIY\nw3LDZ18z4hOnNq8viblAkWvLcFaBeUrw6pUutyy1+MyZLeKiINMWq1NcT7NWK6l5FaGj45WsdT2K\nYpdEa4gsQlq0qXo4aSlJ0spTXmvFgbku7zi2iuOEpFqy2q1zeL5Db1LwDx94jCujhDS11AKfc5OE\nQBnW2g6hm+Kwwz0rOfM1y2JdADOinK1+U1+6lAjislLta/o+kSfZnuQ8v9VjkhlyLbHW0g4Fe1uS\nlaZHO6oROApjC8BBCIurfHKt2Z4Y4sKwPvbZnnoIEeK7Efu7De47uMTdezpsxdscv/oUO+NNJkmf\nk9sjdpNsVuFUO2ZBxF379vKDrzmE7/u4yuOHgZ/95jUePP4CX7rks35xyInNh/mmI8vcf8RiyzPc\nstyhny2zmwdM03WeuFKy3Frlu+7cyzhOefhsyfEtl2e3znD/QcUbDy3QCnw0PrtTzZ88d5JjS20W\n6wm3Lbd48IRknMEPfegRfuA1h/nZ/+5Onl3vo40hVIsYumxPt+lNM2q+y43dBqd6Y0Z5yVzgsJuW\nWODsFA7UCobaJbPQ8B0meclzGyNuXK6zv1Mj12Z2o758yV7ytcVzAB56fp1vvnXfy/a+r8TQxvAj\nv/+FlyT5Fv/k/qP87mPneH5rROgqbl1uM8lKrLEsNfzrHBcJRJ5D4Dgs1D0uD2KOb/bpTTKmpeBw\nx+HwnGK+XmcnNpwfThgkOZHrcXm8wE3zO+xpTmdjni4n1g2umlTJTnZYrOeM05RzW6KyhA2q9p62\nil6smYss3UgQ+W3aAdXEkMm5MrJsjaoMF7kWzymQaJpNiwGWEexp1gmdXdaHE/a3BdPc4okq+Uip\naAQeCk0vqYqFRFdz9L7jkDuSHEM7UKyPUrJSIoRH5AnmIkVSikoC2ErcmQ24K0o04H3FNlZcNwGr\n+E/X0r6ofEe0INeWWMjro21S2KqqlwJXVnwrV1qEMCSFQ1pcm1yxeE41GhwoS+RBzZN40jItxnRD\nS8MTVaFoJEUJ2lbP8EJLjBGkWlxHggeJiwRcxxIpTRRYIsfgOJZACIS0DEyJqwSBIzi0GLBYC4Ax\n47TH+jglyyyHOoq08BGizolNw1PrElf6LNQVd6w2GGUJnzo9pO07fOvXgW/7DVXR/9hnL/P3XnWQ\nP3vyNA9dnX7FMXtqLnfsbfPE+ghjLXsbEWudkI88t359JrnpSeLcXIdEI0fyHbcu4bolz1zZwHMy\nam6JpzKkNF/hYBco8N2AcWrYiSs3qLhQDDNFWjrX0QRjBK0w4t2vPsw9+xfpJzmdwGOpGXJ0scVv\nPfIc7//SKbIio+YWlNYgbU7dK1ntSgSRGRwLAAAgAElEQVQpgVPgSGjOCB35bPEpIWiHHto6JGWV\n9H0lq51+UtCPDbm2eFLju5ZuKFlpeyzW6jQDhcSSm2rxW6OQsz7bJPfoJyEXB4phJom8iNB1uXm5\nxetXazSDCZcH62yOtojzjEv9MevjhH4C40wxyRSp9rlpscMP3HOEm/cs4DkBvhPiqRCDxlU+j539\nGJ3aHj51KuPff/EEaZGRlorbl1PedkSx1NrLbStvRtohn3jhLM9cLTnftxzq9jncFfjOEh96NmZz\ndIXAKal5DvcdXGD/3GHqbkY/GRDnGqPPI4XP2vy9/NmpTR589grWWm7b0+a3v+tVrMfbDBKHfhry\n6IUd/vCZC/SmFSS/txFwameCtpZASYb5i6n24AzGF0LgSEgKXaEvCxF1x+XU9piLw+RlXft/nao+\nACa/8O6Krfw3EH/bK3ptDD/2B1/k9x4/S64Nx5Za/Mh9N/GBJ87zwvaY0BXcstxmWlTuZ53IJS4M\nSV6iZOXu1ok8QiU435/y/NaYQZZRlpZ26HFk3ufG+ZztccGJbY9CC5qBw8FunchVLNXO0Ql6rI8a\nPLM1D2LMWnPKQl2SFIrLA42rkpnXRkQzKNBWM8mbNH2P5XpOPXARIsRaxW5csjNJ2Elgfaj4/E98\nN6/++Q+Q6eI6Ix1rWG17HJmv8exGD2Grtt44y0FYPAU1TxE4DuOsJCsrPRFjBY5UOEpV4jiOopcU\nDGNNbgRSOnQCF2MlG+OUwlYseEkFdytViZd95ahaRcxTMzi+EqKt1vK1FW2AUr+4EWBWvSt57ele\nwflSWBxhkTMinxTgKEvoWDwliZxqZn6UZRURUFhcYUBcIwNWsD+i8j/JZ0JoIJC2QgQyLbFIyhmJ\nUaKIPEmuUyJXU/csKy2fQ3M1lJRMspwrgwnTvETbauMROIqkKMn1td/UpRM22Jw6HN/QrI8lSelw\n83zEr77lwCurov+B19zA//Xxx3n2q56lbz+8gOs6PH6pDwL2dWrsb9V48PjlWZI3zAcWV2Us1Eta\nQcFSZHnz4Ra7yWm2RlOOLRocBWlhictqxnKSVcY00gY4TsTlYcEgsaSzpF6Ya4uuCl/BvWuL/Ogb\nb0JbSIuMfQ3BfM3Qcib8zIOf57nNHjVXs1TTSJFS9zRNX+NJSWH0rPckcITLIDHV4pMuHd9HKsEg\nq/pZRVlJp07zklJrJAbPscw1DIt1n9V2k2YgqXuVUEdaaqRwqAAuj6kOGE5rXBm7rA8rEmE79Flp\nG+7eqzgyVzDJT3Nme8QkKxjEKWd6UzZGlkGqGKQR2ristEK+99UH+M47b8JzAjwnRMlq2STJlPf8\n7ke40L8MQJlZmgtzfNddPt926xy/9LkX+Iuz55iPRjx83uGFXs5PvukK3VrKW29Y494DLf7omaeJ\ns4xnNiShp3jHLQtMEvjTk1eYZJoHnuqxpy349ptcFppNhskFpmnGVjzHTXsDfvN73sA3P36Wn/rI\n45zY7PH9H/xTfuotx7h930HO7Mbcf3iJmufw4DMXOd+fcmGQcLBb4/wgJtWmgiRnZLtzMaxEmqm5\n9pCTTIuC89sxh+YiDnbraG25MklftjV/TSHvr0qzKbA7zZivf31Gcf42hTGWf/bgY/ze42fJSsPN\niw3+p3uP8NuPneVcb0Ldq4RoJpnGcySR5zDOqkredxWh4zBX87HG8uT6gAs7Y6aFBgHLzZAjc3Vc\nJTm5VZm5tAKLKzvcvb/LLUsdzvdGnNjoshRto8Q2pVE0/Xk85TBKd5hkmnEWsNzwqHkwzQ1pKWkH\nkrnIUvNDAqeGEmPG2ZQrY0jyjN5UIBXM1yuxqq2pojCSwJXEU81CzWels8QnT28S5w0kmkGaUFqH\n0DF0QoXAZWeaUZQVNC2loOZKfMciREErEIzSFE9qOhE4StLwwFJZWLfC6vl4TelR20q8LNcCU1Rq\nY0JURwhm34trCfzFJ6ul2iw40uJirr9gXyo2IexsIzCbt5+x+l1ZHVRISehK4qJSxBQ4iHIGt2Nf\ncrzBUxY9O3noaAJsZTtbCKQUOFKDtLNnbmXzXRhQQhJ6De5aXaDmK0Zxwrn+kDifVpr5CmqOIC4t\nozRHGIvvVhoomdYMsx18JbhzL7xWKTwnoO59fcyrvqEq+u9/8AWe/6rZ5Z++/xhPrPd4cr2PtJob\nFgJumFM8evEydb+STGx6+iUVOoSu4s69bc70ci4NCqaFoDA+V/qGXqpIdVWhZ1qyGAV4wrA+KSnt\nfx1KdaRhua54770HuG1Pi6RICV1L4CpWWxEbo4Rf/fwzJEWMY1NqvsGiEdbSCCoHs9JULrmOckj1\ntQpe0g0DfNdhkhfVTZ5XSbkw4AmN52iavqUVuqw0AzqRSzfycZUkLTS5rnpohgBtGkzzFjuJx+VB\nxk6cETiWbpSz2sw5PGdoByWjNKefZMRZxumdgvODks2JZJJ7CCFoBD6vXtnDT77hDpbnOzjSxRhD\nVsac2lzn5z/1CFeHfTy3+rSksHz8H/8zjv7rX8VXbT723teipIPWmlPrX+KTpzf4xCmf57brHJpL\nuHWxyQ++/vW0QoVjLvLcdo9HLwWc61k6wZAj8xHdyOUL53f52PMlC7WEbgR37pljubnBIFFspTdx\ny1KXW/a0eeuRZXYnCf/wP3+MQTKkF4d8xx1H+Zm33c7xjQHDNOepK30+/OwFjm+OcIRgruZzuTcF\nVc3qxi/haizVIC2d2cPJUhhLzXW4aa5GieLM7ojN6ddHuvJavPlAl0/92N/9Gzn339aK3hjLP//j\nx/mNR06RloabFxr8wD1H+P2nL3J5OKXluxyaqzMtNDWvImTm2pDmJTXfwXcVCzWfcVbw7PqAzVFM\nUhoCR1VJfr7OMCnYHGfEZcHhbsq+ls9NiweZb7QoypI/P7fNoxe2OdDe4JbFMYVucKq3l1E6oeMP\nWaqXuI5HL45oBjHNAKxtsdwoiTxBYepMc+jHE9JywiQVjPLK+MZzAhzp8Jvf993c8wu/iSMlcQ6B\n63L36gLnejFXhim5hn5SMkwMZjZeu9TwGCQFcVHgyorH1AyqkVuLJnIs0zwnLYqKVOdU9rKeEmxP\nU6Cq2q8tG3N9+VSJ38z688a+WCy9FL0y5kXRqGtA1TUfkWsbAj0bgavCXifICVFtAgQCX0lCVxG6\nkt1JzmSW3K9tAhxlcWXFR3Aq6VLkNR97R6Oo/l9WWmsw09cXVpCZSjlV22oDeNueJnO1gCy3nO5N\n2BzlpGXljupJxaTQYAvqrqEVGlZaCq01o6xE2xIlwFcOjaDiMRhrCaTPt+17/TcuGc8Yw8/+7M9y\n8uRJPM/j537u59i/f//119///vfz0Y9+FIA3velN/OiP/uhfeq5rif7bH3yB9WmBxDAfaH78/gM8\nt3WV3emQpl+wVHfoRJLTO+MXF4wV5KUgKSq1pXbU5PUHDvCp032e30hItKTmOlwZZf9FEl9t+Whr\n2BwVaJjZIprreseeqvozNy5GvOvOQ4Seg8USOC6NwGFPXfInx8/wxUuXsSZHSo0jKy93ayWOnM2E\nlpXtrZ4RUySSTujTijyGcc4gKxllFquhFlhCt6AVQM11mK+5tCLFQhTSCl0EirgwFNpiiNC2Tao7\nJIXL+ijhyjAh12OWooRWELOnqdnT9BFY+nHKpBRc7WueXs+5MLTYGVxn8Tg0P8+PveE23nrTfkqT\nE2djBpM+j125zINPnWRrGl9vk2hT6U2nWtH2HD7zT3+IhX/5f9MONUpYlhs1funv76M/3WS+voIV\nN/Hjf/hpevGI7alLXga859UB9x/2WWmtsdg4wB8+e5xzO+vsxhmd0Gdve5lJqnn47Em2xpqD3Sl7\nmyVZucZNe2+i4bkYoO67fNORDqEz5H2ffI7fe3KCtfCqlQ4fevd9nBtl9KYZJzYHfPzEFR65uIPF\n0PY9+tOUfNYzTPWLt0LXhxKFsIC0aFP9PW7oRmRWcnpnSD99+Zj4fx0If/K+dxG6Lz8I97cx0Vtr\n+VcffYJf/fzzpKXhpvk633/3IR54+iJXRwndyGN/p8a0MLR8pzIfKjSptrQCF99RzEUeW5OE4xsj\ndqcJubG0A5fVdo3lVsDmMGVjkuBIRSt0uXtfnduXQQnFpVGNvzi/w8Y4YZIVtH3Da/ddxndSHrvS\nZX3a5uh8xmprSlZqjI2o+x7zNU3DVxgq2+VRmnJh6DNJS5pBhhSgZITv+AhgY1zyoX/0Hu79xf+A\ntpbQgRsXm1ghOLMzxlpDnJdMZpNFoSNpBh5ZWSlkljM2uec4uEpVxF8rGSQFvViT6iqZ7ut4+BKu\njiYI+SI876uqwPrqrpKcwfRC2OvJ2rxkiVXfVj16y3+tqq0S+zVVvevbhZmxmAIcRxEoByEUG+MU\nhLhuTVst52o2/xrucG1joVRFAAykxnMqZbyK3U/11RVoY2ZIg2SpHrHUrFMYxaV+yuVhTpxXRWGk\nLAZLbgwVP1syF0VMc8PGuKQ0FXegG8FSQ+BIg7YGYw1CaJpexHesvfYbF7r/5Cc/SZ7nfPCDH+TJ\nJ5/kfe97H7/2a78GwKVLl/jwhz/MAw88gJSSd73rXbztbW/j6NGjf+U5713tURJzuCnYN99kffQs\nvspYaVa+7aEb8IULIwZpNLMyVIwyh1wrsII3Hlzg1pVFPvDEZS4Pc7RVdCOPC/0XE9S1ONLxmRYp\nhS5YbFSJ3f2qBRs5kvsO7eFtN6zhKAVkWJvR9BJEkfD+Ry+wOx0TSk0pJKWxTFIHqSTWCJLMYmR1\nIzFzgGtHDgs1h6tjzZn+FG2gPoP6G0F1I85FLjXPo+E7dOsB3TAi15ZpbrA0SHWLTLcpjMM4zbky\nHDDNtgidKQdbCZEPizWPVqCIC8GVUclgInnkkuDyKCcpLFnpkpcuC40G33vXDfzwfQeY5mOSfMRD\nz3+Wk5s7fO7sBrtJZXObzIh4eSnJS8mels9P338zm2nK8Y0BnwHecbTDI5e2meSSaT7g3z18idCt\n8b//vW9DCMF/evc9PHR6h3/xJxcRjDm+vsXzWz5vPrzI6w4P+aYjDTYXJA9f2ObKMOehM0Pu3Gt5\n5+0HePjMFoHbY33k8oXLBVKNuO/wEsZYRknGR4+f4Mh8yPve8VbuO7jOT33kcZ660ueeX/5Tfv2d\nr2F1roGS/y95bx4mWVrX+X7e9+wnTiwZuWdVZS1dXQ29L2CzL6KiPYjIgHoBvc7ooDJ3HAfH6706\niuMdxcvMHZ9xucy4znUQQVGYFkFBbehuoGno7uru6rWWrsys3DMjYz37e975442sqm4YRWnuA/I+\nTz4ZkRlxIjLyPee3fRdB3XVohi5/9eQGnSQn8j3cMmNYGMR9Ng72nQxaboUWhsYphWZUlJzuJBxv\nBxyaiCi3+wyeJY79l3KUd9/5CG9/1Q3Pyuv9Q15aa372o5eC/JVTEd9z4xHed3KZjUHCdOSz0AoZ\n5iVN16aooFCKvFQ0Q4+aaxPYFme2+zyx2WNYlGgEM5HP0XaEa0nObg3pZyWhY7HQrPHiI9MstEKy\nssPDa+s8ur3Bat+i7bscqPs8sTPg5IbPDbMJx9sjPLtFPwvYHlUcmUgIXYFjTxBaI0qdszMa0k8y\nlMoQusSyPJSuMV3LqHTF1jAnLzP2EsPG6CQ+SsPiRISuatyzsoPGIy9K+rkJdKFrKMZZqYlzU+W6\nFnh2iWcLhDDoprw0Ql41z4CZJ3wPTcVqL8V3DQuo1IKskPQzC4NPFeNqGWzJuJI2zCHH0hcD8DPX\nuP5Ha3lZ8QYgKdU4OAuDqt+/NlvCjNV8CVWlGBYZNVcjx9mAHL+dqjKjBMMGAG2YfCglKEpBgkRK\nI4NuUZkOiqqwZEXkag5P2Dxn2kPKkm7cpZdmqKpipiYQNU2mLAaZJM4tUIJ6EFJUFeuDBFUp6p6g\n7koizwYh6KYGE2BLTc2VNH2L0P4qt6m97777eOlLDb/3xhtv5NSpUxd/Nzc3x2/91m9hWWYTlmX5\nJWUrM7WCFxxsspZ6PLSWcb7rkhY1pqI2hW7xvs+skFVPt/UTgG8Lvun4NIcmmnzwwWU2hym60kxG\nPk91hoaHeRkf80CoGZQjrMsYS5W+5GWcK8lczec11yzwwsMBudokTge4lnF6W+vG3LPUoZcqRhlk\nyjHOeUpgWxZZXpErARqDGrU1juPiOx5rvYKlbkrLKzlQLwldmAhsGr5D5Do4lkXN9ZitB9R9l1zZ\njMo6ZdWiX0ZkZUVVlnTTHQbpJmXZpeEUTHmCyBdMBC6uHdJPLJZ6AZ9bHnFmZ8R2XJEUFknh0fJ9\nvunKJm970UGaoWZ3sMeHHnqKpc6Qz69s000qoxFdWKRlQK4EvgVH2z5vueEo63HKI5t9fuWep8xG\nH7vXbccJr7v2GOd2NgnsXZJC8ldnPX79nj/hu671+A/f+XzecNNNvPGmm3nXx/+Ue5dHPLju8Mmn\nnuCWx57gB593iENTEW+8fpGTG5LPnNtko7fDatfhiqmEQoWcXA/ICs2HHrnAyfU9/pfrD9KuQ5Ln\nnN5x2UnWePVVC3z6X3wr3/m7n+RcZ8hb3vspfuiFJ/ihF1+FlJLXuQ5Nz+LPHlunl+YEjkvL1nTT\nEtcSKKVRQDfX1B2FkCaRFChGRcGZjubKdsiBiYjlzoD42RfP+6LrJz7yED/2yuuR8v+fud7X4tJa\n886PP8iv3vU4aam4YrLOP77+MH/40Ao7o4SZeshc3SPOSuq+Ta7GgQ1NK/Souw6IiofX91jeG5KW\nFa6UzNZ9DrcjhmnBUndEpTStwOHq+RYvXJzGloLzuyPuWeogGGIJzfGJGXq54szugH6SM0jqXDmZ\nMFXL6SR9HL3AVOTSCl1CuyKvBlzo2+TliEIVbA49JgNo+pqadhBCshtLqiomKyVJ7rHQMH/3ZBjT\nrrlcPa15aOMCM1FJXlZ0dUlDCqQQ1D0XR0p2RjlFZfw+bAEVtmHwAIXK2YtNceI7kqbnogRs9WMs\nSxONI8bf1IHab9ur8VempAHAsT+z5xKqfjyvd6yK/W0tn0Gt03o8/1cCS4DjOEghWB+UjEoJuONO\nx3573zzfukyhzxlz412p0cLoB2gECAupjXpgpiokFk3f56aD0zR8n7N7Q05v90hKgYVD5OZjhoOh\nXM/WSoKWRKNJipikFDRcicbFEh6pknRTULpCCkkrcKi5Ek3JsICs+Cp3rxsOh0RRdPG+ZVmUZYlt\n2ziOQ7vdRmvNu971Lq6++mqOHj36tx7ze7/hO3j/qS0+dW6LlW6MLQXXzjVphh7vP7lMVj39AieB\nlu/w4qMztEKXjz+5wjBPmfAVM5HD5mibKycvVekSqHuS7RiS0iIrJZkywb2sYMIvmQpzXnhIcvOi\ny6HGBr04BSrqvk/D97n/woB7lvrEKWTaBHhVXWoQJaUeo6MFWWWhcgHCJnBztO5xqFXgWkZScqbm\nUfd9QODYRid5tlGnFdYpqya9NKKfeXTTgqzoAeeI0x3Sok+hSgJb47gQeSGNoE6uIvZyh6X1hM8s\n9bnQHTLIDZp/OpI874DLG66f4sppnwu9mLueepS1XsxD6wNWe4bKlxW+aQE6RvHpynbIa557gI24\n4OH1hF+9Z4NRIUlLQeTVuHKqwY0H6vw0MCo0d53b5LkzPU5MNXhow+bcXsh8PeX+9Zjr/v0dvOrY\nEX7lDcf5jutm+K7rj/FTH+tw91MX6CUJv3z345yYiXjTDSe4YeEYV01WfOKs4rGNLoO0y6AIuHbh\nCg60Cj67tMtTu0P+06ef4PXX+Fy/0Car6mwOEt5/8jwvOjLDvT/+Gn7gD+7m9kdW+fVPPcHd57f4\n/e95AY4U/KNrFmnWfD740DIb/RTHEkz4Lp20wLJAKE0JDAoILIVjWwhtUaEY5iWnOzEnJkOyRo3V\n7oj8WSjsv5T2/cmlbW4+OvPlv9g/wKW15l1/fYp33fEoaak40o547dUH+ODDy3TinLl6yETNJc5L\nItelVBVJUWFJQct3qXsOw6zg8a0em4OEXBmq5YFmyGzdZ32Q0IkzPNtivhnwgsPTXDEZkeQVdy5v\ncXp7yDDPmY8iFhoJa4NdzndctNZMRwFlpXh8u8mtfofjkzEFMBnNofU2/WyT3XjEWs9FVYLIg/l6\nSVk18JwUigHLXZdcwWQgaAaS2bpDP92vVjyOT07x0GaXvURTVZJeWlJoQzVruoKGLxhkCYGjCIXE\nlmpsY2usqauqYq8oqftgWzAVWAihuNDLKLUxpakqMz83KPj9inwcnMcVPRghHM+61KL/m5a6OM9n\nPCrjYrtdijGyXkLNqfBsG8fSdOIcJNTc/f89FGP1vH24jekM7CP7zBx+nwbo2RW+1OMORYVtVUyG\nNtfMRszXA3aSLg+vxfRShRTgC4XCops67KUCgU/Nc0BrMpUS2AUNr6IdChoeWDJHk47HombEABZJ\nmZFmsJ1UZCWEVvgFn8dXYv29Z/TvfOc7ueGGG7jtttsAeNnLXsadd9558fdZlvFTP/VT1Go13vGO\nd1ys7r/Y2p/R//Gq4s+f3GBzkGJLyQ0LLZqBx+2nlhheJjAv0ERuxeGmz/HpgLpfcWbbSEg6liSw\nJevDHKWNfaHxEzZ89E4qxzOhioarmKzlzEU506GhwB1tuyy2AyaCgErbeHbAfKNBVUrec/I8S52Y\nXJWU2ljfgjAUEC0YKWOVO8wtXFkxM2YANP0CxzKa0A3PZ3EiQkobpSs86RL5IYsTk8zWFxiUNTYG\nks1ej262gaV7aNUjUSlJnoNWSGkjhE/kt4icFpm26YwUnzzT5fHtmLLKaPuKhq843LJ4/mKDG+eb\nrAwyljoxF3oVD60NWB+aNrxlQTAWBLIFHGr4vPz4AVb6FY9uxmyPKrJSUmmYqrk8Z7bFzXM1tkYx\nD65t0kv73Pkvv5+f/NBfUBRbSLFGN3V4YP0ILzk2yRObZ3l8u2C55xO5JbcsDHjJ4Wl++tteh2v7\nPLT8OD//8Qc4uztCo9kYRHz71RO88cYZ5psT3Hn6c5zb2eaB9UnKqsU3LE4TWIIPnFqmqro0vBLL\nanHbNceZDgOGeYlrWxxs1fiWq+a5/eFlfuojDxDnionQ4zfe+A00Q5/tYcq9Szt86NQyp3cGBshp\nSTZjA1DS1SX1Ok+AN3YpKyuFEJLQtrhqMuT8IOfCs0y7+5+tGRvW/+/vfVaP+Q9lRv8f//oU/9df\nPkRSKBZbNV5z9QIfe3KDXlowH3k0Ag+tTdtXA8O8JHBs6q5F6Dl0hjGPbQ3opeba0Q5cFlsRjgWr\n/ZRMVTRchyum6rzk2DSeY/HU7pB7l3fYHmbYUjBX9+nEOZI9HJkzzGtIq0Y1HlBXVLx0cYP5esKw\nmGVUHmRn2AG9S2DH5MpiWDSYi3ICu6KbQlYWWKIAYaF0QOT61L0Ru6OKjaHkz9/2Vn7kfX/MMC9Z\n7Y0olUlGk7GhQmDb1H2XQVYSj8V/HCkIXeNVX2qNVhU7cUElwLFgOvSwBKwPUjPnFibo/l0ZngIT\nvcXY83s/Mdiv7C9+R4NpgqL1WInushm+Zwk8y/Dlh8V+sjFG9I+TgcupemoMBLw82Ovxz0olENIi\nLY2CaOBYXLcwwfMOTrI9Sji10WF3ODIqeRT4jukC7CP2655ECE1aGDqdFBLPtnAtm7KqqCqFJTWB\nCxOBSbQqXZKrElWVF5OfSguaTsQbjrzgq3dGf/PNN3PHHXdw2223cfLkSU6cOHHxd1pr3va2t3Hr\nrbfy1re+9Us+5j3nt9gcpjiWxY0HWtQ8l7947DyIksnwUvu96cJM3aMZaCQxp7cTRpkG4VFUFqe2\nCrIyoKwuWR8KAY5VcqiZMB9lzEQ5Dc+oINmWRleCmXrE0YlpmmENKV3aoUvD93lwbZc/f+wCnaQi\nzTWIfQCbIC0kcemQFtDwFBO1nCvaOQ2vGks+AsJmMqyx2GqAxNBgbJfJ2iTHpg8yWZthva+4b22J\n3ugcadnFEjFSlYwKo2JXVA65ivDsFu2gQeQFDHObvzyzzRPbF6h0StMruG5W4TsWR1oB18xN0s8l\nZ3cLPr084JH1Pp3E8DwDRxA4msBR2AJmo5BbDy2w1K94fCvhd+8bobTpVBxohtww32CxKXlsu8Ny\n5zx/sJ6MKSqXwDR3nD7Pi4/0aAU1BsUiaVnw4Np5QsfmtudeyUcfW6UVDLBlxe89MOSXPvnH/PhL\nD/CjrzjMr73hhXxueYtf+Pg5kkLx2eUVHl7f4JuunGG6NmCiNsk1C0fppwWnNvdoei7ffeNBVrqK\n+y70Ob1VcG7vSV55bJaXH58nLkqWOkPee/9TvPyKOT7zL76V1/7OJ1jaG/Gm99zNP7v1St78/Cuw\npaTuOdz+yAoPrO4SF4rJwKablEaDexzsMw1VrvBdC0talJUiLuHxTsxVEyFlVbEx+MpY216+tkoY\nZgWR53zFX+traf3aXY9cCvLNkG8+MctHH99gkBcs1E31pQHLEhS6YpQpGr5D3XNwLcn5zoCz2wPi\nssIWgvlGwKFmwChXrPQSLCmYjjyef3CKq+daDPOMTz25zdndAaO8ZCZ00UJwrjMkTnNc2+WqKWgF\nJeuDismax3QUoLRmY6RpeCtU1SZntjQXeh5zdZvDE5Kma2yid2MbzxoiRUkvrbHQgKZvZuLdJGFz\noKi5inZggkOuAp7Y2qPUNmmhGeUWQmhqjqDm2RRlQakKfAdsyyJwBI6sqJCIqmQ3LXEdM/+errkI\nARuDdDzevARw29eL2ge2Xb5MsBaXBfPxLf0MzrwWY7DcM49gkol9bRNHGs68Zws8CZkyanR1zzxW\niP2EwARsoyBqnmNbAltcmvFfxA5Ig+ZXCCxhMR2FXD/fIq80J1fPc6GbMco1eVFRaItMBajKjD4i\n1xSJcZ6DqPBtzXQoqPuCrFTYlsKzDfg78sZ8/jgnLioUGltKPOkgJdQci/m6y1RYe3ZOgL9lfdmo\n+yeffBKtNb/4i7/InXfeyeLiIjduUTIAACAASURBVFVV8fa3v50bb7zx4uPf/va3c9NNN33RY+1X\n9P/bHecYlgU3HoiouYL7VzYMZWH/zQJt3yPyXPLKQWnJuU7OKNVjQQnJymWVlSsVh6KciShhrl7Q\n8kt8axzYNYwKyTAz9rfXzE9z9WyLqbqPLQzVJK8kf/XkJg+sdBimBRXG7CApJIU2krxtP6cdFsyM\nM3B3zEctlZGgbQbBxQq+qmwiv8lkNMOVUweBlNPbS6x2N0iKLkoVCCqKSjHMYC91SMqASvu0wyZz\njQZCVGzsdTm1uUFaDBFSIRDY4/bjQmsCTci53ZJBLji91aGoTEJzeTautaTlR9x8YIalXsXSXkpR\nKVSl8W3J8WmPa9oOlSw51+myNUwYjSWFVSWocJiqRdx6eIGXHFngW689wlt//5eRZJzp1Dm9N8Or\njnnUPMW9yxmd1GWhnvKCAwn3rWZ8csm0rI61Eyw0b731KN/z/OcwXT/Er9xxD3/44OOs9jQ3LfRo\n+RrHuppvu/YaFidqfPjRCzy6uYeodjnYcnFEm9sf32a1m4CA+XrA99xymKbnk5amajsx3eAbj8/x\nI3/0GT782Bpaa24+1Obdr7uFlWHBuZ0Bt59a4e6nNslVhWdb9JMC8YzKXmCERgBKpbCkRehIrp5t\ncWqjy27y7FrbfrH1phsO8N++7xufteN9rVf07777MX76Iw8QF4qDjZBXHp/hk+e3GaUFBxoBgeeO\n/dPNMp0dl5prU2nN2Z3+mK1SEbq28YePfDYGKYOsIHJtFiciXnZshprncHZnwOdXdtkZZXi2pOlZ\n7CYFo0xRqIrQtYg8G0/mHJrIqbkRUTBDw3PZ6Mc8tNbl6MQS89GQ9UHI6nCWduAwFcSEbo9hqrkw\n8Kk5FTP1koZr0U090AOgZDuuEXkONy14HGlHfPfzX8vL/tPvkSmIU8VeVqIqI1Pb9F1A0BllKC2Q\nUlJ3bSxLIoVAqZKNQYEei+jM1z1cy8i1amGoavJi1WwCsBzT3yR/9wp/vyyQ4rL9Ji5905rx6NMg\n6z3bIOlHuUaPA/Y+fa8aP1aiEWMZ3H20v5HFNXQ8ZzwKsCTjoA2twOHEdB3Pkax2R+zGGUVZIjD4\nnEIJUyiOBYQKLUkKRV4KisrCsz2UtkgLQamNPHnkBgZ4h6Yfx3TTlIqcuguerWgGkvnI5coZl6YP\nE75gOvSZKg5+9dLrns21H+j/3X2PMRX5eK7N/cvbbI70mJ4mKZXk+rkpSmERpzmpqtiLc8qqouG5\nZEXFTpowHeYs1FPm6xmTYYFrmcq60oK4kPRSm0FmM8gsNBDYmlsX25yYqTEd1bClg23ZDGLF7Y+t\ncH4vphdrCi1ISoklFJNhwWwtZyoscWyjiVwqidIOnuMSOA6h43Cg1aDuhQhZo+FPUnNrtIKUrOhw\nYW+TXjIgKws0ikpLksJjY2Czmzoo7eNLm0MTLtORJCtizmxvszaIycZDqLwARUDkRdTdOp1UI1Cs\ndHdQSrEPadBjtSelLJp+yLXz06wPSjaHKarSQMlMTXJi2mcqxFB6uiO6SY6qNLmyyJRF4AQcn2rz\nLVce4GC7zn0rHZ7Y6VKqAe/9vtfw63f8vyRlyHseaNCJR8xFCRUus415Wk5FUS2DqFjqTnK00eTk\nxgpCjg0yBHRil59/9c289roGtnT4N392D2l+juU9n09faHPdfIt/+03XcGR6gsc31/js8jmWOppB\nHnLVdINznSF3n9ugUOBIyUuOTfGKKxcY5SW+bdEMXF591TyfPLPB//mR+0nyionQ41e/83k0Ao/V\nXsxHHl3ho4+tEhcKW0rSrKSSYwTvM/Zt5Fgm2FuGx3vVTJNH1rt0sy/FrubLW/m/fzOW/J/bdv5d\n1tdyoP+de57kX9/+eeJCcaAe8qKjU3x2eZdRXjDfCAldB0eOgwKQqorJ0COwLeK84PHt/sXrSDNw\nOdgIcCzBaj9FA63Q4/q5FjcfnGSQ5nx+ZZdznSFpqZj0PZKypJsW5GWJZRnwGmgjl1sPuHIypx3C\nIK9xfq9irTtitR9ji4RXXbFFw4Pl3jzL/YjQ6dHyhoROhcADWce3Y7IippeZ2fRsJDgx3eJQa5rd\nkaIT7/KTr34T3/3bv0pRaYaZotQgMZW8FIJBUhq9eSnwbQdrnPSosmI3URcDZst3EUhW+zmFNrK1\n1dg5To0rZ6Oed6kqf/q2uaRwI8YofCEMY0WOg/D+7X2d+2fe319SgG+ZfZk848Tbn92Ly477hfmG\nRo6TANsy71UKaHg2x6dCpiOftf6ItX5iwJhjgIBJJjSeZTzopdQoXSG0eS3fNpinCtOosITEljaO\ntMhLTSdT9BOjrFooi7Ky8Z2Ao1MTHJ+aZDaKuHp+kmtmG3iOZqvbZ+/C6tdXoP+ds1ukyuaTZ7Y5\n3y0vtoR9S/KGaxd4YndEPy0YFSVpoRBasdiCujsg8oZM1go8y1A6NIZb30stOonLXmohtKDpGeU5\nyzY+8NfMTnL13BR13zc2uLbkfCfhTx5aYbuf0c2h7lW0w5yD9YyGry4iO0e5Qeh7lk8zcLGkjZQ2\nB1sNjrRn0bqGbVUIBgT2kLwYshMnxIVRo6q0T64DdkcuKz19ERQyW7eYiyRNX3B+b8CZnSH9tKAf\nS/q5RVx6WDJgvhESeSDJWe93GWUVCnPypaWR8M0LSSuscd38BJvDjH5aYMsC11LMNSyONC2EgK1h\nztYwJS4UaWG0oLVwmIkaPO/QFK+6Yp71Ucqjm3vsDPtAgSMLQqdiru7wjttey7+5/dc42znMYnuW\nlr/HZ5d3+OySIFdw1fSIG+Y1pW7x6SUXyFlsphyKHD63ukcvtzjf9TnYyGj5iv/jFbfgOhdY2unw\n3ocCHtvWoMGxBK+8Ypq3vXiK0JU8sGpxz/Ie64OE6ZrHQj3gY0+ssdKN0Wimaz5vvuUIoeugEdRc\nmxsPTDAX2nzXez7NhW6MYwne+oIr+Y7rD7Pei/nrMxv8yYPn2R7l2FJQKkWpBVWlvyDY12xJqSos\n26LmSI606zy22XtaJ+orsT70vS/i22+84lk51tdqoP//7j3N2z/0OUaFYqEe8LzDU9y3sktalMw3\nQgLXvmR2UoHSmsmaiy0le6OcJ7d7DHOFLQUTocuBRkA/L9mLczzHYr4R8NIjM0yEHmd3B9y/0qGT\n5Pi2mevuxhlJbhLVhu/hWaZinqx5zNcD5hoBlS7I8k02BilP7rikhbgY0K6b2eFgq8vuyOPRrUlK\n7XBsIufo5IgkF1zoGaDwbJQxFVlcObXIdJTRiRN6qUM/KdgcKn7nLd/L897128RFQakNsrzpW0Su\nxSDPqMZBquZauJZpQ6uqMI6MY+paO7BxpGAnyS4KRe3P0C9fWotLc/TLbu//fH8mXulx5V3tu80Z\nDRFVSdRltLd9EJ6qDD1ZI7CFadXnlSmwpLiUQOx/3wfpfUHCMObeS4wVtMAkOJ4tOdKOON6uszGM\nOd8ZMsoLdKVg3BEwSQGm+yPMeS8EWFTUPAtLYsSDpJHP9l2DG1CqNGY/lb6YzLi2pObaTEcBkefQ\nCjymaj5N3yNXMMqhl2oq5XCVM/P1Fej/69mU20+d58LoEut9uubyQy88zp8/ts5ukmDrhNnGkIV6\nzlxUAiYbB0FSCPqZzdbIoRMbPebQqfCdisBWTNUcOrGmm2qqyuKmg9M8d65F03eYqplZycef2OQT\nZ7YRxMw1cubrGYFTYY2VmvqpTVpIVOUwE9m4tott2whs5pptbpiboagSVDVAqT6+oylUSS9NiQuH\nXPmUukaceWzFQ/pJiiMVDV/TDh1mI5+0VJxc7XNut2R9AL3UYZhLJkLJoZbLXN3Ckoq13oheYuQZ\n48IiGQf3spDM1gOeM11jJ8mpyLGF8bBfaJqAlCvYHqbsxCVJLk0GWtnUvICrphu89Ng0h1oRj293\nWe31SPIYS5gOyUTocLgdEVKwPuzRTbZ51+v/Oe/+xF9wartBP93GtXJCZ4KZepvPnDuLa60xKiwe\n2ZrgcCviqtmCjZ6hL/m2RWBNcNf5HabrKaPcouXn3Dg/ZC46xEuuehm+hB+7/fMsdWKmawmzUcUr\nrriS1990LcMs52NPrPP4Zo9hXnKkVaMTZ9z91CZZadp4L1ic5htPmNl93XOYiQK+9TkL/KsPfY4/\nf9y08p+3OMkvfet1bKcVn3pqiw+cPM9SNzbtvKpC6S9e2Ye2pCwrbMd8tgvNGk9u941fwVdoSaD4\nf54dUN7XYqB/3+fP8s8/eC+jvGQu8rnhwAQPb/TICsV83cd37LHdKeRKYUsjgAOw2o0NdU6ZMdV0\nzaMdOGyNcgO48xyummlw6+FpBlnB55d3WeqOKJSi6VkM8oo4V+SVwpMW9cChqox65XzdZ6EZYY/9\n65e6Q/KiT+ikJKVDVTXwbMlWnBKnMa84tkHLK3mqO0OpZ0GPsOUeDT9DVQ6eO8mthyMaTsFunNJN\nXbK8Q6Zy1gceRSn4ox/8p1z9i/+FuDABt2ZLIt+lm5QkpUIgCVyJZ9nYQpCVBduxKaQkmrm6j2NJ\nVroJFZeq8H2Huf2W/UX3zqfdry7KzF5eZRuA3ThhuKzqls+o3J+5bAGWFiTjbsG+8Y3WXAbO20/c\nnp40KC2M4t5Yja8CbGkzVw+4ZqbJIC95YnvI3ignrxRagZb7dD9wbTPOKCvTwreFxnctbCkRssIR\nRqY7sA3+YZjlDLMSpSujLmhpGr7kQMPn8ETAZM1mruEwFTiUFAzSlF6SkpYFeVmgtcaTHteFN3x9\nBfp/8t9P88hlErjPW2jxvc+f4xOnT+HJLg2/GBvCCBzLop9VbA1tNgYOWyOXSgtCt6LuKXyrMm2X\nSgA2hxp1HtnK6OcKT8INBya5eq7JkXYdzw6I44zbH32USu8yGRY4lrlIF0rSTS16qU1eCmYjG89x\nsC2BJTyk9Dk04XPNtI/SPYoqQ2A8jZWy6aYOI+WRFy5lpemnCf00RukCR0pC12K2HtD2Az67knHP\n0oClPUGqJIGtqPua2Uiy0PAIPZvVvSHbo9xU9oUxxUhLiYVgquZy1ZTHSOVYosCWipoDbd9CC0lc\nVmz0S/qZEcCJC4lnG2ndq2cnuPXINKMi50KnyzAfkZUprmXkQWciH6FH9FPzVagEM7k2Z+1vvPkn\nOPhz7+PGuRqvPO7Szy0e3jBK7keb28zX4TMrHp9aKvCtjNkoYzL0mQxdlrslqwOPxVbKXE3yV2di\nXnR4D0tq7jjXplA+d73tm7h+cZbfvPshfu++z7MzVCz3A6ZrPv/qpSd46RUHeGyryx1nNljpjvBs\niwORz93L2yx3THU/Gbi8/rqj1EMbz5ZEnsOLjszw4IVd3vGxkyS5AU390j+6iVbo8eDqHu8/eY5H\nNvqAplIVSWUqIPWMsyawBKXS2LYg8mwmA5/TO4MvyZ3u77vO/+RtHJqZ/LKP87UW6D9w8hw//Eef\nZZiXTIUe1843eWJ7SF4q5ho+nm14Xa4lGeUlNc+h7tqUquJcZ8DWMEVpQ52brQdIBNtxiiMkU3Wf\nFy5OM98IeGK7z8Pre+wlBaFtKlZjAGP2fdN3QUDNdYy2fKtG3bfZG2Usd2O2BwlxUWJbkivaKTUX\nLvR89hLBMFcUpeK62T7XzfWIC5sHN9rsJAFXTCRcN1tw1WwTx47YGgrifI84HbGbSKqqxLEKhJDE\nuct7vv8HuOoX3o0Uxo+jFbikhSLOFVKCKyW+Y2NJQVGW7KWKCpMsToUujiVYH2QXRcVMUB0D3MaV\ne6W/8H51sYoXoE01rtEIvT+J/8JK/FIr3wRSS46TBQGhA6KqqOQYGyAuyd7u3788WbgkmXup+2CN\nkwwpBXXP4VAzBCFY76cMxpbeevy3CbH/zgQCSVFpCmXUQi1hrpkaE/w928K3XVRVsTPKGBWaSptU\nyRl3ca6abbHYqnNsMuLEdJNW6LEXZ6z1Y7aHCf0sIy0UUmiSPGeQJaA0rz90+Osr0H/Hfz/NsBhy\ndCLldVeHLLQUy7sd4kKNvY0hKXwGRcSjG9DPNZGnDMjOrsbay2PDmty0n2ejGtdM17n3wjaDosIW\ncM3cBDccmGOhbqP1Dlv9Dda6O+wTH+LcYiex6ae26QrYFRM1F9cSFKWNbcFkzWahDosTFlJUlFWJ\nJSwCt05SeuwmmrKEUZ5TqIxhrozRAWAJm1bY4FBritUevO/BDTYGJqj6tiJyK+qeZKbuc7BZ48mt\nAUvdgm4qScaiPiBwhWY+khyecKlEji2NQ1V9TJspKugkgu1hRVKI8fMk7dBlcaLGTQfbzNd9duI+\nozRhmI2AgtCxcKVC6IxMJeRVgqrSy1wm9rPpACkbtMJJ/uPrv41jP/+HTNcGSKFJqzYvODTHoXqf\nvXSdXhbQzWY53g650F3i3O6AtFQIIUhVi8Wmg6q67KUWk2HGkWaX+9YcPrc6cXGftH2bT731aurN\niHf+1Rrvvf8CSWFAhscn6/zMt1zHfKvGXWc3ue9Ch51hymzDJ8ly7lkyiHopBLccavPKKxfIlaLl\nuxxuRxyfCPhf33fPxVb+D7/wBN/8nAOc3urz3gfOce/SDqXpVxIrAxR6ZhD3pdmjji1oeA6hZ/NU\nJ/6KnTcn6pLHfu7NX/ZxvpYC/Z8+vMQ/ef+nGWYlLU9yzfwkZ3YGlBpmax6ebcBQtiUY5iWToYfv\nWAySnLM7ffq52QMTgctM5NPPSoZZSc21ONaOeOHRGeKi5L4LO6zsJZSqJHJsenlJWhg/jMix8R0L\nyxJM1nwWmiHToUs/LbnQHbExSIhzQ2NreDaebVHqgqbTJVGCjUFEM3BBa3ZHCS85skY7yFnutmnW\njvDSoxMI3aWfbtCJNWsDj2GW03ATpCzopRHTtQxbKlb7kg/84A+z+HO/gSU0Ld9DVdBPc7QQuFJQ\n8x1TyReKTlKOZ/gwG7l4tsVaP6F6xrz70kz9b6/C/y5LX544cGnWL7Vp7ReXJxHPSCaq8fXH6Nzr\ni8mERuAIo1/v2oIJ3+E50zXqNYfVvQGbw5hSlQhM98EW+qIMriUAUY317zWOLU3yIUyi6DsCR0Cm\nNKNcUVTGgsqSksC2mKr5LE7UmI58DjRDJmtG66AbZ3TizIxCSyMOkOSKYaboZYpRbkDUDdfjTUeP\nfX0F+t87fSeVGHHLgRZFJTi3O+JCT7I5dNmOA3y3RstXdJMuNUddtEIslOGudxObrJRICTM1i+MT\nHkcm63zsyU22YkFZal55hc/NBy0id8ggG7HWS8mUppvYbA4dBqmNY0PoKiQQeRa+Lc0msipm6xUt\n32Y6sgkcC02IJTws6ROXgr04IS8LRoUmLQriXLGXCsrKx7VrHJmYIZQB733oDBv9AXI857akxrUF\nMzWf6+enuH+lxxO7ObuJcYMCgWtVRLZiKhQcaFpIS4/BdBDYgqKySXLJTqzp54JCGcWmwLaYa/gc\nbdd57nSEEgVlmRGXMUme4lkSKXKKYkSlMzQZQufjDN+c4UVlowkJnAkO1Gc5NDGNkg7D3GgX/MiL\nn8N/ufMuHtnc4bMrGUt7gtApuGmhx8FmjUbtOEt7GqW6+HbGdOCzHY+4b7XgQldyqBXT8iW2jDjU\nuECuKu5ZncUi5ExnZIBRfsFslOPLkI9+/6uJoog3vuczfOr8FqWqkFLwkiMz/Myrb2BnlPHXp9c4\nsz0kU4qDdZ/71zo8tTtCC5jwXb7j2oPUA4+G59AKXV55xRz/7uMPXWzl33Kozc++6mq2EsUfPnCe\nv3xynaRUaF2RKr5osPekoSDZlqDpO9iWZLX/7LndPXOl73ozjvXlgfK+VgL9h08t8U/f9xn6WUHL\nlRyfabG8F1NqzWw9wLVMiWZLwag0Dm6WkOyOEs53hqSlCQZTtYC6Z9GJCzTQDj1uPjTB4XaNM9sD\nHl7vMkhLfEuQV5q0VGRKITGBWwjBROgzX/eZa/ikSrPWjVnvjRjkZq4bjlu8mdKM0oJMVUyFGQea\nGlUFLPds+mlOrjTHWiNedqzPTC0E6wp2kyad4QXQHSqdkxYO3cyj7pY0g5xSaTaHNoE9QFWKD/yz\nf81173w3Dd9BCBgkBRUGz1Jz99v1ymjbM2YvBQ6BI1nrZRRcqtb3+ef7rfJ9QJ7WemxQc2ldYrKP\nCwA9BsBdXnE/I2HYb+tfDsKzxSVWy99n2eN5vWvZLDQCpush64OMtV7MKC0pLksauPieJVWlyUqj\nXmpJCymM2YzvuES+hYWmM8roZwVCGGpe4Epm6x5Xz0ZcPdfkinaNo5MGwLk7SrjQGzFIM0ZZTkVF\nkmeM8oJhlpOrctylgKbvmFFPWOOEu/D1Fejff+6TfMOReTaGPn/6+IhT6zkTQcFspDnYMrzIC/2E\nSguGucVeYjNMLTQC19b4doVvwfGpBu1aQOT4fPSxFSZqCQcbGSemoOHb9LKCQVqx0nXYis0xGoHh\nlAN4lqLmCJqBwB+j9kMXmr7NRBDRCGogHKrKRaOIs4peXjHKSpKyop/a7MaSburgiICpWkjDVZzc\n3GWt10NQXNJtltDyfV569BCfXenywGpMNzNb0XcqQlsROhXt8QwfKam0RpUliICktBjmgm6izebV\nptU1EfocbAQca4dM1CxsmVOolLTM0FpTlhll2TdBXeRIUT7NaSqvXBARDX+SxYkFjkxOM8xN9WNJ\nQeQ6RJ7NdOQzGXo8d67FH3zubnqZQOkJljsjzu8+Sln1eaoTsTmqcXzS54WL5gKyE2dGVlZMkqsR\nq3sbrPQk8/WYK9ojLvQanNmbBEzbzBYlebUDwFOdAKUlR1sBZ37mDZxa3eIt772HMztDKl0R2Bbf\nfeNh3nTLMU5tdvn0U9us9mIi16FSFZ9b2WVUFEgpuW6uyTeeWDBArdDj6tkWy50B//bjD5EVppX/\nC7fdRM21+bNHlvngI6t04xyNafMBX+Cj4O1X9pag5TsordmOvzK0u599+ZW847Uv+LKO8bUQ6D/2\n2Apv+f1P0c8Kmrbg8HSLtX6MqjSzjQBPmorQkha5qpgOHUpdsd5LWB8klJXhLk/WfKTU7CUFvm1x\nsBny4mOzJHnJA6u7rPcTlDL2pMNcUaqSsoLIM6YvgWsxH4UstAKoNOuDlI1BQjcxibGxeZUUShPn\nJWlZYVsG0R5YgsDtoqqSrVFE6IZcMRXh23CgfpbQGrDcb3Ch32KYayb9mNmoD9gM84BBLrAYIEXO\nXmIjUfhOxUfe9nZe+sv/GUvYdOKcTAmENHxuKQx+pJMoCkwl3w5cIl+y1kvHyPh9WtqXX70budqn\nV+XqaS3/S7cFRta2ZGx888wt+AzEvhBPp/rZAlzb6PHP1VwOTISM8pK17oi4KMYJCE+jAcoxP6+s\nxl0LS+AIiW1BYFum+6IU/VxdZDdJAZ7tMN8IuHa+zdHJJs+dadCu+SR5xXI3YWeU0UuM/3w/LdiL\nC3bjgmFmsBCOZTFXD5mrh8xEDned3eL+Czs4WvNfb7vq6yvQ77gRj+1scdeZMwzyPq40fOZ26FFU\nDp+/kLKXOOSVwLEgctUlswQN7VqN585Ooqsh7WBIZ7hF4Jpc0bdN1b/cddkZuoxKxWTNJAeWqGh4\nClsqQkcTuoLANjvCAFk8JsIaM7UGjm10odNSM8xLBqnhuw8zm93YZjvWuJZNZBuL2V7SZ30wIL0M\nmFUqQeiF3DhZZzWxuWelR6ZKArsicBSBUxFYFb4jaHkOQhpOf15oktIhKS0GuZklCYymQeQ5tGsu\nRydCFuoWoQdSFJRVSpqlJEVMVcUGmCcLBIpqPJvSWpBXPlJETNVmeO70IVqN+sXALgQXA/tE4JIU\npkW52ktY3huxNRzx4be+mv981ye44cAJ9tKKzd4aSb5MXrr85WmPR7YGNL0BoasIbJcT0yGhM8H5\nHjT9Lp4tGcQ2tnySUV5xx7kppPCZqwdkRU4jzGn5JVt9m4e3nx5ar5+r88BPvI7333+W//3DD7Az\nzNBo2qHHj774BDcuTnH32U0eWu/RS3KmQofHtvZY2UupBNRdh9dcM08z8Jmq+UxHPifaIW/74H2s\n9WJcW/J9zz/GN584wF1nN/n9+84axTCtyZW58DxzZu+MKXnO+CKfqJLes+h2d/kq/8Nbxtzjv9/6\nag/0n3hyne/+b3fSS3MiS7A43WRrkKAqzVw9wJLm/RtxFGn2aF6wvDcwn7nQRK5NO3AZFRW5qmgG\nDtfOTnDldJ3T2wMe3+ozzHNcCXGhyZWiVMZPPPQcXGkxXXNZaIYEjs3WKGGtlxhqnq4IbQvftigr\n4xaXKY0tBQ3fxrMsumlJphQNr+LEdMlVU20W20dY6cU8sLpLkm1zzcwGttCc7kyzm7SYqaU03BGu\nNWQnlmwMHKpKMVPLCBzNat9nvqG4/Yd/lDf85q+QFKWZvQvGgDGDGxlkmmIccCPHwnMc1no5eSUp\nlKCoBLmS5KXhhBsL2f3P9HKHuf0q/FLg3efV7wdgS+ovqOC/2Nqf3P9N63KswL4xzX6iIBHYlqTh\neRxs1SiV5lwnYW+UUYwR/tUYsa8RWELDuP0vJXiOceVzbfM/8i2Is5JBbpgL9hg53645PGeqzg0H\nJjg+HXGgGSCAjX7MxiBmmJWkpWKY5ezFBYMsNyBcDY4lmap5TEUeroQH1vY4vz2gl6uL14tJz+WX\nX/51BsZ7YLTEnU9tshOnFKWk1CE1r0GlA+69sE3NqQgddTE7U5XhtvuW5luO12gGMbYc4lolG4MM\nVUl2E4uNvsde6hK6kskgp9QFDU9RdxWeZVyclAbPEtQ9SaYsCiUReEzXGyy2m7iWQ1xUdGNJP7Pp\nJDbD3KWfwSjLyMsYiwJkiaRga5SS5IbTmo0d31zL57mtkFy6nFzvUFHiO6Zit6U5lTzbOKyV2qEf\nlxTaJy1NG74cGyOAyUwnfI/ZusvxSY/I13h2idAFRZnRS4coNUSIDEsWF1tXQhgp4FIFuHaTufoc\nx+cO0PCDsZ1liaoq6p6Ls3SQmQAAIABJREFUJyr6uWJzkLE1TNkepeyMDAVPoLGlSY6anuIDP/AG\nvuXdH6SoAq6bi3j+gR6ToU0vP0AntUCnnNk6z6kNowdeKMH2KOLmBYfjk7Ax8vHkJjVnj6f2mjy4\nGTFMDXAmcjTPmckoClga1HEtST/J2Bg9vUp+4aEWd//Yt/PTH/48v3nPGUa5CaxH2zV+6uXXoF3J\nJ89ssrRnbGwtASfXOgyzEikkz5lp8MoT81gCZqKAWw5N8qt3PsJfn9k2qPyDbX78ZVfx2O6Q3/3s\nWU5vD6i0Uev6YieRM27ju5ZJ2IZFybB49pH4J//lt3Hd4tTf+/lfzYH+rtPrvPH37qSb5tQswcJE\nnU6SoZVmuu5jjS8GJYbyVHNt+mnJ8t6AUaFwpaQVuISOQzcrcCzBbORz66FJFPDg2h5bgwStDXUy\nL02QR0Pdt3GksXU9NBHQ8j06ccZa32jeZ6UyLmq2jdKmgs+ryqgtuja2LeknJaWu8CyLmcjjhUem\nOdxSDLI+Z3Y05/c0m8OcOC+4ZX6DQ62YThyx0p9gaU8yEfSYq4/QGjqxQ6psGq4R0nGkRVo2+NBb\nv48j7/htMlXhOtD0JKErsXRudDqEMY1p+uZ97cTZGB1/CSm/v/avE2qsNGfkZC/9rFSCYpwg5Mpc\nly5Hve970F9Cw4ORCX96ZW59EW79l/LdEmYsFjoWc5GPbUm2hil7sZEt3v9L9mmVgktcf4Xhvcsx\n9z10LbSGXmLOSwPcFnhjR8JvWJzhuvk21861qfsu3aTgzM6Qvbikl+Z0kpStQUo/yRnlOQiNb1nM\nNTzmGx5JXvD5lW2WOkMz8sN81hbgOwY8/YrD0/zjw9NfX4H+lx88xb1rGd3EYjqqcWzSoW4XnNzs\nXnxsWppN5ciKg/WU6+c1iy1Jqoqxx7LmzK7N+T2fuLCYDCsONioONASqGmLLYiygA4gKR5oNbVkW\nSrt0EwtLeLRCn+fOThP5DTaHks2BGFviGrpckicM0phMxVRVTqUhLkriTDEsjLlCWkikdDhWs6jX\nPZ7a61FREFymUmfAeRLf8ihw6SeKtLApx5m0lCZ7BW1a5TWPxZbNQsvBEgWOLEmzlLjsUSkDphOy\nMNlvJRFCoCqXihDfbXG4vcB1C4uoCtKyZJCVlKrClZqtUc72MKUT5+yOMnppgdIaW1bYUuFIRTMw\nzINDLY9DrRoHWwGh43Dz4et4x0fv597lXSJni4lgRP4/yHvzYEvTu77v8zzPu579nrv3Ot2zaaTR\nSEKSNRKyAckSYBFTJSd2DI5ARREToBzMkrLjVBKSGDt2CmGMS7ERYBPHISkwYINAZhGLMAhZy4w0\nmq2np/e+67lne/f3eZ788bznLjPdMxJIQNW8VV23b99z+55zz/u+39/yXfQSF5bv49HzK6TVFgdJ\nSlJZxknCbzxXc2WUstlNqI0k8CLefGqXtITPbJ+hF3cYxj5P7U5QHODLmq15m8CL8I2mEApfCSZp\nzk56csP37ntX+dlv+fN88//zB/zWc87pTknBG88s8X1vf5CnRymfuD5ia5oxiAOu7E240kQZtwOP\nr31wnWGnzUY34nS/zdZ4zgc+9hRFZVjpRPwP73gYIy0/+QeX+NTNEbW2lMa+PNhHPuO8OpF7/6U4\nYmD+x5Da/VkF+o9f3uYbf+q3GOclsYTNpQ6jeYGQsNaOsEKgpAPnXugjBRxkJTenKXXtOrJB7MbX\nSVXTCQPuX+ny6vU+l/ZmPLs3Iy0qpBQUtaHWmlJbWqHrzjtBwEYvZrUdkJaaW9OM/aQgr2s8KYl8\nZ4WdVxWVtkgp6QUeQjmTGisskac41Y9567lV+i2fg7Tk6e0xQuxR1TXb8xa5VuSVJlYZbzlzGyVr\nPnmrz7N7LTqhZqOTc36poDIeSdEhCiTn+pZeZBhlHj/xzd/Ka//BBxFAHCiXK69hlNXk2oF0x1cM\n4oCbEyehA3GMrd6w27F4TaSsauy71V2A2NFC3DlzNJ5/cXGwMNyptGwmB65YqK08IYk7XiwsioQX\nWuQGUtD2BRv9mGHksZPk7M7Sxm/evQ5fOpMbR9c40tYHDVGv5UvavqSymrSs0ca5pHrKWWFfXOnw\nhlNDLgy7rHZDagO3pxkHWUGSVxxkJaOsYJ67Na224EuPlXbEsBWyn1Q8dnPMjWlKVnE4WUAIIulx\nZqnNWuzzua2E3axgEAb8wjc+8MoC+vd/5PNkuuDi0GepHTAIJB+9MqLWzo2p69ecXcpZblWstAzr\nnZDaSG5PNJdGPpdHAZWB5bZmrVVybmB4eCOgrFP2khJtDQgD1o3C00KSVyFx0GJaCkrtjGge2tzk\nVGeFG7Oa/aREoJGyIisS9tIZSZ5T1BptXcFwkFrGhSAtJUUlaQdwuqVZHsTsJQnangSivJYYo2h5\nEUlpSbWLQTVYFAKpnO9q6HkMWx5n+u5PNwIlatJySlHNsTZDUCCloTY0FamiNiFSdmiHQ169fpoz\nQ5d25gqhinFesDst2J670eO0qJmXVWM5aQiUJvAMy5Fkvedxqhex3o1ZbSroaaGZ5pZpk69s8fjb\nX/0G/q9PPMfZXs3N8VM8tZPze9c6lFrQDQruXYYHV5e4sBIzyRUHWZu82uGJW1v8pxuGU919Nrsp\nT+/2SfU6D671KDVEXk7XS3huVPPZWxbTpGB0A4UnBQaJrwTb05RpebJb/i9ec4p/8A1v4r/8v3+X\nJ7amGOs0++951Sbvff0Ffu/5bZ7cdl7lvoTPbY2Z5iVSKO5bafOWM0us9DtsdGPW2hH/269/9miU\n/8aLvPXCKv/qD5/jo89tUdaaUr94Xw+OLORuCA7s99Lyjo/74xzJP/wmIv/uwVEvdfxZBPpPXdvl\nPR/6KAdZQSRgrd9mkpVIKVhrh+jm5lwaw1LoUxvD7UnKQV5jrKHte3TjgLxyLOlhK+BNZ4YIJfnc\nrQl7SY7RNaUBrQ2VdZaxrcAj9iSrnagxvIGbk4TdeU5eaZQUh6E4ea2ptJOMdX0JUpGW7lqPA4+z\ngxZvOrtMO/DYmqQ8sz/nIM2blU7JSpxSGZ+dJGZ7ljPJK960ecB9qwmj1OfzOz320jYPrhac7RUs\ntwVSdih1yLyoCeSYtKz5uf/6b3HhB3+c2HedrmwSFmuccUw3EAzikFvT/As+7xbRsHASbg8T65px\nvRIOWANpGhvaI5e6hdxNypPdO3A4hj8ePmMa5r1p/q0yzoZWa4GUim4Q0osCxrlma5KTauHue1Yg\nFkVCQ7izzc8PGil2O/SIpGBWVszL+lBe2A4U5wZt3nZhmTefW+a+1Q6BEOwkKbemKbOsYGeWsZvm\nTNKcyhgsLqBstR3QCSS7Scbl/SkHScmx7DUnGQwUm52AXBt25yVZfbIhWIkCPvAXXmGj++//3cdo\nRx6dMGTYCfjI01ustipOdUuGcUnsG4wVDKKQso751C3J5QPHiF9r55zuFSy1nKf9hWGAEpLtWU5e\nW2ormBaKUeaRlj5F7RGoNrmO2M88sDFnBh0e3hwwK3KsLcDm1LpkP0kZ51Uj87NkpWRSCCaZICkh\n9i2dyNIXNct9j3nl3lBwF0vemNlo7dENQrLaUB2CgiD0HBFFSMlS5HG6pzi7pFjretRVQlYmaJsg\nRYYSFQZLbdyu36CodYRSXVbbazy8dp7hoIvA7Y+vH8x4dnfOflowykrmeUlea3xpiQNL5DkS4nLs\nsdHzWO9EDFsRw3aANjArLNPCTVLAw+IDCikE3chn2ApZin2+6r5N/slvfw5ln0eKgmHnfjY7K3z6\n9h5P3r7UkO+cKcVa9xxvPjsg9ieMc8tBZtkef5ob44r/8OwSQoZEvsfpfsBr10osklHex2rBtUnC\njckc3QRYRJ4i9AVYSeBJbo0TkvrolJbAt/+5C3ztq87wA//+U9ycZlgsgyjg295yL6cHbX7/yh43\nJglKCm6NU66PE7SF2Fd81cUVTg16nB20uHely7/+w0v87hVHCvyKM0t856MX+ZVndvm3j18lKWsq\n/WL3PDgO9s6Gcy/70trk/oVTbT76fe/9I33vnzWgf+zGHl//47/JKC0IBKz2WszzCiEFKy13XvpS\nUFnLMA6ZlxW3JgnzygU0tQOfOPBIy5rIV5xf6vDqtT5XR3OeO0jIyxKDoNK2SRtz07LQVwyigNP9\nFp5SbE0TdpOCpKiQQhB7HgbjAk+MA/h2IDFAXjnVRzvwuWepzRtOD/Ckx/OjKVcPEiZ5BdZQNR70\nUkDLm1KbjBsTn73U5YvFXsV7HtyjH2me3RuS22WGcchKO6PjHzDLDVcnPkUFoVfRC0v+3Xd8D2/8\nRx8k8ANMbThINZlxXXMoBMN2zPVJ5nbbwh4Gzyx6ZtFY1bokOMecP75rf6E97Usfx0l97vua9fjh\nz3JfNyeKgON8eMHiiTliayfwaAXO5GucVRQ11E1RYBcpdw3g1816wV35Cl9IpPSYFDV5ZamtG923\ngoAHV/t89X2neWh9yFo3YlbW3Byn7Cc5tycpNyZZs0bWGCvcvasT4knJ1f0ZT+9OGWfVIbhLXADP\ncuyzEksOipJJXjk5Y+O/v/idBsLSCiT3LbX5wbfc98oC+h95/DmEMgy8Mdv5lKW4xhP2kCyyOwso\nTI9ndyVRUHJ+kHKun7MU13TCCikccaMX+RSVZHtu2U0duI+zkHHuMS98BCGduEtWuip0ECkeXItY\n6UiyPCOvC9JSM8oK0sqSNhr0eSHJK4NAE3qabmjxhKYbCfLKUhgotTg0sUkrhUDSkVBJhTFgcCQd\nX8rGbEOy2VWc6/vcM/TJdEqaTxAkeKJEKefDXBnHUK2MR6EjfNXj9GCD122cpdVqIQVcP0h4anvC\ntXHCQVaSlDVlrYk9Q+RbYt/QDQVrHcVKO2CpFbIUB468VFumuQN0i4/Fo8n9oxf5LMUBy+2QYSvk\nyVu7/Oazu3x+d8LNScYkq9j7ob/OD/zcv+cdD/rszUP28lVA4MsJmx2BNpInt/f4zO2K3dRnOZ6z\n3JKcWz7NRmuLvfk2l0fLFGaNqwcJVw8SemFGP6qodIdeNKDjCYTvo7Vhe55xfTynrHFxv0oS+U4i\n4yvJjfGc49w3Bfy9d7yKwA/4kd99inlRAYLT/YjveutD7BcFj90ccZBVYC1P7Rw4ratQnFtq8dYz\nA04Pe5zqt9ieZvyLjz9LURmG7ZDv/wuv4vY85yc+fondee5Gj3c4zxeBHL6Edig5yL60fX31j/8G\n8m7sp5c4/iwB/ZNbI975wd9glOYEApY7MUlRoZTzf6i1xZcCK5wx0SQr2ZrmFFrjS+iELtxq4V3/\n2vU+oa/4/PaUcVpSG8eMttZS1pbQV3R8j3bkXO3akcf+vGR7njHPKxDOgtsIqGpDbayLKF6M7bUb\n47d9xb2rPV67McBay5M7U7amOWlVIRYsbwlYyzgrmZYVWM2ZfoYArk9iwMOTkkc2p7xufY4VITvz\nNZ7ZCwi9GR1/zrBVMC08dhMfAay0az7yXd/NX/nQjyJxWu9FnRtK6EY+o7Q6HCFb3OTP2qMRu/u7\nG6Ef7uetayQWe/dGQAeLj6Lxs+dYkdA8Skp7+LiFfe2CE3DokU9D8FuA5DFTHCkgkJZ2IOmFHlbA\nJHO5HKJ53GLtsHheC2a9k9kJvCbfIK8tlXYTAildXO99Kz0eWl1ipRMBikleMck1O/Oa27OC/XlF\nZZ3ZmpIevcCn1HDtIOXaOGNaGErtig2JIFCCpUgRKMV+VpJW9o6Tk0DCUuwjraGwrjhZ7/j89Nde\neGUB/a9tf4qq2udWqqm0JKkUt2YhNychVa1YbRc8sJKw0S0YxBUt37k+pVVNWkmS0keIFrtpxDO7\nlr3M5yDzmeQeFkkkYa0bESoLsqITGDY7gkHLd2YI2pBUhnEKs0JQ1hIrLBKNpCT0LYHn3uRYuujS\npHSAntcu1U5bR5bre1AJeRjHGEiQSuJLWO9Izg487luJECZjWuzjiRxP5Uhh0IZDYM+1T65jfNnn\ndP80j148h9Wa27Ocy6OEa+OE29OMeVE1chtLqJzt7yAWrLY8llohw3bIIAqIPElWK2eXiY/r0j1A\nOeZ+K2DYCrl5MOW3Lm3zzN7c7ajSkqx2vyNzhzOm/uH38c5/+gFAcG2yxjvuP8df/4ozXN67yryw\ngCbwPM70znJjMuHpnatc2nf8gAtLNyl1QFo+wKMXNojDkPF8zLO713hqN+HJXR8lnPRlrRuy1orw\nfYXWmp15wY1JQl4ZtDUoqYg8QaAU1tTcmpYnNLq+gL//dQ/zmVszfumpGxS1QQrBI5sD/qs3nOUz\nO3Ou7LvQkt1Zxo1JTm0Mse/x1vNLXFgecG6pTS/0+NHffZLtWUGgJH/t9ee5b7XHj33sKa4dJJT6\nzhe78wp0zyP2YPolVN391F99A+97y8Nf9Pf9WQH6p7ZG/MX/8zfYS3I8C0vdiKys8KXHMPYorcUT\nEiUFvoT9pGA/cx7jcSDpBD5Vw3bf6LV49XqP6wcJ16cZaZ5TGYExxnVYUtD2POJAsdaNGcYB86Li\n9ixnmjvOzcIXvtSOJS1x3uZGSAptCZSgG3g8sNbj/uUeqa54cmvKKC2otUs3dLRzS1lrDlJnsVvr\nI9BcbmnOLlVYIirdcyNsXfGm0zdo+QVP7Xa5MWszSSWnBhmnuxmBEhzkEca4CdvvfM/7uP8Hf4qk\n1M5VThi6Aay0fXaTotlZN/v3RTe98IEXsGDTH3b3zb8vxvQLB7lDA5tj7PeTsrmFg57rro8Mbxav\n9sjZbpFtf9LdzhUEgRT04gCFYV5UFPooblYIF4OO5XAvL4Qrnp3cTjq7amMQ0r3eVuDuGxeXOqz1\nYkKlKLQhrWomackoLUmq8jCC1000FFlpm528IS1to0hwr0UCke+RlYJ5ZSnMizkG1ggCJQkklEY5\nNYCRzWRPIgRstD1+6t2vMKD/xau/w+N7NbfnEdcnAUYbHt5IefVKynKnpOU5T2HnYe1jbMCT25Zr\ns5Db04hBPODWTHFlVB3rqCxtz7DWFvQiCDyNNSW+59PylUtp0h7TUjHOnF1lW9UIWSGFdj76Flcl\nG0FSSZJakVWSopbHTmJoKacRN4DCdQsSwXILNnsuHa7tzZmVMxQZgSoRwjiyinEjp7QOyasIJXuc\n6p/l4uqAvNJcHafsTDN3IypylHA2vw7Y3VRhGAcsxT7LnYhhHCClR1pJFmC+6NLbgc9SK2QyLfjY\n9W0u783Znjst8LxxfzJ3QKlFrnOgJL7nupjlVsBqO+DXvvs9vOeDH+CJ7RY3pjFguTjM2ex6vP7U\nCu961QY3Jj5ZHaDYIw4E/WidJ25+glG6x6dv9zgoltnsRDy00eORDUPoWcZFl2e2Mz72/C63Zykg\nUELQj302OhG9OEAIwdYk5cYkZV6UCOnoi4GUCFsT+h43p+WJLjuQ8KPveQP/+nM3+NSNEdoYfCV5\n571rvO3eTT5+dZedWUZZGy6Ppo3MUHKqF/GW0wMuri+z2fH5pSdv8fFrIwAeOdXnmx45w4//p6s8\ncXtMUZu7dvYGd2OKBcy+hI29/iOQ8v4sAP2VvQlv/2e/xt48Q1notwKyuibwFEuR6+Rd+prCGM3O\nLGNWmkMCWthI29qBxwMrXTqhz7N7Mw7SglIb5z1hXF5F7Ltd/CAKWO9GFJVuzv8abV1cK0JQN++f\nxBUWzkDGEnqSbhTwwGqXC8MOu/OcS3szxlkJjb/CYtQ9y0umZU1Vc7gXdiloilag6MceF5dquqHm\nxjTk+tgwzkvOdWe86cyY2iie2Olxa9ZlpV2z3io4t1SiVMAobVFqwy9/x/tofd+/Im/205GEjV7A\ntfHduSCCo8hZF+vaeNgLDv9+/GtHOfHNyF28oChoCoI7fQ5H3BXTsPCP7HYbGZ8VKOEmEFJKsqIi\nrRZrsJNTqoWv/iKCNpQulGYhsZTCvUe9yOPicpv7V7p0Q4XBkFYF07RknDtLXG3ceeW87Z2xUFoV\nFNpg7dH6QdrFOkNQG9ftW2GPhfkcSQClldRIqtoREKvGht1NUASeUIR+QCcMeWDY46+ef4WF2rzv\nw58jCKa8/tSMs72cbqjx1KLSU0gbMipC5tUAbXr8zGdm7KYeIHlgpcMoKdjNSqJGj97yDf1AEyqw\nUlBrZ91q8PFkgBIetdEEqqYTALZgXhpHuLCiYc9LilqRVNLlE7/gCFyJitGWyBP4nqATwHpXcKpt\nOD3QVHqOEpnzp4ZDYC+0IKliqroF9BjES0gVkVWOTLOf5iRFjhQaXzlgDz1DOxB0w4BBK2AYB6y0\nQpDBCTC3+LSDgHmW86mbB1wZzdlJCqZZxazU1EYfVrDHj8XFuRiFdwLVuICFbLR9VBC6PXTT1kth\nGcQpP/Ler+N7f+5nuDYZ8tjNCUk1ZxDlZJUg9i2VlmR6wDe8asDbz3pMTIe0zCiKJ5kXATvZfdQW\nbk1yJCm9KCP0OpxfPsWDywMKo7k1SfntS9s8uz+lqDVKSGJfMWwFrMU+YRSyPcu4Pk6Y5yVCKqw1\neELiC42vPG7NT978Yk/wg+98mA998nmuHaQYa+lEHu99+CztwOfpnSmTomJ3lnNzPEdbiALFG04N\nefXGgIvLXa6NZ/y/n75CpWG5FfDdf/5+/sPTO/z25S2KylC/xBXmCfAsfKm882783fewuTL8or7n\nTxvor+9P+Mof+zV25hnSQj/2yWtNqCTdKMLibrqx75NWFXvznNI4mVLLlwjpIQSstEIeWO+xPU25\nPSvIipKqmazUTSHnfO991jvuprqdZIxTN1lSChSCyjTTGOukXFaANY4PMogDHljrcboXc3WUcnU8\nJ6sce1s331Rpw6RxvStNM1bGeSq0PEUn8unHAZ3AozKGWZHRD6aUteH5g4isdlD4tfeNWOsW3Jx0\nGJdLaNNjtTOj4yeEqmJWhmSlx4e/81t56Ic+6OJmjWAQB9yY6cPmoTYn/9wp1PWLO4728Mdlc+rY\n54sd/wsfd7d9fyeUhFKRVzXpCcKafVFSHtatHnzpTMJq7Ui6EogDn81ei9efGvLgao/A85iVJbtJ\nwa3xnJ1Z5lwOhQsqo/GdT8qSqq4dl6B53koYQgmeWrT6zkJdSfd6Fj78nnC2umLhx998LoVASNHY\n6Trjr26oAMGs1JSVxhcR//k9b3tlAf3H9j+CFnMAB+7Wx1N9jFjhuYMez+175LVE1/Cxq3uHnfNX\n3dPjub19jNInpGuecDvztFJUWhEoST9WrLYlvtKAwRrDQeYcrNJKMi3caDur5F0vCK8hikgLUSDp\n+IZhCzZaNZv9ilZQIURG0OjXtRGURpBVHkkVUeoWhi5p7qOFT2UstdEYU6JE3cjZagLPjYzbgaIT\n+Axin+VWi1YYI4TfALvPQZZzeSfl2dGc/bmzbEwrQ6XvDuYuAMPtGttByFon4FQ/5kw3xCifotbu\neWnTdCKCbigYxJKVlmApsrSCmnZgCL2Kd736XfzUf3yMy2PNjfGc2B9RliVP7ybMioLn9kMKLbk4\nTJEC8nrAX3pgzmqr4LnJJq1wk3uXu6y2AnaT61w9mPO5bZ/auA7+3FKbV6312egEzGvLH1ze5pM3\nDxhnJUK4nVwv9FlttRh2A3ZmGTcmKbO8BOHGeUoIQmFQymMrKU+wXzu+4NvfeJqfeWKPUVpgEWz2\nQt77mnNsJSU3JwmzvOTaQUJSukCR1U7IW88s8cDGMrGv+MlPPMv+vCRQkr/yyFny2vJzj18jKauX\nBHtfuBHol8JOZxXY+iK7+j9NoL+xP+Ht/+zX2ZqlCOs6ulJr1/F6Ctsk0EVKMk4LJmVNrR1oRp4C\n4UhS5/stupHHtXHKJCsotEU3ayYhBS3fO5Sn+p5iPykalrRBNd2nORw5a3ypMIuiV0qG7YgHVrss\nt0Oe2ZmyNc+omo7fGIO1OB+Kqj4k2gqc82VLKVqBRy/2aQceSgjGeck4q6h1jTbQi0pWWiXz0meU\nxVgLp3s5bz+/jxCSywfLbM3bxB6stjNWohm5sXxuK+Txv/vtnPuf/wWhsmy2FXt59ZJmNNpCreWL\nCoDjf/SXpCC483F8mtBSLvVP64q0Ns0O/s7ThEU8uNesHRYrRF8JelHAPUttHlzrMWyFaGvZT1z0\n9u1p4aRwzesqKs0sr5mUmqQwbqp7jP3vGaiFywpZrAteGKgTAJ7nfE2UNAQSYh+UEsS+oR8pliLF\nekdhrWZa5BS6whiNRTd++ppBEPKtD7zCgP739n+VUW44NTzPZvdBbs+67KQlf3h9xM4kJa01Rhuu\nT8ZEvnOze/1Gi6d254c3ycWu3BiXSRQow6AlWG65k0FaS1JWjHI4yCyzQjAr1aGf/N0OhdtTRcrS\nDaHXsmzGOev9mkFY46ucQDmZm7GSSsOsDMiqmFkZkRUdUhOQlDWImlAZOgF4okbKGiUsgVINw9Sj\nHQastltEQUzkRcyTjCf2S57fz9hLSmZFRV4bSq0P92Ives7SjQhjT9GPA1baIfcstTm/2kLiM07z\nw8SmuikKhBB0QslSJFhuK4YtQTesafvO6c+XllqX5FVNqSHXkJbwbV/5Hi7vTkHA9dEtrox2uTmp\n2E2mJKXP7szn6ngbYxJuTn3aQcXbzo0ZZT6fuLbOm8+v8t6H1snIkKQge4Syy5O7U57ZGTMpFg6H\nbjR732qXOPB4euuAjz2/x/Vxgm2IQbGnWGr5rLcCUk3Dei4RQqK1KwQjaUFKdl+gwV8KPb7mdIvf\nvJWSVS544v6VDu+4d5lnRgUHacHuPOfWJEEbCH3Fa9Z7fMXZVc4NQn716W0ev+V8H167OeDNm21+\n+jO33N72pTp7/nh+38eP8h9/M0p+4f73f1pAvzNOeMs//VVuTRzI9yKfXNfEniTwfLyGYeUBB0nO\ntHbJaIGCwHddfC8KuDjsME5LtmYpeanJa41UAq0bsl1TJPfjgEleM0pyF6jUeLMbYTFWgnVTIgMo\nKYmUYNiOuH+1R+S0B7bYAAAgAElEQVRJnt2bMUoLtLHUxmKMQRvLtCipGl7N4i4SKEErUHQDj04U\nEEjJvKqYpE71Ymxj0CKcj0bgCy70czqhZV72OMhgXhrednaHzV7GKGmxlfS4PArphTM6QcawVTIp\nPD72Pf8twff+NBsdj5vzGjjqOO/6R5lDU5m7HbqZPB7/eKei4KXum3c7FjkiwlqS0rzsuS856cjn\nCnvnUfDwRo+LKx1anmCUFezNU25NE/KqwmCc2qGuyeqa0miq+iR/ZkHmc0LMk8dxGSAN30ojwVqU\n8EBIlFS0A49hK2YYR8xrw86kZJxXzIr6MAjHGLf/j31FN/LZ6EY8stHmWy+2X1lAf98DD/LsuGCc\nHo2bH7u5S1pmWFuyFMPWbH7Ysbc92Mtko0t3I7ZIGddlKneR90OPwPPJK8l+Ztiaasa567C/kCMQ\nlm5kiGTJ+aWazV5NL6po+zW+0k7naRxxb5p7zKoW4yzkIG1RIjDWRd62VE0rdEY/Ugh85Tyx275H\nL4rox22KvOLW3HJ1UrI7r5hVhqJ6eTD3pBsJDeKAzU7Efas9HhwOCGLJXlIwzkrKWjfGLoZau9FU\nHHgstRTDWNAPDbGnqU1OZQq0Lql0RW1ram3JK0hqyErIa59C+1TaOfjVRvEL3/ZO3vuTH+V0z+Ph\ndc19K32GLY9pUXFr1uHKaM7+/AZJadmeKTyeQ8mc378+4NrE2Uq2A81r1zQPrA15w/o5tB+790Ap\nZnnJM3tTnt+bUVk3zltph1wcdrh/tccoy/mdSzs8tTslK2ukdESqfuSz0QsJhMel/RkHeYmutVMY\nC0usAAR7L7CnXY48Nv2ay7ly0wApeMOpIQ+u97kymjFKS66OEmZ5hZQwbIc8em7II6dWuDGe80uf\nv0GlYdgK+GuP3MO/eex5bk1SypfYx98pJOePcrz/4RU+9P6v/4If/6cB9LsNyN+cpFjrPOK1tbR8\n54+glEuH09pwkBbkNYcjUCUlnpJsdEP6ccjtacY4zSi1wBpzOMbtRD690GepFZBWmoPMOdAJY5rM\nCDc5NMY0e1rwlCL0JWvtiHuX25Q1XB7NmOdOkldbjTGQlzVpbQ7fz0X3HkpJO/ToRj6RklTaMC1r\nkqI6NGdBuCmVryS+FES+pBv5+KJmuZ1QGcnWrE1WaQKZ8pXndxHUfOpml+vTmFxLzg9yNrs5vrR8\n5Lu+l9f/w5/gdmKPXOyaffHCyOZOJjTO3fKFBcDi7+bw315OyHHHqYA+krrVRpz42ZF0wUN5bV8W\n4AXuupAOW/Eb9cWFYYcH1/qsdyNmpWaUFlw/mDPOSoraUNWapKzJat2QiB1Oy0ZC6DUpdshj64Vj\nEjivmSwsTIN8ZQmUREqBL13mwVLs0/Y9cq2ZNPymsvFXwS6mOpJAeUSeohv7xMqn1JKs1qSVpaU8\n/v6j515ZQJ92N3h+kvD4zS0O0hmX90cUdYEvJd3I47n9lLyWFLU7aSy4IJtmXL9gXgokQkZo6zNL\nNAeldCSzL+CVKmHpBJrYq7g4KNjoVfQjTSes8SSHVW1eCmZlzCiP2E9CJrljUUpZESlnRRkoR1wL\nPEmgJO3Ax6stiYm4MS3YSw2z3JJrt0O8G5hL6VianUAxaIWc6ka8dqPPG0+v0W75bM8yduY5k7wk\nrxygV8ZJgbSuUcJDSevMcDyDJyqULBHkWFs5Qx+rGymMUw4YKym1T2kCytrHEBB6AbEf0o0CBnFA\nv5HdDeOAr3/NWb77Z/+AebGNEhWl9uhFhvXuKq/ZPMO5fkWlU7ZmAU/v3GSaPsmNacDnttbYm1fs\n5wXr7YzYN1yfRJRaMYx93nB6yDvuGaKiFjTv+s1JxnN7U3aSwv1+EJwZtLgw7LDW9vnUzQkfv77H\n3rw49LpuB4rVdsxqO+SZ3Qn7aYk2DvAtlkg6w6JxcRKJV2IPmdfMlKQ2lk6geMvZVcJAsTvPuD3N\nuT1J3MjPk9y/3OHt927QDX3+v89c4SAt8ZTk6x/c4PHbY57cnlLcSbawOP/40oD9F0PK+5MG+r1x\nwqM/9hE3hVmAPNDyVHOjVfgS0rJkVjnPCYnzNRAS2r7PqV7klBHzjKQ55yVgxGJM77MUOQLUqAF4\nYwxCyENNljHWyREt+J7je6x2I871W0zSihvTjEK7VYE2mrI2ZC8wRnLkVGei0wl9Wp6T4qWFYVaU\n1MawEIIJ4e4DgZJEnrPrNcJSaw6L/443J/AKbk0EN2cepYFHzxxw7zBllPk8s9/h+jjmTF9zulty\nYVjzL9/33bzrx3740IXOfVy4zTmW92JsfQT8R4XAiaLgmE/84mu2cZ5z96EFAJrmoz38+FJae2tp\ngmsEeS0OWeonCwR5aHgjWHTwrhiLfcV6L+aepTanBy08oZjkJTvzlN1ZQV5r8kqTVjVF7fIMvpD7\n/QuPRTniCRc37QlFGCh6gcdS7BGHHmVZM8kLkqqiMhph3crBUxB5gsiD2JP0QkXgS4wxVMZNcay1\nzmit8VPY7ER8z2tfYTr6P5jNuHww4foo4cY0pag0hgAhFNvzwu2VPUOgjlE1LGgtETKgqCW18bFI\n0rIkr3nJDgocsLd8zSCqONvP2eyW9ENNO9B4CrRxZI+kChhnEZMiYFI4q1olnYd04LnaLVCi2ak7\ns455oRhlLlQiKSHXjkRyp0MKV622fTdmP9OPeWi9z9c8sMq5wYAbk5SdWc5uUrjqMS+Y5Jp5WZEU\nlSMQWUcu8YUl8Cy+cha57aBsUvhcEt/CV99YhUXhqwBPxgRei27UoRfG9Fot1rstVtoRay0XzOB5\nHtY6stHiwsprQ1FrHljrsz3ZZ5Js89nbCc/sjbg2ybg96+JJzVI0ZRC1Ob+8SSCeZpbPuTbdRJsl\nEIJZMcG3Bzx2u+SJXUFRH2lxVaM/fd2pIV99bomo3QYgqzRXR3Mu78+a+FgHBmf7Le5f7TEvKn79\n2dtcPUgxxs1VfSnpRz5nOj5XpyV7aUFda4xwciBfOiOe2QtOnNWWR+j57CY5xsJKHPDGMyvkWrOX\n5lwdzZkVNQL3XN98zzKvX1/i1y/t8PTOBCElr17tgq741PbcBV98GY+Pfcdf5K33b35Bj/2TBPp5\nWvDGD/wyVw5ccdTywSCJPYlAoJQb1U+zksw0JkPNteErlyO/0IbP85ykdLpqF8sq6YYBvVChlGSS\nVaRVTV3VyCb1ESEw2iCVcrtWBXEQsNmNWO/E7M5ztpOcShvqWjeGVxW5cVp4WOjD3c08ChRtzxlI\nFRpmuVuHLTo6JdwN3fc9IgmtwHPPoZH4SaMpDcyLmrSqqWrDuaUMJS1XDiJKo4i9mv/swV06geHq\neIhUy4Sqy1pnzmg+5d/+zfdz3//6QQJlCZQ5oUc/7mfvOvOGKd48P3PMo951/fJFcbSL48gH/yjO\n9rAoME0x0zzWFQZHe22v0b+rL6QgWJjgWEkU+KzELYbtmFYYUdaGvaRmd14yz2vSunZTT2so6ztL\nWl/uWIzuA+WKrchXdEOfXuQSC/NmGjQva7Q2R46BwjnvBVIQ+s47X0qXXGixhysea20TGazohR7D\nVsCpfoinC5LS8J2veYV53X/omat84saESVFhjCDwBEuxYJwfiY215dBHPq8dSEnrTrG8qKml25Xd\n7VDCOcGttHLO9gvW2yX9uHaZ8EJSa6iMJCkDJnnIpFCM88CZQ4ja7YFxGcZ1qRESslqRa0lSiiOO\nQGMnAceMJoQDEpfIF3C6H/Oa9R5fe/9pHj67zN684Op4zrPbU65P5uwlJdOianyV3VioNu5Ec6AG\nbR/agSX2LJ2wJvZrAqUJPegETkoU+j6x77rxTtiiH3dZ7yyx0eux1uvhSZ9F+pm1lqI+AvKidjvP\no8+dVOmFx9fcv8mvPPFJpLD0ohYt33BqsMnNieXjzz/N86MRlw9CYnnAML7FXhYzL+/l7fdu8PYL\nqxwkN7kymvHcKOTmpCRJcx7bmrKT5BTHvOEXWc6PbA746vNLtDtdp6dPCq7sz7k9zxoCpGWlFXJ6\n0OJsP+D3r4z43M6UWdYYKwnXQd6z0mZvVrI1zai0xgonofKkcIz5F7zOYSQRKOaVS/be6Ia8ZrXD\ntLJcn6RsT1NXrSvJ+aUW73rVaW6OE37ruW2MwZm4bHb56KVdklK/JGnqj3t8oV39nxTQz9OCN/3I\nh3l+1KgXlAOblu+AUjmRNPOyJm3GGoF0q5vIkyy3Q4yx7M1SMmMPJaCuy/foRh6xp5gVNWmlKesa\nsQBV4dYAQkm3E1eWlu+x3o1ZajkP+GlWUltBVVdU2lA0rPnFb0bipoaxr2iHPqK5G8zy4hBkLDSM\nbUHgKcJmRSeEPPya0dp5cBQun+OFoUhtv+bcoMAYj53EpeW9fnPOAytjkkLx9F6XJ7Z9EBUr7ZIn\n//v/htW/96FDKZfjJ9H41b/Ufv6krv3Ipe7I2MbYRZLdkdGNOQS6L243v7C3dQMVN4HhGNHNFy42\nNvQEvdC5FAa+jxTOkGxWlExz996U2pJVUJ7gEciTvAJ9d0KhAEIFvvKIPEHbl3TCEE9CVhsmudvp\na22bbADHowGXoRD6PoGnmrkyjV+/Iy4HStHyHPlyKfaJlGCrUYJMipqi0pTaETk3Wz4//433v7KA\n/nt++3EOygJPCFqNAcblg8IZ0lSODb8gzcXK6WenmXaV6V3+bwfsJae7Jad7Oaudil6giTz3Hc5T\nWZGUHpPcZ14EZLXvbGY1pHVzx7E4SVztOAG5ls0EQZ44lZy0pCGExT7rnZhXb/R454UN7tsYcHk0\n5+rBnJuTlK1pxm6Su11SZcjqmqoyGBpphhCHTk8tX9ANLe1AMGxZhrGhF1m6gaQXS3qBdLKdKGYp\nch154MdEXpvQb+GpAF+GgCSvjwF405XnlabQDsjvdkr4ShJ5ishXhx8XMrxuFPCJK4+TVIq0SJAy\nIPDWkKIgkBOG7T6RbPPLn/91ro0O+MNby5S6x3IrpBumnO1bNvob3Lu8wVLs8dTujEv7c26OU6bz\nhMd3Zs7OWB91+gum/cPrfd5xYUin26Woap4bzbk+SpgWjoAnhGWjE3PPcodZXvF7z++wNSsQ2EY3\nLVnrREghuD5OyWvnRmgRzYX/4sCavg9aKKra7e/PLbVZCQS5lVwazZnl9eGe+NELQ871unz4yZtM\n8xLfU3zFqQGfuDFinNdfNrDP/9E346uXJ+X9SQB9lhe88QMf5um9ecNkd9ds5Ck8JVEC6loza4hZ\njtAGvlR0fEk3DpqUsIq8dteZsTgJaOjMnrKiJqk1ZVmDkFhrEEpgawtKoIQk9AStwGetFRL6iq2Z\nc6+rtCPWFZVb8R0nTkpcUdIKA3wlEdYyLyvK2hzee6QAT7jo1AW4u6RJAcY55KS1Ji0NZf3iAnJx\nPseexyD2uHfFsN4y7Mx9ntipmRcF77pvl25Y8eRum9vzmJvTkLP9nKv/09/koR/64It+58f35VUD\nhEdyOw5Jbcd38v6xPf3LWd/WC1e9Zjpgm7H/YWHAYgT/Qi3+yb2/k6xB6Hn0QkXs+81CzVDWmrR2\n0rfFzxHNL8zp+O2LnP3cpOJo/VA2CgOMREqJJzx8TzUNjmJeGGalOYwmtjjSJ7jVTqDcOepLiZDu\nnuwCDt29ox16tHyFryAtXPc/LWryylBbc0flk1uDKO4bRPzEu8+/soD++3/nMWZast7r0vFifvHz\ne1R3GCP1AkFS3tlTXAlLxy85Oyg43S1YaZd0Qn1oBmGt06/PC49p6ZNXEbszg0YdXnzauM681IuP\nR+Y4ix2OlE6eFnuCYSvi7MBJO+4ftjgoDdvHxuyTrCIpXVzloXFHM9KRQsJiBx+64mClJRm2Fast\nyVoHlmLDsK0YRh7t0Jk7SOlOVCU9PBUR+W1CL0bJAG0UlVEU2jpAr45AvbzTWdccoXcMwL2ToB56\nEiUlZVny1O6cT97c44nbEy7tztiZJ/z+3/7L/J1f/CUePbfOG872kWqFWS64PblGqUt8b42D+TW2\nJk8zK/v02w+z2YvYnSc8v/c8O/OaUd6DZh93cbnDxWGXi0ttbswynt1znuGj6ZzPbs/ZmZ8EfdV4\nyD+40uPd9y7T6/XYm+dc3p9zc5Ic7koDpTjVizjdi/n4tX0u7c2coUrTYfQjn54nuJnUpFWFaTTV\nSsCd4uTbAkyzvw99ycWlNr0o4MY45fYspdaOeHS6H/P1rzrFf7yyy/OjBATcM2yzO3fThC9HUv39\nbXjqf3n5rv7LDfR5UfCmD/wKT+7OAIgETZaAQEqJxK1hysY6WHEkn+uGHhbBNMuYV0c7VCWhG/q0\nAq/JgNcUVQ1uKICQjnUvJY1bouPIDNsu5W57llNpS1m5aNmqhuMmhbJ5HnHgEXpO4ldUmqIyaMGh\n+41qpJ2Rp/CVAxIlBMZoamNJC5dNX9oXM7oXE4Ju5Duzq1aIFLAzyxhlBeudOcZaLo9iaiu5dzjn\nLWemFLXkiZ0O1ycRSaUwP/wtdP/OT57o3n11BNx3A+uFg13VgP+iEKjMInHuZDFwuI9XJwuDlyoG\njOUwve74z9INA92XikgJ+q0QqZxguNba7b/rZoLagLo6Rpw7+pmN5l0e6fhVsybwmhVm1EwJpFQY\nKyi1pagtVW2prcU0qXraiMZD38dahUG51a3xMNYlgYZN4VxrQ1YZ0tqQ186V804mY65Rc8XoguD5\n9gurPHxqyOX9OQfzlHev8soC+v/x49vEcYuiqvnw07e/oO9VwjKMCs4Ock71CoZxRSfQh+MoayGv\nFeNcMs589vOApPQB97VSSwrtuvSydsC+sLEFN64JfEcA6oY+692I84MYKzxmZUVa1s0oqflTnwRz\n4DDxKvYUUeAxiH3WOhGnux5rfZ/Njs9yW+DLEl/VTTWtEcItAJT0UNJvgD0EIhABlVHURjUFiduV\nV3cBciHcczgO5sdBPVTq0Ce9LEueG815/NaY5/ZmXJ84E5qtaco4L9G6RmCcS5a0dIKaT/7A+3nd\n//4vaQc1ZR2g1DKvO+Xz1nMhb7/nHpQf8bFLv82tyYyd9B7OLK3zmo0BgTqgHVi6/ipPj0o+e+uA\nZ3anzIqjnmezF3HPUocLS22MNTy7l3LlYMb2ZM5nt2cuWewY6EvhQP+BlS5fd98K7XabS/tzro8T\n9tMcJRyTvh8FnFtqsTuZ8/j2nIO8ROLG/lGgWGn57M4r5mWFtbYJp4DyDqjs48aOxkLbV1wctpFS\ncGlvfvhaOqHHW8+tgIQ/vLbvEhlDD2Uqbib1S66c/qhH/X/8jZcdsX45gT4vCv7cj/wqT+xMAac/\nduN64RwMrbMXLZvHKxbmIoJOGJDlJfPayVYt7noMQ0W72elnlSEra2wTJNCs4RvzElc0tnzngldb\nwzh1Gvuy0buXL3i+EgiVIPIEAue/kFUGI47+b9VM7UJPEXgCKaRbDdQVhWkKAn1nUqUCIg8Gcciw\nFRAoyTgvGSUl80qfOAe6Qc2pXsGsUNyaRc5E5/59luOSK+OYm9OYK+MI88Pfgvzen77re+BY4+bE\n+N5X5kRh8FLFwKFzZ5NDf9zJszZuJeE1BL2TzH17okCAxjhIut+h+/0pRxyuBbPSkpT2cAVRG3nC\n9Of4GP6Es9+hk58llBB5lk7oNwWaxVhNbWqkrd33SMdjUk0cb6jcc/KVa+BEU5RJ6SYDtcWtdLWl\nbIqCUkuqWlAa54Dnnp/Ckx79uMU9gx4PrffpRTG7ac61ccZekjHLa+ZlTdGsYddij3/+zlcY6/6f\nP5OQacO/+fT1uz5WCct6O+NcP2ejV7IcV7QCfThmskBSehxkit3EZ5SFlFpRN2P3ounOi1o0Eavu\n5JG4LiL2JP0oYLkTNCMkRzJZjINqoyldmD2eEPiKJtRB4ClB21d0ooBOqFiOQjb6LZZi5UJlPEsv\nErT8Ck9W+FKjpAvJsdYgpQJc9WishxURxoYY61FqN1m4G1FGCnECvBdd+OLzQMkX3fAv7Uz4zK0R\nl/Zm3JhkbE1TRklGUuXU2jHxLYuCw5EgW4Gk1WRbb/Qi7lvucqbf4j2PPMp/9/O/xJXRjE/fVqSV\n4UwvQQq4MW3zyMacB1ZmZNUyF1Zfy1de2CT2S/bm2yBiAm8ZcKEkg9jn1iTlia0JT+2MuTHODnkB\nkS85v9ThnkGbXuxxfZxxZZRwYzThs1tTdpP8sPNegH439Lh/ucNfun8VFcQ8sT3h5jQ9jB21tomc\njDye3JpyY5o6v2ztMqz7oc/BPHMWo9YejpbvBsyhcjelXuhxpuszLS03JqkLNhFwuhfztgur/Nal\nbdKyRnmSQeBzY/rS8rs/yvGBb3iEv/U1r3vJx3y5gL6uNW/6wC/z2a0J4IohC4Se28drbciPBQD5\nDYclCtz0KC0q0vqYNl0K2qFCgNvRlvokwOMA3hXWHm1f0I0C0tLt7POyomxIdS8EYR93/ftKYKzr\n+I6PimUD8L4SBL6HJ9xIXlu3x8+qO7O8Ba44aYWOl9MKFGlec1BUJMXLK4FO93I6gebGJCCpfDa7\nGV91zwHGwGd3etyehYx+6NteEuhf/ngBMKtjoH3Ywd/9iR7v2k8WAW5KUGnhIrCVpR26918Kx1Y3\n1nnSq5cpOIATLP3F1EFrFyzje37jdOrOjUprauvWr4sVghCLCYxtgm8sUphmTWrdPVhoZLPKCKTB\nUxA0ROZQNTI7z13jLV/RC336sY+SjWSu8XHIKjcxOFyZND73xioQHpEX0A0jzvU6fNOFwSsL6H/h\nRsUP/c6zJ76mRM25XsGZQcapbsVyq3TM0oY8UhnBLPfYT312Ep+D1CfVfgPmC2AXh126pNkHKckg\n8ujGkdPuKok29kgOJyWh7zUXd7NzkwKMq/xC38eXbie3YGkOIv8wJW4QCTqhxZcFntT4ygF6UTdJ\nUs2b7jKUA4wNqY0PwkcIHym8E78HT8oGwO/cld9tF3ttNOeztw94anfM9YM5u7OEgyxjXhZg6yY/\n2oG5tW6X6EnpCCWBohN6dAOf070WvVbknKVqwbQw7Cc1B2lNYQQ/+/538wM//+9Yag+5sLKJrsY8\nt3ubx7YNV0cpDyzfBCy/d22ZeRmy1gr4irOa+1e6PHr+IR7aXGGcuzXHAtSFEK7jtfDU3pSndyZc\n2pud6PZX2yHnhh2WY595XnJtknNtb8LjWxN27gD6ncDj4rDFX35wnYmWXNqbsz1LoQG7QCmGbZ/d\nec7VUUZaVY00yHXkVVWSaA4TBb2XIH/6zT5vKfIZ+LCVaWaFRuO6/jdtDtlKM25NM6R0hcFeUn7J\nGfkvR8r7cgB9XWve8k8+zGca86DF2RwoN1YvK3tiVB5Kt+OOQo+iqEiPuct5AiJP4kmntc9LTUPy\nPrqJS1coRL5P7Asi3yMpNXldUxSa0r4Y3Bf/9//P3psH3XaVZ36/NezhTN9850FXEkiMagFu09gx\nMbhIO3K7Ux27aIpqka7Y7TLVHgoad9ONm8IxdnBwbAfjwkMcU5bTGAg4SeMYPGHjEE8IYxAgNF/d\n+X7jmfe01sofa+1zzncnXV3dKwnpPFX3nvOdfYZ9hr3fd73v8z5PLEEoSVHaXcG99naIlCfWaSnB\n+bHVUeEJdZeCwJtetRsR7VhhjGM7rxhf5YjvLLS03LzsLWYf2WrgELzu2BZHFrPgY59y77/74acY\n6J8Y9Qo6ukTpXqvdq/YLEQtwQlJWkFeCLBDlLqwSeLLiNLG48N+kKiGC30Zw4jFBuKiw/jityXgm\nPD+hZWARwQk1TD85JpWaWfhpBZ/YNYJk8XLqRcwEjtzYcB4vEVRILFoZEumItKUR+arNQipoJ4ql\nVLPUiFltRexpJiw2YiKtiKQgkhHVRvr8CvT/zf/1INv5mFuWRty0lLFvoWApqdDKIsMhnVeCnVyz\nOYw53Ys5N4rJq2gS1CvjhVK0FD6zVwKtNbGSQSVLs9RIWE4jXzqKfEl9IdEspBqkYlwYhkXp2aWO\nQMQQJJFXQGrHikbkNdw7iWAxdbQiixA5zlU4V1KYiqIqMU6FWVZJ5RRSpCiZIkSMDEFdBGvVyxHd\nasLS5XBmq8+Xz27x9fUtTu0M2BiM6GZjBnmODeUqJabBQ+BIlCLSiljLMMMb0YpikihmXIbeXeUn\nEKyTjCp3EakkVjL8gDX/4z/5Vn7wI59kY7SIFJbltIsQkjTaz6H2OZw9w4ObDb66vkYvK4nlgHZS\nsDVO6GYxaaQ41Gnykv2LvObYGq8+ssLYCvp5NQlCMpjZnOmN+Mb5Hg9v9DjVzSaJQaIk+xca7Os0\ncFjWBzmPnd/h7850fXn/gvGoZuSD/ve+cI3HhpYTOyO6WUkkoXSOpSTGOsvjGz22S4uzjtI6Gtrr\naGUVlNZ7jF9p/j0KetcrzYQ0UpzcGVFZX3bc10nYl2ge7mVYa0m0pjsuuY6mdpx91z9hz8ryZbdf\n70DvnOPVv/j73Htq2z8/U3IdFgo3Jc/WbOs4VMaKypE7H2DBJwBxpAJRbkrUc0ytSWMJUUh+E6UY\nlhVZUZJdpnxev6aWwuuks5vMK4Uv5XpTKgm2orCO/II+/iw0hGqXQCIZlIastBRP8WOVwGqz5EC7\noF8knO1rFpOc77x1C3B85Vybr7zzR1j6979JZaeytper/N1ICGaDtKWhaxGaq1PjMzPVgClPQE7U\n9zy5UWKMgbr0Lu2uWX6tHLGcViAu9/HXiYAN2gEgUVITqQghFcYqLJLSmNCKJcxYiDBWrWhoL3S0\n1k44stjippU2L1jtcOueNvvaTdpJRKTC+KerMKYiK3PGRc6oysmKnKIoYNs8vwL9Rx/9M6QaTEYW\nwOvUb4+8Xe3xXsLWqEFhJMZ6xaFOqllrNFhsRp456xxVZZBChjEIxf6FBu3Iz0Rq5ft2OvjB56G2\n41xgQgZBi2asibWioaSX202hk0CqDIgcXIlzhrIqyE1BZcCiEPjSjJIJUjSQ0gf0NEppREkg91ya\n6HYpOGcx1vo4TsAAACAASURBVLDZ98H84Y0uJ3d6rA9GdLOMYZFj7MX6UlIIL6UbRSQ6QiuFEBop\nNHnlyI2knxmGpR+lUcH+04/ZyMlcf6wkjcjPfu5ppxzopOxpRSynkgfWe/ztiXOc7G7x8f/++/np\nP/g8Lzu4n1G5zantTR7fidgcZextPEJWWf7+7AGaSYc7D3Y4tjhkfWS4/3zCie6YM70xhZmeliMp\n2dtJecFah1ceWubbj+0jjnXwkfdQUqAcPLjV56H1Ho9tDRiEBrpzjuWm1/CPpWJUlDy20eXe07uD\nPviTaStW3LLS5DuPLvPY0HoVO2O9iJCQXl5zVHBumHuN8yC2ooWgl1coAaWbSmleCpH0CehaO6I/\nNvTzCgOkSvCSPYsc7w49x0KAKQ3Dp8nV7noGeucc3/a//D/8zQnv6FcHeR2ic8kF42oCYq2ojGE8\n8341XnwEIJ9RUKsDPIQqgBSksa+8jQJZ7lLENzHzelU9Jsa0IqDwlb5Yh8asgLy8uIc/iwRIIs8n\nyIxjdBmC8JOBJqwmlUA5hxOKwlQcWMhIteXxnYTMaF59eIdbV4asD2M+86//zUWs+3plOytGc2MM\nbp4cpKj79nZXW2CWN1CX74OJYGDyX0wevLBdUPvO16lgXXGouQiJcqSRI1KQRt5yuJ71D9ZpBEPc\nwMNQaKVo6JilRpMDCy2OLHe4ZXWJo8ttDi60WWw0UTMEPT/RZCdTTfX1vLr0aLKwFe3+uedXoP/U\nyT/h3KhkfRBzopfwyHaD3MSsNprcurLAPziyQjvRRFKipaK0FY9vjznbG7I1Lrw4gQWtBavNhKVG\nTDuO0MFXui7Jexa1CAFdoaQkUQItDWkM7ciryEWywFEinKG0JaUpAIsQGoH2l0KjVYNG1CCNGjSj\nhGbSoBnFlyS6zaIO4sZVWFvRH4z5yvkNHtnscbI3YGs4pDvO6ede1vFCeF6AD+StOKEdJyw2UqIo\nRhhYH1k2xyW9cUEe+la1haaWglYSsZxGnriiPN9guaFZbUUspIJECaBinGd8dX2Hx7f6bI3GDArP\n4Dduuk9/929/mM6/+x1SZbl5NSPRKZFa5ZUHunSSbTbHq5zo7UVKRzvqo2VFL+9Q2oiFNOLoUous\nKDi5M+ZM3/+bLdEL4W14j620edn+RV5z0x72L7UYFdP7REqyOch4aKPHw5t9zvZzrPPTDVpJ1poJ\naSTBCk7vdPnC6S5ne2NmxfAEfq77BatN7tjT4tTYsTHIEdJhjSNRklFl2OiPGZkgUhRIelkQO6/c\ndNV5KcRSEEnBgoaNYko029eKcVXJKCiVgSO7RhGQC3ElUt71CvTOOb7jA3/AXz6+uev2OCRBs68g\nwu3O7Q6m9e1SetKjuWAb+ACvQoVNOBgXFWN7+c9bMy3HX/h8kfSVKVNaCG2YK8myxsKP2lX46sNT\n9SeQ+IqEX2j4CsOlqgaxshxbGpNVkse7KY3IcNcL1xHC8bEfeCfL//43LyLBaXnl3jpc3Pc2F1wv\nzW752qcD3sDGXpAE7OYNXOqnLADcjAogEofEhtW5cwrr/G1K+JV5I/Its6Wm4sBCzJHFhFtWWxxZ\nStnbjlluKCLteRulsbuI1/5vF6oQXoBMiPAPf0m4nkyIz9OYkEYKZQ2PPnj/8yvQ/8Jf3Y9eOsLZ\nfk6kBEeWWmG+2TM0C2M508s4uTNgfZgzLAzgwmiaZiGJ/KhKMyHWnm0bq9BHVwIXxDOUsCSRpamh\nGXnShZI5CIOkwjmDcxUIgQyr4CSKSXVKM2nTjBs0o5RW3KCVpCRaXXQStc5ibYVxFcaayfXBIOOB\nrS2Ob/U50x9wbpDTHeX08opxOT1tOKasVqUUqYpJ4piFOKGdpiykKUtpgpKCXlaxkxdsDHI2hznj\nspooMglhaUaSxYZmKfGXjRgaShBH0ktZYj3VFMHmYMyJ7pD1QU4/96IR9RhKfRIwzpe5Uq1ZSlP2\nL7X53bf8Y17yvt+jIbtAzuPdlFQXfNvRbUoj+NxjayiZcvuemJfucyw1FkjSVYa54+TOkHE5PQVL\nIVhtxMQSzg1yzg8zNgZjdnKzKyB1Es2hxRa3rbV55eFVXn5o1Vdo6u/AGh7eGPDQZp+TOyNG4TWM\ntSwkMc3Yq6X1BiO+cLrHuRn2PtRBX3J0qckt7YgNIxjmBkHo7VnHzth/f05KL6kqBKayVGElciX4\nuWlFXpjJSjcWsKcTsTPyKnuVY9c+XSteuy/is//2TZfcdj0CvXOO133w0/zFYxu7br9UhaMeXXPs\nDqr1atZcpp8e4T+zSPt57by8dFC88HEXvrO6bO+MX4Ff6Tl02Nc6gXuqK3ZVX9aM7ifx2NWmJx+v\njyK2xzEv39vj5fsH/M6/fCcvfO+HGOSKwirf/zY12ZhLBv/ZVXQ9tnY5zK6kJ+X12UrB094u8Ep7\nSUgEEu0TgUThr2tP/FPSqynGyht7tVLNajNhf6fJkaUFblrusG+hzWKSksbJpCpQWhlG8PwqPCt9\nK9a5KUHZOc9pchiUsESqJnP6KmgU/sVh/l4pFaTINVIopFQoqTGV5YGvP/z8CvQ/fe86JwclkZCs\ntpOgiOXLrtvjgl7mZ9EdoJ1goRmx0krY20roJJG3toxVkHT0+sOx9OSIZuRoJ9DQnu0uRf2FVUjh\niFVErCNiHZNGKa2kRTtu0koaNKIUrWKU9JSiywVxaw3GVuTFmIc2+xzfHnCml3F+kLE9zulnJePS\nTAwmKiO9spMBJSNvlhDFNOOEhSRhsZmwv5PSiKfEvKKyrA9HrA8y+nnOKPc9akeFxJFG3tWpGUsS\nDa3IyzNGSnofAOGz89JaznWHnB0UbI4repllWHjBkMqpII3pCYPNSLMctAJevG+BVxxYYe9Si2bk\nhUpaseamlTZfPHGKreFZjI352lnLw+tfwtgNvnSmyZfPthE4bl4eIyU8vtPAOM+ZWGsm7Omk7G8l\nrDVbGGk5P8h2zfzrQFbbHuVsDPPJ76GcGV6NlS/337zc5o6Dy/yjI8uoKJ5sP9cb8fBmn8e2B6wP\n/DrSWgfC0Y69LnqZ5XzxzKWDfkNLDi2m7EslPauojF+6G+MY5iX9PKd0ws/eh5X+1fRotfBM5DIo\nsQEsJppxXqHroHYdgv3lyvfXI9B/1698hj975PwT3i+06S8KvlfiOCjqkSzhyXjXUOLQTMv05hKv\nf6n7X839rgZ1cH+qSYLAcdNShpKOx7cTHIJ//MIN/vMPv4N/8eH3+XK9keQhWOWVYFwq+oVmWCgG\nhaI00o+Emd3Oc3V/fdLrlvaSicHVzMzvSgDMxRWDJ9suqPeyJsl5wyqJDtWYVGtaiWYhjVhO/TTQ\nsZU2L1xrcmSpwVJDkWhfsc2ritwUZGVJHhQQLzWS7IW2fDBOdEyqY9IoIo1if46O/N9127WsKsZl\nyc54xNYoY2M4Yms8ZmcwYpAXDMucUVEwrnwMKEpf5m9pxVtffMvzK9C/9c9OYvAl1nFlqIxlWNYq\nZb5v24o1ezop+9opaaQn41FaOhYS6KSwmEAzhlRXSOFH2JTwmVesFYmOSVRMGsU045Rm3CbSiQ/m\nwksuWmdDIA8BPJTXjTV+LrOqeHxnzPHtAae7I872M3bGJd2soJfbcCBJiooJ01Oi0DqiEUw3FpKI\nPe2Ug4sprdgHJGP9zGeiHNZZhnnO1nhMP8sYFgV5VSKxYT7UB8BEe5JgJ4loJiocBJJYRiAVg7zi\n1E7OyV7O5tAEWV1HYXzlwHcVfDa6kHo1v2MrLV6yb4E7Dy7TbqS0E6/h78mImkakd7UjhBCc7z2O\nsSVLjYOc72/yFw99jnP9kq38hexbWGY5HnLf2VPcd9bwwKZjZ1Qyrgy43SeQJFIsN2KW0oiVRsJq\nU9NpNFgf5rt6XJFwjCvD1qikOy7oZyXlzHYpBCvNhMNLTV6yb4FXH15jz6I3xxkXBQ+tDzi+M+LE\nzmDSGimN85KlzpGXBV8502V9XO4KtHXQ39OMWdSWQiU+IDjHqKy8BndRIaSgCqLiV7Ny0wJUKGU7\nfLBpKHzygHvKo3f/9794Dd/zihdcdPtTDfTf/aE/5I8eOndNj71Si0Mx9R4v7OW5D5fD9TIIejK4\n0vu5Hki14ehixqDwvKVOXNF93w/wyv/pl2nFllbsLbC1AoWfE1de5g0b+vZFUBjNK8Gw1PRzxSDX\nDCufCNSr2otX6C5o91vfJhAXz8xfbbvgUva39XUbpGuVlKiJw5+mE3sfkNVmwsGlJkeXWhxdabK/\n1WS5GdOMIxIlqYKM9xMpfXqCqA376xd9Dn9uz01BWZUMi5JBWTHMK+8rUlSMCsO4NIwLy6iEUWkZ\nl16IpzBQVLMGPSJ8crWxj0UJixB+XPlgO+Ld3/I8m6P/ic+f4czQZ1laSix+3GkhjdjTSlhtp97B\nrSFZTDw5rpMIFlJHM/JEDlkHdekmmVgUVuORjlEyQQkZMjYvunFhEJ/F41t9Ht0acro34vyg4Hy/\nZHtUsZMbz8KtvOVtWUHuBMIpGpGkESuakWK1kbLWjtjbatBpCO+Y5mywPYRYg7UFpfHa3IOipJ/7\nwDUqKwpjg+a0XyGmcUwrjllMElZbTVpJQhpFOBTGCjZ7JQ9vDzk7yNgcFnTzgmFeTZYzIpyK0liz\n0og5uNDgltUOLz+wxIv2LftgnvhgXgd2rSTOOQpjufexdT5//DxfOdvl+NaAjUFONy859VNv5Ht+\n9bdpxB32tvfwotVzxKqLVUdZa9/KHQc7NPU2hRFotY9R6W0kt3oD/uL4Jl86ucXxnSEbo5xR7lsP\n9YgT+NGrVqLpJJpOGrOcRCwmgkJEu76vcV7SzQu6uWGYlWRVNSE6CiFox4oDCw1uXe3wrUfXuH3f\nEsDEHOdkd8j5QYHEYZwn2EgERVnw8OaAc6PiotV1qgXLSURDVqi4iRCOcW4YVIY8L7FCeP9yd3XB\n6kJv+kRCbqeXTwWXWtU/lUB/16/9EZ954OxT26kLIJkS7p5sD/xGB9pnA/a2cjpJxflBTL+IsL/w\nll3jdVJYGtrSiAzNyNCIDO3Y0oyqYOFdl+t3y9Fa5wXE6qrAuFAMS0U/98nAuJKUQaDrSj709Sje\nxW0CNxHviSYqdz4hV1IENURNO1EspAlLjZQ97SZ72y1WW00WkpRm4n07rPP74UeV3a6+eVEZSucw\ntYvnhBBnJ54hWWUYFBXDoiIrDGNjKIPrXRVI3cY5nLHIoHfiRdhs+NwqL3usnE+ogi22tw33xGYt\nFUr6kr2UntSX6ohGnNCJY6I4IRHwra3ihgd6/cR3efqwNSoZl5Z2otnTirl1rcXNKyl724rVlmK5\nIelE1o/bSePV47CBJe5LLRKBFAqlIt8LEQKEQhJUnkx+0cnjfH/MQxsDTnQzzvRKzvYKtkYVm6OS\nUelJQWUlPFvX+tWn79lLFlPFkaZmseH3uZ34rmQcArnvKVbEcoAIhimVs4wLw/aoYHNUMMhLRqXz\nIguB/CKFJo0aLKUpa60G+xda7G01cVJNCGbHN/v83akhp7tdNoc521lJXhlE6LfjCeMsNWLWWkno\nZ3d41ZEVDi21J2X3VqyDoA5kpeGr5zf4y0e2+OqZHY5vDVgf5fQyzyGo1f5mUZ8oTnUzjm8LVhob\n7BzZZlhIPveYJNbrvPyA5fCCZrm5nzsPWe667RAv3reIcyt8+wsOMgwHXX3w3fv4Ov/fY+fDnHtG\nLyvYHpfsjEtgHGQxBUmw+2xp7wHeaSr2L7TYH/ZtnFdsjfNJNr4zLuiOCx5Y7/MH958m0Yq97YRj\nSy3uOLTMG19+BCsEXz3T43h3xOnukLyypCrm1r1LHLOWqqo4vjPkXL+gArLKcabyrYBYDujEmpSK\nlWaLKonoZV6NLS/tLtb35VAxDXSWaXAvre/hP5WRrYk163XA9/7GH1/3IC/git4VT4TnepAHWB/G\ndBLDcqNiVKqLPivrJMNSMiwvfXpXwtKIQiKgfTLQjr1jZzM2JMqRKMtSWu3Su6/JeUVoCwwLxbBQ\n9AvFoNCMSuWrmEaG0r1/nCC4+ClPhm7GChkp2g3N3nbM/k7M/oWY1UZEEuy9BRaLoaxK8mrEo5t9\n8soEuWJPgquc89ctlFXdsnBeDK1yEwW7wnqZW+tckLQOv5Ja6jBcSinCyGbNuhdEcUSk4yCO5uWQ\nEy1pRhGxFGilkMr4qRIMjgqcBfx5WAuDlg4rHIIcXE5hBmyOQIwFnUhDa891/41ciGfViv5jx7u8\n7PASL1xLWW7gA7owVKagNDmVrRBCerU64RWRpNQo6RmOXiLWB/hZcpyUip1Bwf0bI45vjznVzTnT\nLzjTy9kY+GBeWSY/BOOMNyRJBM1Y0EkkS4lioaFYThWLzTiwQCGJvJhOI7AoYy3BwaAIFrJB1nFY\nWLLSMcid35ZbxiUorUiUJlGKVqLZ0/ZtibVWSivxWmJlZXlwo8ujm0NO9UZsDMb08gvlbv3o1koz\nZm+nwU2Lfib9VYeXWWw20EqipSAKY4WPrQ/5y8fPct+ZHU50R2yOcvrjakLkuxBKCRKlWEg1K62E\nm5ZavHhvm1cdXuJleyNu3n+M3/nrL/K3J0aMi6+D6/J3pzvcv9miqSoOLeYMC82Zfjp5zjiSdOKI\n1VbCwYWU2/Ys8qrDK/zTFx8gTlJfBs+rSRLw0Pkun33oDF871+VUbzwp/Tu3e/WvhLcLbkaKRixp\nRwkLsaPZ9PLKnt/ge2rj0neMtVLgQEnJcjPi8FKLl+xd4Dtu3cfOqOD+9R6nwueEg9IYTwZ18Ohm\nn81RcVECqQXePdBYOgtNennpx79KQ8HVrT4vvE/991NZuV64qr+WFf33/eaf8n9+7dQ17sEcTxWt\nuOJAO6eXa87+9L+6roI5WlgasaGpLY1QBWjHlU8EIkOsLq4GTAl73iOkqCSl0eSVpqKBIEaLBK1j\nnFAoJELWY4DezrUKxkLGeY6LcWGMVXgdFSksSjkkvvwtscgwSy+FC+56jtpmzAmYeOW5qXiOFRJr\nvYeCcxJrJU545zshxeQAm9B/LnOw1S1Pbz7mH1Sfh4Soh8SFL9cHLRMlHVpM/15JFD9x57HnV+k+\nXSuwMqc0xdTMwIdztIzQOp4YuagQ1LWKJmzG/rjk/vURD54f8vjOiOPbOad6OdvjknFZYY3vjRAk\nXbV0pNoH81YiaMWSxUSy3PBl7UYSTdToYu0tMBuRItGaSGnKStDLDb3C0M8svcy7bxWl99jOSu9y\nNS59UHZ48kisJI1Ys9qIWWl5paR97YTFRsL5/oivne3y4Eafs/0xW8OcfuF5CiBwAnCOVqxZbiQT\n4snL9y9yy9rCpGykpGAny/jbRzf56rkuZ3ojNsc5g1DONiYcJMIbf2jpLxta0kkUa82E/Z2EY6sN\nbt+zwIv3tEApT2apquAqVVvnOt74ym/j9796HOw2p3a+xPZIkfNibt+7zJHmgPvOb/HF04pHtzPO\n9cYTf+fyUmODImidhx790aUWt+1t8+qje/gvj63idDxJADZ7ff7ooXW+eHKT49tDNkYFo7ycJCuz\nCUDtNZ0qb3ByoKVpNpuc6w3ZGJVkhbcDNtZ5kZuQFC2kEXtbKbftXeAfHl2kO3Y8vDXwo3mlTzR6\neUVpDSe3h2yNyot6wwpINMQW0mZMPyspgrre011ufqqB/p9/+LP8H185eb13a44niQOdjERZHnn3\nD5O847e8xOoNH4PzY27NyNKoqwGJTwY6ka8IxDr4z6sp7a72rK+soKgEo0oxyiWDKmKYKcaVZlxK\nCqswLgiPB13jQDGYSJx7y3A8t0dKBC7ofjhfQZUQS0ukhZ+oUrOtA/88U3dQEYh+IizuFS6M4CGU\n/xsZkgJVS+YggghW/XgVzhcSEXgRvjWsAonUy++Gc3NoVUgFy7Hin+6Xz69AL5a7aC3QKkariFg1\niHRCrBrEOkXJiGxsuW+9x33n+zxwfsiJ7RFneyP6eU5elYAJ7kbedMWPX0BDe6vXNBhcrLQS9rYT\nmnEURGv8inoxTWjG6URkBifpF5bu2LA9tuxkJdsjw8443zXb7pwjq7yKUmV8dqpk7UntSzyLQUVp\nX6fBYiR4ZDvjvrM7nOqOODfI2BpmZBc8pxBeRnGlkXBgIeboUpOX7uuw3EoQEvrZmMc2Bzy+3We9\nP2YnLxiVBUVpMNYE8Y3gNy38zzQO5e7FJGK1nXCg3eDIcouX7uuA1JTWG4WMg9tdZR2V9cYOlfUZ\nuHOe8Bcpb+Twlle/gv/1L79Kb3Qf/fEWZwb7EHI/t64K9ncMsV4kiRZpBLngVqJJtWKjN+Ivj2/w\n1bNdju8MWO9nbGclo/LySUAjkrSTiNVmwk1LTV4UJgHuPLSIUxGDouKvHj3P//vIeR7Y6HKul9PL\nC7LKAW4SWJ2rzSy8sFIr0uxdTIiQnOll9MuKojSUtrbYBCkkaaRZayfcutLi2EoHKeGxrSFbowJr\nLaPKMMgrznSHdMfVRSv9WrglceBir8T4VMrVTxbffSjm99/+z3d9pld7GvgXv/3nfOTvH79RuzbH\nk4ASlpuXMx74yR/m9p/50K4xuFkhmYkcrLtyb/16wLP3recGaN8SqBOBVlSRaDchwM2O9Fnnveq9\nFbhiVEqGoR0wLBSZ0WSFxjqFk8K7EiInPf7Zf3W/vO6Z18HVC1wxUdJLdK3Y54iUIKkNb5T1PBHp\nRXSkECAdwnnishMCnJ+Rt05OEgCHxDnpRdNQCCRhrTBZ5QvE5LrEsZwovvfAjQ/0z6oe/dGVF9Nq\ndigywRfP7nDviXUePH+G0/0h26MxwzKnNFXI4EwoHfkvbSGRxC0Z7Cg1i40Ge4OS20IjpZPUs+cp\nzUBgi1UteiMxBrayinO9jAc3M9YHGdujgn5ecGElW0tBJ/Yr/CoQQfKyohUpEH42O9GSlWbMajMi\njQSnt4c8ut3l70+N2Bh6Br2xobrgHE74qYGDC5rFVLPajDi81OCmpQYKy5lBzqlen7O9DR44XzIo\nqpBYsOsk7X9AsJD6sZN2GrOcJuxpN7h1bYHbV5bRidcB95wAR2l88L7vfDCEsBIlUmKliJQi1po0\nVigchfHTAJV1jCrjWxJB0W5cbFCZLplJqdweDnU0hxZKHJqsatAvskCY8QG3njPds9Dgv1puTWSA\n24nvt5/d6XPviR3uP9/l8Z2hJxdmBePKcL6fcb6f8fVzXT4dnA6F8EYT7TRirRVz01KT73rhfv7B\nwVXuPLzKyY0B//n+k/z96S1OdUdsj0tGpSfhjCvL9rjkxM5oogoYBUGlxSRiMBqTC0VWWbZHOd2s\n4LHNPoKzJJFmuRlzaCFlbztFY9nILSvNhKIy9LOCUz3vXGUIfXcHOSAK4+fH8UnHUyXbXQ0+fepK\nem+Xx1t+53PzIP8sgnFePAegm+mJwEyqHa14RpPDzbC+CSNwRk7n4c00CTD2qQnkOASFURRGsXOJ\n7X7m3NLUnhzYSQztyNJOKpqRJdWWTlKx3HRI8slu1BUB4xRZKRmVilEgC47KiMxIxqXGWDm7M9Np\nnvqK89frM+aualrdumfamqjHDKdjh36bDoTG+jmYeb76P7/PPuGyMxoklZOT5GutEfO9B266ps/6\nyeBZFeh/8KOf5/RgROU80U5eUNBsaMliImjEmnbcYKWRcnCxxc0ri+xbaLPUaLDcDIFcR8Rao4Wm\ntEz82EdFxflhxrlzYzZGPnD0s5JBXk4CusCXgBItObTox9ZiCaX1c5jDvKCbDTHO1nkce1rSuxg5\ny3aWsTHI+PLmmO0s9zaX1mGxwUBBsJhKGpFiMYlZa8ccaMeUeGW3rVHJ+YGf+R4VljwosFmnAgNf\ngNNI6RmonSRiuZVyoNPiBasLvGBtgTT2I4oXKjl9ZdM7RkVKEsnAKcA7bxWm8prOzjGqLDtZQVYG\nxmqw4JWTWfyLcWr7UYzNeWRrgaHJgC5ZYTFuBa12iFVw1IsUWgpKrxuM8Ok841KwUeWhiuD3ce9i\nk0PLbWI9FaBoJ5rT20O+enaHR7a88972sKBflIxDP/9cb8xXz3QBnwRI6ZOATuI5AS8/sMQL1xZ5\n4XLKZmH5/KPrPLrdZ2NYMMjKkPD4yYBz/RwgVIi8cp6SgiIrsDqinxX0spLHt4do6RUXO2nMchrR\nSTWrjQar7QZ5ZejnBSe2RgxLM5knrwVbKjsVmLnR5fz7HjnJy245fNX3/4GPfI7//e+O38A9muNa\nUBgf2M4Pd68GL9SdnzWf0dLRiA3tmcO4Nm6yEPrZF7vQ+eAElZuVmn1izIp7O+dtXXcq6GUxZ/vT\n37nD69RHypJGPgHoJCWt2NBODKm2xMrQSSqW0unR4WAyn19UkqxSjCrlKwKFbwtkpSI33mNeQSBp\nu0DiZrL6r8v5GiDM6yv8NmSYEqhZAALiUAXQwk5GD9XE9MdPQAgxs68zB7VzjqX46anjPasCfV4N\n0bKioTSNKGExTdnTaXLryhIv2bfMzWtLPpDHPpArpSiNJSv9uMSoqBhXJRuDilHRY2OUsTPK6ReF\nFy3IC0ZF6TO20MuJJLQjwcGOphVLWpEfi6uspZdnbAwL1vs+4NWQUrAYCQpr2ckqz+IeF3SzMgR1\nP/vsLDghSXREO05oKkcjbqKVoFdYuuOS0z3D8HTBqMh9Jh0SAdA457X5W3HEYlKLA6XcvNrmtj0d\nGkk0kWEsgrxtZS0negWR8uGjMJayMmRBZ9laMM6FwG0oKktp7aS8dakgHitJJ9G0OhGLaRRW3DEr\njZjFNGKpEfMrwM2rjrO9JdY6h7m1AQfbgtxEbGcpw8LQdxcPSznnMNZiXT1e45MPJfxcP8LLxcZa\nECtNLAUGqKzj4HKLo6udiRJVfXlia8CDGz1OdoecG+RsDXKGpU8A+lnJmd6Y+87AH37j7OT7bMWa\ndqI5uthk75EGC5FmJ8tZH+a7Sv/+s67CPL9AFqWf9xX+TFlkjjJS9PMh53qecKSE5xs0tVfoun1f\nBykkN1JehwAAIABJREFUg7zi5M6AfjHhAU/K9ze6n/YPfuWzT+hqV+OHfvcv+PAX5kH+mwl+ZS3w\n1g/qkveZ1Z2/0DEuUn51PVkI76oKiODTPtWdL63EhMqAsT7w2iB1s5syPE1k60uoDYq8emlhJWUe\n0cscYtCY3Mvvr599T7WhHVd04opWXNGM60TA0YxL9oqgNSn8wqhyfp9zo8gKxahUDEJbYGQUebhu\n3MWflZi5cvHZcUrAm4gPienjfJ/eeQtmZaemO0GHIGtd3qzseuJZFej/53/6em7dv+Ynw5zFOUtl\nDeOiZFyW5FXOmd6QvPTSrEVVUVnDqPSrsGHhS7GjoiI3BkntYudJEUup4vCiohnHNLT2fRntmZhb\n45JuVnGun9HNSrLSYX2Hhspqhrnx8/Ojkp28pJtXlGaqq+yDckQ7iZHSkirf9/cJg+Fs35BXDueG\nwLTc7gjGHNorOy00Iva1Ug4uNrl1tc1KuzFZjVfWVwTyynK6n1HuDL3etvHB2jqvi163E6xznvgh\nPJv8wiCeKMVq2ycRrTRiIY7oJNqL1TRjFtOYPe2YRhxzJeTVGIBYRuxfuIWXHT7ATUs5hxYj1jpH\n0DLCGMOgMGyPcjZHOd2s9P/GBf28YpAHcYq82qVxDwT3KBsUBV0QCfIjlbVaVqwVkZCegKMkt+xZ\n4PZ9S8GZr5akFDy43uXRjT6nexlb45ydcUlWGHqZ9wQ4vTNCBP908P29RqS9hkOkKCvrqxxFRoZi\nVBpsYA07HFaAqDw3wmAQlR+H28kqGrom7khi5S1V97RSDiz4VcSp7ph+YZ5VI2L/6j/9Of/bvfNy\n/XMFjdBSXGsn7GulCCmprKEsfTvOWBdm0P05x1JRmiqcjysIltZaWGJtUZFPUu0F7QHrxIxPfbCj\nnWkN+GRAYibKHiHY2zpQBl4MfkRYColUEuHAYhmVEcNScH4kwuu6iRStlpamNr4akFS0IksztiTK\nsJg41holUpV+5Brp990KKhc0BCrN2CjGufbcAKPJKj15Hza8Xi3k5gBrAOFH92woDdtQHS2MoxAC\nShFardPzcH/8PAz0xna573SXwphJubmaGSHzqkR+ZVYTxfLKl1+8MIFCKc1C2qSdxKSR9v7UWhNr\njZISawWj0nC2l3N24OVU+3lQZ0N6ol1mGRQlg8x4y8vShNl1vx9S+mFoHUW+VCs90aNyju64CPf1\nfdD6pC2BJNa0IuWtDZsJBxZSjq0usLedUlrLKDeMipI8vO9Hd0Z8Y71PVf+oHFQuHAByasygpLea\nrdGKNXsjTacR0Y59v3u5kbCQRiylEUvNmD2NlDhWOOcDFK5uLcxet4CvNuAcDl/2Bz/HX1/fGXo7\n0tw2kWKZpi7Y21a0kiW09II2SikWG17Z6thq54q/g8G4YGtcsFUnBCEZ6GYF/dzzE4Z5FZIZfzCN\nSzvhDpgwY1sTalRdjgvB/sBiiyPLbeIgzpFqDbbiRDfjwY0+53tjtjP/mllZ0csKelmxO5sXAiUt\n7cQbq1iHPzFWFSPjHQCN8we+DKO6eRVOADUnf2xo6Hq1ArGU7GnFCAHbg+KKzmnXA+rf3HPFVf2/\n/sjn5kH+OYB9rYgX7V3ilUdWuWm5xeHlFpGUkwpgPdpWWn/OzcqKceHbdf649xweYy1FeExWGK88\nV1ZUrmKUFxRlQWFLSlNhXW0v7ZMD48ogqx0sX52vzBmLbw8YuYsnUIWEILcCYUAIgwitQ18+nx7b\nWgqU9qRg5UAp/1xbuWMnrxMJr4WvlCWSfmKgFRe0IkMzrkiVT1xasSVSxe5Khpuy7g0aYyOMjSmJ\nES7GkiCIkEKjlEJL5XXvpUIrgkqpCnr4PrCbylA4R0M5ng4a7rMq0J8bghWKQQGjQpBVgqwUZJVj\nXBqEiNCqQSQVSkmaWrLa9vPrjUgH+9mglGS921BuLOcHJecHA79qywryyuBMxXZWTcxbRjMBXeBn\nMPOs9LOYSiJrBqUQWGsxQlEFVrirkwApSJXX2+8kEWuthNVmxP5Oi4VmRFZ66US/4naUleHr53a4\n76xftUbSrzzrKoQOjFEtQEtFGglfQk8UnUjRTjULqWYhVnQaEcuNiHak0FriQtB2IUD7ywLncpxz\nbGcWN/bBvK4t1O/DhQBuCQe6c2z2/XTD2cGIbuZFfoZlwTA3OHyV4hN/X6DUo7x8v+WR9RZrbcFt\newtesNym3W5f9e+g3YhpN2KOrlz5MeOiYGtcsjMq2Brm9PKSbu4rBcPMVweGpU8KXVC6GpeWQVFh\ng2pWUdkQjP0Jo5NolvYtEYWqQRopIgHro5xT3RHnBzndcRkSwIp+Vk0ab1IInJDE2iGkQITj14Qk\nre7FKzHth46r6fp9iIHcEIczWTN0coy5sl3qjcCPfPTP+dUvzIP8NzMOtRP+i1v28soja7xo3yI3\nrbR50d5FoqB0WfN3ar330jqvKhfOneOylnutJiPCdVJQ2aA6F67XIl7GujAX75/bGEtmSvKiojIV\nY5OTFSVFWVHYgsqWVEGVtKwsxnkBHBPajJXxyUBRW9FWM1UB4xOBygm8n2Bgt1PziNzMCJw/vqXy\nI26RjNAqJhLe9CaNBc1I0owcqba0E8NCWtFQhlQbtKqQGKSsEJSIcM4zzq/wrfUku8pqCqPJ85iu\n0V5LwGiMVb6d62QYvfOLw9VY8tql1g3/LTyrAv3Dm9AtKz+LKL1/OoBWsDfVpHo6hyiF9w72PVPL\nMK9NUPzfo8IwDKu/Xl5wfmfEeuijZ6Wd3jf4m5cuCCyEPrULCmJS+C6Mc44qlGdiJWkoL8naijQL\nqWKtFbPYiCmrCkMVylYVuJLN4Yh+Joi0I5aChoKOBtWQaOVoRP5Hlka+TNyKoBFLGlEYJYsVDe1A\ngnPFzCrcUhePrHXsDB3bYVueZ2yMc7rjikFWeNW5spz4I9eEt8rMlMWtxQqBMbVgxW6Czmxpjpnb\namGJP30kY3+7z8ntitP9lGE5nbWu1aak8K0KISRJLeKjJGnkGfdpGHNbTGIWmzGLqebgYpO97YS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='minmax', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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FRCkFKJSMiYVEihQlIqSQxGmKFAoZviuERIkIIfxrJRRCKL+8VP4PiZQRSiokCqEUCokQ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\nQwAPrP6O/XlOwH+63gD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='maxabs', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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bRKBK3HQwVQgbDhHdQ5YVYNtV52RUWSNuNjOzYSFNsZkoskKpmiNfTZMvJ6k6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\ndiPCY5OrZKXAtx6dp+Wtc2PwPOPFMYoSC4fQayFqSVan7E9qvnYUMc5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muZkciERJdxspe7tUOqixh2I3ZTeWgKykAE1GVYC0SPgD5GnTbDOsgGh02Y+OOOfKtt1zrZ5Qeo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PqzsTlIKT9YKvvqgIpctOepdBdEwoF7T6BZfVisPVTWLP48dvCT60d4tIubx38S0W+RlZc8HZ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mCdxnlF3SgWFWjdMElKtvudF0BjFM5phPDUVvCp44yDZcuzl0omiaNoBMbGeG+wKCyONLSc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9+zl//mYU+i9nxRjpfQpSaQfPuuk5qzrOqpbzuudk2/DS2YbjbcuTbcPppmHVdmz6+KZH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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='standard', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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UJKswNiy855qKUYPzzF/n8M6qV8kXejbpHlNVGjvoNw/3aJmi8yTRCyHENrrl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\n5Dj69mv58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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='l1', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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/+GrqHLM6Mx0hKQ/0Lf4q1BAk8UOHhOWQtkNiWa3gpRBEcYTUYkzdJO2OYHzD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\nU8U7927np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73Y8hW67aperuaFcKzUKakEJr9C5CPtX1eb5/EcOnQo2dnZAOzbt4/Q0Nrn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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='l2', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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/+GrqHLM6Mx0hKQ/0Lf4q1BAk8UOHhOWQtkNiWa3gpRBEcYTUYkzdJO2OYHzD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\nU8U7927np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73Y8hW67aperuaFcKzUKakEJr9C5CPtX1eb5/EcOnQo2dnZAOzbt4/Q0Nrn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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(normalize='l2', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'ParallelCoordinates' object has no attribute 'normalize'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# should raise YellowbrickValueError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallelCoordinates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bad'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform_poof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/pcoords.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, features, classes, normalize, sample, color, colormap, vlines, vlines_kwds, **kwargs)\u001b[0m\n\u001b[1;32m 205\u001b[0m raise YellowbrickValueError(\n\u001b[1;32m 206\u001b[0m \u001b[0;34m\"'{}' is an unrecognized normalization method\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 208\u001b[0m )\n\u001b[1;32m 209\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ParallelCoordinates' object has no attribute 'normalize'" + ] + } + ], + "source": [ + "# should raise YellowbrickValueError\n", + "visualizer = ParallelCoordinates(normalize='bad', classes=classes)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MNH7y3FGeXJn5wM8N4pi/eH2dP3vtJusDH4nk5EyFzz16lBdOztOahFxujxgE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\nKGSQSTzHsJ6UnG/Z8fyD1rQuzaBLO96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ZuX9Mbx0EZ5Q12U4g7GRDehxP73AyucXxOOPP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h38o/S++T1WsAZvEVhsEMz4uom4Jl9qTnMFzih3cG15klVwhU3OdcTIdy3MtifUrR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OQRh1UPzge+3GhuTyWXhxWXhjDqQ2EkVVkh2ylw5dN+Lz6rJvprrUXpHlGQIj1MltdZmevg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\n1DCOGwZpi3cO6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4YH1gKurvVY1pJn70+oW0sgPP0YZiVcPwvoRbCeGi4Paqo2pHUR1mv6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\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kpqS02xSmTKhEaq4f3OPZgzscrU5Ztfss28iijZw2guMq52idPvh9kMQoKUzJVu6ZlYGp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# quick method\n", + "parallel_coordinates(X, y, normalize='standard');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `sample` argument" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OqXv7uBZWlxuYlkHOsUlHyRkImVbmBiCUJGOaZCyTfi9hyqj80FA7g0dXKIQU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bBi5yl+SEnn+KWa66sqAFq89aNFbXt5wi9zHpeugwoMXPj5NT9Pv6ulc4II4WYS/x5Jz3dL72Sn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\nQrix/wdFUVAW+TSJWgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(classes=classes, sample=200)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OqXv7uBZWlxuYlkHOsUlHyRkImVbmBiCUJGOaZCyTfi9hyqj80FA7g0dXKIQU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\nQrix/wdFUVAW+TSJWgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(classes=classes, sample=0.2)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CJXEIIiWpjGOQ5jQCSW4sDoiUIpSCSW44sBjigMNrAeNUUeicpBgzzfpYa67Z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\nAfQUhMl6Dz/RAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# quick method\n", + "parallel_coordinates(X, y, sample=0.2);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickTypeError", + "evalue": "`sample` parameter must be int or float", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mYellowbrickTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# should raise YellowbrickTypeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallelCoordinates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform_poof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit_transform_poof\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \"\"\"\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mXp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mXp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.virtualenvs/yellowbrick/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;31m# Draw the instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/pcoords.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m raise YellowbrickTypeError(\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0;34m\"`sample` parameter must be int or float\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m )\n\u001b[1;32m 244\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickTypeError\u001b[0m: `sample` parameter must be int or float" + ] + } + ], + "source": [ + "# should raise YellowbrickTypeError\n", + "visualizer = ParallelCoordinates(classes=classes, sample='bad')\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickValueError", + "evalue": "`sample` parameter of type `int` must be greater than 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mYellowbrickValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# should raise YellowbrickValueError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallelCoordinates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform_poof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit_transform_poof\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \"\"\"\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mXp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mXp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.virtualenvs/yellowbrick/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;31m# Draw the instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/pcoords.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m raise YellowbrickValueError(\n\u001b[0;32m--> 231\u001b[0;31m \u001b[0;34m\"`sample` parameter of type `int` must be greater than 1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 232\u001b[0m )\n\u001b[1;32m 233\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickValueError\u001b[0m: `sample` parameter of type `int` must be greater than 1" + ] + } + ], + "source": [ + "# should raise YellowbrickValueError\n", + "visualizer = ParallelCoordinates(classes=classes, sample=-1)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickValueError", + "evalue": "`sample` parameter of type `float` must be between 0 and 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mYellowbrickValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# should raise YellowbrickValueError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallelCoordinates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform_poof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit_transform_poof\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \"\"\"\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mXp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mXp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.virtualenvs/yellowbrick/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;31m# Draw the instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Google Drive/projects/other/yellowbrick/yellowbrick/features/pcoords.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m raise YellowbrickValueError(\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0;34m\"`sample` parameter of type `float` must be between 0 and 1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m )\n\u001b[1;32m 239\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickValueError\u001b[0m: `sample` parameter of type `float` must be between 0 and 1" + ] + } + ], + "source": [ + "# should raise YellowbrickValueError\n", + "visualizer = ParallelCoordinates(classes=classes, sample=1.1)\n", + "visualizer.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py index 7492bf59d..a4dbe1036 100644 --- a/tests/test_features/test_pcoords.py +++ b/tests/test_features/test_pcoords.py @@ -48,10 +48,61 @@ def test_parallel_coords(self): visualizer = ParallelCoordinates() visualizer.fit_transform(self.X, self.y) - @unittest.skip("takes too long with matplotlib 2.0.2; see #230") + def test_normalized_pcoords(self): + """ + Assert no errors occur using 'normalize' argument + """ + visualizer = ParallelCoordinates(normalize='l2') + visualizer.fit_transform(self.X, self.y) + + def test_normalized_pcoords_invalid_arg(self): + """ + Invalid argument to 'normalize' should raise + """ + with self.assertRaises(YellowbrickValueError): + ParallelCoordinates(normalize='foo') + + def test_pcoords_sample_int(self): + """ + Assert no errors occur using integer 'sample' argument + """ + visualizer = ParallelCoordinates(sample=10) + visualizer.fit_transform(self.X, self.y) + + def test_pcoords_sample_int_invalid(self): + """ + Negative int values should raise + """ + with self.assertRaises(YellowbrickValueError): + ParallelCoordinates(sample=-1) + + def test_pcoords_sample_float(self): + """ + Assert no errors occur using float 'sample' argument + """ + visualizer = ParallelCoordinates(sample=0.5) + visualizer.fit_transform(self.X, self.y) + + def test_pcoords_sample_float_invalid(self): + """ + Float values for 'sample' argument outside [0,1] should raise. + """ + with self.assertRaises(YellowbrickValueError): + ParallelCoordinates(sample=-0.2) + with self.assertRaises(YellowbrickValueError): + ParallelCoordinates(sample=1.1) + + def test_pcoords_sample_invalid_type(self): + """ + Non-numeric values for 'sample' argument should raise. + """ + with self.assertRaises(YellowbrickTypeError): + ParallelCoordinates(sample='foo') + def test_integrated_pcoords(self): """ - Test parallel coordinates on a real, occupancy data set + Test parallel coordinates on a real, occupancy data set (downsampled + for speed) """ occupancy = self.load_data('occupancy') @@ -65,5 +116,5 @@ def test_integrated_pcoords(self): X = np.array(X.tolist()) # Test the visualizer - visualizer = ParallelCoordinates() + visualizer = ParallelCoordinates(sample=200) visualizer.fit_transform(X, y) diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 877b0c172..ba14ea238 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -21,18 +21,21 @@ import numpy as np import matplotlib.pyplot as plt +from sklearn.preprocessing import (MinMaxScaler, MaxAbsScaler, Normalizer, + StandardScaler) +from yellowbrick.utils import is_dataframe from yellowbrick.features.base import DataVisualizer -from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.exceptions import YellowbrickTypeError, YellowbrickValueError from yellowbrick.style.colors import resolve_colors, get_color_cycle ########################################################################## ## Quick Methods ########################################################################## -def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, - color=None, colormap=None, vlines=True, - vlines_kwds=None, **kwargs): +def parallel_coordinates(X, y, ax=None, features=None, classes=None, + normalize=None, sample=1.0, color=None, colormap=None, + vlines=True, vlines_kwds=None, **kwargs): """Displays each feature as a vertical axis and each instance as a line. This helper function is a quick wrapper to utilize the ParallelCoordinates @@ -48,25 +51,44 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, An array or series of target or class values ax : matplotlib Axes, default: None - The axes to plot the figure on. + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). - features : list of strings, default: None - The names of the features or columns + features : list, default: None + a list of feature names to use + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. - classes : list of strings, default: None - The names of the classes in the target + classes : list, default: None + a list of class names for the legend + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. - color : list or tuple of colors, default: None - Specify the colors for each individual class + normalize : string or None, default: None + specifies which normalization method to use, if any + Current supported options are 'minmax', 'maxabs', 'standard', 'l1', + and 'l2'. - colormap : string or matplotlib cmap, default: None - Sequential colormap for continuous target + sample : float or int, default: 1.0 + specifies how many examples to display from the data + If int, specifies the maximum number of samples to display. + If float, specifies a fraction between 0 and 1 to display. - vlines : bool, default: True - Display the vertical azis lines + color : list or tuple, default: None + optional list or tuple of colors to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + vlines : boolean, default: True + flag to determine vertical line display vlines_kwds : dict, default: None - Keyword arguments to draw the vlines + options to style or display the vertical lines, default: None kwargs : dict Keyword arguments that are passed to the base class and may influence @@ -79,7 +101,8 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, """ # Instantiate the visualizer visualizer = ParallelCoordinates( - ax, features, classes, color, colormap, vlines, vlines_kwds, **kwargs + ax, features, classes, normalize, sample, color, colormap, vlines, + vlines_kwds, **kwargs ) # Fit and transform the visualizer (calls draw) @@ -117,6 +140,16 @@ class ParallelCoordinates(DataVisualizer): If classes is None and a y value is passed to fit then the classes are selected from the target vector. + normalize : string or None, default: None + specifies which normalization method to use, if any + Current supported options are 'minmax', 'maxabs', 'standard', 'l1', + and 'l2'. + + sample : float or int, default: 1.0 + specifies how many examples to display from the data + If int, specifies the maximum number of samples to display. + If float, specifies a fraction between 0 and 1 to display. + color : list or tuple, default: None optional list or tuple of colors to colorize lines Use either color to colorize the lines on a per class basis or @@ -152,12 +185,47 @@ class ParallelCoordinates(DataVisualizer): process, but can and should be set as early as possible. """ - def __init__(self, ax=None, features=None, classes=None, color=None, - colormap=None, vlines=True, vlines_kwds=None, **kwargs): + normalizers = { + 'minmax': MinMaxScaler(), + 'maxabs': MaxAbsScaler(), + 'standard': StandardScaler(), + 'l1': Normalizer('l1'), + 'l2': Normalizer('l2'), + } + + def __init__(self, ax=None, features=None, classes=None, normalize=None, + sample=1.0, color=None, colormap=None, vlines=True, + vlines_kwds=None, **kwargs): super(ParallelCoordinates, self).__init__( ax, features, classes, color, colormap, **kwargs ) + # Validate 'normalize' argument + if normalize in self.normalizers or normalize is None: + self.normalize = normalize + else: + raise YellowbrickValueError( + "'{}' is an unrecognized normalization method" + .format(normalize) + ) + + # Validate 'sample' argument + if isinstance(sample, int): + if sample < 1: + raise YellowbrickValueError( + "`sample` parameter of type `int` must be greater than 1" + ) + elif isinstance(sample, float): + if sample <= 0 or sample > 1: + raise YellowbrickValueError( + "`sample` parameter of type `float` must be between 0 and 1" + ) + else: + raise YellowbrickTypeError( + "`sample` parameter must be int or float" + ) + self.sample = sample + # Visual Parameters self.show_vlines = vlines self.vlines_kwds = vlines_kwds or { @@ -169,6 +237,22 @@ def draw(self, X, y, **kwargs): Called from the fit method, this method creates the parallel coordinates canvas and draws each instance and vertical lines on it. """ + # Convert from dataframe + if is_dataframe(X): + X = X.as_matrix() + + # Choose a subset of samples + # TODO: allow selection of a random subset of samples instead of head + + if isinstance(self.sample, int): + self.n_samples = min([self.sample, len(X)]) + elif isinstance(self.sample, float): + self.n_samples = int(len(X) * self.sample) + X = X[:self.n_samples, :] + + # Normalize + if self.normalize is not None: + X = self.normalizers[self.normalize].fit_transform(X) # Get the shape of the data nrows, ncols = X.shape @@ -198,9 +282,9 @@ def draw(self, X, y, **kwargs): if label not in used_legends: used_legends.add(label) - self.ax.plot(x, row, color=colors[label], label=label, **kwargs) + self.ax.plot(x, row, color=colors[label], alpha=0.25, label=label, **kwargs) else: - self.ax.plot(x, row, color=colors[label], **kwargs) + self.ax.plot(x, row, color=colors[label], alpha=0.25, **kwargs) # Add the vertical lines # TODO: Make an independent function for override! From 8484519a3419bfd442d95af50d912b98ba80e184 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Wed, 19 Jul 2017 20:31:08 -0400 Subject: [PATCH 27/40] r2 in pe plot and visual test cases --- examples/bbengfort/regression.ipynb | 993 ++++++++++++++++++ .../test_residuals/test_pred_error.png | Bin 0 -> 13223 bytes .../test_residuals/test_resid_plots.png | Bin 0 -> 10743 bytes tests/test_regressor/test_residuals.py | 13 +- yellowbrick/regressor/residuals.py | 16 +- 5 files changed, 1015 insertions(+), 7 deletions(-) create mode 100644 examples/bbengfort/regression.ipynb create mode 100644 tests/baseline_images/test_regressor/test_residuals/test_pred_error.png create mode 100644 tests/baseline_images/test_regressor/test_residuals/test_resid_plots.png diff --git a/examples/bbengfort/regression.ipynb b/examples/bbengfort/regression.ipynb new file mode 100644 index 000000000..486778185 --- /dev/null +++ b/examples/bbengfort/regression.ipynb @@ -0,0 +1,993 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "sys.path.append(\"..\")\n", + "sys.path.append(\"../..\")\n", + "\n", + "import pandas as pd \n", + "import yellowbrick as yb\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt \n", + "\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "DATA = os.path.normpath(\"../data/\")\n", + "\n", + "def load_data(name):\n", + " path = os.path.join(DATA, name, name + \".csv\")\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cementslagashwatersplastcoarsefineagestrength
0540.00.00.0162.02.51040.0676.02879.986111
1540.00.00.0162.02.51055.0676.02861.887366
2332.5142.50.0228.00.0932.0594.027040.269535
3332.5142.50.0228.00.0932.0594.036541.052780
4198.6132.40.0192.00.0978.4825.536044.296075
\n", + "
" + ], + "text/plain": [ + " cement slag ash water splast coarse fine age strength\n", + "0 540.0 0.0 0.0 162.0 2.5 1040.0 676.0 28 79.986111\n", + "1 540.0 0.0 0.0 162.0 2.5 1055.0 676.0 28 61.887366\n", + "2 332.5 142.5 0.0 228.0 0.0 932.0 594.0 270 40.269535\n", + "3 332.5 142.5 0.0 228.0 0.0 932.0 594.0 365 41.052780\n", + "4 198.6 132.4 0.0 192.0 0.0 978.4 825.5 360 44.296075" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = load_data(\"concrete\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split as tts\n", + "\n", + "features = [\"cement\", \"slag\", \"ash\", \"water\", \"splast\", \"coarse\", \"fine\", \"age\"]\n", + "target = \"strength\"\n", + "\n", + "X = df[features]\n", + "y = df[target]\n", + "\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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