version 0.9 (#267)

docs/mkdocs.yml

@@ -53,7 +53,7 @@ pages:
     - user_guide/data/wine_data.md
   - evaluate:
     - user_guide/evaluate/bootstrap.md
-    - user_guide/evaluate/BootstrapOutOufBag.md
+    - user_guide/evaluate/BootstrapOutOfBag.md
     - user_guide/evaluate/confusion_matrix.md
     - user_guide/evaluate/lift_score.md
     - user_guide/evaluate/mcnemar_table.md

docs/sources/CHANGELOG.md

@@ -5,34 +5,34 @@
 The CHANGELOG for the current development version is available at
 [https://github.com/rasbt/mlxtend/blob/master/docs/sources/CHANGELOG.md](https://github.com/rasbt/mlxtend/blob/master/docs/sources/CHANGELOG.md).
 
-### Version 0.8.1dev (TBD)
+### Version 0.9.0 (2017-10-21)
 
 
 ##### Downloads
 
-- [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.8.1.zip)
-- [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.8.1.tar.gz)
+- [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.9.0.zip)
+- [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.9.0.tar.gz)
 
 ##### New Features
 
-- Added `evaluate.permutation_test`, a permutation test for hypothesis testing (or A/B testing) to test if two samples come from the same distribution. Or in other words, a procedure to test the null hypothesis that that two groups are not significantly different (e.g., a treatment and a control group).
-- Added `'leverage'` and `'conviction` as evaluation metrics to the `frequent_patterns.association_rules` function. [#246](https://github.com/rasbt/mlxtend/pull/246) & [#247](https://github.com/rasbt/mlxtend/pull/247)
-- Added a `loadings_` attribute to `PrincipalComponentAnalysis` to compute the factor loadings of the features on the principal components. [#251](https://github.com/rasbt/mlxtend/pull/251)
-- Allow grid search over classifiers/regressors in ensemble and stacking estimators [#259](https://github.com/rasbt/mlxtend/pull/259)
-- New `make_multiplexer_dataset` function that creates a dataset generated by a n-bit Boolean multiplexer for evaluating supervised learning algorithms [#263](https://github.com/rasbt/mlxtend/pull/263)
-- Added a new `BootstrapOutOfBag` class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms [#265](https://github.com/rasbt/mlxtend/pull/265)
+- Added `evaluate.permutation_test`, a permutation test for hypothesis testing (or A/B testing) to test if two samples come from the same distribution. Or in other words, a procedure to test the null hypothesis that that two groups are not significantly different (e.g., a treatment and a control group). ([#250](https://github.com/rasbt/mlxtend/pull/250))
+- Added `'leverage'` and `'conviction` as evaluation metrics to the `frequent_patterns.association_rules` function. ([#246](https://github.com/rasbt/mlxtend/pull/246) & [#247](https://github.com/rasbt/mlxtend/pull/247))
+- Added a `loadings_` attribute to `PrincipalComponentAnalysis` to compute the factor loadings of the features on the principal components. ([#251](https://github.com/rasbt/mlxtend/pull/251))
+- Allow grid search over classifiers/regressors in ensemble and stacking estimators. ([#259](https://github.com/rasbt/mlxtend/pull/259))
+- New `make_multiplexer_dataset` function that creates a dataset generated by a n-bit Boolean multiplexer for evaluating supervised learning algorithms. ([#263](https://github.com/rasbt/mlxtend/pull/263))
+- Added a new `BootstrapOutOfBag` class, an implementation of the out-of-bag bootstrap to evaluate supervised learning algorithms. ([#265](https://github.com/rasbt/mlxtend/pull/265))
+- The parameters for `StackingClassifier`, `StackingCVClassifier`, `StackingRegressor`, `StackingCVRegressor`, and `EnsembleVoteClassifier` can now be tuned using scikit-learn's `GridSearchCV` ([#254](https://github.com/rasbt/mlxtend/pull/254) via [James Bourbeau](https://github.com/jrbourbeau))
 
 ##### Changes
 
-- The `'support'` column returned by `frequent_patterns.association_rules` was changed to compute the support of "antecedant union consequent", and new `antecedant support'` and `'consequent support'` column were added to avoid ambiguity. [#245](https://github.com/rasbt/mlxtend/pull/245)
-- Allow the `OnehotTransactions` to be cloned via scikit-learn's `clone` function, which is required by e.g., scikit-learn's `FeatureUnion` or `GridSearchCV` (via [Iaroslav Shcherbatyi](https://github.com/iaroslav-ai)). [#249](https://github.com/rasbt/mlxtend/pull/249)
+- The `'support'` column returned by `frequent_patterns.association_rules` was changed to compute the support of "antecedant union consequent", and new `antecedant support'` and `'consequent support'` column were added to avoid ambiguity. ([#245](https://github.com/rasbt/mlxtend/pull/245))
+- Allow the `OnehotTransactions` to be cloned via scikit-learn's `clone` function, which is required by e.g., scikit-learn's `FeatureUnion` or `GridSearchCV` (via [Iaroslav Shcherbatyi](https://github.com/iaroslav-ai)). ([#249](https://github.com/rasbt/mlxtend/pull/249))
 
 ##### Bug Fixes
 
-- the "S" vector from SVD in `PrincipalComponentAnalysis` are now scaled so that the eigenvalues via `solver='eigen'` and `solver='svd'` now store eigenvalues that have the same magnitudes. [#251](https://github.com/rasbt/mlxtend/pull/251)
-- The parameters for `StackingClassifier`, `StackingCVClassifier`, `StackingRegressor`, `StackingCVRegressor`, and `EnsembleVoteClassifier` can now be tuned using scikit-learn's `GridSearchCV` ([#254](https://github.com/rasbt/mlxtend/pull/254) via [James Bourbeau](https://github.com/jrbourbeau))
-- Fix issues with `self._init_time` parameter in `_IterativeModel` subclasses. [#256](https://github.com/rasbt/mlxtend/pull/256)
-- Fix imprecision bug that occurred in `plot_ecdf` when run on Python 2.7 that [264](https://github.com/rasbt/mlxtend/pull/264)
+- Fix issues with `self._init_time` parameter in `_IterativeModel` subclasses. ([#256](https://github.com/rasbt/mlxtend/pull/256))
+- Fix imprecision bug that occurred in `plot_ecdf` when run on Python 2.7. ([264](https://github.com/rasbt/mlxtend/pull/264))
+- The vectors from SVD in `PrincipalComponentAnalysis` are no being scaled so that the eigenvalues via `solver='eigen'` and `solver='svd'` now store eigenvalues that have the same magnitudes. ([#251](https://github.com/rasbt/mlxtend/pull/251))
 
 ### Version 0.8.0 (2017-09-09)
 

docs/sources/user_guide/evaluate/BootstrapOutOfBag.ipynb

@@ -34,8 +34,7 @@
    "source": [
     "Originally, the bootstrap method aims to determine the statistical properties of an estimator when the underlying distribution was unknown and additional samples are not available. Now, in order to exploit this method for the evaluation of predictive models, such as hypotheses for classification and regression, we may prefer a slightly different approach to bootstrapping using the so-called Out-Of-Bag (OOB) or Leave-One-Out Bootstrap (LOOB) technique. Here, we use out-of-bag samples as test sets for evaluation instead of evaluating the model on the training data. Out-of-bag samples are the unique sets of instances that are not used for model fitting as shown in the figure below [1].\n",
     "\n",
-    "\n",
-    "![](BootstrapOutOfBag_files/bootrap_concept.png)\n",
+    "![](BootstrapOutOfBag_files/bootstrap_concept.png)\n",
     "\n",
     "\n",
     "The figure above illustrates how three random bootstrap samples drawn from an exemplary ten-sample dataset ($X_1,X_2, ..., X_{10}$) and their out-of-bag sample for testing may look like. In practice, Bradley Efron and Robert Tibshirani recommend drawing 50 to 200 bootstrap samples as being sufficient for reliable estimates [2]."
@@ -74,9 +73,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0 4 4 3 3] [1 2]\n",
-      "[1 1 1 0 3] [2 4]\n",
-      "[4 2 1 4 0] [3]\n"
+      "[4 2 1 3 3] [0]\n",
+      "[2 4 1 2 1] [0 3]\n",
+      "[4 3 3 4 1] [0 2]\n"
      ]
     }
    ],
@@ -198,7 +197,7 @@
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cHZ5MFhE/BmYBwyKiBbgJmBURU4AEmoFPA2Tmhoi4E3gW2AV8NjN390zpkiQd+ao56/uy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\n1K0dHAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11448ce48>"
+       "<matplotlib.figure.Figure at 0x10ba42e80>"
       ]
      },
      "metadata": {},

mlxtend/__init__.py

@@ -4,4 +4,4 @@
 #
 # License: BSD 3 clause
 
-__version__ = '0.8.1dev'
+__version__ = '0.9.0'