Merge pull request #346 from rasbt/0.11.0

0.11.0 release

docs/sources/CHANGELOG.md

@@ -7,7 +7,7 @@ The CHANGELOG for the current development version is available at
 
 ---
 
-### Version 0.11.0dev
+### Version 0.11.0 (2017-03-14)
 
 ##### Downloads
 
@@ -25,7 +25,7 @@ The CHANGELOG for the current development version is available at
 -   New function implementing the 5x2cv paired t-test procedure (`paired_ttest_5x2cv`) proposed by Dieterrich (1998)
     to compare the performance of two models. ([#325](https://github.com/rasbt/mlxtend/issues/325))
 - A `refit` parameter was added to stacking classes (similar to the `refit` parameter in the `EnsembleVoteClassifier`), to support classifiers and regressors that follow the scikit-learn API but are not compatible with scikit-learn's `clone` function. ([#325](https://github.com/rasbt/mlxtend/issues/324))
-- The `ColumnSelector` now has a `drop_axis` argument to use it in pipelines with `CountVectorizers`. ([#333](https://github.com/rasbt/mlxtend/pull/333)
+- The `ColumnSelector` now has a `drop_axis` argument to use it in pipelines with `CountVectorizers`. ([#333](https://github.com/rasbt/mlxtend/pull/333))
 
 ##### Changes
 

docs/sources/user_guide/evaluate/paired_ttest_kfold_cv.ipynb

@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "K-fold cross-validated paired t-test procedure is a common method for comparing the performance of two models (classifiers or regressors) and addresses some of the drawbacks of the [resampled t-test procedure](paired_ttest_resampled.md); however, this method has still the problem that the training sets overlap and is not recommended to be used in practice [1], and techniques such as the [`paired_ttest_5times2cv`](paired_ttest_5times2cv.md) should be used instead.\n",
+    "K-fold cross-validated paired t-test procedure is a common method for comparing the performance of two models (classifiers or regressors) and addresses some of the drawbacks of the [resampled t-test procedure](paired_ttest_resampled.md); however, this method has still the problem that the training sets overlap and is not recommended to be used in practice [1], and techniques such as the [`paired_ttest_5x2cv`](paired_ttest_5x2cv.md) should be used instead.\n",
     "\n",
     "To explain how this method works, let's consider to estimator (e.g., classifiers) A and B. Further, we have a labeled dataset *D*. In the common hold-out method, we typically split the dataset into 2 parts: a training and a test set. In the k-fold cross-validated paired t-test procedure, we split the test set into *k* parts of equal size, and each of these parts is then used for testing while the remaining *k-1* parts (joined together) are used for training a classifier or regressor (i.e., the standard k-fold cross-validation procedure).\n",
     "\n",
@@ -321,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

docs/sources/user_guide/plotting/plot_decision_regions.ipynb

@@ -691,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
      "text": [
       "## plot_decision_regions\n",
       "\n",
-      "*plot_decision_regions(X, y, clf, feature_index=None, filler_feature_values=None, filler_feature_ranges=None, ax=None, X_highlight=None, res=0.02, legend=1, hide_spines=True, markers='s^oxv<>', colors='red,blue,limegreen,gray,cyan')*\n",
+      "*plot_decision_regions(X, y, clf, feature_index=None, filler_feature_values=None, filler_feature_ranges=None, ax=None, X_highlight=None, res=None, legend=1, hide_spines=True, markers='s^oxv<>', colors='red,blue,limegreen,gray,cyan', scatter_kwargs=None, contourf_kwargs=None, scatter_highlight_kwargs=None)*\n",
       "\n",
       "Plot decision regions of a classifier.\n",
       "\n",
@@ -751,12 +751,14 @@
       "\n",
       "    An array with data points that are used to highlight samples in `X`.\n",
       "\n",
-      "- `res` : float or array-like, shape = (2,) (default: 0.02)\n",
+      "- `res` : float or array-like, shape = (2,) (default: None)\n",
       "\n",
-      "    Grid width. If float, same resolution is used for both the x- and\n",
-      "    y-axis. If array-like, the first item is used on the x-axis, the\n",
-      "    second is used on the y-axis. Lower values increase the resolution but\n",
-      "    slow down the plotting.\n",
+      "    This parameter was used to define the grid width,\n",
+      "    but it has been deprecated in favor of\n",
+      "    determining the number of points given the figure DPI and size\n",
+      "    automatically for optimal results and computational efficiency.\n",
+      "    To increase the resolution, it's is recommended to use to provide\n",
+      "    a `dpi argument via matplotlib, e.g., `plt.figure(dpi=600)`.\n",
       "\n",
       "- `hide_spines` : bool (default: True)\n",
       "\n",
@@ -767,14 +769,26 @@
       "    Integer to specify the legend location.\n",
       "    No legend if legend is 0.\n",
       "\n",
-      "- `markers` : str (default 's^oxv<>')\n",
+      "- `markers` : str (default: 's^oxv<>')\n",
       "\n",
       "    Scatterplot markers.\n",
       "\n",
-      "- `colors` : str (default 'red,blue,limegreen,gray,cyan')\n",
+      "- `colors` : str (default: 'red,blue,limegreen,gray,cyan')\n",
       "\n",
       "    Comma separated list of colors.\n",
       "\n",
+      "- `scatter_kwargs` : dict (default: None)\n",
+      "\n",
+      "    Keyword arguments for underlying matplotlib scatter function.\n",
+      "\n",
+      "- `contourf_kwargs` : dict (default: None)\n",
+      "\n",
+      "    Keyword arguments for underlying matplotlib contourf function.\n",
+      "\n",
+      "- `scatter_highlight_kwargs` : dict (default: None)\n",
+      "\n",
+      "    Keyword arguments for underlying matplotlib scatter function.\n",
+      "\n",
       "**Returns**\n",
       "\n",
       "- `ax` : matplotlib.axes.Axes object\n",
@@ -788,15 +802,6 @@
     "with open('../../api_modules/mlxtend.plotting/plot_decision_regions.md', 'r') as f:\n",
     "    print(f.read())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -816,7 +821,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

mlxtend/__init__.py

@@ -4,4 +4,4 @@
 #
 # License: BSD 3 clause
 
-__version__ = '0.11.0dev'
+__version__ = '0.11.0'