update examples and little fix on docs

docs/Quickstart.rst

@@ -108,4 +108,4 @@ And again, that's it! You'll notice that in this plot, all we needed to do was p
 More Plots
 ----------
 
-Want to know the other plots you can generate using Scikit-plot? Visit the :ref:`factoryapidocs`.
+Want to know the other plots you can generate using Scikit-plot? Visit the :ref:`factoryapidocs` or the :ref:`functionsapidocs`.

examples/plot_confusion_matrix.py

@@ -10,9 +10,9 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 rf = RandomForestClassifier()
 rf = rf.fit(X, y)
 preds = rf.predict(X)
-plotters.plot_confusion_matrix(y_true=y, y_pred=preds)
+skplt.plot_confusion_matrix(y_true=y, y_pred=preds)
 plt.show()

examples/plot_feature_importances.py

@@ -12,9 +12,9 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 rf = RandomForestClassifier()
 rf = rf.fit(X, y)
-plotters.plot_feature_importances(rf, feature_names=['petal length', 'petal width',
-  'sepal length', 'sepal width'])
+skplt.plot_feature_importances(rf, feature_names=['petal length', 'petal width',
+ 'sepal length', 'sepal width'])
 plt.show()

examples/plot_ks_statistic.py

@@ -12,9 +12,9 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 lr = LogisticRegression()
 lr = lr.fit(X, y)
 probas = lr.predict_proba(X)
-plotters.plot_ks_statistic(y_true=y, y_probas=probas)
+skplt.plot_ks_statistic(y_true=y, y_probas=probas)
 plt.show()

examples/plot_learning_curve.py

@@ -12,7 +12,7 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 rf = RandomForestClassifier()
-plotters.plot_learning_curve(rf, X, y)
+skplt.plot_learning_curve(rf, X, y)
 plt.show()

examples/plot_precision_recall_curve.py

@@ -12,9 +12,9 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 nb = GaussianNB()
 nb = nb.fit(X, y)
 probas = nb.predict_proba(X)
-plotters.plot_precision_recall_curve(y_true=y, y_probas=probas)
+skplt.plot_precision_recall_curve(y_true=y, y_probas=probas)
 plt.show()

examples/plot_roc_curve.py

@@ -12,9 +12,9 @@
 plt.show()
 
 # Using the more flexible functions API
-from scikitplot import plotters
+from scikitplot import plotters as skplt
 nb = GaussianNB()
 nb = nb.fit(X, y)
 probas = nb.predict_proba(X)
-plotters.plot_roc_curve(y_true=y, y_probas=probas)
+skplt.plot_roc_curve(y_true=y, y_probas=probas)
 plt.show()

scikitplot/plotters.py

@@ -45,10 +45,11 @@ def plot_confusion_matrix(y_true, y_pred, title=None, normalize=False, ax=None):
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
+ >>> import scikitplot.plotters as skplt
  >>> rf = RandomForestClassifier()
  >>> rf = rf.fit(X_train, y_train)
  >>> y_pred = rf.predict(X_test)
- >>> plot_confusion_matrix(y_test, y_pred, normalize=True)
+ >>> skplt.plot_confusion_matrix(y_test, y_pred, normalize=True)
  <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
  >>> plt.show()
 
@@ -112,10 +113,11 @@ def plot_roc_curve(y_true, y_probas, title='ROC Curves', ax=None):
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
+ >>> import scikitplot.plotters as skplt
  >>> nb = GaussianNB()
  >>> nb = nb.fit(X_train, y_train)
  >>> y_probas = nb.predict_proba(X_test)
- >>> plot_roc_curve(y_test, y_probas)
+ >>> skplt.plot_roc_curve(y_test, y_probas)
  <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
  >>> plt.show()
 
@@ -215,10 +217,11 @@ def plot_ks_statistic(y_true, y_probas, title='KS Statistic Plot', ax=None):
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
+ >>> import scikitplot.plotters as skplt
  >>> lr = LogisticRegression()
  >>> lr = lr.fit(X_train, y_train)
  >>> y_probas = lr.predict_proba(X_test)
- >>> plot_ks_statistic(y_test, y_probas)
+ >>> skplt.plot_ks_statistic(y_test, y_probas)
  <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
  >>> plt.show()
 
@@ -275,10 +278,11 @@ def plot_precision_recall_curve(y_true, y_probas, title='Precision-Recall Curve'
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
+ >>> import scikitplot.plotters as skplt
  >>> nb = GaussianNB()
  >>> nb = nb.fit(X_train, y_train)
  >>> y_probas = nb.predict_proba(X_test)
- >>> plot_precision_recall_curve(y_test, y_probas)
+ >>> skplt.plot_precision_recall_curve(y_test, y_probas)
  <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
  >>> plt.show()
 
@@ -360,12 +364,13 @@ def plot_feature_importances(clf, title='Feature Importance', feature_names=None
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
- >>> rf = RandomForestClassifier()
- >>> rf.fit(X, y)
- >>> plot_feature_importances(rf, feature_names=['petal length', 'petal width',
- ... 'sepal length', 'sepal width'])
- <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
- >>> plt.show()
+ >>> import scikitplot.plotters as skplt
+ >>> rf = RandomForestClassifier()
+ >>> rf.fit(X, y)
+ >>> skplt.plot_feature_importances(rf, feature_names=['petal length', 'petal width',
+ ... 'sepal length', 'sepal width'])
+ <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
+ >>> plt.show()
 
  .. image:: _static/examples/plot_feature_importances.png
  :align: center
@@ -465,8 +470,9 @@ def plot_learning_curve(clf, X, y, title='Learning Curve', cv=None, train_sizes=
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
- >>> rf = classifier_factory(RandomForestClassifier())
- >>> rf.plot_learning_curve(X, y)
+ >>> import scikitplot.plotters as skplt
+ >>> rf = RandomForestClassifier()
+ >>> skplt.plot_learning_curve(rf, X, y)
  <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
  >>> plt.show()
 
@@ -530,10 +536,11 @@ def plot_silhouette(clf, X, title='Silhouette Analysis', metric='euclidean', cop
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
- >>> kmeans = KMeans(n_clusters=4, random_state=1)
- >>> plot_silhouette(kmeans, X)
- <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
- >>> plt.show()
+ >>> import scikitplot.plotters as skplt
+ >>> kmeans = KMeans(n_clusters=4, random_state=1)
+ >>> skplt.plot_silhouette(kmeans, X)
+ <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
+ >>> plt.show()
 
  .. image:: _static/examples/plot_silhouette.png
  :align: center
@@ -618,10 +625,11 @@ def plot_elbow_curve(clf, X, title='Elbow Plot', cluster_ranges=None, ax=None):
  ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was drawn.
 
  Example:
- >>> kmeans = KMeans(random_state=1)
- >>> plot_elbow_curve(kmeans, cluster_ranges=range(1, 11))
- <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
- >>> plt.show()
+ >>> import scikitplot.plotters as skplt
+ >>> kmeans = KMeans(random_state=1)
+ >>> skplt.plot_elbow_curve(kmeans, cluster_ranges=range(1, 11))
+ <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
+ >>> plt.show()
 
  .. image:: _static/examples/plot_elbow_curve.png
  :align: center