Functions API Reference

This document contains the stand-alone plotting functions for maximum flexibility. If you want to use factory functions clustering_factory() and classifier_factory(), use the Factory API Reference instead.

This module contains a more flexible API for Scikit-plot users, exposing simple functions to generate plots.

scikitplot.plotters.plot_learning_curve(clf, X, y, title='Learning Curve', cv=None, train_sizes=None, n_jobs=1, scoring=None, ax=None, figsize=None, title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.estimators.plot_learning_curve instead.

Generates a plot of the train and test learning curves for a classifier.

Args:
clf: Classifier instance that implements fit and predict
methods.
X (array-like, shape (n_samples, n_features)):
Training vector, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape (n_samples) or (n_samples, n_features)):
Target relative to X for classification or regression; None for unsupervised learning.
title (string, optional): Title of the generated plot. Defaults to
“Learning Curve”
cv (int, cross-validation generator, iterable, optional): Determines

the cross-validation strategy to be used for splitting.

Possible inputs for cv are:
  • None, to use the default 3-fold cross-validation,
  • integer, to specify the number of folds.
  • An object to be used as a cross-validation generator.
  • An iterable yielding train/test splits.

For integer/None inputs, if y is binary or multiclass, StratifiedKFold used. If the estimator is not a classifier or if y is neither binary nor multiclass, KFold is used.

train_sizes (iterable, optional): Determines the training sizes used to
plot the learning curve. If None, np.linspace(.1, 1.0, 5) is used.
n_jobs (int, optional): Number of jobs to run in parallel. Defaults to
scoring (string, callable or None, optional): default: None
A string (see scikit-learn model evaluation documentation) or a scorerbcallable object / function with signature scorer(estimator, X, y).
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> import scikitplot.plotters as skplt
>>> rf = RandomForestClassifier()
>>> skplt.plot_learning_curve(rf, X, y)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
Learning Curve
scikitplot.plotters.plot_confusion_matrix(y_true, y_pred, labels=None, true_labels=None, pred_labels=None, title=None, normalize=False, hide_zeros=False, x_tick_rotation=0, ax=None, figsize=None, cmap='Blues', title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.metrics.plot_confusion_matrix instead.

Generates confusion matrix plot from predictions and true labels

Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_pred (array-like, shape (n_samples)):
Estimated targets as returned by a classifier.
labels (array-like, shape (n_classes), optional): List of labels to
index the matrix. This may be used to reorder or select a subset of labels. If none is given, those that appear at least once in y_true or y_pred are used in sorted order. (new in v0.2.5)
true_labels (array-like, optional): The true labels to display.
If none is given, then all of the labels are used.
pred_labels (array-like, optional): The predicted labels to display.
If none is given, then all of the labels are used.
title (string, optional): Title of the generated plot. Defaults to
“Confusion Matrix” if normalize is True. Else, defaults to “Normalized Confusion Matrix.
normalize (bool, optional): If True, normalizes the confusion matrix
before plotting. Defaults to False.
hide_zeros (bool, optional): If True, does not plot cells containing a
value of zero. Defaults to False.
x_tick_rotation (int, optional): Rotates x-axis tick labels by the
specified angle. This is useful in cases where there are numerous categories and the labels overlap each other.
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
cmap (string or matplotlib.colors.Colormap instance, optional):
Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (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)
>>> skplt.plot_confusion_matrix(y_test, y_pred, normalize=True)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
Confusion matrix
scikitplot.plotters.plot_roc_curve(y_true, y_probas, title='ROC Curves', curves=('micro', 'macro', 'each_class'), ax=None, figsize=None, cmap='nipy_spectral', title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.metrics.plot_roc_curve instead.

Generates the ROC curves from labels and predicted scores/probabilities

Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probas (array-like, shape (n_samples, n_classes)):
Prediction probabilities for each class returned by a classifier.
title (string, optional): Title of the generated plot. Defaults to
“ROC Curves”.
curves (array-like): A listing of which curves should be plotted on the
resulting plot. Defaults to (“micro”, “macro”, “each_class”) i.e. “micro” for micro-averaged curve, “macro” for macro-averaged curve
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
cmap (string or matplotlib.colors.Colormap instance, optional):
Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (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)
>>> skplt.plot_roc_curve(y_test, y_probas)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
ROC Curves
scikitplot.plotters.plot_ks_statistic(y_true, y_probas, title='KS Statistic Plot', ax=None, figsize=None, title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.metrics.plot_ks_statistic instead.

Generates the KS Statistic plot from labels and scores/probabilities

Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probas (array-like, shape (n_samples, n_classes)):
Prediction probabilities for each class returned by a classifier.
title (string, optional): Title of the generated plot. Defaults to
“KS Statistic Plot”.
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the learning curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (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)
>>> skplt.plot_ks_statistic(y_test, y_probas)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
KS Statistic
scikitplot.plotters.plot_precision_recall_curve(y_true, y_probas, title='Precision-Recall Curve', curves=('micro', 'each_class'), ax=None, figsize=None, cmap='nipy_spectral', title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.metrics.plot_precision_recall_curve instead.

Generates the Precision Recall Curve from labels and probabilities

Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probas (array-like, shape (n_samples, n_classes)):
Prediction probabilities for each class returned by a classifier.
curves (array-like): A listing of which curves should be plotted on the
resulting plot. Defaults to (“micro”, “each_class”) i.e. “micro” for micro-averaged curve
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
cmap (string or matplotlib.colors.Colormap instance, optional):
Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (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)
>>> skplt.plot_precision_recall_curve(y_test, y_probas)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
Precision Recall Curve
scikitplot.plotters.plot_feature_importances(clf, title='Feature Importance', feature_names=None, max_num_features=20, order='descending', x_tick_rotation=0, ax=None, figsize=None, title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.estimators.plot_feature_importances instead.

Generates a plot of a classifier’s feature importances.

Args:
clf: Classifier instance that implements fit and predict_proba
methods. The classifier must also have a feature_importances_ attribute.
title (string, optional): Title of the generated plot. Defaults to
“Feature importances”.
feature_names (None, list of string, optional): Determines the
feature names used to plot the feature importances. If None, feature names will be numbered.
max_num_features (int): Determines the maximum number of features to
plot. Defaults to 20.
order (‘ascending’, ‘descending’, or None, optional): Determines the
order in which the feature importances are plotted. Defaults to ‘descending’.
x_tick_rotation (int, optional): Rotates x-axis tick labels by the
specified angle. This is useful in cases where there are numerous categories and the labels overlap each other.
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> 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()
Feature Importances
scikitplot.plotters.plot_silhouette(clf, X, title='Silhouette Analysis', metric='euclidean', copy=True, ax=None, figsize=None, cmap='nipy_spectral', title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.metrics.plot_silhouette instead.

Plots silhouette analysis of clusters using fit_predict.

Args:
clf: Clusterer instance that implements fit and fit_predict
methods.
X (array-like, shape (n_samples, n_features)):
Data to cluster, where n_samples is the number of samples and n_features is the number of features.
title (string, optional): Title of the generated plot. Defaults to
“Silhouette Analysis”
metric (string or callable, optional): The metric to use when
calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by sklearn.metrics.pairwise.pairwise_distances. If X is the distance array itself, use “precomputed” as the metric.
copy (boolean, optional): Determines whether fit is used on
clf or on a copy of clf.
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
cmap (string or matplotlib.colors.Colormap instance, optional):
Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> 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()
Silhouette Plot
scikitplot.plotters.plot_elbow_curve(clf, X, title='Elbow Plot', cluster_ranges=None, ax=None, figsize=None, title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.cluster.plot_elbow_curve instead.

Plots elbow curve of different values of K for KMeans clustering.

Args:
clf: Clusterer instance that implements fit and fit_predict
methods and a score parameter.
X (array-like, shape (n_samples, n_features)):
Data to cluster, where n_samples is the number of samples and n_features is the number of features.
title (string, optional): Title of the generated plot. Defaults to
“Elbow Plot”
cluster_ranges (None or list of int, optional): List of
n_clusters for which to plot the explained variances. Defaults to range(1, 12, 2).
copy (boolean, optional): Determines whether fit is used on
clf or on a copy of clf.
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> 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()
Elbow Curve
scikitplot.plotters.plot_pca_component_variance(clf, title='PCA Component Explained Variances', target_explained_variance=0.75, ax=None, figsize=None, title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.decomposition.plot_pca_component_variance instead.

Plots PCA components’ explained variance ratios. (new in v0.2.2)

Args:

clf: PCA instance that has the explained_variance_ratio_ attribute.

title (string, optional): Title of the generated plot. Defaults to
“PCA Component Explained Variances”
target_explained_variance (float, optional): Looks for the minimum
number of principal components that satisfies this value and emphasizes it on the plot. Defaults to 0.75
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> import scikitplot.plotters as skplt
>>> pca = PCA(random_state=1)
>>> pca.fit(X)
>>> skplt.plot_pca_component_variance(pca)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
PCA Component variances
scikitplot.plotters.plot_pca_2d_projection(clf, X, y, title='PCA 2-D Projection', ax=None, figsize=None, cmap='Spectral', title_fontsize='large', text_fontsize='medium')

DEPRECATED: This will be removed in v0.4.0. Please use scikitplot.decomposition.plot_pca_component_variance instead.

Plots the 2-dimensional projection of PCA on a given dataset.

Args:
clf: Fitted PCA instance that can transform given data set into 2
dimensions.
X (array-like, shape (n_samples, n_features)):
Feature set to project, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape (n_samples) or (n_samples, n_features)):
Target relative to X for labeling.
title (string, optional): Title of the generated plot. Defaults to
“PCA 2-D Projection”
ax (matplotlib.axes.Axes, optional): The axes upon which to
plot the curve. If None, the plot is drawn on a new set of axes.
figsize (2-tuple, optional): Tuple denoting figure size of the plot
e.g. (6, 6). Defaults to None.
cmap (string or matplotlib.colors.Colormap instance, optional):
Colormap used for plotting the projection. View Matplotlib Colormap documentation for available options. https://matplotlib.org/users/colormaps.html
title_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “large”.
text_fontsize (string or int, optional): Matplotlib-style fontsizes.
Use e.g. “small”, “medium”, “large” or integer-values. Defaults to “medium”.
Returns:
ax (matplotlib.axes.Axes): The axes on which the plot was
drawn.
Example:
>>> import scikitplot.plotters as skplt
>>> pca = PCA(random_state=1)
>>> pca.fit(X)
>>> skplt.plot_pca_2d_projection(pca, X, y)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
PCA 2D Projection