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=u'Learning Curve', cv=None, train_sizes=None, n_jobs=1, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates a plot of the train and test learning curves for a given classifier.
Parameters: - clf – Classifier instance that implements
fit
andpredict
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 ify
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 1.
- 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)Example
>>> import scikitplot.plotters as skplt >>> rf = RandomForestClassifier() >>> skplt.plot_learning_curve(rf, X, y) <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490> >>> plt.show()
- clf – Classifier instance that implements
-
scikitplot.plotters.
plot_confusion_matrix
(y_true, y_pred, labels=None, title=None, normalize=False, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates confusion matrix plot for a given set of ground truth labels and classifier predictions.
Parameters: - 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
ory_pred
are used in sorted order. (new in v0.2.5) - 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.
- 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
-
scikitplot.plotters.
plot_roc_curve
(y_true, y_probas, title=u'ROC Curves', curves=(u'micro', u'macro', u'each_class'), ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates the ROC curves for a set of ground truth labels and classifier probability predictions.
Parameters: - 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 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
-
scikitplot.plotters.
plot_ks_statistic
(y_true, y_probas, title=u'KS Statistic Plot', ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates the KS Statistic plot for a set of ground truth labels and classifier probability predictions.
Parameters: - 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
-
scikitplot.plotters.
plot_precision_recall_curve
(y_true, y_probas, title=u'Precision-Recall Curve', curves=(u'micro', u'each_class'), ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates the Precision Recall Curve for a set of ground truth labels and classifier probability predictions.
Parameters: - 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 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
-
scikitplot.plotters.
plot_feature_importances
(clf, title=u'Feature Importance', feature_names=None, max_num_features=20, order=u'descending', ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Generates a plot of a classifier’s feature importances.
Parameters: - clf – Classifier instance that implements
fit
andpredict_proba
methods. The classifier must also have afeature_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’.
- 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
- clf – Classifier instance that implements
-
scikitplot.plotters.
plot_silhouette
(clf, X, title=u'Silhouette Analysis', metric=u'euclidean', copy=True, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Plots silhouette analysis of clusters using fit_predict.
Parameters: - clf – Clusterer instance that implements
fit
andfit_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 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
- clf – Clusterer instance that implements
-
scikitplot.plotters.
plot_elbow_curve
(clf, X, title=u'Elbow Plot', cluster_ranges=None, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Plots elbow curve of different values of K for KMeans clustering.
Parameters: - clf – Clusterer instance that implements
fit
andfit_predict
methods and ascore
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 torange(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 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
- clf – Clusterer instance that implements
-
scikitplot.plotters.
plot_pca_component_variance
(clf, title=u'PCA Component Explained Variances', target_explained_variance=0.75, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Plots PCA components’ explained variance ratios. (new in v0.2.2)
Parameters: - 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 0.75.4
- 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
- clf – PCA instance that has the
-
scikitplot.plotters.
plot_pca_2d_projection
(clf, X, y, title=u'PCA 2-D Projection', ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')¶ Plots the 2-dimensional projection of PCA on a given dataset. (new in v0.2.2)
Parameters: - clf – 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 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: The axes on which the plot was drawn.
Return type: ax (
matplotlib.axes.Axes
)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()
- clf – PCA instance that can