Factory API Reference

This document contains the plotting methods that are embedded into scikit-learn objects by the factory functions clustering_factory() and classifier_factory().

Important Note

If you want to use stand-alone functions and not bother with the factory functions, view the Functions API Reference instead.

Classifier Plots

scikitplot.classifier_factory(clf)

Takes a scikit-learn classifier instance and embeds scikit-plot instance methods in it.

Parameters:clf – Scikit-learn classifier instance
Returns:The same scikit-learn classifier instance passed in clf with embedded scikit-plot instance methods.
Raises:ValueError – If clf does not contain the instance methods necessary for scikit-plot instance methods.
scikitplot.classifiers.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 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 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()
Learning Curve
scikitplot.classifiers.plot_confusion_matrix(clf, X, y, labels=None, title=None, normalize=False, x_tick_rotation=0, do_cv=True, cv=None, shuffle=True, random_state=None, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')

Generates the confusion matrix for a given classifier and dataset.

Parameters:
  • 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.
  • 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 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.
  • 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.
  • do_cv (bool, optional) – If True, the classifier is cross-validated on the dataset using the cross-validation strategy in cv to generate the confusion matrix. If False, the confusion matrix is generated without training or cross-validating the classifier. This assumes that the classifier has already been called with its fit method beforehand.
  • 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.

  • shuffle (bool, optional) – Used when do_cv is set to True. Determines whether to shuffle the training data before splitting using cross-validation. Default set to True.
  • random_state (int RandomState) – Pseudo-random number generator state used for random sampling.
  • 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

>>> rf = classifier_factory(RandomForestClassifier())
>>> rf.plot_confusion_matrix(X, y, normalize=True)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
Confusion matrix
scikitplot.classifiers.plot_roc_curve(clf, X, y, title=u'ROC Curves', do_cv=True, cv=None, shuffle=True, random_state=None, 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 given classifier and dataset.

Parameters:
  • clf – Classifier instance that implements “fit” and “predict_proba” 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.
  • title (string, optional) – Title of the generated plot. Defaults to “ROC Curves”.
  • do_cv (bool, optional) – If True, the classifier is cross-validated on the dataset using the cross-validation strategy in cv to generate the confusion matrix. If False, the confusion matrix is generated without training or cross-validating the classifier. This assumes that the classifier has already been called with its fit method beforehand.
  • 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.

  • shuffle (bool, optional) – Used when do_cv is set to True. Determines whether to shuffle the training data before splitting using cross-validation. Default set to True.
  • random_state (int RandomState) – Pseudo-random number generator state used for random sampling.
  • 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

>>> nb = classifier_factory(GaussianNB())
>>> nb.plot_roc_curve(X, y, random_state=1)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
ROC Curves
scikitplot.classifiers.plot_ks_statistic(clf, X, y, title=u'KS Statistic Plot', do_cv=True, cv=None, shuffle=True, random_state=None, ax=None, figsize=None, title_fontsize=u'large', text_fontsize=u'medium')

Generates the KS Statistic plot for a given classifier and dataset.

Parameters:
  • clf – Classifier instance that implements “fit” and “predict_proba” 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.
  • title (string, optional) – Title of the generated plot. Defaults to “KS Statistic Plot”.
  • do_cv (bool, optional) – If True, the classifier is cross-validated on the dataset using the cross-validation strategy in cv to generate the confusion matrix. If False, the confusion matrix is generated without training or cross-validating the classifier. This assumes that the classifier has already been called with its fit method beforehand.
  • 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.

  • shuffle (bool, optional) – Used when do_cv is set to True. Determines whether to shuffle the training data before splitting using cross-validation. Default set to True.
  • random_state (int RandomState) – Pseudo-random number generator state used for random sampling.
  • 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

>>> lr = classifier_factory(LogisticRegression())
>>> lr.plot_ks_statistic(X, y, random_state=1)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
KS Statistic
scikitplot.classifiers.plot_precision_recall_curve(clf, X, y, title=u'Precision-Recall Curve', do_cv=True, cv=None, shuffle=True, random_state=None, 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 given classifier and dataset.

Parameters:
  • clf – Classifier instance that implements “fit” and “predict_proba” 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.
  • title (string, optional) – Title of the generated plot. Defaults to “Precision-Recall Curve”.
  • do_cv (bool, optional) – If True, the classifier is cross-validated on the dataset using the cross-validation strategy in cv to generate the confusion matrix. If False, the confusion matrix is generated without training or cross-validating the classifier. This assumes that the classifier has already been called with its fit method beforehand.
  • 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.

  • shuffle (bool, optional) – Used when do_cv is set to True. Determines whether to shuffle the training data before splitting using cross-validation. Default set to True.
  • random_state (int RandomState) – Pseudo-random number generator state used for random sampling.
  • 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

>>> nb = classifier_factory(GaussianNB())
>>> nb.plot_precision_recall_curve(X, y, random_state=1)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
>>> plt.show()
Precision Recall Curve
scikitplot.classifiers.plot_feature_importances(clf, title=u'Feature Importance', feature_names=None, max_num_features=20, order=u'descending', x_tick_rotation=0, 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 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 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()
Feature Importances

Clustering Plots

scikitplot.clustering_factory(clf)

Takes a scikit-learn clusterer and embeds scikit-plot plotting methods in it.

Parameters:clf – Scikit-learn clusterer instance
Returns:The same scikit-learn clusterer instance passed in clf with embedded scikit-plot instance methods.
Raises:ValueError – If clf does not contain the instance methods necessary for scikit-plot instance methods.
scikitplot.clustering.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 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 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()
Silhouette Plot
scikitplot.clustering.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 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 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()
Elbow Curve