kenchi.plotting.plot_anomaly_score(anomaly_score, ax=None, bins='auto', figsize=None, filename=None, hist=True, kde=True, threshold=None, title=None, xlabel='Samples', xlim=None, ylabel='Anomaly score', ylim=None, **kwargs)[source]

Plot the anomaly score for each sample.

Parameters:
  • anomaly_score (array-like of shape (n_samples,)) – Anomaly score for each sample.
  • ax (matplotlib Axes, default None) – Target axes instance.
  • bins (int, str or array-like, default 'auto') – Number of hist bins.
  • figsize (tuple, default None) – Tuple denoting figure size of the plot.
  • filename (str, default None) – If provided, save the current figure.
  • hist (bool, default True) – If True, plot a histogram of anomaly scores.
  • kde (bool, default True) – If True, plot a gaussian kernel density estimate.
  • threshold (float, default None) – Threshold.
  • title (string, default None) – Axes title. To disable, pass None.
  • xlabel (string, default 'Samples') – X axis title label. To disable, pass None.
  • xlim (tuple, default None) – Tuple passed to ax.xlim.
  • ylabel (string, default 'Anomaly score') – Y axis title label. To disable, pass None.
  • ylim (tuple, default None) – Tuple passed to ax.ylim.
  • **kwargs (dict) – Other keywords passed to ax.plot.
Returns:

ax – Axes on which the plot was drawn.
Return type:

matplotlib Axes

Examples

>>> import matplotlib.pyplot as plt
>>> from kenchi.datasets import load_wdbc
>>> from kenchi.outlier_detection import MiniBatchKMeans
>>> from kenchi.plotting import plot_anomaly_score
>>> X, _ = load_wdbc(random_state=0, return_X_y=True)
>>> det = MiniBatchKMeans(random_state=0).fit(X)
>>> anomaly_score = det.anomaly_score(X, normalize=True)
>>> plot_anomaly_score(
...     anomaly_score, threshold=det.threshold_, linestyle='', marker='.'
... ) 
<matplotlib.axes._subplots.AxesSubplot object at 0x...>
>>> plt.show()
_images/plot_anomaly_score.png
kenchi.plotting.plot_graphical_model(G, ax=None, figsize=None, filename=None, random_state=None, title='GGM', **kwargs)[source]

Plot the Gaussian Graphical Model (GGM).

Parameters:
  • G (networkx Graph) – GGM.
  • ax (matplotlib Axes, default None) – Target axes instance.
  • figsize (tuple, default None) – Tuple denoting figure size of the plot.
  • filename (str, default None) – If provided, save the current figure.
  • random_state (int, RandomState instance, default None) – Seed of the pseudo random number generator.
  • title (string, default 'GGM') – Axes title. To disable, pass None.
  • **kwargs (dict) – Other keywords passed to nx.draw_networkx.
Returns:

ax – Axes on which the plot was drawn.
Return type:

matplotlib Axes

Examples

>>> import matplotlib.pyplot as plt
>>> import networkx as nx
>>> from kenchi.plotting import plot_graphical_model
>>> from sklearn.datasets import make_sparse_spd_matrix
>>> A = make_sparse_spd_matrix(dim=20, norm_diag=True, random_state=0)
>>> G = nx.from_numpy_matrix(A)
>>> plot_graphical_model(G, random_state=0) 
<matplotlib.axes._subplots.AxesSubplot object at 0x...>
>>> plt.show()
_images/plot_graphical_model.png
kenchi.plotting.plot_partial_corrcoef(partial_corrcoef, ax=None, cbar=True, figsize=None, filename=None, title='Partial correlation', **kwargs)[source]

Plot the partial correlation coefficient matrix.

Parameters:
  • partial_corrcoef (array-like of shape (n_features, n_features)) – Partial correlation coefficient matrix.
  • ax (matplotlib Axes, default None) – Target axes instance.
  • cbar (bool, default True.) – If True, draw a colorbar.
  • figsize (tuple, default None) – Tuple denoting figure size of the plot.
  • filename (str, default None) – If provided, save the current figure.
  • title (string, default 'Partial correlation') – Axes title. To disable, pass None.
  • **kwargs (dict) – Other keywords passed to ax.pcolormesh.
Returns:

ax – Axes on which the plot was drawn.
Return type:

matplotlib Axes

Examples

>>> import matplotlib.pyplot as plt
>>> from kenchi.plotting import plot_partial_corrcoef
>>> from sklearn.datasets import make_sparse_spd_matrix
>>> A = make_sparse_spd_matrix(dim=20, norm_diag=True, random_state=0)
>>> plot_partial_corrcoef(A) 
<matplotlib.axes._subplots.AxesSubplot object at 0x...>
>>> plt.show()
_images/plot_partial_corrcoef.png
kenchi.plotting.plot_roc_curve(y_true, y_score, ax=None, figsize=None, filename=None, title='ROC curve', xlabel='FPR', ylabel='TPR', **kwargs)[source]

Plot the Receiver Operating Characteristic (ROC) curve.

Parameters:
  • y_true (array-like of shape (n_samples,)) – True Labels.
  • y_score (array-like of shape (n_samples,)) – Target scores.
  • ax (matplotlib Axes, default None) – Target axes instance.
  • figsize (tuple, default None) – Tuple denoting figure size of the plot.
  • filename (str, default None) – If provided, save the current figure.
  • title (string, default 'ROC curve') – Axes title. To disable, pass None.
  • xlabel (string, default 'FPR') – X axis title label. To disable, pass None.
  • ylabel (string, default 'TPR') – Y axis title label. To disable, pass None.
  • **kwargs (dict) – Other keywords passed to ax.plot.
Returns:

ax – Axes on which the plot was drawn.
Return type:

matplotlib Axes

Examples

>>> import matplotlib.pyplot as plt
>>> from kenchi.datasets import load_wdbc
>>> from kenchi.outlier_detection import MiniBatchKMeans
>>> from kenchi.plotting import plot_roc_curve
>>> X, y = load_wdbc(random_state=0, return_X_y=True)
>>> det = MiniBatchKMeans(random_state=0).fit(X)
>>> score_samples = det.score_samples(X)
>>> plot_roc_curve(y, score_samples) 
<matplotlib.axes._subplots.AxesSubplot object at 0x...>
>>> plt.show()
_images/plot_roc_curve.png