Plotting¶

mlflow_extend.plotting.corr_matrix(corr)[source]¶

Plot correlation matrix.

Parameters: corr (array-like) – Correlation matrix.
Returns: Figure object.
Return type: matplotlib.pyplot.Figure

Examples

>>> df = pd.DataFrame([(0.2, 0.3), (0.0, 0.6), (0.6, 0.0), (0.2, 0.1)],
...                   columns=['dogs', 'cats'])
>>> corr_matrix(df.corr())  
<Figure ... with 2 Axes>

mlflow_extend.plotting.confusion_matrix(cm, labels=None, normalize=True)[source]¶

Plot confusion matrix.

Parameters

cm (array-like) – Confusion matrix.
labels (list of str, default None) – Label names.
normalize (bool, default True) – Divide each row by its sum.

Returns

Figure object.

Return type

matplotlib.pyplot.Figure

Examples

>>> cm = [[2, 0, 0],
...       [0, 0, 1],
...       [1, 0, 2]]
>>> confusion_matrix(cm)  
<Figure ... with 2 Axes>

mlflow_extend.plotting.feature_importance(features, importances, importance_type, limit=None, normalize=False)[source]¶

Plot feature importance.

Parameters

features (list of str) – Feature names.
importances (array-like) – Importance of each feature.
importance_type (str) – Feature importance type (e.g. “gain”).
limit (int, default None) – Number of features to plot. If None, all features will be plotted.
normalize (bool, default False) – Divide importance by the sum.

Returns

Figure object.

Return type

matplotlib.pyplot.Figure

Examples

>>> features = ["a", "b", "c"]
>>> importances = [1, 2, 3]
>>> importance_type = "gain"
>>> feature_importance(features, importances, importance_type)  
<Figure ... with 1 Axes>

mlflow_extend.plotting.roc_curve(fpr, tpr, auc=None)[source]¶

Plot ROC curve.

Parameters

fpr (array-like) – False positive rate.
tpr (array-like) – True positive rate.
auc (float, default None) – Area under the curve.

Returns

Figure object.

Return type

matplotlib.pyplot.Figure

Examples

>>> fpr = np.linspace(0, 1, 11)
>>> tpr = -((fpr - 1) ** 2) + 1
>>> roc_curve(fpr, tpr)  
<Figure ... with 1 Axes>

mlflow_extend.plotting.pr_curve(pre, rec, auc=None)[source]¶

Plot precision-recall curve.

Parameters

pre (array-like) – Precision.
rec (array-like) – Recall.
auc (float, default None) – Area under the curve.

Returns

Figure object.

Return type

matplotlib.pyplot.Figure

Examples

>>> rec = np.linspace(0, 1, 11)
>>> pre = -(rec ** 2) + 1
>>> pr_curve(pre, rec)  
<Figure ... with 1 Axes>