scitex_ml.plt

Scitex centralized plotting module.

Note: Metric calculation functions (calc_*) are imported from scitex_ml.metrics but re-exported here for backward compatibility. New code should import directly from scitex_ml.metrics instead.

scitex_ml.plt.stx_conf_mat(cm=None, y_true=None, y_pred=None, y_pred_proba=None, labels=None, sorted_labels=None, pred_labels=None, sorted_pred_labels=None, true_labels=None, sorted_true_labels=None, label_rotation_xy=(15, 15), title='Confusion Matrix', colorbar=True, x_extend_ratio=1.0, y_extend_ratio=1.0, ax=None, spath=None)[source]

Plot confusion matrix as a heatmap with inverted y-axis.

Parameters:

  • cm (array-like, optional) – Pre-computed confusion matrix

  • y_true (array-like, optional) – True labels

  • y_pred (array-like, optional) – Predicted labels

  • y_pred_proba (array-like, optional) – Predicted probabilities

  • labels (list, optional) – List of labels

  • sorted_labels (list, optional) – Sorted list of labels

  • pred_labels (list, optional) – Predicted label names

  • sorted_pred_labels (list, optional) – Sorted predicted label names

  • true_labels (list, optional) – True label names

  • sorted_true_labels (list, optional) – Sorted true label names

  • label_rotation_xy (tuple, optional) – Rotation angles for x and y labels

  • title (str, optional) – Title of the plot

  • colorbar (bool, optional) – Whether to include a colorbar

  • x_extend_ratio (float, optional) – Ratio to extend x-axis

  • y_extend_ratio (float, optional) – Ratio to extend y-axis

  • spath (str, optional) – Path to save the figure

  • plt (deprecated) – Kept for backward compatibility only

  • cm – Pre-computed confusion matrix

  • y_true – True labels

  • y_pred – Predicted labels

  • y_pred_proba – Predicted probabilities

  • labels – List of labels

  • sorted_labels – Sorted list of labels

  • pred_labels – Predicted label names

  • sorted_pred_labels – Sorted predicted label names

  • true_labels – True label names

  • sorted_true_labels – Sorted true label names

  • label_rotation_xy – Rotation angles for x and y labels

  • title – Title of the plot

  • colorbar – Whether to include a colorbar

  • x_extend_ratio – Ratio to extend x-axis

  • y_extend_ratio – Ratio to extend y-axis

  • spath – Path to save the figure

Returns:

  • fig (matplotlib.figure.Figure) – The figure object containing the plot

  • cm (pandas.DataFrame) – The confusion matrix as a DataFrame

Example

y_true = [0, 1, 2, 0, 1, 2] y_pred = [0, 2, 1, 0, 0, 1] labels = [‘A’, ‘B’, ‘C’] fig, cm = conf_mat(plt, y_true=y_true, y_pred=y_pred, labels=labels) plt.show()

scitex_ml.plt.conf_mat(cm=None, y_true=None, y_pred=None, y_pred_proba=None, labels=None, sorted_labels=None, pred_labels=None, sorted_pred_labels=None, true_labels=None, sorted_true_labels=None, label_rotation_xy=(15, 15), title='Confusion Matrix', colorbar=True, x_extend_ratio=1.0, y_extend_ratio=1.0, ax=None, spath=None)

Plot confusion matrix as a heatmap with inverted y-axis.

Parameters:

  • cm (array-like, optional) – Pre-computed confusion matrix

  • y_true (array-like, optional) – True labels

  • y_pred (array-like, optional) – Predicted labels

  • y_pred_proba (array-like, optional) – Predicted probabilities

  • labels (list, optional) – List of labels

  • sorted_labels (list, optional) – Sorted list of labels

  • pred_labels (list, optional) – Predicted label names

  • sorted_pred_labels (list, optional) – Sorted predicted label names

  • true_labels (list, optional) – True label names

  • sorted_true_labels (list, optional) – Sorted true label names

  • label_rotation_xy (tuple, optional) – Rotation angles for x and y labels

  • title (str, optional) – Title of the plot

  • colorbar (bool, optional) – Whether to include a colorbar

  • x_extend_ratio (float, optional) – Ratio to extend x-axis

  • y_extend_ratio (float, optional) – Ratio to extend y-axis

  • spath (str, optional) – Path to save the figure

  • plt (deprecated) – Kept for backward compatibility only

  • cm – Pre-computed confusion matrix

  • y_true – True labels

  • y_pred – Predicted labels

  • y_pred_proba – Predicted probabilities

  • labels – List of labels

  • sorted_labels – Sorted list of labels

  • pred_labels – Predicted label names

  • sorted_pred_labels – Sorted predicted label names

  • true_labels – True label names

  • sorted_true_labels – Sorted true label names

  • label_rotation_xy – Rotation angles for x and y labels

  • title – Title of the plot

  • colorbar – Whether to include a colorbar

  • x_extend_ratio – Ratio to extend x-axis

  • y_extend_ratio – Ratio to extend y-axis

  • spath – Path to save the figure

Returns:

  • fig (matplotlib.figure.Figure) – The figure object containing the plot

  • cm (pandas.DataFrame) – The confusion matrix as a DataFrame

Example

y_true = [0, 1, 2, 0, 1, 2] y_pred = [0, 2, 1, 0, 0, 1] labels = [‘A’, ‘B’, ‘C’] fig, cm = conf_mat(plt, y_true=y_true, y_pred=y_pred, labels=labels) plt.show()

scitex_ml.plt.plot_learning_curve(metrics_df, keys, title='Title', max_n_ticks=4, scattersize=3, linewidth=1, yscale='linear', spath=None)[source]

Plot learning curves from training metrics.

This is mainly used by scitex/ml/training/_LearningCurveLogger.py

Parameters:

  • metrics_df (pd.DataFrame) – DataFrame with columns: step, i_global, i_epoch, i_batch, and metric columns

  • keys (list of str) – Metric names to plot

  • title (str) – Plot title

  • max_n_ticks (int) – Maximum number of ticks on x-axis

  • scattersize (float) – Size of scatter points for validation/test

  • linewidth (float) – Width of training line

  • yscale (str) – Y-axis scale (‘linear’ or ‘log’)

  • spath (str, optional) – Save path for the figure

Returns:

fig – Figure containing learning curves

Return type:

matplotlib.figure.Figure

Example

>>> print(metrics_df)
#                 step  i_global  i_epoch  i_batch      loss
# 0       Training         0        0        0  0.717023
# 1       Training         1        0        1  0.703844
# ...
# [123271 rows x 5 columns]
scitex_ml.plt.learning_curve(metrics_df, keys, title='Title', max_n_ticks=4, scattersize=3, linewidth=1, yscale='linear', spath=None)

Plot learning curves from training metrics.

This is mainly used by scitex/ml/training/_LearningCurveLogger.py

Parameters:

  • metrics_df (pd.DataFrame) – DataFrame with columns: step, i_global, i_epoch, i_batch, and metric columns

  • keys (list of str) – Metric names to plot

  • title (str) – Plot title

  • max_n_ticks (int) – Maximum number of ticks on x-axis

  • scattersize (float) – Size of scatter points for validation/test

  • linewidth (float) – Width of training line

  • yscale (str) – Y-axis scale (‘linear’ or ‘log’)

  • spath (str, optional) – Save path for the figure

Returns:

fig – Figure containing learning curves

Return type:

matplotlib.figure.Figure

Example

>>> print(metrics_df)
#                 step  i_global  i_epoch  i_batch      loss
# 0       Training         0        0        0  0.717023
# 1       Training         1        0        1  0.703844
# ...
# [123271 rows x 5 columns]
scitex_ml.plt.optuna_study(lpath, value_str, sort=False)

Loads an Optuna study and generates various visualizations for each target metric.

Parameters: - lpath (str): Path to the Optuna study database. - value_str (str): The name of the column to be used as the optimization target.

Returns: - None

scitex_ml.plt.plot_optuna_study(lpath, value_str, sort=False)[source]

Loads an Optuna study and generates various visualizations for each target metric.

Parameters: - lpath (str): Path to the Optuna study database. - value_str (str): The name of the column to be used as the optimization target.

Returns: - None

scitex_ml.plt.plot_roc_curve(true_class, pred_proba, labels, ax=None, spath=None)[source]

Plot ROC-AUC curve.

Parameters:

  • true_class (array-like) – True class labels

  • pred_proba (array-like) – Predicted probabilities

  • labels (list) – Class labels

  • ax (matplotlib axis, optional) – Axis to plot on. If None, creates new figure

  • spath (str, optional) – Path to save figure

Returns:

  • fig (matplotlib.figure.Figure) – Figure object

  • metrics (dict) – ROC metrics

scitex_ml.plt.plot_pre_rec_curve(true_class, pred_proba, labels, ax=None, spath=None)[source]

Plot precision-recall curve.

Parameters:

  • true_class (array-like) – True class labels

  • pred_proba (array-like) – Predicted probabilities

  • labels (list) – Class labels

  • ax (matplotlib axis, optional) – Axis to plot on. If None, creates new figure

  • spath (str, optional) – Path to save figure

Returns:

  • fig (matplotlib.figure.Figure) – Figure object

  • metrics (dict) – Precision-recall metrics

scitex_ml.plt.plot_feature_importance(importance, feature_names=None, top_n=20, title='Feature Importance', xlabel='Importance', figsize=(10, 8), spath=None)[source]

Plot feature importance as a horizontal bar chart.

Parameters:

  • importance (np.ndarray or Dict[str, float]) – Feature importance values. If array, must match feature_names length. If dict, keys are feature names and values are importances.

  • feature_names (List[str], optional) – Names of features (required if importance is array)

  • top_n (int, default 20) – Number of top features to display

  • title (str, default "Feature Importance") – Plot title

  • xlabel (str, default "Importance") – X-axis label

  • figsize (tuple, default (10, 8)) – Figure size

  • spath (Union[str, Path], optional) – Path to save the figure

Returns:

fig – The figure object

Return type:

matplotlib.figure.Figure

Examples

>>> from sklearn.ensemble import RandomForestClassifier
>>> import numpy as np
>>> X = np.random.rand(100, 5)
>>> y = np.random.randint(0, 2, 100)
>>> model = RandomForestClassifier().fit(X, y)
>>> fig = plot_feature_importance(
...     model.feature_importances_,
...     feature_names=['f1', 'f2', 'f3', 'f4', 'f5'],
...     spath='feature_importance.jpg'
... )
scitex_ml.plt.plot_feature_importance_cv_summary(all_importances, top_n=20, title=None, figsize=(12, 8), spath=None)[source]

Plot feature importance summary across cross-validation folds with error bars.

Parameters:

  • all_importances (List[Dict[str, float]]) – List of importance dictionaries from each fold

  • top_n (int, default 20) – Number of top features to display

  • title (str, optional) – Plot title (auto-generated if None)

  • figsize (tuple, default (12, 8)) – Figure size

  • spath (Union[str, Path], optional) – Path to save the figure

Returns:

fig – The figure object

Return type:

matplotlib.figure.Figure

Examples

>>> # After cross-validation
>>> all_importances = [
...     {'feature1': 0.3, 'feature2': 0.7},
...     {'feature1': 0.4, 'feature2': 0.6},
... ]
>>> fig = plot_feature_importance_cv_summary(
...     all_importances,
...     spath='feature_importance_cv_summary.jpg'
... )
scitex_ml.plt.plot_tra(ax, metrics_df, metric_key, linewidth=1, color=None)

Plot training phase data as line.

scitex_ml.plt.process_i_global(metrics_df)

Prepare metrics DataFrame with i_global as index.

scitex_ml.plt.scatter_tes(ax, metrics_df, metric_key, markersize=3, color=None)

Plot test phase data as scatter.

scitex_ml.plt.scatter_val(ax, metrics_df, metric_key, markersize=3, color=None)

Plot validation phase data as scatter.

scitex_ml.plt.select_ticks(metrics_df, max_n_ticks=4)

Select representative epoch tick positions and labels.

scitex_ml.plt.set_yaxis_for_acc(ax, metric_key)

Configure y-axis for accuracy metrics.

scitex_ml.plt.vline_at_epochs(ax, metrics_df, color='grey')

Add vertical lines at epoch boundaries.

scitex_ml.plt.calc_bACC_from_conf_mat(cm)

Calculate balanced accuracy from confusion matrix.

Parameters:

cm (np.ndarray) – Confusion matrix

Returns:

Balanced accuracy

Return type:

float

scitex_ml.plt.calc_bacc_from_conf_mat(cm)[source]

Calculate balanced accuracy from confusion matrix.

Parameters:

cm (np.ndarray) – Confusion matrix

Returns:

Balanced accuracy

Return type:

float