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chore: Make the progress bar disappear at the end of their execution #1255

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merged 7 commits into from
Jan 30, 2025
Merged

chore: Make the progress bar disappear at the end of their execution #1255

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81bae22
feat: hide progress bar after execution
ictorv Jan 29, 2025
d297b58
Merge pull request #1 from ictorv/1251-hide-progress-bar-once-the-exe…
ictorv Jan 29, 2025
6c2c5f3
fix: progress-bar-docstring
ictorv Jan 29, 2025
edbfc0d
fix: Removed Processing after disapperance
ictorv Jan 29, 2025
d1ba106
fix: doctests of hiding progress bar
ictorv Jan 30, 2025
2c2a1fd
Merge branch 'main' into fix-progress-bar-docstring
ictorv Jan 30, 2025
cc44134
fix 2 remaining docstrings
glemaitre Jan 30, 2025
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13 changes: 0 additions & 13 deletions skore/src/skore/sklearn/_cross_validation/metrics_accessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,6 @@ def report_metrics(
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.report_metrics(
... scoring=["precision", "recall"], pos_label=1, aggregate=["mean", "std"]
... )
Expand Down Expand Up @@ -211,7 +210,6 @@ def accuracy(self, *, data_source="test", aggregate=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.accuracy()
Compute metric for each split ...
Metric Accuracy (↗︎)
Expand Down Expand Up @@ -291,7 +289,6 @@ def precision(
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.precision()
Compute metric for each split ...
Metric Precision (↗︎)
Expand Down Expand Up @@ -375,7 +372,6 @@ def recall(
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.recall()
Compute metric for each split ...
Metric Recall (↗︎)
Expand Down Expand Up @@ -421,7 +417,6 @@ def brier_score(self, *, data_source="test", aggregate=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.brier_score()
Compute metric for each split ...
Metric Brier score (↘︎)
Expand Down Expand Up @@ -507,7 +502,6 @@ def roc_auc(
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.roc_auc()
Compute metric for each split ...
Metric ROC AUC (↗︎)
Expand Down Expand Up @@ -554,7 +548,6 @@ def log_loss(self, *, data_source="test", aggregate=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.log_loss()
Compute metric for each split ...
Metric Log loss (↘︎)
Expand Down Expand Up @@ -611,7 +604,6 @@ def r2(
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.r2()
Compute metric for each split ...
Metric R² (↗︎)
Expand Down Expand Up @@ -669,7 +661,6 @@ def rmse(
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.rmse()
Compute metric for each split ...
Metric RMSE (↘︎)
Expand Down Expand Up @@ -745,7 +736,6 @@ def custom_metric(
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.metrics.custom_metric(
... metric_function=mean_absolute_error,
... response_method="predict",
Expand Down Expand Up @@ -975,7 +965,6 @@ def roc(self, *, data_source="test", pos_label=None, ax=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> display = report.metrics.plot.roc()
Computing predictions for display ...
>>> display.plot(roc_curve_kwargs={"color": "tab:red"})
Expand Down Expand Up @@ -1026,7 +1015,6 @@ def precision_recall(self, *, data_source="test", pos_label=None, ax=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> display = report.metrics.plot.precision_recall()
Computing predictions for display ...
>>> display.plot()
Expand Down Expand Up @@ -1101,7 +1089,6 @@ def prediction_error(
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> display = report.metrics.plot.prediction_error(
... kind="actual_vs_predicted"
... )
Expand Down
3 changes: 0 additions & 3 deletions skore/src/skore/sklearn/_cross_validation/report.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,6 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
>>> estimator = LogisticRegression()
>>> from skore import CrossValidationReport
>>> report = CrossValidationReport(estimator, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
"""

_ACCESSOR_CONFIG = {
Expand Down Expand Up @@ -181,7 +180,6 @@ def clear_cache(self):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.cache_predictions()
Cross-validation predictions ...
Caching predictions ...
Expand Down Expand Up @@ -216,7 +214,6 @@ def cache_predictions(self, response_methods="auto", n_jobs=None):
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
Processing cross-validation ...
>>> report.cache_predictions()
Cross-validation predictions ...
Caching predictions ...
Expand Down
4 changes: 4 additions & 0 deletions skore/src/skore/utils/_progress_bar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,9 @@
def progress_decorator(description):
"""Decorate class methods to add a progress bar.

This decorator adds a Rich progress bar to class methods, displaying progress
during execution. The progress bar automatically disappears after completion.

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The lint job is failing because you have trailing space here.

Suggested change
during execution. The progress bar automatically disappears after completion.
during execution. The progress bar automatically disappears after completion.

Parameters
----------
description : str or callable
Expand Down Expand Up @@ -43,6 +46,7 @@ def wrapper(*args, **kwargs):
),
TextColumn("[orange1]{task.percentage:>3.0f}%"),
expand=False,
transient=True,
)
progress.start()

Expand Down
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