Nb/docs/warnings fix

deepchecks/core/serialization/suite_result/html.py

@@ -196,7 +196,8 @@ def prepare_summary(self, output_id: t.Optional[str] = None, **kwargs) -> str:
         extra_info = self.prepare_extra_info()
 
         suite_creation_example_link = (
-            'https://docs.deepchecks.com/en/stable/examples/guides/create_a_custom_suite.html'
+            'https://docs.deepchecks.com/stable/general/usage/customizations/'
+            'auto_examples/plot_create_a_custom_suite.html'
             '?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link'
         )
         icons = textwrap.dedent("""

deepchecks/nlp/checks/data_integrity/property_label_correlation.py

@@ -27,7 +27,7 @@
 
 PLC = t.TypeVar('PLC', bound='PropertyLabelCorrelation')
 
-pps_url = 'https://docs.deepchecks.com/en/stable/checks_gallery/tabular/' \
+pps_url = 'https://docs.deepchecks.com/stable/tabular/auto_checks/' \
           'train_test_validation/plot_feature_label_correlation_change.html'
 pps_html = f'<a href={pps_url} target="_blank">Predictive Power Score</a>'
 

deepchecks/nlp/text_data.py

@@ -72,7 +72,7 @@ class TextData:
         The number of rows in the metadata DataFrame must be equal to the number of samples in the dataset, and the
         order of the rows must be the same as the order of the samples in the dataset.
         For more on metadata, see the `NLP Metadata Guide
-        <https://docs.deepchecks.com/en/latest/nlp/nlp-metadata.html>`_.
+        <https://docs.deepchecks.com/stable/nlp/usage_guides/nlp_metadata.html>`_.
     categorical_metadata : t.Optional[t.List[str]] , default: None
         The names of the categorical metadata columns. If None, categorical metadata columns are automatically inferred.
         Only relevant if metadata is not None.
@@ -85,7 +85,7 @@ class TextData:
         In order to calculate the default properties, use the `TextData.calculate_default_properties` function after
         the creation of the TextData object.
         For more on properties, see the `NLP Properties Guide
-        <https://docs.deepchecks.com/en/latest/nlp/nlp-properties.html>`_.
+        <https://docs.deepchecks.com/stable/nlp/usage_guides/nlp_properties.html>`_.
     categorical_properties : t.Optional[t.List[str]] , default: None
         The names of the categorical properties columns. If None, categorical properties columns are automatically
         inferred. Only relevant if properties is not None.

deepchecks/tabular/checks/data_integrity/feature_label_correlation.py

@@ -26,7 +26,7 @@
 FLC = t.TypeVar('FLC', bound='FeatureLabelCorrelation')
 
 
-pps_url = 'https://docs.deepchecks.com/en/stable/checks_gallery/tabular/' \
+pps_url = 'https://docs.deepchecks.com/stable/tabular/auto_checks/' \
           'train_test_validation/plot_feature_label_correlation_change.html'
 pps_html = f'<a href={pps_url} target="_blank">Predictive Power Score</a>'
 

deepchecks/tabular/checks/train_test_validation/feature_label_correlation_change.py

@@ -27,7 +27,7 @@
 
 FLC = t.TypeVar('FLC', bound='FeatureLabelCorrelationChange')
 
-pps_url = 'https://docs.deepchecks.com/en/stable/checks_gallery/tabular/' \
+pps_url = 'https://docs.deepchecks.com/stable/tabular/auto_checks/' \
           'train_test_validation/plot_feature_label_correlation_change.html'
 pps_html = f'<a href={pps_url} target="_blank">Predictive Power Score</a>'
 

deepchecks/tabular/suites/default_suites.py

@@ -51,29 +51,29 @@ def data_integrity(columns: Union[Hashable, List[Hashable]] = None,
 
            * - Check Example
              - API Reference
-           * - :ref:`plot_tabular_is_single_value`
+           * - :ref:`tabular__is_single_value`
              - :class:`~deepchecks.tabular.checks.data_integrity.IsSingleValue`
-           * - :ref:`plot_tabular_special_chars`
+           * - :ref:`tabular__special_chars`
              - :class:`~deepchecks.tabular.checks.data_integrity.SpecialCharacters`
-           * - :ref:`plot_tabular_mixed_nulls`
+           * - :ref:`tabular__mixed_nulls`
              - :class:`~deepchecks.tabular.checks.data_integrity.MixedNulls`
-           * - :ref:`plot_tabular_mixed_data_types`
+           * - :ref:`tabular__mixed_data_types`
              - :class:`~deepchecks.tabular.checks.data_integrity.MixedDataTypes`
-           * - :ref:`plot_tabular_string_mismatch`
+           * - :ref:`tabular__string_mismatch`
              - :class:`~deepchecks.tabular.checks.data_integrity.StringMismatch`
-           * - :ref:`plot_tabular_data_duplicates`
+           * - :ref:`tabular__data_duplicates`
              - :class:`~deepchecks.tabular.checks.data_integrity.DataDuplicates`
-           * - :ref:`plot_tabular_string_length_out_of_bounds`
+           * - :ref:`tabular__string_length_out_of_bounds`
              - :class:`~deepchecks.tabular.checks.data_integrity.StringLengthOutOfBounds`
-           * - :ref:`plot_tabular_conflicting_labels`
+           * - :ref:`tabular__conflicting_labels`
              - :class:`~deepchecks.tabular.checks.data_integrity.ConflictingLabels`
-           * - :ref:`plot_tabular_outlier_sample_detection`
+           * - :ref:`tabular__outlier_sample_detection`
              - :class:`~deepchecks.tabular.checks.data_integrity.OutlierSampleDetection`
-           * - :ref:`plot_tabular_feature_label_correlation`
+           * - :ref:`tabular__feature_label_correlation`
              - :class:`~deepchecks.tabular.checks.data_integrity.FeatureLabelCorrelation`
-           * - :ref:`plot_tabular_identifier_label_correlation`
+           * - :ref:`tabular__identifier_label_correlation`
              - :class:`~deepchecks.tabular.checks.data_integrity.IdentifierLabelCorrelation`
-           * - :ref:`plot_tabular_feature_feature_correlation`
+           * - :ref:`tabular__feature_feature_correlation`
              - :class:`~deepchecks.tabular.checks.data_integrity.FeatureFeatureCorrelation`
 
     Parameters
@@ -148,29 +148,29 @@ def train_test_validation(columns: Union[Hashable, List[Hashable]] = None,
 
            * - Check Example
              - API Reference
-           * - :ref:`plot_tabular_datasets_size_comparison`
+           * - :ref:`tabular__datasets_size_comparison`
              - :class:`~deepchecks.tabular.checks.train_test_validation.DatasetsSizeComparison`
-           * - :ref:`plot_tabular_new_label`
+           * - :ref:`tabular__new_label`
              - :class:`~deepchecks.tabular.checks.train_test_validation.NewLabelTrainTest`
-           * - :ref:`plot_tabular_new_category`
+           * - :ref:`tabular__new_category`
              - :class:`~deepchecks.tabular.checks.train_test_validation.CategoryMismatchTrainTest`
-           * - :ref:`plot_tabular_string_mismatch_comparison`
+           * - :ref:`tabular__string_mismatch_comparison`
              - :class:`~deepchecks.tabular.checks.train_test_validation.StringMismatchComparison`
-           * - :ref:`plot_tabular_date_train_test_validation_leakage_duplicates`
+           * - :ref:`tabular__date_train_test_validation_leakage_duplicates`
              - :class:`~deepchecks.tabular.checks.train_test_validation.DateTrainTestLeakageDuplicates`
-           * - :ref:`plot_tabular_date_train_test_validation_leakage_overlap`
+           * - :ref:`tabular__date_train_test_validation_leakage_overlap`
              - :class:`~deepchecks.tabular.checks.train_test_validation.DateTrainTestLeakageOverlap`
-           * - :ref:`plot_tabular_index_leakage`
+           * - :ref:`tabular__index_leakage`
              - :class:`~deepchecks.tabular.checks.train_test_validation.IndexTrainTestLeakage`
-           * - :ref:`plot_tabular_train_test_samples_mix`
+           * - :ref:`tabular__train_test_samples_mix`
              - :class:`~deepchecks.tabular.checks.train_test_validation.TrainTestSamplesMix`
-           * - :ref:`plot_tabular_feature_label_correlation_change`
+           * - :ref:`tabular__feature_label_correlation_change`
              - :class:`~deepchecks.tabular.checks.train_test_validation.FeatureLabelCorrelationChange`
-           * - :ref:`plot_tabular_feature_drift`
+           * - :ref:`tabular__feature_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.FeatureDrift`
-           * - :ref:`plot_tabular_label_drift`
+           * - :ref:`tabular__label_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.LabelDrift`
-           * - :ref:`plot_tabular_multivariate_drift`
+           * - :ref:`tabular__multivariate_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.MultivariateDrift`
 
     Parameters
@@ -247,29 +247,29 @@ def model_evaluation(alternative_scorers: Dict[str, Callable] = None,
 
            * - Check Example
              - API Reference
-           * - :ref:`plot_tabular_roc_report`
+           * - :ref:`tabular__roc_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RocReport`
-           * - :ref:`plot_tabular_confusion_matrix_report`
+           * - :ref:`tabular__confusion_matrix_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.ConfusionMatrixReport`
-           * - :ref:`plot_tabular_weak_segment_performance`
+           * - :ref:`tabular__weak_segment_performance`
              - :class:`~deepchecks.tabular.checks.model_evaluation.WeakSegmentPerformance`
-           * - :ref:`plot_tabular_prediction_drift`
+           * - :ref:`tabular__prediction_drift`
              - :class:`~deepchecks.tabular.checks.model_evaluation.PredictionDrift`
-           * - :ref:`plot_tabular_simple_model_comparison`
+           * - :ref:`tabular__simple_model_comparison`
              - :class:`~deepchecks.tabular.checks.model_evaluation.SimpleModelComparison`
-           * - :ref:`plot_tabular_calibration_score`
+           * - :ref:`tabular__calibration_score`
              - :class:`~deepchecks.tabular.checks.model_evaluation.CalibrationScore`
-           * - :ref:`plot_tabular_regression_systematic_error`
+           * - :ref:`tabular__regression_systematic_error`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RegressionSystematicError`
-           * - :ref:`plot_tabular_regression_error_distribution`
+           * - :ref:`tabular__regression_error_distribution`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RegressionErrorDistribution`
-           * - :ref:`plot_tabular_unused_features`
+           * - :ref:`tabular__unused_features`
              - :class:`~deepchecks.tabular.checks.model_evaluation.UnusedFeatures`
-           * - :ref:`plot_tabular_boosting_overfit`
+           * - :ref:`tabular__boosting_overfit`
              - :class:`~deepchecks.tabular.checks.model_evaluation.BoostingOverfit`
-           * - :ref:`plot_tabular_model_inference_time`
+           * - :ref:`tabular__model_inference_time`
              - :class:`~deepchecks.tabular.checks.model_evaluation.ModelInferenceTime`
-           * - :ref:`plot_tabular_prediction_drift`
+           * - :ref:`tabular__prediction_drift`
              - :class:`~deepchecks.tabular.checks.model_evaluation.PredictionDrift`
 
     Parameters
@@ -352,35 +352,35 @@ def production_suite(task_type: str = None,
 
            * - Check Example
              - API Reference
-           * - :ref:`plot_tabular_roc_report`
+           * - :ref:`tabular__roc_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RocReport`
-           * - :ref:`plot_tabular_confusion_matrix_report`
+           * - :ref:`tabular__confusion_matrix_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.ConfusionMatrixReport`
-           * - :ref:`plot_tabular_weak_segment_performance`
+           * - :ref:`tabular__weak_segment_performance`
              - :class:`~deepchecks.tabular.checks.model_evaluation.WeakSegmentPerformance`
-           * - :ref:`plot_tabular_regression_error_distribution`
+           * - :ref:`tabular__regression_error_distribution`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RegressionErrorDistribution`
-           * - :ref:`plot_tabular_string_mismatch_comparison`
+           * - :ref:`tabular__string_mismatch_comparison`
              - :class:`~deepchecks.tabular.checks.train_test_validation.StringMismatchComparison`
-           * - :ref:`plot_tabular_feature_label_correlation_change`
+           * - :ref:`tabular__feature_label_correlation_change`
              - :class:`~deepchecks.tabular.checks.train_test_validation.FeatureLabelCorrelationChange`
-           * - :ref:`plot_tabular_feature_drift`
+           * - :ref:`tabular__feature_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.FeatureDrift`
-           * - :ref:`plot_tabular_label_drift`
+           * - :ref:`tabular__label_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.LabelDrift`
-           * - :ref:`plot_tabular_multivariate_drift`
+           * - :ref:`tabular__multivariate_drift`
              - :class:`~deepchecks.tabular.checks.train_test_validation.MultivariateDrift`
-           * - :ref:`plot_tabular_prediction_drift`
+           * - :ref:`tabular__prediction_drift`
              - :class:`~deepchecks.tabular.checks.model_evaluation.PredictionDrift`
-           * - :ref:`plot_tabular_prediction_drift`
+           * - :ref:`tabular__prediction_drift`
              - :class:`~deepchecks.tabular.checks.model_evaluation.PredictionDrift`
-           * - :ref:`plot_tabular_string_mismatch`
+           * - :ref:`tabular__string_mismatch`
              - :class:`~deepchecks.tabular.checks.data_integrity.StringMismatch`
-           * - :ref:`plot_tabular_feature_label_correlation`
+           * - :ref:`tabular__feature_label_correlation`
              - :class:`~deepchecks.tabular.checks.data_integrity.FeatureLabelCorrelation`
-           * - :ref:`plot_tabular_feature_feature_correlation`
+           * - :ref:`tabular__feature_feature_correlation`
              - :class:`~deepchecks.tabular.checks.data_integrity.FeatureFeatureCorrelation`
-           * - :ref:`plot_tabular_single_dataset_performance`
+           * - :ref:`tabular__single_dataset_performance`
              - :class:`~deepchecks.tabular.checks.model_evaluation.SingleDatasetPerformance`
 
     Parameters

deepchecks/tabular/utils/validation.py

@@ -26,7 +26,7 @@
     'ensure_predictions_proba',
 ]
 
-supported_models_link = ('https://docs.deepchecks.com/en/stable/user-guide/supported_models.html'
+supported_models_link = ('https://docs.deepchecks.com/stable/tabular/usage_guides/supported_models.html'
                          '?utm_source=display_output&utm_medium=referral&utm_campaign=exception_link')
 supported_models_html = f'<a href="{supported_models_link}" target="_blank">supported model types</a>'
 

deepchecks/utils/docref.py

@@ -18,9 +18,9 @@
 # TODO:
 links = {
     'default': {
-        'supported-metrics-by-string': 'https://docs.deepchecks.com/en/stable/user-guide/general/metrics_guide.html#list-of-supported-strings', # pylint: disable=line-too-long  # noqa
-        'supported-prediction-format': 'https://docs.deepchecks.com/en/stable/user-guide/tabular/supported_models.html#supported-tasks-and-predictions-format', # pylint: disable=line-too-long  # noqa
-        'supported-predictions-format-nlp': 'https://docs.deepchecks.com/en/stable/user-guide/nlp/supported_tasks.html#supported-labels-and-predictions-format', # pylint: disable=line-too-long  # noqa
+        'supported-metrics-by-string': 'https://docs.deepchecks.com/stable/general/guides/metrics_guide.html#list-of-supported-strings', # pylint: disable=line-too-long  # noqa
+        'supported-prediction-format': 'https://docs.deepchecks.com/stable/tabular/usage_guides/supported_models.html#supported-tasks-and-predictions-format', # pylint: disable=line-too-long  # noqa
+        'supported-predictions-format-nlp': 'https://docs.deepchecks.com/stable/nlp/usage_guides/supported_tasks.html#supported-labels-and-predictions-format', # pylint: disable=line-too-long  # noqa
     },
     # '0.0.1': {},  # noqa
     # '0.0.2': {},  # noqa