Fix api docs (#2508)

* fixing many errors * fixing many errors * fixing many errors * fixing many errors * changing vision__properties * fixing redirects * fixing labels * fixing labels * fixing tf * fixing mf * fixing mf * update welcome and sub package indices --------- Co-authored-by: shir22 <shir@deepchecks.com>
deepchecks · May 9, 2023 · 7e0365b · 7e0365b
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diff --git a/README.md b/README.md
@@ -119,7 +119,7 @@ Head over to one of our following quickstart tutorials, and have deepchecks runn
 - [Data Integrity Quickstart (avocado sales data)](
    https://docs.deepchecks.com/stable/user-guide/tabular/auto_quickstarts/plot_quick_data_integrity.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=try_it_out)
 - [Model Evaluation Quickstart (wine quality data)](
-   https://docs.deepchecks.com/en/stable/user-guide/tabular/auto_quickstarts/plot_quickstart_in_5_minutes.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=try_it_out)
+   https://docs.deepchecks.com/stable/user-guide/tabular/auto_quickstarts/plot_quickstart_in_5_minutes.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=try_it_out)
 
 > **Recommended - download the code and run it locally** on the built-in dataset and (optional) model, or **replace them with your own**.
 
@@ -230,7 +230,7 @@ covering all kinds of common issues, such as:
 - Conflicting Labels 
 
 and [many more
-checks](https://docs.deepchecks.com/stable/checks_gallery/tabular.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=key_concepts__check).
+checks](https://docs.deepchecks.com/stable/tabular/index.html?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=key_concepts__check).
 
 
 Each check can have two types of

diff --git a/deepchecks/nlp/metric_utils/token_classification.py b/deepchecks/nlp/metric_utils/token_classification.py
@@ -31,15 +31,15 @@ def get_scorer_dict(
 ) -> t.Dict[str, t.Callable[[t.List[str], t.List[str]], float]]:
     """Return a dict of scorers for token classification.
 
-    Parameters:
+    Parameters
     -----------
-        mode: str, [None (default), `strict`].
-            if ``None``, the score is compatible with conlleval.pl. Otherwise,
-            the score is calculated strictly.
-        scheme: Token, [IOB2, IOE2, IOBES]
-        suffix: bool, False by default.
+    mode: str, [None (default), `strict`].
+        if ``None``, the score is compatible with conlleval.pl. Otherwise,
+        the score is calculated strictly.
+    scheme: Token, [IOB2, IOE2, IOBES]
+    suffix: bool, False by default.
 
-    Returns:
+    Returns
     --------
         A dict of scorers.
     """

diff --git a/deepchecks/tabular/suites/default_suites.py b/deepchecks/tabular/suites/default_suites.py
@@ -251,7 +251,7 @@ def model_evaluation(alternative_scorers: Dict[str, Callable] = None,
              - :class:`~deepchecks.tabular.checks.model_evaluation.RocReport`
            * - :ref:`tabular__confusion_matrix_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.ConfusionMatrixReport`
-           * - :ref:`tabular__weak_segment_performance`
+           * - :ref:`tabular__weak_segments_performance`
              - :class:`~deepchecks.tabular.checks.model_evaluation.WeakSegmentPerformance`
            * - :ref:`tabular__prediction_drift`
              - :class:`~deepchecks.tabular.checks.model_evaluation.PredictionDrift`
@@ -356,7 +356,7 @@ def production_suite(task_type: str = None,
              - :class:`~deepchecks.tabular.checks.model_evaluation.RocReport`
            * - :ref:`tabular__confusion_matrix_report`
              - :class:`~deepchecks.tabular.checks.model_evaluation.ConfusionMatrixReport`
-           * - :ref:`tabular__weak_segment_performance`
+           * - :ref:`tabular__weak_segments_performance`
              - :class:`~deepchecks.tabular.checks.model_evaluation.WeakSegmentPerformance`
            * - :ref:`tabular__regression_error_distribution`
              - :class:`~deepchecks.tabular.checks.model_evaluation.RegressionErrorDistribution`

diff --git a/deepchecks/vision/checks/data_integrity/abstract_property_outliers.py b/deepchecks/vision/checks/data_integrity/abstract_property_outliers.py
@@ -54,7 +54,7 @@ class AbstractPropertyOutliers(SingleDatasetCheck):
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     property_input_type : PropertiesInputType , default: PropertiesInputType.IMAGES
         The type of input to the properties, required for caching the results after first calculation.
     n_show_top : int , default: 3

diff --git a/deepchecks/vision/checks/data_integrity/image_property_outliers.py b/deepchecks/vision/checks/data_integrity/image_property_outliers.py
@@ -38,7 +38,7 @@ class ImagePropertyOutliers(AbstractPropertyOutliers):
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     n_show_top : int , default: 3
         number of outliers to show from each direction (upper limit and bottom limit)
     iqr_percentiles: Tuple[int, int], default: (25, 75)

diff --git a/deepchecks/vision/checks/data_integrity/label_property_outliers.py b/deepchecks/vision/checks/data_integrity/label_property_outliers.py
@@ -42,7 +42,7 @@ class LabelPropertyOutliers(AbstractPropertyOutliers):
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     n_show_top : int , default: 3
         number of outliers to show from each direction (upper limit and bottom limit)
     iqr_percentiles: Tuple[int, int], default: (25, 75)

diff --git a/deepchecks/vision/checks/data_integrity/property_label_correlation.py b/deepchecks/vision/checks/data_integrity/property_label_correlation.py
@@ -67,7 +67,7 @@ class PropertyLabelCorrelation(SingleDatasetCheck):
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     n_top_properties: int, default: 5
         Number of features to show, sorted by the magnitude of difference in PPS
     min_pps_to_show: float, default 0.05

diff --git a/deepchecks/vision/checks/model_evaluation/prediction_drift.py b/deepchecks/vision/checks/model_evaluation/prediction_drift.py
@@ -83,7 +83,7 @@ class PredictionDrift(TrainTestCheck, ReducePropertyMixin):
           but these numbers do not have inherent value.
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     margin_quantile_filter : float , default : 0.025
         float in range [0,0.5), representing which margins (high and low quantiles) of the distribution will be filtered
         out of the EMD calculation. This is done in order for extreme values not to affect the calculation

diff --git a/deepchecks/vision/checks/train_test_validation/image_dataset_drift.py b/deepchecks/vision/checks/train_test_validation/image_dataset_drift.py
@@ -51,7 +51,7 @@ class ImageDatasetDrift(TrainTestCheck):
         - 'categorical' - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :doc:`/user-guide/vision/vision_properties`
+        For more on image / label properties, see the guide about :doc:`/vision/usage_guides/vision_properties`
     n_top_properties : int , default: 3
         Amount of properties to show ordered by domain classifier feature importance. This limit is used together
         (AND) with min_feature_importance, so less than n_top_columns features can be displayed.

diff --git a/deepchecks/vision/checks/train_test_validation/image_property_drift.py b/deepchecks/vision/checks/train_test_validation/image_property_drift.py
@@ -54,7 +54,7 @@ class ImagePropertyDrift(TrainTestCheck, ReducePropertyMixin):
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     margin_quantile_filter: float, default: 0.025
         float in range [0,0.5), representing which margins (high and low quantiles) of the distribution will be filtered
         out of the EMD calculation. This is done in order for extreme values not to affect the calculation

diff --git a/deepchecks/vision/checks/train_test_validation/label_drift.py b/deepchecks/vision/checks/train_test_validation/label_drift.py
@@ -79,7 +79,7 @@ class LabelDrift(TrainTestCheck, ReducePropertyMixin, ReduceLabelMixin):
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     margin_quantile_filter : float, default: 0.025
         float in range [0,0.5), representing which margins (high and low quantiles) of the distribution will be filtered
         out of the EMD calculation. This is done in order for extreme values not to affect the calculation

diff --git a/deepchecks/vision/checks/train_test_validation/property_label_correlation_change.py b/deepchecks/vision/checks/train_test_validation/property_label_correlation_change.py
@@ -73,7 +73,7 @@ class PropertyLabelCorrelationChange(TrainTestCheck):
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     per_class : bool, default: True
         boolean that indicates whether the results of this check should be calculated for all classes or per class in
         label. If True, the conditions will be run per class as well.

diff --git a/deepchecks/vision/suites/default_suites.py b/deepchecks/vision/suites/default_suites.py
@@ -48,7 +48,7 @@ def train_test_validation(label_properties: List[Dict[str, Any]] = None, image_p
              - :class:`~deepchecks.vision.checks.train_test_validation.ImagePropertyDrift`
            * - :ref:`vision__image_dataset_drift`
              - :class:`~deepchecks.vision.checks.train_test_validation.ImageDatasetDrift`
-           * - :ref:`vision__property_label_correlation `
+           * - :ref:`vision__property_label_correlation_change`
              - :class:`~deepchecks.vision.checks.train_test_validation.PropertyLabelCorrelationChange`
 
     Parameters
@@ -64,7 +64,7 @@ def train_test_validation(label_properties: List[Dict[str, Any]] = None, image_p
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
 
     image_properties : List[Dict[str, Any]], default: None
         List of properties. Replaces the default deepchecks properties.
@@ -75,7 +75,7 @@ def train_test_validation(label_properties: List[Dict[str, Any]] = None, image_p
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     **kwargs : dict
         additional arguments to pass to the checks.
 
@@ -134,7 +134,7 @@ def model_evaluation(scorers: Union[Dict[str, Union[Callable, str]], List[Any]]
              - :class:`~deepchecks.vision.checks.model_evaluation.PredictionDrift`
            * - :ref:`vision__simple_model_comparison`
              - :class:`~deepchecks.vision.checks.model_evaluation.SimpleModelComparison`
-           * - :ref:`vision__weak_segment_performance`
+           * - :ref:`vision__weak_segments_performance`
              - :class:`~deepchecks.vision.checks.model_evaluation.WeakSegmentPerformance`
 
     Parameters
@@ -153,7 +153,7 @@ def model_evaluation(scorers: Union[Dict[str, Union[Callable, str]], List[Any]]
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     prediction_properties : List[Dict[str, Any]], default: None
         List of properties. Replaces the default deepchecks properties.
         Each property is a dictionary with keys ``'name'`` (str), ``method`` (Callable) and ``'output_type'`` (str),
@@ -165,7 +165,7 @@ def model_evaluation(scorers: Union[Dict[str, Union[Callable, str]], List[Any]]
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     **kwargs : dict
         additional arguments to pass to the checks.
 
@@ -230,7 +230,7 @@ def data_integrity(image_properties: List[Dict[str, Any]] = None, label_properti
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     label_properties : List[Dict[str, Any]], default: None
         List of properties. Replaces the default deepchecks properties.
         Each property is a dictionary with keys ``'name'`` (str), ``method`` (Callable) and ``'output_type'`` (str),
@@ -242,7 +242,7 @@ def data_integrity(image_properties: List[Dict[str, Any]] = None, label_properti
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     **kwargs : dict
         additional arguments to pass to the checks.
 
@@ -293,7 +293,7 @@ def full_suite(n_samples: Optional[int] = 5000, image_properties: List[Dict[str,
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     label_properties : List[Dict[str, Any]], default: None
         List of properties. Replaces the default deepchecks properties.
         Each property is a dictionary with keys ``'name'`` (str), ``method`` (Callable) and ``'output_type'`` (str),
@@ -305,7 +305,7 @@ def full_suite(n_samples: Optional[int] = 5000, image_properties: List[Dict[str,
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
 
     scorers: Union[Dict[str, Union[Callable, str]], List[Any]], default: None
         Scorers to override the default scorers (metrics), find more about the supported formats at
@@ -321,7 +321,7 @@ def full_suite(n_samples: Optional[int] = 5000, image_properties: List[Dict[str,
         - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
           but these numbers do not have inherent value.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     prediction_properties : List[Dict[str, Any]], default: None
         List of properties. Replaces the default deepchecks properties.
         Each property is a dictionary with keys ``'name'`` (str), ``method`` (Callable) and ``'output_type'`` (str),
@@ -333,7 +333,7 @@ def full_suite(n_samples: Optional[int] = 5000, image_properties: List[Dict[str,
         - ``'class_id'`` - for properties that return the class_id. This is used because these
           properties are later matched with the ``VisionData.label_map``, if one was given.
 
-        For more on image / label properties, see the guide about :ref:`vision_properties_guide`.
+        For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
     Returns
     -------
     Suite

diff --git a/docs/source/_templates/autosummary/check.rst b/docs/source/_templates/autosummary/check.rst
@@ -22,10 +22,10 @@ Examples
 
 .. only:: html
 
- .. figure:: /checks_gallery/{{ submoduletype }}/{{ checktype}}/images/thumb/sphx_glr_plot_{{ to_snake_case(objname).lower() }}_thumb.png
+ .. figure:: /{{ submoduletype }}/auto_checks/{{ checktype}}/images/thumb/sphx_glr_plot_{{ to_snake_case(objname).lower() }}_thumb.png
      :alt: {{ objname }}
 
-     :ref:`sphx_glr_checks_gallery_{{submoduletype}}_{{ checktype }}_plot_{{ to_snake_case(objname).lower() }}.py`
+     :ref:`sphx_glr_{{submoduletype}}_auto_checks_{{ checktype }}_plot_{{ to_snake_case(objname).lower() }}.py`
 
 .. raw:: html
 

diff --git a/docs/source/checks/nlp/model_evaluation/plot_prediction_drift.py b/docs/source/checks/nlp/model_evaluation/plot_prediction_drift.py
@@ -2,8 +2,9 @@
 """
 .. _nlp__prediction_drift:
 
+================
 Prediction Drift
-****************
+================
 
 This notebook provides an overview for using and understanding the NLP prediction drift check.
 
@@ -43,7 +44,6 @@
 #
 # For this example, we'll use the tweet emotion dataset, which is a dataset of tweets labeled by one of four emotions:
 # happiness, anger, sadness and optimism.
-#%%
 
 import numpy as np
 from deepchecks.nlp.checks import PredictionDrift
@@ -57,6 +57,7 @@
 
 #%%
 # Let's see how our data looks like:
+
 train_ds.head()
 
 #%%

diff --git a/docs/source/checks/vision/train_test_validation/plot_new_labels.py b/docs/source/checks/vision/train_test_validation/plot_new_labels.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """
-.. _plot_vision_new_labels:
+.. _vision__new_labels:
 
 New Labels
 ==========