Fix read more (#1217)

* Fix read more

* Fix test

* Rename files for the docs

* Fix lint

* Fix init

* Fix docs import

deepchecks/tabular/checks/integrity/__init__.py

@@ -18,8 +18,8 @@
 from .string_mismatch_comparison import StringMismatchComparison
 from .dominant_frequency_change import DominantFrequencyChange
 from .data_duplicates import DataDuplicates
-from .new_category import CategoryMismatchTrainTest
-from .new_label import NewLabelTrainTest
+from .category_mismatch_train_test import CategoryMismatchTrainTest
+from .new_label_train_test import NewLabelTrainTest
 from .label_ambiguity import LabelAmbiguity
 
 

deepchecks/tabular/checks/performance/__init__.py

@@ -9,7 +9,8 @@
 # ----------------------------------------------------------------------------
 #
 """Module contains checks of model performance metrics."""
-from .performance_report import PerformanceReport, MultiModelPerformanceReport
+from .performance_report import PerformanceReport
+from .multi_model_performance_report import MultiModelPerformanceReport
 from .confusion_matrix_report import ConfusionMatrixReport
 from .roc_report import RocReport
 from .simple_model_comparison import SimpleModelComparison

deepchecks/tabular/checks/performance/multi_model_performance_report.py

@@ -0,0 +1,94 @@
+# ----------------------------------------------------------------------------
+# Copyright (C) 2021-2022 Deepchecks (https://www.deepchecks.com)
+#
+# This file is part of Deepchecks.
+# Deepchecks is distributed under the terms of the GNU Affero General
+# Public License (version 3 or later).
+# You should have received a copy of the GNU Affero General Public License
+# along with Deepchecks.  If not, see <http://www.gnu.org/licenses/>.
+# ----------------------------------------------------------------------------
+#
+"""Module containing multi model performance report check."""
+from typing import Dict, Callable, cast
+
+import pandas as pd
+import plotly.express as px
+
+from deepchecks.core import CheckResult
+from deepchecks.tabular import ModelComparisonCheck, ModelComparisonContext
+from deepchecks.utils.metrics import ModelType
+
+
+__all__ = ['MultiModelPerformanceReport']
+
+
+class MultiModelPerformanceReport(ModelComparisonCheck):
+    """Summarize performance scores for multiple models on test datasets.
+
+    Parameters
+    ----------
+    alternative_scorers : Dict[str, Callable] , default: None
+        An optional dictionary of scorer name to scorer functions.
+        If none given, using default scorers
+    """
+
+    def __init__(self, alternative_scorers: Dict[str, Callable] = None, **kwargs):
+        super().__init__(**kwargs)
+        self.user_scorers = alternative_scorers
+
+    def run_logic(self, multi_context: ModelComparisonContext):
+        """Run check logic."""
+        first_context = multi_context[0]
+        scorers = first_context.get_scorers(self.user_scorers, class_avg=False)
+
+        if multi_context.task_type in [ModelType.MULTICLASS, ModelType.BINARY]:
+            plot_x_axis = ['Class', 'Model']
+            results = []
+
+            for context in multi_context:
+                test = context.test
+                model = context.model
+                label = cast(pd.Series, test.label_col)
+                n_samples = label.groupby(label).count()
+                results.extend(
+                    [context.model_name, class_score, scorer.name, class_name, n_samples[class_name]]
+                    for scorer in scorers
+                    # scorer returns numpy array of results with item per class
+                    for class_score, class_name in zip(scorer(model, test), test.classes)
+                )
+
+            results_df = pd.DataFrame(results, columns=['Model', 'Value', 'Metric', 'Class', 'Number of samples'])
+
+        else:
+            plot_x_axis = 'Model'
+            results = [
+                [context.model_name, scorer(context.model, context.test), scorer.name,
+                 cast(pd.Series, context.test.label_col).count()]
+                for context in multi_context
+                for scorer in scorers
+            ]
+            results_df = pd.DataFrame(results, columns=['Model', 'Value', 'Metric', 'Number of samples'])
+
+        fig = px.histogram(
+            results_df,
+            x=plot_x_axis,
+            y='Value',
+            color='Model',
+            barmode='group',
+            facet_col='Metric',
+            facet_col_spacing=0.05,
+            hover_data=['Number of samples'],
+        )
+
+        if multi_context.task_type in [ModelType.MULTICLASS, ModelType.BINARY]:
+            fig.update_xaxes(title=None, tickprefix='Class ', tickangle=60)
+        else:
+            fig.update_xaxes(title=None)
+
+        fig = (
+            fig.update_yaxes(title=None, matches=None)
+            .for_each_annotation(lambda a: a.update(text=a.text.split('=')[-1]))
+            .for_each_yaxis(lambda yaxis: yaxis.update(showticklabels=True))
+        )
+
+        return CheckResult(results_df, display=[fig])

deepchecks/tabular/checks/performance/performance_report.py

@@ -10,18 +10,19 @@
 #
 """Module containing performance report check."""
 from typing import Callable, TypeVar, Dict, cast
+
 import pandas as pd
 import plotly.express as px
 
 from deepchecks.core import CheckResult, ConditionResult
 from deepchecks.core.condition import ConditionCategory
 from deepchecks.core.errors import DeepchecksValueError
-from deepchecks.tabular import Context, ModelComparisonContext, TrainTestCheck, ModelComparisonCheck
+from deepchecks.tabular import Context, TrainTestCheck
 from deepchecks.utils.strings import format_percent, format_number
 from deepchecks.utils.metrics import MULTICLASS_SCORERS_NON_AVERAGE, ModelType
 
 
-__all__ = ['PerformanceReport', 'MultiModelPerformanceReport']
+__all__ = ['PerformanceReport']
 
 
 PR = TypeVar('PR', bound='PerformanceReport')
@@ -301,75 +302,3 @@ def condition(check_result: pd.DataFrame) -> ConditionResult:
             ),
             condition_func=condition
         )
-
-
-class MultiModelPerformanceReport(ModelComparisonCheck):
-    """Summarize performance scores for multiple models on test datasets.
-
-    Parameters
-    ----------
-    alternative_scorers : Dict[str, Callable] , default: None
-        An optional dictionary of scorer name to scorer functions.
-        If none given, using default scorers
-    """
-
-    def __init__(self, alternative_scorers: Dict[str, Callable] = None, **kwargs):
-        super().__init__(**kwargs)
-        self.user_scorers = alternative_scorers
-
-    def run_logic(self, multi_context: ModelComparisonContext):
-        """Run check logic."""
-        first_context = multi_context[0]
-        scorers = first_context.get_scorers(self.user_scorers, class_avg=False)
-
-        if multi_context.task_type in [ModelType.MULTICLASS, ModelType.BINARY]:
-            plot_x_axis = ['Class', 'Model']
-            results = []
-
-            for context in multi_context:
-                test = context.test
-                model = context.model
-                label = cast(pd.Series, test.label_col)
-                n_samples = label.groupby(label).count()
-                results.extend(
-                    [context.model_name, class_score, scorer.name, class_name, n_samples[class_name]]
-                    for scorer in scorers
-                    # scorer returns numpy array of results with item per class
-                    for class_score, class_name in zip(scorer(model, test), test.classes)
-                )
-
-            results_df = pd.DataFrame(results, columns=['Model', 'Value', 'Metric', 'Class', 'Number of samples'])
-
-        else:
-            plot_x_axis = 'Model'
-            results = [
-                [context.model_name, scorer(context.model, context.test), scorer.name,
-                 cast(pd.Series, context.test.label_col).count()]
-                for context in multi_context
-                for scorer in scorers
-            ]
-            results_df = pd.DataFrame(results, columns=['Model', 'Value', 'Metric', 'Number of samples'])
-
-        fig = px.histogram(
-            results_df,
-            x=plot_x_axis,
-            y='Value',
-            color='Model',
-            barmode='group',
-            facet_col='Metric',
-            facet_col_spacing=0.05,
-            hover_data=['Number of samples'],
-        )
-
-        if multi_context.task_type in [ModelType.MULTICLASS, ModelType.BINARY]:
-            fig.update_xaxes(title=None, tickprefix='Class ', tickangle=60)
-        else:
-            fig.update_xaxes(title=None)
-
-        fig = (
-            fig.update_yaxes(title=None, matches=None)
-            .for_each_annotation(lambda a: a.update(text=a.text.split('=')[-1]))
-            .for_each_yaxis(lambda yaxis: yaxis.update(showticklabels=True))
-        )
-
-        return CheckResult(results_df, display=[fig])

deepchecks/utils/strings.py

@@ -122,16 +122,15 @@ def _generate_check_docs_link_html(check):
     # compare check full name and link to the notebook to
     # understand how link is formatted:
     #
-    # - deepchecks.tabular.checks.integrity.new_category.CategoryMismatchTrainTest
-    # - docs.deepchecks.com/{version}/examples/tabular/checks/integrity/category_mismatch_train_test.html # noqa: E501 # pylint: disable=line-too-long
+    # - deepchecks.tabular.checks.integrity.StringMismatchComparison
+    # - https://docs.deepchecks.com/{version}/examples/tabular/checks/integrity/examples/plot_string_mismatch_comparison.html # noqa: E501 # pylint: disable=line-too-long
 
     # Remove deepchecks from the start
     module_path = module_path[len('deepchecks.'):]
-    # There is a bug in doc rendering where the "tabular" is omitted, so do it for now
-    if module_path.startswith('tabular.'):
-        module_path = module_path[len('tabular.'):]
-
-    url = '/'.join([*module_path.split('.')])
+    module_parts = module_path.split('.')
+    module_parts[-1] = f'plot_{module_parts[-1]}'
+    module_parts.insert(len(module_parts) - 1, 'examples')
+    url = '/'.join([*module_parts])
     version = deepchecks.__version__ or 'stable'
     link = link_template.format(version=version, path=url)
     return f' <a href="{link}" target="_blank">Read More...</a>'

docs/source/examples/tabular/checks/integrity/source/plot_category_mismatch_train_test.py

@@ -6,7 +6,7 @@
 
 #%%
 
-from deepchecks.tabular.checks.integrity.new_category import CategoryMismatchTrainTest
+from deepchecks.tabular.checks.integrity import CategoryMismatchTrainTest
 from deepchecks.tabular import Dataset
 import pandas as pd
 

docs/source/examples/tabular/checks/integrity/source/plot_new_label_train_test.py

@@ -6,7 +6,7 @@
 
 #%%
 
-from deepchecks.tabular.checks.integrity.new_label import NewLabelTrainTest
+from deepchecks.tabular.checks.integrity import NewLabelTrainTest
 from deepchecks.tabular import Dataset
 import pandas as pd
 

tests/utils/test_string_utils.py

@@ -75,9 +75,9 @@ def test_generate_check_docs_link_html():
     # Act
     html = _generate_check_docs_link_html(check)
     # Assert
-    assert_that(html, equal_to(f' <a href="https://docs.deepchecks.com/{version}/examples/checks/overview/model_info'
-                               f'.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link" '
-                               f'target="_blank">Read More...</a>'))
+    assert_that(html, equal_to(f' <a href="https://docs.deepchecks.com/{version}/examples/tabular/checks/overview/'
+                               f'examples/plot_model_info.html?utm_source=display_output&utm_medium=referral'
+                               f'&utm_campaign=check_link" target="_blank">Read More...</a>'))
 
 
 def test_generate_check_docs_link_html_not_a_check():