update style

_doc/examples/plot_constraint_kmeans.py

@@ -10,6 +10,7 @@
 Data
 ====
 """
+
 from collections import Counter
 
 import matplotlib.pyplot as plt

_doc/examples/plot_decision_tree_logreg.py

@@ -13,6 +13,7 @@
 models on the `Iris
 dataset <https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html>`_.
 """
+
 import numpy
 from scipy.spatial.distance import cdist
 import matplotlib.pyplot as plt

_doc/examples/plot_digitize.py

@@ -13,6 +13,7 @@
 Simple example
 ==============
 """
+
 import numpy
 import matplotlib.pyplot as plt
 from onnxruntime import InferenceSession

_doc/examples/plot_kmeans_l1.py

@@ -8,7 +8,6 @@
 algorithm.
 """
 
-
 import matplotlib.pyplot as plt
 import numpy
 import numpy.random as rnd

_doc/examples/plot_leave_neighbors.py

@@ -7,7 +7,6 @@
 the predict the same value (or class). Do they share a border?
 """
 
-
 ##############################
 # A simple tree
 # +++++++++++++

_doc/examples/plot_logistic_regression_clustering.py

@@ -13,7 +13,6 @@
 -------------------------------
 """
 
-
 import numpy
 import pandas
 import matplotlib.pyplot as plt

_doc/examples/plot_piecewise_linear_regression_criterion.py

@@ -17,7 +17,6 @@
 Let's build a toy problem based on two linear models.
 """
 
-
 import matplotlib.pyplot as plt
 import numpy
 import numpy.random as npr

_doc/examples/plot_quantile_mlpregression.py

@@ -11,7 +11,6 @@
 We first generate some dummy data.
 """
 
-
 import numpy
 from pandas import DataFrame
 import matplotlib.pyplot as plt

_doc/examples/plot_traceable_ngrams_tfidf.py

@@ -15,7 +15,6 @@
 ~~~~~~~~~~~~~~~~~~~~
 """
 
-
 import numpy
 from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 from mlinsights.mlmodel.sklearn_text import (

_doc/sg_execution_times.rst

@@ -0,0 +1,85 @@
+
+:orphan:
+
+.. _sphx_glr_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.000** total execution time for 17 files **from all galleries**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_auto_examples_plot_constraint_kmeans.py` (``examples/plot_constraint_kmeans.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_decision_tree_logreg.py` (``examples/plot_decision_tree_logreg.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_digitize.py` (``examples/plot_digitize.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_kmeans_l1.py` (``examples/plot_kmeans_l1.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_leave_neighbors.py` (``examples/plot_leave_neighbors.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_logistic_regression_clustering.py` (``examples/plot_logistic_regression_clustering.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_piecewise_classification.py` (``examples/plot_piecewise_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_piecewise_linear_regression.py` (``examples/plot_piecewise_linear_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_piecewise_linear_regression_criterion.py` (``examples/plot_piecewise_linear_regression_criterion.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_predictable_tsne.py` (``examples/plot_predictable_tsne.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_quantile_mlpregression.py` (``examples/plot_quantile_mlpregression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_quantile_regression.py` (``examples/plot_quantile_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_regression_confidence_interval.py` (``examples/plot_regression_confidence_interval.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_search_images_torch.py` (``examples/plot_search_images_torch.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_sklearn_transformed_target.py` (``examples/plot_sklearn_transformed_target.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_traceable_ngrams_tfidf.py` (``examples/plot_traceable_ngrams_tfidf.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_visualize_pipeline.py` (``examples/plot_visualize_pipeline.py``)
+     - 00:00.000
+     - 0.0

mlinsights/helpers/pipeline.py

@@ -105,9 +105,9 @@ def __init__(self, model):
             )
         if hasattr(model, "decision_function") and callable(model.decision_function):
             model._debug_decision_function = model.decision_function
-            self.methods[
-                "decision_function"
-            ] = lambda model, X: model._debug_decision_function(X)
+            self.methods["decision_function"] = (
+                lambda model, X: model._debug_decision_function(X)
+            )
 
     def __repr__(self):
         """

mlinsights/sklapi/sklearn_base_learner.py

@@ -3,7 +3,6 @@
 
 
 class SkBaseLearner(SkBase):
-
     """
     Pattern of a *learner* qui suit la même API que :epkg:`scikit-learn`.
     """

mlinsights/sklapi/sklearn_parameters.py

@@ -3,7 +3,6 @@
 
 
 class SkException(Exception):
-
     """
     custom exception
     """
@@ -12,7 +11,6 @@ class SkException(Exception):
 
 
 class SkLearnParameters:
-
     """
     Defines a class to store parameters of a *learner* or a *transform*.
     """