From 29f2006fccf0b27fdb8c6f4c597199cf44f84df1 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 12:11:34 +0200
Subject: [PATCH 01/43] Rename file

---
 ..._extraction.py => plot_grid_search_text_feature_extraction.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/model_selection/{grid_search_text_feature_extraction.py => plot_grid_search_text_feature_extraction.py} (100%)

diff --git a/examples/model_selection/grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
similarity index 100%
rename from examples/model_selection/grid_search_text_feature_extraction.py
rename to examples/model_selection/plot_grid_search_text_feature_extraction.py

From 9fe3d7d83c572eaaf43e165e865c958ec3262fad Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 12:13:36 +0200
Subject: [PATCH 02/43] Update references to new file name

---
 doc/modules/compose.rst                      | 2 +-
 doc/modules/cross_validation.rst             | 2 +-
 doc/modules/feature_extraction.rst           | 2 +-
 doc/modules/grid_search.rst                  | 2 +-
 sklearn/datasets/descr/twenty_newsgroups.rst | 4 ++--
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
index c797eb288c6e5..4a61b5ec5f118 100644
--- a/doc/modules/compose.rst
+++ b/doc/modules/compose.rst
@@ -163,7 +163,7 @@ You can also provide custom feature names for the input data using
 .. topic:: Examples:
 
  * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
- * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
+ * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
  * :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`
  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
  * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
index 3ecab9bd8eb04..d353c3ed326e2 100644
--- a/doc/modules/cross_validation.rst
+++ b/doc/modules/cross_validation.rst
@@ -295,7 +295,7 @@ section.
     * :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`,
     * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`,
     * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`,
-    * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`,
+    * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`,
     * :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`,
     * :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`.
 
diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst
index a07d722defb9b..28053571f6ff0 100644
--- a/doc/modules/feature_extraction.rst
+++ b/doc/modules/feature_extraction.rst
@@ -607,7 +607,7 @@ As usual the best way to adjust the feature extraction parameters
 is to use a cross-validated grid search, for instance by pipelining the
 feature extractor with a classifier:
 
- * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
+ * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
 
 
 Decoding text files
diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst
index ad3f24f23980a..e4cc62b7773f3 100644
--- a/doc/modules/grid_search.rst
+++ b/doc/modules/grid_search.rst
@@ -77,7 +77,7 @@ evaluated and the best combination is retained.
     - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of
       Grid Search computation on the digits dataset.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py` for an example
+    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example
       of Grid Search coupling parameters from a text documents feature
       extractor (n-gram count vectorizer and TF-IDF transformer) with a
       classifier (here a linear SVM trained with SGD with either elastic
diff --git a/sklearn/datasets/descr/twenty_newsgroups.rst b/sklearn/datasets/descr/twenty_newsgroups.rst
index a7542ea57d529..595e04e18caa2 100644
--- a/sklearn/datasets/descr/twenty_newsgroups.rst
+++ b/sklearn/datasets/descr/twenty_newsgroups.rst
@@ -216,7 +216,7 @@ It loses even more if we also strip this metadata from the training data:
   0.76995...
 
 Some other classifiers cope better with this harder version of the task. Try
-running :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py` with and without
+running :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` with and without
 the ``--filter`` option to compare the results.
 
 .. topic:: Data Considerations
@@ -248,6 +248,6 @@ the ``--filter`` option to compare the results.
 
 .. topic:: Examples
 
-   * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
+   * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
 
    * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`

From 497178a00121144fab5f91762821ae6d15ec4139 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 12:14:15 +0200
Subject: [PATCH 03/43] First step to improve notebook style

---
 ...lot_grid_search_text_feature_extraction.py | 97 ++++++-------------
 1 file changed, 32 insertions(+), 65 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 91801b361265b..be97bc64ed4b0 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -10,35 +10,6 @@
 You can adjust the number of categories by giving their names to the dataset
 loader or setting them to None to get the 20 of them.
 
-Here is a sample output of a run on a quad-core machine::
-
-  Loading 20 newsgroups dataset for categories:
-  ['alt.atheism', 'talk.religion.misc']
-  1427 documents
-  2 categories
-
-  Performing grid search...
-  pipeline: ['vect', 'tfidf', 'clf']
-  parameters:
-  {'clf__alpha': (1.0000000000000001e-05, 9.9999999999999995e-07),
-   'clf__max_iter': (10, 50, 80),
-   'clf__penalty': ('l2', 'elasticnet'),
-   'tfidf__use_idf': (True, False),
-   'vect__max_n': (1, 2),
-   'vect__max_df': (0.5, 0.75, 1.0),
-   'vect__max_features': (None, 5000, 10000, 50000)}
-  done in 1737.030s
-
-  Best score: 0.940
-  Best parameters set:
-      clf__alpha: 9.9999999999999995e-07
-      clf__max_iter: 50
-      clf__penalty: 'elasticnet'
-      tfidf__use_idf: True
-      vect__max_n: 2
-      vect__max_df: 0.75
-      vect__max_features: 50000
-
 """
 
 # Author: Olivier Grisel <olivier.grisel@ensta.org>
@@ -49,69 +20,63 @@
 # %%
 # Data loading
 # ------------
-
-from pprint import pprint
-from time import time
-import logging
+# We load two categories from the training set. Set `categories=None` to do the
+# analysis on all the categories.
 
 from sklearn.datasets import fetch_20newsgroups
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.linear_model import SGDClassifier
-from sklearn.model_selection import GridSearchCV
-from sklearn.pipeline import Pipeline
-
-# Display progress logs on stdout
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
 
-# Load some categories from the training set
 categories = [
     "alt.atheism",
     "talk.religion.misc",
 ]
 
-# Uncomment the following to do the analysis on all the categories
-# categories = None
-
-print("Loading 20 newsgroups dataset for categories:")
-print(categories)
-
 data = fetch_20newsgroups(subset="train", categories=categories)
-print("%d documents" % len(data.filenames))
-print("%d categories" % len(data.target_names))
-print()
+
+print(f"Loading 20 newsgroups dataset for {len(data.target_names)} categories:")
+print(data.target_names)
+print(f"{len(data.data)} documents")
 
 # %%
 # Pipeline with hyperparameter tuning
 # -----------------------------------
+# We define a pipeline combining a text feature vectorizer with a simple
+# classifier. We also define a set of parameters to use for grid search.
+# Uncommenting more parameters will give better exploring power but will
+# increase processing time in a combinatorial way.
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline
 
-# Define a pipeline combining a text feature extractor with a simple classifier
 pipeline = Pipeline(
     [
-        ("vect", CountVectorizer()),
-        ("tfidf", TfidfTransformer()),
+        ("vect", TfidfVectorizer),
         ("clf", SGDClassifier()),
     ]
 )
 
-# Parameters to use for grid search. Uncommenting more parameters will give
-# better exploring power but will increase processing time in a combinatorial
-# way
 parameters = {
     "vect__max_df": (0.5, 0.75, 1.0),
     # 'vect__max_features': (None, 5000, 10000, 50000),
     "vect__ngram_range": ((1, 1), (1, 2)),  # unigrams or bigrams
-    # 'tfidf__use_idf': (True, False),
-    # 'tfidf__norm': ('l1', 'l2'),
+    # 'vect__use_idf': (True, False),
+    # 'vect__norm': ('l1', 'l2'),
     "clf__max_iter": (20,),
     "clf__alpha": (0.00001, 0.000001),
     "clf__penalty": ("l2", "elasticnet"),
     # 'clf__max_iter': (10, 50, 80),
 }
 
-# Find the best parameters for both the feature extraction and the
-# classifier
-grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
+# %%
+# Find the best parameters for both the feature extraction and the classifier.
+
+from pprint import pprint
+from time import time
+from sklearn.model_selection import GridSearchCV
+
+grid_search = GridSearchCV(
+    estimator=pipeline, param_grid=parameters, n_jobs=-1, verbose=1
+)
 
 print("Performing grid search...")
 print("pipeline:", [name for name, _ in pipeline.steps])
@@ -119,11 +84,13 @@
 pprint(parameters)
 t0 = time()
 grid_search.fit(data.data, data.target)
-print("done in %0.3fs" % (time() - t0))
+print(f"done in {time() - t0:.3f}s")
 print()
 
-print("Best score: %0.3f" % grid_search.best_score_)
+print("Best score: {grid_search.best_score_:.3f}")
 print("Best parameters set:")
 best_parameters = grid_search.best_estimator_.get_params()
 for param_name in sorted(parameters.keys()):
-    print("\t%s: %r" % (param_name, best_parameters[param_name]))
+    print(f"{param_name}: {best_parameters[param_name]}")
+
+# %%

From 8765fc631a6a21b0bee6d9ce65bf3de7be993df7 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 15:45:32 +0200
Subject: [PATCH 04/43] Tune ComplementNB instead of SGDClassifier

---
 ...lot_grid_search_text_feature_extraction.py | 29 ++++++++++---------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index be97bc64ed4b0..1877c9837489f 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -30,11 +30,17 @@
     "talk.religion.misc",
 ]
 
-data = fetch_20newsgroups(subset="train", categories=categories)
+data_train = fetch_20newsgroups(
+    subset="train",
+    categories=categories,
+    shuffle=True,
+    random_state=42,
+    remove=("headers", "footers", "quotes"),
+)
 
-print(f"Loading 20 newsgroups dataset for {len(data.target_names)} categories:")
-print(data.target_names)
-print(f"{len(data.data)} documents")
+print(f"Loading 20 newsgroups dataset for {len(data_train.target_names)} categories:")
+print(data_train.target_names)
+print(f"{len(data_train.data)} documents")
 
 # %%
 # Pipeline with hyperparameter tuning
@@ -45,26 +51,23 @@
 # increase processing time in a combinatorial way.
 
 from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import SGDClassifier
+from sklearn.naive_bayes import ComplementNB
 from sklearn.pipeline import Pipeline
 
 pipeline = Pipeline(
     [
-        ("vect", TfidfVectorizer),
-        ("clf", SGDClassifier()),
+        ("vect", TfidfVectorizer()),
+        ("clf", ComplementNB()),
     ]
 )
 
 parameters = {
     "vect__max_df": (0.5, 0.75, 1.0),
+    "vect__min_df": (1, 3, 5),
     # 'vect__max_features': (None, 5000, 10000, 50000),
     "vect__ngram_range": ((1, 1), (1, 2)),  # unigrams or bigrams
-    # 'vect__use_idf': (True, False),
     # 'vect__norm': ('l1', 'l2'),
-    "clf__max_iter": (20,),
-    "clf__alpha": (0.00001, 0.000001),
-    "clf__penalty": ("l2", "elasticnet"),
-    # 'clf__max_iter': (10, 50, 80),
+    "clf__alpha": (0.01, 0.1),
 }
 
 # %%
@@ -83,7 +86,7 @@
 print("parameters:")
 pprint(parameters)
 t0 = time()
-grid_search.fit(data.data, data.target)
+grid_search.fit(data_train.data, data_train.target)
 print(f"done in {time() - t0:.3f}s")
 print()
 

From ce1f9a9eb62e5882e91bcdaf9d5e5bf3e582f9fe Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 16:28:06 +0200
Subject: [PATCH 05/43] Add score accuracy on test set

---
 .../plot_grid_search_text_feature_extraction.py    | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 1877c9837489f..b19b037250145 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -38,6 +38,14 @@
     remove=("headers", "footers", "quotes"),
 )
 
+data_test = fetch_20newsgroups(
+    subset="test",
+    categories=categories,
+    shuffle=True,
+    random_state=42,
+    remove=("headers", "footers", "quotes"),
+)
+
 print(f"Loading 20 newsgroups dataset for {len(data_train.target_names)} categories:")
 print(data_train.target_names)
 print(f"{len(data_train.data)} documents")
@@ -89,11 +97,11 @@
 grid_search.fit(data_train.data, data_train.target)
 print(f"done in {time() - t0:.3f}s")
 print()
-
-print("Best score: {grid_search.best_score_:.3f}")
 print("Best parameters set:")
 best_parameters = grid_search.best_estimator_.get_params()
 for param_name in sorted(parameters.keys()):
     print(f"{param_name}: {best_parameters[param_name]}")
 
-# %%
+accuracy = grid_search.score(data_test.data, data_test.target)
+print(f"Best train accuracy: {grid_search.best_score_:.3f}")
+print(f"Accuracy on test set: {accuracy:.3f}")

From 1c2a102f57de16092a5906d7db96d4f3d752219c Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 16:29:18 +0200
Subject: [PATCH 06/43] Add reference to grid search in text classification
 example

---
 examples/text/plot_document_classification_20newsgroups.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/examples/text/plot_document_classification_20newsgroups.py b/examples/text/plot_document_classification_20newsgroups.py
index b53920178aa86..ea3b26cd1b6b0 100644
--- a/examples/text/plot_document_classification_20newsgroups.py
+++ b/examples/text/plot_document_classification_20newsgroups.py
@@ -334,7 +334,10 @@ def benchmark(clf, custom_name=False):
 # such a multi-class text classification problem.
 #
 # Notice that the most important hyperparameters values were tuned using a grid
-# search procedure not shown in this notebook for the sake of simplicity.
+# search procedure not shown in this notebook for the sake of simplicity. See
+# the example script
+# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+# for a demo on how such tuning can be done.
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.svm import LinearSVC

From 4f08c16833b2e73d8e3a58d8f09c83a89ada49eb Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 17:01:58 +0200
Subject: [PATCH 07/43] Add reference to text classification example

---
 .../plot_grid_search_text_feature_extraction.py    | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index b19b037250145..6075ccd0bd760 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -4,11 +4,14 @@
 ==========================================================
 
 The dataset used in this example is the 20 newsgroups dataset which will be
-automatically downloaded and then cached and reused for the document
+automatically downloaded, cached and reused for the document
 classification example.
 
-You can adjust the number of categories by giving their names to the dataset
-loader or setting them to None to get the 20 of them.
+In this example we tune the hyperparameters of a particular classifier using a
+:class:`~sklearn.model_selection.GridSearchCV`. For a demo on the performance of
+some other classifiers, see the
+:ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+notebook.
 
 """
 
@@ -20,8 +23,9 @@
 # %%
 # Data loading
 # ------------
-# We load two categories from the training set. Set `categories=None` to do the
-# analysis on all the categories.
+# We load two categories from the training set. You can adjust the number of
+# categories by giving their names to the dataset loader or setting them to None
+# to get the 20 of them.
 
 from sklearn.datasets import fetch_20newsgroups
 

From 58ed079c1437118a833f0e72b146735e620bbca7 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Thu, 23 Jun 2022 17:03:16 +0200
Subject: [PATCH 08/43] Improve references to the twenty_newsgroup description

---
 sklearn/datasets/descr/twenty_newsgroups.rst | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/sklearn/datasets/descr/twenty_newsgroups.rst b/sklearn/datasets/descr/twenty_newsgroups.rst
index 595e04e18caa2..77b0712e4c122 100644
--- a/sklearn/datasets/descr/twenty_newsgroups.rst
+++ b/sklearn/datasets/descr/twenty_newsgroups.rst
@@ -215,9 +215,9 @@ It loses even more if we also strip this metadata from the training data:
   >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
   0.76995...
 
-Some other classifiers cope better with this harder version of the task. Try
-running :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` with and without
-the ``--filter`` option to compare the results.
+Some other classifiers cope better with this harder version of the task. Try the
+:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+example with and without the `remove` option to compare the results.
 
 .. topic:: Data Considerations
 
@@ -251,3 +251,7 @@ the ``--filter`` option to compare the results.
    * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
 
    * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+
+   * :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`
+
+   * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`

From e62f74ecb4db5497c4350f9b0d52675f643a3a1f Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 27 Jun 2022 11:47:24 +0200
Subject: [PATCH 09/43] Add plotly to CI

---
 build_tools/update_environments_and_lock_files.py | 1 +
 sklearn/_min_dependencies.py                      | 1 +
 2 files changed, 2 insertions(+)

diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index a06fded6c3469..da411d53601f2 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -238,6 +238,7 @@ def remove_from(alist, to_remove):
             "numpydoc": "min",
             "sphinx-prompt": "min",
             "sphinxext-opengraph": "min",
+            "plotly": "min",
         },
     },
     {
diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py
index f055037d78be9..2f7e069af5df7 100644
--- a/sklearn/_min_dependencies.py
+++ b/sklearn/_min_dependencies.py
@@ -47,6 +47,7 @@
     "Pillow": ("7.1.2", "docs"),
     "sphinx-prompt": ("1.3.0", "docs"),
     "sphinxext-opengraph": ("0.4.2", "docs"),
+    "plotly": ("4.14.3", "docs, examples"),
     # XXX: Pin conda-lock to the latest released version (needs manual update
     # from time to time)
     "conda-lock": ("1.0.5", "maintenance"),

From 1b12759cfd3183a05333a36bb627520564bb58da Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 27 Jun 2022 17:31:32 +0200
Subject: [PATCH 10/43] Add parallel coordinate plot and interpretation

---
 ...lot_grid_search_text_feature_extraction.py | 69 ++++++++++++++++++-
 1 file changed, 68 insertions(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 6075ccd0bd760..fa3604617b67d 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -107,5 +107,72 @@
     print(f"{param_name}: {best_parameters[param_name]}")
 
 accuracy = grid_search.score(data_test.data, data_test.target)
-print(f"Best train accuracy: {grid_search.best_score_:.3f}")
+print(f"Best accuracy on the grid search: {grid_search.best_score_:.3f}")
 print(f"Accuracy on test set: {accuracy:.3f}")
+
+# %%
+# Finally, we use a `plotly.express.parallel_coordinates
+# <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
+# to visualize the results from the
+# :class:`~sklearn.model_selection.GridSearchCV`.
+
+import pandas as pd
+import plotly.express as px
+
+
+def shorten_param(param_name):
+    if "__" in param_name:
+        return param_name.rsplit("__", 1)[1]
+    return param_name
+
+
+cv_results = pd.DataFrame(grid_search.cv_results_)
+# monograms are mapped to index 1 and bigrams to index 2
+cv_results["param_vect__ngram_range"] = cv_results["param_vect__ngram_range"].apply(
+    lambda x: x[1]
+)
+
+column_results = [f"param_{name}" for name in parameters.keys()]
+column_results += ["mean_test_score"]
+
+fig = px.parallel_coordinates(
+    cv_results[column_results]
+    .rename(shorten_param, axis=1)
+    .apply(dict.fromkeys(map(shorten_param, column_results), lambda x: x)),
+    color="mean_test_score",
+    color_continuous_scale=px.colors.sequential.Jet,
+)
+fig.show()
+
+# %%
+# The parallel coordinates plot displays the values of the hyperparameters on
+# different columns while the performance metric is color coded. It allows us to
+# quickly inspect the combinations of hyperparameters that maximize the
+# performance.
+#
+# It is possible to select a range of results by clicking and holding on any
+# axis of the parallel coordinate plot. You can then slide (move) the range
+# selection and cross two selections to see the intersections. You can undo a
+# selection by clicking once again on the same axis.
+#
+# In particular for this hyperparameter search, it is interesting to notice that
+# the top performing models (dark red lines with mean test score > 0.82) are
+# reached when `min_df=1` and `alpha=0.01`, regardless of the value of
+# `max_df` and the `ngram_range`.
+
+# %%
+column_results += ["std_test_score"]
+cv_results = (
+    cv_results[column_results]
+    .rename(shorten_param, axis=1)
+    .sort_values("mean_test_score", ascending=False)
+)
+cv_results
+
+# %%
+# By a manual inspection of the results, one can notice that the top performing
+# models overlap within one standard deviation of their test score, showing that
+# `max_df` and `ngram_range` are indeed not meaningful parameters in this
+# particular case. For more information on how to customize a
+# :class:`~sklearn.model_selection.GridSearchCV`, see the example notebook
+# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.

From a0157a6afc5f998ce59997e71c7c635485f45bc4 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 27 Jun 2022 17:33:39 +0200
Subject: [PATCH 11/43] Update plotly version

---
 sklearn/_min_dependencies.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py
index 2f7e069af5df7..977d6a680f842 100644
--- a/sklearn/_min_dependencies.py
+++ b/sklearn/_min_dependencies.py
@@ -47,7 +47,7 @@
     "Pillow": ("7.1.2", "docs"),
     "sphinx-prompt": ("1.3.0", "docs"),
     "sphinxext-opengraph": ("0.4.2", "docs"),
-    "plotly": ("4.14.3", "docs, examples"),
+    "plotly": ("5.9.0", "docs, examples"),
     # XXX: Pin conda-lock to the latest released version (needs manual update
     # from time to time)
     "conda-lock": ("1.0.5", "maintenance"),

From fc4667b11ef5a0eaa8ce3b6604d82d615063c7bd Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 27 Jun 2022 23:14:47 +0200
Subject: [PATCH 12/43] Add plotly to conda dependencies

---
 build_tools/update_environments_and_lock_files.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index da411d53601f2..cd060d8412680 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -223,6 +223,7 @@ def remove_from(alist, to_remove):
             "sphinx-gallery",
             "numpydoc",
             "sphinx-prompt",
+            "plotly",
         ],
         "pip_dependencies": ["sphinxext-opengraph"],
         "package_constraints": {

From 24db4a16918cf455782f8cf83b09454396c05cc0 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 27 Jun 2022 23:28:13 +0200
Subject: [PATCH 13/43] Add plotly to build name doc

---
 build_tools/update_environments_and_lock_files.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index cd060d8412680..43bceca9b2152 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -257,6 +257,7 @@ def remove_from(alist, to_remove):
             "sphinx-gallery",
             "numpydoc",
             "sphinx-prompt",
+            "plotly",
         ],
         "pip_dependencies": ["sphinxext-opengraph"],
         "package_constraints": {

From 0e48c5949a44eb6a914a563663668d57ee4daded Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 28 Jun 2022 11:10:13 +0200
Subject: [PATCH 14/43] Update enviroments and lock files

---
 build_tools/github/doc_environment.yml        |   1 +
 build_tools/github/doc_linux-64_conda.lock    | 111 ++++++++++--------
 .../doc_min_dependencies_environment.yml      |   1 +
 .../doc_min_dependencies_linux-64_conda.lock  |  53 ++++-----
 4 files changed, 88 insertions(+), 78 deletions(-)

diff --git a/build_tools/github/doc_environment.yml b/build_tools/github/doc_environment.yml
index f0a933e31552a..0e332269b68ca 100644
--- a/build_tools/github/doc_environment.yml
+++ b/build_tools/github/doc_environment.yml
@@ -25,6 +25,7 @@ dependencies:
   - sphinx-gallery
   - numpydoc
   - sphinx-prompt
+  - plotly
   - pip
   - pip:
     - sphinxext-opengraph
diff --git a/build_tools/github/doc_linux-64_conda.lock b/build_tools/github/doc_linux-64_conda.lock
index 0ad634faa86c5..16a86e01244d4 100644
--- a/build_tools/github/doc_linux-64_conda.lock
+++ b/build_tools/github/doc_linux-64_conda.lock
@@ -1,9 +1,9 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 1b1e977a1b5dcedea55d0a8b53d501d1c1211b70a4419ec48dafe1b2658b4ef8
+# input_hash: 51824060cd657aa08f97c645d9c3c1efd6eec66aedaf8949d8d7cb5d9d8e69e6
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.5.18.1-ha878542_0.tar.bz2#352e93bbe1d604002b11bbcf425bf866
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.6.15-ha878542_0.tar.bz2#c320890f77fd1d617fa876e0982002c2
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
@@ -25,7 +25,7 @@ https://conda.anaconda.org/conda-forge/linux-64/binutils-2.36.1-hdd6e379_2.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.36-hf3e587d_10.tar.bz2#9d5cdbfe24b182d4c749b86d500ac9d2
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-12.1.0-h8d9b700_16.tar.bz2#4f05bc9844f7c101e6e147dab3c88d5c
-https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.3.2-h166bdaf_0.tar.bz2#b7607b7b62dce55c194ad84f99464e5f
+https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.6.1-h7f98852_0.tar.bz2#0347ce6a34f8b55b544b141432c6d4c7
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.3.0-h27087fc_1.tar.bz2#fe863d1e92331e69c8f231df5eaf5e16
 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_0.tar.bz2#ec47e97c8e0b27dcadbebc4d17764548
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54
@@ -35,16 +35,14 @@ https://conda.anaconda.org/conda-forge/linux-64/expat-2.4.8-h27087fc_0.tar.bz2#e
 https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_h77c792f_102.tar.bz2#208f18b1d596b50c6a92a12b30ebe31f
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h36c2ea0_2.tar.bz2#626e68ae9cc5912d6adb79d318cf962d
 https://conda.anaconda.org/conda-forge/linux-64/icu-70.1-h27087fc_0.tar.bz2#87473a15119779e021c314249d4b4aed
-https://conda.anaconda.org/conda-forge/linux-64/jbig-2.1-h7f98852_2003.tar.bz2#1aa0cee79792fa97b7ff4545110b60bf
 https://conda.anaconda.org/conda-forge/linux-64/jpeg-9e-h166bdaf_1.tar.bz2#4828c7f7208321cfbede4880463f4930
-https://conda.anaconda.org/conda-forge/linux-64/json-c-0.15-h98cffda_0.tar.bz2#f32d45a88e7462be446824654dbcf4a4
 https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-h7f98852_2.tar.bz2#8e787b08fe19986d99d034b839df2961
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
 https://conda.anaconda.org/conda-forge/linux-64/lerc-3.0-h9c3ff4c_0.tar.bz2#7fcefde484980d23f0ec24c11e314d2e
 https://conda.anaconda.org/conda-forge/linux-64/libaec-1.0.6-h9c3ff4c_0.tar.bz2#c77f5e4e418fa47d699d6afa54c5d444
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_7.tar.bz2#f82dc1c78bcf73583f2656433ce2933c
 https://conda.anaconda.org/conda-forge/linux-64/libdb-6.2.32-h9c3ff4c_0.tar.bz2#3f3258d8f841fbac63b36b75bdac1afd
-https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.10-h7f98852_0.tar.bz2#ffa3a757a97e851293909b49f49f28fb
+https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.12-h166bdaf_0.tar.bz2#d56e3db8fa642fb383f18f5be35eeef2
 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2#6f8720dff19e17ce5d48cfe7f3d2f0a3
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.16-h516909a_0.tar.bz2#5c0f338a513a2943c659ae619fca9211
@@ -54,14 +52,15 @@ https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.20-pthreads_h78a
 https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f
 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-10.3.0-h26c7422_16.tar.bz2#4076c395c7fa53cd708949e4be48154e
 https://conda.anaconda.org/conda-forge/linux-64/libtool-2.4.6-h9c3ff4c_1008.tar.bz2#16e143a1ed4b4fd169536373957f6fee
+https://conda.anaconda.org/conda-forge/linux-64/libudev1-249-h166bdaf_4.tar.bz2#dc075ff6fcb46b3d3c7652e543d5f334
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h7f98852_1000.tar.bz2#772d69f030955d9646d3d0eaf21d859d
 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.2-h7f98852_1.tar.bz2#46cf26ecc8775a0aab300ea1821aaa3c
-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.11-h166bdaf_1014.tar.bz2#757138ba3ddc6777b82e91d9ff62e7b9
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.12-h166bdaf_1.tar.bz2#58eaff4f91891978af3625e7bbf958af
 https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.3-h9c3ff4c_1.tar.bz2#fbe97e8fa6f275d7c76a09e795adc3e6
 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.3-h27087fc_1.tar.bz2#4acfc691e64342b9dae57cf2adc63238
 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.32-h9c3ff4c_1.tar.bz2#29ded371806431b0499aaee146abfc3e
-https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1o-h166bdaf_0.tar.bz2#6172048796b123e542945d998f5150b7
+https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1p-h166bdaf_0.tar.bz2#995e819f901ee0c4411e4f50d9b31a82
 https://conda.anaconda.org/conda-forge/linux-64/pcre-8.45-h9c3ff4c_0.tar.bz2#c05d1820a6d34ff07aaaab7a9b7eddaa
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.9-hbd366e4_1.tar.bz2#418adb239781d9690afc6b1a05514c37
@@ -74,21 +73,22 @@ https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.0.6-h166bdaf_0.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-10.3.0-hf2f2afa_16.tar.bz2#a340c8ce9e702836e999984fcabd1b6e
 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-h73d1719_1008.tar.bz2#af49250eca8e139378f8ff0ae9e57251
 https://conda.anaconda.org/conda-forge/linux-64/libavif-0.10.1-h166bdaf_0.tar.bz2#eb9a725cb66435aaf81f13ce585a64eb
-https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-14_linux64_openblas.tar.bz2#fb31fbbde682414550bbe15e3964420f
+https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-15_linux64_openblas.tar.bz2#04eb983975a1be3e57d6d667414cd774
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.0.9-h166bdaf_7.tar.bz2#37a460703214d0d1b421e2a47eb5e6d0
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.0.9-h166bdaf_7.tar.bz2#785a9296ea478eb78c47593c4da6550f
-https://conda.anaconda.org/conda-forge/linux-64/libcap-2.51-h166bdaf_1.tar.bz2#875e5931fb2faa6dacb4c13672630ae3
+https://conda.anaconda.org/conda-forge/linux-64/libcap-2.64-ha37c62d_0.tar.bz2#5896fbd58d0376df8556a4aba1ce4f71
 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1
 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.10-h9b69904_4.tar.bz2#390026683aef81db27ff1b8570ca1336
-https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.3-he0ac6c6_0.tar.bz2#8ab9f9c0fc641b53c5f87368e41af18d
+https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-he0ac6c6_0.tar.bz2#f5759f0c80708fbf9c4836c0cb46d0fe
 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h7f98852_1004.tar.bz2#b3653fdc58d03face9724f602218a904
-https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-14.0.3-he0ac6c6_0.tar.bz2#f3ab3fe1a6e6cef77d4d3e7523b603cb
+https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-14.0.4-he0ac6c6_0.tar.bz2#cecc6e3cb66570ffcfb820c637890f54
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-https://conda.anaconda.org/conda-forge/linux-64/readline-8.1-h46c0cb4_0.tar.bz2#5788de3c8d7a7d64ac56c784c4ef48e6
+https://conda.anaconda.org/conda-forge/linux-64/portaudio-19.6.0-h57a0ea0_5.tar.bz2#5469312a373f481c05c380897fd7c923
+https://conda.anaconda.org/conda-forge/linux-64/readline-8.1.2-h0f457ee_0.tar.bz2#db2ebbe2943aae81ed051a6a9af8e0fa
 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.12-h27826a3_0.tar.bz2#5b8c42eb62e9fc961af70bdd6a26e168
-https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h166bdaf_1014.tar.bz2#def3b82d1a03aa695bb38ac1dd072ff2
+https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.12-h166bdaf_1.tar.bz2#e4b67f2b4096807cd7d836227c026a43
 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-h8a70e8d_1.tar.bz2#3db63b53bb194dbaa7dc3d8833e98da2
 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.1-h83bc5f7_3.tar.bz2#37baca23e60af4130cfc03e8ab9f8e22
 https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.0.9-h166bdaf_7.tar.bz2#1699c1211d56a23c66047524cd76796e
@@ -98,61 +98,66 @@ https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-10.3.0-hc39de41_10.
 https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-10.3.0-h73f4979_16.tar.bz2#0407b066da49b4562ce054c06b40558c
 https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-10.3.0-hf2f2afa_16.tar.bz2#f825336ce3d4dba77ffd1edc78e7abf9
 https://conda.anaconda.org/conda-forge/linux-64/krb5-1.19.3-h3790be6_0.tar.bz2#7d862b05445123144bec92cb1acc8ef8
-https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-14_linux64_openblas.tar.bz2#1b41ea4c32014d878e84de4e5690df7a
-https://conda.anaconda.org/conda-forge/linux-64/libclang13-14.0.3-default_h3a83d3e_0.tar.bz2#1a571eb098b894a66642cd8b638f5f1d
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 https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.33.3-py39hb9d737c_0.tar.bz2#43f3c538bbcf6ed0da225891e11bf0a8
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-https://conda.anaconda.org/conda-forge/noarch/imageio-2.19.2-pyhcf75d05_0.tar.bz2#91806152074cba0105cf95350581376c
+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.20.3-hf6a322e_0.tar.bz2#6ea2ce6265c3207876ef2369b7479f08
+https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2022.2.22-py39hfcd4fa2_6.tar.bz2#fb284401002dea344eada09798cd07ec
+https://conda.anaconda.org/conda-forge/noarch/imageio-2.19.3-pyhcf75d05_0.tar.bz2#9a5e536d761271c400310ec5dff8c5e1
 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.0.3-pyhd8ed1ab_0.tar.bz2#036d872c653780cb26e797e2e2f61b4c
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.0-pyhd8ed1ab_0.tar.bz2#07d1b5c8cde14d95998fd4767e1e62d2
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.60.0-pyhd8ed1ab_0.tar.bz2#f769ad93cd67c6eb7e932c255ed7d642
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-https://conda.anaconda.org/conda-forge/noarch/pip-22.1.1-pyhd8ed1ab_0.tar.bz2#6affaf2f490f479c73d819735f80a104
+https://conda.anaconda.org/conda-forge/linux-64/pandas-1.4.3-py39h1832856_0.tar.bz2#74e00961703972cf33b44a6fca7c3d51
+https://conda.anaconda.org/conda-forge/noarch/pip-22.1.2-pyhd8ed1ab_0.tar.bz2#d29185c662a424f8bea1103270b85c96
 https://conda.anaconda.org/conda-forge/noarch/pygments-2.12.0-pyhd8ed1ab_0.tar.bz2#cb27e2ded147e5bcc7eafc1c6d343cb3
 https://conda.anaconda.org/conda-forge/linux-64/pytest-7.1.2-py39hf3d152e_0.tar.bz2#a6bcf633d12aabdfc4cb32a09ebc0f31
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.3.0-py39hd257fcd_1.tar.bz2#c4b698994b2d8d2e659ae02202e6abe4
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 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.2-pyhd8ed1ab_0.tar.bz2#2e4e8be763551f60bbfcc22b650e5d49
 https://conda.anaconda.org/conda-forge/linux-64/pyamg-4.2.3-py39hac2352c_0.tar.bz2#9df63dea0e4d2163be63b8e40c17ceaf
 https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_0.tar.bz2#1d7e241dfaf5475e893d4b824bb71b44
-https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.9.0-py39h5a03fae_0.tar.bz2#772c3115b4e857f3fd2051435f04f68d
+https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.9.0-py39h5a03fae_1.tar.bz2#bff0f5f688af0dc337d11517c5a46402
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+https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.4-ha5833f6_2.tar.bz2#dd3aa6715b9e9efaf842febf18ce4261
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-https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.4-py39h5a03fae_0.tar.bz2#af520e0af4a0772f90f9ec79ca91f603
+https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.4-py39h18e9c17_1.tar.bz2#111647767bc84d4795f829bdc07dbb27
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
-https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.19.2-py39hde0f152_0.tar.bz2#200ceba1a31bf22629232285a550506f
+https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.19.3-py39h1832856_0.tar.bz2#5d638481c48c57fdb986490a4a6983fc
 https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.11.2-pyhd8ed1ab_0.tar.bz2#fe2303dc8f1febeb82d927ce8ad153ed
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+https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.13.2-py39hd257fcd_0.tar.bz2#bd7cdadf70e34a19333c3aacc40206e8
 https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.9-pyhd8ed1ab_0.tar.bz2#0ea179ee251aa7100807c35bc0252693
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.2-py39hf3d152e_0.tar.bz2#d65d073d186977a2a9a9d5a68b2b77ef
-https://conda.anaconda.org/conda-forge/noarch/requests-2.27.1-pyhd8ed1ab_0.tar.bz2#7c1c427246b057b8fa97200ecdb2ed62
+https://conda.anaconda.org/conda-forge/noarch/requests-2.28.0-pyhd8ed1ab_1.tar.bz2#5db4d14905f98da161e2153b1c9d2bce
 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.11.2-hd8ed1ab_0.tar.bz2#e56b6a19f4b717eca7c68ad78196b075
-https://conda.anaconda.org/conda-forge/noarch/sphinx-4.5.0-pyh6c4a22f_0.tar.bz2#46b38d88c4270ff9ba78a89c83c66345
-https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.3.1-pyhd8ed1ab_0.tar.bz2#31b43675aa1bb68e049c2f65c15da864
+https://conda.anaconda.org/conda-forge/noarch/sphinx-5.0.2-pyh6c4a22f_0.tar.bz2#d4eaa1f50733a377480ce1d5aac556c7
+https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.4.0-pyhd8ed1ab_0.tar.bz2#d1c1320c63843ca639a638cb4259e346
 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.10.1-pyhd8ed1ab_0.tar.bz2#4918585fe5e5341740f7e63c61743efb
 https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2
 # pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/58/ed/59df64b8400caf736f38bd3725ab9b1d9e50874f629980973aea090c1a8b/sphinxext_opengraph-0.6.3-py3-none-any.whl#md5=None
diff --git a/build_tools/github/doc_min_dependencies_environment.yml b/build_tools/github/doc_min_dependencies_environment.yml
index 9f20b9e900336..d1c5c4f2053e2 100644
--- a/build_tools/github/doc_min_dependencies_environment.yml
+++ b/build_tools/github/doc_min_dependencies_environment.yml
@@ -25,6 +25,7 @@ dependencies:
   - sphinx-gallery=0.7.0  # min
   - numpydoc=1.2.0  # min
   - sphinx-prompt=1.3.0  # min
+  - plotly=5.9.0  # min
   - pip
   - pip:
     - sphinxext-opengraph==0.4.2  # min
diff --git a/build_tools/github/doc_min_dependencies_linux-64_conda.lock b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
index 3c3a948784955..70a7bacddcd6e 100644
--- a/build_tools/github/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
@@ -1,14 +1,12 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: a6aaffac15d19e8ed6fc40eddb398e18e9252fb737bf8c60f99f93e7d41ac5ce
+# input_hash: 53d568af487a4ac9a260bfa1bcd5263594b6a4aef53e17e7ccf17e2e2165ab96
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.5.18.1-ha878542_0.tar.bz2#352e93bbe1d604002b11bbcf425bf866
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.6.15-ha878542_0.tar.bz2#c320890f77fd1d617fa876e0982002c2
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_15.tar.bz2#5dd5127afd710f91f6a75821bac0a4f0
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.36.1-hea4e1c9_2.tar.bz2#bd4f2e711b39af170e7ff15163fe87ee
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-7.5.0-hda03d7c_20.tar.bz2#2146b25eb2a762a44fab709338a7b6d9
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran4-7.5.0-h14aa051_20.tar.bz2#a072eab836c3a9578ce72b5640ce592d
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-7.5.0-hb016644_20.tar.bz2#31d5500f621954679ee41d7f5d1089fb
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-12.1.0-ha89aaad_16.tar.bz2#6f5ba041a41eb102a1027d9e68731be7
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-7.5.0-h14aa051_20.tar.bz2#c3b2ad091c043c08689e64b10741484b
 https://conda.anaconda.org/conda-forge/linux-64/libgomp-12.1.0-h8d9b700_16.tar.bz2#f013cf7749536ce43d82afbffdf499ab
@@ -19,23 +17,22 @@ https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.36-hf3e587d_
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-12.1.0-h8d9b700_16.tar.bz2#4f05bc9844f7c101e6e147dab3c88d5c
 https://conda.anaconda.org/conda-forge/linux-64/expat-2.4.8-h27087fc_0.tar.bz2#e1b07832504eeba765d648389cc387a9
-https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-7.5.0-habd7529_20.tar.bz2#42140612518a7ce78f571d64b6a50ba3
+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-7.5.0-hda68d29_13.tar.bz2#fe83fa08b9fe7ceccfd0068571b92827
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h36c2ea0_2.tar.bz2#626e68ae9cc5912d6adb79d318cf962d
 https://conda.anaconda.org/conda-forge/linux-64/icu-64.2-he1b5a44_1.tar.bz2#8e881214a23508f1541eb7a3135d6fcb
-https://conda.anaconda.org/conda-forge/linux-64/jbig-2.1-h7f98852_2003.tar.bz2#1aa0cee79792fa97b7ff4545110b60bf
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-https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.10-h7f98852_0.tar.bz2#ffa3a757a97e851293909b49f49f28fb
+https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.12-h166bdaf_0.tar.bz2#d56e3db8fa642fb383f18f5be35eeef2
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1007.tar.bz2#11389072d7d6036fd811c3d9460475cd
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-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.11-h166bdaf_1014.tar.bz2#757138ba3ddc6777b82e91d9ff62e7b9
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.12-h166bdaf_1.tar.bz2#58eaff4f91891978af3625e7bbf958af
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.3-h9c3ff4c_1.tar.bz2#fbe97e8fa6f275d7c76a09e795adc3e6
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-https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1o-h166bdaf_0.tar.bz2#6172048796b123e542945d998f5150b7
+https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1p-h166bdaf_0.tar.bz2#995e819f901ee0c4411e4f50d9b31a82
 https://conda.anaconda.org/conda-forge/linux-64/pcre-8.45-h9c3ff4c_0.tar.bz2#c05d1820a6d34ff07aaaab7a9b7eddaa
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h7f98852_0.tar.bz2#bf6f803a544f26ebbdc3bfff272eb179
@@ -45,13 +42,13 @@ https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4c
 https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-7.5.0-h47867f9_33.tar.bz2#3a31c3f430a31184a5d07e67d3b24e2c
 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hf34092f_1004.tar.bz2#5582e1349bee4a25705adca745bf6845
 https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-7.5.0-h56cb351_20.tar.bz2#8f897b30195bd3a2251b4c51c3cc91cf
-https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-7.5.0-hd0bb8aa_20.tar.bz2#dbe78fc5fb9c339f8e55426559e12f7b
+https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-7.5.0-h64c220c_13.tar.bz2#4000478f05b985c952ad8e100d1c13fd
 https://conda.anaconda.org/conda-forge/linux-64/libllvm9-9.0.1-default_hc23dcda_7.tar.bz2#9f4686a2c319355fe8636ca13783c3b4
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h7f98852_1004.tar.bz2#b3653fdc58d03face9724f602218a904
-https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-14.0.3-he0ac6c6_0.tar.bz2#f3ab3fe1a6e6cef77d4d3e7523b603cb
-https://conda.anaconda.org/conda-forge/linux-64/readline-8.1-h46c0cb4_0.tar.bz2#5788de3c8d7a7d64ac56c784c4ef48e6
+https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-14.0.4-he0ac6c6_0.tar.bz2#cecc6e3cb66570ffcfb820c637890f54
+https://conda.anaconda.org/conda-forge/linux-64/readline-8.1.2-h0f457ee_0.tar.bz2#db2ebbe2943aae81ed051a6a9af8e0fa
 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.12-h27826a3_0.tar.bz2#5b8c42eb62e9fc961af70bdd6a26e168
-https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h166bdaf_1014.tar.bz2#def3b82d1a03aa695bb38ac1dd072ff2
+https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.12-h166bdaf_1.tar.bz2#e4b67f2b4096807cd7d836227c026a43
 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-h8a70e8d_1.tar.bz2#3db63b53bb194dbaa7dc3d8833e98da2
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@@ -59,10 +56,10 @@ https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-7.5.0-h555fc39_33.t
 https://conda.anaconda.org/conda-forge/linux-64/libclang-9.0.1-default_hb4e5071_5.tar.bz2#9dde69aa2a8ecd575a16e44987bdc9f7
 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.66.3-hbe7bbb4_0.tar.bz2#d5a09a9e981849b751cb75656b7302a0
 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-h21135ba_2.tar.bz2#b6acf807307d033d4b7e758b4f44b036
-https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.3.0-h542a066_3.tar.bz2#1a0efb4dfd880b0376da8e1ba39fa838
+https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-hc85c160_1.tar.bz2#151f9fae3ab50f039c8735e47770aa2d
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.10-hee79883_0.tar.bz2#0217b0926808b1adf93247bba489d733
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-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.38.5-h4ff8645_0.tar.bz2#a1448f0c31baec3946d2dcf09f905c9e
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.0-h4ff8645_0.tar.bz2#ead30581ba8cfd52d69632868b844d4a
 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.1.1-hc9558a2_0.tar.bz2#1eb7c67eb11eab0c98a87f84174fdde1
 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.1.1-he991be0_0.tar.bz2#e38ac82cc517b9e245c1ae99f9f140da
 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.4-h0708190_1.tar.bz2#4a06f2ac2e5bfae7b6b245171c3f07aa
@@ -76,7 +73,7 @@ https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.12-py_0.tar.bz2#2489
 https://conda.anaconda.org/conda-forge/noarch/attrs-21.4.0-pyhd8ed1ab_0.tar.bz2#f70280205d7044c8b8358c8de3190e5d
 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-2.0.12-pyhd8ed1ab_0.tar.bz2#1f5b32dabae0f1893ae3283dac7f799e
 https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.1.0-pyhd8ed1ab_0.tar.bz2#f7551a8a008dfad2b7ac9662dd124614
-https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.4-pyh9f0ad1d_0.tar.bz2#c08b4c1326b880ed44f3ffb04803332f
+https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.5-pyhd8ed1ab_0.tar.bz2#c267da48ce208905d7d976d49dfd9433
 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.1.1-0.tar.bz2#1ba267e19dbaf3db9dd0404e6fb9cdb9
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb
 https://conda.anaconda.org/conda-forge/noarch/execnet-1.9.0-pyhd8ed1ab_0.tar.bz2#0e521f7a5e60d508b121d38b04874fb2
@@ -89,7 +86,7 @@ https://conda.anaconda.org/conda-forge/noarch/iniconfig-1.1.1-pyh9f0ad1d_0.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-20_mkl.tar.bz2#14b25490fdcc44e879ac6c10fe764f68
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-20_mkl.tar.bz2#52c0ae3606eeae7e1d493f37f336f4f5
 https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2#91e27ef3d05cc772ce627e51cff111c4
-https://conda.anaconda.org/conda-forge/noarch/networkx-2.8.2-pyhd8ed1ab_0.tar.bz2#fa13a75da1377a7a5a5df3299aea6893
+https://conda.anaconda.org/conda-forge/noarch/networkx-2.8.4-pyhd8ed1ab_0.tar.bz2#d5ee17f3f60079d79fbd027f9944d18e
 https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pycparser-2.21-pyhd8ed1ab_0.tar.bz2#076becd9e05608f8dc72757d5f3a91ff
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.0.9-pyhd8ed1ab_0.tar.bz2#e8fbc1b54b25f4b08281467bc13b70cc
@@ -103,25 +100,27 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.0-pyhd8
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-py_0.tar.bz2#67cd9d9c0382d37479b4d306c369a2d4
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.3-py_0.tar.bz2#d01180388e6d1838c3e1ad029590aa7a
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.5-pyhd8ed1ab_2.tar.bz2#9ff55a0901cf952f05c654394de76bf7
+https://conda.anaconda.org/conda-forge/noarch/tenacity-8.0.1-pyhd8ed1ab_0.tar.bz2#8b29b2c12cb21dbd057755e5fd22d005
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.1.0-pyh8a188c0_0.tar.bz2#a2995ee828f65687ac5b1e71a2ab1e0c
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
 https://conda.anaconda.org/conda-forge/noarch/toolz-0.11.2-pyhd8ed1ab_0.tar.bz2#f348d1590550371edfac5ed3c1d44f7e
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.37.1-pyhd8ed1ab_0.tar.bz2#1ca02aaf78d9c70d9a81a3bed5752022
-https://conda.anaconda.org/conda-forge/noarch/babel-2.10.1-pyhd8ed1ab_0.tar.bz2#2ec70a4a964b696170d730466c668f60
-https://conda.anaconda.org/conda-forge/linux-64/certifi-2022.5.18.1-py38h578d9bd_0.tar.bz2#429a49d95358a078211aad97c4fc286c
+https://conda.anaconda.org/conda-forge/noarch/babel-2.10.3-pyhd8ed1ab_0.tar.bz2#72f1c6d03109d7a70087bc1d029a8eda
+https://conda.anaconda.org/conda-forge/linux-64/certifi-2022.6.15-py38h578d9bd_0.tar.bz2#1f4339b25d1030cfbf4ee0b06690bbce
 https://conda.anaconda.org/conda-forge/linux-64/cffi-1.14.4-py38ha312104_0.tar.bz2#8f82b87522fbb1d4b24e8b5e2b1d0501
 https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.24-py38h709712a_1.tar.bz2#9e5fe389471a13ae523ae980de4ad1f4
 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.11.2-py38h0a891b7_2.tar.bz2#71f54bf65927270177d66e26664f11ea
 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-hfdff14a_1.tar.bz2#4caaca6356992ee545080c7d7193b5a3
 https://conda.anaconda.org/conda-forge/linux-64/docutils-0.17.1-py38h578d9bd_2.tar.bz2#affd6b87adb2b0c98da0e3ad274349be
 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.5-h36ae1b5_2.tar.bz2#00084ab2657be5bf0ba0757ccde797ef
-https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.2-py38h43d8883_1.tar.bz2#34c284cb94bd8c5118ccfe6d6fc3dd3f
+https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.3-py38h43d8883_0.tar.bz2#0719de23a2c5aa0b4db25ee34394e8f3
 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.8.0-20_mkl.tar.bz2#8274dc30518af9df1de47f5d9e73165c
-https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py38h497a2fe_3.tar.bz2#d51cdb48d2792a63174c0ee66a602e7e
+https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py38h0a891b7_4.tar.bz2#d182e0c60439427453ed4a7abd28ef0d
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.17.3-py38h95a1406_0.tar.bz2#bc0cbf611fe2f86eab29b98e51404f5e
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.2.0-pyhd8ed1ab_0.tar.bz2#0c32f563d7f22e3a34c95cad8cc95651
-https://conda.anaconda.org/conda-forge/linux-64/pillow-9.1.1-py38h0ee0e06_0.tar.bz2#b2135f7c9f4ba2ef94666946c8238d03
+https://conda.anaconda.org/conda-forge/linux-64/pillow-9.1.1-py38h0ee0e06_1.tar.bz2#cd653a4a951ca80adb96ff6cd3b36883
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py38h578d9bd_3.tar.bz2#6ce4ce3d4490a56eb33b52c179609193
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.1-py38h0a891b7_0.tar.bz2#e3908bd184030e7f4a3d837959ebf6d7
 https://conda.anaconda.org/conda-forge/linux-64/pysocks-1.7.1-py38h578d9bd_5.tar.bz2#11113c7e50bb81f30762fe8325f305e1
@@ -132,15 +131,15 @@ https://conda.anaconda.org/conda-forge/linux-64/tornado-6.1-py38h0a891b7_3.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/blas-2.20-mkl.tar.bz2#e7d09a07f5413e53dca5282b8fa50bed
 https://conda.anaconda.org/conda-forge/linux-64/brotlipy-0.7.0-py38h0a891b7_1004.tar.bz2#9fcaaca218dcfeb8da806d4fd4824aa0
 https://conda.anaconda.org/conda-forge/linux-64/cryptography-37.0.2-py38h2b5fc30_0.tar.bz2#bcc387154aae535f8b4f84822621b5f7
-https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.5.1-pyhd8ed1ab_0.tar.bz2#8aaa4107e044e68c8d8af030b9c171f6
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.6.1-pyhd8ed1ab_0.tar.bz2#69655c7e78034d4293130f5a5ecf7421
 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.5-h0935bb2_2.tar.bz2#eb125ee86480e00a4a1ed45a577c3311
-https://conda.anaconda.org/conda-forge/noarch/imageio-2.19.2-pyhcf75d05_0.tar.bz2#91806152074cba0105cf95350581376c
-https://conda.anaconda.org/conda-forge/noarch/jinja2-2.11.3-pyhd8ed1ab_1.tar.bz2#d32c1aa8047d2d11163abbe4a8f852ac
+https://conda.anaconda.org/conda-forge/noarch/imageio-2.19.3-pyhcf75d05_0.tar.bz2#9a5e536d761271c400310ec5dff8c5e1
+https://conda.anaconda.org/conda-forge/noarch/jinja2-2.11.3-pyhd8ed1ab_2.tar.bz2#bdedf6199eec03402a0c5db1f25e891e
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.0-pyhd8ed1ab_0.tar.bz2#07d1b5c8cde14d95998fd4767e1e62d2
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.1.2-py38h250f245_1.tar.bz2#0ae46309d21c964547792bac48162fc8
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.60.0-pyhd8ed1ab_0.tar.bz2#f769ad93cd67c6eb7e932c255ed7d642
 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.0.5-py38hcb8c335_0.tar.bz2#1e1b4382170fd26cf722ef008ffb651e
-https://conda.anaconda.org/conda-forge/noarch/pip-22.1.1-pyhd8ed1ab_0.tar.bz2#6affaf2f490f479c73d819735f80a104
+https://conda.anaconda.org/conda-forge/noarch/pip-22.1.2-pyhd8ed1ab_0.tar.bz2#d29185c662a424f8bea1103270b85c96
 https://conda.anaconda.org/conda-forge/noarch/pygments-2.12.0-pyhd8ed1ab_0.tar.bz2#cb27e2ded147e5bcc7eafc1c6d343cb3
 https://conda.anaconda.org/conda-forge/linux-64/pytest-7.1.2-py38h578d9bd_0.tar.bz2#626d2b8f96c8c3d20198e6bd84d1cfb7
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.1.1-py38h5c078b8_3.tar.bz2#dafeef887e68bd18ec84681747ca0fd5
@@ -157,7 +156,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.ta
 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.12.2-py38h5c078b8_0.tar.bz2#33787719ad03d33cffc4e2e3ea82bc9e
 https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.9-pyhd8ed1ab_0.tar.bz2#0ea179ee251aa7100807c35bc0252693
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.1.2-py38_1.tar.bz2#c2b9671a19c01716c37fe0a0e18b0aec
-https://conda.anaconda.org/conda-forge/noarch/requests-2.27.1-pyhd8ed1ab_0.tar.bz2#7c1c427246b057b8fa97200ecdb2ed62
+https://conda.anaconda.org/conda-forge/noarch/requests-2.28.0-pyhd8ed1ab_1.tar.bz2#5db4d14905f98da161e2153b1c9d2bce
 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.11.2-hd8ed1ab_0.tar.bz2#e56b6a19f4b717eca7c68ad78196b075
 https://conda.anaconda.org/conda-forge/noarch/sphinx-4.0.1-pyh6c4a22f_2.tar.bz2#c203dcc46f262853ecbb9552c50d664e
 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb

From 999c7c3d256a380ab1c54190c6ee9c7ab2734928 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 28 Jun 2022 15:21:50 +0200
Subject: [PATCH 15/43] Fix plotly figure

---
 .../plot_grid_search_text_feature_extraction.py              | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index fa3604617b67d..8f3caeeba42d0 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -118,6 +118,9 @@
 
 import pandas as pd
 import plotly.express as px
+import plotly.io as pio
+
+pio.renderers.default = "sphinx_gallery"
 
 
 def shorten_param(param_name):
@@ -142,7 +145,7 @@ def shorten_param(param_name):
     color="mean_test_score",
     color_continuous_scale=px.colors.sequential.Jet,
 )
-fig.show()
+fig
 
 # %%
 # The parallel coordinates plot displays the values of the hyperparameters on

From ce37d607352cc58a3cf3eac19aa07d06ffeb61f4 Mon Sep 17 00:00:00 2001
From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
Date: Tue, 28 Jun 2022 18:21:41 +0200
Subject: [PATCH 16/43] Apply suggestions from code review

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
---
 .../plot_grid_search_text_feature_extraction.py        | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 8f3caeeba42d0..e466a1bae6843 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -72,7 +72,9 @@
         ("clf", ComplementNB()),
     ]
 )
+pipeline
 
+# %%
 parameters = {
     "vect__max_df": (0.5, 0.75, 1.0),
     "vect__min_df": (1, 3, 5),
@@ -90,11 +92,10 @@
 from sklearn.model_selection import GridSearchCV
 
 grid_search = GridSearchCV(
-    estimator=pipeline, param_grid=parameters, n_jobs=-1, verbose=1
+    estimator=pipeline, param_grid=parameters, n_jobs=2, verbose=1
 )
 
 print("Performing grid search...")
-print("pipeline:", [name for name, _ in pipeline.steps])
 print("parameters:")
 pprint(parameters)
 t0 = time()
@@ -107,7 +108,10 @@
     print(f"{param_name}: {best_parameters[param_name]}")
 
 accuracy = grid_search.score(data_test.data, data_test.target)
-print(f"Best accuracy on the grid search: {grid_search.best_score_:.3f}")
+print(
+    "Accuracy of the best parameters using the inner CV of "
+    f"the grid search: {grid_search.best_score_:.3f}"
+)
 print(f"Accuracy on test set: {accuracy:.3f}")
 
 # %%

From 02975f162542f790f80882bfeb676bfffc1f608a Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 10:18:45 +0200
Subject: [PATCH 17/43] Move config of the default renderer of plotly to
 conf.py

---
 doc/conf.py                                           | 11 +++++++++++
 .../plot_grid_search_text_feature_extraction.py       |  5 +----
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/doc/conf.py b/doc/conf.py
index b4c944a928c3c..24b47d49b9a75 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -29,6 +29,17 @@
 import sphinx_gallery
 from sphinx_gallery.sorting import ExampleTitleSortKey
 
+try:
+    # Configure plotly to integrate its output into the HTML pages generated by
+    # sphinx-gallery.
+    import plotly.io as pio
+
+    pio.renderers.default = "sphinx_gallery"
+except ImportError:
+    # Make it possible to render the doc when not running the examples
+    # that need plotly.
+    pass
+
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index e466a1bae6843..0e8e5d6b635bd 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -122,9 +122,6 @@
 
 import pandas as pd
 import plotly.express as px
-import plotly.io as pio
-
-pio.renderers.default = "sphinx_gallery"
 
 
 def shorten_param(param_name):
@@ -134,7 +131,7 @@ def shorten_param(param_name):
 
 
 cv_results = pd.DataFrame(grid_search.cv_results_)
-# monograms are mapped to index 1 and bigrams to index 2
+# unigrams are mapped to index 1 and bigrams to index 2
 cv_results["param_vect__ngram_range"] = cv_results["param_vect__ngram_range"].apply(
     lambda x: x[1]
 )

From 01910dd31c5504c98b934a8b63e64103a87ce534 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 11:27:47 +0200
Subject: [PATCH 18/43] Change grid search for randomized search

---
 ...lot_grid_search_text_feature_extraction.py | 28 ++++++++++++-------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 0e8e5d6b635bd..94e0303e37828 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -75,13 +75,16 @@
 pipeline
 
 # %%
+import numpy as np
+
 parameters = {
     "vect__max_df": (0.5, 0.75, 1.0),
     "vect__min_df": (1, 3, 5),
     # 'vect__max_features': (None, 5000, 10000, 50000),
     "vect__ngram_range": ((1, 1), (1, 2)),  # unigrams or bigrams
     # 'vect__norm': ('l1', 'l2'),
-    "clf__alpha": (0.01, 0.1),
+    "clf__alpha": np.logspace(-2.5, -1, 20),
+    # (0.01, 0.1),
 }
 
 # %%
@@ -89,10 +92,15 @@
 
 from pprint import pprint
 from time import time
-from sklearn.model_selection import GridSearchCV
-
-grid_search = GridSearchCV(
-    estimator=pipeline, param_grid=parameters, n_jobs=2, verbose=1
+from sklearn.model_selection import RandomizedSearchCV
+
+grid_search = RandomizedSearchCV(
+    estimator=pipeline,
+    param_distributions=parameters,
+    n_iter=40,
+    random_state=0,
+    n_jobs=2,
+    verbose=1,
 )
 
 print("Performing grid search...")
@@ -160,9 +168,9 @@ def shorten_param(param_name):
 # selection by clicking once again on the same axis.
 #
 # In particular for this hyperparameter search, it is interesting to notice that
-# the top performing models (dark red lines with mean test score > 0.82) are
-# reached when `min_df=1` and `alpha=0.01`, regardless of the value of
-# `max_df` and the `ngram_range`.
+# the top performing models (mean test score > 0.82) are reached when `min_df=1`
+# and `alpha` is close to 0.01, regardless of the values of `max_df` and
+# `ngram_range`.
 
 # %%
 column_results += ["std_test_score"]
@@ -177,6 +185,6 @@ def shorten_param(param_name):
 # By a manual inspection of the results, one can notice that the top performing
 # models overlap within one standard deviation of their test score, showing that
 # `max_df` and `ngram_range` are indeed not meaningful parameters in this
-# particular case. For more information on how to customize a
-# :class:`~sklearn.model_selection.GridSearchCV`, see the example notebook
+# particular case. For more information on how to customize an automated tuning,
+# see the example notebook
 # :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.

From 675315e65951c74ded2f4fe2413c146d08f24664 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 11:53:46 +0200
Subject: [PATCH 19/43] Split code cells for easier reading

---
 .../plot_grid_search_text_feature_extraction.py     | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 94e0303e37828..7931cbdf7ab40 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -91,7 +91,6 @@
 # Find the best parameters for both the feature extraction and the classifier.
 
 from pprint import pprint
-from time import time
 from sklearn.model_selection import RandomizedSearchCV
 
 grid_search = RandomizedSearchCV(
@@ -106,21 +105,27 @@
 print("Performing grid search...")
 print("parameters:")
 pprint(parameters)
+
+# %%
+from time import time
+
 t0 = time()
 grid_search.fit(data_train.data, data_train.target)
 print(f"done in {time() - t0:.3f}s")
-print()
+
+# %%
 print("Best parameters set:")
 best_parameters = grid_search.best_estimator_.get_params()
 for param_name in sorted(parameters.keys()):
     print(f"{param_name}: {best_parameters[param_name]}")
 
-accuracy = grid_search.score(data_test.data, data_test.target)
+# %%
+test_accuracy = grid_search.score(data_test.data, data_test.target)
 print(
     "Accuracy of the best parameters using the inner CV of "
     f"the grid search: {grid_search.best_score_:.3f}"
 )
-print(f"Accuracy on test set: {accuracy:.3f}")
+print(f"Accuracy on test set: {test_accuracy:.3f}")
 
 # %%
 # Finally, we use a `plotly.express.parallel_coordinates

From 72452af4b86ffd22e91e9cb35c767f6ef353781a Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 11:57:33 +0200
Subject: [PATCH 20/43] Wording tweak

---
 .../plot_grid_search_text_feature_extraction.py                | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 7931cbdf7ab40..985b4bb311a62 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -88,7 +88,8 @@
 }
 
 # %%
-# Find the best parameters for both the feature extraction and the classifier.
+# We search for the best parameters for both the feature extraction and the
+# classifier.
 
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV

From 59d501d66212d13df2d99a5f3d967ed762bf8902 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 12:02:15 +0200
Subject: [PATCH 21/43] Reset pandas index

---
 .../model_selection/plot_grid_search_text_feature_extraction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 985b4bb311a62..10f3c51b85d5c 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -185,7 +185,7 @@ def shorten_param(param_name):
     .rename(shorten_param, axis=1)
     .sort_values("mean_test_score", ascending=False)
 )
-cv_results
+cv_results.reset_index(drop=True)
 
 # %%
 # By a manual inspection of the results, one can notice that the top performing

From ec8e5443ee97c329b2dc83bcfb7c513af9f02962 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 12:11:36 +0200
Subject: [PATCH 22/43] improve narrative

---
 .../plot_grid_search_text_feature_extraction.py              | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 10f3c51b85d5c..d79bea917d4b1 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -177,8 +177,11 @@ def shorten_param(param_name):
 # the top performing models (mean test score > 0.82) are reached when `min_df=1`
 # and `alpha` is close to 0.01, regardless of the values of `max_df` and
 # `ngram_range`.
+#
+# The parallel coordinates plot does not provide information on the variability
+# of the score accross the different folds of the cross-validation. For such
+# purpose we can further inspect the `cv_results` as follows:
 
-# %%
 column_results += ["std_test_score"]
 cv_results = (
     cv_results[column_results]

From 3add47210035f4c2ad02570711e00eb9a2f2b938 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 16:40:26 +0200
Subject: [PATCH 23/43] Add plotly scatterplot and related narrative

---
 ...lot_grid_search_text_feature_extraction.py | 64 ++++++++++++-------
 1 file changed, 41 insertions(+), 23 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index d79bea917d4b1..eb40295de1d18 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -129,13 +129,12 @@
 print(f"Accuracy on test set: {test_accuracy:.3f}")
 
 # %%
-# Finally, we use a `plotly.express.parallel_coordinates
-# <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
-# to visualize the results from the
-# :class:`~sklearn.model_selection.GridSearchCV`.
+# The prefixes `vect` and `clf` are required to avoid possible ambiguities in
+# the pipeline, but are not necessary for visualizing the results. Because of
+# this, we define a function that will rename the tuned hyperparameters and
+# improve the readability.
 
 import pandas as pd
-import plotly.express as px
 
 
 def shorten_param(param_name):
@@ -145,18 +144,42 @@ def shorten_param(param_name):
 
 
 cv_results = pd.DataFrame(grid_search.cv_results_)
+cv_results = cv_results.rename(shorten_param, axis=1)
 # unigrams are mapped to index 1 and bigrams to index 2
-cv_results["param_vect__ngram_range"] = cv_results["param_vect__ngram_range"].apply(
-    lambda x: x[1]
+cv_results["ngram_range"] = cv_results["ngram_range"].apply(lambda x: x[1])
+
+# %%
+# We can use a `plotly.express.scatter
+# <https://plotly.com/python-api-reference/generated/plotly.express.scatter.html>`_
+# to visualize the trade-off between scoring time and mean test score. Passing
+# the cursor over a given point displays the corresponding parameters.
+
+import plotly.express as px
+
+param_names = [shorten_param(name) for name in parameters.keys()]
+fig = px.scatter(
+    cv_results, x="mean_score_time", y="mean_test_score", hover_data=param_names
 )
+fig
 
-column_results = [f"param_{name}" for name in parameters.keys()]
-column_results += ["mean_test_score"]
+# %%
+# Notice that the cluster of models in the upper-left corner of the plot are the
+# most optimal in terms of accuracy and scoring time. For more information on
+# how to customize an automated tuning to maximize score and minimize scoring
+# time, see the example notebook
+# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
+#
+# We can also use a `plotly.express.parallel_coordinates
+# <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
+# to further visualize the mean test score as a function of the tuned
+# hyperparameters. This helps finding interactions between (more than two)
+# hyperparameters and provide an intuition on the relevance they have for
+# maximizing the performance of a pipeline.
+
+column_results = param_names + ["mean_test_score"]
 
 fig = px.parallel_coordinates(
-    cv_results[column_results]
-    .rename(shorten_param, axis=1)
-    .apply(dict.fromkeys(map(shorten_param, column_results), lambda x: x)),
+    cv_results[column_results].apply(dict.fromkeys(column_results, lambda x: x)),
     color="mean_test_score",
     color_continuous_scale=px.colors.sequential.Jet,
 )
@@ -164,14 +187,11 @@ def shorten_param(param_name):
 
 # %%
 # The parallel coordinates plot displays the values of the hyperparameters on
-# different columns while the performance metric is color coded. It allows us to
-# quickly inspect the combinations of hyperparameters that maximize the
-# performance.
-#
-# It is possible to select a range of results by clicking and holding on any
-# axis of the parallel coordinate plot. You can then slide (move) the range
-# selection and cross two selections to see the intersections. You can undo a
-# selection by clicking once again on the same axis.
+# different columns while the performance metric is color coded. It is possible
+# to select a range of results by clicking and holding on any axis of the
+# parallel coordinate plot. You can then slide (move) the range selection and
+# cross two selections to see the intersections. You can undo a selection by
+# clicking once again on the same axis.
 #
 # In particular for this hyperparameter search, it is interesting to notice that
 # the top performing models (mean test score > 0.82) are reached when `min_df=1`
@@ -194,6 +214,4 @@ def shorten_param(param_name):
 # By a manual inspection of the results, one can notice that the top performing
 # models overlap within one standard deviation of their test score, showing that
 # `max_df` and `ngram_range` are indeed not meaningful parameters in this
-# particular case. For more information on how to customize an automated tuning,
-# see the example notebook
-# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
+# particular case.

From 6842945f11254d525fdf6c261a1b29770c48f6b6 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 16:40:42 +0200
Subject: [PATCH 24/43] Add author

---
 .../model_selection/plot_grid_search_text_feature_extraction.py  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index eb40295de1d18..269dbdeddb8ac 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -18,6 +18,7 @@
 # Author: Olivier Grisel <olivier.grisel@ensta.org>
 #         Peter Prettenhofer <peter.prettenhofer@gmail.com>
 #         Mathieu Blondel <mathieu@mblondel.org>
+#         Arturo Amor <david-arturo.amor-quiroz@inria.fr>
 # License: BSD 3 clause
 
 # %%

From ef7657b89bd744d595f7d61dd57018b7bd6e4fc3 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 29 Jun 2022 16:47:44 +0200
Subject: [PATCH 25/43] Add interpretation to scatterplot

---
 .../plot_grid_search_text_feature_extraction.py          | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 269dbdeddb8ac..829abdea09617 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -165,9 +165,12 @@ def shorten_param(param_name):
 
 # %%
 # Notice that the cluster of models in the upper-left corner of the plot are the
-# most optimal in terms of accuracy and scoring time. For more information on
-# how to customize an automated tuning to maximize score and minimize scoring
-# time, see the example notebook
+# most optimal in terms of accuracy and scoring time. In this case, using
+# bigrams increases the required scoring time without improving considerably the
+# accuracy of the pipeline.
+#
+# For more information on how to customize an automated tuning to maximize score
+# and minimize scoring time, see the example notebook
 # :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
 #
 # We can also use a `plotly.express.parallel_coordinates

From 43d0d49673f0e9d67e914e88eab670dc2411f2f5 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Mon, 4 Jul 2022 18:44:52 +0200
Subject: [PATCH 26/43] Remove max_features and improve narrative

---
 ...lot_grid_search_text_feature_extraction.py | 103 +++++++++---------
 1 file changed, 53 insertions(+), 50 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 829abdea09617..80830f643e424 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -8,8 +8,8 @@
 classification example.
 
 In this example we tune the hyperparameters of a particular classifier using a
-:class:`~sklearn.model_selection.GridSearchCV`. For a demo on the performance of
-some other classifiers, see the
+:class:`~sklearn.model_selection.RandomizedSearchCV`. For a demo on the
+performance of some other classifiers, see the
 :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
 notebook.
 
@@ -25,8 +25,8 @@
 # Data loading
 # ------------
 # We load two categories from the training set. You can adjust the number of
-# categories by giving their names to the dataset loader or setting them to None
-# to get the 20 of them.
+# categories by giving their names to the dataset loader or setting them to
+# `None` to get the 20 of them.
 
 from sklearn.datasets import fetch_20newsgroups
 
@@ -59,9 +59,7 @@
 # Pipeline with hyperparameter tuning
 # -----------------------------------
 # We define a pipeline combining a text feature vectorizer with a simple
-# classifier. We also define a set of parameters to use for grid search.
-# Uncommenting more parameters will give better exploring power but will
-# increase processing time in a combinatorial way.
+# classifier. We also define a set of parameters to use for random search.
 
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.naive_bayes import ComplementNB
@@ -79,13 +77,11 @@
 import numpy as np
 
 parameters = {
-    "vect__max_df": (0.5, 0.75, 1.0),
-    "vect__min_df": (1, 3, 5),
-    # 'vect__max_features': (None, 5000, 10000, 50000),
+    "vect__max_df": (0.2, 0.4, 0.6, 0.8, 1.0),
+    "vect__min_df": (1, 3, 5, 10),
     "vect__ngram_range": ((1, 1), (1, 2)),  # unigrams or bigrams
-    # 'vect__norm': ('l1', 'l2'),
-    "clf__alpha": np.logspace(-2.5, -1, 20),
-    # (0.01, 0.1),
+    "vect__norm": ("l1", "l2"),
+    "clf__alpha": np.logspace(-6, 6, 13),
 }
 
 # %%
@@ -95,7 +91,7 @@
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV
 
-grid_search = RandomizedSearchCV(
+random_search = RandomizedSearchCV(
     estimator=pipeline,
     param_distributions=parameters,
     n_iter=40,
@@ -112,20 +108,20 @@
 from time import time
 
 t0 = time()
-grid_search.fit(data_train.data, data_train.target)
+random_search.fit(data_train.data, data_train.target)
 print(f"done in {time() - t0:.3f}s")
 
 # %%
 print("Best parameters set:")
-best_parameters = grid_search.best_estimator_.get_params()
+best_parameters = random_search.best_estimator_.get_params()
 for param_name in sorted(parameters.keys()):
     print(f"{param_name}: {best_parameters[param_name]}")
 
 # %%
-test_accuracy = grid_search.score(data_test.data, data_test.target)
+test_accuracy = random_search.score(data_test.data, data_test.target)
 print(
     "Accuracy of the best parameters using the inner CV of "
-    f"the grid search: {grid_search.best_score_:.3f}"
+    f"the random search: {random_search.best_score_:.3f}"
 )
 print(f"Accuracy on test set: {test_accuracy:.3f}")
 
@@ -144,7 +140,7 @@ def shorten_param(param_name):
     return param_name
 
 
-cv_results = pd.DataFrame(grid_search.cv_results_)
+cv_results = pd.DataFrame(random_search.cv_results_)
 cv_results = cv_results.rename(shorten_param, axis=1)
 # unigrams are mapped to index 1 and bigrams to index 2
 cv_results["ngram_range"] = cv_results["ngram_range"].apply(lambda x: x[1])
@@ -158,34 +154,47 @@ def shorten_param(param_name):
 import plotly.express as px
 
 param_names = [shorten_param(name) for name in parameters.keys()]
+labels = {"mean_score_time": "score time (s)", "mean_test_score": "CV score"}
 fig = px.scatter(
-    cv_results, x="mean_score_time", y="mean_test_score", hover_data=param_names
+    cv_results,
+    x="mean_score_time",
+    y="mean_test_score",
+    error_x="std_score_time",
+    error_y="std_test_score",
+    hover_data=param_names,
+    labels=labels,
 )
 fig
 
 # %%
-# Notice that the cluster of models in the upper-left corner of the plot are the
-# most optimal in terms of accuracy and scoring time. In this case, using
+# Notice that the cluster of models in the upper-left corner of the plot have
+# the best trade-off between accuracy and scoring time. In this case, using
 # bigrams increases the required scoring time without improving considerably the
-# accuracy of the pipeline.
-#
-# For more information on how to customize an automated tuning to maximize score
-# and minimize scoring time, see the example notebook
+# accuracy of the pipeline. For more information on how to customize an
+# automated tuning to maximize score and minimize scoring time, see the example
+# notebook
 # :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
 #
 # We can also use a `plotly.express.parallel_coordinates
 # <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
 # to further visualize the mean test score as a function of the tuned
-# hyperparameters. This helps finding interactions between (more than two)
+# hyperparameters. This helps finding interactions between more than two
 # hyperparameters and provide an intuition on the relevance they have for
 # maximizing the performance of a pipeline.
 
-column_results = param_names + ["mean_test_score"]
+import math
+
+column_results = param_names + ["mean_test_score", "mean_score_time"]
+
+transform_funcs = dict.fromkeys(column_results, lambda x: x)
+transform_funcs["alpha"] = math.log10
+transform_funcs["norm"] = lambda x: 2 if x == "l2" else 1
 
 fig = px.parallel_coordinates(
-    cv_results[column_results].apply(dict.fromkeys(column_results, lambda x: x)),
+    cv_results[column_results].apply(transform_funcs),
     color="mean_test_score",
-    color_continuous_scale=px.colors.sequential.Jet,
+    color_continuous_scale=px.colors.sequential.Viridis_r,
+    labels=labels,
 )
 fig
 
@@ -197,25 +206,19 @@ def shorten_param(param_name):
 # cross two selections to see the intersections. You can undo a selection by
 # clicking once again on the same axis.
 #
+# .. note:: We applied a `math.log10` transformation on the `alpha` axis to
+#    spread the active range and improve the readability of the plot. A value
+#    :math:`x` on said axis is to be understood as :math:`10^x`.
+#
 # In particular for this hyperparameter search, it is interesting to notice that
-# the top performing models (mean test score > 0.82) are reached when `min_df=1`
-# and `alpha` is close to 0.01, regardless of the values of `max_df` and
-# `ngram_range`.
+# the top performing models do not seem to depend on the regularization `norm`,
+# but they do depend on a trade-off between `max_df`, `min_df` and the
+# regularization strenght `alpha`. The reason is that including noisy features
+# (i.e. `max_df` close to 1.0 or `min_df` close to 0) tend to overfit and
+# therefore require a stronger regularization to compensate. Having less
+# features require less regularization and less scoring time.
 #
-# The parallel coordinates plot does not provide information on the variability
-# of the score accross the different folds of the cross-validation. For such
-# purpose we can further inspect the `cv_results` as follows:
-
-column_results += ["std_test_score"]
-cv_results = (
-    cv_results[column_results]
-    .rename(shorten_param, axis=1)
-    .sort_values("mean_test_score", ascending=False)
-)
-cv_results.reset_index(drop=True)
-
-# %%
-# By a manual inspection of the results, one can notice that the top performing
-# models overlap within one standard deviation of their test score, showing that
-# `max_df` and `ngram_range` are indeed not meaningful parameters in this
-# particular case.
+# In practice it would be interesting to increase the parameter `n_iter` of the
+# :class:`~sklearn.model_selection.RandomizedSearchCV` to get a more informative
+# analysis. This requires more computing time and can be done increasing the
+# number of CPUs of the machine and via increasing the `n_jobs` parameter.

From bb8580592d59ff90f1609dad2757ff470d8d333d Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 5 Jul 2022 10:43:05 +0200
Subject: [PATCH 27/43] Add simil to GridSearchCV

---
 ...lot_grid_search_text_feature_extraction.py | 20 +++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 80830f643e424..ca3f5222a7454 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -74,6 +74,15 @@
 pipeline
 
 # %%
+# We define a grid of hyperparameters to be explored by the
+# :class:`~sklearn.model_selection.RandomizedSearchCV`. Using a
+# :class:`~sklearn.model_selection.GridSearchCV` instead would explore all the
+# possible combinations on the grid, which can be costly to compute, whereas the
+# parameter `n_iter` of the :class:`~sklearn.model_selection.RandomizedSearchCV`
+# chooses a different random combination for each iteration. Notice that setting
+# `n_iter` larger than the number of possible combinations in a grid would lead
+# to repeating already-explored combinations.
+
 import numpy as np
 
 parameters = {
@@ -86,7 +95,11 @@
 
 # %%
 # We search for the best parameters for both the feature extraction and the
-# classifier.
+# classifier. In this case `n_iter=40` is not an exhaustive search of the
+# hyperparameter grid. In practice it would be interesting to increase the
+# parameter `n_iter` to get a more informative analysis. The consequent increase
+# in computing time can be handled by increasing the number of CPUs via the
+# `n_jobs` parameter.
 
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV
@@ -217,8 +230,3 @@ def shorten_param(param_name):
 # (i.e. `max_df` close to 1.0 or `min_df` close to 0) tend to overfit and
 # therefore require a stronger regularization to compensate. Having less
 # features require less regularization and less scoring time.
-#
-# In practice it would be interesting to increase the parameter `n_iter` of the
-# :class:`~sklearn.model_selection.RandomizedSearchCV` to get a more informative
-# analysis. This requires more computing time and can be done increasing the
-# number of CPUs of the machine and via increasing the `n_jobs` parameter.

From c05788ae42b8b4ea86d37d96c6f67acbbb44474e Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 5 Jul 2022 17:02:54 +0200
Subject: [PATCH 28/43] Improve wording

---
 ...lot_grid_search_text_feature_extraction.py | 21 +++++++++++--------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index ca3f5222a7454..f7383d9332cd4 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -3,9 +3,9 @@
 Sample pipeline for text feature extraction and evaluation
 ==========================================================
 
-The dataset used in this example is the 20 newsgroups dataset which will be
-automatically downloaded, cached and reused for the document
-classification example.
+The dataset used in this example is :ref:`20newsgroups_dataset` which will be
+automatically downloaded, cached and reused for the document classification
+example.
 
 In this example we tune the hyperparameters of a particular classifier using a
 :class:`~sklearn.model_selection.RandomizedSearchCV`. For a demo on the
@@ -25,8 +25,8 @@
 # Data loading
 # ------------
 # We load two categories from the training set. You can adjust the number of
-# categories by giving their names to the dataset loader or setting them to
-# `None` to get the 20 of them.
+# categories by adding their names to the list or setting `categories=None` in
+# the dataset loader to get the 20 of them.
 
 from sklearn.datasets import fetch_20newsgroups
 
@@ -59,7 +59,7 @@
 # Pipeline with hyperparameter tuning
 # -----------------------------------
 # We define a pipeline combining a text feature vectorizer with a simple
-# classifier. We also define a set of parameters to use for random search.
+# classifier.
 
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.naive_bayes import ComplementNB
@@ -79,9 +79,9 @@
 # :class:`~sklearn.model_selection.GridSearchCV` instead would explore all the
 # possible combinations on the grid, which can be costly to compute, whereas the
 # parameter `n_iter` of the :class:`~sklearn.model_selection.RandomizedSearchCV`
-# chooses a different random combination for each iteration. Notice that setting
-# `n_iter` larger than the number of possible combinations in a grid would lead
-# to repeating already-explored combinations.
+# controls the number of different random combination that are evaluated. Notice
+# that setting `n_iter` larger than the number of possible combinations in a
+# grid would lead to repeating already-explored combinations.
 
 import numpy as np
 
@@ -230,3 +230,6 @@ def shorten_param(param_name):
 # (i.e. `max_df` close to 1.0 or `min_df` close to 0) tend to overfit and
 # therefore require a stronger regularization to compensate. Having less
 # features require less regularization and less scoring time.
+#
+# The best accuracies are obtained when `alpha` is between :math:`10^{-6}` and
+# :math:`10^0`, regardless of the hyperparameter `norm`.

From f98598f6e80bc1a0e4bf3b0d0ab3c8aacf293f43 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 5 Jul 2022 17:09:57 +0200
Subject: [PATCH 29/43] Move ngram_range mapping for consistency

---
 .../plot_grid_search_text_feature_extraction.py               | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index f7383d9332cd4..3f9e5ee7e7a61 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -155,8 +155,6 @@ def shorten_param(param_name):
 
 cv_results = pd.DataFrame(random_search.cv_results_)
 cv_results = cv_results.rename(shorten_param, axis=1)
-# unigrams are mapped to index 1 and bigrams to index 2
-cv_results["ngram_range"] = cv_results["ngram_range"].apply(lambda x: x[1])
 
 # %%
 # We can use a `plotly.express.scatter
@@ -202,6 +200,8 @@ def shorten_param(param_name):
 transform_funcs = dict.fromkeys(column_results, lambda x: x)
 transform_funcs["alpha"] = math.log10
 transform_funcs["norm"] = lambda x: 2 if x == "l2" else 1
+# unigrams are mapped to index 1 and bigrams to index 2
+transform_funcs["ngram_range"] = lambda x: x[1]
 
 fig = px.parallel_coordinates(
     cv_results[column_results].apply(transform_funcs),

From 7b32e976ce428492de996b728d9664de34d9dbf1 Mon Sep 17 00:00:00 2001
From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
Date: Wed, 20 Jul 2022 15:00:20 +0200
Subject: [PATCH 30/43] Apply suggestions from code review

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
---
 .../model_selection/plot_grid_search_text_feature_extraction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 3f9e5ee7e7a61..539f72426cbb3 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -226,7 +226,7 @@ def shorten_param(param_name):
 # In particular for this hyperparameter search, it is interesting to notice that
 # the top performing models do not seem to depend on the regularization `norm`,
 # but they do depend on a trade-off between `max_df`, `min_df` and the
-# regularization strenght `alpha`. The reason is that including noisy features
+# regularization strength `alpha`. The reason is that including noisy features
 # (i.e. `max_df` close to 1.0 or `min_df` close to 0) tend to overfit and
 # therefore require a stronger regularization to compensate. Having less
 # features require less regularization and less scoring time.

From 91bd385951c6adad74eb54146b550b5a64c6cbb0 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Fri, 22 Jul 2022 15:32:48 +0200
Subject: [PATCH 31/43] Iter

---
 build_tools/github/doc_linux-64_conda.lock                  | 5 -----
 build_tools/github/doc_min_dependencies_linux-64_conda.lock | 5 -----
 2 files changed, 10 deletions(-)

diff --git a/build_tools/github/doc_linux-64_conda.lock b/build_tools/github/doc_linux-64_conda.lock
index 8ff333e9315bb..7097a26ceb7f1 100644
--- a/build_tools/github/doc_linux-64_conda.lock
+++ b/build_tools/github/doc_linux-64_conda.lock
@@ -194,12 +194,7 @@ https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.1-py39hb9d737c_1.
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.23.1-py39hba7629e_0.tar.bz2#ee8dff1fb28e0e2c458e845aae9d915a
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.2.0-pyhd8ed1ab_0.tar.bz2#0c32f563d7f22e3a34c95cad8cc95651
-<<<<<<< HEAD
-https://conda.anaconda.org/conda-forge/linux-64/pillow-9.1.1-py39hae2aec6_1.tar.bz2#9039c6b86ddb65d7e8bf464337ae9053
-https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
-=======
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py39hae2aec6_0.tar.bz2#ea9760ab3700e63809de6ae71a11664b
->>>>>>> c1d6d7850410cc219efe489aeb9ba9c9d5402bda
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py39hf3d152e_3.tar.bz2#c375c89340e563053f3656c7f134d265
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.1-py39hb9d737c_0.tar.bz2#5852c69cad74811dc3c95f9ab6a184ef
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-14.0-h7f54b18_8.tar.bz2#f9dbcfbb942ec9a3c0249cb71da5c7d1
diff --git a/build_tools/github/doc_min_dependencies_linux-64_conda.lock b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
index b4f693f6dc628..7de2492e44987 100644
--- a/build_tools/github/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
@@ -119,12 +119,7 @@ https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.1-py38h0a891b7_4.
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.17.3-py38h95a1406_0.tar.bz2#bc0cbf611fe2f86eab29b98e51404f5e
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.2.0-pyhd8ed1ab_0.tar.bz2#0c32f563d7f22e3a34c95cad8cc95651
-<<<<<<< HEAD
-https://conda.anaconda.org/conda-forge/linux-64/pillow-9.1.1-py38h0ee0e06_1.tar.bz2#cd653a4a951ca80adb96ff6cd3b36883
-https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
-=======
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py38h0ee0e06_0.tar.bz2#7ef61f9084bda76b2f7a668ec5d1499a
->>>>>>> c1d6d7850410cc219efe489aeb9ba9c9d5402bda
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py38h578d9bd_3.tar.bz2#6ce4ce3d4490a56eb33b52c179609193
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.1-py38h0a891b7_0.tar.bz2#e3908bd184030e7f4a3d837959ebf6d7
 https://conda.anaconda.org/conda-forge/linux-64/pysocks-1.7.1-py38h578d9bd_5.tar.bz2#11113c7e50bb81f30762fe8325f305e1

From b0b6365289c2dfec095c0eccd0b1af95dc5fb7d4 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Fri, 22 Jul 2022 19:05:08 +0200
Subject: [PATCH 32/43] Iter

---
 build_tools/github/doc_linux-64_conda.lock                 | 7 ++++---
 .../github/doc_min_dependencies_linux-64_conda.lock        | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/build_tools/github/doc_linux-64_conda.lock b/build_tools/github/doc_linux-64_conda.lock
index 7097a26ceb7f1..8208980afa9e0 100644
--- a/build_tools/github/doc_linux-64_conda.lock
+++ b/build_tools/github/doc_linux-64_conda.lock
@@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-10.3.0-h26c7422_16.
 https://conda.anaconda.org/conda-forge/linux-64/libtool-2.4.6-h9c3ff4c_1008.tar.bz2#16e143a1ed4b4fd169536373957f6fee
 https://conda.anaconda.org/conda-forge/linux-64/libudev1-249-h166bdaf_4.tar.bz2#dc075ff6fcb46b3d3c7652e543d5f334
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h7f98852_1000.tar.bz2#772d69f030955d9646d3d0eaf21d859d
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.3-h166bdaf_0.tar.bz2#3d6168ac3560d473e52a7cb836400135
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.3-h166bdaf_1.tar.bz2#9c2a9c67255426d22174f888ba10b4c6
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.12-h166bdaf_2.tar.bz2#8302381297332ea50532cf2c67961080
 https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.3-h9c3ff4c_1.tar.bz2#fbe97e8fa6f275d7c76a09e795adc3e6
@@ -109,7 +109,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.10.0-ha56f1ee_2.tar.bz
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-hc85c160_1.tar.bz2#151f9fae3ab50f039c8735e47770aa2d
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.14-h22db469_3.tar.bz2#b6f4a0850ba620030a48b88c25497aaa
 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.29-h28c427c_1.tar.bz2#36dbdbf505b131c7e79a3857f3537185
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.1-h4ff8645_0.tar.bz2#6acda9d2a3ea84b58637b8f880bbf29b
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.2-h4ff8645_0.tar.bz2#2cf5cb4cd116a78e639977eb61ad9987
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-h166bdaf_0.tar.bz2#384e7fcb3cd162ba3e4aed4b687df566
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h166bdaf_0.tar.bz2#637054603bb7594302e3bf83f0a99879
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-h166bdaf_0.tar.bz2#732e22f1741bccea861f5668cf7342a7
@@ -195,6 +195,7 @@ https://conda.anaconda.org/conda-forge/linux-64/numpy-1.23.1-py39hba7629e_0.tar.
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.2.0-pyhd8ed1ab_0.tar.bz2#0c32f563d7f22e3a34c95cad8cc95651
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py39hae2aec6_0.tar.bz2#ea9760ab3700e63809de6ae71a11664b
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py39hf3d152e_3.tar.bz2#c375c89340e563053f3656c7f134d265
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.1-py39hb9d737c_0.tar.bz2#5852c69cad74811dc3c95f9ab6a184ef
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-14.0-h7f54b18_8.tar.bz2#f9dbcfbb942ec9a3c0249cb71da5c7d1
@@ -215,7 +216,7 @@ https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2#
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.0-pyhd8ed1ab_0.tar.bz2#07d1b5c8cde14d95998fd4767e1e62d2
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.60.0-pyhd8ed1ab_0.tar.bz2#f769ad93cd67c6eb7e932c255ed7d642
 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.4.3-py39h1832856_0.tar.bz2#74e00961703972cf33b44a6fca7c3d51
-https://conda.anaconda.org/conda-forge/noarch/pip-22.1.2-pyhd8ed1ab_0.tar.bz2#d29185c662a424f8bea1103270b85c96
+https://conda.anaconda.org/conda-forge/noarch/pip-22.2-pyhd8ed1ab_0.tar.bz2#2e92a2c7eb81581410550bec3f28560d
 https://conda.anaconda.org/conda-forge/noarch/pygments-2.12.0-pyhd8ed1ab_0.tar.bz2#cb27e2ded147e5bcc7eafc1c6d343cb3
 https://conda.anaconda.org/conda-forge/linux-64/pytest-7.1.2-py39hf3d152e_0.tar.bz2#a6bcf633d12aabdfc4cb32a09ebc0f31
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.3.0-py39hd257fcd_1.tar.bz2#c4b698994b2d8d2e659ae02202e6abe4
diff --git a/build_tools/github/doc_min_dependencies_linux-64_conda.lock b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
index 7de2492e44987..9dd412468a30a 100644
--- a/build_tools/github/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
@@ -26,7 +26,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.12-h166bdaf_0.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1007.tar.bz2#11389072d7d6036fd811c3d9460475cd
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.16-h516909a_0.tar.bz2#5c0f338a513a2943c659ae619fca9211
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h7f98852_1000.tar.bz2#772d69f030955d9646d3d0eaf21d859d
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.3-h166bdaf_0.tar.bz2#3d6168ac3560d473e52a7cb836400135
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.3-h166bdaf_1.tar.bz2#9c2a9c67255426d22174f888ba10b4c6
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-0.10.0-he1b5a44_0.tar.bz2#78ccac2098edcd3673af2ceb3e95f932
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.12-h166bdaf_2.tar.bz2#8302381297332ea50532cf2c67961080
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.3-h9c3ff4c_1.tar.bz2#fbe97e8fa6f275d7c76a09e795adc3e6
@@ -59,7 +59,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-h753d276_3.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-hc85c160_1.tar.bz2#151f9fae3ab50f039c8735e47770aa2d
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.10-hee79883_0.tar.bz2#0217b0926808b1adf93247bba489d733
 https://conda.anaconda.org/conda-forge/linux-64/mkl-2020.4-h726a3e6_304.tar.bz2#b9b35a50e5377b19da6ec0709ae77fc3
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.1-h4ff8645_0.tar.bz2#6acda9d2a3ea84b58637b8f880bbf29b
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.2-h4ff8645_0.tar.bz2#2cf5cb4cd116a78e639977eb61ad9987
 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.1.1-hc9558a2_0.tar.bz2#1eb7c67eb11eab0c98a87f84174fdde1
 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.1.1-he991be0_0.tar.bz2#e38ac82cc517b9e245c1ae99f9f140da
 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.4-h0708190_1.tar.bz2#4a06f2ac2e5bfae7b6b245171c3f07aa
@@ -120,6 +120,7 @@ https://conda.anaconda.org/conda-forge/linux-64/numpy-1.17.3-py38h95a1406_0.tar.
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.2.0-pyhd8ed1ab_0.tar.bz2#0c32f563d7f22e3a34c95cad8cc95651
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py38h0ee0e06_0.tar.bz2#7ef61f9084bda76b2f7a668ec5d1499a
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py38h578d9bd_3.tar.bz2#6ce4ce3d4490a56eb33b52c179609193
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.1-py38h0a891b7_0.tar.bz2#e3908bd184030e7f4a3d837959ebf6d7
 https://conda.anaconda.org/conda-forge/linux-64/pysocks-1.7.1-py38h578d9bd_5.tar.bz2#11113c7e50bb81f30762fe8325f305e1
@@ -138,7 +139,7 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.0-pyhd8ed1ab_0.tar.bz2#
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.1.2-py38h250f245_1.tar.bz2#0ae46309d21c964547792bac48162fc8
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.60.0-pyhd8ed1ab_0.tar.bz2#f769ad93cd67c6eb7e932c255ed7d642
 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.0.5-py38hcb8c335_0.tar.bz2#1e1b4382170fd26cf722ef008ffb651e
-https://conda.anaconda.org/conda-forge/noarch/pip-22.1.2-pyhd8ed1ab_0.tar.bz2#d29185c662a424f8bea1103270b85c96
+https://conda.anaconda.org/conda-forge/noarch/pip-22.2-pyhd8ed1ab_0.tar.bz2#2e92a2c7eb81581410550bec3f28560d
 https://conda.anaconda.org/conda-forge/noarch/pygments-2.12.0-pyhd8ed1ab_0.tar.bz2#cb27e2ded147e5bcc7eafc1c6d343cb3
 https://conda.anaconda.org/conda-forge/linux-64/pytest-7.1.2-py38h578d9bd_0.tar.bz2#626d2b8f96c8c3d20198e6bd84d1cfb7
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.1.1-py38h5c078b8_3.tar.bz2#dafeef887e68bd18ec84681747ca0fd5

From af2baebdffe58a37346c316368807fa70e954cc3 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 7 Sep 2022 14:08:10 +0200
Subject: [PATCH 33/43] Update lock files

---
 build_tools/github/doc_linux-64_conda.lock         | 10 ++++++----
 .../doc_min_dependencies_linux-64_conda.lock       | 14 +++++++++-----
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/build_tools/github/doc_linux-64_conda.lock b/build_tools/github/doc_linux-64_conda.lock
index 6850a0f56a010..9ec0dfc306540 100644
--- a/build_tools/github/doc_linux-64_conda.lock
+++ b/build_tools/github/doc_linux-64_conda.lock
@@ -83,7 +83,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.10-h9b69904_4.tar.b
 https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-he0ac6c6_0.tar.bz2#f5759f0c80708fbf9c4836c0cb46d0fe
 https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.47.0-hdcd2b5c_1.tar.bz2#6fe9e31c2b8d0b022626ccac13e6ca3c
 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-h753d276_4.tar.bz2#6b611734b73d639c084ac4be2fcd996a
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.39.2-h753d276_1.tar.bz2#90136dc0a305db4e1df24945d431457b
+https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.39.3-h753d276_0.tar.bz2#ccb2457c73609f2622b8a4b3e42e5d8b
 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.10.0-haa6b8db_3.tar.bz2#89acee135f0809a18a1f4537390aa2dd
 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h7f98852_1004.tar.bz2#b3653fdc58d03face9724f602218a904
@@ -112,7 +112,7 @@ https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-16_linux64_openb
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-h0e0dad5_3.tar.bz2#5627d42c13a9b117ae1701c6e195624f
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.0.3-he3ba5ed_0.tar.bz2#f9dbabc7e01c459ed7a1d1d64b206e9b
 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.30-h28c427c_1.tar.bz2#0bd292db365c83624316efc2764d9f16
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.2-h4ff8645_1.tar.bz2#2676ec698ce91567fca50654ac1b18ba
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.3-h4ff8645_0.tar.bz2#f03cf4ec974e32b6c5d349f62637e36e
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-h166bdaf_0.tar.bz2#384e7fcb3cd162ba3e4aed4b687df566
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h166bdaf_0.tar.bz2#637054603bb7594302e3bf83f0a99879
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-h166bdaf_0.tar.bz2#732e22f1741bccea861f5668cf7342a7
@@ -180,6 +180,7 @@ https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.1.0-pyh8a188c0_0.t
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.0-pyhd8ed1ab_0.tar.bz2#92facfec94bc02d6ccf42e7173831a36
+https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.3.0-pyha770c72_0.tar.bz2#a9d85960bc62d53cc4ea0d1d27f73c98
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.37.1-pyhd8ed1ab_0.tar.bz2#1ca02aaf78d9c70d9a81a3bed5752022
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.8.1-pyhd8ed1ab_0.tar.bz2#a3508a0c850745b875de88aea4c40cc5
 https://conda.anaconda.org/conda-forge/noarch/babel-2.10.3-pyhd8ed1ab_0.tar.bz2#72f1c6d03109d7a70087bc1d029a8eda
@@ -199,6 +200,7 @@ https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz
 https://conda.anaconda.org/conda-forge/noarch/partd-1.3.0-pyhd8ed1ab_0.tar.bz2#af8c82d121e63082926062d61d9abb54
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py39hd5dbb17_2.tar.bz2#3b74a959f6a8008f5901de60b3572c09
 https://conda.anaconda.org/conda-forge/noarch/pip-22.2.2-pyhd8ed1ab_0.tar.bz2#0b43abe4d3ee93e82742d37def53a836
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.10.0-pyhd8ed1ab_0.tar.bz2#e95502aa0f8e3db05d198214472575de
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py39hf3d152e_3.tar.bz2#c375c89340e563053f3656c7f134d265
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.2-py39hb9d737c_0.tar.bz2#1e7ffe59e21862559e06b981817e5058
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-14.0-h7f54b18_8.tar.bz2#f9dbcfbb942ec9a3c0249cb71da5c7d1
@@ -232,12 +234,12 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2022.8.12-pyhd8ed1ab_0.ta
 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.7-py39h18e9c17_0.tar.bz2#5ed8f83afff3b64fa91f7a6af8d7ff04
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.19.3-py39h1832856_1.tar.bz2#472bb5b9d9eb26ac697902da265ee551
-https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.11.2-pyhd8ed1ab_0.tar.bz2#fe2303dc8f1febeb82d927ce8ad153ed
+https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.12.0-pyhd8ed1ab_0.tar.bz2#05ee2fb22c1eca4309c06d11aff049f3
 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.13.2-py39hd257fcd_0.tar.bz2#bd7cdadf70e34a19333c3aacc40206e8
 https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.11-pyhd8ed1ab_0.tar.bz2#0738978569b10669bdef41c671252dd1
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.3-py39hf3d152e_2.tar.bz2#98bf9bdfbac2ac73bbd1dc12a61519eb
 https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_1.tar.bz2#089382ee0e2dc2eae33a04cc3c2bddb0
-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.11.2-hd8ed1ab_0.tar.bz2#e56b6a19f4b717eca7c68ad78196b075
+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.0-hd8ed1ab_0.tar.bz2#c22474d96fa1725ae47def82b5668686
 https://conda.anaconda.org/conda-forge/noarch/sphinx-5.1.1-pyhd8ed1ab_1.tar.bz2#cd1129e88f6278787212624e1b7a8001
 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.4.0-pyhd8ed1ab_1.tar.bz2#0aac89c61a466b0f9c4fd0ec44d81f1d
 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.11.1-pyhd8ed1ab_0.tar.bz2#729254314a5d178eefca50acbc2687b8
diff --git a/build_tools/github/doc_min_dependencies_linux-64_conda.lock b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
index dfb1a20e90d93..dac55e8cec09d 100644
--- a/build_tools/github/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/github/doc_min_dependencies_linux-64_conda.lock
@@ -6,7 +6,9 @@ https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.ta
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.6.15-ha878542_0.tar.bz2#c320890f77fd1d617fa876e0982002c2
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_15.tar.bz2#5dd5127afd710f91f6a75821bac0a4f0
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.36.1-hea4e1c9_2.tar.bz2#bd4f2e711b39af170e7ff15163fe87ee
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-7.5.0-hda03d7c_20.tar.bz2#2146b25eb2a762a44fab709338a7b6d9
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran4-7.5.0-h14aa051_20.tar.bz2#a072eab836c3a9578ce72b5640ce592d
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-7.5.0-hb016644_20.tar.bz2#31d5500f621954679ee41d7f5d1089fb
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-12.1.0-ha89aaad_16.tar.bz2#6f5ba041a41eb102a1027d9e68731be7
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-7.5.0-h14aa051_20.tar.bz2#c3b2ad091c043c08689e64b10741484b
 https://conda.anaconda.org/conda-forge/linux-64/libgomp-12.1.0-h8d9b700_16.tar.bz2#f013cf7749536ce43d82afbffdf499ab
@@ -40,10 +42,10 @@ https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4c
 https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-7.5.0-h47867f9_33.tar.bz2#3a31c3f430a31184a5d07e67d3b24e2c
 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hf34092f_1004.tar.bz2#5582e1349bee4a25705adca745bf6845
 https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-7.5.0-h56cb351_20.tar.bz2#8f897b30195bd3a2251b4c51c3cc91cf
-https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-7.5.0-h64c220c_13.tar.bz2#4000478f05b985c952ad8e100d1c13fd
+https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-7.5.0-hd0bb8aa_20.tar.bz2#dbe78fc5fb9c339f8e55426559e12f7b
 https://conda.anaconda.org/conda-forge/linux-64/libllvm9-9.0.1-default_hc23dcda_7.tar.bz2#9f4686a2c319355fe8636ca13783c3b4
 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-h753d276_4.tar.bz2#6b611734b73d639c084ac4be2fcd996a
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.39.2-h753d276_1.tar.bz2#90136dc0a305db4e1df24945d431457b
+https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.39.3-h753d276_0.tar.bz2#ccb2457c73609f2622b8a4b3e42e5d8b
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h7f98852_1004.tar.bz2#b3653fdc58d03face9724f602218a904
 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-14.0.4-he0ac6c6_0.tar.bz2#cecc6e3cb66570ffcfb820c637890f54
 https://conda.anaconda.org/conda-forge/linux-64/readline-8.1.2-h0f457ee_0.tar.bz2#db2ebbe2943aae81ed051a6a9af8e0fa
@@ -59,7 +61,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libglib-2.66.3-hbe7bbb4_0.tar.bz
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-h0e0dad5_3.tar.bz2#5627d42c13a9b117ae1701c6e195624f
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.10-hee79883_0.tar.bz2#0217b0926808b1adf93247bba489d733
 https://conda.anaconda.org/conda-forge/linux-64/mkl-2020.4-h726a3e6_304.tar.bz2#b9b35a50e5377b19da6ec0709ae77fc3
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.2-h4ff8645_1.tar.bz2#2676ec698ce91567fca50654ac1b18ba
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.3-h4ff8645_0.tar.bz2#f03cf4ec974e32b6c5d349f62637e36e
 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.1.1-hc9558a2_0.tar.bz2#1eb7c67eb11eab0c98a87f84174fdde1
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@@ -104,6 +106,7 @@ https://conda.anaconda.org/conda-forge/noarch/tenacity-8.0.1-pyhd8ed1ab_0.tar.bz
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.1.0-pyh8a188c0_0.tar.bz2#a2995ee828f65687ac5b1e71a2ab1e0c
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.0-pyhd8ed1ab_0.tar.bz2#92facfec94bc02d6ccf42e7173831a36
+https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.3.0-pyha770c72_0.tar.bz2#a9d85960bc62d53cc4ea0d1d27f73c98
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.37.1-pyhd8ed1ab_0.tar.bz2#1ca02aaf78d9c70d9a81a3bed5752022
 https://conda.anaconda.org/conda-forge/noarch/babel-2.10.3-pyhd8ed1ab_0.tar.bz2#72f1c6d03109d7a70087bc1d029a8eda
 https://conda.anaconda.org/conda-forge/linux-64/cffi-1.14.4-py38ha312104_0.tar.bz2#8f82b87522fbb1d4b24e8b5e2b1d0501
@@ -119,6 +122,7 @@ https://conda.anaconda.org/conda-forge/linux-64/numpy-1.17.3-py38h95a1406_0.tar.
 https://conda.anaconda.org/conda-forge/noarch/packaging-21.3-pyhd8ed1ab_0.tar.bz2#71f1ab2de48613876becddd496371c85
 https://conda.anaconda.org/conda-forge/noarch/partd-1.3.0-pyhd8ed1ab_0.tar.bz2#af8c82d121e63082926062d61d9abb54
 https://conda.anaconda.org/conda-forge/linux-64/pillow-9.2.0-py38ha3b2c9c_2.tar.bz2#a077cc2bb9d854074b1cf4607252da7a
+https://conda.anaconda.org/conda-forge/noarch/plotly-5.9.0-pyhd8ed1ab_0.tar.bz2#00a668931d448ce0ce42d1b02005d636
 https://conda.anaconda.org/conda-forge/linux-64/pluggy-1.0.0-py38h578d9bd_3.tar.bz2#6ce4ce3d4490a56eb33b52c179609193
 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.2-py38h0a891b7_0.tar.bz2#907a39b6d7443f770ed755885694f864
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@@ -147,14 +151,14 @@ https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_0.tar.
 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.4.0-pyhd8ed1ab_0.tar.bz2#95286e05a617de9ebfe3246cecbfb72f
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-https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.11.2-pyhd8ed1ab_0.tar.bz2#fe2303dc8f1febeb82d927ce8ad153ed
+https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.12.0-pyhd8ed1ab_0.tar.bz2#05ee2fb22c1eca4309c06d11aff049f3
 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.12.3-py38ha8c2ead_3.tar.bz2#242c206b0c30fdc4c18aea16f04c4262
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.12.2-py38h5c078b8_0.tar.bz2#33787719ad03d33cffc4e2e3ea82bc9e
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-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.11.2-hd8ed1ab_0.tar.bz2#e56b6a19f4b717eca7c68ad78196b075
+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.0-hd8ed1ab_0.tar.bz2#c22474d96fa1725ae47def82b5668686
 https://conda.anaconda.org/conda-forge/noarch/sphinx-4.0.1-pyh6c4a22f_2.tar.bz2#c203dcc46f262853ecbb9552c50d664e
 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb
 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.7.0-py_0.tar.bz2#80bad3f857ecc86a4ab73f3e57addd13

From 5c95135062d7b47b60c435b2d00071cf93fcc631 Mon Sep 17 00:00:00 2001
From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
Date: Tue, 13 Sep 2022 11:43:54 +0200
Subject: [PATCH 34/43] Apply suggestions from code review

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
---
 ...lot_grid_search_text_feature_extraction.py | 44 ++++++++++---------
 1 file changed, 24 insertions(+), 20 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 539f72426cbb3..043b0564585a4 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -81,7 +81,9 @@
 # parameter `n_iter` of the :class:`~sklearn.model_selection.RandomizedSearchCV`
 # controls the number of different random combination that are evaluated. Notice
 # that setting `n_iter` larger than the number of possible combinations in a
-# grid would lead to repeating already-explored combinations.
+# grid would lead to repeating already-explored combinations. We search for
+# the best parameters for both the feature extraction (`vect__`) and the
+# classifier (`clf__`).
 
 import numpy as np
 
@@ -94,12 +96,10 @@
 }
 
 # %%
-# We search for the best parameters for both the feature extraction and the
-# classifier. In this case `n_iter=40` is not an exhaustive search of the
-# hyperparameter grid. In practice it would be interesting to increase the
-# parameter `n_iter` to get a more informative analysis. The consequent increase
-# in computing time can be handled by increasing the number of CPUs via the
-# `n_jobs` parameter.
+# In this case `n_iter=40` is not an exhaustive search of the hyperparameters'
+# grid. In practice it would be interesting to increase the parameter `n_iter
+# to get a more informative analysis. The consequent increase in computing time
+# can be handled by increasing the number of CPUs via the `n_jobs` parameter.
 
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV
@@ -122,7 +122,7 @@
 
 t0 = time()
 random_search.fit(data_train.data, data_train.target)
-print(f"done in {time() - t0:.3f}s")
+print(f"Done in {time() - t0:.3f}s")
 
 # %%
 print("Best parameters set:")
@@ -148,6 +148,7 @@
 
 
 def shorten_param(param_name):
+    """Remove components' prefixes in param_name."""
     if "__" in param_name:
         return param_name.rsplit("__", 1)[1]
     return param_name
@@ -165,7 +166,7 @@ def shorten_param(param_name):
 import plotly.express as px
 
 param_names = [shorten_param(name) for name in parameters.keys()]
-labels = {"mean_score_time": "score time (s)", "mean_test_score": "CV score"}
+labels = {"mean_score_time": "CV Score time (s)", "mean_test_score": "CV score (accuracy)"}
 fig = px.scatter(
     cv_results,
     x="mean_score_time",
@@ -181,26 +182,29 @@ def shorten_param(param_name):
 # Notice that the cluster of models in the upper-left corner of the plot have
 # the best trade-off between accuracy and scoring time. In this case, using
 # bigrams increases the required scoring time without improving considerably the
-# accuracy of the pipeline. For more information on how to customize an
-# automated tuning to maximize score and minimize scoring time, see the example
-# notebook
-# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
+# accuracy of the pipeline. 
+# 
+# .. note:: For more information on how to customize an automated tuning 
+#    to maximize score and minimize scoring time, see the example notebook
+#    :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
 #
 # We can also use a `plotly.express.parallel_coordinates
 # <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
 # to further visualize the mean test score as a function of the tuned
 # hyperparameters. This helps finding interactions between more than two
-# hyperparameters and provide an intuition on the relevance they have for
-# maximizing the performance of a pipeline.
+# hyperparameters and provide intuition on their relevance for improving
+# the performance of a pipeline.
 
 import math
 
 column_results = param_names + ["mean_test_score", "mean_score_time"]
 
 transform_funcs = dict.fromkeys(column_results, lambda x: x)
+# Using a logarithmic scale for alpha
 transform_funcs["alpha"] = math.log10
+# L1 norms are mapped to index 1, and L2 norms to index 2
 transform_funcs["norm"] = lambda x: 2 if x == "l2" else 1
-# unigrams are mapped to index 1 and bigrams to index 2
+# Unigrams are mapped to index 1 and bigrams to index 2
 transform_funcs["ngram_range"] = lambda x: x[1]
 
 fig = px.parallel_coordinates(
@@ -227,9 +231,9 @@ def shorten_param(param_name):
 # the top performing models do not seem to depend on the regularization `norm`,
 # but they do depend on a trade-off between `max_df`, `min_df` and the
 # regularization strength `alpha`. The reason is that including noisy features
-# (i.e. `max_df` close to 1.0 or `min_df` close to 0) tend to overfit and
-# therefore require a stronger regularization to compensate. Having less
-# features require less regularization and less scoring time.
+# (i.e. `max_df` close to :math:`1.0` or `min_df` close to :math:`0`) tend 
+# to overfit and therefore require a stronger regularization to compensate.
+# Having less features require less regularization and less scoring time.
 #
-# The best accuracies are obtained when `alpha` is between :math:`10^{-6}` and
+# The best accuracy scores are obtained when `alpha` is between :math:`10^{-6}` and
 # :math:`10^0`, regardless of the hyperparameter `norm`.

From 3202572d8a1dc993b0379aa1300589f4c8b22395 Mon Sep 17 00:00:00 2001
From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
Date: Tue, 13 Sep 2022 11:48:30 +0200
Subject: [PATCH 35/43] Apply suggestions from code review

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
---
 ...lot_grid_search_text_feature_extraction.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 043b0564585a4..be704acdc257d 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -7,12 +7,11 @@
 automatically downloaded, cached and reused for the document classification
 example.
 
-In this example we tune the hyperparameters of a particular classifier using a
+In this example, we tune the hyperparameters of a particular classifier using a
 :class:`~sklearn.model_selection.RandomizedSearchCV`. For a demo on the
 performance of some other classifiers, see the
 :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
 notebook.
-
 """
 
 # Author: Olivier Grisel <olivier.grisel@ensta.org>
@@ -25,9 +24,9 @@
 # Data loading
 # ------------
 # We load two categories from the training set. You can adjust the number of
-# categories by adding their names to the list or setting `categories=None` in
-# the dataset loader to get the 20 of them.
-
+# categories by adding their names to the list or setting `categories=None`
+# when calling the dataset loader :func:`~sklearn.datasets.fetch20newsgroups`
+# to get the 20 of them.
 from sklearn.datasets import fetch_20newsgroups
 
 categories = [
@@ -59,7 +58,7 @@
 # Pipeline with hyperparameter tuning
 # -----------------------------------
 # We define a pipeline combining a text feature vectorizer with a simple
-# classifier.
+# classifier yet effective for text classification.
 
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.naive_bayes import ComplementNB
@@ -125,7 +124,7 @@
 print(f"Done in {time() - t0:.3f}s")
 
 # %%
-print("Best parameters set:")
+print("Best parameters combination found:")
 best_parameters = random_search.best_estimator_.get_params()
 for param_name in sorted(parameters.keys()):
     print(f"{param_name}: {best_parameters[param_name]}")
@@ -160,9 +159,9 @@ def shorten_param(param_name):
 # %%
 # We can use a `plotly.express.scatter
 # <https://plotly.com/python-api-reference/generated/plotly.express.scatter.html>`_
-# to visualize the trade-off between scoring time and mean test score. Passing
-# the cursor over a given point displays the corresponding parameters.
-
+# to visualize the trade-off between scoring time and mean test score
+# (i.e. "CV score"). Passing the cursor over a given point displays the
+# corresponding parameters.
 import plotly.express as px
 
 param_names = [shorten_param(name) for name in parameters.keys()]

From ba6fba54b251368beb8f69a75bcfd055d2934279 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 11:58:04 +0200
Subject: [PATCH 36/43] Format

---
 ...lot_grid_search_text_feature_extraction.py | 54 ++++++++++---------
 1 file changed, 30 insertions(+), 24 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index be704acdc257d..2c823d39d0efd 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -24,9 +24,10 @@
 # Data loading
 # ------------
 # We load two categories from the training set. You can adjust the number of
-# categories by adding their names to the list or setting `categories=None`
-# when calling the dataset loader :func:`~sklearn.datasets.fetch20newsgroups`
-# to get the 20 of them.
+# categories by adding their names to the list or setting `categories=None` when
+# calling the dataset loader :func:`~sklearn.datasets.fetch20newsgroups` to get
+# the 20 of them.
+
 from sklearn.datasets import fetch_20newsgroups
 
 categories = [
@@ -57,6 +58,7 @@
 # %%
 # Pipeline with hyperparameter tuning
 # -----------------------------------
+#
 # We define a pipeline combining a text feature vectorizer with a simple
 # classifier yet effective for text classification.
 
@@ -80,9 +82,9 @@
 # parameter `n_iter` of the :class:`~sklearn.model_selection.RandomizedSearchCV`
 # controls the number of different random combination that are evaluated. Notice
 # that setting `n_iter` larger than the number of possible combinations in a
-# grid would lead to repeating already-explored combinations. We search for
-# the best parameters for both the feature extraction (`vect__`) and the
-# classifier (`clf__`).
+# grid would lead to repeating already-explored combinations. We search for the
+# best parameters for both the feature extraction (`vect__`) and the classifier
+# (`clf__`).
 
 import numpy as np
 
@@ -96,9 +98,9 @@
 
 # %%
 # In this case `n_iter=40` is not an exhaustive search of the hyperparameters'
-# grid. In practice it would be interesting to increase the parameter `n_iter
-# to get a more informative analysis. The consequent increase in computing time
-# can be handled by increasing the number of CPUs via the `n_jobs` parameter.
+# grid. In practice it would be interesting to increase the parameter `n_iter to
+# get a more informative analysis. The consequent increase in computing time can
+# be handled by increasing the number of CPUs via the `n_jobs` parameter.
 
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV
@@ -159,13 +161,17 @@ def shorten_param(param_name):
 # %%
 # We can use a `plotly.express.scatter
 # <https://plotly.com/python-api-reference/generated/plotly.express.scatter.html>`_
-# to visualize the trade-off between scoring time and mean test score
-# (i.e. "CV score"). Passing the cursor over a given point displays the
-# corresponding parameters.
+# to visualize the trade-off between scoring time and mean test score (i.e. "CV
+# score"). Passing the cursor over a given point displays the corresponding
+# parameters.
+
 import plotly.express as px
 
 param_names = [shorten_param(name) for name in parameters.keys()]
-labels = {"mean_score_time": "CV Score time (s)", "mean_test_score": "CV score (accuracy)"}
+labels = {
+    "mean_score_time": "CV Score time (s)",
+    "mean_test_score": "CV score (accuracy)",
+}
 fig = px.scatter(
     cv_results,
     x="mean_score_time",
@@ -181,18 +187,18 @@ def shorten_param(param_name):
 # Notice that the cluster of models in the upper-left corner of the plot have
 # the best trade-off between accuracy and scoring time. In this case, using
 # bigrams increases the required scoring time without improving considerably the
-# accuracy of the pipeline. 
-# 
-# .. note:: For more information on how to customize an automated tuning 
-#    to maximize score and minimize scoring time, see the example notebook
+# accuracy of the pipeline.
+#
+# .. note:: For more information on how to customize an automated tuning to
+#    maximize score and minimize scoring time, see the example notebook
 #    :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`.
 #
 # We can also use a `plotly.express.parallel_coordinates
 # <https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates.html>`_
 # to further visualize the mean test score as a function of the tuned
 # hyperparameters. This helps finding interactions between more than two
-# hyperparameters and provide intuition on their relevance for improving
-# the performance of a pipeline.
+# hyperparameters and provide intuition on their relevance for improving the
+# performance of a pipeline.
 
 import math
 
@@ -230,9 +236,9 @@ def shorten_param(param_name):
 # the top performing models do not seem to depend on the regularization `norm`,
 # but they do depend on a trade-off between `max_df`, `min_df` and the
 # regularization strength `alpha`. The reason is that including noisy features
-# (i.e. `max_df` close to :math:`1.0` or `min_df` close to :math:`0`) tend 
-# to overfit and therefore require a stronger regularization to compensate.
-# Having less features require less regularization and less scoring time.
+# (i.e. `max_df` close to :math:`1.0` or `min_df` close to :math:`0`) tend to
+# overfit and therefore require a stronger regularization to compensate. Having
+# less features require less regularization and less scoring time.
 #
-# The best accuracy scores are obtained when `alpha` is between :math:`10^{-6}` and
-# :math:`10^0`, regardless of the hyperparameter `norm`.
+# The best accuracy scores are obtained when `alpha` is between :math:`10^{-6}`
+# and :math:`10^0`, regardless of the hyperparameter `norm`.

From f665a0187ef83d98287ec5ea3d0d057a07063577 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 12:04:15 +0200
Subject: [PATCH 37/43] Redirect link to renamed example

---
 doc/conf.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/doc/conf.py b/doc/conf.py
index 741c5ec6caa82..9ec60d4b34092 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -268,6 +268,9 @@
     "auto_examples/linear_model/plot_bayesian_ridge": (
         "auto_examples/linear_model/plot_ard"
     ),
+    "examples/model_selection/grid_search_text_feature_extraction.py": (
+        "examples/model_selection/plot_grid_search_text_feature_extraction.py"
+    ),
 }
 html_context["redirects"] = redirects
 for old_link in redirects:

From 06254c413092ca3a4dffc3546cb30b6abf1d2231 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 14:37:15 +0200
Subject: [PATCH 38/43] Apply suggestions from code review

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
---
 ...lot_grid_search_text_feature_extraction.py | 24 +++++++++++++------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 2c823d39d0efd..dc44772a64500 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -88,7 +88,7 @@
 
 import numpy as np
 
-parameters = {
+parameter_grid = {
     "vect__max_df": (0.2, 0.4, 0.6, 0.8, 1.0),
     "vect__min_df": (1, 3, 5, 10),
     "vect__ngram_range": ((1, 1), (1, 2)),  # unigrams or bigrams
@@ -107,7 +107,7 @@
 
 random_search = RandomizedSearchCV(
     estimator=pipeline,
-    param_distributions=parameters,
+    param_distributions=parameter_grid,
     n_iter=40,
     random_state=0,
     n_jobs=2,
@@ -115,8 +115,8 @@
 )
 
 print("Performing grid search...")
-print("parameters:")
-pprint(parameters)
+print("Hyperparameters to be evaluated:")
+pprint(parameter_grid)
 
 # %%
 from time import time
@@ -128,7 +128,7 @@
 # %%
 print("Best parameters combination found:")
 best_parameters = random_search.best_estimator_.get_params()
-for param_name in sorted(parameters.keys()):
+for param_name in sorted(parameter_grid.keys()):
     print(f"{param_name}: {best_parameters[param_name]}")
 
 # %%
@@ -163,11 +163,12 @@ def shorten_param(param_name):
 # <https://plotly.com/python-api-reference/generated/plotly.express.scatter.html>`_
 # to visualize the trade-off between scoring time and mean test score (i.e. "CV
 # score"). Passing the cursor over a given point displays the corresponding
-# parameters.
+# parameters. Error bars correspond to one standard deviation as computed in the
+# different folds of the cross-validation.
 
 import plotly.express as px
 
-param_names = [shorten_param(name) for name in parameters.keys()]
+param_names = [shorten_param(name) for name in parameter_grid.keys()]
 labels = {
     "mean_score_time": "CV Score time (s)",
     "mean_test_score": "CV score (accuracy)",
@@ -181,6 +182,15 @@ def shorten_param(param_name):
     hover_data=param_names,
     labels=labels,
 )
+fig.update_layout(
+    title={
+        "text": "trade-off between scoring time and mean test score",
+        "y": 0.95,
+        "x": 0.5,
+        "xanchor": "center",
+        "yanchor": "top",
+    }
+)
 fig
 
 # %%

From 3e2d9e0c7ed77317add2745681fd724e0a6b3efd Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 17:43:27 +0200
Subject: [PATCH 39/43] Add plotly as requirement with minimum associated
 version

---
 README.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.rst b/README.rst
index 089929278c75d..4d0480845c735 100644
--- a/README.rst
+++ b/README.rst
@@ -42,6 +42,7 @@
 .. |PandasMinVersion| replace:: 1.0.5
 .. |SeabornMinVersion| replace:: 0.9.0
 .. |PytestMinVersion| replace:: 5.0.1
+.. |PlotlyMinVersion| replace:: 5.9.0
 
 .. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png
   :target: https://scikit-learn.org/

From 75f62b909060cf9d3a700c7c85ae9784ec5663f4 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 17:53:14 +0200
Subject: [PATCH 40/43] Apply suggestions from code review

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
---
 .../plot_grid_search_text_feature_extraction.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index dc44772a64500..592202d0088f6 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -209,6 +209,10 @@ def shorten_param(param_name):
 # hyperparameters. This helps finding interactions between more than two
 # hyperparameters and provide intuition on their relevance for improving the
 # performance of a pipeline.
+#
+# We apply a `math.log10` transformation on the `alpha` axis to spread the
+# active range and improve the readability of the plot. A value :math:`x` on
+# said axis is to be understood as :math:`10^x`.
 
 import math
 
@@ -228,6 +232,15 @@ def shorten_param(param_name):
     color_continuous_scale=px.colors.sequential.Viridis_r,
     labels=labels,
 )
+fig.update_layout(
+    title={
+        "text": "Parallel coordinates plot of text classifier pipeline",
+        "y": 0.95,
+        "x": 0.5,
+        "xanchor": "center",
+        "yanchor": "top",
+    }
+)
 fig
 
 # %%
@@ -238,10 +251,6 @@ def shorten_param(param_name):
 # cross two selections to see the intersections. You can undo a selection by
 # clicking once again on the same axis.
 #
-# .. note:: We applied a `math.log10` transformation on the `alpha` axis to
-#    spread the active range and improve the readability of the plot. A value
-#    :math:`x` on said axis is to be understood as :math:`10^x`.
-#
 # In particular for this hyperparameter search, it is interesting to notice that
 # the top performing models do not seem to depend on the regularization `norm`,
 # but they do depend on a trade-off between `max_df`, `min_df` and the

From ea40c9ae57e3c1d858c73c6e81cabdcae3d31016 Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Tue, 13 Sep 2022 17:59:37 +0200
Subject: [PATCH 41/43] Apply suggestions from code review

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
---
 .../plot_grid_search_text_feature_extraction.py      | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 592202d0088f6..88e4c9aa13d9e 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -83,8 +83,8 @@
 # controls the number of different random combination that are evaluated. Notice
 # that setting `n_iter` larger than the number of possible combinations in a
 # grid would lead to repeating already-explored combinations. We search for the
-# best parameters for both the feature extraction (`vect__`) and the classifier
-# (`clf__`).
+# best parameter combination for both the feature extraction (`vect__`) and the
+# classifier (`clf__`).
 
 import numpy as np
 
@@ -98,9 +98,11 @@
 
 # %%
 # In this case `n_iter=40` is not an exhaustive search of the hyperparameters'
-# grid. In practice it would be interesting to increase the parameter `n_iter to
-# get a more informative analysis. The consequent increase in computing time can
-# be handled by increasing the number of CPUs via the `n_jobs` parameter.
+# grid. In practice it would be interesting to increase the parameter `n_iter`
+# to get a more informative analysis. As a consequence, the computional time
+# increases. We can reduce it by taking advantage of the parallelisation over
+# the parameter combinations evaluation by increasing the number of CPUs used
+# via the parameter `n_jobs`.
 
 from pprint import pprint
 from sklearn.model_selection import RandomizedSearchCV

From bce3a9ebd8f23b6cfc07adb393b0c3f9ffec124f Mon Sep 17 00:00:00 2001
From: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Date: Wed, 14 Sep 2022 10:41:16 +0200
Subject: [PATCH 42/43] Fix plot title position

---
 .../model_selection/plot_grid_search_text_feature_extraction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py
index 88e4c9aa13d9e..9ad4296aad9b4 100644
--- a/examples/model_selection/plot_grid_search_text_feature_extraction.py
+++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py
@@ -237,7 +237,7 @@ def shorten_param(param_name):
 fig.update_layout(
     title={
         "text": "Parallel coordinates plot of text classifier pipeline",
-        "y": 0.95,
+        "y": 0.99,
         "x": 0.5,
         "xanchor": "center",
         "yanchor": "top",

From 2bbcebacd6445489dd59aedaaec60dedfa6270bc Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 15 Sep 2022 10:09:16 +0200
Subject: [PATCH 43/43] Update README.rst

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 4d0480845c735..7d4e4ed9568ba 100644
--- a/README.rst
+++ b/README.rst
@@ -84,7 +84,7 @@ classes end with "Display") require Matplotlib (>= |MatplotlibMinVersion|).
 For running the examples Matplotlib >= |MatplotlibMinVersion| is required.
 A few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples
 require pandas >= |PandasMinVersion|, some examples require seaborn >=
-|SeabornMinVersion|.
+|SeabornMinVersion| and plotly >= |PlotlyMinVersion|.
 
 User installation
 ~~~~~~~~~~~~~~~~~