refactor(docs): improve imports (sklearn_fork is now sklearn) [cd build]

docs/API/data_preparation/aggregation_function.md

@@ -131,7 +131,7 @@ Apply the aggregation function to the feature groups in the dataset.
 ``` py title="Example 1: Basic Usage with Mean Aggregation" linenums="1" hl_lines="15-25"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
 from scikit_longitudinal.data_preparation.aggregation_function import AggrFunc
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -156,7 +156,7 @@ agg_func.prepare_data(dataset.X_train)
 transformed_dataset, transformed_features_group, transformed_non_longitudinal_features, transformed_feature_list_names = agg_func.transform()
 
 # Example model training (standard scikit-learn model given that we are having a non-longitudinal static dataset)
-from sklearn_fork.tree import DecisionTreeClassifier
+from sklearn.tree import DecisionTreeClassifier
 
 clf = DecisionTreeClassifier()
 clf.fit(transformed_dataset, dataset.y_train)
@@ -172,7 +172,7 @@ accuracy = accuracy_score(dataset.y_test, y_pred)
 ``` py title="Example 2: Using Custom Aggregation Function" linenums="1" hl_lines="15-28"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
 from scikit_longitudinal.data_preparation.aggregation_function import AggrFunc
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -200,7 +200,7 @@ agg_func.prepare_data(dataset.X_train)
 transformed_dataset, transformed_features_group, transformed_non_longitudinal_features, transformed_feature_list_names = agg_func.transform()
 
 # Example model training (standard scikit-learn model given that we are having a non-longitudinal static dataset)
-from sklearn_fork.tree import DecisionTreeClassifier
+from sklearn.tree import DecisionTreeClassifier
 
 clf = DecisionTreeClassifier()
 

docs/API/data_preparation/longitudinal_dataset.md

@@ -337,7 +337,7 @@ Set the test target data attribute.
 
 ``` py title="Example 1: Basic Usage" linenums="1" hl_lines="7-20"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -380,7 +380,7 @@ accuracy = accuracy_score(y_test, y_pred)
 
 ``` py title="Example 2: Use faster setup with load_data_target_train_test_split " linenums="1" hl_lines="7-18"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -420,7 +420,7 @@ accuracy = accuracy_score(y_test, y_pred)
 
 ``` py title="Example 2: Using Custom Feature Groups" linenums="1" hl_lines="7-24"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'

docs/API/data_preparation/sepwav.md

@@ -188,8 +188,8 @@ Predict class for X, using the classifier for the specified wave number.
 ``` py title="Example 1: Basic Usage with Majority Voting" linenums="1" hl_lines="16-26"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
 from scikit_longitudinal.data_preparation.separate_waves import SepWav
-from sklearn_fork.ensemble import RandomForestClassifier
-from sklearn_fork.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -229,9 +229,9 @@ accuracy = accuracy_score(dataset.y_test, y_pred)
 ``` py title="Example 2: Using Stacking Ensemble" linenums="1" hl_lines="17-28"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
 from scikit_longitudinal.data_preparation.separate_waves import SepWav
-from sklearn_fork.ensemble import RandomForestClassifier
-from sklearn_fork.linear_model import LogisticRegression
-from sklearn_fork.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'
@@ -272,8 +272,8 @@ accuracy = accuracy_score(dataset.y_test, y_pred)
 ``` py title="Example 3: Using Parallel Processing" linenums="1" hl_lines="20-31"
 from scikit_longitudinal.data_preparation import LongitudinalDataset
 from scikit_longitudinal.data_preparation.separate_waves import SepWav
-from sklearn_fork.ensemble import RandomForestClassifier
-from sklearn_fork.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
 
 # Define your dataset
 input_file = './stroke.csv'

docs/API/estimators/ensemble/lexico_deep_forest.md

@@ -134,7 +134,7 @@ Predict class probabilities for samples in X.
 ### Example 1: Basic Usage
 
 ``` py title="example_1: Basic Usage" linenums="1" hl_lines="6-14"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.trees import LexicoDeepForestClassifier
 
 features_group = [[0, 1], [2, 3]] # (1)
@@ -162,7 +162,7 @@ accuracy_score(y, clf.predict(X)) # (3)
 ### Example 2: Using Multiple Types of Longitudinal Estimators
 
 ``` py title="example_2: Using Multiple Types of Longitudinal Estimators" linenums="1" hl_lines="6-19"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.trees import LexicoDeepForestClassifier
 
 features_group = [[0, 1], [2, 3]] # (1)
@@ -196,7 +196,7 @@ accuracy_score(y, clf.predict(X)) # (4)
 ### Example 3: Disabling Diversity Estimators
 
 ``` py title="example_3: Disabling Diversity Estimators" linenums="1" hl_lines="6-15"
-import sklearn_fork.metrics import accuracy_score
+import sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.trees import LexicoDeepForestClassifier
 
 features_group = [[0, 1], [2, 3]] # (1)

docs/API/estimators/ensemble/lexico_gradient_boosting.md

@@ -153,7 +153,7 @@ Predict class probabilities for samples in X.
 ### Example 1: Basic Usage
 
 ``` py title="Example_1: Default Parameters" linenums="1" hl_lines="7-9"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import \
     LexicoGradientBoostingClassifier
 
@@ -174,7 +174,7 @@ accuracy_score(y, y_pred)  # (2)
 ### Example 2: Using Specific Parameters
 
 ``` py title="Example_2: Using Specific Parameters" linenums="1" hl_lines="7-12"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import \
     LexicoGradientBoostingClassifier
 
@@ -199,7 +199,7 @@ accuracy_score(y, y_pred)  # (3)
 ### Exemple 3: Using the learning rate
 
 ``` py title="Example_3: Using the learning rate" linenums="1" hl_lines="7-11"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import \
     LexicoGradientBoostingClassifier
 

docs/API/estimators/ensemble/lexico_random_forest.md

@@ -150,8 +150,8 @@ Predict class probabilities for samples in X.
 
 ### Example 1: Basic Usage
 
-```py title="Example_1: Default Parameters" linenums="1" hl_lines="6-8"
-from sklearn_fork.metrics import accuracy_score
+``` py title="Example_1: Default Parameters" linenums="1" hl_lines="6-8"
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 
 features_group = [(0, 1), (2, 3)]  # (1)
@@ -170,8 +170,8 @@ accuracy_score(y, y_pred)  # (2)
 
 ### Example 2: How-To Set Threshold Gain of the Lexicographical Approach
 
-```py title="Example_2: How-To Set Threshold Gain of the Lexicographical Approach" linenums="1" hl_lines="6-9"
-from sklearn_fork.metrics import accuracy_score
+``` py title="Example_2: How-To Set Threshold Gain of the Lexicographical Approach" linenums="1" hl_lines="6-9"
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 
 features_group = [(0, 1), (2, 3)]  # (1)
@@ -193,8 +193,8 @@ accuracy_score(y, y_pred)  # (3)
 
 ### Example 3: How-To Set the Number of Estimators
 
-```py title="Example_3: How-To Set the Number of Estimators" linenums="1" hl_lines="6-9"
-from sklearn_fork.metrics import accuracy_score
+``` py title="Example_3: How-To Set the Number of Estimators" linenums="1" hl_lines="6-9"
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 
 features_group = [(0, 1), (2, 3)]  # (1)

docs/API/estimators/ensemble/longitudinal_stacking.md

@@ -143,7 +143,7 @@ Predicts the target data probabilities for the given input data.
 from scikit_longitudinal.estimators.ensemble.longitudinal_stacking.longitudinal_stacking import (
     LongitudinalStackingClassifier,
 )
-from sklearn_fork.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 from sklearn.metrics import accuracy_score
 
@@ -182,7 +182,7 @@ accuracy_score(y, y_pred) # (6)
 from scikit_longitudinal.estimators.ensemble.longitudinal_stacking.longitudinal_stacking import (
     LongitudinalStackingClassifier,
 )
-from sklearn_fork.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 from sklearn.metrics import accuracy_score
 

docs/API/estimators/ensemble/longitudinal_voting.md

@@ -161,7 +161,7 @@ Predict probabilities using the ensemble model.
 from scikit_longitudinal.estimators.ensemble.longitudinal_voting.longitudinal_voting import (
     LongitudinalVotingClassifier,
 )
-from sklearn_fork.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 from sklearn.metrics import accuracy_score
 
@@ -198,7 +198,7 @@ accuracy_score(y, y_pred) # (5)
 from scikit_longitudinal.estimators.ensemble.longitudinal_voting.longitudinal_voting import (
     LongitudinalVotingClassifier,
 )
-from sklearn_fork.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier
 from scikit_longitudinal.estimators.ensemble.lexicographical import LexicoRandomForestClassifier
 from sklearn.metrics import accuracy_score
 

docs/API/estimators/ensemble/nested_trees.md

@@ -160,8 +160,8 @@ Print the structure of the nested tree classifier.
 
 ``` py title="Example 1: Basic Usage" linenums="1" hl_lines="8-11"
 from scikit_longitudinal.estimators.ensemble import NestedTreesClassifier
-from sklearn_fork.model_selection import train_test_split
-from sklearn_fork.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 features_group = [(0, 1), (2, 3)] # (1)
 non_longitudinal_features = [4, 5] # (2)
@@ -185,8 +185,8 @@ accuracy_score(y_test, y_pred) # (3)
 
 ``` py title="Example 2: Using Custom Hyperparameters for Inner Estimators" linenums="1" hl_lines="8-18"
 from scikit_longitudinal.estimators.ensemble import NestedTreesClassifier
-from sklearn_fork.model_selection import train_test_split
-from sklearn_fork.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 features_group = [(0, 1), (2, 3)] # (1)
 non_longitudinal_features = [4, 5] # (2)
@@ -218,8 +218,8 @@ accuracy_score(y_test, y_pred) # (4)
 
 ``` py title="Example 3: Using Parallel Processing" linenums="1" hl_lines="8-13"
 from scikit_longitudinal.estimators.ensemble import NestedTreesClassifier
-from sklearn_fork.model_selection import train_test_split
-from sklearn_fork.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 features_group = [(0, 1), (2, 3)] # (1)
 non_longitudinal_features = [4, 5] # (2)
@@ -248,8 +248,8 @@ accuracy_score(y_test, y_pred) # (5)
 
 ``` py title="Example 4: Saving the Nested Trees Structure" linenums="1" hl_lines="8-12"
 from scikit_longitudinal.estimators.ensemble import NestedTreesClassifier
-from sklearn_fork.model_selection import train_test_split
-from sklearn_fork.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 features_group = [(0, 1), (2, 3)] # (1)
 non_longitudinal_features = [4, 5] # (2)
@@ -276,8 +276,8 @@ accuracy_score(y_test, y_pred) # (4)
 
 ``` py title="Example 5: Printing the Nested Trees Structure" linenums="1" hl_lines="8-11"
 from scikit_longitudinal.estimators.ensemble import NestedTreesClassifier
-from sklearn_fork.model_selection import train_test_split
-from sklearn_fork.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
 
 features_group = [(0, 1), (2, 3)] # (1)
 non_longitudinal_features = [4, 5] # (2)

docs/API/estimators/trees/lexico_decision_tree_classifier.md

@@ -162,7 +162,8 @@ The predicted class probabilities of an input sample are computed as the mean pr
 ### Example 1: Basic Usage
 
 ``` py title="Example_1: Default Parameters" linenums="1" hl_lines="5-7"
-from sklearn_fork.metrics imp mators.tree import LexicoDecisionTreeClassifier
+from sklearn.metrics import accuracy
+from scikit_longitudinal.estimaators.tree import LexicoDecisionTreeClassifier
 
 features_group = [(0,1), (2,3)] # (1)
 
@@ -181,7 +182,7 @@ accuracy_score(y, y_pred) # (2)
 ### Example 2: How-To Set Threshold Gain of the Lexicographical Approach?
 
 ``` py title="example_1: How-To Set Threshold Gain of the Lexicographical Approach" linenums="1" hl_lines="6-9"
-from sklearn_fork.metrics import accuracy_score
+from sklearn.metrics import accuracy_score
 from scikit_longitudinal.estimators.tree import LexicoDecisionTreeClassifier
 
 features_group = [(0,1), (2,3)] # (1)

docs/API/estimators/trees/lexico_decision_tree_regressor.md

@@ -252,7 +252,7 @@ Predicts the target data for the given input data.
 ### Example 1: Basic Usage
 
 ``` py title="Example_1: Default Parameters" linenums="1" hl_lines="5-7"
-from sklearn_fork.metrics import mean_squared_error
+from sklearn.metrics import mean_squared_error
 from scikit_longitudinal.estimators.tree import LexicoDecisionTreeRegressor
 
 features_group = [(0, 1), (2, 3)]  # (1)
@@ -272,7 +272,7 @@ mean_squared_error(y, y_pred)  # (2)
 ### Example 2: How-To Set Threshold Gain of the Lexicographical Approach?
 
 ``` py title="Example_2: How-To Set Threshold Gain of the Lexicographical Approach" linenums="1" hl_lines="6-9"
-from sklearn_fork.metrics import mean_squared_error
+from sklearn.metrics import mean_squared_error
 from scikit_longitudinal.estimators.tree import LexicoDecisionTreeRegressor
 
 features_group = [(0, 1), (2, 3)]  # (1)

docs/quick-start.md

@@ -113,6 +113,9 @@ model = LexicoGradientBoostingClassifier(
 
 model.fit(dataset.X_train, dataset.y_train)
 y_pred = model.predict(dataset.X_test)
+
+# Classification report
+print(classification_report(y_test, y_pred))
 ```
 
 !!! warning "Neural Networks models"

docs/theme/overrides/home.html

@@ -386,7 +386,7 @@ <h3>
             </h3>
             <hr/>
             <cite>
-              Simon designed, developed and maintain the <a href="http://scikit-longitudinal.org" target="_blank">Scikit-Longitudinal</a> library. Simon is a Ph.D student in computer science at the University of Kent, United-Kingdom. His research interests are in Machine Learning (ML), Automated Machine Learning and ML-applied Healthcare.
+              Simon designed, developed and maintain the <a href="https://github.com/simonprovost/scikit-longitudinal" target="_blank">Scikit-Longitudinal</a> library. Simon is a Ph.D student in computer science at the University of Kent, United-Kingdom. His research interests are in Machine Learning (ML), Automated Machine Learning and ML-applied Healthcare.
             </cite>
           </figcaption>
         </figure>
@@ -403,7 +403,7 @@ <h3>
             </h3>
             <hr/>
             <cite>
-              Alex is a Professor of Computational Intelligence at the University of Kent, United-Kingdom. He is the main supervisor of Simon's Ph.D., and also designed the <a href="http://scikit-longitudinal.org" target="_blank">Scikit-Longitudinal</a> library with. Alex's research interests are in machine learning, data mining, and bioinformatics.
+              Alex is a Professor of Computational Intelligence at the University of Kent, United-Kingdom. He is the main supervisor of Simon's Ph.D., and also designed the <a href="https://github.com/simonprovost/scikit-longitudinal" target="_blank">Scikit-Longitudinal</a> library with. Alex's research interests are in machine learning, data mining, and bioinformatics.
             </cite>
           </figcaption>
         </figure>