[ENH] Adding leaf node samples to be stored when "quantile" tree is turned on (#45)

#### Reference Issues/PRs
Addresses the quantile-trees part of:
neurodata/treeple#29


#### What does this implement/fix? Explain your changes.
1. Stores for each leaf node a 2D numpy array of the y-samples (remember
`y` is (n_samples, n_outputs))
2. Does this all the way in Criterion
3. Only supports supervised tree/splitter/criterion
4. merges in `main` changes.

#### Any other comments?


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---------

Signed-off-by: Adam Li <adam2392@gmail.com>

Author: adam2392

doc/authors_emeritus.rst

@@ -20,6 +20,7 @@
 - Wei Li
 - Paolo Losi
 - Gilles Louppe
+- Chiara Marmo
 - Vincent Michel
 - Jarrod Millman
 - Alexandre Passos

doc/contributor_experience_team.rst

@@ -18,6 +18,10 @@
     <p>Lucy Liu</p>
     </div>
     <div>
+    <a href='https://github.com/MaxwellLZH'><img src='https://avatars.githubusercontent.com/u/16646940?v=4' class='avatar' /></a> <br />
+    <p>Maxwell Liu</p>
+    </div>
+    <div>
     <a href='https://github.com/jmloyola'><img src='https://avatars.githubusercontent.com/u/2133361?v=4' class='avatar' /></a> <br />
     <p>Juan Martin Loyola</p>
     </div>
@@ -26,14 +30,6 @@
     <p>Sylvain Marié</p>
     </div>
     <div>
-    <a href='https://github.com/cmarmo'><img src='https://avatars.githubusercontent.com/u/1662261?v=4' class='avatar' /></a> <br />
-    <p>Chiara Marmo</p>
-    </div>
-    <div>
-    <a href='https://github.com/MaxwellLZH'><img src='https://avatars.githubusercontent.com/u/16646940?v=4' class='avatar' /></a> <br />
-    <p>Maxwell Liu</p>
-    </div>
-    <div>
     <a href='https://github.com/norbusan'><img src='https://avatars.githubusercontent.com/u/1735589?v=4' class='avatar' /></a> <br />
     <p>Norbert Preining</p>
     </div>

doc/modules/classes.rst

@@ -1247,6 +1247,7 @@ Visualization
    :template: display_only_from_estimator.rst
 
    model_selection.LearningCurveDisplay
+   model_selection.ValidationCurveDisplay
 
 .. _multiclass_ref:
 

doc/modules/learning_curve.rst

@@ -71,7 +71,7 @@ The function :func:`validation_curve` can help in this case::
   >>> import numpy as np
   >>> from sklearn.model_selection import validation_curve
   >>> from sklearn.datasets import load_iris
-  >>> from sklearn.linear_model import Ridge
+  >>> from sklearn.svm import SVC
 
   >>> np.random.seed(0)
   >>> X, y = load_iris(return_X_y=True)
@@ -80,30 +80,50 @@ The function :func:`validation_curve` can help in this case::
   >>> X, y = X[indices], y[indices]
 
   >>> train_scores, valid_scores = validation_curve(
-  ...     Ridge(), X, y, param_name="alpha", param_range=np.logspace(-7, 3, 3),
-  ...     cv=5)
+  ...     SVC(kernel="linear"), X, y, param_name="C", param_range=np.logspace(-7, 3, 3),
+  ... )
   >>> train_scores
-  array([[0.93..., 0.94..., 0.92..., 0.91..., 0.92...],
-         [0.93..., 0.94..., 0.92..., 0.91..., 0.92...],
-         [0.51..., 0.52..., 0.49..., 0.47..., 0.49...]])
+  array([[0.90..., 0.94..., 0.91..., 0.89..., 0.92...],
+         [0.9... , 0.92..., 0.93..., 0.92..., 0.93...],
+         [0.97..., 1...   , 0.98..., 0.97..., 0.99...]])
   >>> valid_scores
-  array([[0.90..., 0.84..., 0.94..., 0.96..., 0.93...],
-         [0.90..., 0.84..., 0.94..., 0.96..., 0.93...],
-         [0.46..., 0.25..., 0.50..., 0.49..., 0.52...]])
+  array([[0.9..., 0.9... , 0.9... , 0.96..., 0.9... ],
+         [0.9..., 0.83..., 0.96..., 0.96..., 0.93...],
+         [1.... , 0.93..., 1....  , 1....  , 0.9... ]])
+
+If you intend to plot the validation curves only, the class
+:class:`~sklearn.model_selection.ValidationCurveDisplay` is more direct than
+using matplotlib manually on the results of a call to :func:`validation_curve`.
+You can use the method
+:meth:`~sklearn.model_selection.ValidationCurveDisplay.from_estimator` similarly
+to :func:`validation_curve` to generate and plot the validation curve:
+
+.. plot::
+   :context: close-figs
+   :align: center
+
+      from sklearn.datasets import load_iris
+      from sklearn.model_selection import ValidationCurveDisplay
+      from sklearn.svm import SVC
+      from sklearn.utils import shuffle
+      X, y = load_iris(return_X_y=True)
+      X, y = shuffle(X, y, random_state=0)
+      ValidationCurveDisplay.from_estimator(
+         SVC(kernel="linear"), X, y, param_name="C", param_range=np.logspace(-7, 3, 10)
+      )
 
 If the training score and the validation score are both low, the estimator will
 be underfitting. If the training score is high and the validation score is low,
 the estimator is overfitting and otherwise it is working very well. A low
 training score and a high validation score is usually not possible. Underfitting,
 overfitting, and a working model are shown in the in the plot below where we vary
-the parameter :math:`\gamma` of an SVM on the digits dataset.
+the parameter `gamma` of an SVM with an RBF kernel on the digits dataset.
 
 .. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
    :target: ../auto_examples/model_selection/plot_validation_curve.html
    :align: center
    :scale: 50%
 
-
 .. _learning_curve:
 
 Learning curve

doc/visualizations.rst

@@ -89,3 +89,4 @@ Display Objects
    metrics.PredictionErrorDisplay
    metrics.RocCurveDisplay
    model_selection.LearningCurveDisplay
+   model_selection.ValidationCurveDisplay