Attempt to bring in categorical support (#46)

<!--
Thanks for contributing a pull request! Please ensure you have taken a
look at
the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
-->

#### Reference Issues/PRs
Helps bring in fork wrt changes in
scikit-learn#12866

#### What does this implement/fix? Explain your changes.


#### Any other comments?


<!--
Please be aware that we are a loose team of volunteers so patience is
necessary; assistance handling other issues is very welcome. We value
all user contributions, no matter how minor they are. If we are slow to
review, either the pull request needs some benchmarking, tinkering,
convincing, etc. or more likely the reviewers are simply busy. In either
case, we ask for your understanding during the review process.
For more information, see our FAQ on this topic:

http://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention.

Thanks for contributing!
-->

---------

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

Author: adam2392

benchmarks/bench_tree_nocats.py

@@ -0,0 +1,128 @@
+from itertools import product
+from timeit import timeit
+
+import numpy as np
+import pandas as pd
+
+from sklearn.datasets import fetch_openml
+from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import StratifiedKFold
+from sklearn.preprocessing import OneHotEncoder
+
+
+def get_data(trunc_ncat):
+    # the data is located here: https://www.openml.org/d/4135
+    X, y = fetch_openml(data_id=4135, return_X_y=True)
+    X = pd.DataFrame(X)
+
+    Xdicts = []
+    for trunc in trunc_ncat:
+        X_trunc = X % trunc if trunc > 0 else X
+        keep_idx = np.array(
+            [idx[0] for idx in X_trunc.groupby(list(X.columns)).groups.values()]
+        )
+        X_trunc = X_trunc.values[keep_idx]
+        y_trunc = y[keep_idx]
+
+        X_ohe = OneHotEncoder(categories="auto").fit_transform(X_trunc)
+
+        Xdicts.append({"X": X_trunc, "y": y_trunc, "ohe": False, "trunc": trunc})
+        Xdicts.append({"X": X_ohe, "y": y_trunc, "ohe": True, "trunc": trunc})
+
+    return Xdicts
+
+
+# Training dataset
+trunc_factor = [2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 64, 0]
+data = get_data(trunc_factor)
+results = []
+# Loop over classifiers and datasets
+for Xydict, clf_type in product(data, [RandomForestClassifier, ExtraTreesClassifier]):
+    # Can't use non-truncated categorical data with RandomForest
+    # and it becomes intractable with too many categories
+    if (
+        clf_type is RandomForestClassifier
+        and not Xydict["ohe"]
+        and (not Xydict["trunc"] or Xydict["trunc"] > 16)
+    ):
+        continue
+
+    X, y = Xydict["X"], Xydict["y"]
+    tech = "One-hot" if Xydict["ohe"] else "NOCATS"
+    trunc = "truncated({})".format(Xydict["trunc"]) if Xydict["trunc"] > 0 else "full"
+    cat = "none" if Xydict["ohe"] else "all"
+    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=17).split(X, y)
+
+    traintimes = []
+    testtimes = []
+    aucs = []
+    name = "({}, {}, {})".format(clf_type.__name__, trunc, tech)
+
+    for train, test in cv:
+        # Train
+        clf = clf_type(
+            n_estimators=10,
+            max_features=None,
+            min_samples_leaf=1,
+            random_state=23,
+            bootstrap=False,
+            max_depth=None,
+            categorical=cat,
+        )
+
+        traintimes.append(
+            timeit(
+                "clf.fit(X[train], y[train])".format(),
+                "from __main__ import clf, X, y, train",
+                number=1,
+            )
+        )
+
+        """
+        # Check that all leaf nodes are pure
+        for est in clf.estimators_:
+            leaves = est.tree_.children_left < 0
+            print(np.max(est.tree_.impurity[leaves]))
+            #assert(np.all(est.tree_.impurity[leaves] == 0))
+        """
+
+        # Test
+        probs = []
+        testtimes.append(
+            timeit(
+                "probs.append(clf.predict_proba(X[test]))",
+                "from __main__ import probs, clf, X, test",
+                number=1,
+            )
+        )
+
+        aucs.append(roc_auc_score(y[test], probs[0][:, 1]))
+
+    traintimes = np.array(traintimes)
+    testtimes = np.array(testtimes)
+    aucs = np.array(aucs)
+    results.append(
+        [
+            name,
+            traintimes.mean(),
+            traintimes.std(),
+            testtimes.mean(),
+            testtimes.std(),
+            aucs.mean(),
+            aucs.std(),
+        ]
+    )
+
+    results_df = pd.DataFrame(results)
+    results_df.columns = [
+        "name",
+        "train time mean",
+        "train time std",
+        "test time mean",
+        "test time std",
+        "auc mean",
+        "auc std",
+    ]
+    results_df = results_df.set_index("name")
+    print(results_df)

sklearn/ensemble/_forest.py

@@ -725,7 +725,7 @@ def predict_quantiles(self, X, quantiles=0.5, method="nearest"):
         ----------
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             Input data.
-        quantiles : float, optional
+        quantiles : array-like, float, optional
             The quantiles at which to evaluate, by default 0.5 (median).
         method : str, optional
             The method to interpolate, by default 'linear'. Can be any keyword
@@ -746,7 +746,7 @@ def predict_quantiles(self, X, quantiles=0.5, method="nearest"):
         X = self._validate_X_predict(X)
 
         if not isinstance(quantiles, (np.ndarray, list)):
-            quantiles = np.array([quantiles])
+            quantiles = np.atleast_1d(np.array(quantiles))
 
         # if we trained a binning tree, then we should re-bin the data
         # XXX: this is inefficient and should be improved to be in line with what
@@ -777,15 +777,15 @@ def predict_quantiles(self, X, quantiles=0.5, method="nearest"):
 
             # (n_total_leaf_samples, n_outputs)
             leaf_node_samples = np.vstack(
-                (
+                [
                     est.leaf_nodes_samples_[leaf_nodes[jdx]]
                     for jdx, est in enumerate(self.estimators_)
-                )
+                ]
             )
 
             # get quantiles across all leaf node samples
             y_hat[idx, ...] = np.quantile(
-                leaf_node_samples, quantiles, axis=0, interpolation=method
+                leaf_node_samples, quantiles, axis=0, method=method
             )
 
             if is_classifier(self):
@@ -1550,6 +1550,17 @@ class RandomForestClassifier(ForestClassifier):
 
         .. versionadded:: 1.4
 
+    categorical : array-like or str
+        Array of feature indices, boolean array of length n_features,
+        ``'all'`` or `None`. Indicates which features should be
+        considered as categorical rather than ordinal. For decision trees,
+        the maximum number of categories is 64. In practice, the limit will
+        often be lower because the process of searching for the best possible
+        split grows exponentially with the number of categories. However, a
+        shortcut due to Breiman (1984) is used when fitting data with binary
+        labels using the ``Gini`` or ``Entropy`` criteria. In this case,
+        the runtime is linear in the number of categories.
+
     Attributes
     ----------
     estimator_ : :class:`~sklearn.tree.DecisionTreeClassifier`
@@ -1693,6 +1704,7 @@ def __init__(
         max_bins=None,
         store_leaf_values=False,
         monotonic_cst=None,
+        categorical=None,
     ):
         super().__init__(
             estimator=DecisionTreeClassifier(),
@@ -1710,6 +1722,7 @@ def __init__(
                 "ccp_alpha",
                 "store_leaf_values",
                 "monotonic_cst",
+                "categorical",
             ),
             bootstrap=bootstrap,
             oob_score=oob_score,
@@ -1733,6 +1746,7 @@ def __init__(
         self.min_impurity_decrease = min_impurity_decrease
         self.monotonic_cst = monotonic_cst
         self.ccp_alpha = ccp_alpha
+        self.categorical = categorical
 
 
 class RandomForestRegressor(ForestRegressor):
@@ -1935,6 +1949,17 @@ class RandomForestRegressor(ForestRegressor):
 
         .. versionadded:: 1.4
 
+    categorical : array-like or str
+        Array of feature indices, boolean array of length n_features,
+        ``'all'`` or `None`. Indicates which features should be
+        considered as categorical rather than ordinal. For decision trees,
+        the maximum number of categories is 64. In practice, the limit will
+        often be lower because the process of searching for the best possible
+        split grows exponentially with the number of categories. However, a
+        shortcut due to Breiman (1984) is used when fitting data with binary
+        labels using the ``Gini`` or ``Entropy`` criteria. In this case,
+        the runtime is linear in the number of categories.
+
     Attributes
     ----------
     estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`
@@ -2065,6 +2090,7 @@ def __init__(
         max_bins=None,
         store_leaf_values=False,
         monotonic_cst=None,
+        categorical=None,
     ):
         super().__init__(
             estimator=DecisionTreeRegressor(),
@@ -2082,6 +2108,7 @@ def __init__(
                 "ccp_alpha",
                 "store_leaf_values",
                 "monotonic_cst",
+                "categorical",
             ),
             bootstrap=bootstrap,
             oob_score=oob_score,
@@ -2104,6 +2131,7 @@ def __init__(
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
         self.monotonic_cst = monotonic_cst
+        self.categorical = categorical
 
 
 class ExtraTreesClassifier(ForestClassifier):
@@ -2316,24 +2344,16 @@ class ExtraTreesClassifier(ForestClassifier):
 
         .. versionadded:: 1.4
 
-    monotonic_cst : array-like of int of shape (n_features), default=None
-        Indicates the monotonicity constraint to enforce on each feature.
-          - 1: monotonically increasing
-          - 0: no constraint
-          - -1: monotonically decreasing
-
-        If monotonic_cst is None, no constraints are applied.
-
-        Monotonicity constraints are not supported for:
-          - multiclass classifications (i.e. when `n_classes > 2`),
-          - multioutput classifications (i.e. when `n_outputs_ > 1`),
-          - classifications trained on data with missing values.
-
-        The constraints hold over the probability of the positive class.
-
-        Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
-
-        .. versionadded:: 1.4
+    categorical : array-like or str
+        Array of feature indices, boolean array of length n_features,
+        ``'all'`` or `None`. Indicates which features should be
+        considered as categorical rather than ordinal. For decision trees,
+        the maximum number of categories is 64. In practice, the limit will
+        often be lower because the process of searching for the best possible
+        split grows exponentially with the number of categories. However, a
+        shortcut due to Breiman (1984) is used when fitting data with binary
+        labels using the ``Gini`` or ``Entropy`` criteria. In this case,
+        the runtime is linear in the number of categories.
 
     Attributes
     ----------
@@ -2467,6 +2487,7 @@ def __init__(
         max_bins=None,
         store_leaf_values=False,
         monotonic_cst=None,
+        categorical=None,
     ):
         super().__init__(
             estimator=ExtraTreeClassifier(),
@@ -2484,6 +2505,7 @@ def __init__(
                 "ccp_alpha",
                 "store_leaf_values",
                 "monotonic_cst",
+                "categorical",
             ),
             bootstrap=bootstrap,
             oob_score=oob_score,
@@ -2507,6 +2529,7 @@ def __init__(
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
         self.monotonic_cst = monotonic_cst
+        self.categorical = categorical
 
 
 class ExtraTreesRegressor(ForestRegressor):
@@ -2704,6 +2727,17 @@ class ExtraTreesRegressor(ForestRegressor):
 
         .. versionadded:: 1.4
 
+    categorical : array-like or str
+        Array of feature indices, boolean array of length n_features,
+        ``'all'`` or `None`. Indicates which features should be
+        considered as categorical rather than ordinal. For decision trees,
+        the maximum number of categories is 64. In practice, the limit will
+        often be lower because the process of searching for the best possible
+        split grows exponentially with the number of categories. However, a
+        shortcut due to Breiman (1984) is used when fitting data with binary
+        labels using the ``Gini`` or ``Entropy`` criteria. In this case,
+        the runtime is linear in the number of categories.
+
     Attributes
     ----------
     estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor`
@@ -2819,6 +2853,7 @@ def __init__(
         max_bins=None,
         store_leaf_values=False,
         monotonic_cst=None,
+        categorical=None,
     ):
         super().__init__(
             estimator=ExtraTreeRegressor(),
@@ -2836,6 +2871,7 @@ def __init__(
                 "ccp_alpha",
                 "store_leaf_values",
                 "monotonic_cst",
+                "categorical",
             ),
             bootstrap=bootstrap,
             oob_score=oob_score,
@@ -2858,6 +2894,7 @@ def __init__(
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
         self.monotonic_cst = monotonic_cst
+        self.categorical = categorical
 
 
 class RandomTreesEmbedding(TransformerMixin, BaseForest):
@@ -2969,6 +3006,17 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):
         new forest. See :term:`Glossary <warm_start>` and
         :ref:`gradient_boosting_warm_start` for details.
 
+    categorical : array-like or str
+        Array of feature indices, boolean array of length n_features,
+        ``'all'`` or `None`. Indicates which features should be
+        considered as categorical rather than ordinal. For decision trees,
+        the maximum number of categories is 64. In practice, the limit will
+        often be lower because the process of searching for the best possible
+        split grows exponentially with the number of categories. However, a
+        shortcut due to Breiman (1984) is used when fitting data with binary
+        labels using the ``Gini`` or ``Entropy`` criteria. In this case,
+        the runtime is linear in the number of categories.
+
     Attributes
     ----------
     estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` instance
@@ -3073,6 +3121,7 @@ def __init__(
         verbose=0,
         warm_start=False,
         store_leaf_values=False,
+        categorical=None,
     ):
         super().__init__(
             estimator=ExtraTreeRegressor(),
@@ -3088,6 +3137,7 @@ def __init__(
                 "min_impurity_decrease",
                 "random_state",
                 "store_leaf_values",
+                "categorical",
             ),
             bootstrap=False,
             oob_score=False,
@@ -3106,6 +3156,7 @@ def __init__(
         self.max_leaf_nodes = max_leaf_nodes
         self.min_impurity_decrease = min_impurity_decrease
         self.sparse_output = sparse_output
+        self.categorical = categorical
 
     def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
         raise NotImplementedError("OOB score not supported by tree embedding")