Trees Module

dislib/classification/__init__.py

@@ -1,4 +1,4 @@
 from dislib.classification.csvm.base import CascadeSVM
-from dislib.commons.rf.forest import RandomForestClassifier
+from dislib.trees.forest import RandomForestClassifier
 
 __all__ = ["CascadeSVM", "RandomForestClassifier"]

dislib/regression/__init__.py

@@ -1,5 +1,5 @@
 from dislib.regression.linear.base import LinearRegression
 from dislib.regression.lasso.base import Lasso
-from dislib.commons.rf.forest import RandomForestRegressor
+from dislib.trees.forest import RandomForestRegressor
 
 __all__ = ["LinearRegression", "Lasso", "RandomForestRegressor"]

dislib/trees/__init__.py

@@ -0,0 +1,14 @@
+from dislib.trees.forest import RandomForestClassifier, RandomForestRegressor
+from dislib.trees.decision_tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+)
+from dislib.trees.data import transform_to_rf_dataset
+
+__all__ = [
+    "RandomForestClassifier",
+    "RandomForestRegressor",
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "transform_to_rf_dataset",
+]

dislib/commons/rf/data.py → dislib/trees/data.py

@@ -1,5 +1,4 @@
 import tempfile
-
 import numpy as np
 from numpy.lib import format
 from pycompss.api.parameter import (
@@ -10,7 +9,6 @@
     Type,
 )
 from pycompss.api.task import task
-
 from dislib.data.array import Array
 
 
@@ -43,7 +41,6 @@ def get_n_samples(self):
             If self.n_samples is None and self.samples_path is not a string.
         ValueError
             If invalid content is encountered in the samples file.
-
         """
         if self.n_samples is None:
             if not isinstance(self.samples_path, str):
@@ -68,7 +65,6 @@ def get_n_features(self):
             If self.n_features is None and self.samples_path is not a string.
         ValueError
             If invalid content is encountered in the samples file.
-
         """
         if self.n_features is None:
             if not isinstance(self.samples_path, str):
@@ -88,7 +84,6 @@ def validate_features_file(self):
         ValueError
             If the shape of the array in the features_file doesn't match this
             class n_samples and n_features or if the array is in fortran order.
-
         """
         features_npy_file = _NpyFile(self.features_path)
         shape = features_npy_file.get_shape()
@@ -156,7 +151,6 @@ def get_y_targets(self):
         Returns
         -------
         y_targets: ndarray
-
         """
         if self.y_targets is None:
             labels = _get_labels(self.targets_path)
@@ -169,7 +163,6 @@ def get_classes(self):
         Returns
         -------
         y_categories: ndarray
-
         """
         if self.y_categories is None:
             labels = _get_labels(self.targets_path)
@@ -182,7 +175,6 @@ def get_n_classes(self):
         Returns
         -------
         n_classes: int
-
         """
         if self.n_classes is None:
             labels = _get_labels(self.targets_path)
@@ -238,7 +230,6 @@ def get_y_targets(self):
         Returns
         -------
         y_targets: ndarray
-
         """
         if self.y_targets is None:
             targets = _get_values(self.targets_path)
@@ -253,11 +244,14 @@ def get_classes(self):
 
 
 def transform_to_rf_dataset(
-    x: Array, y: Array, task: str, features_file=False
+    x: Array,
+    y: Array,
+    base_dataset: RfRegressorDataset or RfClassifierDataset,
+    features_file=False,
 ) -> RfRegressorDataset or RfClassifierDataset:
     """Creates a RfDataset object from samples x and targets y.
 
-    This function creates a dislib.commons.rf.data.RfDataset by saving
+    This function creates a `RfDataset` by saving
     x and y in files.
 
     Parameters
@@ -323,16 +317,7 @@ def transform_to_rf_dataset(
     else:
         features_path = None
 
-    if task == "classification":
-        rf_dataset = RfClassifierDataset(
-            samples_path, targets_path, features_path
-        )
-    elif task == "regression":
-        rf_dataset = RfRegressorDataset(
-            samples_path, targets_path, features_path
-        )
-    else:
-        raise ValueError("task must be either classification or regression.")
+    rf_dataset = base_dataset(samples_path, targets_path, features_path)
     rf_dataset.n_samples = n_samples
     rf_dataset.n_features = n_features
     return rf_dataset

dislib/commons/rf/decision_tree.py → dislib/trees/decision_tree.py

@@ -8,7 +8,7 @@
 from sklearn.tree import DecisionTreeClassifier as SklearnDTClassifier
 from sklearn.tree import DecisionTreeRegressor as SklearnDTRegressor
 
-from dislib.commons.rf.test_split import test_split
+from dislib.trees.test_split import test_split
 from dislib.data.array import Array
 
 
@@ -27,13 +27,17 @@ def __init__(
         sklearn_max,
         bootstrap,
         random_state,
+        base_node,
+        base_tree,
     ):
         self.try_features = try_features
         self.max_depth = max_depth
         self.distr_depth = distr_depth
         self.sklearn_max = sklearn_max
         self.bootstrap = bootstrap
         self.random_state = random_state
+        self.base_node = base_node
+        self.base_tree = base_tree
 
         self.n_features = None
         self.n_classes = None
@@ -48,7 +52,6 @@ def fit(self, dataset):
         Parameters
         ----------
         dataset : dislib.classification.rf._data.RfDataset
-
         """
 
         self.n_features = dataset.get_n_features()
@@ -63,9 +66,8 @@ def fit(self, dataset):
         sample, y_s = _sample_selection(
             n_samples, y_targets, self.bootstrap, seed
         )
-        Node = _ClassificationNode if self.n_classes else _RegressionNode
 
-        self.tree = Node()
+        self.tree = self.base_node()
         self.nodes_info = []
         self.subtrees = []
         tree_traversal = [(self.tree, sample, y_s, 0)]
@@ -87,8 +89,8 @@ def fit(self, dataset):
                 compss_delete_object(y_s)
                 node.content = len(self.nodes_info)
                 self.nodes_info.append(node_info)
-                node.left = Node()
-                node.right = Node()
+                node.left = self.base_node()
+                node.right = self.base_node()
                 depth = depth + 1
                 tree_traversal.append((node.right, right_group, y_r, depth))
                 tree_traversal.append((node.left, left_group, y_l, depth))
@@ -102,6 +104,8 @@ def fit(self, dataset):
                     self.try_features,
                     self.sklearn_max,
                     self.random_state,
+                    self.base_node,
+                    self.base_tree,
                     samples_path,
                     features_path,
                 )
@@ -216,6 +220,8 @@ def __init__(
             sklearn_max,
             bootstrap,
             random_state,
+            _ClassificationNode,
+            SklearnDTClassifier,
         )
 
     def predict_proba(self, x_row):
@@ -234,7 +240,6 @@ def predict_proba(self, x_row):
             of the column being codes of the fitted
             dislib.classification.rf.data.RfDataset. The returned object can be
             a pycompss.runtime.Future object.
-
         """
 
         assert self.tree is not None, "The decision tree is not fitted."
@@ -319,6 +324,8 @@ def __init__(
             sklearn_max,
             bootstrap,
             random_state,
+            _RegressionNode,
+            SklearnDTRegressor,
         )
 
 
@@ -539,6 +546,8 @@ def _build_subtree_wrapper(
     m_try,
     sklearn_max,
     random_state,
+    base_node,
+    base_tree,
     samples_file,
     features_file,
 ):
@@ -553,6 +562,8 @@ def _build_subtree_wrapper(
             m_try,
             sklearn_max,
             seed,
+            base_node,
+            base_tree,
             samples_file,
             features_file,
         )
@@ -566,6 +577,8 @@ def _build_subtree_wrapper(
             m_try,
             sklearn_max,
             seed,
+            base_node,
+            base_tree,
             samples_file,
         )
 
@@ -580,6 +593,8 @@ def _build_subtree_using_features(
     m_try,
     sklearn_max,
     seed,
+    base_node,
+    base_tree,
     samples_file,
     features_file,
 ):
@@ -593,6 +608,8 @@ def _build_subtree_using_features(
         m_try,
         sklearn_max,
         random_state,
+        base_node,
+        base_tree,
         samples_file,
         features_file=features_file,
     )
@@ -608,6 +625,8 @@ def _build_subtree(
     m_try,
     sklearn_max,
     seed,
+    base_node,
+    base_tree,
     samples_file,
 ):
     random_state = RandomState(seed)
@@ -620,6 +639,8 @@ def _build_subtree(
         m_try,
         sklearn_max,
         random_state,
+        base_node,
+        base_tree,
         samples_file,
     )
 
@@ -633,19 +654,19 @@ def _compute_build_subtree(
     m_try,
     sklearn_max,
     random_state,
+    base_node,
+    base_tree,
     samples_file,
     features_file=None,
     use_sklearn=True,
 ):
-    Node = _ClassificationNode if n_classes else _RegressionNode
-    SklearnDT = SklearnDTClassifier if n_classes else SklearnDTRegressor
     if not sample.size:
-        return Node()
+        return base_node()
     if features_file is not None:
         mmap = np.load(features_file, mmap_mode="r", allow_pickle=False)
     else:
         mmap = np.load(samples_file, mmap_mode="r", allow_pickle=False).T
-    subtree = Node()
+    subtree = base_node()
     tree_traversal = [(subtree, sample, y_s, 0)]
     while tree_traversal:
         node, sample, y_s, depth = tree_traversal.pop()
@@ -655,7 +676,7 @@ def _compute_build_subtree(
                     sklearn_max_depth = None
                 else:
                     sklearn_max_depth = max_depth - depth
-                dt = SklearnDT(
+                dt = base_tree(
                     max_features=m_try,
                     max_depth=sklearn_max_depth,
                     random_state=random_state,
@@ -681,8 +702,8 @@ def _compute_build_subtree(
                 node_info, left_group, y_l, right_group, y_r = split
                 node.content = node_info
                 if isinstance(node_info, _InnerNodeInfo):
-                    node.left = Node()
-                    node.right = Node()
+                    node.left = base_node()
+                    node.right = base_node()
                     tree_traversal.append(
                         (node.right, right_group, y_r, depth + 1)
                     )