836 lines
32 KiB
Python
836 lines
32 KiB
Python
"""
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This module gathers tree-based methods, including decision, regression and
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randomized trees. Single and multi-output problems are both handled.
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"""
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# Authors: Gilles Louppe <g.louppe@gmail.com>
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# Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# Brian Holt <bdholt1@gmail.com>
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# Noel Dawe <noel@dawe.me>
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# Satrajit Gosh <satrajit.ghosh@gmail.com>
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# Joly Arnaud <arnaud.v.joly@gmail.com>
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# Fares Hedayati <fares.hedayati@gmail.com>
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#
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# Licence: BSD 3 clause
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# MODIFICATIONS:
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# - Removed base classes
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# - Incorporated required functions from six, utils
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from __future__ import division
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import numbers
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from abc import abstractmethod
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import numpy as np
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from utils import NotFittedError, check_is_fitted, compute_sample_weight, check_array, check_random_state, ClassifierMixin, RegressorMixin, string_types
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from utils import BaseEstimator
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from ._tree import Criterion
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from ._tree import Splitter
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from ._tree import DepthFirstTreeBuilder, BestFirstTreeBuilder
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from ._tree import Tree
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from . import _tree
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__all__ = ["DecisionTreeClassifier", "DecisionTreeRegressor",
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"ExtraTreeClassifier", "ExtraTreeRegressor"]
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# =============================================================================
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# Types and constants
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# =============================================================================
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DTYPE = _tree.DTYPE
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DOUBLE = _tree.DOUBLE
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CRITERIA_CLF = {"gini": _tree.Gini, "entropy": _tree.Entropy}
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CRITERIA_REG = {"mse": _tree.MSE, "friedman_mse": _tree.FriedmanMSE}
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DENSE_SPLITTERS = {"best": _tree.BestSplitter,
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"presort-best": _tree.PresortBestSplitter,
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"random": _tree.RandomSplitter}
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SPARSE_SPLITTERS = {"best": _tree.BestSparseSplitter,
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"random": _tree.RandomSparseSplitter}
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# =============================================================================
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# Base decision tree
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# =============================================================================
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class BaseDecisionTree(BaseEstimator):
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"""Base class for decision trees.
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Warning: This class should not be used directly.
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Use derived classes instead.
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"""
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@abstractmethod
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def __init__(self,
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criterion,
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splitter,
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max_depth,
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min_samples_split,
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min_samples_leaf,
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min_weight_fraction_leaf,
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max_features,
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max_leaf_nodes,
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random_state,
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class_weight=None):
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self.criterion = criterion
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self.splitter = splitter
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self.max_depth = max_depth
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self.min_samples_split = min_samples_split
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self.min_samples_leaf = min_samples_leaf
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self.min_weight_fraction_leaf = min_weight_fraction_leaf
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self.max_features = max_features
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self.random_state = random_state
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self.max_leaf_nodes = max_leaf_nodes
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self.class_weight = class_weight
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self.n_features_ = None
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self.n_outputs_ = None
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self.classes_ = None
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self.n_classes_ = None
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self.tree_ = None
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self.max_features_ = None
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def fit(self, X, y, sample_weight=None, check_input=True):
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"""Build a decision tree from the training set (X, y).
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Parameters
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----------
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X : array-like or sparse matrix, shape = [n_samples, n_features]
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The training input samples. Internally, it will be converted to
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``dtype=np.float32`` and if a sparse matrix is provided
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to a sparse ``csc_matrix``.
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y : array-like, shape = [n_samples] or [n_samples, n_outputs]
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The target values (class labels in classification, real numbers in
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regression). In the regression case, use ``dtype=np.float64`` and
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``order='C'`` for maximum efficiency.
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sample_weight : array-like, shape = [n_samples] or None
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Sample weights. If None, then samples are equally weighted. Splits
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that would create child nodes with net zero or negative weight are
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ignored while searching for a split in each node. In the case of
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classification, splits are also ignored if they would result in any
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single class carrying a negative weight in either child node.
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check_input : boolean, (default=True)
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Allow to bypass several input checking.
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Don't use this parameter unless you know what you do.
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Returns
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-------
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self : object
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Returns self.
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"""
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random_state = check_random_state(self.random_state)
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if check_input:
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X = check_array(X, dtype=DTYPE)
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# Determine output settings
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n_samples, self.n_features_ = X.shape
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is_classification = isinstance(self, ClassifierMixin)
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y = np.atleast_1d(y)
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expanded_class_weight = None
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if y.ndim == 1:
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# reshape is necessary to preserve the data contiguity against vs
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# [:, np.newaxis] that does not.
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y = np.reshape(y, (-1, 1))
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self.n_outputs_ = y.shape[1]
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if is_classification:
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y = np.copy(y)
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self.classes_ = []
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self.n_classes_ = []
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if self.class_weight is not None:
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y_original = np.copy(y)
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for k in range(self.n_outputs_):
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classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True)
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self.classes_.append(classes_k)
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self.n_classes_.append(classes_k.shape[0])
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if self.class_weight is not None:
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expanded_class_weight = compute_sample_weight(
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self.class_weight, y_original)
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else:
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self.classes_ = [None] * self.n_outputs_
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self.n_classes_ = [1] * self.n_outputs_
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self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
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if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
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y = np.ascontiguousarray(y, dtype=DOUBLE)
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# Check parameters
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max_depth = ((2 ** 31) - 1 if self.max_depth is None
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else self.max_depth)
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max_leaf_nodes = (-1 if self.max_leaf_nodes is None
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else self.max_leaf_nodes)
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if isinstance(self.max_features, string_types):
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if self.max_features == "auto":
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if is_classification:
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max_features = max(1, int(np.sqrt(self.n_features_)))
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else:
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max_features = self.n_features_
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elif self.max_features == "sqrt":
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max_features = max(1, int(np.sqrt(self.n_features_)))
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elif self.max_features == "log2":
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max_features = max(1, int(np.log2(self.n_features_)))
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else:
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raise ValueError(
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'Invalid value for max_features. Allowed string '
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'values are "auto", "sqrt" or "log2".')
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elif self.max_features is None:
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max_features = self.n_features_
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elif isinstance(self.max_features, (numbers.Integral, np.integer)):
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max_features = self.max_features
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else: # float
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if self.max_features > 0.0:
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max_features = max(1, int(self.max_features * self.n_features_))
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else:
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max_features = 0
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self.max_features_ = max_features
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if len(y) != n_samples:
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raise ValueError("Number of labels=%d does not match "
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"number of samples=%d" % (len(y), n_samples))
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if self.min_samples_split <= 0:
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raise ValueError("min_samples_split must be greater than zero.")
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if self.min_samples_leaf <= 0:
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raise ValueError("min_samples_leaf must be greater than zero.")
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if not 0 <= self.min_weight_fraction_leaf <= 0.5:
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raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
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if max_depth <= 0:
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raise ValueError("max_depth must be greater than zero. ")
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if not (0 < max_features <= self.n_features_):
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raise ValueError("max_features must be in (0, n_features]")
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if not isinstance(max_leaf_nodes, (numbers.Integral, np.integer)):
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raise ValueError("max_leaf_nodes must be integral number but was "
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"%r" % max_leaf_nodes)
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if -1 < max_leaf_nodes < 2:
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raise ValueError(("max_leaf_nodes {0} must be either smaller than "
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"0 or larger than 1").format(max_leaf_nodes))
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if sample_weight is not None:
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if (getattr(sample_weight, "dtype", None) != DOUBLE or
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not sample_weight.flags.contiguous):
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sample_weight = np.ascontiguousarray(
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sample_weight, dtype=DOUBLE)
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if len(sample_weight.shape) > 1:
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raise ValueError("Sample weights array has more "
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"than one dimension: %d" %
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len(sample_weight.shape))
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if len(sample_weight) != n_samples:
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raise ValueError("Number of weights=%d does not match "
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"number of samples=%d" %
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(len(sample_weight), n_samples))
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if expanded_class_weight is not None:
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if sample_weight is not None:
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sample_weight = sample_weight * expanded_class_weight
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else:
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sample_weight = expanded_class_weight
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# Set min_weight_leaf from min_weight_fraction_leaf
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if self.min_weight_fraction_leaf != 0. and sample_weight is not None:
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min_weight_leaf = (self.min_weight_fraction_leaf *
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np.sum(sample_weight))
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else:
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min_weight_leaf = 0.
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# Set min_samples_split sensibly
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min_samples_split = max(self.min_samples_split,
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2 * self.min_samples_leaf)
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# Build tree
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criterion = self.criterion
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if not isinstance(criterion, Criterion):
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if is_classification:
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criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,
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self.n_classes_)
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else:
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criterion = CRITERIA_REG[self.criterion](self.n_outputs_)
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SPLITTERS = DENSE_SPLITTERS
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splitter = self.splitter
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if not isinstance(self.splitter, Splitter):
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splitter = SPLITTERS[self.splitter](criterion,
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self.max_features_,
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self.min_samples_leaf,
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min_weight_leaf,
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random_state)
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self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)
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# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
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if max_leaf_nodes < 0:
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builder = DepthFirstTreeBuilder(splitter, min_samples_split,
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self.min_samples_leaf,
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min_weight_leaf,
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max_depth)
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else:
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builder = BestFirstTreeBuilder(splitter, min_samples_split,
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self.min_samples_leaf,
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min_weight_leaf,
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max_depth,
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max_leaf_nodes)
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builder.build(self.tree_, X, y, sample_weight)
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if self.n_outputs_ == 1:
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self.n_classes_ = self.n_classes_[0]
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self.classes_ = self.classes_[0]
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return self
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def predict(self, X, check_input=True):
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"""Predict class or regression value for X.
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For a classification model, the predicted class for each sample in X is
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returned. For a regression model, the predicted value based on X is
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returned.
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Parameters
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----------
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X : array-like or sparse matrix of shape = [n_samples, n_features]
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The input samples. Internally, it will be converted to
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``dtype=np.float32`` and if a sparse matrix is provided
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to a sparse ``csr_matrix``.
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check_input : boolean, (default=True)
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Allow to bypass several input checking.
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Don't use this parameter unless you know what you do.
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Returns
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-------
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y : array of shape = [n_samples] or [n_samples, n_outputs]
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The predicted classes, or the predict values.
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"""
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if check_input:
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X = check_array(X, dtype=DTYPE)
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n_samples, n_features = X.shape
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if self.tree_ is None:
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raise NotFittedError("Tree not initialized. Perform a fit first")
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if self.n_features_ != n_features:
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raise ValueError("Number of features of the model must "
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" match the input. Model n_features is %s and "
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" input n_features is %s "
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% (self.n_features_, n_features))
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proba = self.tree_.predict(X)
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# Classification
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if isinstance(self, ClassifierMixin):
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if self.n_outputs_ == 1:
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return self.classes_.take(np.argmax(proba, axis=1), axis=0)
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else:
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predictions = np.zeros((n_samples, self.n_outputs_))
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for k in range(self.n_outputs_):
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predictions[:, k] = self.classes_[k].take(
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np.argmax(proba[:, k], axis=1),
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axis=0)
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return predictions
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# Regression
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else:
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if self.n_outputs_ == 1:
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return proba[:, 0]
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else:
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return proba[:, :, 0]
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@property
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def feature_importances_(self):
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"""Return the feature importances.
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The importance of a feature is computed as the (normalized) total
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reduction of the criterion brought by that feature.
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It is also known as the Gini importance.
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Returns
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-------
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feature_importances_ : array, shape = [n_features]
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"""
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if self.tree_ is None:
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raise NotFittedError("Estimator not fitted, call `fit` before"
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" `feature_importances_`.")
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return self.tree_.compute_feature_importances()
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# =============================================================================
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# Public estimators
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# =============================================================================
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class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
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"""A decision tree classifier.
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Parameters
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----------
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criterion : string, optional (default="gini")
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The function to measure the quality of a split. Supported criteria are
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"gini" for the Gini impurity and "entropy" for the information gain.
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splitter : string, optional (default="best")
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The strategy used to choose the split at each node. Supported
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strategies are "best" to choose the best split and "random" to choose
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the best random split.
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max_features : int, float, string or None, optional (default=None)
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The number of features to consider when looking for the best split:
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- If int, then consider `max_features` features at each split.
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- If float, then `max_features` is a percentage and
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`int(max_features * n_features)` features are considered at each
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split.
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- If "auto", then `max_features=sqrt(n_features)`.
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- If "sqrt", then `max_features=sqrt(n_features)`.
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- If "log2", then `max_features=log2(n_features)`.
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- If None, then `max_features=n_features`.
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Note: the search for a split does not stop until at least one
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valid partition of the node samples is found, even if it requires to
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effectively inspect more than ``max_features`` features.
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max_depth : int or None, optional (default=None)
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The maximum depth of the tree. If None, then nodes are expanded until
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all leaves are pure or until all leaves contain less than
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min_samples_split samples.
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Ignored if ``max_leaf_nodes`` is not None.
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min_samples_split : int, optional (default=2)
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The minimum number of samples required to split an internal node.
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min_samples_leaf : int, optional (default=1)
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The minimum number of samples required to be at a leaf node.
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min_weight_fraction_leaf : float, optional (default=0.)
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The minimum weighted fraction of the input samples required to be at a
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leaf node.
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max_leaf_nodes : int or None, optional (default=None)
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Grow a tree with ``max_leaf_nodes`` in best-first fashion.
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Best nodes are defined as relative reduction in impurity.
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If None then unlimited number of leaf nodes.
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If not None then ``max_depth`` will be ignored.
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class_weight : dict, list of dicts, "auto" or None, optional (default=None)
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Weights associated with classes in the form ``{class_label: weight}``.
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If not given, all classes are supposed to have weight one. For
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multi-output problems, a list of dicts can be provided in the same
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order as the columns of y.
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The "auto" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies in the input data.
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For multi-output, the weights of each column of y will be multiplied.
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Note that these weights will be multiplied with sample_weight (passed
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through the fit method) if sample_weight is specified.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Attributes
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----------
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tree_ : Tree object
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The underlying Tree object.
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max_features_ : int,
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The inferred value of max_features.
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classes_ : array of shape = [n_classes] or a list of such arrays
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The classes labels (single output problem),
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or a list of arrays of class labels (multi-output problem).
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n_classes_ : int or list
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The number of classes (for single output problems),
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or a list containing the number of classes for each
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output (for multi-output problems).
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feature_importances_ : array of shape = [n_features]
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The feature importances. The higher, the more important the
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feature. The importance of a feature is computed as the (normalized)
|
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total reduction of the criterion brought by that feature. It is also
|
|
known as the Gini importance [4]_.
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See also
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--------
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DecisionTreeRegressor
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|
|
References
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----------
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|
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.. [1] http://en.wikipedia.org/wiki/Decision_tree_learning
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.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
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and Regression Trees", Wadsworth, Belmont, CA, 1984.
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.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
|
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Learning", Springer, 2009.
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.. [4] L. Breiman, and A. Cutler, "Random Forests",
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|
http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
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Examples
|
|
--------
|
|
>>> from sklearn.datasets import load_iris
|
|
>>> from sklearn.cross_validation import cross_val_score
|
|
>>> from sklearn.tree import DecisionTreeClassifier
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|
>>> clf = DecisionTreeClassifier(random_state=0)
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>>> iris = load_iris()
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>>> cross_val_score(clf, iris.data, iris.target, cv=10)
|
|
... # doctest: +SKIP
|
|
...
|
|
array([ 1. , 0.93..., 0.86..., 0.93..., 0.93...,
|
|
0.93..., 0.93..., 1. , 0.93..., 1. ])
|
|
"""
|
|
def __init__(self,
|
|
criterion="gini",
|
|
splitter="best",
|
|
max_depth=None,
|
|
min_samples_split=2,
|
|
min_samples_leaf=1,
|
|
min_weight_fraction_leaf=0.,
|
|
max_features=None,
|
|
random_state=None,
|
|
max_leaf_nodes=None,
|
|
class_weight=None):
|
|
super(DecisionTreeClassifier, self).__init__(
|
|
criterion=criterion,
|
|
splitter=splitter,
|
|
max_depth=max_depth,
|
|
min_samples_split=min_samples_split,
|
|
min_samples_leaf=min_samples_leaf,
|
|
min_weight_fraction_leaf=min_weight_fraction_leaf,
|
|
max_features=max_features,
|
|
max_leaf_nodes=max_leaf_nodes,
|
|
class_weight=class_weight,
|
|
random_state=random_state)
|
|
|
|
def predict_proba(self, X, check_input=True):
|
|
"""Predict class probabilities of the input samples X.
|
|
|
|
The predicted class probability is the fraction of samples of the same
|
|
class in a leaf.
|
|
|
|
check_input : boolean, (default=True)
|
|
Allow to bypass several input checking.
|
|
Don't use this parameter unless you know what you do.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like or sparse matrix of shape = [n_samples, n_features]
|
|
The input samples. Internally, it will be converted to
|
|
``dtype=np.float32`` and if a sparse matrix is provided
|
|
to a sparse ``csr_matrix``.
|
|
|
|
Returns
|
|
-------
|
|
p : array of shape = [n_samples, n_classes], or a list of n_outputs
|
|
such arrays if n_outputs > 1.
|
|
The class probabilities of the input samples. The order of the
|
|
classes corresponds to that in the attribute `classes_`.
|
|
"""
|
|
check_is_fitted(self, 'n_outputs_')
|
|
if check_input:
|
|
X = check_array(X, dtype=DTYPE)
|
|
|
|
n_samples, n_features = X.shape
|
|
|
|
if self.tree_ is None:
|
|
raise NotFittedError("Tree not initialized. Perform a fit first.")
|
|
|
|
if self.n_features_ != n_features:
|
|
raise ValueError("Number of features of the model must "
|
|
" match the input. Model n_features is %s and "
|
|
" input n_features is %s "
|
|
% (self.n_features_, n_features))
|
|
|
|
proba = self.tree_.predict(X)
|
|
|
|
if self.n_outputs_ == 1:
|
|
proba = proba[:, :self.n_classes_]
|
|
normalizer = proba.sum(axis=1)[:, np.newaxis]
|
|
normalizer[normalizer == 0.0] = 1.0
|
|
proba /= normalizer
|
|
|
|
return proba
|
|
|
|
else:
|
|
all_proba = []
|
|
|
|
for k in range(self.n_outputs_):
|
|
proba_k = proba[:, k, :self.n_classes_[k]]
|
|
normalizer = proba_k.sum(axis=1)[:, np.newaxis]
|
|
normalizer[normalizer == 0.0] = 1.0
|
|
proba_k /= normalizer
|
|
all_proba.append(proba_k)
|
|
|
|
return all_proba
|
|
|
|
def predict_log_proba(self, X):
|
|
"""Predict class log-probabilities of the input samples X.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like or sparse matrix of shape = [n_samples, n_features]
|
|
The input samples. Internally, it will be converted to
|
|
``dtype=np.float32`` and if a sparse matrix is provided
|
|
to a sparse ``csr_matrix``.
|
|
|
|
Returns
|
|
-------
|
|
p : array of shape = [n_samples, n_classes], or a list of n_outputs
|
|
such arrays if n_outputs > 1.
|
|
The class log-probabilities of the input samples. The order of the
|
|
classes corresponds to that in the attribute `classes_`.
|
|
"""
|
|
proba = self.predict_proba(X)
|
|
|
|
if self.n_outputs_ == 1:
|
|
return np.log(proba)
|
|
|
|
else:
|
|
for k in range(self.n_outputs_):
|
|
proba[k] = np.log(proba[k])
|
|
|
|
return proba
|
|
|
|
class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
|
|
"""A decision tree regressor.
|
|
|
|
Parameters
|
|
----------
|
|
criterion : string, optional (default="mse")
|
|
The function to measure the quality of a split. The only supported
|
|
criterion is "mse" for the mean squared error.
|
|
|
|
splitter : string, optional (default="best")
|
|
The strategy used to choose the split at each node. Supported
|
|
strategies are "best" to choose the best split and "random" to choose
|
|
the best random split.
|
|
|
|
max_features : int, float, string or None, optional (default=None)
|
|
The number of features to consider when looking for the best split:
|
|
- If int, then consider `max_features` features at each split.
|
|
- If float, then `max_features` is a percentage and
|
|
`int(max_features * n_features)` features are considered at each
|
|
split.
|
|
- If "auto", then `max_features=n_features`.
|
|
- If "sqrt", then `max_features=sqrt(n_features)`.
|
|
- If "log2", then `max_features=log2(n_features)`.
|
|
- If None, then `max_features=n_features`.
|
|
|
|
Note: the search for a split does not stop until at least one
|
|
valid partition of the node samples is found, even if it requires to
|
|
effectively inspect more than ``max_features`` features.
|
|
|
|
max_depth : int or None, optional (default=None)
|
|
The maximum depth of the tree. If None, then nodes are expanded until
|
|
all leaves are pure or until all leaves contain less than
|
|
min_samples_split samples.
|
|
Ignored if ``max_leaf_nodes`` is not None.
|
|
|
|
min_samples_split : int, optional (default=2)
|
|
The minimum number of samples required to split an internal node.
|
|
|
|
min_samples_leaf : int, optional (default=1)
|
|
The minimum number of samples required to be at a leaf node.
|
|
|
|
min_weight_fraction_leaf : float, optional (default=0.)
|
|
The minimum weighted fraction of the input samples required to be at a
|
|
leaf node.
|
|
|
|
max_leaf_nodes : int or None, optional (default=None)
|
|
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
|
|
Best nodes are defined as relative reduction in impurity.
|
|
If None then unlimited number of leaf nodes.
|
|
If not None then ``max_depth`` will be ignored.
|
|
|
|
random_state : int, RandomState instance or None, optional (default=None)
|
|
If int, random_state is the seed used by the random number generator;
|
|
If RandomState instance, random_state is the random number generator;
|
|
If None, the random number generator is the RandomState instance used
|
|
by `np.random`.
|
|
|
|
Attributes
|
|
----------
|
|
tree_ : Tree object
|
|
The underlying Tree object.
|
|
|
|
max_features_ : int,
|
|
The inferred value of max_features.
|
|
|
|
feature_importances_ : array of shape = [n_features]
|
|
The feature importances.
|
|
The higher, the more important the feature.
|
|
The importance of a feature is computed as the
|
|
(normalized) total reduction of the criterion brought
|
|
by that feature. It is also known as the Gini importance [4]_.
|
|
|
|
See also
|
|
--------
|
|
DecisionTreeClassifier
|
|
|
|
References
|
|
----------
|
|
|
|
.. [1] http://en.wikipedia.org/wiki/Decision_tree_learning
|
|
|
|
.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
|
|
and Regression Trees", Wadsworth, Belmont, CA, 1984.
|
|
|
|
.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
|
|
Learning", Springer, 2009.
|
|
|
|
.. [4] L. Breiman, and A. Cutler, "Random Forests",
|
|
http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.datasets import load_boston
|
|
>>> from sklearn.cross_validation import cross_val_score
|
|
>>> from sklearn.tree import DecisionTreeRegressor
|
|
>>> boston = load_boston()
|
|
>>> regressor = DecisionTreeRegressor(random_state=0)
|
|
>>> cross_val_score(regressor, boston.data, boston.target, cv=10)
|
|
... # doctest: +SKIP
|
|
...
|
|
array([ 0.61..., 0.57..., -0.34..., 0.41..., 0.75...,
|
|
0.07..., 0.29..., 0.33..., -1.42..., -1.77...])
|
|
"""
|
|
def __init__(self,
|
|
criterion="mse",
|
|
splitter="best",
|
|
max_depth=None,
|
|
min_samples_split=2,
|
|
min_samples_leaf=1,
|
|
min_weight_fraction_leaf=0.,
|
|
max_features=None,
|
|
random_state=None,
|
|
max_leaf_nodes=None):
|
|
super(DecisionTreeRegressor, self).__init__(
|
|
criterion=criterion,
|
|
splitter=splitter,
|
|
max_depth=max_depth,
|
|
min_samples_split=min_samples_split,
|
|
min_samples_leaf=min_samples_leaf,
|
|
min_weight_fraction_leaf=min_weight_fraction_leaf,
|
|
max_features=max_features,
|
|
max_leaf_nodes=max_leaf_nodes,
|
|
random_state=random_state)
|
|
|
|
|
|
class ExtraTreeClassifier(DecisionTreeClassifier):
|
|
"""An extremely randomized tree classifier.
|
|
|
|
Extra-trees differ from classic decision trees in the way they are built.
|
|
When looking for the best split to separate the samples of a node into two
|
|
groups, random splits are drawn for each of the `max_features` randomly
|
|
selected features and the best split among those is chosen. When
|
|
`max_features` is set 1, this amounts to building a totally random
|
|
decision tree.
|
|
|
|
Warning: Extra-trees should only be used within ensemble methods.
|
|
|
|
See also
|
|
--------
|
|
ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor
|
|
|
|
References
|
|
----------
|
|
|
|
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
|
|
Machine Learning, 63(1), 3-42, 2006.
|
|
"""
|
|
def __init__(self,
|
|
criterion="gini",
|
|
splitter="random",
|
|
max_depth=None,
|
|
min_samples_split=2,
|
|
min_samples_leaf=1,
|
|
min_weight_fraction_leaf=0.,
|
|
max_features="auto",
|
|
random_state=None,
|
|
max_leaf_nodes=None,
|
|
class_weight=None):
|
|
super(ExtraTreeClassifier, self).__init__(
|
|
criterion=criterion,
|
|
splitter=splitter,
|
|
max_depth=max_depth,
|
|
min_samples_split=min_samples_split,
|
|
min_samples_leaf=min_samples_leaf,
|
|
min_weight_fraction_leaf=min_weight_fraction_leaf,
|
|
max_features=max_features,
|
|
max_leaf_nodes=max_leaf_nodes,
|
|
class_weight=class_weight,
|
|
random_state=random_state)
|
|
|
|
|
|
class ExtraTreeRegressor(DecisionTreeRegressor):
|
|
"""An extremely randomized tree regressor.
|
|
|
|
Extra-trees differ from classic decision trees in the way they are built.
|
|
When looking for the best split to separate the samples of a node into two
|
|
groups, random splits are drawn for each of the `max_features` randomly
|
|
selected features and the best split among those is chosen. When
|
|
`max_features` is set 1, this amounts to building a totally random
|
|
decision tree.
|
|
|
|
Warning: Extra-trees should only be used within ensemble methods.
|
|
|
|
See also
|
|
--------
|
|
ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor
|
|
|
|
References
|
|
----------
|
|
|
|
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
|
|
Machine Learning, 63(1), 3-42, 2006.
|
|
"""
|
|
def __init__(self,
|
|
criterion="mse",
|
|
splitter="random",
|
|
max_depth=None,
|
|
min_samples_split=2,
|
|
min_samples_leaf=1,
|
|
min_weight_fraction_leaf=0.,
|
|
max_features="auto",
|
|
random_state=None,
|
|
max_leaf_nodes=None):
|
|
super(ExtraTreeRegressor, self).__init__(
|
|
criterion=criterion,
|
|
splitter=splitter,
|
|
max_depth=max_depth,
|
|
min_samples_split=min_samples_split,
|
|
min_samples_leaf=min_samples_leaf,
|
|
min_weight_fraction_leaf=min_weight_fraction_leaf,
|
|
max_features=max_features,
|
|
max_leaf_nodes=max_leaf_nodes,
|
|
random_state=random_state)
|