機械学習の続き：tree.DecisionTreeClassifier()について調べてみる。
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class sklearn.tree.DecisionTreeClassifier( criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)[source]¶ |
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Methods apply(X[, check_input]) Returns the index of the leaf that each sample is predicted as. decision_path(X[, check_input]) Return the decision path in the tree fit(X, y[, sample_weight, check_input, …]) Build a decision tree classifier from the training set (X, y). get_params([deep]) Get parameters for this estimator. predict(X[, check_input]) Predict class or regression value for X. predict_log_proba(X) Predict class log-probabilities of the input samples X. predict_proba(X[, check_input]) Predict class probabilities of the input samples X. score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator. |
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とくに# 分類器の生成メソッドfit()について
fit(X, y, sample_weight=None, check_input=True, X_idx_sorted=None)[source]
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Build a decision tree classifier from the training set (X, y). Parameters: X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : boolean, (default=True) Allow to bypass several input checking. Don’t use this parameter unless you know what you do. X_idx_sorted : array-like, shape = [n_samples, n_features], optional The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don’t use this parameter unless you know what to do. Returns: self : object Returns self. |
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fit（）のsource
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""" This module gathers tree-based methods, including decision, regression and randomized trees. Single and multi-output problems are both handled. """ # Authors: Gilles Louppe <g.louppe@gmail.com> # Peter Prettenhofer <peter.prettenhofer@gmail.com> # Brian Holt <bdholt1@gmail.com> # Noel Dawe <noel@dawe.me> # Satrajit Gosh <satrajit.ghosh@gmail.com> # Joly Arnaud <arnaud.v.joly@gmail.com> # Fares Hedayati <fares.hedayati@gmail.com> # Nelson Liu <nelson@nelsonliu.me> # # License: BSD 3 clause from __future__ import division import numbers import warnings from abc import ABCMeta from abc import abstractmethod from math import ceil import numpy as np from scipy.sparse import issparse from ..base import BaseEstimator from ..base import ClassifierMixin from ..base import RegressorMixin from ..base import is_classifier from ..externals import six from ..utils import check_array from ..utils import check_random_state from ..utils import compute_sample_weight from ..utils.multiclass import check_classification_targets from ..utils.validation import check_is_fitted from ._criterion import Criterion from ._splitter import Splitter from ._tree import DepthFirstTreeBuilder from ._tree import BestFirstTreeBuilder from ._tree import Tree from . import _tree, _splitter, _criterion __all__ = ["DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor"] # ============================================================================= # Types and constants # ============================================================================= DTYPE = _tree.DTYPE DOUBLE = _tree.DOUBLE CRITERIA_CLF = {"gini": _criterion.Gini, "entropy": _criterion.Entropy} CRITERIA_REG = {"mse": _criterion.MSE, "friedman_mse": _criterion.FriedmanMSE, "mae": _criterion.MAE} DENSE_SPLITTERS = {"best": _splitter.BestSplitter, "random": _splitter.RandomSplitter} SPARSE_SPLITTERS = {"best": _splitter.BestSparseSplitter, "random": _splitter.RandomSparseSplitter} # ============================================================================= # Base decision tree # ============================================================================= class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator)): """Base class for decision trees. Warning: This class should not be used directly. Use derived classes instead. """ @abstractmethod def __init__(self, criterion, splitter, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, random_state, min_impurity_decrease, min_impurity_split, class_weight=None, presort=False): self.criterion = criterion self.splitter = splitter self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.random_state = random_state self.max_leaf_nodes = max_leaf_nodes self.min_impurity_decrease = min_impurity_decrease self.min_impurity_split = min_impurity_split self.class_weight = class_weight self.presort = presort def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): random_state = check_random_state(self.random_state) if check_input: X = check_array(X, dtype=DTYPE, accept_sparse="csc") y = check_array(y, ensure_2d=False, dtype=None) if issparse(X): X.sort_indices() if X.indices.dtype != np.intc or X.indptr.dtype != np.intc: raise ValueError("No support for np.int64 index based " "sparse matrices") # Determine output settings n_samples, self.n_features_ = X.shape is_classification = is_classifier(self) y = np.atleast_1d(y) expanded_class_weight = None if y.ndim == 1: # reshape is necessary to preserve the data contiguity against vs # [:, np.newaxis] that does not. y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] if is_classification: check_classification_targets(y) y = np.copy(y) self.classes_ = [] self.n_classes_ = [] if self.class_weight is not None: y_original = np.copy(y) y_encoded = np.zeros(y.shape, dtype=np.int) for k in range(self.n_outputs_): classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes_k) self.n_classes_.append(classes_k.shape[0]) y = y_encoded if self.class_weight is not None: expanded_class_weight = compute_sample_weight( self.class_weight, y_original) else: self.classes_ = [None] * self.n_outputs_ self.n_classes_ = [1] * self.n_outputs_ self.n_classes_ = np.array(self.n_classes_, dtype=np.intp) if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous: y = np.ascontiguousarray(y, dtype=DOUBLE) # Check parameters max_depth = ((2 ** 31) - 1 if self.max_depth is None else self.max_depth) max_leaf_nodes = (-1 if self.max_leaf_nodes is None else self.max_leaf_nodes) if isinstance(self.min_samples_leaf, (numbers.Integral, np.integer)): if not 1 <= self.min_samples_leaf: raise ValueError("min_samples_leaf must be at least 1 " "or in (0, 0.5], got %s" % self.min_samples_leaf) min_samples_leaf = self.min_samples_leaf else: # float if not 0. < self.min_samples_leaf <= 0.5: raise ValueError("min_samples_leaf must be at least 1 " "or in (0, 0.5], got %s" % self.min_samples_leaf) min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples)) if isinstance(self.min_samples_split, (numbers.Integral, np.integer)): if not 2 <= self.min_samples_split: raise ValueError("min_samples_split must be an integer " "greater than 1 or a float in (0.0, 1.0]; " "got the integer %s" % self.min_samples_split) min_samples_split = self.min_samples_split else: # float if not 0. < self.min_samples_split <= 1.: raise ValueError("min_samples_split must be an integer " "greater than 1 or a float in (0.0, 1.0]; " "got the float %s" % self.min_samples_split) min_samples_split = int(ceil(self.min_samples_split * n_samples)) min_samples_split = max(2, min_samples_split) min_samples_split = max(min_samples_split, 2 * min_samples_leaf) if isinstance(self.max_features, six.string_types): if self.max_features == "auto": if is_classification: max_features = max(1, int(np.sqrt(self.n_features_))) else: max_features = self.n_features_ elif self.max_features == "sqrt": max_features = max(1, int(np.sqrt(self.n_features_))) elif self.max_features == "log2": max_features = max(1, int(np.log2(self.n_features_))) else: raise ValueError( 'Invalid value for max_features. Allowed string ' 'values are "auto", "sqrt" or "log2".') elif self.max_features is None: max_features = self.n_features_ elif isinstance(self.max_features, (numbers.Integral, np.integer)): max_features = self.max_features else: # float if self.max_features > 0.0: max_features = max(1, int(self.max_features * self.n_features_)) else: max_features = 0 self.max_features_ = max_features if len(y) != n_samples: raise ValueError("Number of labels=%d does not match " "number of samples=%d" % (len(y), n_samples)) if not 0 <= self.min_weight_fraction_leaf <= 0.5: raise ValueError("min_weight_fraction_leaf must in [0, 0.5]") if max_depth <= 0: raise ValueError("max_depth must be greater than zero. ") if not (0 < max_features <= self.n_features_): raise ValueError("max_features must be in (0, n_features]") if not isinstance(max_leaf_nodes, (numbers.Integral, np.integer)): raise ValueError("max_leaf_nodes must be integral number but was " "%r" % max_leaf_nodes) if -1 < max_leaf_nodes < 2: raise ValueError(("max_leaf_nodes {0} must be either None " "or larger than 1").format(max_leaf_nodes)) if sample_weight is not None: if (getattr(sample_weight, "dtype", None) != DOUBLE or not sample_weight.flags.contiguous): sample_weight = np.ascontiguousarray( sample_weight, dtype=DOUBLE) if len(sample_weight.shape) > 1: raise ValueError("Sample weights array has more " "than one dimension: %d" % len(sample_weight.shape)) if len(sample_weight) != n_samples: raise ValueError("Number of weights=%d does not match " "number of samples=%d" % (len(sample_weight), n_samples)) if expanded_class_weight is not None: if sample_weight is not None: sample_weight = sample_weight * expanded_class_weight else: sample_weight = expanded_class_weight # Set min_weight_leaf from min_weight_fraction_leaf if sample_weight is None: min_weight_leaf = (self.min_weight_fraction_leaf * n_samples) else: min_weight_leaf = (self.min_weight_fraction_leaf * np.sum(sample_weight)) if self.min_impurity_split is not None: warnings.warn("The min_impurity_split parameter is deprecated and" " will be removed in version 0.21. " "Use the min_impurity_decrease parameter instead.", DeprecationWarning) min_impurity_split = self.min_impurity_split else: min_impurity_split = 1e-7 if min_impurity_split < 0.: raise ValueError("min_impurity_split must be greater than " "or equal to 0") if self.min_impurity_decrease < 0.: raise ValueError("min_impurity_decrease must be greater than " "or equal to 0") presort = self.presort # Allow presort to be 'auto', which means True if the dataset is dense, # otherwise it will be False. if self.presort == 'auto' and issparse(X): presort = False elif self.presort == 'auto': presort = True if presort is True and issparse(X): raise ValueError("Presorting is not supported for sparse " "matrices.") # If multiple trees are built on the same dataset, we only want to # presort once. Splitters now can accept presorted indices if desired, # but do not handle any presorting themselves. Ensemble algorithms # which desire presorting must do presorting themselves and pass that # matrix into each tree. if X_idx_sorted is None and presort: X_idx_sorted = np.asfortranarray(np.argsort(X, axis=0), dtype=np.int32) if presort and X_idx_sorted.shape != X.shape: raise ValueError("The shape of X (X.shape = {}) doesn't match " "the shape of X_idx_sorted (X_idx_sorted" ".shape = {})".format(X.shape, X_idx_sorted.shape)) # Build tree criterion = self.criterion if not isinstance(criterion, Criterion): if is_classification: criterion = CRITERIA_CLF[self.criterion](self.n_outputs_, self.n_classes_) else: criterion = CRITERIA_REG[self.criterion](self.n_outputs_, n_samples) SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS splitter = self.splitter if not isinstance(self.splitter, Splitter): splitter = SPLITTERS[self.splitter](criterion, self.max_features_, min_samples_leaf, min_weight_leaf, random_state, self.presort) self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_) # Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise if max_leaf_nodes < 0: builder = DepthFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, self.min_impurity_decrease, min_impurity_split) else: builder = BestFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf, min_weight_leaf, max_depth, max_leaf_nodes, self.min_impurity_decrease, min_impurity_split) builder.build(self.tree_, X, y, sample_weight, X_idx_sorted) if self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] return self def _validate_X_predict(self, X, check_input): """Validate X whenever one tries to predict, apply, predict_proba""" if check_input: X = check_array(X, dtype=DTYPE, accept_sparse="csr") if issparse(X) and (X.indices.dtype != np.intc or X.indptr.dtype != np.intc): raise ValueError("No support for np.int64 index based " "sparse matrices") n_features = X.shape[1] 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)) return X def predict(self, X, check_input=True): """Predict class or regression value for X. For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. 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``. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted classes, or the predict values. """ check_is_fitted(self, 'tree_') X = self._validate_X_predict(X, check_input) proba = self.tree_.predict(X) n_samples = X.shape[0] # Classification if is_classifier(self): if self.n_outputs_ == 1: return self.classes_.take(np.argmax(proba, axis=1), axis=0) else: predictions = np.zeros((n_samples, self.n_outputs_)) for k in range(self.n_outputs_): predictions[:, k] = self.classes_[k].take( np.argmax(proba[:, k], axis=1), axis=0) return predictions # Regression else: if self.n_outputs_ == 1: return proba[:, 0] else: return proba[:, :, 0] def apply(self, X, check_input=True): """ Returns the index of the leaf that each sample is predicted as. .. versionadded:: 0.17 Parameters ---------- X : array_like or sparse matrix, 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``. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- X_leaves : array_like, shape = [n_samples,] For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within ``[0; self.tree_.node_count)``, possibly with gaps in the numbering. """ check_is_fitted(self, 'tree_') X = self._validate_X_predict(X, check_input) return self.tree_.apply(X) def decision_path(self, X, check_input=True): """Return the decision path in the tree .. versionadded:: 0.18 Parameters ---------- X : array_like or sparse matrix, 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``. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- indicator : sparse csr array, shape = [n_samples, n_nodes] Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. """ X = self._validate_X_predict(X, check_input) return self.tree_.decision_path(X) @property def feature_importances_(self): """Return the feature importances. 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. Returns ------- feature_importances_ : array, shape = [n_features] """ check_is_fitted(self, 'tree_') return self.tree_.compute_feature_importances() # ============================================================================= # Public estimators # ============================================================================= class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin): """A decision tree classifier. Read more in the :ref:`User Guide <tree>`. Parameters ---------- criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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_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. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. 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=sqrt(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. 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`. 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. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. class_weight : dict, list of dicts, "balanced" or None, default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. presort : bool, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. Attributes ---------- classes_ : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). 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]_. max_features_ : int, The inferred value of max_features. n_classes_ : int or list The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). n_features_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree object The underlying Tree object. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. See also -------- DecisionTreeRegressor References ---------- .. [1] https://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_iris >>> from sklearn.model_selection import cross_val_score >>> from sklearn.tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> 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, min_impurity_decrease=0., min_impurity_split=None, class_weight=None, presort=False): 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, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, presort=presort) def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """Build a decision tree classifier from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. X_idx_sorted : array-like, shape = [n_samples, n_features], optional The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. Returns ------- self : object Returns self. """ super(DecisionTreeClassifier, self).fit( X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted) return self 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``. check_input : bool Run check_array on X. 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, 'tree_') X = self._validate_X_predict(X, check_input) 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. Read more in the :ref:`User Guide <tree>`. Parameters ---------- criterion : string, optional (default="mse") The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, "friedman_mse", which uses mean squared error with Friedman's improvement score for potential splits, and "mae" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. 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_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. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. 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. 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`. 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. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. presort : bool, optional (default=False) Whether to presort the data to speed up the finding of best splits in fitting. For the default settings of a decision tree on large datasets, setting this to true may slow down the training process. When using either a smaller dataset or a restricted depth, this may speed up the training. Attributes ---------- 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]_. max_features_ : int, The inferred value of max_features. n_features_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree object The underlying Tree object. Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. See also -------- DecisionTreeClassifier References ---------- .. [1] https://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.model_selection 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, min_impurity_decrease=0., min_impurity_split=None, presort=False): 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, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, presort=presort) def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): """Build a decision tree regressor from the training set (X, y). Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (real numbers). Use ``dtype=np.float64`` and ``order='C'`` for maximum efficiency. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. X_idx_sorted : array-like, shape = [n_samples, n_features], optional The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do. Returns ------- self : object Returns self. """ super(DecisionTreeRegressor, self).fit( X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted) return self 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. Read more in the :ref:`User Guide <tree>`. Parameters ---------- criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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_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. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. 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=sqrt(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. 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`. 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. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. class_weight : dict, list of dicts, "balanced" or None, default=None Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. See also -------- ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. 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, min_impurity_decrease=0., min_impurity_split=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, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, 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. Read more in the :ref:`User Guide <tree>`. Parameters ---------- criterion : string, optional (default="mse") The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute error. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. 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_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. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for percentages. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for percentages. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. 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. 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`. min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 min_impurity_split : float, Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19 and will be removed in 0.21. Use ``min_impurity_decrease`` instead. 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. See also -------- ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. 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, min_impurity_decrease=0., min_impurity_split=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, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, random_state=random_state) |