1478 lines
63 KiB
Python
1478 lines
63 KiB
Python
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"""Fast Gradient Boosting decision trees for classification and regression."""
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# Author: Nicolas Hug
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from abc import ABC, abstractmethod
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from functools import partial
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import numpy as np
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from timeit import default_timer as time
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from ...base import (BaseEstimator, RegressorMixin, ClassifierMixin,
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is_classifier)
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from ...utils import check_random_state, check_array, resample
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from ...utils.validation import (check_is_fitted,
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check_consistent_length,
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_check_sample_weight,
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_deprecate_positional_args)
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from ...utils.multiclass import check_classification_targets
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from ...metrics import check_scoring
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from ...model_selection import train_test_split
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from ...preprocessing import LabelEncoder
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from ._gradient_boosting import _update_raw_predictions
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from .common import Y_DTYPE, X_DTYPE, X_BINNED_DTYPE
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from .binning import _BinMapper
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from .grower import TreeGrower
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from .loss import _LOSSES
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from .loss import BaseLoss
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class BaseHistGradientBoosting(BaseEstimator, ABC):
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"""Base class for histogram-based gradient boosting estimators."""
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@abstractmethod
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def __init__(self, loss, *, learning_rate, max_iter, max_leaf_nodes,
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max_depth, min_samples_leaf, l2_regularization, max_bins,
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categorical_features, monotonic_cst,
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warm_start, early_stopping, scoring,
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validation_fraction, n_iter_no_change, tol, verbose,
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random_state):
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self.loss = loss
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self.learning_rate = learning_rate
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self.max_iter = max_iter
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self.max_leaf_nodes = max_leaf_nodes
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self.max_depth = max_depth
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self.min_samples_leaf = min_samples_leaf
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self.l2_regularization = l2_regularization
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self.max_bins = max_bins
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self.monotonic_cst = monotonic_cst
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self.categorical_features = categorical_features
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self.warm_start = warm_start
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self.early_stopping = early_stopping
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self.scoring = scoring
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self.validation_fraction = validation_fraction
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self.n_iter_no_change = n_iter_no_change
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self.tol = tol
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self.verbose = verbose
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self.random_state = random_state
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def _validate_parameters(self):
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"""Validate parameters passed to __init__.
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The parameters that are directly passed to the grower are checked in
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TreeGrower."""
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if (self.loss not in self._VALID_LOSSES and
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not isinstance(self.loss, BaseLoss)):
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raise ValueError(
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"Loss {} is not supported for {}. Accepted losses: "
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"{}.".format(self.loss, self.__class__.__name__,
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', '.join(self._VALID_LOSSES)))
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if self.learning_rate <= 0:
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raise ValueError('learning_rate={} must '
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'be strictly positive'.format(self.learning_rate))
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if self.max_iter < 1:
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raise ValueError('max_iter={} must not be smaller '
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'than 1.'.format(self.max_iter))
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if self.n_iter_no_change < 0:
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raise ValueError('n_iter_no_change={} must be '
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'positive.'.format(self.n_iter_no_change))
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if (self.validation_fraction is not None and
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self.validation_fraction <= 0):
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raise ValueError(
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'validation_fraction={} must be strictly '
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'positive, or None.'.format(self.validation_fraction))
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if self.tol is not None and self.tol < 0:
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raise ValueError('tol={} '
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'must not be smaller than 0.'.format(self.tol))
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if not (2 <= self.max_bins <= 255):
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raise ValueError('max_bins={} should be no smaller than 2 '
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'and no larger than 255.'.format(self.max_bins))
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if self.monotonic_cst is not None and self.n_trees_per_iteration_ != 1:
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raise ValueError(
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'monotonic constraints are not supported for '
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'multiclass classification.'
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)
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def _check_categories(self, X):
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"""Check and validate categorical features in X
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Return
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------
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is_categorical : ndarray of shape (n_features,) or None, dtype=bool
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Indicates whether a feature is categorical. If no feature is
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categorical, this is None.
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known_categories : list of size n_features or None
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The list contains, for each feature:
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- an array of shape (n_categories,) with the unique cat values
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- None if the feature is not categorical
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None if no feature is categorical.
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"""
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if self.categorical_features is None:
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return None, None
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categorical_features = np.asarray(self.categorical_features)
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if categorical_features.size == 0:
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return None, None
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if categorical_features.dtype.kind not in ('i', 'b'):
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raise ValueError("categorical_features must be an array-like of "
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"bools or array-like of ints.")
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n_features = X.shape[1]
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# check for categorical features as indices
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if categorical_features.dtype.kind == 'i':
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if (np.max(categorical_features) >= n_features
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or np.min(categorical_features) < 0):
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raise ValueError("categorical_features set as integer "
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"indices must be in [0, n_features - 1]")
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is_categorical = np.zeros(n_features, dtype=bool)
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is_categorical[categorical_features] = True
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else:
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if categorical_features.shape[0] != n_features:
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raise ValueError("categorical_features set as a boolean mask "
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"must have shape (n_features,), got: "
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f"{categorical_features.shape}")
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is_categorical = categorical_features
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if not np.any(is_categorical):
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return None, None
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# compute the known categories in the training data. We need to do
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# that here instead of in the BinMapper because in case of early
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# stopping, the mapper only gets a fraction of the training data.
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known_categories = []
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for f_idx in range(n_features):
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if is_categorical[f_idx]:
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categories = np.unique(X[:, f_idx])
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missing = np.isnan(categories)
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if missing.any():
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categories = categories[~missing]
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if categories.size > self.max_bins:
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raise ValueError(
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f"Categorical feature at index {f_idx} is "
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f"expected to have a "
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f"cardinality <= {self.max_bins}"
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)
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if (categories >= self.max_bins).any():
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raise ValueError(
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f"Categorical feature at index {f_idx} is "
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f"expected to be encoded with "
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f"values < {self.max_bins}"
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)
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else:
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categories = None
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known_categories.append(categories)
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return is_categorical, known_categories
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def fit(self, X, y, sample_weight=None):
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"""Fit the gradient boosting model.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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The input samples.
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y : array-like of shape (n_samples,)
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Target values.
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sample_weight : array-like of shape (n_samples,) default=None
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Weights of training data.
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.. versionadded:: 0.23
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Returns
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-------
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self : object
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"""
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fit_start_time = time()
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acc_find_split_time = 0. # time spent finding the best splits
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acc_apply_split_time = 0. # time spent splitting nodes
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acc_compute_hist_time = 0. # time spent computing histograms
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# time spent predicting X for gradient and hessians update
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acc_prediction_time = 0.
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X, y = self._validate_data(X, y, dtype=[X_DTYPE],
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force_all_finite=False)
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y = self._encode_y(y)
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check_consistent_length(X, y)
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# Do not create unit sample weights by default to later skip some
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# computation
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if sample_weight is not None:
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sample_weight = _check_sample_weight(sample_weight, X,
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dtype=np.float64)
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# TODO: remove when PDP suports sample weights
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self._fitted_with_sw = True
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rng = check_random_state(self.random_state)
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# When warm starting, we want to re-use the same seed that was used
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# the first time fit was called (e.g. for subsampling or for the
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# train/val split).
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if not (self.warm_start and self._is_fitted()):
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self._random_seed = rng.randint(np.iinfo(np.uint32).max,
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dtype='u8')
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self._validate_parameters()
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# used for validation in predict
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n_samples, self._n_features = X.shape
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self.is_categorical_, known_categories = self._check_categories(X)
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# we need this stateful variable to tell raw_predict() that it was
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# called from fit() (this current method), and that the data it has
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# received is pre-binned.
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# predicting is faster on pre-binned data, so we want early stopping
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# predictions to be made on pre-binned data. Unfortunately the _scorer
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# can only call predict() or predict_proba(), not raw_predict(), and
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# there's no way to tell the scorer that it needs to predict binned
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# data.
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self._in_fit = True
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if isinstance(self.loss, str):
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self._loss = self._get_loss(sample_weight=sample_weight)
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elif isinstance(self.loss, BaseLoss):
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self._loss = self.loss
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if self.early_stopping == 'auto':
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self.do_early_stopping_ = n_samples > 10000
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else:
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self.do_early_stopping_ = self.early_stopping
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# create validation data if needed
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self._use_validation_data = self.validation_fraction is not None
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if self.do_early_stopping_ and self._use_validation_data:
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# stratify for classification
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stratify = y if hasattr(self._loss, 'predict_proba') else None
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# Save the state of the RNG for the training and validation split.
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# This is needed in order to have the same split when using
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# warm starting.
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if sample_weight is None:
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X_train, X_val, y_train, y_val = train_test_split(
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X, y, test_size=self.validation_fraction,
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stratify=stratify,
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random_state=self._random_seed)
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sample_weight_train = sample_weight_val = None
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else:
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# TODO: incorporate sample_weight in sampling here, as well as
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# stratify
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(X_train, X_val, y_train, y_val, sample_weight_train,
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sample_weight_val) = train_test_split(
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X, y, sample_weight, test_size=self.validation_fraction,
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stratify=stratify,
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random_state=self._random_seed)
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else:
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X_train, y_train, sample_weight_train = X, y, sample_weight
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X_val = y_val = sample_weight_val = None
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# Bin the data
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# For ease of use of the API, the user-facing GBDT classes accept the
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# parameter max_bins, which doesn't take into account the bin for
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# missing values (which is always allocated). However, since max_bins
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# isn't the true maximal number of bins, all other private classes
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# (binmapper, histbuilder...) accept n_bins instead, which is the
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# actual total number of bins. Everywhere in the code, the
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# convention is that n_bins == max_bins + 1
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n_bins = self.max_bins + 1 # + 1 for missing values
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self._bin_mapper = _BinMapper(
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n_bins=n_bins,
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is_categorical=self.is_categorical_,
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known_categories=known_categories,
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random_state=self._random_seed)
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X_binned_train = self._bin_data(X_train, is_training_data=True)
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if X_val is not None:
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X_binned_val = self._bin_data(X_val, is_training_data=False)
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else:
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X_binned_val = None
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# Uses binned data to check for missing values
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has_missing_values = (
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X_binned_train == self._bin_mapper.missing_values_bin_idx_).any(
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axis=0).astype(np.uint8)
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if self.verbose:
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print("Fitting gradient boosted rounds:")
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n_samples = X_binned_train.shape[0]
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# First time calling fit, or no warm start
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if not (self._is_fitted() and self.warm_start):
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# Clear random state and score attributes
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self._clear_state()
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# initialize raw_predictions: those are the accumulated values
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# predicted by the trees for the training data. raw_predictions has
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# shape (n_trees_per_iteration, n_samples) where
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# n_trees_per_iterations is n_classes in multiclass classification,
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# else 1.
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self._baseline_prediction = self._loss.get_baseline_prediction(
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y_train, sample_weight_train, self.n_trees_per_iteration_
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)
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raw_predictions = np.zeros(
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shape=(self.n_trees_per_iteration_, n_samples),
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dtype=self._baseline_prediction.dtype
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)
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raw_predictions += self._baseline_prediction
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# predictors is a matrix (list of lists) of TreePredictor objects
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# with shape (n_iter_, n_trees_per_iteration)
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self._predictors = predictors = []
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# Initialize structures and attributes related to early stopping
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self._scorer = None # set if scoring != loss
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raw_predictions_val = None # set if scoring == loss and use val
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self.train_score_ = []
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self.validation_score_ = []
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if self.do_early_stopping_:
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# populate train_score and validation_score with the
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# predictions of the initial model (before the first tree)
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if self.scoring == 'loss':
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# we're going to compute scoring w.r.t the loss. As losses
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# take raw predictions as input (unlike the scorers), we
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# can optimize a bit and avoid repeating computing the
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# predictions of the previous trees. We'll re-use
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# raw_predictions (as it's needed for training anyway) for
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# evaluating the training loss, and create
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# raw_predictions_val for storing the raw predictions of
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# the validation data.
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if self._use_validation_data:
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raw_predictions_val = np.zeros(
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shape=(self.n_trees_per_iteration_,
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X_binned_val.shape[0]),
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dtype=self._baseline_prediction.dtype
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)
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raw_predictions_val += self._baseline_prediction
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self._check_early_stopping_loss(raw_predictions, y_train,
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sample_weight_train,
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raw_predictions_val, y_val,
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sample_weight_val)
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else:
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self._scorer = check_scoring(self, self.scoring)
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# _scorer is a callable with signature (est, X, y) and
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# calls est.predict() or est.predict_proba() depending on
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# its nature.
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# Unfortunately, each call to _scorer() will compute
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# the predictions of all the trees. So we use a subset of
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# the training set to compute train scores.
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# Compute the subsample set
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(X_binned_small_train,
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y_small_train,
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sample_weight_small_train) = self._get_small_trainset(
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X_binned_train, y_train, sample_weight_train,
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self._random_seed)
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self._check_early_stopping_scorer(
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X_binned_small_train, y_small_train,
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sample_weight_small_train,
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X_binned_val, y_val, sample_weight_val,
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)
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begin_at_stage = 0
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# warm start: this is not the first time fit was called
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else:
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# Check that the maximum number of iterations is not smaller
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# than the number of iterations from the previous fit
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if self.max_iter < self.n_iter_:
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raise ValueError(
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'max_iter=%d must be larger than or equal to '
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'n_iter_=%d when warm_start==True'
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% (self.max_iter, self.n_iter_)
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)
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# Convert array attributes to lists
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self.train_score_ = self.train_score_.tolist()
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self.validation_score_ = self.validation_score_.tolist()
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# Compute raw predictions
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raw_predictions = self._raw_predict(X_binned_train)
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if self.do_early_stopping_ and self._use_validation_data:
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raw_predictions_val = self._raw_predict(X_binned_val)
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else:
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raw_predictions_val = None
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||
|
if self.do_early_stopping_ and self.scoring != 'loss':
|
||
|
# Compute the subsample set
|
||
|
(X_binned_small_train,
|
||
|
y_small_train,
|
||
|
sample_weight_small_train) = self._get_small_trainset(
|
||
|
X_binned_train, y_train, sample_weight_train,
|
||
|
self._random_seed)
|
||
|
|
||
|
# Get the predictors from the previous fit
|
||
|
predictors = self._predictors
|
||
|
|
||
|
begin_at_stage = self.n_iter_
|
||
|
|
||
|
# initialize gradients and hessians (empty arrays).
|
||
|
# shape = (n_trees_per_iteration, n_samples).
|
||
|
gradients, hessians = self._loss.init_gradients_and_hessians(
|
||
|
n_samples=n_samples,
|
||
|
prediction_dim=self.n_trees_per_iteration_,
|
||
|
sample_weight=sample_weight_train
|
||
|
)
|
||
|
|
||
|
for iteration in range(begin_at_stage, self.max_iter):
|
||
|
|
||
|
if self.verbose:
|
||
|
iteration_start_time = time()
|
||
|
print("[{}/{}] ".format(iteration + 1, self.max_iter),
|
||
|
end='', flush=True)
|
||
|
|
||
|
# Update gradients and hessians, inplace
|
||
|
self._loss.update_gradients_and_hessians(gradients, hessians,
|
||
|
y_train, raw_predictions,
|
||
|
sample_weight_train)
|
||
|
|
||
|
# Append a list since there may be more than 1 predictor per iter
|
||
|
predictors.append([])
|
||
|
|
||
|
# Build `n_trees_per_iteration` trees.
|
||
|
for k in range(self.n_trees_per_iteration_):
|
||
|
grower = TreeGrower(
|
||
|
X_binned_train, gradients[k, :], hessians[k, :],
|
||
|
n_bins=n_bins,
|
||
|
n_bins_non_missing=self._bin_mapper.n_bins_non_missing_,
|
||
|
has_missing_values=has_missing_values,
|
||
|
is_categorical=self.is_categorical_,
|
||
|
monotonic_cst=self.monotonic_cst,
|
||
|
max_leaf_nodes=self.max_leaf_nodes,
|
||
|
max_depth=self.max_depth,
|
||
|
min_samples_leaf=self.min_samples_leaf,
|
||
|
l2_regularization=self.l2_regularization,
|
||
|
shrinkage=self.learning_rate)
|
||
|
grower.grow()
|
||
|
|
||
|
acc_apply_split_time += grower.total_apply_split_time
|
||
|
acc_find_split_time += grower.total_find_split_time
|
||
|
acc_compute_hist_time += grower.total_compute_hist_time
|
||
|
|
||
|
if self._loss.need_update_leaves_values:
|
||
|
self._loss.update_leaves_values(grower, y_train,
|
||
|
raw_predictions[k, :],
|
||
|
sample_weight_train)
|
||
|
|
||
|
predictor = grower.make_predictor(
|
||
|
binning_thresholds=self._bin_mapper.bin_thresholds_
|
||
|
)
|
||
|
predictors[-1].append(predictor)
|
||
|
|
||
|
# Update raw_predictions with the predictions of the newly
|
||
|
# created tree.
|
||
|
tic_pred = time()
|
||
|
_update_raw_predictions(raw_predictions[k, :], grower)
|
||
|
toc_pred = time()
|
||
|
acc_prediction_time += toc_pred - tic_pred
|
||
|
|
||
|
should_early_stop = False
|
||
|
if self.do_early_stopping_:
|
||
|
if self.scoring == 'loss':
|
||
|
# Update raw_predictions_val with the newest tree(s)
|
||
|
if self._use_validation_data:
|
||
|
for k, pred in enumerate(self._predictors[-1]):
|
||
|
raw_predictions_val[k, :] += (
|
||
|
pred.predict_binned(
|
||
|
X_binned_val,
|
||
|
self._bin_mapper.missing_values_bin_idx_
|
||
|
)
|
||
|
)
|
||
|
|
||
|
should_early_stop = self._check_early_stopping_loss(
|
||
|
raw_predictions, y_train, sample_weight_train,
|
||
|
raw_predictions_val, y_val, sample_weight_val
|
||
|
)
|
||
|
|
||
|
else:
|
||
|
should_early_stop = self._check_early_stopping_scorer(
|
||
|
X_binned_small_train, y_small_train,
|
||
|
sample_weight_small_train,
|
||
|
X_binned_val, y_val, sample_weight_val
|
||
|
)
|
||
|
|
||
|
if self.verbose:
|
||
|
self._print_iteration_stats(iteration_start_time)
|
||
|
|
||
|
# maybe we could also early stop if all the trees are stumps?
|
||
|
if should_early_stop:
|
||
|
break
|
||
|
|
||
|
if self.verbose:
|
||
|
duration = time() - fit_start_time
|
||
|
n_total_leaves = sum(
|
||
|
predictor.get_n_leaf_nodes()
|
||
|
for predictors_at_ith_iteration in self._predictors
|
||
|
for predictor in predictors_at_ith_iteration
|
||
|
)
|
||
|
n_predictors = sum(
|
||
|
len(predictors_at_ith_iteration)
|
||
|
for predictors_at_ith_iteration in self._predictors)
|
||
|
print("Fit {} trees in {:.3f} s, ({} total leaves)".format(
|
||
|
n_predictors, duration, n_total_leaves))
|
||
|
print("{:<32} {:.3f}s".format('Time spent computing histograms:',
|
||
|
acc_compute_hist_time))
|
||
|
print("{:<32} {:.3f}s".format('Time spent finding best splits:',
|
||
|
acc_find_split_time))
|
||
|
print("{:<32} {:.3f}s".format('Time spent applying splits:',
|
||
|
acc_apply_split_time))
|
||
|
print("{:<32} {:.3f}s".format('Time spent predicting:',
|
||
|
acc_prediction_time))
|
||
|
|
||
|
self.train_score_ = np.asarray(self.train_score_)
|
||
|
self.validation_score_ = np.asarray(self.validation_score_)
|
||
|
del self._in_fit # hard delete so we're sure it can't be used anymore
|
||
|
return self
|
||
|
|
||
|
def _is_fitted(self):
|
||
|
return len(getattr(self, '_predictors', [])) > 0
|
||
|
|
||
|
def _clear_state(self):
|
||
|
"""Clear the state of the gradient boosting model."""
|
||
|
for var in ('train_score_', 'validation_score_'):
|
||
|
if hasattr(self, var):
|
||
|
delattr(self, var)
|
||
|
|
||
|
def _get_small_trainset(self, X_binned_train, y_train, sample_weight_train,
|
||
|
seed):
|
||
|
"""Compute the indices of the subsample set and return this set.
|
||
|
|
||
|
For efficiency, we need to subsample the training set to compute scores
|
||
|
with scorers.
|
||
|
"""
|
||
|
# TODO: incorporate sample_weights here in `resample`
|
||
|
subsample_size = 10000
|
||
|
if X_binned_train.shape[0] > subsample_size:
|
||
|
indices = np.arange(X_binned_train.shape[0])
|
||
|
stratify = y_train if is_classifier(self) else None
|
||
|
indices = resample(indices, n_samples=subsample_size,
|
||
|
replace=False, random_state=seed,
|
||
|
stratify=stratify)
|
||
|
X_binned_small_train = X_binned_train[indices]
|
||
|
y_small_train = y_train[indices]
|
||
|
if sample_weight_train is not None:
|
||
|
sample_weight_small_train = sample_weight_train[indices]
|
||
|
else:
|
||
|
sample_weight_small_train = None
|
||
|
X_binned_small_train = np.ascontiguousarray(X_binned_small_train)
|
||
|
return (X_binned_small_train, y_small_train,
|
||
|
sample_weight_small_train)
|
||
|
else:
|
||
|
return X_binned_train, y_train, sample_weight_train
|
||
|
|
||
|
def _check_early_stopping_scorer(self, X_binned_small_train, y_small_train,
|
||
|
sample_weight_small_train,
|
||
|
X_binned_val, y_val, sample_weight_val):
|
||
|
"""Check if fitting should be early-stopped based on scorer.
|
||
|
|
||
|
Scores are computed on validation data or on training data.
|
||
|
"""
|
||
|
if is_classifier(self):
|
||
|
y_small_train = self.classes_[y_small_train.astype(int)]
|
||
|
|
||
|
if sample_weight_small_train is None:
|
||
|
self.train_score_.append(
|
||
|
self._scorer(self, X_binned_small_train, y_small_train)
|
||
|
)
|
||
|
else:
|
||
|
self.train_score_.append(
|
||
|
self._scorer(self, X_binned_small_train, y_small_train,
|
||
|
sample_weight=sample_weight_small_train)
|
||
|
)
|
||
|
|
||
|
if self._use_validation_data:
|
||
|
if is_classifier(self):
|
||
|
y_val = self.classes_[y_val.astype(int)]
|
||
|
if sample_weight_val is None:
|
||
|
self.validation_score_.append(
|
||
|
self._scorer(self, X_binned_val, y_val)
|
||
|
)
|
||
|
else:
|
||
|
self.validation_score_.append(
|
||
|
self._scorer(self, X_binned_val, y_val,
|
||
|
sample_weight=sample_weight_val)
|
||
|
)
|
||
|
return self._should_stop(self.validation_score_)
|
||
|
else:
|
||
|
return self._should_stop(self.train_score_)
|
||
|
|
||
|
def _check_early_stopping_loss(self,
|
||
|
raw_predictions,
|
||
|
y_train,
|
||
|
sample_weight_train,
|
||
|
raw_predictions_val,
|
||
|
y_val,
|
||
|
sample_weight_val):
|
||
|
"""Check if fitting should be early-stopped based on loss.
|
||
|
|
||
|
Scores are computed on validation data or on training data.
|
||
|
"""
|
||
|
|
||
|
self.train_score_.append(
|
||
|
-self._loss(y_train, raw_predictions, sample_weight_train)
|
||
|
)
|
||
|
|
||
|
if self._use_validation_data:
|
||
|
self.validation_score_.append(
|
||
|
-self._loss(y_val, raw_predictions_val, sample_weight_val)
|
||
|
)
|
||
|
return self._should_stop(self.validation_score_)
|
||
|
else:
|
||
|
return self._should_stop(self.train_score_)
|
||
|
|
||
|
def _should_stop(self, scores):
|
||
|
"""
|
||
|
Return True (do early stopping) if the last n scores aren't better
|
||
|
than the (n-1)th-to-last score, up to some tolerance.
|
||
|
"""
|
||
|
reference_position = self.n_iter_no_change + 1
|
||
|
if len(scores) < reference_position:
|
||
|
return False
|
||
|
|
||
|
# A higher score is always better. Higher tol means that it will be
|
||
|
# harder for subsequent iteration to be considered an improvement upon
|
||
|
# the reference score, and therefore it is more likely to early stop
|
||
|
# because of the lack of significant improvement.
|
||
|
tol = 0 if self.tol is None else self.tol
|
||
|
reference_score = scores[-reference_position] + tol
|
||
|
recent_scores = scores[-reference_position + 1:]
|
||
|
recent_improvements = [score > reference_score
|
||
|
for score in recent_scores]
|
||
|
return not any(recent_improvements)
|
||
|
|
||
|
def _bin_data(self, X, is_training_data):
|
||
|
"""Bin data X.
|
||
|
|
||
|
If is_training_data, then fit the _bin_mapper attribute.
|
||
|
Else, the binned data is converted to a C-contiguous array.
|
||
|
"""
|
||
|
|
||
|
description = 'training' if is_training_data else 'validation'
|
||
|
if self.verbose:
|
||
|
print("Binning {:.3f} GB of {} data: ".format(
|
||
|
X.nbytes / 1e9, description), end="", flush=True)
|
||
|
tic = time()
|
||
|
if is_training_data:
|
||
|
X_binned = self._bin_mapper.fit_transform(X) # F-aligned array
|
||
|
else:
|
||
|
X_binned = self._bin_mapper.transform(X) # F-aligned array
|
||
|
# We convert the array to C-contiguous since predicting is faster
|
||
|
# with this layout (training is faster on F-arrays though)
|
||
|
X_binned = np.ascontiguousarray(X_binned)
|
||
|
toc = time()
|
||
|
if self.verbose:
|
||
|
duration = toc - tic
|
||
|
print("{:.3f} s".format(duration))
|
||
|
|
||
|
return X_binned
|
||
|
|
||
|
def _print_iteration_stats(self, iteration_start_time):
|
||
|
"""Print info about the current fitting iteration."""
|
||
|
log_msg = ''
|
||
|
|
||
|
predictors_of_ith_iteration = [
|
||
|
predictors_list for predictors_list in self._predictors[-1]
|
||
|
if predictors_list
|
||
|
]
|
||
|
n_trees = len(predictors_of_ith_iteration)
|
||
|
max_depth = max(predictor.get_max_depth()
|
||
|
for predictor in predictors_of_ith_iteration)
|
||
|
n_leaves = sum(predictor.get_n_leaf_nodes()
|
||
|
for predictor in predictors_of_ith_iteration)
|
||
|
|
||
|
if n_trees == 1:
|
||
|
log_msg += ("{} tree, {} leaves, ".format(n_trees, n_leaves))
|
||
|
else:
|
||
|
log_msg += ("{} trees, {} leaves ".format(n_trees, n_leaves))
|
||
|
log_msg += ("({} on avg), ".format(int(n_leaves / n_trees)))
|
||
|
|
||
|
log_msg += "max depth = {}, ".format(max_depth)
|
||
|
|
||
|
if self.do_early_stopping_:
|
||
|
if self.scoring == 'loss':
|
||
|
factor = -1 # score_ arrays contain the negative loss
|
||
|
name = 'loss'
|
||
|
else:
|
||
|
factor = 1
|
||
|
name = 'score'
|
||
|
log_msg += "train {}: {:.5f}, ".format(name, factor *
|
||
|
self.train_score_[-1])
|
||
|
if self._use_validation_data:
|
||
|
log_msg += "val {}: {:.5f}, ".format(
|
||
|
name, factor * self.validation_score_[-1])
|
||
|
|
||
|
iteration_time = time() - iteration_start_time
|
||
|
log_msg += "in {:0.3f}s".format(iteration_time)
|
||
|
|
||
|
print(log_msg)
|
||
|
|
||
|
def _raw_predict(self, X):
|
||
|
"""Return the sum of the leaves values over all predictors.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
raw_predictions : array, shape (n_trees_per_iteration, n_samples)
|
||
|
The raw predicted values.
|
||
|
"""
|
||
|
is_binned = getattr(self, '_in_fit', False)
|
||
|
dtype = X_BINNED_DTYPE if is_binned else X_DTYPE
|
||
|
X = check_array(X, dtype=dtype, force_all_finite=False)
|
||
|
check_is_fitted(self)
|
||
|
if X.shape[1] != self._n_features:
|
||
|
raise ValueError(
|
||
|
'X has {} features but this estimator was trained with '
|
||
|
'{} features.'.format(X.shape[1], self._n_features)
|
||
|
)
|
||
|
n_samples = X.shape[0]
|
||
|
raw_predictions = np.zeros(
|
||
|
shape=(self.n_trees_per_iteration_, n_samples),
|
||
|
dtype=self._baseline_prediction.dtype
|
||
|
)
|
||
|
raw_predictions += self._baseline_prediction
|
||
|
self._predict_iterations(
|
||
|
X, self._predictors, raw_predictions, is_binned
|
||
|
)
|
||
|
return raw_predictions
|
||
|
|
||
|
def _predict_iterations(self, X, predictors, raw_predictions, is_binned):
|
||
|
"""Add the predictions of the predictors to raw_predictions."""
|
||
|
if not is_binned:
|
||
|
known_cat_bitsets, f_idx_map = (
|
||
|
self._bin_mapper.make_known_categories_bitsets())
|
||
|
|
||
|
for predictors_of_ith_iteration in predictors:
|
||
|
for k, predictor in enumerate(predictors_of_ith_iteration):
|
||
|
if is_binned:
|
||
|
predict = partial(
|
||
|
predictor.predict_binned,
|
||
|
missing_values_bin_idx=self._bin_mapper.missing_values_bin_idx_ # noqa
|
||
|
)
|
||
|
else:
|
||
|
predict = partial(
|
||
|
predictor.predict,
|
||
|
known_cat_bitsets=known_cat_bitsets,
|
||
|
f_idx_map=f_idx_map)
|
||
|
raw_predictions[k, :] += predict(X)
|
||
|
|
||
|
def _staged_raw_predict(self, X):
|
||
|
"""Compute raw predictions of ``X`` for each iteration.
|
||
|
|
||
|
This method allows monitoring (i.e. determine error on testing set)
|
||
|
after each stage.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Yields
|
||
|
-------
|
||
|
raw_predictions : generator of ndarray of shape \
|
||
|
(n_trees_per_iteration, n_samples)
|
||
|
The raw predictions of the input samples. The order of the
|
||
|
classes corresponds to that in the attribute :term:`classes_`.
|
||
|
"""
|
||
|
X = check_array(X, dtype=X_DTYPE, force_all_finite=False)
|
||
|
check_is_fitted(self)
|
||
|
if X.shape[1] != self._n_features:
|
||
|
raise ValueError(
|
||
|
'X has {} features but this estimator was trained with '
|
||
|
'{} features.'.format(X.shape[1], self._n_features)
|
||
|
)
|
||
|
n_samples = X.shape[0]
|
||
|
raw_predictions = np.zeros(
|
||
|
shape=(self.n_trees_per_iteration_, n_samples),
|
||
|
dtype=self._baseline_prediction.dtype
|
||
|
)
|
||
|
raw_predictions += self._baseline_prediction
|
||
|
for iteration in range(len(self._predictors)):
|
||
|
self._predict_iterations(
|
||
|
X,
|
||
|
self._predictors[iteration:iteration + 1],
|
||
|
raw_predictions,
|
||
|
is_binned=False
|
||
|
)
|
||
|
yield raw_predictions.copy()
|
||
|
|
||
|
def _compute_partial_dependence_recursion(self, grid, target_features):
|
||
|
"""Fast partial dependence computation.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
grid : ndarray, shape (n_samples, n_target_features)
|
||
|
The grid points on which the partial dependence should be
|
||
|
evaluated.
|
||
|
target_features : ndarray, shape (n_target_features)
|
||
|
The set of target features for which the partial dependence
|
||
|
should be evaluated.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
averaged_predictions : ndarray, shape \
|
||
|
(n_trees_per_iteration, n_samples)
|
||
|
The value of the partial dependence function on each grid point.
|
||
|
"""
|
||
|
|
||
|
if getattr(self, '_fitted_with_sw', False):
|
||
|
raise NotImplementedError("{} does not support partial dependence "
|
||
|
"plots with the 'recursion' method when "
|
||
|
"sample weights were given during fit "
|
||
|
"time.".format(self.__class__.__name__))
|
||
|
|
||
|
grid = np.asarray(grid, dtype=X_DTYPE, order='C')
|
||
|
averaged_predictions = np.zeros(
|
||
|
(self.n_trees_per_iteration_, grid.shape[0]), dtype=Y_DTYPE)
|
||
|
|
||
|
for predictors_of_ith_iteration in self._predictors:
|
||
|
for k, predictor in enumerate(predictors_of_ith_iteration):
|
||
|
predictor.compute_partial_dependence(grid, target_features,
|
||
|
averaged_predictions[k])
|
||
|
# Note that the learning rate is already accounted for in the leaves
|
||
|
# values.
|
||
|
|
||
|
return averaged_predictions
|
||
|
|
||
|
def _more_tags(self):
|
||
|
return {'allow_nan': True}
|
||
|
|
||
|
@abstractmethod
|
||
|
def _get_loss(self, sample_weight):
|
||
|
pass
|
||
|
|
||
|
@abstractmethod
|
||
|
def _encode_y(self, y=None):
|
||
|
pass
|
||
|
|
||
|
@property
|
||
|
def n_iter_(self):
|
||
|
check_is_fitted(self)
|
||
|
return len(self._predictors)
|
||
|
|
||
|
|
||
|
class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
|
||
|
"""Histogram-based Gradient Boosting Regression Tree.
|
||
|
|
||
|
This estimator is much faster than
|
||
|
:class:`GradientBoostingRegressor<sklearn.ensemble.GradientBoostingRegressor>`
|
||
|
for big datasets (n_samples >= 10 000).
|
||
|
|
||
|
This estimator has native support for missing values (NaNs). During
|
||
|
training, the tree grower learns at each split point whether samples
|
||
|
with missing values should go to the left or right child, based on the
|
||
|
potential gain. When predicting, samples with missing values are
|
||
|
assigned to the left or right child consequently. If no missing values
|
||
|
were encountered for a given feature during training, then samples with
|
||
|
missing values are mapped to whichever child has the most samples.
|
||
|
|
||
|
This implementation is inspired by
|
||
|
`LightGBM <https://github.com/Microsoft/LightGBM>`_.
|
||
|
|
||
|
.. note::
|
||
|
|
||
|
This estimator is still **experimental** for now: the predictions
|
||
|
and the API might change without any deprecation cycle. To use it,
|
||
|
you need to explicitly import ``enable_hist_gradient_boosting``::
|
||
|
|
||
|
>>> # explicitly require this experimental feature
|
||
|
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
|
||
|
>>> # now you can import normally from ensemble
|
||
|
>>> from sklearn.ensemble import HistGradientBoostingRegressor
|
||
|
|
||
|
Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`.
|
||
|
|
||
|
.. versionadded:: 0.21
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
loss : {'least_squares', 'least_absolute_deviation', 'poisson'}, \
|
||
|
default='least_squares'
|
||
|
The loss function to use in the boosting process. Note that the
|
||
|
"least squares" and "poisson" losses actually implement
|
||
|
"half least squares loss" and "half poisson deviance" to simplify the
|
||
|
computation of the gradient. Furthermore, "poisson" loss internally
|
||
|
uses a log-link and requires ``y >= 0``
|
||
|
|
||
|
.. versionchanged:: 0.23
|
||
|
Added option 'poisson'.
|
||
|
|
||
|
learning_rate : float, default=0.1
|
||
|
The learning rate, also known as *shrinkage*. This is used as a
|
||
|
multiplicative factor for the leaves values. Use ``1`` for no
|
||
|
shrinkage.
|
||
|
max_iter : int, default=100
|
||
|
The maximum number of iterations of the boosting process, i.e. the
|
||
|
maximum number of trees.
|
||
|
max_leaf_nodes : int or None, default=31
|
||
|
The maximum number of leaves for each tree. Must be strictly greater
|
||
|
than 1. If None, there is no maximum limit.
|
||
|
max_depth : int or None, default=None
|
||
|
The maximum depth of each tree. The depth of a tree is the number of
|
||
|
edges to go from the root to the deepest leaf.
|
||
|
Depth isn't constrained by default.
|
||
|
min_samples_leaf : int, default=20
|
||
|
The minimum number of samples per leaf. For small datasets with less
|
||
|
than a few hundred samples, it is recommended to lower this value
|
||
|
since only very shallow trees would be built.
|
||
|
l2_regularization : float, default=0
|
||
|
The L2 regularization parameter. Use ``0`` for no regularization
|
||
|
(default).
|
||
|
max_bins : int, default=255
|
||
|
The maximum number of bins to use for non-missing values. Before
|
||
|
training, each feature of the input array `X` is binned into
|
||
|
integer-valued bins, which allows for a much faster training stage.
|
||
|
Features with a small number of unique values may use less than
|
||
|
``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
|
||
|
is always reserved for missing values. Must be no larger than 255.
|
||
|
categorical_features : array-like of {bool, int} of shape (n_features) \
|
||
|
or shape (n_categorical_features,), default=None.
|
||
|
Indicates the categorical features.
|
||
|
|
||
|
- None : no feature will be considered categorical.
|
||
|
- boolean array-like : boolean mask indicating categorical features.
|
||
|
- integer array-like : integer indices indicating categorical
|
||
|
features.
|
||
|
|
||
|
For each categorical feature, there must be at most `max_bins` unique
|
||
|
categories, and each categorical value must be in [0, max_bins -1].
|
||
|
|
||
|
Read more in the :ref:`User Guide <categorical_support_gbdt>`.
|
||
|
|
||
|
.. versionadded:: 0.24
|
||
|
|
||
|
monotonic_cst : array-like of int of shape (n_features), default=None
|
||
|
Indicates the monotonic constraint to enforce on each feature. -1, 1
|
||
|
and 0 respectively correspond to a negative constraint, positive
|
||
|
constraint and no constraint. Read more in the :ref:`User Guide
|
||
|
<monotonic_cst_gbdt>`.
|
||
|
|
||
|
.. versionadded:: 0.23
|
||
|
|
||
|
warm_start : bool, default=False
|
||
|
When set to ``True``, reuse the solution of the previous call to fit
|
||
|
and add more estimators to the ensemble. For results to be valid, the
|
||
|
estimator should be re-trained on the same data only.
|
||
|
See :term:`the Glossary <warm_start>`.
|
||
|
early_stopping : 'auto' or bool, default='auto'
|
||
|
If 'auto', early stopping is enabled if the sample size is larger than
|
||
|
10000. If True, early stopping is enabled, otherwise early stopping is
|
||
|
disabled.
|
||
|
|
||
|
.. versionadded:: 0.23
|
||
|
|
||
|
scoring : str or callable or None, default='loss'
|
||
|
Scoring parameter to use for early stopping. It can be a single
|
||
|
string (see :ref:`scoring_parameter`) or a callable (see
|
||
|
:ref:`scoring`). If None, the estimator's default scorer is used. If
|
||
|
``scoring='loss'``, early stopping is checked w.r.t the loss value.
|
||
|
Only used if early stopping is performed.
|
||
|
validation_fraction : int or float or None, default=0.1
|
||
|
Proportion (or absolute size) of training data to set aside as
|
||
|
validation data for early stopping. If None, early stopping is done on
|
||
|
the training data. Only used if early stopping is performed.
|
||
|
n_iter_no_change : int, default=10
|
||
|
Used to determine when to "early stop". The fitting process is
|
||
|
stopped when none of the last ``n_iter_no_change`` scores are better
|
||
|
than the ``n_iter_no_change - 1`` -th-to-last one, up to some
|
||
|
tolerance. Only used if early stopping is performed.
|
||
|
tol : float or None, default=1e-7
|
||
|
The absolute tolerance to use when comparing scores during early
|
||
|
stopping. The higher the tolerance, the more likely we are to early
|
||
|
stop: higher tolerance means that it will be harder for subsequent
|
||
|
iterations to be considered an improvement upon the reference score.
|
||
|
verbose : int, default=0
|
||
|
The verbosity level. If not zero, print some information about the
|
||
|
fitting process.
|
||
|
random_state : int, RandomState instance or None, default=None
|
||
|
Pseudo-random number generator to control the subsampling in the
|
||
|
binning process, and the train/validation data split if early stopping
|
||
|
is enabled.
|
||
|
Pass an int for reproducible output across multiple function calls.
|
||
|
See :term:`Glossary <random_state>`.
|
||
|
|
||
|
Attributes
|
||
|
----------
|
||
|
do_early_stopping_ : bool
|
||
|
Indicates whether early stopping is used during training.
|
||
|
n_iter_ : int
|
||
|
The number of iterations as selected by early stopping, depending on
|
||
|
the `early_stopping` parameter. Otherwise it corresponds to max_iter.
|
||
|
n_trees_per_iteration_ : int
|
||
|
The number of tree that are built at each iteration. For regressors,
|
||
|
this is always 1.
|
||
|
train_score_ : ndarray, shape (n_iter_+1,)
|
||
|
The scores at each iteration on the training data. The first entry
|
||
|
is the score of the ensemble before the first iteration. Scores are
|
||
|
computed according to the ``scoring`` parameter. If ``scoring`` is
|
||
|
not 'loss', scores are computed on a subset of at most 10 000
|
||
|
samples. Empty if no early stopping.
|
||
|
validation_score_ : ndarray, shape (n_iter_+1,)
|
||
|
The scores at each iteration on the held-out validation data. The
|
||
|
first entry is the score of the ensemble before the first iteration.
|
||
|
Scores are computed according to the ``scoring`` parameter. Empty if
|
||
|
no early stopping or if ``validation_fraction`` is None.
|
||
|
is_categorical_ : ndarray, shape (n_features, ) or None
|
||
|
Boolean mask for the categorical features. ``None`` if there are no
|
||
|
categorical features.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> # To use this experimental feature, we need to explicitly ask for it:
|
||
|
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
|
||
|
>>> from sklearn.ensemble import HistGradientBoostingRegressor
|
||
|
>>> from sklearn.datasets import load_diabetes
|
||
|
>>> X, y = load_diabetes(return_X_y=True)
|
||
|
>>> est = HistGradientBoostingRegressor().fit(X, y)
|
||
|
>>> est.score(X, y)
|
||
|
0.92...
|
||
|
"""
|
||
|
|
||
|
_VALID_LOSSES = ('least_squares', 'least_absolute_deviation',
|
||
|
'poisson')
|
||
|
|
||
|
@_deprecate_positional_args
|
||
|
def __init__(self, loss='least_squares', *, learning_rate=0.1,
|
||
|
max_iter=100, max_leaf_nodes=31, max_depth=None,
|
||
|
min_samples_leaf=20, l2_regularization=0., max_bins=255,
|
||
|
categorical_features=None, monotonic_cst=None,
|
||
|
warm_start=False, early_stopping='auto',
|
||
|
scoring='loss', validation_fraction=0.1,
|
||
|
n_iter_no_change=10, tol=1e-7,
|
||
|
verbose=0, random_state=None):
|
||
|
super(HistGradientBoostingRegressor, self).__init__(
|
||
|
loss=loss, learning_rate=learning_rate, max_iter=max_iter,
|
||
|
max_leaf_nodes=max_leaf_nodes, max_depth=max_depth,
|
||
|
min_samples_leaf=min_samples_leaf,
|
||
|
l2_regularization=l2_regularization, max_bins=max_bins,
|
||
|
monotonic_cst=monotonic_cst,
|
||
|
categorical_features=categorical_features,
|
||
|
early_stopping=early_stopping,
|
||
|
warm_start=warm_start, scoring=scoring,
|
||
|
validation_fraction=validation_fraction,
|
||
|
n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose,
|
||
|
random_state=random_state)
|
||
|
|
||
|
def predict(self, X):
|
||
|
"""Predict values for X.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like, shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
y : ndarray, shape (n_samples,)
|
||
|
The predicted values.
|
||
|
"""
|
||
|
check_is_fitted(self)
|
||
|
# Return inverse link of raw predictions after converting
|
||
|
# shape (n_samples, 1) to (n_samples,)
|
||
|
return self._loss.inverse_link_function(self._raw_predict(X).ravel())
|
||
|
|
||
|
def staged_predict(self, X):
|
||
|
"""Predict regression target for each iteration
|
||
|
|
||
|
This method allows monitoring (i.e. determine error on testing set)
|
||
|
after each stage.
|
||
|
|
||
|
.. versionadded:: 0.24
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Yields
|
||
|
-------
|
||
|
y : generator of ndarray of shape (n_samples,)
|
||
|
The predicted values of the input samples, for each iteration.
|
||
|
"""
|
||
|
for raw_predictions in self._staged_raw_predict(X):
|
||
|
yield self._loss.inverse_link_function(raw_predictions.ravel())
|
||
|
|
||
|
def _encode_y(self, y):
|
||
|
# Just convert y to the expected dtype
|
||
|
self.n_trees_per_iteration_ = 1
|
||
|
y = y.astype(Y_DTYPE, copy=False)
|
||
|
if self.loss == 'poisson':
|
||
|
# Ensure y >= 0 and sum(y) > 0
|
||
|
if not (np.all(y >= 0) and np.sum(y) > 0):
|
||
|
raise ValueError("loss='poisson' requires non-negative y and "
|
||
|
"sum(y) > 0.")
|
||
|
return y
|
||
|
|
||
|
def _get_loss(self, sample_weight):
|
||
|
return _LOSSES[self.loss](sample_weight=sample_weight)
|
||
|
|
||
|
|
||
|
class HistGradientBoostingClassifier(ClassifierMixin,
|
||
|
BaseHistGradientBoosting):
|
||
|
"""Histogram-based Gradient Boosting Classification Tree.
|
||
|
|
||
|
This estimator is much faster than
|
||
|
:class:`GradientBoostingClassifier<sklearn.ensemble.GradientBoostingClassifier>`
|
||
|
for big datasets (n_samples >= 10 000).
|
||
|
|
||
|
This estimator has native support for missing values (NaNs). During
|
||
|
training, the tree grower learns at each split point whether samples
|
||
|
with missing values should go to the left or right child, based on the
|
||
|
potential gain. When predicting, samples with missing values are
|
||
|
assigned to the left or right child consequently. If no missing values
|
||
|
were encountered for a given feature during training, then samples with
|
||
|
missing values are mapped to whichever child has the most samples.
|
||
|
|
||
|
This implementation is inspired by
|
||
|
`LightGBM <https://github.com/Microsoft/LightGBM>`_.
|
||
|
|
||
|
.. note::
|
||
|
|
||
|
This estimator is still **experimental** for now: the predictions
|
||
|
and the API might change without any deprecation cycle. To use it,
|
||
|
you need to explicitly import ``enable_hist_gradient_boosting``::
|
||
|
|
||
|
>>> # explicitly require this experimental feature
|
||
|
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
|
||
|
>>> # now you can import normally from ensemble
|
||
|
>>> from sklearn.ensemble import HistGradientBoostingClassifier
|
||
|
|
||
|
Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`.
|
||
|
|
||
|
.. versionadded:: 0.21
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
loss : {'auto', 'binary_crossentropy', 'categorical_crossentropy'}, \
|
||
|
default='auto'
|
||
|
The loss function to use in the boosting process. 'binary_crossentropy'
|
||
|
(also known as logistic loss) is used for binary classification and
|
||
|
generalizes to 'categorical_crossentropy' for multiclass
|
||
|
classification. 'auto' will automatically choose either loss depending
|
||
|
on the nature of the problem.
|
||
|
learning_rate : float, default=0.1
|
||
|
The learning rate, also known as *shrinkage*. This is used as a
|
||
|
multiplicative factor for the leaves values. Use ``1`` for no
|
||
|
shrinkage.
|
||
|
max_iter : int, default=100
|
||
|
The maximum number of iterations of the boosting process, i.e. the
|
||
|
maximum number of trees for binary classification. For multiclass
|
||
|
classification, `n_classes` trees per iteration are built.
|
||
|
max_leaf_nodes : int or None, default=31
|
||
|
The maximum number of leaves for each tree. Must be strictly greater
|
||
|
than 1. If None, there is no maximum limit.
|
||
|
max_depth : int or None, default=None
|
||
|
The maximum depth of each tree. The depth of a tree is the number of
|
||
|
edges to go from the root to the deepest leaf.
|
||
|
Depth isn't constrained by default.
|
||
|
min_samples_leaf : int, default=20
|
||
|
The minimum number of samples per leaf. For small datasets with less
|
||
|
than a few hundred samples, it is recommended to lower this value
|
||
|
since only very shallow trees would be built.
|
||
|
l2_regularization : float, default=0
|
||
|
The L2 regularization parameter. Use 0 for no regularization.
|
||
|
max_bins : int, default=255
|
||
|
The maximum number of bins to use for non-missing values. Before
|
||
|
training, each feature of the input array `X` is binned into
|
||
|
integer-valued bins, which allows for a much faster training stage.
|
||
|
Features with a small number of unique values may use less than
|
||
|
``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
|
||
|
is always reserved for missing values. Must be no larger than 255.
|
||
|
categorical_features : array-like of {bool, int} of shape (n_features) \
|
||
|
or shape (n_categorical_features,), default=None.
|
||
|
Indicates the categorical features.
|
||
|
|
||
|
- None : no feature will be considered categorical.
|
||
|
- boolean array-like : boolean mask indicating categorical features.
|
||
|
- integer array-like : integer indices indicating categorical
|
||
|
features.
|
||
|
|
||
|
For each categorical feature, there must be at most `max_bins` unique
|
||
|
categories, and each categorical value must be in [0, max_bins -1].
|
||
|
|
||
|
Read more in the :ref:`User Guide <categorical_support_gbdt>`.
|
||
|
|
||
|
.. versionadded:: 0.24
|
||
|
|
||
|
monotonic_cst : array-like of int of shape (n_features), default=None
|
||
|
Indicates the monotonic constraint to enforce on each feature. -1, 1
|
||
|
and 0 respectively correspond to a negative constraint, positive
|
||
|
constraint and no constraint. Read more in the :ref:`User Guide
|
||
|
<monotonic_cst_gbdt>`.
|
||
|
|
||
|
.. versionadded:: 0.23
|
||
|
|
||
|
warm_start : bool, default=False
|
||
|
When set to ``True``, reuse the solution of the previous call to fit
|
||
|
and add more estimators to the ensemble. For results to be valid, the
|
||
|
estimator should be re-trained on the same data only.
|
||
|
See :term:`the Glossary <warm_start>`.
|
||
|
early_stopping : 'auto' or bool, default='auto'
|
||
|
If 'auto', early stopping is enabled if the sample size is larger than
|
||
|
10000. If True, early stopping is enabled, otherwise early stopping is
|
||
|
disabled.
|
||
|
|
||
|
.. versionadded:: 0.23
|
||
|
|
||
|
scoring : str or callable or None, default='loss'
|
||
|
Scoring parameter to use for early stopping. It can be a single
|
||
|
string (see :ref:`scoring_parameter`) or a callable (see
|
||
|
:ref:`scoring`). If None, the estimator's default scorer
|
||
|
is used. If ``scoring='loss'``, early stopping is checked
|
||
|
w.r.t the loss value. Only used if early stopping is performed.
|
||
|
validation_fraction : int or float or None, default=0.1
|
||
|
Proportion (or absolute size) of training data to set aside as
|
||
|
validation data for early stopping. If None, early stopping is done on
|
||
|
the training data. Only used if early stopping is performed.
|
||
|
n_iter_no_change : int, default=10
|
||
|
Used to determine when to "early stop". The fitting process is
|
||
|
stopped when none of the last ``n_iter_no_change`` scores are better
|
||
|
than the ``n_iter_no_change - 1`` -th-to-last one, up to some
|
||
|
tolerance. Only used if early stopping is performed.
|
||
|
tol : float or None, default=1e-7
|
||
|
The absolute tolerance to use when comparing scores. The higher the
|
||
|
tolerance, the more likely we are to early stop: higher tolerance
|
||
|
means that it will be harder for subsequent iterations to be
|
||
|
considered an improvement upon the reference score.
|
||
|
verbose : int, default=0
|
||
|
The verbosity level. If not zero, print some information about the
|
||
|
fitting process.
|
||
|
random_state : int, RandomState instance or None, default=None
|
||
|
Pseudo-random number generator to control the subsampling in the
|
||
|
binning process, and the train/validation data split if early stopping
|
||
|
is enabled.
|
||
|
Pass an int for reproducible output across multiple function calls.
|
||
|
See :term:`Glossary <random_state>`.
|
||
|
|
||
|
Attributes
|
||
|
----------
|
||
|
classes_ : array, shape = (n_classes,)
|
||
|
Class labels.
|
||
|
do_early_stopping_ : bool
|
||
|
Indicates whether early stopping is used during training.
|
||
|
n_iter_ : int
|
||
|
The number of iterations as selected by early stopping, depending on
|
||
|
the `early_stopping` parameter. Otherwise it corresponds to max_iter.
|
||
|
n_trees_per_iteration_ : int
|
||
|
The number of tree that are built at each iteration. This is equal to 1
|
||
|
for binary classification, and to ``n_classes`` for multiclass
|
||
|
classification.
|
||
|
train_score_ : ndarray, shape (n_iter_+1,)
|
||
|
The scores at each iteration on the training data. The first entry
|
||
|
is the score of the ensemble before the first iteration. Scores are
|
||
|
computed according to the ``scoring`` parameter. If ``scoring`` is
|
||
|
not 'loss', scores are computed on a subset of at most 10 000
|
||
|
samples. Empty if no early stopping.
|
||
|
validation_score_ : ndarray, shape (n_iter_+1,)
|
||
|
The scores at each iteration on the held-out validation data. The
|
||
|
first entry is the score of the ensemble before the first iteration.
|
||
|
Scores are computed according to the ``scoring`` parameter. Empty if
|
||
|
no early stopping or if ``validation_fraction`` is None.
|
||
|
is_categorical_ : ndarray, shape (n_features, ) or None
|
||
|
Boolean mask for the categorical features. ``None`` if there are no
|
||
|
categorical features.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> # To use this experimental feature, we need to explicitly ask for it:
|
||
|
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
|
||
|
>>> from sklearn.ensemble import HistGradientBoostingClassifier
|
||
|
>>> from sklearn.datasets import load_iris
|
||
|
>>> X, y = load_iris(return_X_y=True)
|
||
|
>>> clf = HistGradientBoostingClassifier().fit(X, y)
|
||
|
>>> clf.score(X, y)
|
||
|
1.0
|
||
|
"""
|
||
|
|
||
|
_VALID_LOSSES = ('binary_crossentropy', 'categorical_crossentropy',
|
||
|
'auto')
|
||
|
|
||
|
@_deprecate_positional_args
|
||
|
def __init__(self, loss='auto', *, learning_rate=0.1, max_iter=100,
|
||
|
max_leaf_nodes=31, max_depth=None, min_samples_leaf=20,
|
||
|
l2_regularization=0., max_bins=255,
|
||
|
categorical_features=None, monotonic_cst=None,
|
||
|
warm_start=False, early_stopping='auto', scoring='loss',
|
||
|
validation_fraction=0.1, n_iter_no_change=10, tol=1e-7,
|
||
|
verbose=0, random_state=None):
|
||
|
super(HistGradientBoostingClassifier, self).__init__(
|
||
|
loss=loss, learning_rate=learning_rate, max_iter=max_iter,
|
||
|
max_leaf_nodes=max_leaf_nodes, max_depth=max_depth,
|
||
|
min_samples_leaf=min_samples_leaf,
|
||
|
l2_regularization=l2_regularization, max_bins=max_bins,
|
||
|
categorical_features=categorical_features,
|
||
|
monotonic_cst=monotonic_cst,
|
||
|
warm_start=warm_start,
|
||
|
early_stopping=early_stopping, scoring=scoring,
|
||
|
validation_fraction=validation_fraction,
|
||
|
n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose,
|
||
|
random_state=random_state)
|
||
|
|
||
|
def predict(self, X):
|
||
|
"""Predict classes for X.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like, shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
y : ndarray, shape (n_samples,)
|
||
|
The predicted classes.
|
||
|
"""
|
||
|
# TODO: This could be done in parallel
|
||
|
encoded_classes = np.argmax(self.predict_proba(X), axis=1)
|
||
|
return self.classes_[encoded_classes]
|
||
|
|
||
|
def staged_predict(self, X):
|
||
|
"""Predict classes at each iteration.
|
||
|
|
||
|
This method allows monitoring (i.e. determine error on testing set)
|
||
|
after each stage.
|
||
|
|
||
|
.. versionadded:: 0.24
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Yields
|
||
|
-------
|
||
|
y : generator of ndarray of shape (n_samples,)
|
||
|
The predicted classes of the input samples, for each iteration.
|
||
|
"""
|
||
|
for proba in self.staged_predict_proba(X):
|
||
|
encoded_classes = np.argmax(proba, axis=1)
|
||
|
yield self.classes_.take(encoded_classes, axis=0)
|
||
|
|
||
|
def predict_proba(self, X):
|
||
|
"""Predict class probabilities for X.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like, shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
p : ndarray, shape (n_samples, n_classes)
|
||
|
The class probabilities of the input samples.
|
||
|
"""
|
||
|
raw_predictions = self._raw_predict(X)
|
||
|
return self._loss.predict_proba(raw_predictions)
|
||
|
|
||
|
def staged_predict_proba(self, X):
|
||
|
"""Predict class probabilities at each iteration.
|
||
|
|
||
|
This method allows monitoring (i.e. determine error on testing set)
|
||
|
after each stage.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Yields
|
||
|
-------
|
||
|
y : generator of ndarray of shape (n_samples,)
|
||
|
The predicted class probabilities of the input samples,
|
||
|
for each iteration.
|
||
|
"""
|
||
|
for raw_predictions in self._staged_raw_predict(X):
|
||
|
yield self._loss.predict_proba(raw_predictions)
|
||
|
|
||
|
def decision_function(self, X):
|
||
|
"""Compute the decision function of ``X``.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like, shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
decision : ndarray, shape (n_samples,) or \
|
||
|
(n_samples, n_trees_per_iteration)
|
||
|
The raw predicted values (i.e. the sum of the trees leaves) for
|
||
|
each sample. n_trees_per_iteration is equal to the number of
|
||
|
classes in multiclass classification.
|
||
|
"""
|
||
|
decision = self._raw_predict(X)
|
||
|
if decision.shape[0] == 1:
|
||
|
decision = decision.ravel()
|
||
|
return decision.T
|
||
|
|
||
|
def staged_decision_function(self, X):
|
||
|
"""Compute decision function of ``X`` for each iteration.
|
||
|
|
||
|
This method allows monitoring (i.e. determine error on testing set)
|
||
|
after each stage.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : array-like of shape (n_samples, n_features)
|
||
|
The input samples.
|
||
|
|
||
|
Yields
|
||
|
-------
|
||
|
decision : generator of ndarray of shape (n_samples,) or \
|
||
|
(n_samples, n_trees_per_iteration)
|
||
|
The decision function of the input samples, which corresponds to
|
||
|
the raw values predicted from the trees of the ensemble . The
|
||
|
classes corresponds to that in the attribute :term:`classes_`.
|
||
|
"""
|
||
|
for staged_decision in self._staged_raw_predict(X):
|
||
|
if staged_decision.shape[0] == 1:
|
||
|
staged_decision = staged_decision.ravel()
|
||
|
yield staged_decision.T
|
||
|
|
||
|
def _encode_y(self, y):
|
||
|
# encode classes into 0 ... n_classes - 1 and sets attributes classes_
|
||
|
# and n_trees_per_iteration_
|
||
|
check_classification_targets(y)
|
||
|
|
||
|
label_encoder = LabelEncoder()
|
||
|
encoded_y = label_encoder.fit_transform(y)
|
||
|
self.classes_ = label_encoder.classes_
|
||
|
n_classes = self.classes_.shape[0]
|
||
|
# only 1 tree for binary classification. For multiclass classification,
|
||
|
# we build 1 tree per class.
|
||
|
self.n_trees_per_iteration_ = 1 if n_classes <= 2 else n_classes
|
||
|
encoded_y = encoded_y.astype(Y_DTYPE, copy=False)
|
||
|
return encoded_y
|
||
|
|
||
|
def _get_loss(self, sample_weight):
|
||
|
if (self.loss == 'categorical_crossentropy' and
|
||
|
self.n_trees_per_iteration_ == 1):
|
||
|
raise ValueError("'categorical_crossentropy' is not suitable for "
|
||
|
"a binary classification problem. Please use "
|
||
|
"'auto' or 'binary_crossentropy' instead.")
|
||
|
|
||
|
if self.loss == 'auto':
|
||
|
if self.n_trees_per_iteration_ == 1:
|
||
|
return _LOSSES['binary_crossentropy'](
|
||
|
sample_weight=sample_weight)
|
||
|
else:
|
||
|
return _LOSSES['categorical_crossentropy'](
|
||
|
sample_weight=sample_weight)
|
||
|
|
||
|
return _LOSSES[self.loss](sample_weight=sample_weight)
|