8000 ENH make base classes abstract meta classes · pfdevilliers/scikit-learn@26c31bf · GitHub
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ENH make base classes abstract meta classes
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‎sklearn/ensemble/forest.py

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@@ -35,6 +35,7 @@ class calls the ``fit`` method of each sub-estimator on random samples
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import itertools
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import numpy as np
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from abc import ABCMeta, abstractmethod
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from ..base import ClassifierMixin, RegressorMixin
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from ..externals.joblib import Parallel, delayed, cpu_count
@@ -164,6 +165,9 @@ class BaseForest(BaseEnsemble, SelectorMixin):
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Warning: This class should not be used directly. Use derived classes
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instead.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def __init__(self, base_estimator,
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n_estimators=10,
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estimator_params=[],
@@ -294,6 +298,9 @@ class ForestClassifier(BaseForest, ClassifierMixin):
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Warning: This class should not be used directly. Use derived classes
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instead.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def __init__(self, base_estimator,
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n_estimators=10,
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estimator_params=[],
@@ -394,6 +401,9 @@ class ForestRegressor(BaseForest, RegressorMixin):
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Warning: This class should not be used directly. Use derived classes
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instead.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def __init__(self, base_estimator,
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n_estimators=10,
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estimator_params=[],

‎sklearn/ensemble/gradient_boosting.py

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@@ -326,6 +326,9 @@ def _update_terminal_region(self, tree, terminal_regions, leaf, X, y,
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class BaseGradientBoosting(BaseEnsemble):
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"""Abstract base class for Gradient Boosting. """
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__metaclass__ = ABCMeta
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@abstractmethod
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def __init__(self, loss, learn_rate, n_estimators, min_samples_split,
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min_samples_leaf, max_depth, init, subsample, random_state):
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if n_estimators <= 0:

‎sklearn/svm/base.py

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@@ -1,6 +1,7 @@
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import numpy as np
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import scipy.sparse as sp
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import warnings
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from abc import ABCMeta, abstractmethod
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from . import libsvm, liblinear
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from . import libsvm_sparse
@@ -71,8 +72,10 @@ class BaseLibSVM(BaseEstimator):
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This implements support vector machine classification and regression.
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"""
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__metaclass__ = ABCMeta
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_sparse_kernels = ["linear", "poly", "rbf", "sigmoid", "precomputed"]
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@abstractmethod
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def __init__(self, impl, kernel, degree, gamma, coef0,
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tol, C, nu, epsilon, shrinking, probability, cache_size,
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sparse, class_weight, verbose):

‎sklearn/tree/tree.py

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@@ -12,6 +12,7 @@
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from __future__ import division
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import numpy as np
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from abc import ABCMeta, abstractmethod
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from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
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from ..feature_selection.selector_mixin import SelectorMixin
@@ -408,6 +409,9 @@ class BaseDecisionTree(BaseEstimator, SelectorMixin):
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Warning: This class should not be used directly.
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Use derived classes instead.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def __init__(self, criterion,
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max_depth,
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min_samples_split,

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