685 lines
23 KiB
Python
685 lines
23 KiB
Python
"""
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Base classes for all estimators.
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Used for VotingClassifier
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"""
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD 3 clause
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import copy
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import warnings
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from collections import defaultdict
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import platform
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import inspect
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import re
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import numpy as np
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from . import __version__
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from .utils import _IS_32BIT
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_DEFAULT_TAGS = {
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'non_deterministic': False,
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'requires_positive_X': False,
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'requires_positive_y': False,
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'X_types': ['2darray'],
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'poor_score': False,
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'no_validation': False,
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'multioutput': False,
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"allow_nan": False,
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'stateless': False,
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'multilabel': False,
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'_skip_test': False,
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'multioutput_only': False,
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'binary_only': False,
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'requires_fit': True}
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def clone(estimator, safe=True):
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"""Constructs a new estimator with the same parameters.
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Clone does a deep copy of the model in an estimator
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without actually copying attached data. It yields a new estimator
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with the same parameters that has not been fit on any data.
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Parameters
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----------
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estimator : estimator object, or list, tuple or set of objects
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The estimator or group of estimators to be cloned
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safe : boolean, optional
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If safe is false, clone will fall back to a deep copy on objects
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that are not estimators.
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"""
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estimator_type = type(estimator)
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# XXX: not handling dictionaries
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if estimator_type in (list, tuple, set, frozenset):
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return estimator_type([clone(e, safe=safe) for e in estimator])
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elif not hasattr(estimator, 'get_params') or isinstance(estimator, type):
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if not safe:
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return copy.deepcopy(estimator)
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else:
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raise TypeError("Cannot clone object '%s' (type %s): "
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"it does not seem to be a scikit-learn estimator "
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"as it does not implement a 'get_params' methods."
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% (repr(estimator), type(estimator)))
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klass = estimator.__class__
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new_object_params = estimator.get_params(deep=False)
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for name, param in new_object_params.items():
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new_object_params[name] = clone(param, safe=False)
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new_object = klass(**new_object_params)
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params_set = new_object.get_params(deep=False)
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# quick sanity check of the parameters of the clone
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for name in new_object_params:
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param1 = new_object_params[name]
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param2 = params_set[name]
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if param1 is not param2:
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raise RuntimeError('Cannot clone object %s, as the constructor '
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'either does not set or modifies parameter %s' %
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(estimator, name))
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return new_object
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def _pprint(params, offset=0, printer=repr):
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"""Pretty print the dictionary 'params'
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Parameters
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----------
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params : dict
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The dictionary to pretty print
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offset : int
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The offset in characters to add at the begin of each line.
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printer : callable
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The function to convert entries to strings, typically
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the builtin str or repr
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"""
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# Do a multi-line justified repr:
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options = np.get_printoptions()
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np.set_printoptions(precision=5, threshold=64, edgeitems=2)
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params_list = list()
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this_line_length = offset
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line_sep = ',\n' + (1 + offset // 2) * ' '
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for i, (k, v) in enumerate(sorted(params.items())):
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if type(v) is float:
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# use str for representing floating point numbers
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# this way we get consistent representation across
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# architectures and versions.
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this_repr = '%s=%s' % (k, str(v))
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else:
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# use repr of the rest
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this_repr = '%s=%s' % (k, printer(v))
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if len(this_repr) > 500:
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this_repr = this_repr[:300] + '...' + this_repr[-100:]
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if i > 0:
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if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr):
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params_list.append(line_sep)
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this_line_length = len(line_sep)
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else:
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params_list.append(', ')
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this_line_length += 2
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params_list.append(this_repr)
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this_line_length += len(this_repr)
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np.set_printoptions(**options)
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lines = ''.join(params_list)
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# Strip trailing space to avoid nightmare in doctests
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lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n'))
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return lines
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class BaseEstimator:
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"""Base class for all estimators in scikit-learn
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Notes
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-----
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All estimators should specify all the parameters that can be set
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at the class level in their ``__init__`` as explicit keyword
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arguments (no ``*args`` or ``**kwargs``).
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"""
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@classmethod
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def _get_param_names(cls):
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"""Get parameter names for the estimator"""
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# fetch the constructor or the original constructor before
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# deprecation wrapping if any
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init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
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if init is object.__init__:
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# No explicit constructor to introspect
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return []
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# introspect the constructor arguments to find the model parameters
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# to represent
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init_signature = inspect.signature(init)
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# Consider the constructor parameters excluding 'self'
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parameters = [p for p in init_signature.parameters.values()
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if p.name != 'self' and p.kind != p.VAR_KEYWORD]
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for p in parameters:
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if p.kind == p.VAR_POSITIONAL:
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raise RuntimeError("scikit-learn estimators should always "
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"specify their parameters in the signature"
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" of their __init__ (no varargs)."
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" %s with constructor %s doesn't "
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" follow this convention."
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% (cls, init_signature))
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# Extract and sort argument names excluding 'self'
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return sorted([p.name for p in parameters])
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def get_params(self, deep=True):
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"""
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Get parameters for this estimator.
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Parameters
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----------
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deep : bool, default=True
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If True, will return the parameters for this estimator and
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contained subobjects that are estimators.
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Returns
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-------
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params : mapping of string to any
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Parameter names mapped to their values.
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"""
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out = dict()
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for key in self._get_param_names():
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try:
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value = getattr(self, key)
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except AttributeError:
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warnings.warn('From version 0.24, get_params will raise an '
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'AttributeError if a parameter cannot be '
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'retrieved as an instance attribute. Previously '
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'it would return None.',
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FutureWarning)
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value = None
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if deep and hasattr(value, 'get_params'):
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deep_items = value.get_params().items()
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out.update((key + '__' + k, val) for k, val in deep_items)
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out[key] = value
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return out
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def set_params(self, **params):
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"""
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Set the parameters of this estimator.
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The method works on simple estimators as well as on nested objects
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(such as pipelines). The latter have parameters of the form
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``<component>__<parameter>`` so that it's possible to update each
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component of a nested object.
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Parameters
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----------
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**params : dict
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Estimator parameters.
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Returns
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-------
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self : object
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Estimator instance.
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"""
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if not params:
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# Simple optimization to gain speed (inspect is slow)
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return self
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valid_params = self.get_params(deep=True)
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nested_params = defaultdict(dict) # grouped by prefix
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for key, value in params.items():
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key, delim, sub_key = key.partition('__')
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if key not in valid_params:
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raise ValueError('Invalid parameter %s for estimator %s. '
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'Check the list of available parameters '
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'with `estimator.get_params().keys()`.' %
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(key, self))
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if delim:
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nested_params[key][sub_key] = value
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else:
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setattr(self, key, value)
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valid_params[key] = value
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for key, sub_params in nested_params.items():
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valid_params[key].set_params(**sub_params)
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return self
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def __repr__(self, N_CHAR_MAX=700):
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# N_CHAR_MAX is the (approximate) maximum number of non-blank
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# characters to render. We pass it as an optional parameter to ease
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# the tests.
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from .utils._pprint import _EstimatorPrettyPrinter
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N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences
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# use ellipsis for sequences with a lot of elements
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pp = _EstimatorPrettyPrinter(
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compact=True, indent=1, indent_at_name=True,
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n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW)
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repr_ = pp.pformat(self)
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# Use bruteforce ellipsis when there are a lot of non-blank characters
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n_nonblank = len(''.join(repr_.split()))
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if n_nonblank > N_CHAR_MAX:
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lim = N_CHAR_MAX // 2 # apprx number of chars to keep on both ends
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regex = r'^(\s*\S){%d}' % lim
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# The regex '^(\s*\S){%d}' % n
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# matches from the start of the string until the nth non-blank
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# character:
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# - ^ matches the start of string
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# - (pattern){n} matches n repetitions of pattern
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# - \s*\S matches a non-blank char following zero or more blanks
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left_lim = re.match(regex, repr_).end()
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right_lim = re.match(regex, repr_[::-1]).end()
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if '\n' in repr_[left_lim:-right_lim]:
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# The left side and right side aren't on the same line.
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# To avoid weird cuts, e.g.:
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# categoric...ore',
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# we need to start the right side with an appropriate newline
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# character so that it renders properly as:
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# categoric...
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# handle_unknown='ignore',
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# so we add [^\n]*\n which matches until the next \n
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regex += r'[^\n]*\n'
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right_lim = re.match(regex, repr_[::-1]).end()
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ellipsis = '...'
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if left_lim + len(ellipsis) < len(repr_) - right_lim:
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# Only add ellipsis if it results in a shorter repr
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repr_ = repr_[:left_lim] + '...' + repr_[-right_lim:]
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return repr_
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def __getstate__(self):
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try:
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state = super().__getstate__()
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except AttributeError:
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state = self.__dict__.copy()
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if type(self).__module__.startswith('sklearn.'):
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return dict(state.items(), _sklearn_version=__version__)
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else:
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return state
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def __setstate__(self, state):
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if type(self).__module__.startswith('sklearn.'):
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pickle_version = state.pop("_sklearn_version", "pre-0.18")
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if pickle_version != __version__:
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warnings.warn(
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"Trying to unpickle estimator {0} from version {1} when "
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"using version {2}. This might lead to breaking code or "
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"invalid results. Use at your own risk.".format(
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self.__class__.__name__, pickle_version, __version__),
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UserWarning)
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try:
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super().__setstate__(state)
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except AttributeError:
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self.__dict__.update(state)
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def _more_tags(self):
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return _DEFAULT_TAGS
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def _get_tags(self):
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collected_tags = {}
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for base_class in reversed(inspect.getmro(self.__class__)):
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if hasattr(base_class, '_more_tags'):
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# need the if because mixins might not have _more_tags
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# but might do redundant work in estimators
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# (i.e. calling more tags on BaseEstimator multiple times)
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more_tags = base_class._more_tags(self)
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collected_tags.update(more_tags)
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return collected_tags
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class ClassifierMixin:
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"""Mixin class for all classifiers in scikit-learn."""
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_estimator_type = "classifier"
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def score(self, X, y, sample_weight=None):
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"""
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Return the mean accuracy on the given test data and labels.
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In multi-label classification, this is the subset accuracy
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which is a harsh metric since you require for each sample that
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each label set be correctly predicted.
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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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Test samples.
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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True labels for X.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
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-------
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score : float
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Mean accuracy of self.predict(X) wrt. y.
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"""
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from .metrics import accuracy_score
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return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
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class RegressorMixin:
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"""Mixin class for all regression estimators in scikit-learn."""
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_estimator_type = "regressor"
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def score(self, X, y, sample_weight=None):
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"""Return the coefficient of determination R^2 of the prediction.
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The coefficient R^2 is defined as (1 - u/v), where u is the residual
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sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
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sum of squares ((y_true - y_true.mean()) ** 2).sum().
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The best possible score is 1.0 and it can be negative (because the
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model can be arbitrarily worse). A constant model that always
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predicts the expected value of y, disregarding the input features,
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would get a R^2 score of 0.0.
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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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Test samples. For some estimators this may be a
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precomputed kernel matrix or a list of generic objects instead,
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shape = (n_samples, n_samples_fitted),
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where n_samples_fitted is the number of
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samples used in the fitting for the estimator.
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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True values for X.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
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-------
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score : float
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R^2 of self.predict(X) wrt. y.
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Notes
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-----
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The R2 score used when calling ``score`` on a regressor will use
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``multioutput='uniform_average'`` from version 0.23 to keep consistent
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with :func:`~sklearn.metrics.r2_score`. This will influence the
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``score`` method of all the multioutput regressors (except for
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:class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the
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default value manually and avoid the warning, please either call
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:func:`~sklearn.metrics.r2_score` directly or make a custom scorer with
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:func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses
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``multioutput='uniform_average'``).
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"""
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from .metrics import r2_score
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from .metrics._regression import _check_reg_targets
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y_pred = self.predict(X)
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# XXX: Remove the check in 0.23
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y_type, _, _, _ = _check_reg_targets(y, y_pred, None)
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if y_type == 'continuous-multioutput':
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warnings.warn("The default value of multioutput (not exposed in "
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"score method) will change from 'variance_weighted' "
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"to 'uniform_average' in 0.23 to keep consistent "
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"with 'metrics.r2_score'. To specify the default "
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"value manually and avoid the warning, please "
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"either call 'metrics.r2_score' directly or make a "
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"custom scorer with 'metrics.make_scorer' (the "
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"built-in scorer 'r2' uses "
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"multioutput='uniform_average').", FutureWarning)
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return r2_score(y, y_pred, sample_weight=sample_weight,
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multioutput='variance_weighted')
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class ClusterMixin:
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"""Mixin class for all cluster estimators in scikit-learn."""
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_estimator_type = "clusterer"
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def fit_predict(self, X, y=None):
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"""
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Perform clustering on X and returns cluster labels.
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Parameters
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----------
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X : ndarray, shape (n_samples, n_features)
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Input data.
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y : Ignored
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Not used, present for API consistency by convention.
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Returns
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-------
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labels : ndarray, shape (n_samples,)
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Cluster labels.
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"""
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# non-optimized default implementation; override when a better
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# method is possible for a given clustering algorithm
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self.fit(X)
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return self.labels_
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class BiclusterMixin:
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"""Mixin class for all bicluster estimators in scikit-learn"""
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@property
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def biclusters_(self):
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"""Convenient way to get row and column indicators together.
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Returns the ``rows_`` and ``columns_`` members.
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"""
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return self.rows_, self.columns_
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def get_indices(self, i):
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"""Row and column indices of the i'th bicluster.
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Only works if ``rows_`` and ``columns_`` attributes exist.
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Parameters
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----------
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i : int
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The index of the cluster.
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Returns
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-------
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row_ind : np.array, dtype=np.intp
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Indices of rows in the dataset that belong to the bicluster.
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col_ind : np.array, dtype=np.intp
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Indices of columns in the dataset that belong to the bicluster.
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"""
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rows = self.rows_[i]
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columns = self.columns_[i]
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return np.nonzero(rows)[0], np.nonzero(columns)[0]
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def get_shape(self, i):
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"""Shape of the i'th bicluster.
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Parameters
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----------
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i : int
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The index of the cluster.
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Returns
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-------
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shape : (int, int)
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Number of rows and columns (resp.) in the bicluster.
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"""
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indices = self.get_indices(i)
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return tuple(len(i) for i in indices)
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def get_submatrix(self, i, data):
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"""Return the submatrix corresponding to bicluster `i`.
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Parameters
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----------
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i : int
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The index of the cluster.
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data : array
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The data.
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Returns
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-------
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submatrix : array
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The submatrix corresponding to bicluster i.
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Notes
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-----
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Works with sparse matrices. Only works if ``rows_`` and
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``columns_`` attributes exist.
|
|
"""
|
|
from .utils.validation import check_array
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|
data = check_array(data, accept_sparse='csr')
|
|
row_ind, col_ind = self.get_indices(i)
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|
return data[row_ind[:, np.newaxis], col_ind]
|
|
|
|
|
|
class TransformerMixin:
|
|
"""Mixin class for all transformers in scikit-learn."""
|
|
|
|
def fit_transform(self, X, y=None, **fit_params):
|
|
"""
|
|
Fit to data, then transform it.
|
|
|
|
Fits transformer to X and y with optional parameters fit_params
|
|
and returns a transformed version of X.
|
|
|
|
Parameters
|
|
----------
|
|
X : numpy array of shape [n_samples, n_features]
|
|
Training set.
|
|
|
|
y : numpy array of shape [n_samples]
|
|
Target values.
|
|
|
|
**fit_params : dict
|
|
Additional fit parameters.
|
|
|
|
Returns
|
|
-------
|
|
X_new : numpy array of shape [n_samples, n_features_new]
|
|
Transformed array.
|
|
"""
|
|
# non-optimized default implementation; override when a better
|
|
# method is possible for a given clustering algorithm
|
|
if y is None:
|
|
# fit method of arity 1 (unsupervised transformation)
|
|
return self.fit(X, **fit_params).transform(X)
|
|
else:
|
|
# fit method of arity 2 (supervised transformation)
|
|
return self.fit(X, y, **fit_params).transform(X)
|
|
|
|
|
|
class DensityMixin:
|
|
"""Mixin class for all density estimators in scikit-learn."""
|
|
_estimator_type = "DensityEstimator"
|
|
|
|
def score(self, X, y=None):
|
|
"""Return the score of the model on the data X
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
"""
|
|
pass
|
|
|
|
|
|
class OutlierMixin:
|
|
"""Mixin class for all outlier detection estimators in scikit-learn."""
|
|
_estimator_type = "outlier_detector"
|
|
|
|
def fit_predict(self, X, y=None):
|
|
"""Perform fit on X and returns labels for X.
|
|
|
|
Returns -1 for outliers and 1 for inliers.
|
|
|
|
Parameters
|
|
----------
|
|
X : ndarray, shape (n_samples, n_features)
|
|
Input data.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
Returns
|
|
-------
|
|
y : ndarray, shape (n_samples,)
|
|
1 for inliers, -1 for outliers.
|
|
"""
|
|
# override for transductive outlier detectors like LocalOulierFactor
|
|
return self.fit(X).predict(X)
|
|
|
|
|
|
class MetaEstimatorMixin:
|
|
_required_parameters = ["estimator"]
|
|
"""Mixin class for all meta estimators in scikit-learn."""
|
|
|
|
|
|
class MultiOutputMixin:
|
|
"""Mixin to mark estimators that support multioutput."""
|
|
def _more_tags(self):
|
|
return {'multioutput': True}
|
|
|
|
|
|
class _UnstableArchMixin:
|
|
"""Mark estimators that are non-determinstic on 32bit or PowerPC"""
|
|
def _more_tags(self):
|
|
return {'non_deterministic': (
|
|
_IS_32BIT or platform.machine().startswith(('ppc', 'powerpc')))}
|
|
|
|
|
|
def is_classifier(estimator):
|
|
"""Return True if the given estimator is (probably) a classifier.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : object
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is a classifier and False otherwise.
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "classifier"
|
|
|
|
|
|
def is_regressor(estimator):
|
|
"""Return True if the given estimator is (probably) a regressor.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : object
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is a regressor and False otherwise.
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "regressor"
|
|
|
|
|
|
def is_outlier_detector(estimator):
|
|
"""Return True if the given estimator is (probably) an outlier detector.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : object
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is an outlier detector and False otherwise.
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "outlier_detector"
|