858 lines
30 KiB
Python
858 lines
30 KiB
Python
"""Base classes for all estimators."""
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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 ._config import get_config
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from .utils import _IS_32BIT
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from .utils._tags import (
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_DEFAULT_TAGS,
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_safe_tags,
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)
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from .utils.validation import check_X_y
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from .utils.validation import check_array
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from .utils._estimator_html_repr import estimator_html_repr
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from .utils.validation import _deprecate_positional_args
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@_deprecate_positional_args
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def clone(estimator, *, safe=True):
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"""Constructs a new unfitted 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 fitted on any data.
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If the estimator's `random_state` parameter is an integer (or if the
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estimator doesn't have a `random_state` parameter), an *exact clone* is
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returned: the clone and the original estimator will give the exact same
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results. Otherwise, *statistical clone* is returned: the clone might
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yield different results from the original estimator. More details can be
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found in :ref:`randomness`.
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Parameters
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----------
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estimator : {list, tuple, set} of estimator instance or a single \
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estimator instance
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The estimator or group of estimators to be cloned.
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safe : bool, default=True
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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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if isinstance(estimator, type):
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raise TypeError("Cannot clone object. " +
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"You should provide an instance of " +
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"scikit-learn estimator instead of a class.")
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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 "
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"estimator as it does not implement a "
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"'get_params' method."
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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, default=0
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The offset in characters to add at the begin of each line.
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printer : callable, default=repr
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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 : dict
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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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value = getattr(self, key)
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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 :class:`~sklearn.pipeline.Pipeline`). The latter have
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parameters of the form ``<component>__<parameter>`` so that it's
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possible to update each 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 : estimator instance
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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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def _check_n_features(self, X, reset):
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"""Set the `n_features_in_` attribute, or check against it.
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Parameters
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----------
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X : {ndarray, sparse matrix} of shape (n_samples, n_features)
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The input samples.
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reset : bool
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If True, the `n_features_in_` attribute is set to `X.shape[1]`.
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If False and the attribute exists, then check that it is equal to
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`X.shape[1]`. If False and the attribute does *not* exist, then
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the check is skipped.
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.. note::
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It is recommended to call reset=True in `fit` and in the first
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call to `partial_fit`. All other methods that validate `X`
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should set `reset=False`.
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"""
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n_features = X.shape[1]
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if reset:
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self.n_features_in_ = n_features
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return
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if not hasattr(self, "n_features_in_"):
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# Skip this check if the expected number of expected input features
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# was not recorded by calling fit first. This is typically the case
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# for stateless transformers.
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return
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if n_features != self.n_features_in_:
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raise ValueError(
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f"X has {n_features} features, but {self.__class__.__name__} "
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f"is expecting {self.n_features_in_} features as input.")
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def _validate_data(self, X, y='no_validation', reset=True,
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validate_separately=False, **check_params):
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"""Validate input data and set or check the `n_features_in_` attribute.
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Parameters
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----------
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X : {array-like, sparse matrix, dataframe} of shape \
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(n_samples, n_features)
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The input samples.
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y : array-like of shape (n_samples,), default='no_validation'
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The targets.
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- If `None`, `check_array` is called on `X`. If the estimator's
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requires_y tag is True, then an error will be raised.
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- If `'no_validation'`, `check_array` is called on `X` and the
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estimator's requires_y tag is ignored. This is a default
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placeholder and is never meant to be explicitly set.
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- Otherwise, both `X` and `y` are checked with either `check_array`
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or `check_X_y` depending on `validate_separately`.
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reset : bool, default=True
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Whether to reset the `n_features_in_` attribute.
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If False, the input will be checked for consistency with data
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provided when reset was last True.
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.. note::
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It is recommended to call reset=True in `fit` and in the first
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call to `partial_fit`. All other methods that validate `X`
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should set `reset=False`.
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validate_separately : False or tuple of dicts, default=False
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Only used if y is not None.
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If False, call validate_X_y(). Else, it must be a tuple of kwargs
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to be used for calling check_array() on X and y respectively.
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**check_params : kwargs
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Parameters passed to :func:`sklearn.utils.check_array` or
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:func:`sklearn.utils.check_X_y`. Ignored if validate_separately
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is not False.
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Returns
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-------
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out : {ndarray, sparse matrix} or tuple of these
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The validated input. A tuple is returned if `y` is not None.
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"""
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if y is None:
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if self._get_tags()['requires_y']:
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raise ValueError(
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f"This {self.__class__.__name__} estimator "
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f"requires y to be passed, but the target y is None."
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)
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X = check_array(X, **check_params)
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out = X
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elif isinstance(y, str) and y == 'no_validation':
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X = check_array(X, **check_params)
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out = X
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else:
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if validate_separately:
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# We need this because some estimators validate X and y
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# separately, and in general, separately calling check_array()
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# on X and y isn't equivalent to just calling check_X_y()
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# :(
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check_X_params, check_y_params = validate_separately
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X = check_array(X, **check_X_params)
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y = check_array(y, **check_y_params)
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else:
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X, y = check_X_y(X, y, **check_params)
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out = X, y
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if check_params.get('ensure_2d', True):
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self._check_n_features(X, reset=reset)
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return out
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@property
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def _repr_html_(self):
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"""HTML representation of estimator.
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This is redundant with the logic of `_repr_mimebundle_`. The latter
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should be favorted in the long term, `_repr_html_` is only
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implemented for consumers who do not interpret `_repr_mimbundle_`.
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"""
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if get_config()["display"] != 'diagram':
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raise AttributeError("_repr_html_ is only defined when the "
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"'display' configuration option is set to "
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"'diagram'")
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return self._repr_html_inner
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def _repr_html_inner(self):
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"""This function is returned by the @property `_repr_html_` to make
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`hasattr(estimator, "_repr_html_") return `True` or `False` depending
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on `get_config()["display"]`.
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"""
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return estimator_html_repr(self)
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def _repr_mimebundle_(self, **kwargs):
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"""Mime bundle used by jupyter kernels to display estimator"""
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output = {"text/plain": repr(self)}
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if get_config()["display"] == 'diagram':
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output["text/html"] = estimator_html_repr(self)
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return output
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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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def _more_tags(self):
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return {'requires_y': True}
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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):
|
|
"""Return the coefficient of determination :math:`R^2` of the
|
|
prediction.
|
|
|
|
The coefficient :math:`R^2` is defined as :math:`(1 - \\frac{u}{v})`,
|
|
where :math:`u` is the residual sum of squares ``((y_true - y_pred)
|
|
** 2).sum()`` and :math:`v` is the total sum of squares ``((y_true -
|
|
y_true.mean()) ** 2).sum()``. The best possible score is 1.0 and it
|
|
can be negative (because the model can be arbitrarily worse). A
|
|
constant model that always predicts the expected value of `y`,
|
|
disregarding the input features, would get a :math:`R^2` score of
|
|
0.0.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Test samples. For some estimators this may be a precomputed
|
|
kernel matrix or a list of generic objects instead with shape
|
|
``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
|
|
is the number of samples used in the fitting for the estimator.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
|
True values for `X`.
|
|
|
|
sample_weight : array-like of shape (n_samples,), default=None
|
|
Sample weights.
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
:math:`R^2` of ``self.predict(X)`` wrt. `y`.
|
|
|
|
Notes
|
|
-----
|
|
The :math:`R^2` score used when calling ``score`` on a regressor uses
|
|
``multioutput='uniform_average'`` from version 0.23 to keep consistent
|
|
with default value of :func:`~sklearn.metrics.r2_score`.
|
|
This influences the ``score`` method of all the multioutput
|
|
regressors (except for
|
|
:class:`~sklearn.multioutput.MultiOutputRegressor`).
|
|
"""
|
|
|
|
from .metrics import r2_score
|
|
y_pred = self.predict(X)
|
|
return r2_score(y, y_pred, sample_weight=sample_weight)
|
|
|
|
def _more_tags(self):
|
|
return {'requires_y': True}
|
|
|
|
|
|
class ClusterMixin:
|
|
"""Mixin class for all cluster estimators in scikit-learn."""
|
|
_estimator_type = "clusterer"
|
|
|
|
def fit_predict(self, X, y=None):
|
|
"""
|
|
Perform clustering on `X` and returns cluster labels.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Input data.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
Returns
|
|
-------
|
|
labels : ndarray of shape (n_samples,), dtype=np.int64
|
|
Cluster labels.
|
|
"""
|
|
# non-optimized default implementation; override when a better
|
|
# method is possible for a given clustering algorithm
|
|
self.fit(X)
|
|
return self.labels_
|
|
|
|
def _more_tags(self):
|
|
return {"preserves_dtype": []}
|
|
|
|
|
|
class BiclusterMixin:
|
|
"""Mixin class for all bicluster estimators in scikit-learn."""
|
|
|
|
@property
|
|
def biclusters_(self):
|
|
"""Convenient way to get row and column indicators together.
|
|
|
|
Returns the ``rows_`` and ``columns_`` members.
|
|
"""
|
|
return self.rows_, self.columns_
|
|
|
|
def get_indices(self, i):
|
|
"""Row and column indices of the `i`'th bicluster.
|
|
|
|
Only works if ``rows_`` and ``columns_`` attributes exist.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
|
|
Returns
|
|
-------
|
|
row_ind : ndarray, dtype=np.intp
|
|
Indices of rows in the dataset that belong to the bicluster.
|
|
col_ind : ndarray, dtype=np.intp
|
|
Indices of columns in the dataset that belong to the bicluster.
|
|
|
|
"""
|
|
rows = self.rows_[i]
|
|
columns = self.columns_[i]
|
|
return np.nonzero(rows)[0], np.nonzero(columns)[0]
|
|
|
|
def get_shape(self, i):
|
|
"""Shape of the `i`'th bicluster.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
|
|
Returns
|
|
-------
|
|
n_rows : int
|
|
Number of rows in the bicluster.
|
|
|
|
n_cols : int
|
|
Number of columns in the bicluster.
|
|
"""
|
|
indices = self.get_indices(i)
|
|
return tuple(len(i) for i in indices)
|
|
|
|
def get_submatrix(self, i, data):
|
|
"""Return the submatrix corresponding to bicluster `i`.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
data : array-like of shape (n_samples, n_features)
|
|
The data.
|
|
|
|
Returns
|
|
-------
|
|
submatrix : ndarray of shape (n_rows, n_cols)
|
|
The submatrix corresponding to bicluster `i`.
|
|
|
|
Notes
|
|
-----
|
|
Works with sparse matrices. Only works if ``rows_`` and
|
|
``columns_`` attributes exist.
|
|
"""
|
|
from .utils.validation import check_array
|
|
data = check_array(data, accept_sparse='csr')
|
|
row_ind, col_ind = self.get_indices(i)
|
|
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 : array-like of shape (n_samples, n_features)
|
|
Input samples.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs), \
|
|
default=None
|
|
Target values (None for unsupervised transformations).
|
|
|
|
**fit_params : dict
|
|
Additional fit parameters.
|
|
|
|
Returns
|
|
-------
|
|
X_new : ndarray 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)
|
|
Test samples.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
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 : {array-like, sparse matrix, dataframe} of shape \
|
|
(n_samples, n_features)
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
Returns
|
|
-------
|
|
y : ndarray of 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 : estimator instance
|
|
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 : estimator instance
|
|
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"
|
|
|
|
|
|
def _is_pairwise(estimator):
|
|
"""Returns True if estimator is pairwise.
|
|
|
|
- If the `_pairwise` attribute and the tag are present and consistent,
|
|
then use the value and not issue a warning.
|
|
- If the `_pairwise` attribute and the tag are present and not
|
|
consistent, use the `_pairwise` value and issue a deprecation
|
|
warning.
|
|
- If only the `_pairwise` attribute is present and it is not False,
|
|
issue a deprecation warning and use the `_pairwise` value.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : object
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if the estimator is pairwise and False otherwise.
|
|
"""
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings('ignore', category=FutureWarning)
|
|
has_pairwise_attribute = hasattr(estimator, '_pairwise')
|
|
pairwise_attribute = getattr(estimator, '_pairwise', False)
|
|
pairwise_tag = _safe_tags(estimator, key="pairwise")
|
|
|
|
if has_pairwise_attribute:
|
|
if pairwise_attribute != pairwise_tag:
|
|
warnings.warn(
|
|
"_pairwise was deprecated in 0.24 and will be removed in 1.1 "
|
|
"(renaming of 0.26). Set the estimator tags of your estimator "
|
|
"instead",
|
|
FutureWarning
|
|
)
|
|
return pairwise_attribute
|
|
|
|
# use pairwise tag when the attribute is not present
|
|
return pairwise_tag
|