484 lines
18 KiB
Python
484 lines
18 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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import numpy as np
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from scipy import sparse
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from .externals import six
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from .utils.fixes import signature
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from .utils.deprecation import deprecated
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from .exceptions import ChangedBehaviorWarning as ChangedBehaviorWarning_
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class ChangedBehaviorWarning(ChangedBehaviorWarning_):
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pass
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ChangedBehaviorWarning = deprecated("ChangedBehaviorWarning has been moved "
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"into the sklearn.exceptions module. "
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"It will not be available here from "
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"version 0.19")(ChangedBehaviorWarning)
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##############################################################################
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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 deepcopy 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'):
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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 six.iteritems(new_object_params):
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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 isinstance(param1, np.ndarray):
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# For most ndarrays, we do not test for complete equality
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if not isinstance(param2, type(param1)):
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equality_test = False
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elif (param1.ndim > 0
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and param1.shape[0] > 0
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and isinstance(param2, np.ndarray)
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and param2.ndim > 0
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and param2.shape[0] > 0):
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equality_test = (
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param1.shape == param2.shape
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and param1.dtype == param2.dtype
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# We have to use '.flat' for 2D arrays
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and param1.flat[0] == param2.flat[0]
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and param1.flat[-1] == param2.flat[-1]
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)
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else:
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equality_test = np.all(param1 == param2)
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elif sparse.issparse(param1):
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# For sparse matrices equality doesn't work
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if not sparse.issparse(param2):
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equality_test = False
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elif param1.size == 0 or param2.size == 0:
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equality_test = (
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param1.__class__ == param2.__class__
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and param1.size == 0
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and param2.size == 0
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)
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else:
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equality_test = (
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param1.__class__ == param2.__class__
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and param1.data[0] == param2.data[0]
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and param1.data[-1] == param2.data[-1]
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and param1.nnz == param2.nnz
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and param1.shape == param2.shape
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)
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else:
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new_obj_val = new_object_params[name]
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params_set_val = params_set[name]
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# The following construct is required to check equality on special
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# singletons such as np.nan that are not equal to them-selves:
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equality_test = (new_obj_val == params_set_val or
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new_obj_val is params_set_val)
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if not equality_test:
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raise RuntimeError('Cannot clone object %s, as the constructor '
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'does not seem to set parameter %s' %
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(estimator, name))
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return new_object
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###############################################################################
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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:
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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(six.iteritems(params))):
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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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###############################################################################
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class BaseEstimator(object):
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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 = 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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"""Get parameters for this estimator.
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Parameters
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----------
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deep: boolean, optional
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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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# We need deprecation warnings to always be on in order to
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# catch deprecated param values.
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# This is set in utils/__init__.py but it gets overwritten
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# when running under python3 somehow.
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warnings.simplefilter("always", DeprecationWarning)
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try:
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with warnings.catch_warnings(record=True) as w:
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value = getattr(self, key, None)
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if len(w) and w[0].category == DeprecationWarning:
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# if the parameter is deprecated, don't show it
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continue
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finally:
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warnings.filters.pop(0)
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# XXX: should we rather test if instance of estimator?
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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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"""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 former 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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Returns
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-------
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self
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"""
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if not params:
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# Simple optimisation 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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for key, value in six.iteritems(params):
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split = key.split('__', 1)
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if len(split) > 1:
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# nested objects case
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name, sub_name = split
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if name 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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(name, self))
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sub_object = valid_params[name]
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sub_object.set_params(**{sub_name: value})
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else:
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# simple objects case
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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.__class__.__name__))
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setattr(self, key, value)
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return self
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def __repr__(self):
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class_name = self.__class__.__name__
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return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
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offset=len(class_name),),)
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###############################################################################
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class ClassifierMixin(object):
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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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"""Returns 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, shape = (n_samples, n_features)
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Test samples.
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y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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True labels for X.
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sample_weight : array-like, shape = [n_samples], optional
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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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###############################################################################
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class RegressorMixin(object):
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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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"""Returns 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 regression
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sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
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sum of squares ((y_true - y_true.mean()) ** 2).sum().
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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, shape = (n_samples, n_features)
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Test samples.
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y : array-like, shape = (n_samples) or (n_samples, n_outputs)
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True values for X.
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sample_weight : array-like, shape = [n_samples], optional
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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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"""
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from .metrics import r2_score
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return r2_score(y, self.predict(X), sample_weight=sample_weight,
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multioutput='variance_weighted')
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###############################################################################
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class ClusterMixin(object):
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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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"""Performs 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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Returns
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-------
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y : 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(object):
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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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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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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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"""Returns the submatrix corresponding to bicluster `i`.
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Works with sparse matrices. Only works if ``rows_`` and
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``columns_`` attributes exist.
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"""
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from .utils.validation import check_array
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data = check_array(data, accept_sparse='csr')
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row_ind, col_ind = self.get_indices(i)
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return data[row_ind[:, np.newaxis], col_ind]
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###############################################################################
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class TransformerMixin(object):
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"""Mixin class for all transformers in scikit-learn."""
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def fit_transform(self, X, y=None, **fit_params):
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"""Fit to data, then transform it.
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Fits transformer to X and y with optional parameters fit_params
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and returns a transformed version of X.
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Parameters
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----------
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X : numpy array of shape [n_samples, n_features]
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Training set.
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y : numpy array of shape [n_samples]
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Target values.
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Returns
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-------
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X_new : numpy array of shape [n_samples, n_features_new]
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Transformed array.
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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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if y is None:
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# fit method of arity 1 (unsupervised transformation)
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return self.fit(X, **fit_params).transform(X)
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else:
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# fit method of arity 2 (supervised transformation)
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return self.fit(X, y, **fit_params).transform(X)
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###############################################################################
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class MetaEstimatorMixin(object):
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"""Mixin class for all meta estimators in scikit-learn."""
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# this is just a tag for the moment
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###############################################################################
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def is_classifier(estimator):
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"""Returns True if the given estimator is (probably) a classifier."""
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return getattr(estimator, "_estimator_type", None) == "classifier"
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def is_regressor(estimator):
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"""Returns True if the given estimator is (probably) a regressor."""
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return getattr(estimator, "_estimator_type", None) == "regressor"
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