384 lines
13 KiB
Python
384 lines
13 KiB
Python
"""Base class for all estimators."""
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD Style
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import copy
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import inspect
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import numpy as np
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from scipy import sparse
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import warnings
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from .metrics import r2_score
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from .utils import deprecated
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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 as "
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"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.iteritems():
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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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equality_test = new_object_params[name] == params_set[name]
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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' % (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(params.iteritems())):
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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
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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, **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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try:
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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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# introspect the constructor arguments to find the model parameters
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# to represent
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args, varargs, kw, default = inspect.getargspec(init)
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if not varargs is None:
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raise RuntimeError('scikit learn estimators should always '
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'specify their parameters in the signature of '
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'their init (no varargs).')
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# Remove 'self'
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# XXX: This is going to fail if the init is a staticmethod, but
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# who would do this?
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args.pop(0)
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except TypeError:
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# No explicit __init__
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args = []
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args.sort()
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return args
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@deprecated("to be removed in v0.12; use get_params() instead")
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def _get_params(self, deep=True):
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return self.get_params(deep)
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def get_params(self, deep=True):
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"""Get parameters for the 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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"""
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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, 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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"""Set the parameters of the 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
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valid_params = self.get_params(deep=True)
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for key, value in params.iteritems():
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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 not name in valid_params:
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raise ValueError('Invalid parameter %s for estimator %s'
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% (name, self))
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sub_object = valid_params[name]
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if not hasattr(sub_object, 'get_params'):
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raise TypeError(
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'Parameter %s of %s is not an estimator, cannot set '
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'sub parameter %s' %
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(sub_name, self.__class__.__name__, sub_name)
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)
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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 not key in valid_params:
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raise ValueError('Invalid parameter %s ' 'for estimator %s'
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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 _set_params(self, **params):
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if params != {}:
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warnings.warn("Passing estimator parameters to fit is deprecated;"
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" use set_params instead",
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category=DeprecationWarning)
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return self.set_params(**params)
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def __repr__(self):
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class_name = self.__class__.__name__
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return '%s(%s)' % (
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class_name,
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_pprint(self.get_params(deep=False),
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offset=len(class_name),
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),
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)
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def __str__(self):
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class_name = self.__class__.__name__
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return '%s(%s)' % (
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class_name,
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_pprint(self.get_params(deep=True),
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offset=len(class_name),
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printer=str,
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),
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)
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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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def score(self, X, y):
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"""Returns the mean accuracy on the given test data and labels.
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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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Training set.
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y : array-like, shape = [n_samples]
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Labels for X.
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Returns
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-------
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z : float
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"""
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return np.mean(self.predict(X) == y)
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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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def score(self, X, y):
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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
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regression sum of squares ((y - y_pred) ** 2).sum() and v is the
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residual sum of squares ((y_true - y_true.mean()) ** 2).sum().
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Best possible score is 1.0, lower values are worse.
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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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Training set.
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y : array-like, shape = [n_samples]
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Returns
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-------
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z : float
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"""
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return r2_score(y, self.predict(X))
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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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Notes
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-----
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This method just calls fit and transform consecutively, i.e., it is not
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an optimized implementation of fit_transform, unlike other transformers
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such as PCA.
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"""
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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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# XXX: Temporary solution to figure out if an estimator is a classifier
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def _get_sub_estimator(estimator):
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"""Returns the final estimator if there is any."""
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if hasattr(estimator, 'estimator'):
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# GridSearchCV and other CV-tuned estimators
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return _get_sub_estimator(estimator.estimator)
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if hasattr(estimator, 'steps'):
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# Pipeline
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return _get_sub_estimator(estimator.steps[-1][1])
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return estimator
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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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estimator = _get_sub_estimator(estimator)
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return isinstance(estimator, ClassifierMixin)
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