"""

The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters

of an estimator.

"""

from __future__ import print_function

# Author: Alexandre Gramfort <[email protected]>,

# Gael Varoquaux <[email protected]>

# License: BSD 3 clause

from abc import ABCMeta, abstractmethod

from collections import Mapping, namedtuple

from functools import partial, reduce

from itertools import product

import numbers

import operator

import time

import warnings

import numpy as np

import numpy.ma.mrecords as mrecords

from .base import BaseEstimator, is_classifier, clone

from .base import MetaEstimatorMixin

from .cross_validation import check_cv

from .externals.joblib import Parallel, delayed, logger

from .externals import six

from .externals.six import iteritems, iterkeys

from .externals.six.moves import zip

from .utils import safe_mask, check_random_state

from .utils.validation import _num_samples, check_arrays

from .metrics import SCORERS, Scorer

__all__ = ['GridSearchCV', 'ParameterGrid', 'fit_grid_point',

'ParameterSampler', 'RandomizedSearchCV']

class ParameterGrid(object):

"""Grid of parameters with a discrete number of values for each.

Can be used to iterate over parameter value combinations with the

Python built-in function iter.

Parameters

----------

param_grid : dict of string to sequence

The parameter grid to explore, as a dictionary mapping estimator

parameters to sequences of allowed values.

Examples

--------

>>> from sklearn.grid_search import ParameterGrid

>>> param_grid = {'a':[1, 2], 'b':[True, False]}

>>> list(ParameterGrid(param_grid)) #doctest: +NORMALIZE_WHITESPACE

[{'a': 1, 'b': True}, {'a': 1, 'b': False},

{'a': 2, 'b': True}, {'a': 2, 'b': False}]

See also

--------

:class:`GridSearchCV`:

uses ``ParameterGrid`` to perform a full parallelized parameter search.

"""

def __init__(self, param_grid):

if isinstance(param_grid, Mapping):

# wrap dictionary in a singleton list

# XXX Why? The behavior when passing a list is undocumented,

# but not doing this breaks one of the tests.

param_grid = [param_grid]

self.param_grid = param_grid

def __iter__(self):

"""Iterate over the points in the grid.

Returns

-------

params : iterator over dict of string to any

Yields dictionaries mapping each estimator parameter to one of its

allowed values.

"""

for p in self.param_grid:

# Always sort the keys of a dictionary, for reproducibility

items = sorted(p.items())

keys, values = zip(*items)

for v in product(*values):

params = dict(zip(keys, v))

yield params

def __len__(self):

"""Number of points on the grid."""

# Product function that can handle iterables (np.product can't).

product = partial(reduce, operator.mul)

return sum(product(len(v) for v in p.values())

for p in self.param_grid)

class IterGrid(ParameterGrid):

"""Generators on the combination of the various parameter lists given.

This class is DEPRECATED. It was renamed to ``ParameterGrid``. The name

``IterGrid`` will be removed in 0.15.

Parameters

----------

param_grid: dict of string to sequence

The parameter grid to explore, as a dictionary mapping estimator

parameters to sequences of allowed values.

Returns

-------

params: dict of string to any

**Yields** dictionaries mapping each estimator parameter to one of its

allowed values.

Examples

--------

>>> from sklearn.grid_search import IterGrid

>>> param_grid = {'a':[1, 2], 'b':[True, False]}

>>> list(IterGrid(param_grid)) #doctest: +NORMALIZE_WHITESPACE

[{'a': 1, 'b': True}, {'a': 1, 'b': False},

{'a': 2, 'b': True}, {'a': 2, 'b': False}]

See also

--------

:class:`GridSearchCV`:

uses ``IterGrid`` to perform a full parallelized parameter search.

"""

def __init__(self, param_grid):

warnings.warn("IterGrid was renamed to ParameterGrid and will be"

" removed in 0.15.", DeprecationWarning)

super(IterGrid, self).__init__(param_grid)

class ParameterSampler(object):

"""Generator on parameters sampled from given distributions.

Parameters

----------

param_distributions : dict

Dictionary where the keys are parameters and values

are distributions from which a parameter is to be sampled.

Distributions either have to provide a ``rvs`` function

to sample from them, or can be given as a list of values,

where a uniform distribution is assumed.

n_iter : integer

Number of parameter settings that are produced.

random_state : int or RandomState

Pseudo number generator state used for random sampling.

Returns

-------

params: dict of string to any

**Yields** dictionaries mapping each estimator parameter to

as sampled value.

Examples

--------

>>> from sklearn.grid_search import ParameterSampler

>>> from scipy.stats.distributions import expon

>>> import numpy as np

>>> np.random.seed(0)

>>> param_grid = {'a':[1, 2], 'b': expon()}

>>> list(ParameterSampler(param_grid, n_iter=4))

... #doctest: +NORMALIZE_WHITESPACE +ELLIPSIS

[{'a': 1, 'b': 0.89...}, {'a': 1, 'b': 0.92...},

{'a': 2, 'b': 1.87...}, {'a': 2, 'b': 1.03...}]

"""

def __init__(self, param_distributions, n_iter, random_state=None):

self.param_distributions = param_distributions

self.n_iter = n_iter

self.random_state = random_state

def __iter__(self):

rnd = check_random_state(self.random_state)

# Always sort the keys of a dictionary, for reproducibility

items = sorted(self.param_distributions.items())

for _ in range(self.n_iter):

params = dict()

for k, v in items:

if hasattr(v, "rvs"):

params[k] = v.rvs()

else:

params[k] = v[rnd.randint(len(v))]

yield params

def __len__(self):

"""Number of points that will be sampled."""

return self.n_iter

def fit_grid_point(X, y, base_clf, clf_params, train, test, scorer,

verbose, loss_func=None, **fit_params):

"""Run fit on one set of parameters.

Parameters

----------

X : array-like, sparse matrix or list

Input data.

y : array-like or None

Targets for input data.

base_clf : estimator object

This estimator will be cloned and then fitted.

clf_params : dict

Parameters to be set on base_estimator clone for this grid point.

train : ndarray, dtype int or bool

Boolean mask or indices for training set.

test : ndarray, dtype int or bool

Boolean mask or indices for test set.

scorer : callable or None.

If provided must be a scoring object / function with signature

``scorer(estimator, X, y)``.

verbose : int

Verbosity level.

**fit_params : kwargs

Additional parameter passed to the fit function of the estimator.

Returns

-------

score : float

Score of this parameter setting on given training / test split.

estimator : estimator object

Estimator object of type base_clf that was fitted using clf_params

and provided train / test split.

n_samples_test : int

Number of test samples in this split.

"""

if verbose > 1:

start_time = time.time()

msg = '%s' % (', '.join('%s=%s' % (k, v)

for k, v in clf_params.items()))

print("[GridSearchCV] %s %s" % (msg, (64 - len(msg)) * '.'))

# update parameters of the classifier after a copy of its base structure

clf = clone(base_clf)

clf.set_params(**clf_params)

if hasattr(base_clf, 'kernel') and callable(base_clf.kernel):

# cannot compute the kernel values with custom function

raise ValueError("Cannot use a custom kernel function. "

"Precompute the kernel matrix instead.")

if not hasattr(X, "shape"):

if getattr(base_clf, "_pairwise", False):

raise ValueError("Precomputed kernels or affinity matrices have "

"to be passed as arrays or sparse matrices.")

X_train = [X[idx] for idx in train]

X_test = [X[idx] for idx in test]

else:

if getattr(base_clf, "_pairwise", False):

# X is a precomputed square kernel matrix

if X.shape[0] != X.shape[1]:

raise ValueError("X should be a square kernel matrix")

X_train = X[np.ix_(train, train)]

X_test = X[np.ix_(test, train)]

else:

X_train = X[safe_mask(X, train)]

X_test = X[safe_mask(X, test)]

if y is not None:

y_test = y[safe_mask(y, test)]

y_train = y[safe_mask(y, train)]

clf.fit(X_train, y_train, **fit_params)

if scorer is not None:

this_score = scorer(clf, X_test, y_test)

else:

this_score = clf.score(X_test, y_test)

else:

clf.fit(X_train, **fit_params)

if scorer is not None:

this_score = scorer(clf, X_test)

else:

this_score = clf.score(X_test)

if not isinstance(this_score, numbers.Number):

raise ValueError("scoring must return a number, got %s (%s)"

" instead." % (str(this_score), type(this_score)))

if verbose > 2:

msg += ", score=%f" % this_score

if verbose > 1:

end_msg = "%s -%s" % (msg,

logger.short_format_time(time.time() -

start_time))

print("[GridSearchCV] %s %s" % ((64 - len(end_msg)) * '.', end_msg))

return this_score, clf_params, _num_samples(X_test)

def _check_param_grid(param_grid):

if hasattr(param_grid, 'items'):

param_grid = [param_grid]

for p in param_grid:

for v in p.values():

if isinstance(v, np.ndarray) and v.ndim > 1:

raise ValueError("Parameter array should be one-dimensional.")

check = [isinstance(v, k) for k in (list, tuple, np.ndarray)]

if not True in check:

raise ValueError("Parameter values should be a list.")

if len(v) == 0:

raise ValueError("Parameter values should be a non-empty "

"list.")

class SearchResult(object):

"""

>>> from __future__ import print_function

>>> from sklearn.grid_search import GridSearchCV

>>> from sklearn.datasets import load_iris

>>> from sklearn.svm import SVC

>>> iris = load_iris()

>>> grid = {'C': [0.01, 0.1, 1], 'degree': [1, 2, 3]}

>>> search = GridSearchCV(SVC(kernel='poly'), param_grid=grid)

>>> search = search.fit(iris.data, iris.target)

>>> res = search.results_

>>> res.best().mean_test_score # doctest: +ELLIPSIS

0.973...

>>> res # doctest: +ELLIPSIS

<9 candidates. Best results:

<0.973 for {'C': 0.1..., 'degree': 3}>,

<0.967 for {'C': 1.0, 'degree': 3}>,

<0.967 for {'C': 1.0, 'degree': 2}>, ...>

>>> res[res.param_degree == 2] # doctest: +ELLIPSIS

<3 candidates. Best results:

<0.967 for {'C': 1.0, 'degree': 2}>,

<0.967 for {'C': 0.1..., 'degree': 2}>,

<0.927 for {'C': 0.01, 'degree': 2}>>

>>> res.group_best(['degree']) # doctest: +ELLIPSIS

<3 candidates. Best results:

<0.973 for {'C': 0.1..., 'degree': 3}>,

<0.967 for {'C': 1.0, 'degree': 2}>,

<0.967 for {'C': 1.0, 'degree': 1}>>

>>> for tup in res.zipped('parameters', 'mean_test_score',

... 'std_test_score'):

... print(*tup)

... # doctest: +ELLIPSIS

{'C': 0.01, 'degree': 1} 0.67... 0.03...

{'C': 0.01, 'degree': 2} 0.92... 0.00...

{'C': 0.01, 'degree': 3} 0.96... 0.01...

{'C': 0.10..., 'degree': 1} 0.94 0.01...

{'C': 0.10..., 'degree': 2} 0.96... 0.01...

{'C': 0.10..., 'degree': 3} 0.97... 0.00...

{'C': 1.0, 'degree': 1} 0.96... 0.02...

{'C': 1.0, 'degree': 2} 0.96... 0.00...

{'C': 1.0, 'degree': 3} 0.96... 0.01...

"""

__slots__ = ('_param_arrays', '_data_arrays', '_fold_weight',

'_score_field', '_greater_is_better')

def __init__(self, param_arrays, data_arrays, fold_weight=None,

score_field='test_score', greater_is_better=True):

self._param_arrays = param_arrays

self._data_arrays = data_arrays

self._fold_weight = fold_weight

self._score_field = score_field

self._greater_is_better = greater_is_better

def __getattr__(self, attr):

try:

prefix, field = attr.split('_', 1)

except ValueError:

raise AttributeError('%r has no attribute %r'

% (self.__class__.__name__, attr))

if prefix == 'param':

try:

return self._param_arrays[field]

except (KeyError, ValueError):

raise AttributeError('%r has no attribute %r'

% (self.__class__.__name__, attr))

try:

data = self._data_arrays[field]

except (KeyError, ValueError):

raise AttributeError('%r has no attribute %r'

% (self.__class__.__name__, attr))

if prefix == 'fold':

return data

elif prefix == 'mean':

return np.average(data, axis=-1, weights=self._fold_weight)

elif prefix == 'std':

weight = self._fold_weight

if weight is not None:

avg = np.average(data, axis=-1, weights=weight)

avg.shape = data.shape[:-1]

squares = (data.T - avg) ** 2

return np.sqrt(np.dot(weight, squares) / np.sum(weight))

return np.std(data, axis=-1)

raise AttributeError('%r has no attribute %r'

% (self.__class__.__name__, attr))

def __getitem__(self, index):

index = np.asarray(index)

if index.dtype == np.bool:

index = np.flatnonzero(index)

# TODO: validate

return self.__class__(

{k: v[index] for k, v in iteritems(self._param_arrays)},

{k: v[index] for k, v in iteritems(self._data_arrays)},

self._fold_weight, self._score_field, self._greater_is_better

)

def __len__(self):

shape = np.shape(self._data_arrays[self._score_field])

if len(shape) == 1:

raise TypeError('Singleton results have no length')

return shape[0]

@property

def is_singleton(self):

"""True if result is for a single candidate"""

shape = np.shape(self._data_arrays[self._score_field])

return len(shape) == 1

def __iter__(self):

for i in xrange(len(self)):

yield self[i]

def for_field(self, field, greater_is_better):

"""Create a new SearchResult, using a given score field."""

# TODO: validate

return self.__class__(self._param_arrays, self._data_arrays,

self._fold_weight, field, greater_is_better)

@property

def score(self):

"""Mean score for each candidate"""

return getattr(self, 'mean_' + self._score_field)

def best(self, k=None):

"""Return a ``SearchResult`` with only the best ``k`` candidates.

Results will be ordered in decreasing perormance order.

For k = None, the best result will be returned, with means given as

single values rather than arrays.

"""

order = np.argsort(self.score)

if self._greater_is_better:

order = order[::-1]

if k is not None:

return self[order[:k]]

return self[order[0]]

def best_in_margin(self, margin=0.001):

scores = self.score

if self._greater_is_better:

return self[scores >= scores.max() - margin]

else:

return self[scores <= scores.min() + margin]

def zipped(self, *attrs):

return zip(*[getattr(self, attr) for attr in attrs])

def group(self, fields=None, negate=False):

"""Index candidates by distinct settings of `fields`.

Requires all parameter values for grouping fields to be hashable and

comparable.

"""

items = [(k, v) for k, v in iteritems(self._param_arrays)

if (k in fields) ^ negate]

fields, values = zip(*items)

values = list(zip(*values))

values_arr = np.zeros(len(values), dtype=object)

values_arr[:] = values

distinct, inverse = np.unique(values_arr, return_inverse=True)

return inverse, [dict(zip(fields, values)) for values in distinct]

def group_best(self, fields=None, negate=False):

"""Select the best scoring candidate for each setting of ``fields``.

"""

if self._greater_is_better:

scores = self.score

else:

scores = -self.score

# Sort with major key groups, minor key score:

groups, group_values = self.group(fields, negate)

order = np.lexsort((scores, groups))

groups = groups[order]

# Index marks change from one group to next, i.e. within-group max

index = np.empty(len(groups), 'bool')

index[-1] = True

index[:-1] = groups[1:] != groups[:-1]

return self[order[index]]

@property

def parameters(self):

masked = np.ma.masked

names, values = zip(*list(iteritems(self._param_arrays)))

if self.is_singleton:

return {name: val for name, val in zip(names, values)

if val is not masked}

out = []

for candidate in zip(*values):

out.append({name: val for name, val in zip(names, candidate)

if val is not masked})

return out

def __repr__(self, show_top=3):

try:

n = len(self)

except TypeError:

return '<%0.3f for %r>' % (self.score, self.parameters)

if show_top < n:

suff = ', ...'

else:

suff = ''

return ('<%d candidates. Best results:\n %s%s>'

% (n, ',\n '.join(repr(sr) for sr in self.best(show_top)),

suff))

def __array__(self):

arrays = [('param_' + k, v) for k, v in iteritems(self._param_arrays)]

for field in iterkeys(self._data_arrays):

try:

arrays.append(('mean_' + field,

getattr(self, 'mean_' + field)))

arrays.append(('std_' + field,

getattr(self, 'std_' + field)))

except TypeError:

continue

fields, arrays = zip(*arrays)

return mrecords.fromarrays(arrays, names=fields)

def _params_to_arrays(parameter_dicts):

fields = {}

for params in parameter_dicts:

for name, value in iteritems(params):

fields[name] = value # take an example for masking

field_names = sorted(iterkeys(fields))

data = []

mask = []

for params in parameter_dicts:

row = [(params[name], False) if name in params

else (fields[name], True)

for name in field_names]

rdata, rmask = zip(*row)

data.append(rdata)

mask.append(rmask)

recs = mrecords.fromrecords(data, mask=mask, names=field_names)

return {field: recs[field] for field in field_names}

_CVScoreTuple = namedtuple('_CVScoreTuple',

('parameters', 'mean_validation_score',

'cv_validation_scores'))

class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,

MetaEstimatorMixin)):

"""Base class for hyper parameter search with cross-validation.

"""

@abstractmethod

def __init__(self, estimator, scoring=None, loss_func=None,

score_func=None, fit_params=None, n_jobs=1, iid=True,

refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs'):

self.scoring = scoring

self.estimator = estimator

self.loss_func = loss_func

self.score_func = score_func

self.n_jobs = n_jobs

self.fit_params = fit_params if fit_params is not None else {}

self.iid = iid

self.refit = refit

self.cv = cv

self.verbose = verbose

self.pre_dispatch = pre_dispatch

self._check_estimator()

def score(self, X, y=None):

"""Returns the score on the given test data and labels, if the search

estimator has been refit. The ``score`` function of the best estimator

is used, or the ``scoring`` parameter where unavailable.

Parameters

----------

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples], optional

Labels for X.

Returns

-------

score : float

"""

if hasattr(self.best_estimator_, 'score'):

return self.best_estimator_.score(X, y)

if self.scorer_ is None:

raise ValueError("No score function explicitly defined, "

"and the estimator doesn't provide one %s"

% self.best_estimator_)

return self.scorer_(self.best_estimator_, X, y)

@property

def predict(self):

return self.best_estimator_.predict

@property

def predict_proba(self):

return self.best_estimator_.predict_proba

@property

def decision_function(self):

return self.best_estimator_.decision_function

@property

def transform(self):

return self.best_estimator_.transform

def _check_estimator(self):

"""Check that estimator can be fitted and score can be computed."""

if (not hasattr(self.estimator, 'fit') or

not (hasattr(self.estimator, 'predict')

or hasattr(self.estimator, 'score'))):

raise TypeError("estimator should a be an estimator implementing"

" 'fit' and 'predict' or 'score' methods,"

" %s (type %s) was passed" %

(self.estimator, type(self.estimator)))

if (self.scoring is None and self.loss_func is None and self.score_func

is None):

if not hasattr(self.estimator, 'score'):

raise TypeError(

"If no scoring is specified, the estimator passed "

"should have a 'score' method. The estimator %s "

"does not." % self.estimator)

def _fit(self, X, y, parameter_iterator, **params):

"""Actual fitting, performing the search over parameters."""

if params:

warnings.warn("Passing additional parameters to GridSearchCV "

"is ignored! The option will be removed in 0.15.")

estimator = self.estimator

cv = self.cv

n_samples = _num_samples(X)

X, y = check_arrays(X, y, allow_lists=True, sparse_format='csr')

if self.loss_func is not None:

warnings.warn("Passing a loss function is "

"deprecated and will be removed in 0.15. "

"Either use strings or score objects."

"The relevant new parameter is called ''scoring''. ")

scorer = Scorer(self.loss_func, greater_is_better=False)

elif self.score_func is not None:

warnings.warn("Passing function as ``score_func`` is "

"deprecated and will be removed in 0.15. "

"Either use strings or score objects."

"The relevant new parameter is called ''scoring''.")

scorer = Scorer(self.score_func)

elif isinstance(self.scoring, six.string_types):

scorer = SCORERS[self.scoring]

else:

scorer = self.scoring

self.scorer_ = scorer

if y is not None:

if len(y) != n_samples:

raise ValueError('Target variable (y) has a different number '

'of samples (%i) than data (X: %i samples)'

% (len(y), n_samples))

y = np.asarray(y)

cv = check_cv(cv, X, y, classifier=is_classifier(estimator))

base_clf = clone(self.estimator)

pre_dispatch = self.pre_dispatch

out = Parallel(

n_jobs=self.n_jobs, verbose=self.verbose,

pre_dispatch=pre_dispatch)(

delayed(fit_grid_point)(

X, y, base_clf, clf_params, train, test, scorer,

self.verbose, **self.fit_params) for clf_params in

parameter_iterator for train, test in cv)

# Out is a list of triplet: score, estimator, n_test_samples

n_param_points = len(list(parameter_iterator))

n_fits = len(out)

n_folds = n_fits // n_param_points

scores = list()

cv_scores = list()

for grid_start in range(0, n_fits, n_folds):

n_test_samples = 0

score = 0

these_points = list()

for this_score, clf_params, this_n_test_samples in \

out[grid_start:grid_start + n_folds]:

these_points.append(this_score)

if self.iid:

this_score *= this_n_test_samples

n_test_samples += this_n_test_samples

score += this_score

if self.iid:

score /= float(n_test_samples)

else:

score /= float(n_folds)

scores.append((score, clf_params))

cv_scores.append(these_points)

cv_scores = np.asarray(cv_scores)

self.results_ = SearchResult(

_params_to_arrays(list(parameter_iterator)),

{'test_score': cv_scores},

[len(y[test]) for train, test in cv] if self.iid else None,

'test_score', getattr(scorer, 'greater_is_better', True))

# Note: we do not use max(out) to make ties deterministic even if

# comparison on estimator instances is not deterministic

if scorer is not None:

greater_is_better = scorer.greater_is_better

else:

greater_is_better = True

if greater_is_better:

best_score = -np.inf

else:

best_score = np.inf

for score, params in scores:

if ((score > best_score and greater_is_better)

or (score < best_score and not greater_is_better)):

best_score = score

best_params = params

self.best_params_ = best_params

self.best_score_ = best_score

if self.refit:

# fit the best estimator using the entire dataset

# clone first to work around broken estimators

best_estimator = clone(base_clf).set_params(**best_params)

if y is not None:

best_estimator.fit(X, y, **self.fit_params)

else:

best_estimator.fit(X, **self.fit_params)

self.best_estimator_ = best_estimator

# Store the computed scores

self.cv_scores_ = [

_CVScoreTuple(clf_params, score, all_scores)

for clf_params, (score, _), all_scores

in zip(parameter_iterator, scores, cv_scores)]

return self

class GridSearchCV(BaseSearchCV):

"""Exhaustive search over specified parameter values for an estimator.

Important members are fit, predict.

GridSearchCV implements a "fit" method and a "predict" method like

any classifier except that the parameters of the classifier

used to predict is optimized by cross-validation.

Parameters

----------

estimator : object type that implements the "fit" and "predict" methods

A object of that type is instantiated for each grid point.

param_grid : dict or list of dictionaries

Dictionary with parameters names (string) as keys and lists of

parameter settings to try as values, or a list of such

dictionaries, in which case the grids spanned by each dictionary

in the list are explored. This enables searching over any sequence

of parameter settings.

scoring : string or callable, optional

Either one of either a string ("zero_one", "f1", "roc_auc", ... for

classification, "mse", "r2",... for regression) or a callable.

See 'Scoring objects' in the model evaluation section of the user guide

for details.

fit_params : dict, optional

Parameters to pass to the fit method.

n_jobs : int, optional

Number of jobs to run in parallel (default 1).

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel

execution. Reducing this number can be useful to avoid an

explosion of memory consumption when more jobs get dispatched

than CPUs can process. This parameter can be:

- None, in which case all the jobs are immediately

created and spawned. Use this for lightweight and

fast-running jobs, to avoid delays due to on-demand

spawning of the jobs

- An int, giving the exact number of total jobs that are

spawned

- A string, giving an expression as a function of n_jobs,

as in '2*n_jobs'

iid : boolean, optional

If True, the data is assumed to be identically distributed across

the folds, and the loss minimized is the total loss per sample,

and not the mean loss across the folds.

cv : integer or cross-validation generator, optional

If an integer is passed, it is the number of folds (default 3).

Specific cross-validation objects can be passed, see

sklearn.cross_validation module for the list of possible objects

refit : boolean

Refit the best estimator with the entire dataset.

If "False", it is impossible to make predictions using

this GridSearchCV instance after fitting.

verbose : integer

Controls the verbosity: the higher, the more messages.

Examples

--------

>>> from sklearn import svm, grid_search, datasets

>>> iris = datasets.load_iris()

>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}

>>> svr = svm.SVC()

>>> clf = grid_search.GridSearchCV(svr, parameters)

>>> clf.fit(iris.data, iris.target)

... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS

GridSearchCV(cv=None,

estimator=SVC(C=1.0, cache_size=..., coef0=..., degree=...,

gamma=..., kernel='rbf', max_iter=-1, probability=False,

shrinking=True, tol=...),

fit_params={}, iid=True, loss_func=None, n_jobs=1,

param_grid=...,

...)

Attributes

----------

`cv_scores_` : list of named tuples

Contains scores for all parameter combinations in param_grid.

Each entry corresponds to one parameter setting.

Each named tuple has the attributes:

* ``parameters``, a dict of parameter settings

* ``mean_validation_score``, the mean score over the

cross-validation folds

* ``cv_validation_scores``, the list of scores for each fold

`best_estimator_` : estimator

Estimator that was chosen by the search, i.e. estimator

which gave highest score (or smallest loss if specified)

on the left out data.

`best_score_` : float

Score of best_estimator on the left out data.

`best_params_` : dict

Parameter setting that gave the best results on the hold out data.

Notes

------

The parameters selected are those that maximize the score of the left out

data, unless an explicit score is passed in which case it is used instead.

If `n_jobs` was set to a value higher than one, the data is copied for each

point in the grid (and not `n_jobs` times). This is done for efficiency

reasons if individual jobs take very little time, but may raise errors if

the dataset is large and not enough memory is available. A workaround in

this case is to set `pre_dispatch`. Then, the memory is copied only

`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *

n_jobs`.

See Also

---------

:class:`ParameterGrid`:

generates all the combinations of a an hyperparameter grid.

:func:`sklearn.cross_validation.train_test_split`:

utility function to split the data into a development set usable

for fitting a GridSearchCV instance and an evaluation set for

its final evaluation.

"""

def __init__(self, estimator, param_grid, scoring=None, loss_func=None,

score_func=None, fit_params=None, n_jobs=1, iid=True,

refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs'):

super(GridSearchCV, self).__init__(

estimator, scoring, loss_func, score_func, fit_params, n_jobs, iid,

refit, cv, verbose, pre_dispatch)

self.param_grid = param_grid

_check_param_grid(param_grid)

@property

def grid_scores_(self):

warnings.warn("grid_scores_ is deprecated and will be removed in 0.15."

" Use cv_scores_ instead.", DeprecationWarning)

return self.cv_scores_

def fit(self, X, y=None, **params):

"""Run fit with all sets of parameters.

Parameters

----------

X: array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and

n_features is the number of features.

y: array-like, shape = [n_samples], optional

Target vector relative to X for classification;

None for unsupervised learning.

"""

return self._fit(X, y, ParameterGrid(self.param_grid), **params)

class RandomizedSearchCV(BaseSearchCV):

"""Randomized search on hyper parameters.

RandomizedSearchCV implements a "fit" method and a "predict" method like

any classifier except that the parameters of the classifier

used to predict is optimized by cross-validation.

In constrast to GridSearchCV, not all parameter values are tried out, but

rather a fixed number of parameter settings is sampled from the specified

distributions. The number of parameter settings that are tried is

given by n_iter.

Parameters

----------

estimator : object type that implements the "fit" and "predict" methods

A object of that type is instantiated for each parameter setting.

param_distributions : dict

Dictionary with parameters names (string) as keys and distributions

or lists of parameters to try. Distributions must provide a ``rvs``

method for sampling (such as those from scipy.stats.distributions).

If a list is given, it is sampled uniformly.

n_iter : int, default=10

Number of parameter settings that are sampled. n_iter trades

off runtime vs qualitiy of the solution.

scoring : string or callable, optional

Either one of either a string ("zero_one", "f1", "roc_auc", ... for

classification, "mse", "r2",... for regression) or a callable.

See 'Scoring objects' in the model evaluation section of the user guide

for details.

fit_params : dict, optional

Parameters to pass to the fit method.

n_jobs : int, optional

Number of jobs to run in parallel (default 1).

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel

execution. Reducing this number can be useful to avoid an

explosion of memory consumption when more jobs get dispatched

than CPUs can process. This parameter can be:

- None, in which case all the jobs are immediately

created and spawned. Use this for lightweight and

fast-running jobs, to avoid delays due to on-demand

spawning of the jobs

- An int, giving the exact number of total jobs that are

spawned

- A string, giving an expression as a function of n_jobs,

as in '2*n_jobs'

iid : boolean, optional

If True, the data is assumed to be identically distributed across

the folds, and the loss minimized is the total loss per sample,

and not the mean loss across the folds.

cv : integer or cross-validation generator, optional

If an integer is passed, it is the number of folds (default 3).

Specific cross-validation objects can be passed, see