343 lines
12 KiB
Python
343 lines
12 KiB
Python
"""Tune the parameters of an estimator by cross-validation"""
|
|
|
|
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
|
|
# Gael Varoquaux <gael.varoquaux@normalesup.org>
|
|
# License: BSD Style.
|
|
|
|
import copy
|
|
import time
|
|
|
|
import numpy as np
|
|
import scipy.sparse as sp
|
|
|
|
from .externals.joblib import Parallel, delayed, logger
|
|
from .cross_val import check_cv
|
|
from .base import BaseEstimator, is_classifier, clone
|
|
from .utils.fixes import product
|
|
|
|
|
|
class IterGrid(object):
|
|
"""Generators on the combination of the various parameter lists given
|
|
|
|
Parameters
|
|
-----------
|
|
param_grid: dict of string to sequence
|
|
The parameter grid to explore, as a dictionary mapping estimator
|
|
parameters to sequences of allowed values.
|
|
|
|
Yields
|
|
------
|
|
params: dict of string to any
|
|
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}]
|
|
"""
|
|
|
|
def __init__(self, param_grid):
|
|
self.param_grid = param_grid
|
|
|
|
def __iter__(self):
|
|
param_grid = self.param_grid
|
|
if hasattr(param_grid, 'has_key'):
|
|
param_grid = [param_grid]
|
|
for p in 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 fit_grid_point(X, y, base_clf, clf_params, train, test, loss_func,
|
|
score_func, verbose, **fit_params):
|
|
"""Run fit on one set of parameters
|
|
|
|
Returns the score and the instance of the classifier
|
|
"""
|
|
if verbose > 1:
|
|
start_time = time.time()
|
|
msg = '%s' % (', '.join('%s=%s' % (k, v)
|
|
for k, v in clf_params.iteritems()))
|
|
print "[GridSearchCV] %s %s" % (msg, (64 - len(msg)) * '.')
|
|
|
|
# update parameters of the classifier after a copy of its base structure
|
|
# FIXME we should be doing a clone here
|
|
clf = copy.deepcopy(base_clf)
|
|
clf.set_params(**clf_params)
|
|
|
|
if isinstance(X, list) or isinstance(X, tuple):
|
|
X_train = [X[i] for i, cond in enumerate(train) if cond]
|
|
X_test = [X[i] for i, cond in enumerate(test) if cond]
|
|
else:
|
|
if sp.issparse(X):
|
|
# For sparse matrices, slicing only works with indices
|
|
# (no masked array). Convert to CSR format for efficiency and
|
|
# because some sparse formats don't support row slicing.
|
|
X = sp.csr_matrix(X)
|
|
ind = np.arange(X.shape[0])
|
|
train = ind[train]
|
|
test = ind[test]
|
|
X_train = X[train]
|
|
X_test = X[test]
|
|
if y is not None:
|
|
y_test = y[test]
|
|
y_train = y[train]
|
|
else:
|
|
y_test = None
|
|
y_train = None
|
|
|
|
clf.fit(X_train, y_train, **fit_params)
|
|
|
|
if loss_func is not None:
|
|
y_pred = clf.predict(X_test)
|
|
this_score = -loss_func(y_test, y_pred)
|
|
elif score_func is not None:
|
|
y_pred = clf.predict(X_test)
|
|
this_score = score_func(y_test, y_pred)
|
|
else:
|
|
this_score = clf.score(X_test, y_test)
|
|
|
|
if y is not None:
|
|
if hasattr(y, 'shape'):
|
|
this_n_test_samples = y.shape[0]
|
|
else:
|
|
this_n_test_samples = len(y)
|
|
else:
|
|
if hasattr(X, 'shape'):
|
|
this_n_test_samples = X.shape[0]
|
|
else:
|
|
this_n_test_samples = len(X)
|
|
|
|
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, this_n_test_samples
|
|
|
|
|
|
class GridSearchCV(BaseEstimator):
|
|
"""Grid search on the parameters of a classifier
|
|
|
|
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
|
|
Dictionary with parameters names (string) as keys and lists of
|
|
parameter settings to try as values.
|
|
|
|
loss_func: callable, optional
|
|
function that takes 2 arguments and compares them in
|
|
order to evaluate the performance of prediciton (small is good)
|
|
if None is passed, the score of the estimator is maximized
|
|
|
|
score_func: callable, optional
|
|
A function that takes 2 arguments and compares them in
|
|
order to evaluate the performance of prediction (high is good).
|
|
If None is passed, the score of the estimator is maximized.
|
|
|
|
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 immediatly
|
|
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 crossvalidation generator, optional
|
|
If an integer is passed, it is the number of fold (default 3).
|
|
Specific crossvalidation objects can be passed, see
|
|
sklearn.cross_val module for the list of possible objects
|
|
|
|
refit: boolean
|
|
refit the best estimator with the entire dataset
|
|
|
|
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.SVR()
|
|
>>> clf = grid_search.GridSearchCV(svr, parameters)
|
|
>>> clf.fit(iris.data, iris.target) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
|
|
GridSearchCV(cv=None,
|
|
estimator=SVR(C=1.0, coef0=..., degree=..., epsilon=..., gamma=..., kernel='rbf',
|
|
probability=False, shrinking=True, tol=...),
|
|
fit_params={}, iid=True, loss_func=None, n_jobs=1,
|
|
param_grid=...,
|
|
...)
|
|
|
|
Notes
|
|
------
|
|
The parameters selected are those that maximize the score of the
|
|
left out data, unless an explicit score_func is passed in which
|
|
case it is used instead. If a loss function loss_func is passed,
|
|
it overrides the score functions and is minimized.
|
|
"""
|
|
|
|
def __init__(self, estimator, param_grid, 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',
|
|
):
|
|
assert hasattr(estimator, 'fit') and (hasattr(estimator, 'predict')
|
|
or hasattr(estimator, 'score')), (
|
|
"estimator should a be an estimator implementing 'fit' and "
|
|
"'predict' or 'score' methods, %s (type %s) was passed" %
|
|
(estimator, type(estimator)))
|
|
if loss_func is None and score_func is None:
|
|
assert hasattr(estimator, 'score'), ValueError(
|
|
"If no loss_func is specified, the estimator passed "
|
|
"should have a 'score' method. The estimator %s "
|
|
"does not." % estimator)
|
|
|
|
self.estimator = estimator
|
|
self.param_grid = param_grid
|
|
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
|
|
|
|
def fit(self, X, y=None, **params):
|
|
"""Run fit with all sets of parameters
|
|
|
|
Returns the best classifier
|
|
|
|
Parameters
|
|
----------
|
|
|
|
X: array, [n_samples, n_features]
|
|
Training vector, where n_samples in the number of samples and
|
|
n_features is the number of features.
|
|
|
|
y: array, [n_samples] or None
|
|
Target vector relative to X, None for unsupervised problems
|
|
|
|
"""
|
|
self._set_params(**params)
|
|
estimator = self.estimator
|
|
cv = self.cv
|
|
if hasattr(X, 'shape'):
|
|
n_samples = X.shape[0]
|
|
else:
|
|
# support list of unstructured objects on which feature
|
|
# extraction will be applied later in the tranformer chain
|
|
n_samples = len(X)
|
|
if y is not None and 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))
|
|
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
|
|
|
|
grid = IterGrid(self.param_grid)
|
|
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, self.loss_func,
|
|
self.score_func, self.verbose, **self.fit_params)
|
|
for clf_params in grid for train, test in cv)
|
|
|
|
# Out is a list of triplet: score, estimator, n_test_samples
|
|
n_grid_points = len(list(grid))
|
|
n_fits = len(out)
|
|
n_folds = n_fits // n_grid_points
|
|
|
|
scores = list()
|
|
for grid_start in range(0, n_fits, n_folds):
|
|
n_test_samples = 0
|
|
score = 0
|
|
for this_score, estimator, this_n_test_samples in \
|
|
out[grid_start:grid_start + n_folds]:
|
|
if self.iid:
|
|
this_score *= this_n_test_samples
|
|
score += this_score
|
|
n_test_samples += this_n_test_samples
|
|
if self.iid:
|
|
score /= float(n_test_samples)
|
|
scores.append((score, estimator))
|
|
|
|
# Note: we do not use max(out) to make ties deterministic even if
|
|
# comparison on estimator instances is not deterministic
|
|
best_score = None
|
|
for score, estimator in scores:
|
|
if best_score is None:
|
|
best_score = score
|
|
best_estimator = estimator
|
|
else:
|
|
if score > best_score:
|
|
best_score = score
|
|
best_estimator = estimator
|
|
|
|
if best_score is None:
|
|
raise ValueError('Best score could not be found')
|
|
self.best_score = best_score
|
|
|
|
if self.refit:
|
|
# fit the best estimator using the entire dataset
|
|
best_estimator.fit(X, y, **self.fit_params)
|
|
|
|
self.best_estimator = best_estimator
|
|
if hasattr(best_estimator, 'predict'):
|
|
self.predict = best_estimator.predict
|
|
if hasattr(best_estimator, 'score'):
|
|
self.score = best_estimator.score
|
|
|
|
# Store the computed scores
|
|
# XXX: the name is too specific, it shouldn't have
|
|
# 'grid' in it. Also, we should be retrieving/storing variance
|
|
self.grid_scores_ = [
|
|
(clf_params, score) for clf_params, (score, _)
|
|
in zip(grid, scores)]
|
|
return self
|
|
|
|
def score(self, X, y=None):
|
|
# This method is overridden during the fit if the best estimator
|
|
# found has a score function.
|
|
y_predicted = self.predict(X)
|
|
return self.score_func(y, y_predicted)
|