diff --git a/doc/developers/index.rst b/doc/developers/index.rst
index 23dd7dfff623c33a10bfbf40e441c0c96551f710..9dd0d9b7bcc77bf9e5e96b87cf4e584442b68f1b 100644
--- a/doc/developers/index.rst
+++ b/doc/developers/index.rst
@@ -328,6 +328,11 @@ classifier or a regressor. All estimators implement the fit method::
estimator.fit(X, y)
+All built-in estimators also have a ``set_params`` method, which sets
+data-independent parameters (overriding previous parameter values passed
+to ``__init__``). This method is not required for an object to be an
+estimator, but is used by the :class:`grid_search.GridSearchCV` class.
+
Instantiation
^^^^^^^^^^^^^
diff --git a/examples/svm/plot_svm_anova.py b/examples/svm/plot_svm_anova.py
index 6187afe801218d78b39c388146e0e357cf133f22..100cd0593f67c024cf957690befb2263e8068bb1 100644
--- a/examples/svm/plot_svm_anova.py
+++ b/examples/svm/plot_svm_anova.py
@@ -40,7 +40,7 @@ score_stds = list()
percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
for percentile in percentiles:
- clf._set_params(anova__percentile=percentile)
+ clf.set_params(anova__percentile=percentile)
# Compute cross-validation score using all CPUs
this_scores = cross_val.cross_val_score(clf, X, y, n_jobs=1)
score_means.append(this_scores.mean())
diff --git a/scikits/learn/base.py b/scikits/learn/base.py
index 255b995240c7576e09206e68f36e5a7cd8e2cfaf..944fa82209129dcad2ab345af765a794d24c2458 100644
--- a/scikits/learn/base.py
+++ b/scikits/learn/base.py
@@ -6,8 +6,10 @@ import copy
import inspect
import numpy as np
from scipy import sparse
+import warnings
from .metrics import r2_score
+from .utils import deprecated
###############################################################################
@@ -172,13 +174,17 @@ class BaseEstimator(object):
out[key] = value
return out
- def _set_params(self, **params):
+ def set_params(self, **params):
""" Set the parameters of the estimator.
The method works on simple estimators as well as on nested
objects (such as pipelines). The former have parameters of the
- form <component>__<parameter> so that the its possible to
- update each component of the nested object.
+ form <component>__<parameter> so that it's possible to update
+ each component of a nested object.
+
+ Returns
+ -------
+ self
"""
if not params:
# Simple optimisation to gain speed (inspect is slow)
@@ -198,7 +204,7 @@ class BaseEstimator(object):
'sub parameter %s' %
(sub_name, self.__class__.__name__, sub_name)
)
- sub_object._set_params(**{sub_name: value})
+ sub_object.set_params(**{sub_name: value})
else:
# simple objects case
assert key in valid_params, ('Invalid parameter %s '
@@ -207,6 +213,13 @@ class BaseEstimator(object):
setattr(self, key, value)
return self
+ def _set_params(self, **params):
+ if params != {}:
+ warnings.warn("Passing estimator parameters to fit is deprecated;"
+ " use set_params instead",
+ category=DeprecationWarning)
+ return self.set_params(**params)
+
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (
diff --git a/scikits/learn/cluster/dbscan_.py b/scikits/learn/cluster/dbscan_.py
index 7ba2c0cc680a376aff384ac3dc2513b83299a09f..00b68b2d63f2b8ee8f42287c7820d75023704b32 100644
--- a/scikits/learn/cluster/dbscan_.py
+++ b/scikits/learn/cluster/dbscan_.py
@@ -188,7 +188,7 @@ class DBSCAN(BaseEstimator):
Overwrite keywords from __init__.
"""
- self._set_params(**params)
+ self.set_params(**params)
self.core_sample_indices_, self.labels_ = dbscan(X,
**self._get_params())
self.components_ = X[self.core_sample_indices_].copy()
diff --git a/scikits/learn/cluster/k_means_.py b/scikits/learn/cluster/k_means_.py
index b91cee3329240ed9cfdd6a0941a2e4236f13dbe4..cc54f0e9222ebbc21c726b94627a5da1b9e66b85 100644
--- a/scikits/learn/cluster/k_means_.py
+++ b/scikits/learn/cluster/k_means_.py
@@ -482,25 +482,25 @@ class KMeans(BaseEstimator):
self.random_state = random_state
self.copy_x = copy_x
- def _check_data(self, X, **params):
- """
- Set parameters and check the sample given is larger than k
- """
+ def _check_data(self, X):
+ """Verify that the number of samples given is larger than k"""
if sp.issparse(X):
raise ValueError("K-Means does not support sparse input matrices.")
X = np.asanyarray(X)
if X.shape[0] < self.k:
raise ValueError("n_samples=%d should be larger than k=%d" % (
X.shape[0], self.k))
- self._set_params(**params)
return X
- def fit(self, X, **params):
+ def fit(self, X, k=None, **params):
"""Compute k-means"""
self.random_state = check_random_state(self.random_state)
X = self._check_data(X, **params)
+ if k != None:
+ self.k = k
+ self._set_params(**params)
self.cluster_centers_, self.labels_, self.inertia_ = k_means(
X, k=self.k, init=self.init, n_init=self.n_init,
@@ -647,7 +647,7 @@ class MiniBatchKMeans(KMeans):
self.cluster_centers_ = None
self.chunk_size = chunk_size
- def fit(self, X, y=None, **params):
+ def fit(self, X, y=None):
"""
Calculates the centroids on a batch X
@@ -656,7 +656,6 @@ class MiniBatchKMeans(KMeans):
X: array-like, shape = [n_samples, n_features]
Coordinates of the data points to cluster
"""
- self._set_params(**params)
self.random_state = check_random_state(self.random_state)
X = check_arrays(X, sparse_format="csr", copy=False)[0]
n_samples, n_features = X.shape
@@ -709,7 +708,7 @@ class MiniBatchKMeans(KMeans):
return self
- def partial_fit(self, X, y=None, **params):
+ def partial_fit(self, X, y=None):
"""Update k means estimate on a single mini-batch X.
Parameters
diff --git a/scikits/learn/cluster/tests/test_dbscan.py b/scikits/learn/cluster/tests/test_dbscan.py
index 8b05d98f3cd9b81f20efa4fbf7e8d71965009f55..7c36e710e7cd2bd5928e7bb75131a078f04ff846 100644
--- a/scikits/learn/cluster/tests/test_dbscan.py
+++ b/scikits/learn/cluster/tests/test_dbscan.py
@@ -55,9 +55,8 @@ def test_dbscan_feature():
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
- db = DBSCAN()
- labels = db.fit(X, metric=metric,
- eps=eps, min_samples=min_samples).labels_
+ db = DBSCAN(metric=metric)
+ labels = db.fit(X, eps=eps, min_samples=min_samples).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
@@ -80,9 +79,8 @@ def test_dbscan_callable():
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_1, n_clusters)
- db = DBSCAN()
- labels = db.fit(X, metric=metric,
- eps=eps, min_samples=min_samples).labels_
+ db = DBSCAN(metric=metric)
+ labels = db.fit(X, eps=eps, min_samples=min_samples).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert_equal(n_clusters_2, n_clusters)
diff --git a/scikits/learn/feature_selection/univariate_selection.py b/scikits/learn/feature_selection/univariate_selection.py
index 534e15398a85c33f7cf63df630a4a62db71f9e67..8306cc651b8e33611910bf735aebd77e673c52d7 100644
--- a/scikits/learn/feature_selection/univariate_selection.py
+++ b/scikits/learn/feature_selection/univariate_selection.py
@@ -446,9 +446,9 @@ class GenericUnivariateSelect(_AbstractUnivariateFilter):
selector = self._selection_modes[self.mode](lambda x: x)
selector._pvalues = self._pvalues
selector._scores = self._scores
- # Now make some acrobaties to set the right named parameter in
+ # Now perform some acrobatics to set the right named parameter in
# the selector
possible_params = selector._get_param_names()
possible_params.remove('score_func')
- selector._set_params(**{possible_params[0]: self.param})
+ selector.set_params(**{possible_params[0]: self.param})
return selector._get_support_mask()
diff --git a/scikits/learn/grid_search.py b/scikits/learn/grid_search.py
index 9056996b762e4ed115559092292fdc928f82f067..9f9f87bbb090ea202e66d5736fa60d965de13b38 100644
--- a/scikits/learn/grid_search.py
+++ b/scikits/learn/grid_search.py
@@ -69,7 +69,7 @@ def fit_grid_point(X, y, base_clf, clf_params, train, test, loss_func,
print "[GridSearchCV] %s %s" % (msg, (64 - len(msg)) * '.')
# update parameters of the classifier after a copy of its base structure
clf = copy.deepcopy(base_clf)
- clf._set_params(**clf_params)
+ 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]
diff --git a/scikits/learn/tests/test_base.py b/scikits/learn/tests/test_base.py
index 53a422f5cbc0cb4a62160b2472495c42ca917017..4d1fb4ab572b3b7e9797b2f64c0de3fb91a42375 100644
--- a/scikits/learn/tests/test_base.py
+++ b/scikits/learn/tests/test_base.py
@@ -93,9 +93,9 @@ def test_get_params():
assert_true('a__d' in test._get_params(deep=True))
assert_true('a__d' not in test._get_params(deep=False))
- test._set_params(a__d=2)
+ test.set_params(a__d=2)
assert test.a.d == 2
- assert_raises(AssertionError, test._set_params, a__a=2)
+ assert_raises(AssertionError, test.set_params, a__a=2)
def test_is_classifier():
diff --git a/scikits/learn/tests/test_cross_val.py b/scikits/learn/tests/test_cross_val.py
index c849ae1328748acb4f51eddfa510a367414b0065..c130d4178825d4394899a332c0348317a77b201e 100644
--- a/scikits/learn/tests/test_cross_val.py
+++ b/scikits/learn/tests/test_cross_val.py
@@ -23,8 +23,7 @@ class MockClassifier(BaseEstimator):
def __init__(self, a=0):
self.a = a
- def fit(self, X, Y, **params):
- self._set_params(**params)
+ def fit(self, X, Y):
return self
def predict(self, T):
diff --git a/scikits/learn/tests/test_grid_search.py b/scikits/learn/tests/test_grid_search.py
index e52e06ba2a08263bd21bb4d61d8af4f4e954bd1d..83e18ab46766ddf0bbfc928c47db1db50312e4d0 100644
--- a/scikits/learn/tests/test_grid_search.py
+++ b/scikits/learn/tests/test_grid_search.py
@@ -21,8 +21,7 @@ class MockClassifier(BaseEstimator):
def __init__(self, foo_param=0):
self.foo_param = foo_param
- def fit(self, X, Y, **params):
- self._set_params(**params)
+ def fit(self, X, Y):
return self
def predict(self, T):
@@ -88,7 +87,7 @@ def test_grid_search_sparse_score_func():
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
- cv.fit(X_[:180], y_[:180], refit=False)
+ cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
@@ -96,7 +95,7 @@ def test_grid_search_sparse_score_func():
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
- cv.fit(X_[:180], y_[:180], refit=False)
+ cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
diff --git a/scikits/learn/tests/test_naive_bayes.py b/scikits/learn/tests/test_naive_bayes.py
index 81321931d1122a7b60623148f39ff5f825746019..730b7e5866ab204078dcfd39593a0c1b189be012 100644
--- a/scikits/learn/tests/test_naive_bayes.py
+++ b/scikits/learn/tests/test_naive_bayes.py
@@ -123,7 +123,8 @@ def test_discretenb_uniform_prior():
when fit_prior=False and class_prior=None"""
for cls in [BernoulliNB, MultinomialNB]:
- clf = cls(fit_prior=False)
+ clf = cls()
+ clf.set_params(fit_prior=False)
clf.fit([[0], [0], [1]], [0, 0, 1])
prior = np.exp(clf.class_log_prior_)
assert prior[0] == prior[1]
diff --git a/scikits/learn/tests/test_pipeline.py b/scikits/learn/tests/test_pipeline.py
index 98911a44c0a6b3328795ad351510801c0120968a..42e1f7f78fa1eda79f35ac5003cf0a227548f79c 100644
--- a/scikits/learn/tests/test_pipeline.py
+++ b/scikits/learn/tests/test_pipeline.py
@@ -44,7 +44,7 @@ def test_pipeline_init():
dict(svc__a=None, svc__b=None, svc=clf))
# Check that params are set
- pipe._set_params(svc__a=0.1)
+ pipe.set_params(svc__a=0.1)
assert_equal(clf.a, 0.1)
# Smoke test the repr:
repr(pipe)
@@ -55,13 +55,13 @@ def test_pipeline_init():
pipe = Pipeline([('anova', filter1), ('svc', clf)])
# Check that params are set
- pipe._set_params(svc__C=0.1)
+ pipe.set_params(svc__C=0.1)
assert_equal(clf.C, 0.1)
# Smoke test the repr:
repr(pipe)
# Check that params are not set when naming them wrong
- assert_raises(AssertionError, pipe._set_params, anova__C=0.1)
+ assert_raises(AssertionError, pipe.set_params, anova__C=0.1)
# Test clone
pipe2 = clone(pipe)