scikit-learn · amueller · Oct 8, 2016 · Oct 8, 2016 · Oct 8, 2016 · Oct 8, 2016
diff --git a/sklearn/base.py b/sklearn/base.py
@@ -5,6 +5,7 @@
 
 import copy
 import warnings
+from uuid import uuid4
 
 import numpy as np
 from scipy import sparse
@@ -121,7 +122,7 @@ def clone(estimator, safe=True):
 
 
 ###############################################################################
-def _pprint(params, offset=0, printer=repr):
+def _pprint(params, offset=0, printer=repr, cutoff=500):
     """Pretty print the dictionary 'params'
 
     Parameters
@@ -150,9 +151,9 @@ def _pprint(params, offset=0, printer=repr):
             # architectures and versions.
             this_repr = '%s=%s' % (k, str(v))
         else:
-            # use repr of the rest
+            # use printer of the rest
             this_repr = '%s=%s' % (k, printer(v))
-        if len(this_repr) > 500:
+        if cutoff is not None and len(this_repr) > cutoff:
             this_repr = this_repr[:300] + '...' + this_repr[-100:]
         if i > 0:
             if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr):
@@ -171,6 +172,16 @@ def _pprint(params, offset=0, printer=repr):
     return lines
 
 
+def _html_repr(thing):
+    if hasattr(thing, "_repr_html_"):
+        return thing._repr_html_()
+    elif isinstance(thing, tuple):
+        return "({})".format(", ". join([_html_repr(vv) for vv in thing]))
+    elif isinstance(thing, list):
+        return "[{}]".format(", ". join([_html_repr(vv) for vv in thing]))
+    return repr(thing)
+
+
 ###############################################################################
 class BaseEstimator(object):
     """Base class for all estimators in scikit-learn
@@ -284,11 +295,61 @@ def set_params(self, **params):
                 setattr(self, key, value)
         return self
 
+    def _changed_params(self):
+        params = self.get_params(deep=False)
+        filtered_params = {}
+        default_params = {}
+        init_params = signature(self.__init__).parameters
+        for k, v in params.items():
+            if v == init_params[k].default:
+                default_params[k] = v
+            else:
+                filtered_params[k] = v
+        return filtered_params, default_params
+
     def __repr__(self):
         class_name = self.__class__.__name__
-        return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
+        params = self.get_params(deep=False)
+        return '%s(%s)' % (class_name, _pprint(params,
                                                offset=len(class_name),),)
 
+    def _repr_html_(self):
+        class_name = self.__class__.__name__
+        changed_params, default_params = self._changed_params()
+        this_id = uuid4()
+        js = """
+            <script type="text/javascript">
+
+            $(document).ready(function(){{
+                $("#default_params_{0}").hide();
+                $("#more_params_{0}").click(function(){{
+                    $("#default_params_{0}").toggle();
+                }});
+            }});
+
+            </script>""".format(this_id)
+        more_params_str = ("<a id='more_params_{0}'>...</a>"
+                           "<span id='default_params_{0}'>, {1}</span>".format(
+                               this_id, _pprint(default_params,
+                                                printer=_html_repr,
+                                                cutoff=None))
+                           if default_params else "")
+        if changed_params:
+            more_params_str = ", " + more_params_str
+        my_repr = "{}<b>{}</b>({}{})".format(js, class_name, _pprint(
+            changed_params, printer=_html_repr, cutoff=None), more_params_str)
+        if False:
+            print_attributes = ['classes_', 'n_outputs_']
+            for attr in print_attributes:
+                value = getattr(self, attr, None)
+                if value is not None:
+                    my_repr += "\n- {0}={1}".format(attr[:-1], value)
+            n_features = self.get_n_features()
+            if n_features is not None:
+                my_repr += "\n- {0}={1}".format('n_features', n_features)
+
+        return my_repr
+
     def __getstate__(self):
         if type(self).__module__.startswith('sklearn.'):
             return dict(self.__dict__.items(), _sklearn_version=__version__)
@@ -307,6 +368,26 @@ def __setstate__(self, state):
                     UserWarning)
         self.__dict__.update(state)
 
+    def get_n_features(self):
+        """Return number of features of a fitted estimator."""
+        n_features = getattr(self, 'n_features_', None)
+        if n_features is not None:
+            return n_features
+
+        components = getattr(self, 'components_', None)
+        if components is not None:
+            return components.shape[1]
+
+        coef = getattr(self, 'coef_', None)
+        if coef is not None:
+            return coef.shape[-1]
+
+        scale = getattr(self, 'scale_', None)
+        if scale is not None:
+            return scale.shape[0]
+
+        return None
+
 
 ###############################################################################
 class ClassifierMixin(object):

diff --git a/sklearn/feature_selection/univariate_selection.py b/sklearn/feature_selection/univariate_selection.py
@@ -339,6 +339,11 @@ def fit(self, X, y):
 
         return self
 
+    def get_n_features(self):
+        if hasattr(self, 'scores_'):
+            return self.scores_.shape[0]
+        return None
+
     def _check_params(self, X, y):
         pass
 

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -382,7 +382,7 @@ def __init__(self, estimator, scoring=None,
         self.scoring = scoring
         self.estimator = estimator
         self.n_jobs = n_jobs
-        self.fit_params = fit_params if fit_params is not None else {}
+        self.fit_params = fit_params
         self.iid = iid
         self.refit = refit
         self.cv = cv
@@ -550,6 +550,7 @@ def fit(self, X, y=None, groups=None):
             Group labels for the samples used while splitting the dataset into
             train/test set.
         """
+        fit_params = self.fit_params if self.fit_params is not None else {}
         estimator = self.estimator
         cv = check_cv(self.cv, y, classifier=is_classifier(estimator))
         self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
@@ -572,7 +573,7 @@ def fit(self, X, y=None, groups=None):
             pre_dispatch=pre_dispatch
         )(delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,
                                   train, test, self.verbose, parameters,
-                                  fit_params=self.fit_params,
+                                  fit_params=fit_params,
                                   return_train_score=self.return_train_score,
                                   return_n_test_samples=True,
                                   return_times=True, return_parameters=False,
@@ -655,9 +656,9 @@ def _store(key_name, array, weights=None, splits=False, rank=False):
             best_estimator = clone(base_estimator).set_params(
                 **best_parameters)
             if y is not None:
-                best_estimator.fit(X, y, **self.fit_params)
+                best_estimator.fit(X, y, **fit_params)
             else:
-                best_estimator.fit(X, **self.fit_params)
+                best_estimator.fit(X, **fit_params)
             self.best_estimator_ = best_estimator
         return self
 
@@ -808,7 +809,7 @@ class GridSearchCV(BaseSearchCV):
                          kernel='rbf', max_iter=-1, probability=False,
                          random_state=None, shrinking=True, tol=...,
                          verbose=False),
-           fit_params={}, iid=..., n_jobs=1,
+           fit_params=None, iid=..., n_jobs=1,
            param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
            scoring=..., verbose=...)
     >>> sorted(clf.cv_results_.keys())
@@ -1159,10 +1160,10 @@ def __init__(self, estimator, param_distributions, n_iter=10, scoring=None,
         self.n_iter = n_iter
         self.random_state = random_state
         super(RandomizedSearchCV, self).__init__(
-             estimator=estimator, scoring=scoring, fit_params=fit_params,
-             n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
-             pre_dispatch=pre_dispatch, error_score=error_score,
-             return_train_score=return_train_score)
+            estimator=estimator, scoring=scoring, fit_params=fit_params,
+            n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
+            pre_dispatch=pre_dispatch, error_score=error_score,
+            return_train_score=return_train_score)
 
     def _get_param_iterator(self):
         """Return ParameterSampler instance for the given distributions"""

diff --git a/sklearn/neighbors/base.py b/sklearn/neighbors/base.py
@@ -262,6 +262,11 @@ def _pairwise(self):
         # For cross-validation routines to split data correctly
         return self.metric == 'precomputed'
 
+    def get_n_features(self):
+        if getattr(self, '_fit_X', None) is not None:
+            return self._fit_X.shape[1]
+        return None
+
 
 class KNeighborsMixin(object):
     """Mixin for k-neighbors searches"""

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -1403,7 +1403,8 @@ def check_no_fit_attributes_set_in_init(name, Estimator):
     """Check that Estimator.__init__ doesn't set trailing-_ attributes."""
     estimator = Estimator()
     for attr in dir(estimator):
-        if attr.endswith("_") and not attr.startswith("__"):
+        if (attr.endswith("_") and not attr.startswith("__") and not
+                hasattr(Estimator, attr)):
             # This check is for properties, they can be listed in dir
             # while at the same time have hasattr return False as long
             # as the property getter raises an AttributeError