Adds fit_params support for ExhaustiveFeatureSelector

docs/sources/CHANGELOG.md

@@ -18,7 +18,10 @@ The CHANGELOG for the current development version is available at
 ##### New Features
 
 
-The fit method of the SequentialFeatureSelector now optionally accepts **fit_params for the estimator that is used for the feature selection. ([#350](https://github.com/rasbt/mlxtend/pull/350) by Zach Griffith)
+-   The fit method of the ExhaustiveFeatureSelector now optionally accepts
+    **fit_params for the estimator that is used for the feature selection. ([#354](https://github.com/rasbt/mlxtend/pull/354) by Zach Griffith)
+-   The fit method of the SequentialFeatureSelector now optionally accepts
+    **fit_params for the estimator that is used for the feature selection. ([#350](https://github.com/rasbt/mlxtend/pull/350) by Zach Griffith)
 
 
 - -

docs/sources/user_guide/feature_selection/ExhaustiveFeatureSelector.ipynb

@@ -1666,7 +1666,7 @@
       "\n",
       "<hr>\n",
       "\n",
-      "*fit(X, y)*\n",
+      "*fit(X, y, **fit_params)*\n",
       "\n",
       "Perform feature selection and learn model from training data.\n",
       "\n",
@@ -1681,14 +1681,18 @@
       "\n",
       "    Target values.\n",
       "\n",
+      "- `fit_params` : dict of string -> object, optional\n",
+      "\n",
+      "    Parameters to pass to to the fit method of classifier.\n",
+      "\n",
       "**Returns**\n",
       "\n",
       "- `self` : object\n",
       "\n",
       "\n",
       "<hr>\n",
       "\n",
-      "*fit_transform(X, y)*\n",
+      "*fit_transform(X, y, **fit_params)*\n",
       "\n",
       "Fit to training data and return the best selected features from X.\n",
       "\n",
@@ -1699,6 +1703,14 @@
       "    Training vectors, where n_samples is the number of samples and\n",
       "    n_features is the number of features.\n",
       "\n",
+      "- `y` : array-like, shape = [n_samples]\n",
+      "\n",
+      "    Target values.\n",
+      "\n",
+      "- `fit_params` : dict of string -> object, optional\n",
+      "\n",
+      "    Parameters to pass to to the fit method of classifier.\n",
+      "\n",
       "**Returns**\n",
       "\n",
       "Feature subset of X, shape={n_samples, k_features}\n",
@@ -1815,7 +1827,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,

docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb

@@ -1592,6 +1592,10 @@
       "    Training vectors, where n_samples is the number of samples and\n",
       "    n_features is the number of features.\n",
       "\n",
+      "- `y` : array-like, shape = [n_samples]\n",
+      "\n",
+      "    Target values.\n",
+      "\n",
       "- `fit_params` : dict of string -> object, optional\n",
       "\n",
       "    Parameters to pass to to the fit method of classifier.\n",

mlxtend/feature_selection/exhaustive_feature_selector.py

@@ -25,16 +25,17 @@
 from sklearn.externals.joblib import Parallel, delayed
 
 
-def _calc_score(selector, X, y, indices):
+def _calc_score(selector, X, y, indices, **fit_params):
     if selector.cv:
         scores = cross_val_score(selector.est_,
                                  X[:, indices], y,
                                  cv=selector.cv,
                                  scoring=selector.scorer,
                                  n_jobs=1,
-                                 pre_dispatch=selector.pre_dispatch)
+                                 pre_dispatch=selector.pre_dispatch,
+                                 fit_params=fit_params)
     else:
-        selector.est_.fit(X[:, indices], y)
+        selector.est_.fit(X[:, indices], y, **fit_params)
         scores = np.array([selector.scorer(selector.est_, X[:, indices], y)])
     return indices, scores
 
@@ -127,7 +128,7 @@ def __init__(self, estimator, min_features=1, max_features=1,
             self.est_ = self.estimator
         self.fitted = False
 
-    def fit(self, X, y):
+    def fit(self, X, y, **fit_params):
         """Perform feature selection and learn model from training data.
 
         Parameters
@@ -137,6 +138,8 @@ def fit(self, X, y):
             n_features is the number of features.
         y : array-like, shape = [n_samples]
             Target values.
+        fit_params : dict of string -> object, optional
+            Parameters to pass to to the fit method of classifier.
 
         Returns
         -------
@@ -160,41 +163,42 @@ def fit(self, X, y):
             raise AttributeError('min_features must be <= max_features')
 
         candidates = chain(*((combinations(range(X.shape[1]), r=i))
-                          for i in range(self.min_features,
-                                         self.max_features + 1)))
+                           for i in range(self.min_features,
+                                          self.max_features + 1)))
 
         self.subsets_ = {}
-        
+
         def ncr(n, r):
             """Return the number of combinations of length r from n items.
-            
+
             Parameters
             ----------
             n : {integer}
             Total number of items
             r : {integer}
             Number of items to select from n
-            
+
             Returns
             -------
             Number of combinations, integer
-            
+
             """
-            
+
             r = min(r, n-r)
             if r == 0:
                 return 1
             numer = reduce(op.mul, range(n, n-r, -1))
             denom = reduce(op.mul, range(1, r+1))
             return numer//denom
-        
+
         all_comb = np.sum([ncr(n=X.shape[1], r=i)
                            for i in range(self.min_features,
                                           self.max_features + 1)])
-        
+
         n_jobs = min(self.n_jobs, all_comb)
         parallel = Parallel(n_jobs=n_jobs, pre_dispatch=self.pre_dispatch)
-        work = enumerate(parallel(delayed(_calc_score)(self, X, y, c)
+        work = enumerate(parallel(delayed(_calc_score)
+                                  (self, X, y, c, **fit_params)
                                   for c in candidates))
 
         for iteration, (c, cv_scores) in work:
@@ -239,21 +243,25 @@ def transform(self, X):
         self._check_fitted()
         return X[:, self.best_idx_]
 
-    def fit_transform(self, X, y):
+    def fit_transform(self, X, y, **fit_params):
         """Fit to training data and return the best selected features from X.
 
         Parameters
         ----------
         X : {array-like, sparse matrix}, shape = [n_samples, n_features]
             Training vectors, where n_samples is the number of samples and
             n_features is the number of features.
+        y : array-like, shape = [n_samples]
+            Target values.
+        fit_params : dict of string -> object, optional
+            Parameters to pass to to the fit method of classifier.
 
         Returns
         -------
         Feature subset of X, shape={n_samples, k_features}
 
         """
-        self.fit(X, y)
+        self.fit(X, y, **fit_params)
         return self.transform(X)
 
     def get_metric_dict(self, confidence_interval=0.95):

mlxtend/feature_selection/sequential_feature_selector.py

@@ -481,6 +481,8 @@ def fit_transform(self, X, y, **fit_params):
         X : {array-like, sparse matrix}, shape = [n_samples, n_features]
             Training vectors, where n_samples is the number of samples and
             n_features is the number of features.
+        y : array-like, shape = [n_samples]
+            Target values.
         fit_params : dict of string -> object, optional
             Parameters to pass to to the fit method of classifier.
 

mlxtend/feature_selection/tests/test_exhaustive_feature_selector.py

@@ -8,6 +8,7 @@
 import numpy as np
 from numpy.testing import assert_almost_equal
 from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from mlxtend.classifier import SoftmaxRegression
 from sklearn.datasets import load_iris
@@ -164,6 +165,40 @@ def test_knn_cv3():
     assert round(efs1.best_score_, 4) == 0.9728
 
 
+def test_fit_params():
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    sample_weight = np.ones(X.shape[0])
+    forest = RandomForestClassifier(n_estimators=100, random_state=123)
+    efs1 = EFS(forest,
+               min_features=3,
+               max_features=3,
+               scoring='accuracy',
+               cv=4,
+               print_progress=False)
+    efs1 = efs1.fit(X, y, sample_weight=sample_weight)
+    expect = {0: {'feature_idx': (0, 1, 2),
+                  'cv_scores': np.array([0.94871795, 0.92307692,
+                                         0.91666667, 0.97222222]),
+                  'avg_score': 0.9401709401709402},
+              1: {'feature_idx': (0, 1, 3),
+                  'cv_scores': np.array([0.92307692, 0.92307692,
+                                         0.88888889, 1.]),
+                  'avg_score': 0.9337606837606838},
+              2: {'feature_idx': (0, 2, 3),
+                  'cv_scores': np.array([0.97435897, 0.94871795,
+                                         0.94444444, 0.97222222]),
+                  'avg_score': 0.9599358974358974},
+              3: {'feature_idx': (1, 2, 3),
+                  'cv_scores': np.array([0.97435897, 0.94871795,
+                                         0.91666667, 1.]),
+                  'avg_score': 0.9599358974358974}}
+    dict_compare_utility(d1=expect, d2=efs1.subsets_)
+    assert efs1.best_idx_ == (0, 2, 3)
+    assert round(efs1.best_score_, 4) == 0.9599
+
+
 def test_regression():
     boston = load_boston()
     X, y = boston.data[:, [1, 2, 6, 8, 12]], boston.target