wip: docstring

Author: mrahim

examples/multitask_example.py

@@ -13,9 +13,8 @@
 Y = np.array(n*[y]).T
 
 mt = MultiTaskEstimator(
-                        # estimator=MultiTaskLassoCV(alphas=np.logspace(-3, 3, 7)),
-                        estimator=MultiTaskElasticNetCV(),
-                        output_types=n*['binary'])
+    estimator=MultiTaskLassoCV(alphas=np.logspace(-3, 3, 7)),
+    output_types=n*['binary'])
 
 mt.fit(X, Y)
 print(mt.score(X, Y))

stlearn/multitask.py

@@ -55,6 +55,24 @@ def predict(self, X):
         return Ypred
 
     def score(self, X, Y):
+        """Returns accuracy for each outputs.
+
+        r2_score is used for continuous outputs,
+        accuracy_score is used for binary outputs.
+
+        Parameters
+        ----------
+        X : array-like, shape = (n_samples, n_features)
+            The multi-input samples.
+
+        y : array-like, shape = (n_samples) or (n_samples, n_outputs)
+            True labels for X.
+
+        Returns
+        -------
+        score : list of float, shape (n_outputs,)
+            Mean accuracy of self.predict(X) wrt. Y.
+        """
         # predict multiple outputs
         # accuracy for regression and classification
         Ypred = self.predict(X)

stlearn/stacking.py

@@ -15,15 +15,15 @@
 def stack_features(X):
     """Stack features from sources
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     X : a list of 2d matrices
 
-    Returns:
-    --------
-    - Xstacked : (n_samples x (n_features*n_sources)) stacked 2d matrix
+    Returns
+    -------
+    Xstacked : (n_samples x (n_features*n_sources)) stacked 2d matrix
 
-    - features_indices : (n_features*n_sources) list of indices
+    features_indices : (n_features*n_sources) list of indices
     """
     X_stacked = np.hstack(X)