Moved fit/predict params to init. Changed test accordingly. This addr…

…esses #558 .

keras/wrappers/scikit_learn.py

@@ -11,11 +11,36 @@ class BaseWrapper(object):
     Base class for the Keras scikit-learn wrapper.
 
     Warning: This class should not be used directly. Use derived classes instead.
+
+    Parameters
+    ----------
+    train_batch_size : int, optional
+        Number of training samples evaluated at a time.
+    test_batch_size : int, optional
+        Number of test samples evaluated at a time.
+    nb_epochs : int, optional
+        Number of training epochs.
+    shuffle : boolean, optional
+        Whether to shuffle the samples at each epoch.
+    show_accuracy : boolean, optional
+        Whether to display class accuracy in the logs at each epoch.
+    validation_split : float [0, 1], optional
+        Fraction of the data to use as held-out validation data.
+    validation_data : tuple (X, y), optional
+        Data to be used as held-out validation data. Will override validation_split.
+    callbacks : list, optional
+        List of callbacks to apply during training.
+    verbose : int, optional
+        Verbosity level.
     """
     __metaclass__ = abc.ABCMeta
 
     @abc.abstractmethod
-    def __init__(self, model, optimizer, loss):
+    def __init__(self, model, optimizer, loss,
+                 train_batch_size=128, test_batch_size=128,
+                 nb_epoch=100, shuffle=True, show_accuracy=False,
+                 validation_split=0, validation_data=None, callbacks=None,
+                 verbose=0,):
         self.model = model
         self.optimizer = optimizer
         self.loss = loss
@@ -24,6 +49,17 @@ def __init__(self, model, optimizer, loss):
         self.config_ = []
         self.weights_ = []
 
+        self.train_batch_size = train_batch_size
+        self.test_batch_size = test_batch_size
+        self.nb_epoch = nb_epoch
+        self.shuffle = shuffle
+        self.show_accuracy = show_accuracy
+        self.validation_split = validation_split
+        self.validation_data = validation_data
+        self.callbacks = [] if callbacks is None else callbacks
+
+        self.verbose = verbose
+
     def get_params(self, deep=True):
         """
         Get parameters for this estimator.
@@ -58,8 +94,7 @@ def set_params(self, **params):
             setattr(self, parameter, value)
         return self
 
-    def fit(self, X, y, batch_size=128, nb_epoch=100, verbose=0, shuffle=True, show_accuracy=False,
-            validation_split=0, validation_data=None, callbacks=[]):
+    def fit(self, X, y):
         """
         Fit the model according to the given training data.
 
@@ -74,22 +109,6 @@ def fit(self, X, y, batch_size=128, nb_epoch=100, verbose=0, shuffle=True, show_
             and n_features is the number of features.
         y : array-like, shape = (n_samples) or (n_samples, n_outputs)
             True labels for X.
-        batch_size : int, optional
-            Number of training samples evaluated at a time.
-        nb_epochs : int, optional
-            Number of training epochs.
-        verbose : int, optional
-            Verbosity level.
-        shuffle : boolean, optional
-            Whether to shuffle the samples at each epoch.
-        show_accuracy : boolean, optional
-            Whether to display class accuracy in the logs at each epoch.
-        validation_split : float [0, 1], optional
-            Fraction of the data to use as held-out validation data.
-        validation_data : tuple (X, y), optional
-            Data to be used as held-out validation data. Will override validation_split.
-        callbacks : list, optional
-            List of callbacks to apply during training.
 
         Returns
         -------
@@ -105,10 +124,11 @@ def fit(self, X, y, batch_size=128, nb_epoch=100, verbose=0, shuffle=True, show_
 
         self.compiled_model_ = copy.deepcopy(self.model)
         self.compiled_model_.compile(optimizer=self.optimizer, loss=self.loss)
-        history = self.compiled_model_.fit(X, y, batch_size=batch_size, nb_epoch=nb_epoch, verbose=verbose,
-                                           shuffle=shuffle, show_accuracy=show_accuracy,
-                                           validation_split=validation_split, validation_data=validation_data,
-                                           callbacks=callbacks)
+        history = self.compiled_model_.fit(
+            X, y, batch_size=self.train_batch_size, nb_epoch=self.nb_epoch, verbose=self.verbose,
+            shuffle=self.shuffle, show_accuracy=self.show_accuracy,
+            validation_split=self.validation_split, validation_data=self.validation_data,
+            callbacks=self.callbacks)
 
         self.config_ = self.model.get_config()
         self.weights_ = self.model.get_weights()
@@ -129,10 +149,10 @@ class KerasClassifier(BaseWrapper):
     loss : string
         Loss function used by the model during compilation/training.
     """
-    def __init__(self, model, optimizer='adam', loss='categorical_crossentropy'):
-        super(KerasClassifier, self).__init__(model, optimizer, loss)
+    def __init__(self, model, optimizer='adam', loss='categorical_crossentropy', **kwargs):
+        super(KerasClassifier, self).__init__(model, optimizer, loss, **kwargs)
 
-    def predict(self, X, batch_size=128, verbose=0):
+    def predict(self, X):
         """
         Returns the class predictions for the given test data.
 
@@ -141,19 +161,16 @@ def predict(self, X, batch_size=128, verbose=0):
         X : array-like, shape = (n_samples, n_features)
             Test samples where n_samples in the number of samples
             and n_features is the number of features.
-        batch_size : int, optional
-            Number of test samples evaluated at a time.
-        verbose : int, optional
-            Verbosity level.
 
         Returns
         -------
         preds : array-like, shape = (n_samples)
             Class predictions.
         """
-        return self.compiled_model_.predict_classes(X, batch_size=batch_size, verbose=verbose)
+        return self.compiled_model_.predict_classes(
+            X, batch_size=self.test_batch_size, verbose=self.verbose)
 
-    def predict_proba(self, X, batch_size=128, verbose=0):
+    def predict_proba(self, X):
         """
         Returns class probability estimates for the given test data.
 
@@ -162,19 +179,16 @@ def predict_proba(self, X, batch_size=128, verbose=0):
         X : array-like, shape = (n_samples, n_features)
             Test samples where n_samples in the number of samples
             and n_features is the number of features.
-        batch_size : int, optional
-            Number of test samples evaluated at a time.
-        verbose : int, optional
-            Verbosity level.
 
         Returns
         -------
         proba : array-like, shape = (n_samples, n_outputs)
             Class probability estimates.
         """
-        return self.compiled_model_.predict_proba(X, batch_size=batch_size, verbose=verbose)
+        return self.compiled_model_.predict_proba(
+            X, batch_size=self.test_batch_size, verbose=self.verbose)
 
-    def score(self, X, y, batch_size=128, verbose=0):
+    def score(self, X, y):
         """
         Returns the mean accuracy on the given test data and labels.
 
@@ -185,17 +199,14 @@ def score(self, X, y, batch_size=128, verbose=0):
             and n_features is the number of features.
         y : array-like, shape = (n_samples) or (n_samples, n_outputs)
             True labels for X.
-        batch_size : int, optional
-            Number of test samples evaluated at a time.
-        verbose : int, optional
-            Verbosity level.
 
         Returns
         -------
         score : float
             Mean accuracy of predictions on X wrt. y.
         """
-        loss, accuracy = self.compiled_model_.evaluate(X, y, batch_size=batch_size, show_accuracy=True, verbose=verbose)
+        loss, accuracy = self.compiled_model_.evaluate(
+            X, y, batch_size=self.test_batch_size, show_accuracy=True, verbose=self.verbose)
         return accuracy
 
 
@@ -212,10 +223,10 @@ class KerasRegressor(BaseWrapper):
     loss : string
         Loss function used by the model during compilation/training.
     """
-    def __init__(self, model, optimizer='adam', loss='mean_squared_error'):
-        super(KerasRegressor, self).__init__(model, optimizer, loss)
+    def __init__(self, model, optimizer='adam', loss='mean_squared_error', **kwargs):
+        super(KerasRegressor, self).__init__(model, optimizer, loss, **kwargs)
 
-    def predict(self, X, batch_size=128, verbose=0):
+    def predict(self, X):
         """
         Returns predictions for the given test data.
 
@@ -224,19 +235,16 @@ def predict(self, X, batch_size=128, verbose=0):
         X : array-like, shape = (n_samples, n_features)
             Test samples where n_samples in the number of samples
             and n_features is the number of features.
-        batch_size : int, optional
-            Number of test samples evaluated at a time.
-        verbose : int, optional
-            Verbosity level.
 
         Returns
         -------
         preds : array-like, shape = (n_samples)
             Predictions.
         """
-        return self.compiled_model_.predict(X, batch_size=batch_size, verbose=verbose).ravel()
+        return self.compiled_model_.predict(
+            X, batch_size=self.test_batch_size, verbose=self.verbose).ravel()
 
-    def score(self, X, y, batch_size=128, verbose=0):
+    def score(self, X, y):
         """
         Returns the mean accuracy on the given test data and labels.
 
@@ -247,15 +255,12 @@ def score(self, X, y, batch_size=128, verbose=0):
             and n_features is the number of features.
         y : array-like, shape = (n_samples)
             True labels for X.
-        batch_size : int, optional
-            Number of test samples evaluated at a time.
-        verbose : int, optional
-            Verbosity level.
 
         Returns
         -------
         score : float
             Loss from predictions on X wrt. y.
         """
-        loss = self.compiled_model_.evaluate(X, y, batch_size=batch_size, show_accuracy=False, verbose=verbose)
+        loss = self.compiled_model_.evaluate(
+            X, y, batch_size=self.test_batch_size, show_accuracy=False, verbose=self.verbose)
         return loss

tests/manual/check_wrappers.py

@@ -42,10 +42,10 @@
 model.add(Activation('softmax'))
 
 print('Creating wrapper')
-classifier = KerasClassifier(model)
+classifier = KerasClassifier(model, train_batch_size=batch_size, nb_epoch=nb_epoch)
 
 print('Fitting model')
-classifier.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch)
+classifier.fit(X_train, Y_train)
 
 print('Testing score function')
 score = classifier.score(X_train, Y_train)
@@ -95,10 +95,10 @@
 model.add(Activation('linear'))
 
 print('Creating wrapper')
-regressor = KerasRegressor(model)
+regressor = KerasRegressor(model, train_batch_size=batch_size, nb_epoch=nb_epoch)
 
 print('Fitting model')
-regressor.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch)
+regressor.fit(X_train, y_train)
 
 print('Testing score function')
 score = regressor.score(X_train, y_train)