fix(examples/trails/sklearn)-fix the incorrect input name for run fun…

examples/trials/sklearn/classification/main.py

@@ -23,13 +23,13 @@
 import logging
 import numpy as np
 
-
 LOG = logging.getLogger('sklearn_classification')
 
 def load_data():
     '''Load dataset, use 20newsgroups dataset'''
     digits = load_digits()
-    X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
+    X_train, X_test, y_train, y_test = train_test_split(
+        digits.data, digits.target, random_state=99, test_size=0.25)
 
     ss = StandardScaler()
     X_train = ss.fit_transform(X_train)
@@ -59,7 +59,7 @@ def get_model(PARAMS):
 
     return model
 
-def run(X_train, X_test, y_train, y_test, PARAMS):
+def run(X_train, X_test, y_train, y_test, model):
     '''Train model and predict result'''
     model.fit(X_train, y_train)
     score = model.score(X_test, y_test)

examples/trials/sklearn/regression/main.py

@@ -33,23 +33,22 @@
 def load_data():
     '''Load dataset, use boston dataset'''
     boston = load_boston()
-    X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=99, test_size=0.25)
+    X_train, X_test, y_train, y_test = train_test_split(
+        boston.data, boston.target, random_state=99, test_size=0.25)
     #normalize data
     ss_X = StandardScaler()
     ss_y = StandardScaler()
 
     X_train = ss_X.fit_transform(X_train)
     X_test = ss_X.transform(X_test)
-    y_train = ss_y.fit_transform(y_train[:, None])[:,0]
-    y_test = ss_y.transform(y_test[:, None])[:,0]
+    y_train = ss_y.fit_transform(y_train[:, None])[:, 0]
+    y_test = ss_y.transform(y_test[:, None])[:, 0]
 
     return X_train, X_test, y_train, y_test
 
 def get_default_parameters():
     '''get default parameters'''
-    params = {
-        'model_name': 'LinearRegression'
-    }
+    params = {'model_name': 'LinearRegression'}
     return params
 
 def get_model(PARAMS):
@@ -76,8 +75,7 @@ def get_model(PARAMS):
         raise
     return model
 
-
-def run(X_train, X_test, y_train, y_test, PARAMS):
+def run(X_train, X_test, y_train, y_test, model):
     '''Train model and predict result'''
     model.fit(X_train, y_train)
     predict_y = model.predict(X_test)