update deprecated package and parameters

.gitignore

@@ -1 +1,3 @@
-.idea
+.idea
+.DS_Store
+sklearnTUT/save

sklearnTUT/sk10_cross_validation3.py

@@ -7,7 +7,7 @@
 Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
 """
 from __future__ import print_function
-from sklearn.learning_curve import  validation_curve
+from sklearn.model_selection import  validation_curve
 from sklearn.datasets import load_digits
 from sklearn.svm import SVC
 import matplotlib.pyplot as plt
@@ -19,7 +19,7 @@
 param_range = np.logspace(-6, -2.3, 5)
 train_loss, test_loss = validation_curve(
         SVC(), X, y, param_name='gamma', param_range=param_range, cv=10,
-        scoring='mean_squared_error')
+        scoring='neg_mean_squared_error')
 train_loss_mean = -np.mean(train_loss, axis=1)
 test_loss_mean = -np.mean(test_loss, axis=1)
 

sklearnTUT/sk11_save.py

@@ -26,7 +26,7 @@
    print(clf2.predict(X[0:1]))
 
 # method 2: joblib
-from sklearn.externals import joblib
+import joblib
 # Save
 joblib.dump(clf, 'save/clf.pkl')
 # restore

sklearnTUT/sk7_normalization.py

@@ -10,7 +10,7 @@
 from sklearn import preprocessing
 import numpy as np
 from sklearn.model_selection import train_test_split
-from sklearn.datasets.samples_generator import make_classification
+from sklearn.datasets import make_classification
 from sklearn.svm import SVC
 import matplotlib.pyplot as plt
 

sklearnTUT/sk8_cross_validation/full_code.py

@@ -8,7 +8,7 @@
 """
 from __future__ import print_function
 from sklearn.datasets import load_iris
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
 
 iris = load_iris()
@@ -23,13 +23,13 @@
 print(knn.score(X_test, y_test))
 
 # this is cross_val_score #
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import cross_val_score
 knn = KNeighborsClassifier(n_neighbors=5)
 scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')
 print(scores)
 
 # this is how to use cross_val_score to choose model and configs #
-from sklearn.cross_validation import cross_val_score
+from sklearn.model_selection import cross_val_score
 import matplotlib.pyplot as plt
 k_range = range(1, 31)
 k_scores = []

sklearnTUT/sk9_cross_validation2.py

@@ -7,7 +7,7 @@
 Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
 """
 from __future__ import print_function
-from sklearn.learning_curve import  learning_curve
+from sklearn.model_selection import  learning_curve
 from sklearn.datasets import load_digits
 from sklearn.svm import SVC
 import matplotlib.pyplot as plt
@@ -17,7 +17,7 @@
 X = digits.data
 y = digits.target
 train_sizes, train_loss, test_loss= learning_curve(
-        SVC(gamma=0.01), X, y, cv=10, scoring='mean_squared_error',
+        SVC(gamma=0.01), X, y, cv=10, scoring='neg_mean_squared_error',
         train_sizes=[0.1, 0.25, 0.5, 0.75, 1])
 train_loss_mean = -np.mean(train_loss, axis=1)
 test_loss_mean = -np.mean(test_loss, axis=1)