add sklearn 0.18 layer to scripts

code/ch09/movieclassifier/vectorizer.py

@@ -0,0 +1,24 @@
+from sklearn.feature_extraction.text import HashingVectorizer
+import re
+import os
+import pickle
+
+cur_dir = os.path.dirname(__file__)
+stop = pickle.load(open(
+                os.path.join(cur_dir, 
+                'pkl_objects', 
+                'stopwords.pkl'), 'rb'))
+
+def tokenizer(text):
+    text = re.sub('<[^>]*>', '', text)
+    emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)',
+                           text.lower())
+    text = re.sub('[\W]+', ' ', text.lower()) \
+                   + ' '.join(emoticons).replace('-', '')
+    tokenized = [w for w in text.split() if w not in stop]
+    return tokenized
+
+vect = HashingVectorizer(decode_error='ignore',
+                         n_features=2**21,
+                         preprocessor=None,
+                         tokenizer=tokenizer)

code/optional-py-scripts/ch03.py

@@ -12,7 +12,6 @@
 
 import numpy as np
 from sklearn import datasets
-from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score
 from sklearn.linear_model import LogisticRegression
@@ -25,6 +24,14 @@
 from matplotlib.colors import ListedColormap
 import matplotlib.pyplot as plt
 
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+    from sklearn.grid_search import train_test_split
+else:
+    from sklearn.model_selection import train_test_split
+
 #############################################################################
 print(50 * '=')
 print('Section: First steps with scikit-learn')
@@ -191,7 +198,8 @@ def cost_0(z):
 # plt.savefig('./figures/logistic_regression.png', dpi=300)
 plt.show()
 
-print('Predicted probabilities', lr.predict_proba(X_test_std[0, :]))
+print('Predicted probabilities', lr.predict_proba(X_test_std[0, :]
+                                                  .reshape(1, -1)))
 
 #############################################################################
 print(50 * '=')

code/optional-py-scripts/ch04.py

@@ -18,7 +18,6 @@
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.preprocessing import StandardScaler
-from sklearn.cross_validation import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestClassifier
@@ -27,6 +26,13 @@
 from itertools import combinations
 import matplotlib.pyplot as plt
 
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+    from sklearn.grid_search import train_test_split
+else:
+    from sklearn.model_selection import train_test_split
 
 #############################################################################
 print(50 * '=')
@@ -382,5 +388,11 @@ def _calc_score(self, X_train, y_train, X_test, y_test, indices):
 # plt.savefig('./random_forest.png', dpi=300)
 plt.show()
 
-X_selected = forest.transform(X_train, threshold=0.15)
+if Version(sklearn_version) < '0.18':
+    X_selected = forest.transform(X_train, threshold=0.15)
+else:
+    from sklearn.feature_selection import SelectFromModel
+    sfm = SelectFromModel(forest, threshold=0.15, prefit=True)
+    X_selected = sfm.transform(X_train)
+
 X_selected.shape

code/optional-py-scripts/ch05.py

@@ -12,7 +12,6 @@
 
 import pandas as pd
 import numpy as np
-from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 import matplotlib.pyplot as plt
@@ -27,6 +26,17 @@
 from scipy.linalg import eigh
 from matplotlib.ticker import FormatStrFormatter
 
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+    from sklearn.grid_search import train_test_split
+    from sklearn.lda import LDA
+else:
+    from sklearn.model_selection import train_test_split
+    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+
+
 #############################################################################
 print(50 * '=')
 print('Section: Unsupervised dimensionality reduction'

code/optional-py-scripts/ch06.py

@@ -15,16 +15,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import LabelEncoder
-from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
-from sklearn.cross_validation import StratifiedKFold
-from sklearn.cross_validation import cross_val_score
-from sklearn.learning_curve import learning_curve
-from sklearn.learning_curve import validation_curve
-from sklearn.grid_search import GridSearchCV
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.svm import SVC
 from sklearn.metrics import confusion_matrix
@@ -38,6 +32,24 @@
 from sklearn.metrics import accuracy_score
 from scipy import interp
 
+# for sklearn 0.18's alternative syntax
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+    from sklearn.grid_search import train_test_split
+    from sklearn.cross_validation import StratifiedKFold
+    from sklearn.cross_validation import cross_val_score
+    from sklearn.learning_curve import learning_curve
+    from sklearn.learning_curve import validation_curve
+    from sklearn.grid_search import GridSearchCV
+else:
+    from sklearn.model_selection import train_test_split
+    from sklearn.model_selection import StratifiedKFold
+    from sklearn.model_selection import cross_val_score
+    from sklearn.model_selection import learning_curve
+    from sklearn.model_selection import validation_curve
+    from sklearn.model_selection import GridSearchCV
+
 #############################################################################
 print(50 * '=')
 print('Section: Loading the Breast Cancer Wisconsin dataset')
@@ -83,31 +95,39 @@
 print('Section: K-fold cross-validation')
 print(50 * '-')
 
-kfold = StratifiedKFold(y=y_train,
-                        n_folds=10,
-                        random_state=1)
+if Version(sklearn_version) < '0.18':
+    kfold = StratifiedKFold(y=y_train,
+                            n_folds=10,
+                            random_state=1)
+else:
+    kfold = StratifiedKFold(n_splits=10,
+                            random_state=1).split(X_train, y_train)
 
 scores = []
 for k, (train, test) in enumerate(kfold):
     pipe_lr.fit(X_train[train], y_train[train])
     score = pipe_lr.score(X_train[test], y_train[test])
     scores.append(score)
-    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1,
+    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k + 1,
           np.bincount(y_train[train]), score))
 
 print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
 
 print('Using StratifiedKFold')
-kfold = StratifiedKFold(y=y_train,
-                        n_folds=10,
-                        random_state=1)
+if Version(sklearn_version) < '0.18':
+    kfold = StratifiedKFold(y=y_train,
+                            n_folds=10,
+                            random_state=1)
+else:
+    kfold = StratifiedKFold(n_splits=10,
+                            random_state=1).split(X_train, y_train)
 
 scores = []
 for k, (train, test) in enumerate(kfold):
     pipe_lr.fit(X_train[train], y_train[train])
     score = pipe_lr.score(X_train[test], y_train[test])
     scores.append(score)
-    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1,
+    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k + 1,
           np.bincount(y_train[train]), score))
 
 print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
@@ -134,12 +154,12 @@
                     ('clf', LogisticRegression(penalty='l2', random_state=0))])
 
 train_sizes, train_scores, test_scores =\
-                learning_curve(estimator=pipe_lr,
-                               X=X_train,
-                               y=y_train,
-                               train_sizes=np.linspace(0.1, 1.0, 10),
-                               cv=10,
-                               n_jobs=1)
+    learning_curve(estimator=pipe_lr,
+                   X=X_train,
+                   y=y_train,
+                   train_sizes=np.linspace(0.1, 1.0, 10),
+                   cv=10,
+                   n_jobs=1)
 
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)
@@ -182,12 +202,12 @@
 
 param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
 train_scores, test_scores = validation_curve(
-                estimator=pipe_lr,
-                X=X_train,
-                y=y_train,
-                param_name='clf__C',
-                param_range=param_range,
-                cv=10)
+    estimator=pipe_lr,
+    X=X_train,
+    y=y_train,
+    param_name='clf__C',
+    param_range=param_range,
+    cv=10)
 
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)
@@ -345,7 +365,14 @@
 
 X_train2 = X_train[:, [4, 14]]
 
-cv = StratifiedKFold(y_train, n_folds=3, random_state=1)
+if Version(sklearn_version) < '0.18':
+    cv = StratifiedKFold(y_train,
+                         n_folds=3,
+                         random_state=1)
+
+else:
+    cv = list(StratifiedKFold(n_splits=3,
+                              random_state=1).split(X_train, y_train))
 
 fig = plt.figure(figsize=(7, 5))
 
@@ -367,7 +394,7 @@
              tpr,
              lw=1,
              label='ROC fold %d (area = %0.2f)'
-                   % (i+1, roc_auc))
+                   % (i + 1, roc_auc))
 
 plt.plot([0, 1],
          [0, 1],

code/optional-py-scripts/ch07.py

@@ -23,22 +23,30 @@
 from sklearn.base import clone
 from sklearn.pipeline import _name_estimators
 from sklearn import datasets
-from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.preprocessing import LabelEncoder
-from sklearn.cross_validation import cross_val_score
 from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.pipeline import Pipeline
 from sklearn.metrics import roc_curve
 from sklearn.metrics import auc
 from sklearn.metrics import accuracy_score
-from sklearn.grid_search import GridSearchCV
 from sklearn.ensemble import BaggingClassifier
 from sklearn.ensemble import AdaBoostClassifier
 from itertools import product
 
+# Added version check for recent scikit-learn 0.18 checks
+from distutils.version import LooseVersion as Version
+from sklearn import __version__ as sklearn_version
+if Version(sklearn_version) < '0.18':
+    from sklearn.cross_validation import train_test_split
+    from sklearn.cross_validation import cross_val_score
+    from sklearn.cross_validation import GridSearchCV
+else:
+    from sklearn.model_selection import train_test_split
+    from sklearn.model_selection import cross_val_score
+    from sklearn.model_selection import GridSearchCV
 
 #############################################################################
 print(50 * '=')
@@ -48,7 +56,7 @@
 
 def ensemble_error(n_classifier, error):
     k_start = math.ceil(n_classifier / 2.0)
-    probs = [comb(n_classifier, k) * error**k * (1-error)**(n_classifier - k)
+    probs = [comb(n_classifier, k) * error**k * (1 - error)**(n_classifier - k)
              for k in range(k_start, n_classifier + 1)]
     return sum(probs)
 
@@ -185,11 +193,11 @@ def predict(self, X):
                                       for clf in self.classifiers_]).T
 
             maj_vote = np.apply_along_axis(
-                                      lambda x:
-                                      np.argmax(np.bincount(x,
-                                                weights=self.weights)),
-                                      axis=1,
-                                      arr=predictions)
+                lambda x:
+                np.argmax(np.bincount(x,
+                                      weights=self.weights)),
+                axis=1,
+                arr=predictions)
         maj_vote = self.lablenc_.inverse_transform(maj_vote)
         return maj_vote
 
@@ -237,9 +245,9 @@ def get_params(self, deep=True):
 y = le.fit_transform(y)
 
 X_train, X_test, y_train, y_test =\
-       train_test_split(X, y,
-                        test_size=0.5,
-                        random_state=1)
+    train_test_split(X, y,
+                     test_size=0.5,
+                     random_state=1)
 
 clf1 = LogisticRegression(penalty='l2',
                           C=0.001,
@@ -391,9 +399,19 @@ def get_params(self, deep=True):
                     scoring='roc_auc')
 grid.fit(X_train, y_train)
 
-for params, mean_score, scores in grid.grid_scores_:
-    print("%0.3f+/-%0.2f %r"
-          % (mean_score, scores.std() / 2.0, params))
+if Version(sklearn_version) < '0.18':
+    for params, mean_score, scores in grid.grid_scores_:
+        print("%0.3f +/- %0.2f %r"
+              % (mean_score, scores.std() / 2.0, params))
+
+else:
+    cv_keys = ('mean_test_score', 'std_test_score', 'params')
+
+    for r, _ in enumerate(grid.cv_results_['mean_test_score']):
+        print("%0.3f +/- %0.2f %r"
+              % (grid.cv_results_[cv_keys[0]][r],
+                 grid.cv_results_[cv_keys[1]][r] / 2.0,
+                 grid.cv_results_[cv_keys[2]][r]))
 
 print('Best parameters: %s' % grid.best_params_)
 print('Accuracy: %.2f' % grid.best_score_)
@@ -426,9 +444,9 @@ def get_params(self, deep=True):
 y = le.fit_transform(y)
 
 X_train, X_test, y_train, y_test =\
-            train_test_split(X, y,
-                             test_size=0.40,
-                             random_state=1)
+    train_test_split(X, y,
+                     test_size=0.40,
+                     random_state=1)
 
 tree = DecisionTreeClassifier(criterion='entropy',
                               max_depth=None,