Extend backwards compatibility by refactoring dependency handling, te…

…sts & examples
nok · Jan 29, 2017 · d038ade · d038ade
1 parent ee66a1d
commit d038ade
Show file tree

Hide file tree

Showing 48 changed files with 223 additions and 148 deletions.
diff --git a/environment.yml b/environment.yml
@@ -1,14 +1,13 @@
 name: sklearn-porter
 dependencies:
-- libgcc=4.8.5=1
-- mkl=11.3.3=0
-- numpy=1.11.2=py27_0
-- openssl=1.0.2j=0
-- pip=8.1.2=py27_0
-- python=2.7.12=1
+- mkl=2017.0.1=0
+- numpy=1.11.3=py27_0
+- openssl=1.0.2k=0
+- pip=9.0.1=py27_1
+- python=2.7.13=0
 - readline=6.2=2
-- scikit-learn=0.18=np111py27_0
-- scipy=0.18.1=np111py27_0
+- scikit-learn=0.18.1=np111py27_1
+- scipy=0.18.1=np111py27_1
 - setuptools=27.2.0=py27_0
 - sqlite=3.13.0=0
 - tk=8.5.18=0

diff --git a/examples/classifier/AdaBoostClassifier/c/basics.py b/examples/classifier/AdaBoostClassifier/c/basics.py
@@ -7,7 +7,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 base_estimator = DecisionTreeClassifier(max_depth=4, random_state=0)
 clf = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=100,
                          random_state=0)

diff --git a/examples/classifier/AdaBoostClassifier/java/basics.py b/examples/classifier/AdaBoostClassifier/java/basics.py
@@ -7,7 +7,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 base_estimator = DecisionTreeClassifier(max_depth=4, random_state=0)
 clf = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=100,
                          random_state=0)

diff --git a/examples/classifier/AdaBoostClassifier/js/basics.py b/examples/classifier/AdaBoostClassifier/js/basics.py
@@ -7,7 +7,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 base_estimator = DecisionTreeClassifier(max_depth=4, random_state=0)
 clf = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=100,
                          random_state=0)

diff --git a/examples/classifier/BernoulliNB/java/basics.py b/examples/classifier/BernoulliNB/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = BernoulliNB()
 clf.fit(X, y)
 

diff --git a/examples/classifier/DecisionTreeClassifier/c/basics.py b/examples/classifier/DecisionTreeClassifier/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = tree.DecisionTreeClassifier()
 clf.fit(X, y)
 

diff --git a/examples/classifier/DecisionTreeClassifier/java/basics.py b/examples/classifier/DecisionTreeClassifier/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = tree.DecisionTreeClassifier()
 clf.fit(X, y)
 

diff --git a/examples/classifier/DecisionTreeClassifier/js/basics.py b/examples/classifier/DecisionTreeClassifier/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = tree.DecisionTreeClassifier()
 clf.fit(X, y)
 

diff --git a/examples/classifier/DecisionTreeClassifier/php/basics.py b/examples/classifier/DecisionTreeClassifier/php/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = tree.DecisionTreeClassifier()
 clf.fit(X, y)
 

diff --git a/examples/classifier/ExtraTreesClassifier/c/basics.py b/examples/classifier/ExtraTreesClassifier/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = ExtraTreesClassifier(n_estimators=15, random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/ExtraTreesClassifier/java/basics.py b/examples/classifier/ExtraTreesClassifier/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = ExtraTreesClassifier(n_estimators=15, random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/ExtraTreesClassifier/js/basics.py b/examples/classifier/ExtraTreesClassifier/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = ExtraTreesClassifier(n_estimators=15, random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/GaussianNB/java/basics.py b/examples/classifier/GaussianNB/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = GaussianNB()
 clf.fit(X, y)
 

diff --git a/examples/classifier/KNeighborsClassifier/java/basics.py b/examples/classifier/KNeighborsClassifier/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = KNeighborsClassifier(algorithm='brute', n_neighbors=3, weights='uniform')
 clf.fit(X, y)
 

diff --git a/examples/classifier/KNeighborsClassifier/js/basics.py b/examples/classifier/KNeighborsClassifier/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = KNeighborsClassifier(algorithm='brute', n_neighbors=3, weights='uniform')
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/c/basics.py b/examples/classifier/LinearSVC/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/c/compiling.py b/examples/classifier/LinearSVC/c/compiling.py
@@ -8,7 +8,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/go/basics.py b/examples/classifier/LinearSVC/go/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/java/basics.py b/examples/classifier/LinearSVC/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/js/basics.py b/examples/classifier/LinearSVC/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/php/basics.py b/examples/classifier/LinearSVC/php/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/LinearSVC/ruby/basics.py b/examples/classifier/LinearSVC/ruby/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.LinearSVC(C=1., random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/MLPClassifier/java/basics.py b/examples/classifier/MLPClassifier/java/basics.py
@@ -9,7 +9,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 
 X = shuffle(X, random_state=0)
 y = shuffle(y, random_state=0)

diff --git a/examples/classifier/MLPClassifier/js/basics.py b/examples/classifier/MLPClassifier/js/basics.py
@@ -9,7 +9,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 
 X = shuffle(X, random_state=0)
 y = shuffle(y, random_state=0)

diff --git a/examples/classifier/NuSVC/c/basics.py b/examples/classifier/NuSVC/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/NuSVC/java/basics.py b/examples/classifier/NuSVC/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/NuSVC/js/basics.py b/examples/classifier/NuSVC/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/NuSVC/php/basics.py b/examples/classifier/NuSVC/php/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/RandomForestClassifier/c/basics.py b/examples/classifier/RandomForestClassifier/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = RandomForestClassifier(n_estimators=15, max_depth=None,
                              min_samples_split=2, random_state=0)
 clf.fit(X, y)

diff --git a/examples/classifier/RandomForestClassifier/java/basics.py b/examples/classifier/RandomForestClassifier/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = RandomForestClassifier(n_estimators=15, max_depth=None,
                              min_samples_split=2, random_state=0)
 clf.fit(X, y)

diff --git a/examples/classifier/RandomForestClassifier/js/basics.py b/examples/classifier/RandomForestClassifier/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = RandomForestClassifier(n_estimators=15, max_depth=None,
                              min_samples_split=2, random_state=0)
 clf.fit(X, y)

diff --git a/examples/classifier/SVC/c/basics.py b/examples/classifier/SVC/c/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.SVC(C=1., gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/SVC/java/basics.py b/examples/classifier/SVC/java/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.SVC(C=1., gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/SVC/js/basics.py b/examples/classifier/SVC/js/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.SVC(C=1., gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/classifier/SVC/php/basics.py b/examples/classifier/SVC/php/basics.py
@@ -6,7 +6,8 @@
 from sklearn_porter import Porter
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = svm.SVC(C=1., gamma=0.001, kernel='rbf', random_state=0)
 clf.fit(X, y)
 

diff --git a/examples/cli/dump_pickle_file.py b/examples/cli/dump_pickle_file.py
@@ -5,7 +5,8 @@
 from sklearn.externals import joblib
 
 
-X, y = load_iris(return_X_y=True)
+iris_data = load_iris()
+X, y = iris_data.data, iris_data.target
 clf = tree.DecisionTreeClassifier()
 clf.fit(X, y)
 

diff --git a/license.txt b/license.txt
@@ -1,4 +1,4 @@
-Copyright 2016, Darius Morawiec
+Copyright 2016-2017, Darius Morawiec
 
 Permission is hereby granted, free of charge, to any person obtaining
 a copy of this software and associated documentation files (the