re-enable plotting tests

ci/.travis_install.sh

@@ -23,7 +23,7 @@ fi
 
 source activate test-environment
 
-pip install nose nose-exclude;
+pip install nose;
 
 if [ "${COVERAGE}" = "true" ]; then
     pip install coverage coveralls codecov;

ci/.travis_test.sh

@@ -4,9 +4,7 @@ set -e
 
 
 if [[ "$COVERAGE" == "true" ]]; then
-    nosetests -s -v --with-coverage --exclude-dir=mlxtend/tf_classifier --exclude-dir=mlxtend/tf_regressor --exclude-dir=mlxtend/tf_cluster --exclude-dir=mlxtend/plotting
-else
-    nosetests -s -v --exclude-dir=mlxtend/tf_classifier --exclude-dir=mlxtend/tf_regressor --exclude-dir=mlxtend/tf_cluster --exclude-dir=mlxtend/plotting
+    nosetests -s -v --with-coverage
 fi
 
 if [[ "$NOTEBOOKS" == "true" ]]; then

docs/sources/CHANGELOG.md

@@ -5,6 +5,27 @@
 The CHANGELOG for the current development version is available at
 [https://github.com/rasbt/mlxtend/blob/master/docs/sources/CHANGELOG.md](https://github.com/rasbt/mlxtend/blob/master/docs/sources/CHANGELOG.md).
 
+### Version 0.7.1dev (TBD)
+
+
+
+##### Downloads
+
+- [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.7.1.zip)
+- [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.7.1.tar.gz)
+
+##### New Features
+
+- /
+
+##### Changes
+
+- /
+
+##### Bug Fixes
+
+- /
+
 
 ### Version 0.7.0 (2017-06-22)
 

mlxtend/__init__.py

@@ -4,4 +4,4 @@
 #
 # License: BSD 3 clause
 
-__version__ = '0.7.0'
+__version__ = '0.7.1dev'

mlxtend/plotting/tests/test_checkerboard.py

@@ -5,8 +5,11 @@
 # License: BSD 3 clause
 
 from mlxtend.plotting import checkerboard_plot
+import matplotlib.pyplot as plt
 import numpy as np
 
+plt.switch_backend('agg')
+
 
 def test_runs():
 

mlxtend/plotting/tests/test_learning_curves.py

@@ -23,8 +23,8 @@ def test_training_size():
     training_errors, test_errors = (plot_learning_curves(X_train, y_train,
                                     X_test, y_test, clf, suppress_plot=True))
 
-    desired1 = [0.32, 0.33, 0.32, 0.33, 0.30, 0.31, 0.31, 0.22, 0.22, 0.22]
-    desired2 = [0.35, 0.35, 0.35, 0.35, 0.43, 0.45, 0.35, 0.35, 0.45, 0.45]
+    desired1 = [0.22, 0.22, 0.22, 0.31, 0.31, 0.3, 0.33, 0.32, 0.33, 0.32]
+    desired2 = [0.45, 0.45, 0.35, 0.35, 0.45, 0.43, 0.35, 0.35, 0.35, 0.35]
 
     np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
     np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
@@ -34,22 +34,18 @@ def test_scikit_metrics():
     iris = datasets.load_iris()
     X = iris.data
     y = iris.target
-    X_train, X_test, y_train, y_test = train_test_split(X,
-                                                        y,
-                                                        train_size=0.6,
-                                                        random_state=2)
+    X_train, X_test, y_train, y_test = (train_test_split(X, y,
+                                        train_size=0.6, random_state=2))
 
     clf = DecisionTreeClassifier(max_depth=1, random_state=1)
-    training_errors, test_errors = plot_learning_curves(X_train,
-                                                        y_train,
-                                                        X_test,
-                                                        y_test,
-                                                        clf,
-                                                        suppress_plot=True,
-                                                        scoring='accuracy')
+    training_acc, test_acc = (plot_learning_curves(X_train, y_train,
+                              X_test, y_test, clf,
+                              scoring='accuracy',
+                              suppress_plot=True))
 
-    desired1 = [0.68, 0.67, 0.68, 0.67, 0.7, 0.69, 0.69, 0.78, 0.78, 0.78]
-    desired2 = [0.65, 0.65, 0.65, 0.65, 0.57, 0.55, 0.65, 0.65, 0.55, 0.55]
-
-    np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
-    np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
+    desired1 = np.array([0.22, 0.22, 0.22, 0.31, 0.31,
+                         0.3, 0.33, 0.32, 0.33, 0.32])
+    desired2 = np.array([0.45, 0.45, 0.35, 0.35, 0.45,
+                         0.43, 0.35, 0.35, 0.35, 0.35])
+    np.testing.assert_almost_equal(training_acc, 1 - desired1, decimal=2)
+    np.testing.assert_almost_equal(test_acc, 1 - desired2, decimal=2)