iter

Author: glemaitre

build_tools/travis/install.sh

@@ -39,7 +39,10 @@ if [[ "$DISTRIB" == "conda" ]]; then
     conda create -n testenv --yes python=$PYTHON_VERSION pip
     source activate testenv
     conda install --yes numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION
-    conda install --yes pandas keras
+    # only install optional dependency in python 3.6
+    if [[ $PYTHON_VERSION == "3.6" ]]; then
+        conda install --yes pandas keras
+    fi
 
     if [[ "$SKLEARN_VERSION" == "master" ]]; then
         conda install --yes cython
@@ -70,7 +73,7 @@ python --version
 python -c "import numpy; print('numpy %s' % numpy.__version__)"
 python -c "import scipy; print('scipy %s' % scipy.__version__)"
 
-python setup.py develop
+pip install -e .
 ccache --show-stats
 # Useful for debugging how ccache is used
 # cat $CCACHE_LOGFILE

imblearn/keras/_generator.py

@@ -1,7 +1,13 @@
 """Implement generators for ``keras`` which will balance the data."""
 from __future__ import division
 
-import keras
+try:
+    import keras
+    ParentClass = keras.utils.Sequence
+    HAS_KERAS = True
+except ImportError:
+    ParentClass = object
+    HAS_KERAS = False
 
 from scipy.sparse import issparse
 
@@ -16,7 +22,7 @@
 from ..tensorflow import balanced_batch_generator as tf_bbg
 
 
-class BalancedBatchGenerator(keras.utils.Sequence):
+class BalancedBatchGenerator(ParentClass):
     """Create balanced batches when training a keras model.
 
     Create a keras ``Sequence`` which is given to ``fit_generator``. The
@@ -90,6 +96,8 @@ class BalancedBatchGenerator(keras.utils.Sequence):
     """
     def __init__(self, X, y, sample_weight=None, sampler=None, batch_size=32,
                  sparse=False, random_state=None):
+        if not HAS_KERAS:
+            raise ImportError("'No module named 'keras'")
         self.X = X
         self.y = y
         self.sample_weight = sample_weight

imblearn/keras/tests/test_generator.py

@@ -5,18 +5,18 @@
 
 from sklearn.datasets import load_iris
 
+keras = pytest.importorskip('keras')
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import to_categorical
+
 from imblearn.datasets import make_imbalance
 from imblearn.under_sampling import ClusterCentroids
 from imblearn.under_sampling import NearMiss
 
 from imblearn.keras import BalancedBatchGenerator
 from imblearn.keras import balanced_batch_generator
 
-keras = pytest.importorskip('keras')
-from keras.models import Sequential
-from keras.layers import Dense
-from keras.utils import to_categorical
-
 iris = load_iris()
 X, y = make_imbalance(iris.data, iris.target, {0: 30, 1: 50, 2: 40})
 y = to_categorical(y, 3)