Explain text predictions of Keras classifiers

.travis.yml

@@ -41,13 +41,13 @@ addons:
             - libatlas-base-dev
             - liblapack-dev
             - gfortran
-            - cmake
 
 before_install:
     - sudo apt-get -qq update
     - if [[ "$TOXENV" != "py35-nodeps" ]]; then sudo apt-get install graphviz; fi
 
 install:
+    - pip install --upgrade cmake
     - pip install -U pip tox codecov
 
 script: tox

README.rst

@@ -39,7 +39,7 @@ It provides support for the following machine learning frameworks and packages:
   It also allows to debug scikit-learn pipelines which contain
   HashingVectorizer, by undoing hashing.
 
-* Keras_ - explain predictions of image classifiers via Grad-CAM visualizations.
+* Keras_ - explain predictions of image and text classifiers via Grad-CAM visualizations.
 
 * xgboost_ - show feature importances and explain predictions of XGBClassifier,
   XGBRegressor and xgboost.Booster.
@@ -81,7 +81,7 @@ and formatting on a client.
 .. _xgboost: https://github.com/dmlc/xgboost
 .. _LightGBM: https://github.com/Microsoft/LightGBM
 .. _Catboost: https://github.com/catboost/catboost
-.. _Keras: https://keras.io/
+.. _Keras: https://github.com/keras-team/keras
 .. _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html
 .. _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html
 

_ci/runtests_default.sh

@@ -7,4 +7,7 @@ py.test --doctest-modules \
         --ignore eli5/keras \
         --ignore eli5/formatters/image.py \
         --ignore tests/utils_image.py \
+        --ignore tests/utils_gradcam.py \
+        --ignore tests/estimators/keras_sentiment_classifier/keras_sentiment_classifier.py \
+        --ignore tests/estimators/keras_multiclass_text_classifier/keras_multiclass_text_classifier.py \
         --cov=eli5 --cov-report=html --cov-report=term  "$@"

_ci/runtests_nodeps.sh

@@ -10,4 +10,7 @@ py.test --doctest-modules \
         --ignore eli5/formatters/as_dataframe.py \
         --ignore eli5/formatters/image.py \
         --ignore tests/utils_image.py \
+        --ignore tests/utils_gradcam.py \
+        --ignore tests/estimators/keras_sentiment_classifier/keras_sentiment_classifier.py \
+        --ignore tests/estimators/keras_multiclass_text_classifier/keras_multiclass_text_classifier.py \
         --cov=eli5 --cov-report=html --cov-report=term "$@"