Save the evaluations to a csv, in case the user wants to inspect, ana…

…lyze, or otherwise use the classifications generated by the neural net

eval.py

@@ -8,6 +8,7 @@
 import data_helpers
 from text_cnn import TextCNN
 from tensorflow.contrib import learn
+import csv
 
 # Parameters
 # ==================================================
@@ -85,3 +86,10 @@
     correct_predictions = float(sum(all_predictions == y_test))
     print("Total number of test examples: {}".format(len(y_test)))
     print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))
+
+# Save the evaluation to a csv
+predictions_human_readable = np.column_stack((np.array(x_raw), all_predictions))
+out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
+print("Saving evaluation to {0}".format(out_path))
+with open(out_path, 'w') as f:
+    csv.writer(f).writerows(predictions_human_readable)