def _process_file(filename, base_dir=None):
"""Preprocesses supplied data file."""
if not base_dir:
base_dir = FLAGS.sigtyp_dir
full_path = os.path.join(base_dir, filename + ".csv")
_, df, data_info = sigtyp.read(
full_path, categorical_as_ints=FLAGS.categorical_as_ints)
_write_dict(data_info, filename, const.DATA_INFO_FILENAME)
# Save preprocessed data frames to a csv.
output_file = os.path.join(FLAGS.output_dir, filename + ".csv")
logging.info("Saving preprocessed data to \"%s\" ...", output_file)
df.to_csv(output_file, sep="|", index=False, float_format="%g")
return data_info
def main(unused_argv):
# Check flags.
if not FLAGS.sigtyp_dir:
raise ValueError("Specify --sigtyp_dir for input data!")
if not FLAGS.training_data_dir:
raise ValueError("Specify --training_data_dir!")
if FLAGS.prediction_mode and not FLAGS.output_sigtyp_predictions_file:
raise ValueError("In prediction mode specify "
"--output_sigtyp_predictions_file!")
# Load the test data (note: we read the vanilla test data in SIGTYP format).
filename = os.path.join(FLAGS.sigtyp_dir, FLAGS.test_set_name + ".csv")
vanilla_test_df, _, vanilla_data_info = sigtyp.read(
filename, categorical_as_ints=False)
if not len(vanilla_test_df): # pylint: disable=g-explicit-length-test
raise ValueError("Test dataset is empty!")
if len(vanilla_test_df.columns) != 8:
raise ValueError("Wrong number of columns: %d" %
len(vanilla_test_df.columns))
# Run evaluation/prediction.
features_to_predict = _features_to_predict(vanilla_test_df)
_evaluate(vanilla_test_df.copy(), vanilla_data_info,
_make_model(features_to_predict))
def _evaluate(test_df, test_data_info, model):
"""Evaluates the model on the supplied dataframe."""
# Prepare the test languages. This lets the model precompute some of the
# information that is based solely on the language context, not the features.
logging.info("Preparing test languages ...")
test_languages = test_df.to_dict(orient="row")
for test_language_df in test_languages:
model.prepare_target(test_language_df)
# Run actual evaluation.
mode_info = "Prediction" if FLAGS.prediction_mode else "Evaluation"
logging.info("[%s] Running over %d languages ...", mode_info, len(test_df))
total_num_evals = 0
total_num_correct = 0.0
feature_counts = {}
all_languages_predictions = []
for _, test_language_df in test_df.iterrows():
lang_num_evals, lang_num_correct, predictions = _evaluate_language(
test_language_df, model, feature_counts)
total_num_evals += lang_num_evals
total_num_correct += lang_num_correct
all_languages_predictions.append(predictions)
logging.info("Total number of evals: %d", total_num_evals)
if total_num_evals == 0:
logging.warning(
"No features to predict found. You are probably not using "
"the blind test set and should either use it or switch the "
"prediction mode off with '--noprediction_mode'.")
if not FLAGS.prediction_mode:
logging.info("Global Accuracy: %f%%",
total_num_correct / total_num_evals * 100.0)
for feature in sorted(feature_counts):
stats = feature_counts[feature]
if not FLAGS.prediction_mode:
logging.info("%s: [n=%d] Accuracy: %f%%", feature, stats["total"],
(stats["correct"] / stats["total"] *
100.0 if stats["total"] != 0.0 else 0.0))
else:
logging.info("%s: [%d predictions]", feature, stats["total"])
# Save the test dataset with all the features filled in.
if not FLAGS.prediction_mode:
return
logging.info("Saving predictions to \"%s\" ...",
FLAGS.output_sigtyp_predictions_file)
columns = [
"wals_code", "name", "latitude", "longitude", "genus", "family",
"countrycodes", "features"
]
result_test_df = pd.DataFrame(all_languages_predictions, columns=columns)
result_test_df.to_csv(FLAGS.output_sigtyp_predictions_file,
index=False,
sep="\t",
encoding=const.ENCODING,
quotechar='"')
# Sanity check. Read the data back in and make sure the values are sane.
logging.info("Sanity check ...")
read_result_test_df, _, read_result_data_info = sigtyp.read(
FLAGS.output_sigtyp_predictions_file,
categorical_as_ints=False,
verbose=False)
if len(test_df) != len(read_result_test_df):
raise ValueError("Expected %s languages in the resulting file!" %
len(test_df))
vanilla_num_feats = len(test_data_info[const.DATA_KEY_FEATURES])
read_num_feats = len(read_result_data_info[const.DATA_KEY_FEATURES])
if vanilla_num_feats > read_num_feats:
raise ValueError(
"Expected same or larger number of feature to be present: "
"Original %d, read %d" % (vanilla_num_feats, read_num_feats))
test_df_nonzero_values = test_df.count(axis=1).sum()
result_df_non_zero_values = read_result_test_df.count(axis=1).sum()
if test_df_nonzero_values != result_df_non_zero_values:
raise ValueError(
"Expected same number of non-zero values in predictions!")
