def run_job(self, request):
""" Used by the web application."""
features = []
count = len(request.feature) - 1
while count >= 0:
features.append(str(request.feature[count]))
count -= 1
prepped_features = Pairwise.prepare_features(request.cohort_id, features)
outputs = Pairwise.run_pairwise(prepped_features)
results = PairwiseResults(result_vectors=[], filter_messages=[])
logging.info(results)
for row_label, row in outputs.items():
if type(row) is dict:
results.result_vectors.append(
PairwiseResultVector(
feature_1=row["feature_A"],
feature_2=row["feature_B"],
comparison_type=row["comparison_type"],
correlation_coefficient=row["correlation_coefficient"],
n=int(row["n"]),
_logp=float(row["_logp"]),
n_A=int(row["n_A"]),
p_A=float(row["p_A"]),
n_B=int(row["n_B"]),
p_B=float(row["p_B"]),
exclusion_rules=row["exclusion_rules"],
)
)
elif type(row) is unicode:
results.filter_messages.append(PairwiseFilterMessage(filter_message=row[0]))
return results
def get_pairwise_result(self, feature_array):
# Format the feature vectors for pairwise
input_vectors = Pairwise.prepare_feature_vector(feature_array)
outputs = None
results = None
try:
outputs = Pairwise.run_pairwise(input_vectors)
if outputs is not None:
results = PairwiseResults(result_vectors=[], filter_messages=[])
for row_label, row in outputs.items():
if type(row) is dict:
results.result_vectors.append(PairwiseResultVector(feature_1=row['feature_A'],
feature_2=row['feature_B'],
comparison_type=row['comparison_type'],
correlation_coefficient=row['correlation_coefficient'],
n=int(row['n']),
_logp=float(row['_logp']),
n_A=int(row['n_A']),
p_A=float(row['p_A']),
n_B=int(row['n_B']),
p_B=float(row['p_B']),
exclusion_rules=row['exclusion_rules']))
elif type(row) is unicode:
results.filter_messages.append(PairwiseFilterMessage(filter_message=row[0]))
except Exception as e:
outputs = None
results = None
logger.error(traceback.format_exc())
return results
