def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name,
type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
print api_form_values['Origin']
print api_form_values['Dest']
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'], api_form_values['Dest'])
print prediction_features['Distance']
# # Turn the date into DayOfYear, DayOfMonth, DayOfWeek
# date_features_dict = predict_utils.get_regression_date_args(
# api_form_values['FlightDate']
# )
# for api_field_name, api_field_value in date_features_dict.items():
# prediction_features[api_field_name] = api_field_value
# # Add a timestamp
# prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
# # Create a unique ID for this message
# unique_id = str(uuid.uuid4())
# prediction_features['UUID'] = unique_id
# message_bytes = json.dumps(prediction_features).encode()
# producer.send(PREDICTION_TOPIC, message_bytes)
# response = {"status": "OK", "id": unique_id}
# return json_util.dumps(response)
return "done"
def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = \
{
"dep_delay": int,
"carrier": str,
"FlightDate": str,
"dest": str,
"FlightNum": str,
"origin": str,
"route": str,
"arr_time": int,
"dep_time": int
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name,
type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['distance'] = predict_utils.get_flight_distance(
client, api_form_values['origin'], api_form_values['dest'])
# Turn the date into DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate'])
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Create a unique ID for this message
unique_id = str(uuid.uuid4())
prediction_features['UUID'] = unique_id
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
print(json.dumps(prediction_features, sort_keys=True, indent=4))
message_bytes = json.dumps(prediction_features).encode()
producer.send(PREDICTION_TOPIC, message_bytes)
response = {"status": "OK", "id": unique_id}
return json_util.dumps(response)
def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = {
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str,
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name,
type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features["Distance"] = predict_utils.get_flight_distance(
client, api_form_values["Origin"], api_form_values["Dest"])
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values["FlightDate"])
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features["Timestamp"] = predict_utils.get_current_timestamp()
# Create a unique ID for this message
unique_id = str(uuid.uuid4())
prediction_features["UUID"] = unique_id
# Encode the JSON prediction message in bytes
print(json.dumps(prediction_features, sort_keys=True, indent=4))
message_bytes = json.dumps(prediction_features).encode()
# Send the prediction message using
producer.produce(PREDICTION_TOPIC, message_bytes, callback=delivery_report)
producer.flush()
response = {"status": "OK", "id": unique_id}
return json_util.dumps(response)
def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
# Create a unique ID for this message
unique_id = str(uuid.uuid4())
prediction_features['UUID'] = unique_id
message_bytes = json.dumps(prediction_features).encode()
producer.send(PREDICTION_TOPIC, message_bytes)
response = {"status": "OK", "id": unique_id}
return json_util.dumps(response)
def regress_flight_delays():
api_field_type_map = \
{
"DepDelay": int,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name,
type=api_field_type)
# Set the direct values
prediction_features = {}
prediction_features['Origin'] = api_form_values['Origin']
prediction_features['Dest'] = api_form_values['Dest']
prediction_features['FlightNum'] = api_form_values['FlightNum']
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'], api_form_values['Dest'])
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate'])
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Vectorize the features
feature_vectors = vectorizer.transform([prediction_features])
# Make the prediction!
result = regressor.predict(feature_vectors)[0]
# Return a JSON object
result_obj = {"Delay": result}
return json.dumps(result_obj)
def classify_flight_delays():
"""POST API for classifying flight delays"""
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
client.agile_data_science.prediction_tasks.insert_one(
prediction_features
)
return json_util.dumps(prediction_features)
def classify_flight_delays():
"""POST API for classifying flight delays"""
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
client.agile_data_science.prediction_tasks.insert_one(
prediction_features
)
return json_util.dumps(prediction_features)
def regress_flight_delays():
api_field_type_map = \
{
"DepDelay": int,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values
prediction_features = {}
prediction_features['Origin'] = api_form_values['Origin']
prediction_features['Dest'] = api_form_values['Dest']
prediction_features['FlightNum'] = api_form_values['FlightNum']
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(client, api_form_values['Origin'], api_form_values['Dest'])
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(api_form_values['FlightDate'])
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Vectorize the features
feature_vectors = vectorizer.transform([prediction_features])
# Make the prediction!
result = regressor.predict(feature_vectors)[0]
# Return a JSON object
result_obj = {"Delay": result}
return json.dumps(result_obj)
