def ez_predict():
try:
global auth_token
global model_id
global prediction_dataset_id
options = {}
response = eazyml.ez_predict(auth_token, model_id, filepath_test,
options)
prediction_dataset_id = response['prediction_dataset_id']
except Exception as e:
print('The function ez_predict was not executed properly', e)
def predict(token, model_id, model_name, predict_file):
options = {"model_name": model_name}
response = ez.ez_predict(token, model_id, predict_file, options)
if response and response["status_code"] != 200:
print("Could not predict", response["message"])
return None
prediction_id = response["prediction_dataset_id"]
#options = {
#
# "record_number": list(range(1, len(response['predictions']['indices'])+1))
#
# }
#response_explain = ez.ez_explain(token, model_id, prediction_id, options)
return response
def ez_predict():
'''
This function calls the API which allows you to predict the outcome variable
for each record in the given prediction (or test) dataset
'''
try:
global auth_token
global model_id
global prediction_dataset_id
#The options dictionary that is to be provided as part of the request payload.
#Please check the API guide for more parameters
options = {}
#Calling the eazyml library function for prediction
response = eazyml.ez_predict(auth_token, model_id, filepath_test, options)
prediction_dataset_id = response['prediction_dataset_id']
print("Output of ez_predict function is", response)
except Exception as e:
print('The function ez_predict was not executed properly', e)
def ez_predict():
'''
This function calls the API which allows you to predict the outcome variable
for each record in the given prediction (or test) dataset
'''
try:
##global auth_token
##global model_id
#WHAT IS THIS ID??????####
global prediction_dataset_id
#The options dictionary that is to be provided as part of the request payload.
#Please check the API guide for more parameters
options = {}
#Calling the eazyml library function for prediction
#put exact values here
response = eazyml.ez_predict(auth_token, model_id = 12411, filepath_test="C:\Users\csankar1\Downloads\eazyml-client-mastercsankar1\Downloads\eazyml-client-master\book1.csv", options)
##prediction_dataset_id = response['predict_dataset_id']
print(response)
except Exception as e:
print('The function ez_predict was not executed properly', e)
ez_model_config = {
"model_type": "predictive",
"derive_text": "no",
"derive_numeric": "no",
"accelerate": "yes"
}
#loading the training data
resp = eazyml.ez_load(auth_token, train_file_path, options)
dataset_id = resp["dataset_id"]
#building the model
resp = eazyml.ez_init_model(auth_token, dataset_id, ez_model_config)
model_id = resp["model_id"]
model_name = resp["model_performance"]["data"][0][0]
options = {"model_name": model_name}
#getting final response with answers and displaying to user
response = eazyml.ez_predict(auth_token, model_id, 'prediction.csv', options)
for row in response['predictions']['data']:
answer = row[-1]
if answer == 'TRUE':
print(
"There is a likely chance that a wildfire here would become a major incident. "
)
if answer == 'FALSE':
print(
"There is an unlikely chance that a wildfire here would become a major incident. "
)
def main_function(county_name):
big_df = pd.read_csv("webserver\\big_data.csv")
dates = big_df["Date"]
county_column = big_df[county_name]
average = sum(list(big_df[county_name])) / 310
result = pd.concat([dates, county_column], axis=1)
result.to_csv("webserver\\dataset.csv", index=False)
#authentication to use api
username = '******'
password = '******'
train_file_path = 'webserver\\dataset.csv'
resp = eazyml.ez_auth(username, None, password)
auth_token = resp["token"]
options = {
"id": "null",
"impute": "yes",
"outlier": "yes",
"discard": "null",
"accelerate": "yes",
"shuffle": "no",
"outcome": county_name
}
ez_model_config = {
"model_type": "timeseries",
"derive_text": "no",
"derive_numeric": "no",
"accelerate": "yes",
"date_time_column": "Date"
}
#loading the training data
resp = eazyml.ez_load(auth_token, train_file_path, options)
print(resp)
dataset_id = resp["dataset_id"]
#building the model
resp = eazyml.ez_init_model(auth_token, dataset_id, ez_model_config)
resp = eazyml.ez_get_models(auth_token)
print(resp)
model_id = resp["dataframe"]["data"][0][4]
prediction_df = pd.DataFrame({
'Date': ["6/30/2020", "12/31/2020"],
'Monmouth': ["", ""]
})
prediction_df.to_csv("webserver\\prediction.csv", index=False)
#getting final response with answers and displaying to user
response = eazyml.ez_predict(auth_token, model_id,
'webserver\\prediction.csv')
half_year = float(response["predictions"]["data"][0][2])
if half_year > average:
half_year_statement = True
else:
half_year_statement = False
full_year = float(response["predictions"]["data"][1][2])
if full_year > average:
full_year_statement = True
else:
full_year_statement = False
return half_year, half_year_statement, full_year, full_year_statement