def ez_load():
'''
This function calls the API which allows the user to upload training data in a file.
The accepted file formats are .csv and .xlsx.
'''
try:
global auth_token
global 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 = {"accelerate":"no"}
#Calling the eazyml library function for loading data
response = eazyml.ez_load(auth_token, filepath_train, options)
dataset_id = response['dataset_id']
print("Output of ez_load function is", response)
except Exception as e:
print('The function ez_load was not executed properly', e)
def train_data(token, file_path):
options = {
"id":
"null",
"impute":
"yes",
"outlier":
"yes",
"discard": [
"started", "extinguished", "counties", "latitude", "longitude",
"acresBurned", "visibility", "uvIndex"
],
"accelerate":
"yes",
"outcome":
"wildfireIntensity"
}
response = ez.ez_load(token, file_path, options)
if response and response["status_code"] != 200:
print("Dataset could not uploaded", response["message"])
return None, None
dataset_id = response["dataset_id"]
#Now building the model
options = {
"model_type": "predictive",
"derive_text": "no",
"derive_numeric": "no",
"accelerate": "yes"
}
response = ez.ez_init_model(token, dataset_id, options)
if response and response["status_code"] != 200:
print("Model could not build", response["message"])
return None, None
model_id = response["model_id"]
best_model = response["model_performance"]["data"][0][0]
return model_id, best_model
"impute": "yes",
"outlier": "yes",
"discard": "null",
"accelerate": "yes",
"outcome": "Major Incident"
}
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(
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