import mlflow
from cls.rfr_model import RFRModel
from cls.utils import Utils
if __name__ == "__main__":
# Use sqlite:///mlruns.db as the local store for tracking and registery
mlflow.set_tracking_uri("sqlite:///mlruns.db")
# load and print dataset
csv_path = "data/windfarm_data.csv"
wind_farm_data = Utils.load_data(csv_path, index_col=0)
Utils.print_pandas_dataset(wind_farm_data)
# Get Validation data
X_train, y_train = Utils.get_training_data(wind_farm_data)
val_x, val_y = Utils.get_validation_data(wind_farm_data)
# train, fit and register our model
params_list = [
{"n_estimators": 100},
{"n_estimators": 200},
{"n_estimators": 300}]
# Iterate over few different tuning parameters
model_name = "SKLearnWeatherForestModel"
for params in params_list:
rfr = RFRModel.new_instance(params)
print("Using paramerts={}".format(params))
runID = rfr.mlflow_run(X_train, y_train, val_x, val_y, model_name)
print("MLflow run_id={} completed with MSE={} and RMSE={}".format(runID, rfr.mse, rfr.rsme))
Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.
Lieberman-Cribbin, W., C. Draxl, and A. Clifton. 2014. Guide to Using the WIND Toolkit Validation Code (Technical Report, NREL/TP-5000-62595). Golden, CO: National Renewable Energy Laboratory.
King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.
"""
if __name__ == "__main__":
# Use sqlite:///mlruns.db as the local store for tracking and registery
mlflow.set_tracking_uri("sqlite:///mlruns.db")
# Load and print dataset
csv_path = "data/windfarm_data.csv"
# Use column 0 (date) as the index
wind_farm_data = Utils.load_data(csv_path, index_col=0)
Utils.print_pandas_dataset(wind_farm_data)
# Get Validation data
X_train, y_train = Utils.get_training_data(wind_farm_data)
val_x, val_y = Utils.get_validation_data(wind_farm_data)
# Train, fit and register our model
params_list = [{
"n_estimators": 100
}, {
"n_estimators": 200
}, {
"n_estimators": 300
}]
import requests
from cls.utils import Utils
import json
(x_train, y_train), (val_x, val_y) = Utils.load_data()
data = val_x[0].reshape(1, -1)
data_json = json.dumps(data.tolist())
# print(data_json)
headers = {'Content-Type': 'application/json; format=pandas-records'}
request_uri = 'http://127.0.0.1:5000/invocations'
if __name__ == '__main__':
try:
response = requests.post(request_uri, data=data_json, headers=headers)
print(response.content)
print('done!!!')
except Exception as ex:
raise (ex)