if __name__ == "__main__":
from gdayf.core.controller import Controller
from gdayf.common.constants import *
from pandas import set_option
from gdayf.common.dataload import DataLoad
#Analysis
controller = Controller()
if controller.config_checks():
data_train, data_test = DataLoad().dm()
status, recomendations = controller.exec_analysis(
datapath=data_train,
objective_column='Weather_Temperature',
amode=POC,
metric='test_rmse',
deep_impact=5)
controller.reconstruct_execution_tree(arlist=None,
metric='test_rmse',
store=True)
controller.remove_models(recomendations, mode=EACH_BEST)
set_option('display.max_rows', 500)
set_option('display.max_columns', 50)
set_option('display.max_colwidth', 100)
set_option('display.precision', 4)
set_option('display.width', 1024)
#Prediction
print('Starting Prediction\'s Phase')
if __name__ == "__main__":
from gdayf.core.controller import Controller
from gdayf.common.constants import *
from pandas import set_option
from gdayf.common.dataload import DataLoad
#Analysis
controller = Controller()
if controller.config_checks():
data_train, data_test = DataLoad().footset()
status, recomendations = controller.exec_analysis(
datapath=data_train,
objective_column='HomeWin',
amode=FAST_PARANOIAC,
metric='combined_accuracy',
deep_impact=3)
controller.reconstruct_execution_tree(metric='test_accuracy',
store=True)
controller.remove_models(arlist=recomendations, mode=EACH_BEST)
set_option('display.max_rows', 500)
set_option('display.max_columns', 50)
set_option('display.max_colwidth', 100)
set_option('display.precision', 4)
set_option('display.width', 1024)
#Prediction
print('Starting Prediction\'s Phase')
print(recomendations[0]['load_path'][0]['value'])
if __name__ == "__main__":
from gdayf.workflow.workflow import Workflow
from gdayf.common.dataload import DataLoad
_, data_test = DataLoad().dm()
del _
workflow_data = list()
workflow_data.append("../json/predict_model_workflow.json")
workflow = Workflow(user_id='WF_POC')
workflow.workflow(datapath=data_test, workflow=''.join(workflow_data))
if __name__ == "__main__":
from gdayf.core.controller import Controller
from gdayf.common.constants import *
from pandas import set_option
from gdayf.common.dataload import DataLoad
# Analysis
controller = Controller()
if controller.config_checks():
data_train, data_test = DataLoad().arm()
status, recomendations = controller.exec_analysis(datapath=data_train,
objective_column='ATYPE',
amode=FAST, metric='test_accuracy', deep_impact=3)
controller.reconstruct_execution_tree(metric='test_accuracy', store=True)
controller.remove_models(arlist=recomendations, mode=EACH_BEST)
set_option('display.max_rows', 500)
set_option('display.max_columns', 50)
set_option('display.max_colwidth', 100)
set_option('display.precision', 4)
set_option('display.width', 1024)
# Prediccion
print('Starting Prediction\'s Phase')
prediction_frame = controller.exec_prediction(datapath=data_test,
model_file=recomendations[0]['json_path'][0]['value'])
if 'predict' in prediction_frame.columns.values:
print(prediction_frame[['ATYPE', 'predict']])
if __name__ == "__main__":
from gdayf.core.controller import Controller
from gdayf.common.constants import *
from pandas import set_option
from gdayf.common.dataload import DataLoad
source_data = list()
source_data.append("/Data/Data/datasheets/Anomalies/CCPP/")
source_data.append("CPP_base_ampliado.csv")
#Analysis
controller = Controller()
if controller.config_checks():
data_train, data_test = DataLoad().cpp()
status, recomendations = controller.exec_analysis(
datapath=data_train,
objective_column=None,
amode=CLUSTERING,
metric='cdistance',
deep_impact=4,
k=12,
estimate_k=False)
controller.reconstruct_execution_tree(recomendations,
metric='cdistance')
controller.remove_models(recomendations, mode=EACH_BEST)
set_option('display.max_rows', 500)
set_option('display.max_columns', 50)
set_option('display.max_colwidth', 100)
set_option('display.precision', 4)
if __name__ == "__main__":
from gdayf.core.controller import Controller
from gdayf.common.constants import *
from pandas import set_option
from gdayf.common.dataload import DataLoad
#Analysis
controller = Controller()
if controller.config_checks():
data_train, data_test = DataLoad().enb()
status, recomendations = controller.exec_analysis(
datapath=data_train,
objective_column='Y2',
amode=FAST,
metric='train_accuracy',
deep_impact=5)
controller.reconstruct_execution_tree(metric='train_accuracy',
store=True)
controller.remove_models(arlist=recomendations, mode=EACH_BEST)
set_option('display.max_rows', 500)
set_option('display.max_columns', 50)
set_option('display.max_colwidth', 100)
set_option('display.precision', 4)
set_option('display.width', 1024)
#Prediction
print('Starting Prediction\'s Phase')