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')