def main():
X_train = pd.read_csv("../Datos/Train_TP2_Datos_2020-2C.csv")
X_test = pd.read_csv("../Datos/Test_TP2_Datos_2020-2C.csv")
X_train = X_train.loc[(X_train["Stage"] == "Closed Won")|(X_train["Stage"] == "Closed Lost"),:]
X_train["Stage"] = X_train['Stage'].apply(lambda x: 1 if x == 'Closed Won' else 0)
pipe = MyPipeline(X_train, X_test)
preprocess(pipe, X_train)
set_xgb_model(pipe, xgb_params)
pipe.preprocess()
pipe.train_xgb(verbose=True)
#pipe.predict()
pipe.score_xgb(verbose=True)
pipe.output()
#pipe.submit()
print("TODO OK")
def main():
X_train = pd.read_csv("../Datos/Train_TP2_Datos_2020-2C.csv")
X_test = pd.read_csv("../Datos/Test_TP2_Datos_2020-2C.csv")
X_train = X_train.loc[(X_train["Stage"] == "Closed Won") |
(X_train["Stage"] == "Closed Lost"), :]
X_train["Stage"] = X_train['Stage'].apply(lambda x: 1
if x == 'Closed Won' else 0)
pipe = MyPipeline(X_train, X_test)
preprocess(pipe, X_train)
pipe.set_model(
RandomForestClassifier(max_depth=21,
max_features=13,
n_estimators=31,
random_state=123))
pipe.set_folds(10)
pipe.preprocess()
pipe.train()
pipe.predict()
#pipe.score(verbose=True)
#pipe.output()
#pipe.submit()
print("TODO OK")
def main():
X_train = pd.read_csv("../Datos/Train_TP2_Datos_2020-2C.csv")
X_test = pd.read_csv("../Datos/Test_TP2_Datos_2020-2C.csv")
X_train = X_train.loc[(X_train["Stage"] == "Closed Won") |
(X_train["Stage"] == "Closed Lost"), :]
X_train["Stage"] = X_train['Stage'].apply(lambda x: 1
if x == 'Closed Won' else 0)
X = X_train.loc[X_train["TRF"] == 0, :]
X = X.drop_duplicates(subset="Opportunity_ID")
print(X["Stage"].value_counts())
pipe = MyPipeline(X_train, X_test)
preprocess(pipe, X_train)
set_xgb_model(pipe, xgb_params)
pipe.set_time_folds(10)
pipe.preprocess()
pipe.train_xgb(verbose=True)
#pipe.predict()
pipe.score_xgb(verbose=True)
pipe.output()
#pipe.submit()
print("TODO OK")
