def df_ny_box():
df = get_data(nrows=10000)
df = clean_df(df)
#setting boundries
df = df[df["pickup_latitude"].between(left=40, right=42)]
df = df[df["pickup_longitude"].between(left=-74.3, right=-72.9)]
df = df[df["dropoff_latitude"].between(left=40, right=42)]
df = df[df["dropoff_longitude"].between(left=-74, right=-72.9)]
return df
def load_data(params):
print("############ Loading Data ############")
df = get_data(**params)
df = clean_df(df)
y = df["fare_amount"]
X = df.drop("fare_amount", axis=1)
print("shape: {}".format(X.shape))
print("size: {} Mb".format(X.memory_usage().sum() / 1e6))
return X, y
if __name__ == "__main__":
warnings.simplefilter(action='ignore', category=FutureWarning)
# Get and clean data
experiment = "taxifare_test_jean"
params = dict(
nrows=100000,
upload=True,
local=False, # set to False to get data from GCP (Storage or BigQuery)
gridsearch=False,
optimize=False,
estimator="xgboost",
mlflow=True, # set to True to log params to mlflow
experiment_name=experiment)
print("############ Loading Data ############")
df = get_data(**params)
df = clean_df(df)
y_train = df["fare_amount"]
X_train = df.drop("fare_amount", axis=1)
del df
print("shape: {}".format(X_train.shape))
print("size: {} Mb".format(X_train.memory_usage().sum() / 1e6))
# Train and save model, locally and
t = Trainer(X=X_train, y=y_train, **params)
del X_train, y_train
print(colored("############ Training model ############", "red"))
t.train()
print(colored("############ Evaluating model ############", "blue"))
t.evaluate()
print(colored("############ Saving model ############", "green"))
t.save_model()
