from sklearn.model_selection import train_test_split
from tpot import TPOTRegressor
#import data
from olist.order import Order
from olist.data import Olist
data = Olist().get_data()
training_orders = Order().get_training_data()
orders = data['olist_orders_dataset']
orders['estimate_wait_time'] = (pd.to_datetime(orders['order_estimated_delivery_date'])\
- pd.to_datetime(orders['order_purchase_timestamp'])) / np.timedelta64(24, 'h')
training_orders =\
training_orders.merge(orders[['estimate_wait_time', 'order_id']], on='order_id')
X = training_orders.drop(['order_id', 'wait_time', 'delay_vs_expected'],
axis=1)
y = training_orders['wait_time']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_boston_pipeline.py')
'product_id'],
as_index=False)\
.agg({'order_item_id': 'count'})\
.merge(products.drop(['product_category_name'],
axis=1),
on='product_id')
order_items_products = order_items_products\
.groupby('order_id',
as_index=False)\
.agg({'product_weight_g': 'sum',
'product_length_cm': 'max',
'product_height_cm': 'max',
'product_width_cm': 'max'})
orders_2 = orders.merge(order_items_products, on='order_id',
how='left').dropna()
# Create train and target variable
X_2 = orders_2.drop(
['order_id', 'expected_wait_time', 'delay_vs_expected', 'wait_time'],
axis=1)
y_2 = orders_2['wait_time']
# Training test split
X_2_train, X_2_test, y_2_train, y_2_test = train_test_split(X_2,
y_2,
test_size=0.30,
random_state=42)
# Average CV score on the training set was:-62.99467662836531
exported_pipeline = RandomForestRegressor(bootstrap=True,