def train_model(X_train, X_test, y_train, y_test, sample_weight,
additional_trees):
# DEFINE TRAIN POOL AND VAL POOL
cat_features = ['year', 'month', 'store_type', 'activity_desc']
train_pool = catpool(data=X_train,
label=y_train,
cat_features=cat_features,
weight=sample_weight)
val_pool = catpool(data=X_test, label=y_test, cat_features=cat_features)
# DEFINE CATBOOST MODEL
model = CatBoostRegressor(
iterations=1000,
# learning_rate=0.1,
loss_function='RMSE',
one_hot_max_size=50,
# loss_function='Tweedie:variance_power=1.9',
eval_metric='RMSE',
verbose=100)
# TRAIN MODEL
model.fit(X=train_pool,
eval_set=val_pool,
use_best_model=False,
early_stopping_rounds=additional_trees)
return model
def train_model(X_train, X_test, y_train, y_test, sample_weight):
cat_features = ['year', 'month', 'store_type', 'activity_desc']
train_pool = catpool(data=X_train,
label=y_train,
cat_features=cat_features,
weight=sample_weight)
val_pool = catpool(data=X_test, label=y_test, cat_features=cat_features)
# CatBoost model
model = CatBoostRegressor(
iterations=1000,
learning_rate=0.1,
loss_function='RMSE',
one_hot_max_size=50,
# loss_function='Tweedie:variance_power=1.9',
eval_metric='RMSE',
verbose=200)
model.fit(X=train_pool,
eval_set=val_pool,
use_best_model=False,
early_stopping_rounds=50)
return model
def train_model(X_train, X_test, y_train, y_test):
train_pool = catpool(data=X_train, label=y_train)
val_pool = catpool(data=X_test, label=y_test)
# CatBoost model
model = CatBoostRegressor(
iterations=500,
# learning_rate=0.1,
loss_function='RMSE',
eval_metric='RMSE',
verbose=False
)
model.fit(
X=train_pool,
eval_set=val_pool,
early_stopping_rounds=20
)
return model
def retrain_model(X, y, trees, learning_rate, sample_weight):
# DEFINE TRAIN POOL
cat_features = ['year','month','store_type','activity_desc']
train_pool = catpool(data=X, label=y, cat_features=cat_features, weight=sample_weight)
# DEFINE CATBOOST MODEL
#* PARAMETERS DEPEND ON PRIOR MODEL
model = CatBoostRegressor(
iterations=trees,
learning_rate=learning_rate,
loss_function='RMSE',
one_hot_max_size=50,
# loss_function='Tweedie:variance_power=1.5',
eval_metric='RMSE',
verbose=200
)
# FIT CATBOOST MODEL
model.fit(X=train_pool)
return model
def main(item, threshold):
# REDIRECT STDOUT AND STDERR TO LOG FILE
with open(f"{log_path}{item}.log", "w") as train_log, contextlib.redirect_stdout(train_log), contextlib.redirect_stderr(train_log):
try:
print(f"ITEM {item} STARTING AT {now()}, PROCESS: {item_list.index(item)+1} OUT OF {len(item_list)}")
print(f"Loading data from {data_path}")
raw_train_val_df, raw_test_df = load_data(f"{data_path}{item}.csv.gz",
train_start=train_val_start, val_end=train_val_end, test_start=test_start, test_end=test_end)
# print TIME RANGE
print(f"train_val_df: {raw_train_val_df.day_dt.min()} to {raw_train_val_df.day_dt.max()}")
print(f"test_df: {raw_test_df.day_dt.min()} to {raw_test_df.day_dt.max()}")
# IF EMPTY, CONTINUE WITH NEXT ITEM
if len(raw_train_val_df[raw_train_val_df.day_dt < v1_start]) == 0:
print(f"EMTPY TRAIN AND VAL DATA >>>")
return None
if len(raw_test_df) == 0:
print(f"EMPTY TEST DATA >>>")
return None
# PRINT OFFER DESC FROM TRAIN, VAL AND TEST
try:
print("\nOFFERS IN TRAIN AND VAL DATA:")
for offer in raw_train_val_df.offer_code.dropna().unique():
tmp = prom_info[(prom_info.offer_code == int(offer)) & (prom_info.item == int(item))][['item_desc','offer_start_date','offer_end_date','promotion_merch','buyer_note','retail_price', 'prom_price','unit_price']]
print(tmp.to_markdown(showindex=False))
print(prom_info[prom_info.offer_code == int(offer)].item_desc)
print("\nOFFERS IN TEST DATA:")
for offer in raw_test_df.offer_code.dropna().unique():
tmp = prom_info[(prom_info.offer_code == int(offer)) & (prom_info.item == int(item))][['item_desc','offer_start_date','offer_end_date','promotion_merch','buyer_note','retail_price', 'prom_price','unit_price']]
print(tmp.to_markdown(showindex=False))
print(prom_info[prom_info.offer_code == int(offer)].item_desc)
except Exception as e:
print(f"Error when printing offer desc: {e}")
# MAKE NEW COPIES OF DATAFRAMES
train_val_df, test_df = raw_train_val_df.copy(), raw_test_df.copy()
del raw_train_val_df, raw_test_df
# ADD NUM ITEMS TO TRAIN AND TEST
num_items_dict = prom_info.groupby(['offer_code']).item.count().to_frame().reset_index().rename({'item':'num_items_in_offer'},axis=1)
train_val_df.offer_code.fillna('0',inplace=True)
train_val_df.offer_code = train_val_df.offer_code.astype(int)
train_val_df = train_val_df.merge(num_items_dict, how='left', on='offer_code')
train_val_df.num_items_in_offer.fillna(100,inplace=True)
test_df.offer_code.fillna('0',inplace=True)
test_df.offer_code = test_df.offer_code.astype(int)
test_df = test_df.merge(num_items_dict, how='left', on='offer_code')
test_df.num_items_in_offer.fillna(100,inplace=True)
# ADD LUNAR NEW YEAR FEATURE
train_val_df = train_val_df.merge(lunar, how='left', on='day_dt')
test_df = test_df.merge(lunar, how='left', on='day_dt')
# ADD PROMOTION SCHEDULE FEATURE
prom_schedule = get_prom_schedule(item=item, prom_info=prom_info) #* GET PROMOTION SCHEDULE FOR CURRENT ITEM
train_val_df = train_val_df.merge(prom_schedule, how='left', on='day_dt')
test_df = test_df.merge(prom_schedule, how='left', on='day_dt')
train_val_df.day_of_prom.fillna(-1,inplace=True)
test_df.day_of_prom.fillna(-1,inplace=True)
# DEFINE FEATURES TO SELECT
index_features = ['day_dt','store_id','item_code','loc_wh']
X_features = ['year','month','day_of_prom','weekday','day_type','is_5th','store_type','activity_desc',
'prom0','prom1','prom2','prom3','prom4','prom5','prom6','prom7', 'is_vip','free_gift',
'required_num','unit_price','p_rate','dis_spring','num_items_in_offer','loc_selling_area','pds_grace',
]
y_feature = ['ttl_quantity']
print(f"\nSELECTED FEATURES: {X_features}")
print(f"Cleaning train data...")
train_val_df = clean_data(train_val_df)
train_val_df = impute_data(train_val_df)
train_val_df = select_features(index_features+X_features+y_feature, train_val_df)
train_val_df = label_encoder(train_val_df,X_features)
print(f"Cleaning test data...")
test_df = clean_data(test_df)
test_df = impute_data(test_df)
test_df = select_features(index_features+X_features+y_feature, test_df)
test_df = label_encoder(test_df,X_features)
#* SORT train_val_df FOR WEIGHT ASSIGNMENT
train_val_df = train_val_df.sort_values('day_dt')
# SPLIT TRAIN DATA INTO TRAIN AND VALIDATION
train_start, train_end, val_start, val_end = train_val_start, v1_start, v1_start, v2_end
train_index = train_val_df[train_val_df.day_dt.between(train_start, train_end)].index
val_index = train_val_df[train_val_df.day_dt.between(val_start, val_end)].index
#* ASSIGN SAMPLE WEIGHT BY DAY_DT BEFORE VALIDATION SET
print(f"ASSIGNING SAMPLE WEIGHT FOR TRAIN DATA FROM {train_start} TO {train_end}")
date_range = pd.date_range(train_start, train_end)
weight_dict = dict(zip(date_range, np.linspace(0,1,len(date_range)) ** 4))
sample_weight_train = [weight_dict[day_dt] for day_dt in train_val_df.loc[train_index].day_dt]
# plt.figure(figsize=(12,8))
# plt.plot(train_val_df.loc[train_index].day_dt, sample_weight_train)
# plt.savefig("./test/sample_weight_train.png")
#* ASSIGN SAMPLE WEIGHT BY DAY_DT BEFORE TEST SET
print(f"ASSIGNING SAMPLE WEIGHT FOR TRAIN AND VAL FROM {train_start} TO {val_end}")
date_range = pd.date_range(train_start, val_end)
weight_dict = dict(zip(date_range, np.linspace(0,1,len(date_range)) ** 4))
sample_weight_train_val = [weight_dict[day_dt] for day_dt in train_val_df.day_dt]
# plt.figure(figsize=(12,8))
# plt.plot(train_val_df.day_dt, sample_weight_train_val)
# plt.savefig("./test/sample_weight_train_val.png")
# Define X and y
X = train_val_df[X_features]
X['year'] = X['year'].apply(lambda x: 2020 if int(x) == 2021 else x)
y = train_val_df[y_feature[0]]
X_train, X_test, y_train, y_test = X.loc[train_index], X.loc[val_index], y.loc[train_index], y.loc[val_index]
print(f"Training model...")
model = train_model(X_train, X_test, y_train, y_test, sample_weight=sample_weight_train)
# print(model.get_feature_importance(type='ShapValues', data=catpool(X_train, y_train, cat_features=['year','month','store_type','activity_desc'])))
print("FEATURE IMPORTANCE - PREDICTION VALUES CHANGE:")
print(pd.Series(index=X_train.columns,
data=model.get_feature_importance(type='PredictionValuesChange')).sort_values(ascending=False))
print("FEATURE IMPORTANCE - LOSS FUNCTION CHANGE:")
print(pd.Series(index=X_train.columns,
data=model.get_feature_importance(type='LossFunctionChange',
data=catpool(X_train, y_train, cat_features=['year','month','store_type','activity_desc']))).sort_values(ascending=False))
print(f"Evaluating model with {model.best_iteration_+50} trees and {round(model.learning_rate_,4)} learning rate...")
y_pred_val = model.predict(X_test)
y_pred_train = model.predict(X_train)
# GET EVALUATE RESULTS
train_evaluate, val_evaluate = train_val_df.loc[train_index], train_val_df.loc[val_index]
train_evaluate['y_pred'], val_evaluate['y_pred'] = y_pred_train, y_pred_val
# VALIDATION RESULTS
item_wh = agg_results(df=val_evaluate, v1_start=v1_start, v1_end=v1_end, v2_start=v2_start, v2_end=v2_end)
val_passed = np.mean([1 if r>=0.65 and r<=1.2 else 0 for r in item_wh.ratio])
print(f"ALL VALIDATION {val_start, val_end} RESULTS________________________________________________________________________")
show_results(item_wh)
print(item_wh)
if val_passed >= threshold: #* IF 75% SELL THRU BETWEEN 65% AND 120%
print(f"ITEM PASSED!!!")
if test_df.duplicated().any(): # CHECK DUPLICATES IN TEST DATA
print("DUPLICATED DATA IN TEST DATA...REMOVED")
test_df = test_df.drop_duplicates()
# ADD VALIDATION DATASET INTO TRAINING
print(f"TRAINING NEW MODEL WITH ADDED VALIDATION DATA USING {model.best_iteration_+50} TREES AND {round(model.learning_rate_,4)} LEARNING RATE...")
retrained_model = retrain_model(X=X, y=y, trees=model.best_iteration_+50, learning_rate=model.learning_rate_, sample_weight=sample_weight_train_val)
# PREDICT ON TEST DATASET
print(f"PREDICTING ON TEST DATA...")
test_X = test_df[X_features]
test_X['year'] = test_X.year.apply(lambda x: 2020 if int(x) == 2021 else x)
test_df['y_pred'] = [round(x,4) for x in retrained_model.predict(test_X)]
print(f"PREDICTION RESULTS:")
pred_results = test_df.groupby('day_dt').agg({'ttl_quantity':'sum', 'y_pred':'sum', 'unit_price':'mean'})
pred_results['ratio'] = pred_results.ttl_quantity / pred_results.y_pred
print(pred_results)
print(f"EXPORTING RESULT DATA, META DATA, IMG, LOG...")
export_results(item_code=item,
train=train_evaluate,
val1=val_evaluate[val_evaluate.day_dt.between(v1_start, v1_end)],
val2=val_evaluate[val_evaluate.day_dt.between(v2_start, v2_end)],
test=test_df,
export_result_meta_data=export_result_meta_data,
export_img=export_img)
print(f"EXPORTED RESULTS TO {img_path} AT {now()}")
print(f"ITEM TRAINING FINISHED AT {now()}")
except:
traceback.print_exc()
