def update_history_for_level3_inv(level3_inv_data: Level3InvDataLoader,
model_config: Bunch,
db_config: Bunch,
start_pred_year: int,
start_pred_month: int,
gap: int,
need_unitize: bool = True) -> None:
"""Update inventory forecast result of level3 in specified month."""
# Step 1: Prepare training and testing set
# ============================================================================================ #
last_train_year, last_train_month = infer_month(start_pred_year,
start_pred_month,
offset=-gap)
train_months = get_pre_months(last_train_year,
last_train_month,
left_bound='2018-06')
true_pred_year, true_pred_month = infer_month(start_pred_year,
start_pred_month, gap)
X_train, y_train = level3_inv_data.prepare_training_set(train_months,
gap=gap)
X_test = level3_inv_data.prepare_testing_set(start_pred_year,
start_pred_month,
gap=gap)
# Step 2: Training and predicting
# ============================================================================================ #
level3_inv_infer = LGBMSalesInfer(model_config)
level3_inv_infer.fit(X_train, y_train)
preds_test = level3_inv_infer.predict(X_test)
# Step 3: Process forecast result
# ============================================================================================ #
df_test = level3_inv_data.get_true_data(true_pred_year, true_pred_month)
df_preds_test = level3_inv_data.decorate_pred_result(
preds_test, true_pred_year, true_pred_month, use_unitize=need_unitize)
result = df_test.join(df_preds_test, how='left').reset_index()
result['bu_code'] = 'M111'
result['bu_name'] = '厨房热水器事业部'
result['comb_name'] = 'Default'
customer_info_dict = level3_inv_data.customer_info.to_dict()
result['customer_name'] = result.customer_code.map(
customer_info_dict['customer_name'])
result['sales_cen_code'] = result.customer_code.map(
customer_info_dict['sales_cen_code'])
result['sales_cen_name'] = result.customer_code.map(
customer_info_dict['sales_cen_name'])
# result['province_code'] = result.customer_code.map(customer_info['province_id'])
# result['city_code'] = result.customer_code.map(customer_info['city_id'])
# result['district_code'] = result.customer_code.map(customer_info['district_id'])
# result['channel_code'] = result.customer_code.map(customer_info['channel_name_id'])
result['province_name'] = result.customer_code.map(
customer_info_dict['province'])
result['city_name'] = result.customer_code.map(customer_info_dict['city'])
result['district_name'] = result.customer_code.map(
customer_info_dict['district'])
sku_info_dict = level3_inv_data.sku_info.to_dict()
result['item_name'] = result.item_code.map(sku_info_dict['item_name'])
result['first_cate_code'] = result.item_code.map(
sku_info_dict['first_cate_code'])
result['second_cate_code'] = result.item_code.map(
sku_info_dict['second_cate_code'])
result['first_cate_name'] = result.item_code.map(
sku_info_dict['first_cate_name'])
result['second_cate_name'] = result.item_code.map(
sku_info_dict['second_cate_name'])
result['channel_name'] = result.item_code.map(
sku_info_dict['channel_name'])
result['item_price'] = result.item_code.map(sku_info_dict['item_price'])
result['act_inv_amount'] = np.round(result.act_inv_qty * result.item_price,
decimals=4 if need_unitize else 0)
result['pred_inv_amount'] = np.round(result.pred_inv_qty *
result.item_price,
decimals=4 if need_unitize else 0)
result['inv_pred_time'] = timestamp_to_time(time.time())
add_accuracy(result, 'inv_acc', 'act_inv_qty', 'pred_inv_qty')
result['inv_weighted_acc'] = (result.act_inv_qty * result.inv_acc).astype(
np.float32)
customer_list = CustomerList()
item_list = ItemList(start_pred_year, start_pred_month)
result = result.loc[result.customer_code.apply(
lambda x: customer_list.is_white_customer(x))]
result = result.loc[result.item_code.apply(
lambda x: item_list.is_white_items(x))]
print()
print("[INFO] The average accuracy is: %.2f" %
(result.inv_acc.mean() * 100))
print("[INFO] The weighted accuracy is: %.2f" %
(result.inv_weighted_acc.sum() / result.act_inv_qty.sum() * 100))
# Step 4: Write into database (Kudu)
# ============================================================================================ #
if db_config.env == 'SIT':
level3_order_writer = KuduResultWriter(Bunch(SIT_DB_CONFIG))
elif db_config.env == 'UAT':
level3_order_writer = KuduResultWriter(Bunch(UAT_DB_CONFIG))
elif db_config.env == 'PROD':
level3_order_writer = KuduResultWriter(Bunch(PROD_DB_CONFIG))
else:
raise Exception(
"[INFO] The environment name of database to write result is illegal!!!"
)
level3_order_writer.clear_one_month(db_config.table_name, 'order_date',
true_pred_year, true_pred_month)
level3_order_writer.upsert(result, db_config.table_name,
db_config.batch_size)
def update_future_for_level3_order(model_config: Bunch,
db_config: Bunch,
start_pred_year: int,
start_pred_month: int,
periods: int = 4,
categories: Union[str, List[str]] = 'all',
need_unitize: bool = True) -> None:
"""Update order future result of level3."""
# Step 1: Read in data
# ============================================================================================ #
data_loader = Level3OrderDataLoader(start_pred_year,
start_pred_month,
categories=categories,
need_unitize=need_unitize)
# Step 2: Training and predicting
# ============================================================================================ #
year_upper_bound, month_upper_bound = infer_month(start_pred_year,
start_pred_month,
offset=-periods)
train_months = get_pre_months(year_upper_bound,
month_upper_bound,
left_bound='2016-03')
preds_test = []
for i in range(periods):
X_train, y_train = data_loader.prepare_training_set(train_months,
gap=i)
X_train, y_train = modify_training_set(X_train, y_train)
X_test = data_loader.prepare_testing_set(start_pred_year,
start_pred_month,
gap=i)
predictor = LGBMSalesInfer(model_config)
predictor.fit(X_train, y_train)
preds_test.append(predictor.predict(X_test))
# Step 3: Process forecast result
# ============================================================================================ #
df_test = data_loader.get_true_data(start_pred_year, start_pred_month)
df_pred_test = data_loader.add_index(preds_test, start_pred_year,
start_pred_month)
df_pred_test_more = data_loader.predict_by_history(start_pred_year,
start_pred_month,
gap=periods)
df_pred_test = pd.concat([df_pred_test, df_pred_test_more],
axis=1).stack().to_frame('pred_ord_qty')
df_pred_test.index.set_names(['customer_code', 'item_code', 'order_date'],
inplace=True)
df_pred_test['pred_ord_qty'] = df_pred_test.pred_ord_qty.apply(
lambda x: x if x > 0 else 0)
df_pred_test['pred_ord_qty'] = np.round(df_pred_test.pred_ord_qty,
decimals=4 if need_unitize else 0)
result = df_pred_test.join(df_test, how='left').reset_index()
result.act_ord_qty.fillna(0, inplace=True)
result['bu_code'] = 'M111'
result['bu_name'] = '厨房热水器事业部'
result['comb_name'] = 'Default'
customer_info_dict = data_loader.customer_info.to_dict()
result['customer_name'] = result.customer_code.map(
customer_info_dict['customer_name'])
result['sales_cen_code'] = result.customer_code.map(
customer_info_dict['sales_cen_code'])
result['sales_cen_name'] = result.customer_code.map(
customer_info_dict['sales_cen_name'])
# result['province_code'] = result.customer_code.map(customer_info['province_id'])
# result['city_code'] = result.customer_code.map(customer_info['city_id'])
# result['district_code'] = result.customer_code.map(customer_info['district_id'])
# result['channel_code'] = result.customer_code.map(customer_info['channel_name_id'])
result['province_name'] = result.customer_code.map(
customer_info_dict['province'])
result['city_name'] = result.customer_code.map(customer_info_dict['city'])
result['district_name'] = result.customer_code.map(
customer_info_dict['district'])
sku_info_dict = data_loader.sku_info.to_dict()
result['item_name'] = result.item_code.map(sku_info_dict['item_name'])
result['first_cate_code'] = result.item_code.map(
sku_info_dict['first_cate_code'])
result['second_cate_code'] = result.item_code.map(
sku_info_dict['second_cate_code'])
result['first_cate_name'] = result.item_code.map(
sku_info_dict['first_cate_name'])
result['second_cate_name'] = result.item_code.map(
sku_info_dict['second_cate_name'])
result['channel_name'] = result.item_code.map(
sku_info_dict['channel_name'])
result['item_price'] = result.item_code.map(sku_info_dict['item_price'])
result['act_ord_amount'] = np.round(result.act_ord_qty * result.item_price,
decimals=4)
result['pred_ord_amount'] = np.round(result.pred_ord_qty *
result.item_price,
decimals=4)
result['ord_pred_time'] = timestamp_to_time(time.time())
customer_list = CustomerList()
item_list = ItemList(start_pred_year, start_pred_month)
result = result.loc[result.customer_code.apply(
lambda x: customer_list.is_white_customer(x))]
result = result.loc[result.item_code.apply(
lambda x: item_list.is_white_items(x))]
# Step 4: Write into database (Kudu)
# ============================================================================================ #
if db_config.env == 'SIT':
writer = KuduResultWriter(Bunch(SIT_DB_CONFIG))
elif db_config.env == 'UAT':
writer = KuduResultWriter(Bunch(UAT_DB_CONFIG))
elif db_config.env == 'PROD':
writer = KuduResultWriter(Bunch(PROD_DB_CONFIG))
else:
raise Exception(
"[INFO] The environment name of database to write result is illegal!!!"
)
writer.clear_months_after(db_config.table_name, 'order_date',
start_pred_year, start_pred_month)
writer.upsert(result, db_config.table_name, db_config.batch_size)
def update_history_for_level2_order(level2_data: Level2DataLoader,
plan_data: PlanData,
model_config: Bunch,
db_config: Bunch,
start_pred_year: int,
start_pred_month: int,
gap: int,
use_unitize: bool = True) -> None:
"""Update order forecast result of level2 in specified month."""
# Step 1: Prepare training and testing set
# ============================================================================================ #
last_train_year, last_train_month = infer_month(start_pred_year,
start_pred_month,
offset=-gap)
train_months = get_pre_months(last_train_year,
last_train_month,
left_bound='2016-03')
true_pred_year, true_pred_month = infer_month(start_pred_year,
start_pred_month, gap)
X_train, y_train = level2_data.prepare_training_set(train_months, gap=gap)
X_test = level2_data.prepare_testing_set(start_pred_year,
start_pred_month,
gap=gap)
# Step 2: Training and predicting
# ============================================================================================ #
level2_order_infer = LGBMSalesInfer(model_config)
level2_order_infer.fit(X_train, y_train)
preds_test = level2_order_infer.predict(X_test)
# Step 3: Process forecast result
# ============================================================================================ #
df_test = level2_data.get_true_order_data(true_pred_year, true_pred_month)
df_pred_test = level2_data.decorate_pred_result(preds_test,
true_pred_year,
true_pred_month,
use_unitize=use_unitize)
result = df_test.join(df_pred_test, how='left').reset_index()
result['bu_code'] = 'M111'
result['bu_name'] = '厨房热水器事业部'
result['comb_name'] = 'Default'
sku_info_dict = level2_data.sku_info.to_dict()
result['item_name'] = result.item_code.map(sku_info_dict['item_name'])
result['first_cate_code'] = result.item_code.map(
sku_info_dict['first_cate_code'])
result['second_cate_code'] = result.item_code.map(
sku_info_dict['second_cate_code'])
result['first_cate_name'] = result.item_code.map(
sku_info_dict['first_cate_name'])
result['second_cate_name'] = result.item_code.map(
sku_info_dict['second_cate_name'])
result['channel_name'] = result.item_code.map(
sku_info_dict['channel_name'])
result['item_price'] = result.item_code.map(sku_info_dict['item_price'])
result['act_ord_amount'] = np.round(result.act_ord_qty * result.item_price,
decimals=4 if use_unitize else 0)
result['pred_ord_amount'] = np.round(result.pred_ord_qty *
result.item_price,
decimals=4 if use_unitize else 0)
result['ord_pred_time'] = timestamp_to_time(time.time())
add_accuracy(result, 'ord_acc', 'act_ord_qty', 'pred_ord_qty')
result['ord_weighted_acc'] = (result.act_ord_qty * result.ord_acc).astype(
np.float32)
item_list = ItemList(start_pred_year, start_pred_month)
result = result.loc[result.item_code.apply(
lambda x: item_list.is_white_items(x))]
print()
print("[INFO] The average accuracy of model is: %.2f" %
(result.ord_acc.mean() * 100))
print("[INFO] The weighted accuracy of model is: %.2f" %
(result.ord_weighted_acc.sum() / result.act_ord_qty.sum() * 100))
# Step 4: Ensemble with rule
# ============================================================================================ #
rule_res = result.copy()
order_sku_month_pre6_mean = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 6,
True).replace(0, np.nan).mean(axis=1)
order_sku_month_pre1 = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 1, True).mean(axis=1)
dis_sku_month_pre3_mean = level2_data.get_pre_dis_vals(
start_pred_year, start_pred_month, 3,
True).replace(0, np.nan).mean(axis=1)
dis_sku_month_pre1 = level2_data.get_pre_dis_vals(start_pred_year,
start_pred_month, 1,
True).mean(axis=1)
plan_sku_month_mean = plan_data.plan_sku_month_mean
rule_res['ord_sku_month_pre6_mean'] = rule_res.item_code.map(
order_sku_month_pre6_mean)
rule_res['ord_sku_month_pre1'] = rule_res.item_code.map(
order_sku_month_pre1)
rule_res['dis_sku_month_pre3_mean'] = rule_res.item_code.map(
dis_sku_month_pre3_mean)
rule_res['dis_sku_month_pre1'] = rule_res.item_code.map(dis_sku_month_pre1)
rule_res['plan_sku_month_mean'] = rule_res.item_code.map(
plan_sku_month_mean)
rule_res['is_aver_ord_na'] = (rule_res.ord_sku_month_pre6_mean.isna()) * 1
rule_res['is_aver_dis_na'] = (rule_res.dis_sku_month_pre3_mean.isna()) * 1
rule_res['is_aver_plan_na'] = (rule_res.plan_sku_month_mean.isna()) * 1
rule_res['is_ord_pre1_na'] = (rule_res.ord_sku_month_pre1.isna()) * 1
rule_res['is_dis_pre1_na'] = (rule_res.dis_sku_month_pre1.isna()) * 1
rule_res['online_offline_flag'] = rule_res.item_code.map(
sku_info_dict['sales_chan_name']).fillna('未知')
rule_res['project_flag'] = rule_res.item_code.map(
sku_info_dict['project_flag']).fillna('未知')
order_sku_month_pre24_mean = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 24,
True).replace(0, np.nan).mean(axis=1)
curr_new_items = set(order_sku_month_pre24_mean.loc[
order_sku_month_pre24_mean.isna()].index)
dis_sku_month_pre3 = level2_data.get_pre_dis_vals(start_pred_year,
start_pred_month, 3,
True)
dis_sku_month_pre3['num_not_null'] = ((dis_sku_month_pre3 > 0) *
1).sum(axis=1)
new_items_by_dis = set(
dis_sku_month_pre3.loc[(dis_sku_month_pre3.num_not_null == 1)
& (dis_sku_month_pre3.iloc[:, 2] > 0)].index)
demand = plan_data.get_one_month(true_pred_year, true_pred_month, True)
rule_res['demand'] = rule_res.item_code.map(demand)
rule_res['is_curr_new'] = rule_res.item_code.apply(
lambda x: 1 if x in curr_new_items else 0)
rule_res['is_new_by_dis'] = rule_res.item_code.apply(
lambda x: 1 if x in new_items_by_dis else 0)
rule_res[
'demand_dis_ratio'] = rule_res.demand / rule_res.dis_sku_month_pre3_mean
rule_res['pred_ord_qty_rule'] = rule_res.apply(rule_func, axis=1)
rule_res['pred_ord_qty_rule'] = rule_res.pred_ord_qty_rule.replace(
np.nan, 0)
rule_res['pred_ord_qty_rule'] = rule_res.apply(
lambda x: x.pred_ord_qty
if x.pred_ord_qty_rule == 0 else x.pred_ord_qty_rule,
axis=1)
add_accuracy(rule_res, 'rule_ord_acc', 'act_ord_qty', 'pred_ord_qty_rule')
rule_res['rule_ord_weighted_acc'] = (rule_res.act_ord_qty *
rule_res.rule_ord_acc).astype(
np.float32)
print()
print("[INFO] The average accuracy of rule is: %.2f" %
(rule_res.rule_ord_acc.mean() * 100))
print("[INFO] The weighted accuracy of rule is: %.2f" %
(rule_res.rule_ord_weighted_acc.sum() / rule_res.act_ord_qty.sum() *
100))
result[
'pred_ord_qty'] = result.pred_ord_qty * 0.5 + rule_res.pred_ord_qty_rule * 0.5
add_accuracy(result, 'ord_acc', 'act_ord_qty', 'pred_ord_qty')
result['ord_weighted_acc'] = (result.act_ord_qty * result.ord_acc).astype(
np.float32)
print()
print("[INFO] The average accuracy of ensemble is: %.2f" %
(result.ord_acc.mean() * 100))
print("[INFO] The weighted accuracy of ensemble is: %.2f" %
(result.ord_weighted_acc.sum() / result.act_ord_qty.sum() * 100))
# Step 5: Write into database (Kudu)
# ============================================================================================ #
if db_config.env == 'SIT':
level2_order_writer = KuduResultWriter(Bunch(SIT_DB_CONFIG))
elif db_config.env == 'UAT':
level2_order_writer = KuduResultWriter(Bunch(UAT_DB_CONFIG))
elif db_config.env == 'PROD':
level2_order_writer = KuduResultWriter(Bunch(PROD_DB_CONFIG))
else:
raise Exception(
"[INFO] The environment name of database to write result is illegal!!!"
)
level2_order_writer.clear_one_month(db_config.table_name, 'order_date',
true_pred_year, true_pred_month)
level2_order_writer.upsert(result, db_config.table_name,
db_config.batch_size)
def update_future_for_level2_order(model_config: Bunch,
db_config: Bunch,
start_pred_year: int,
start_pred_month: int,
periods: int = 4,
categories: Union[str, List[str]] = 'all',
need_unitize: bool = True) -> None:
"""Update order future result of level2."""
# Step 1: Read in data
# ============================================================================================ #
level2_data = Level2DataLoader(start_pred_year,
start_pred_month,
categories=categories,
need_unitize=need_unitize,
label_data='order')
plan_data = PlanData(start_pred_year,
start_pred_month,
need_unitize=need_unitize)
item_list = ItemList(start_pred_year, start_pred_month)
# Step 2: Training and predicting
# ============================================================================================ #
year_upper_bound, month_upper_bound = infer_month(start_pred_year,
start_pred_month,
offset=-periods)
train_months = get_pre_months(year_upper_bound,
month_upper_bound,
left_bound='2016-03')
preds_test = []
for i in range(periods):
X_train, y_train = level2_data.prepare_training_set(train_months,
gap=i)
X_train, y_train = modify_training_set(X_train, y_train)
X_test = level2_data.prepare_testing_set(start_pred_year,
start_pred_month,
gap=i)
predictor = LGBMSalesInfer(model_config)
predictor.fit(X_train, y_train)
preds_test.append(predictor.predict(X_test))
# Step 3: Process forecast result & write into "明细表"
# ============================================================================================ #
m1_year, m1_month = infer_month(start_pred_year, start_pred_month, 1)
result = level2_data.add_index_v2(preds_test[1:])
m1_to_m3_res = result.copy(
) # | item_code (index) | pred_ord_qty_m1 | pred_ord_qty_m2 | pred_ord_qty_m3 |
if need_unitize:
for col in result.columns:
result[col] = result[col].apply(lambda x: 0.0025 if x < 0 else x)
else:
for col in result.columns:
result[col] = result[col].apply(lambda x: 25 if x < 0 else x)
result = result.reset_index(
) # | item_code | pred_ord_qty_m1 | pred_ord_qty_m2 | pred_ord_qty_m3 |
result['bu_code'] = '30015305'
result['bu_name'] = '厨房热水器事业部'
result['comb_name'] = 'Default'
result['sales_type'] = "内销"
result['forecast_type'] = "内销整机预测"
result['order_date'] = "%d-%02d-%02d" % (
m1_year, m1_month, get_days_of_month(m1_year, m1_month))
sku_info_dict = level2_data.sku_info.to_dict()
result['item_name'] = result.item_code.map(sku_info_dict['item_name'])
result['first_cate_code'] = result.item_code.map(
sku_info_dict['first_cate_code'])
result['second_cate_code'] = result.item_code.map(
sku_info_dict['second_cate_code'])
result['first_cate_name'] = result.item_code.map(
sku_info_dict['first_cate_name'])
result['second_cate_name'] = result.item_code.map(
sku_info_dict['second_cate_name'])
result['item_price'] = result.item_code.map(sku_info_dict['item_price'])
item_list_dict = item_list.items.copy().set_index('item_code').to_dict()
result['manu_code'] = result.item_code.map(
item_list_dict['manu_code']).fillna('')
result['area_name'] = ''
rule_res = result.copy()
rule_res['pred_ord_qty'] = rule_res['pred_ord_qty_m1']
order_sku_month_pre6_mean = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 6,
True).replace(0, np.nan).mean(axis=1)
order_sku_month_pre1 = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 1, True).mean(axis=1)
dis_sku_month_pre3_mean = level2_data.get_pre_dis_vals(
start_pred_year, start_pred_month, 3,
True).replace(0, np.nan).mean(axis=1)
dis_sku_month_pre1 = level2_data.get_pre_dis_vals(start_pred_year,
start_pred_month, 1,
True).mean(axis=1)
plan_sku_month_mean = plan_data.plan_sku_month_mean
rule_res['ord_sku_month_pre6_mean'] = rule_res.item_code.map(
order_sku_month_pre6_mean)
rule_res['ord_sku_month_pre1'] = rule_res.item_code.map(
order_sku_month_pre1)
rule_res['dis_sku_month_pre3_mean'] = rule_res.item_code.map(
dis_sku_month_pre3_mean)
rule_res['dis_sku_month_pre1'] = rule_res.item_code.map(dis_sku_month_pre1)
rule_res['plan_sku_month_mean'] = rule_res.item_code.map(
plan_sku_month_mean)
rule_res['is_aver_ord_na'] = (rule_res.ord_sku_month_pre6_mean.isna()) * 1
rule_res['is_aver_dis_na'] = (rule_res.dis_sku_month_pre3_mean.isna()) * 1
rule_res['is_aver_plan_na'] = (rule_res.plan_sku_month_mean.isna()) * 1
rule_res['is_ord_pre1_na'] = (rule_res.ord_sku_month_pre1.isna()) * 1
rule_res['is_dis_pre1_na'] = (rule_res.dis_sku_month_pre1.isna()) * 1
rule_res['online_offline_flag'] = rule_res.item_code.map(
sku_info_dict['sales_chan_name']).fillna('未知')
rule_res['project_flag'] = rule_res.item_code.map(
sku_info_dict['project_flag']).fillna('未知')
order_sku_month_pre24_mean = level2_data.get_pre_order_vals(
start_pred_year, start_pred_month, 24,
True).replace(0, np.nan).mean(axis=1)
curr_new_items = set(order_sku_month_pre24_mean.loc[
order_sku_month_pre24_mean.isna()].index)
dis_sku_month_pre3 = level2_data.get_pre_dis_vals(start_pred_year,
start_pred_month, 3,
True)
dis_sku_month_pre3['num_not_null'] = ((dis_sku_month_pre3 > 0) *
1).sum(axis=1)
new_items_by_dis = set(
dis_sku_month_pre3.loc[(dis_sku_month_pre3.num_not_null == 1)
& (dis_sku_month_pre3.iloc[:, 2] > 0)].index)
demand = plan_data.get_one_month(m1_year, m1_month, True)
rule_res['demand'] = rule_res.item_code.map(demand)
rule_res['is_curr_new'] = rule_res.item_code.apply(
lambda x: 1 if x in curr_new_items else 0)
rule_res['is_new_by_dis'] = rule_res.item_code.apply(
lambda x: 1 if x in new_items_by_dis else 0)
rule_res[
'demand_dis_ratio'] = rule_res.demand / rule_res.dis_sku_month_pre3_mean
rule_res['pred_ord_qty_rule'] = rule_res.apply(rule_func, axis=1)
rule_res['pred_ord_qty_rule'] = rule_res.pred_ord_qty_rule.replace(
np.nan, 0)
rule_res['pred_ord_qty_rule'] = rule_res.apply(
lambda x: x.pred_ord_qty
if x.pred_ord_qty_rule == 0 else x.pred_ord_qty_rule,
axis=1)
result[
'pred_ord_qty_m1'] = result.pred_ord_qty_m1 * 0.5 + rule_res.pred_ord_qty_rule * 0.5
result['avg_dis'] = rule_res['dis_sku_month_pre3_mean'].fillna(0.0)
result['pred_ord_amount_m1'] = np.round(result.pred_ord_qty_m1 *
result.item_price,
decimals=4 if need_unitize else 0)
result['pred_ord_amount_m2'] = np.round(result.pred_ord_qty_m2 *
result.item_price,
decimals=4 if need_unitize else 0)
result['pred_ord_amount_m3'] = np.round(result.pred_ord_qty_m3 *
result.item_price,
decimals=4 if need_unitize else 0)
result['ord_pred_time'] = timestamp_to_time(time.time())
if need_unitize:
result['avg_dis'] = np.round(result.avg_dis * 10000)
result['pred_ord_qty_m1'] = np.round(result.pred_ord_qty_m1 * 10000)
result['pred_ord_qty_m2'] = np.round(result.pred_ord_qty_m2 * 10000)
result['pred_ord_qty_m3'] = np.round(result.pred_ord_qty_m3 * 10000)
result['pred_ord_amount_m1'] = np.round(result.pred_ord_amount_m1 *
10000)
result['pred_ord_amount_m2'] = np.round(result.pred_ord_amount_m2 *
10000)
result['pred_ord_amount_m3'] = np.round(result.pred_ord_amount_m3 *
10000)
result = result.loc[~result.item_code.
apply(lambda x: item_list.is_delisting_items(x))]
result = result.loc[~(result.manu_code == '')]
if db_config.env == 'SIT':
kudu_config = SIT_DB_CONFIG
esb_url = UAT_ESB_URL
elif db_config.env == 'UAT':
kudu_config = UAT_DB_CONFIG
esb_url = UAT_ESB_URL
elif db_config.env == 'PROD':
kudu_config = PROD_DB_CONFIG
esb_url = PROD_ESB_URL
else:
raise Exception(
"[INFO] The environment name of database to write result is illegal!!!"
)
writer = KuduResultWriter(Bunch(kudu_config))
writer.clear_one_month(db_config.table2_name, 'order_date', m1_year,
m1_month)
writer.upsert(result, db_config.table2_name, db_config.batch_size)
# Step 4: Push to ESB
# ============================================================================================ #
result['customer_code'] = ''
result['attribute1'] = ''
result['attribute2'] = ''
result['attribute3'] = ''
result['attribute4'] = ''
result['attribute5'] = ''
result.rename(columns={'manu_code': 'manu_name'}, inplace=True)
result = result[[
'bu_code', 'sales_type', 'manu_name', 'area_name', 'customer_code',
'order_date', 'first_cate_name', 'second_cate_name', 'item_code',
'forecast_type', 'avg_dis', 'item_price', 'pred_ord_qty_m1',
'pred_ord_qty_m2', 'pred_ord_qty_m3', 'attribute1', 'attribute2',
'attribute3', 'attribute4', 'attribute5'
]]
push_to_esb(result, esb_url)
del result
gc.collect()
# Step 5: Process forecast result & write into "水晶球"
# ============================================================================================ #
df_test = level2_data.get_true_order_data(start_pred_year,
start_pred_month) # 847
df_preds_test = level2_data.add_index(preds_test, start_pred_year,
start_pred_month)
df_preds_test['%d%02d' %
(m1_year, m1_month)] = m1_to_m3_res['pred_ord_qty_m1']
df_pred_test_more = level2_data.predict_by_history(start_pred_year,
start_pred_month,
gap=periods)
df_preds_test = pd.concat([df_preds_test, df_pred_test_more],
axis=1).stack().to_frame('pred_ord_qty')
df_preds_test.index.set_names(['item_code', 'order_date'], inplace=True)
if need_unitize:
df_preds_test['pred_ord_qty'] = df_preds_test.pred_ord_qty.apply(
lambda x: x if x > 0 else 0.0025)
else:
df_preds_test['pred_ord_qty'] = df_preds_test.pred_ord_qty.apply(
lambda x: x if x > 0 else 25)
df_preds_test['pred_ord_qty'] = np.round(df_preds_test.pred_ord_qty,
decimals=4 if need_unitize else 0)
result = df_preds_test.join(df_test, how='left').reset_index()
result.act_ord_qty.fillna(0, inplace=True)
result['bu_code'] = 'M111'
result['bu_name'] = '厨房热水器事业部'
result['comb_name'] = 'Default'
sku_info_dict = level2_data.sku_info.to_dict()
result['item_name'] = result.item_code.map(sku_info_dict['item_name'])
result['first_cate_code'] = result.item_code.map(
sku_info_dict['first_cate_code'])
result['second_cate_code'] = result.item_code.map(
sku_info_dict['second_cate_code'])
result['first_cate_name'] = result.item_code.map(
sku_info_dict['first_cate_name'])
result['second_cate_name'] = result.item_code.map(
sku_info_dict['second_cate_name'])
result['channel_name'] = result.item_code.map(
sku_info_dict['channel_name'])
result['item_price'] = result.item_code.map(sku_info_dict['item_price'])
# m1_res = result.loc[result.order_date == "%d%02d" % (m1_year, m1_month)]
# other_res = result.loc[~(result.order_date == "%d%02d" % (m1_year, m1_month))]
#
# rule_res = m1_res.copy()
# order_sku_month_pre6_mean = level2_data.get_pre_order_vals(
# start_pred_year, start_pred_month, 6, True).replace(0, np.nan).mean(axis=1)
# order_sku_month_pre1 = level2_data.get_pre_order_vals(
# start_pred_year, start_pred_month, 1, True).mean(axis=1)
# dis_sku_month_pre3_mean = level2_data.get_pre_dis_vals(
# start_pred_year, start_pred_month, 3, True).replace(0, np.nan).mean(axis=1)
# dis_sku_month_pre1 = level2_data.get_pre_dis_vals(
# start_pred_year, start_pred_month, 1, True).mean(axis=1)
# plan_sku_month_mean = plan_data.plan_sku_month_mean
#
# rule_res['ord_sku_month_pre6_mean'] = rule_res.item_code.map(order_sku_month_pre6_mean)
# rule_res['ord_sku_month_pre1'] = rule_res.item_code.map(order_sku_month_pre1)
# rule_res['dis_sku_month_pre3_mean'] = rule_res.item_code.map(dis_sku_month_pre3_mean)
# rule_res['dis_sku_month_pre1'] = rule_res.item_code.map(dis_sku_month_pre1)
# rule_res['plan_sku_month_mean'] = rule_res.item_code.map(plan_sku_month_mean)
#
# rule_res['is_aver_ord_na'] = (rule_res.ord_sku_month_pre6_mean.isna()) * 1
# rule_res['is_aver_dis_na'] = (rule_res.dis_sku_month_pre3_mean.isna()) * 1
# rule_res['is_aver_plan_na'] = (rule_res.plan_sku_month_mean.isna()) * 1
# rule_res['is_ord_pre1_na'] = (rule_res.ord_sku_month_pre1.isna()) * 1
# rule_res['is_dis_pre1_na'] = (rule_res.dis_sku_month_pre1.isna()) * 1
#
# rule_res['online_offline_flag'] = rule_res.item_code.map(sku_info_dict['sales_chan_name']).fillna('未知')
# rule_res['project_flag'] = rule_res.item_code.map(sku_info_dict['project_flag']).fillna('未知')
#
# order_sku_month_pre24_mean = level2_data.get_pre_order_vals(
# start_pred_year, start_pred_month, 24, True).replace(0, np.nan).mean(axis=1)
# curr_new_items = set(order_sku_month_pre24_mean.loc[order_sku_month_pre24_mean.isna()].index)
#
# dis_sku_month_pre3 = level2_data.get_pre_dis_vals(start_pred_year, start_pred_month, 3, True)
# dis_sku_month_pre3['num_not_null'] = ((dis_sku_month_pre3 > 0) * 1).sum(axis=1)
# new_items_by_dis = set(
# dis_sku_month_pre3.loc[(dis_sku_month_pre3.num_not_null == 1) & (dis_sku_month_pre3.iloc[:, 2] > 0)].index)
#
# demand = plan_data.get_one_month(m1_year, m1_month, True)
# rule_res['demand'] = rule_res.item_code.map(demand)
# rule_res['is_curr_new'] = rule_res.item_code.apply(lambda x: 1 if x in curr_new_items else 0)
# rule_res['is_new_by_dis'] = rule_res.item_code.apply(lambda x: 1 if x in new_items_by_dis else 0)
# rule_res['demand_dis_ratio'] = rule_res.demand / rule_res.dis_sku_month_pre3_mean
#
# rule_res['pred_ord_qty_rule'] = rule_res.apply(rule_func, axis=1)
# rule_res['pred_ord_qty_rule'] = rule_res.pred_ord_qty_rule.replace(np.nan, 0)
# rule_res['pred_ord_qty_rule'] = rule_res.apply(
# lambda x: x.pred_ord_qty if x.pred_ord_qty_rule == 0 else x.pred_ord_qty_rule,
# axis=1
# )
#
# m1_res['pred_ord_qty'] = m1_res.pred_ord_qty * 0.5 + rule_res.pred_ord_qty_rule * 0.5
# result = pd.concat([m1_res, other_res], axis=0)
result['act_ord_amount'] = np.round(result.act_ord_qty * result.item_price,
decimals=4 if need_unitize else 0)
result['pred_ord_amount'] = np.round(result.pred_ord_qty *
result.item_price,
decimals=4 if need_unitize else 0)
result['ord_pred_time'] = timestamp_to_time(time.time())
result = result.loc[result.item_code.apply(
lambda x: item_list.is_white_items(x))]
writer = KuduResultWriter(Bunch(kudu_config))
writer.clear_months_after(db_config.table1_name, 'order_date',
start_pred_year, start_pred_month)
writer.upsert(result, db_config.table1_name, db_config.batch_size)