Kaggle competition: M5 Forecasting - Accuracy
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CAL_DTYPES={"event_name_1": "category", "event_name_2": "category", "event_type_1": "category",
"event_type_2": "category", "weekday": "category", 'wm_yr_wk': 'int16', "wday": "int16",
"month": "int16", "year": "int16", "snap_CA": "float32", 'snap_TX': 'float32', 'snap_WI': 'float32' }
PRICE_DTYPES = {"store_id": "category", "item_id": "category", "wm_yr_wk": "int16","sell_price":"float32" }
pd.options.display.max_columns = 50
h = 28
max_lags = 70
tr_last = 1913
fday = datetime(2016,4, 25)
 fday
def create_dt(is_train = True, nrows = None, first_day = 1200):
prices = pd.read_csv(".. /input/m5-forecasting-accuracy/sell_prices.csv", dtype = PRICE_DTYPES)
 for col, col_dtype in PRICE_DTYPES.items() :
if col_dtype == "category":
prices[col] = prices[col].cat.codes.astype("int16")
 prices[col] -= prices[col].min()
cal = pd.read_csv(".. /input/m5-forecasting-accuracy/calendar.csv", dtype = CAL_DTYPES)
 cal["date"] = pd.to_datetime(cal["date"])
 for col, col_dtype in CAL_DTYPES.items() :
if col_dtype == "category":
cal[col] = cal[col].cat.codes.astype("int16")
 cal[col] -= cal[col].min()
start_day = max(1 if is_train else tr_last-max_lags, first_day)
 numcols = [f"d_{day}" for day in range(start_day,tr_last+1)]
catcols = ['id', 'item_id', 'dept_id','store_id', 'cat_id', 'state_id']
dtype = {numcol:"float32" for numcol in numcols}
dtype.update({col: "category" for col in catcols if col != "id"})
 dt = pd.read_csv(".. /input/m5-forecasting-accuracy/sales_train_validation.csv",
nrows = nrows, usecols = catcols + numcols, dtype = dtype)
 for col in catcols:
if col != "id":
dt[col] = dt[col].cat.codes.astype("int16")
 dt[col] -= dt[col].min()
if not is_train:
for day in range(tr_last+1, tr_last+ 28 +1):
dt[f"d_{day}"] = np.nan
dt = pd.melt(dt,
id_vars = catcols,
value_vars = [col for col in dt.columns if col.startswith("d_")],
var_name = "d",
value_name = "sales")
 dt = dt.merge(cal, on= "d", copy = False)
 dt = dt.merge(prices, on = ["store_id", "item_id", "wm_yr_wk"], copy = False)
 return dt
def create_fea(dt):
lags = [7, 28]
lag_cols = [f"lag_{lag}" for lag in lags ]
for lag, lag_col in zip(lags, lag_cols):
dt[lag_col] = dt[["id","sales"]].groupby("id")["sales"].shift(lag)
 wins = [7, 28]
for win in wins :
for lag,lag_col in zip(lags, lag_cols):
dt[f"rmean_{lag}_{win}"] = dt[["id", lag_col]].groupby("id")[lag_col].transform(lambda x : x.rolling(win ).mean())
 date_features = {
"wday": "weekday",
"week": "weekofyear",
"month": "month",
"mday": "day",
}
for date_feat_name, date_feat_func in date_features.items() :
if date_feat_name in dt.columns:
dt[date_feat_name] = dt[date_feat_name].astype("int16")
 else:
dt[date_feat_name] = getattr(dt["date"].dt, date_feat_func ).astype("int16")
 FIRST_DAY = 700
df = create_dt(is_train=True, first_day= FIRST_DAY)
 df.shape
df.head()
df.info()
create_fea(df)
 df.shape
df.info()
df.head()
df.dropna(inplace = True)
 df.shape
cat_feats = ['item_id', 'dept_id','store_id', 'cat_id', 'state_id'] + ["event_name_1", "event_name_2", "event_type_1", "event_type_2"]
useless_cols = ["id", "date", "sales","d", "wm_yr_wk", "weekday"]
train_cols = df.columns[~df.columns.isin(useless_cols)]
X_train = df[train_cols]
y_train = df["sales"]
np.random.seed(777)
 fake_valid_inds = np.random.choice(X_train.index.values, 2_000_000, replace = False)
 train_inds = np.setdiff1d(X_train.index.values, fake_valid_inds)
 train_data = lgb.Dataset(X_train.loc[train_inds] , label = y_train.loc[train_inds],
categorical_feature=cat_feats, free_raw_data=False)
 fake_valid_data = lgb.Dataset(X_train.loc[fake_valid_inds], label = y_train.loc[fake_valid_inds],
categorical_feature=cat_feats,
free_raw_data=False)
 del df, X_train, y_train, fake_valid_inds,train_inds ; gc.collect()
params = {
"objective" : "poisson",
"metric" :"rmse",
"force_row_wise" : True,
"learning_rate" : 0.1,
"sub_row" : 0.8,
"bagging_freq" : 1,
"lambda_l2" : 0.09,
"metric": ["rmse"],
'verbosity': 1,
'num_iterations' : 400,
'num_leaves': 40,
"min_data_in_leaf": 50,
}
m_lgb = lgb.train(params, train_data, valid_sets = [fake_valid_data], verbose_eval=100)
 plt.rcParams['figure.figsize'] =(18.0, 4)
 %matplotlib inline
fig, ax = plt.subplots(figsize=(12,8))
lgb.plot_importance(m_lgb, max_num_features=50, height=0.8, ax=ax)
 ax.grid(False)
 plt.title("LightGBM - Feature Importance", fontsize=15)
 plt.show()
m_lgb.save_model("model.lgb")
 def create_lag_features_for_test(dt, day):
lags = [7, 28]
lag_cols = [f"lag_{lag}" for lag in lags]
for lag, lag_col in zip(lags, lag_cols):
dt.loc[dt.date == day, lag_col] = \
dt.loc[dt.date ==day-timedelta(days=lag), 'sales'].values
windows = [7, 28]
for window in windows:
for lag in lags:
df_window = dt[(dt.date <= day-timedelta(days=lag)) &(dt.date > day-timedelta(days=lag+window)) ]
df_window_grouped = df_window.groupby("id" ).agg({'sales':'mean'} ).reindex(dt.loc[dt.date==day,'id'])
 dt.loc[dt.date == day,f"rmean_{lag}_{window}"] = \
df_window_grouped.sales.values
def create_date_features_for_test(dt):
date_features = {
"wday": "weekday",
"week": "weekofyear",
"month": "month",
"mday": "day",
}
for date_feat_name, date_feat_func in date_features.items() :
if date_feat_name in dt.columns:
dt[date_feat_name] = dt[date_feat_name].astype("int16")
 else:
dt[date_feat_name] = getattr(
 dt["date"].dt, date_feat_func ).astype("int16")
 alphas = [1.02]
weights = [1/len(alphas)]*len(alphas)
 te0 = create_dt(False)
 create_date_features_for_test(te0)
 for icount,(alpha, weight)in enumerate(zip(alphas, weights)) :
te = te0.copy()
cols = [f"F{i}" for i in range(1, 29)]
for tdelta in range(0, 28):
day = fday + timedelta(days=tdelta)
 print(tdelta, day.date())
 tst = te[(te.date >= day - timedelta(days=max_lags))
&(te.date <= day)].copy()
create_lag_features_for_test(tst, day)
 tst = tst.loc[tst.date == day, train_cols]
te.loc[te.date == day, "sales"] = \
alpha * m_lgb.predict(tst)
 te_sub = te.loc[te.date >= fday, ["id", "sales"]].copy()
te_sub["F"] = [f"F{rank}" for rank in te_sub.groupby("id")[
"id"].cumcount() +1]
te_sub = te_sub.set_index(["id", "F"] ).unstack() [
"sales"][cols].reset_index()
te_sub.fillna(0., inplace=True)
 te_sub.sort_values("id", inplace=True)
 te_sub.reset_index(drop=True, inplace=True)
 te_sub.to_csv(f"submission_{icount}.csv", index=False)
 if icount == 0:
sub = te_sub
sub[cols] *= weight
else:
sub[cols] += te_sub[cols]*weight
print(icount, alpha, weight)
 sub.head(10)
 sub.id.nunique() , sub["id"].str.contains("validation$" ).sum()
sub.shape
sub2 = sub.copy()
sub2["id"] = sub2["id"].str.replace("validation$", "evaluation")
 sub = pd.concat([sub, sub2], axis=0, sort=False)
 sub.to_csv("submission.csv",index=False )