Kaggle competition: M5 Forecasting - Accuracy
<SOS> metric: M5_WRMSSE Kaggle data source: m5-forecasting-accuracy<find_best_params>
from datetime import datetime, timedelta
import gc
import numpy as np, pandas as pd
import lightgbm as lgb
las_best = las.best_estimator_<compute_test_metric>
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" }
print('R-squared value for lasso regression on train data is ', r2_score(y_train,las_best.predict(x_train)) )<predict_on_test>
pd.options.display.max_columns = 50
lasso_pred = las_best.predict(x_test )<train_on_grid>
h = 28
max_lags = 70
tr_last = 1913
fday = datetime(2016,4, 25)
 fday
elastic = ElasticNet()
parameters = {'alpha' : [.5],
'l1_ratio' : [1e-5,0.1,1]}
elas = GridSearchCV(elastic,param_grid = parameters, cv=kfold, scoring= sklearn.metrics.make_scorer(mean_squared_error), n_jobs= -1, verbose = 1)
 elas.fit(x_train,y_train)
 print(math.sqrt(elas.best_score_))
print(elas.best_params_ )<find_best_params>
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
elas_best = elas.best_estimator_<compute_test_metric>
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",
"quarter": "quarter",
"year": "year",
"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" )
print('R-squared value for elasticnet regression on train data is ', r2_score(y_train,elas_best.predict(x_train)) )<predict_on_test>
FIRST_DAY = 800
elastic_pred = elas_best.predict(x_test )<train_on_grid>
df.dropna(inplace = True)
 df.shape
SVMC = SVR(kernel = 'rbf')
 svc_param_grid = {
'gamma': [500],
'C': [500,1000]}
gsSVMC = GridSearchCV(SVMC,param_grid = svc_param_grid, cv=kfold, scoring= sklearn.metrics.make_scorer(mean_squared_log_error), n_jobs= -1, verbose = 1)
 gsSVMC.fit(x_train,y_train)
 print(math.sqrt(gsSVMC.best_score_))
print(gsSVMC.best_params_ )<find_best_params>
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"]
svm_best = gsSVMC.best_estimator_<compute_test_metric>
train_data = lgb.Dataset(X_train, label = y_train, categorical_feature=cat_feats, free_raw_data=False)
 fake_valid_inds = np.random.choice(len(X_train), 1000000)
 fake_valid_data = lgb.Dataset(X_train.iloc[fake_valid_inds], label = y_train.iloc[fake_valid_inds],categorical_feature=cat_feats,
free_raw_data=False )
print('R-squared value for Supprt vector regression on train data is ', r2_score(y_train,svm_best.predict(x_train)) )<predict_on_test>
params = {
"objective" : "poisson",
"metric" :"rmse",
"force_row_wise" : True,
"learning_rate" : 0.075,
"sub_row" : 0.75,
"bagging_freq" : 1,
"lambda_l2" : 0.1,
"metric": ["rmse"],
'verbosity': 1,
'num_iterations' : 2500,
}
svm_pred = svm_best.predict(x_test )<train_on_grid>
%%time
m_lgb = lgb.train(params, train_data, valid_sets = [fake_valid_data], verbose_eval=100 )
SVML = SVR(kernel = 'linear')
 svc_param_grid = {
'gamma': [0.001],
'C': [10]}
gsSVML = GridSearchCV(SVML,param_grid = svc_param_grid, cv=kfold, scoring= sklearn.metrics.make_scorer(mean_squared_log_error), n_jobs= -1, verbose = 1)
 gsSVML.fit(x_train,y_train)
 print(math.sqrt(gsSVML.best_score_))
print(gsSVML.best_params_ )<find_best_params>
m_lgb.save_model("model.lgb" )
<compute_test_metric><EOS> 
sub.id.nunique() , sub["id"].str.contains("validation$" ).sum()