Kaggle competition: M5 Forecasting - Accuracy
<SOS> metric: M5_WRMSSE Kaggle data source: m5-forecasting-accuracy<set_options>
from datetime import datetime, timedelta
import gc
import numpy as np, pandas as pd
import lightgbm as lgb
tf.enable_eager_execution()
AUTOTUNE = tf.data.experimental.AUTOTUNE
print(os.listdir(".. /input")) 
<define_variables>
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" }
SIZE = 156
BATCH_SIZE = 64<load_from_csv>
pd.options.display.max_columns = 50
train_val_df = pd.read_csv(".. /input/imetmetadata/weird_images_w_labels.csv")
 train_val_df.head()<load_from_csv>
h = 28
max_lags = 57
tr_last = 1913 + 28
fday = datetime(2016,4, 25)+ timedelta(days= 28)
 fday,tr_last,max_lags
labels_df = pd.read_csv(".. /input/imet-2019-fgvc6/labels.csv")
 labels_df.head()<define_variables>
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_evaluation.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
N_CLASSES = len(labels_df)
 print("Number of classes:", N_CLASSES )<define_variables>
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" )
n_width_300 = len(train_val_df[train_val_df['width'] == 300])
 n_height_300 = len(train_val_df[train_val_df['height'] == 300])
 n_width_height_300 = len(train_val_df[(train_val_df['height'] == 300)&(train_val_df['width'] == 300)])
 n_all_images = len(train_val_df)
 print("Total images: ", n_all_images)
 print("Total images with width or height = 300: ", n_height_300 + n_width_300 - n_width_height_300 )<filter>
FIRST_DAY = 350
width_larger_equal_height = train_val_df[train_val_df['width'] >= train_val_df['height']]
width_smaller_equal_height = train_val_df[train_val_df['width'] < train_val_df['height']]
print("Number of image with width >= height: ", len(width_larger_equal_height))
print("Number of image with height = 300: ", sum(width_larger_equal_height['height'] == 300))
print("
")
 print("Number of image with width <= height: ", len(width_smaller_equal_height))
print("Number of image with width = 300: ", sum(width_smaller_equal_height['width'] == 300))<load_from_csv>
df.dropna(inplace = True)
 df.shape
test_ids = pd.read_csv('.. /input/imet-2019-fgvc6/sample_submission.csv')['id']
print("Total number of images in test set: ", len(test_ids))
test_ids.head()<feature_engineering>
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"]
train_val_df['aspect_ratio'] = train_val_df['width'] / train_val_df['height']<filter>
%%time
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 )
ar_larger_2 = train_val_df[(train_val_df['aspect_ratio'] > 3)&(train_val_df['height'] == 300)]
ar_larger_2['id'].head()<filter>
del df, X_train, y_train, fake_valid_inds,train_inds ; gc.collect()
ar_smaller_1_2 = train_val_df[(train_val_df['aspect_ratio'] < 1/3.0)&(train_val_df['width'] == 300)]
ar_smaller_1_2['id'].head()<string_transform>
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' : 1500,
'num_leaves': 128,
"min_data_in_leaf": 100,
}
categorical_labels = np.zeros(shape=(len(train_val_df['attribute_ids']), N_CLASSES))
for i, label in enumerate(train_val_df['attribute_ids']):
list_indices = [int(k)for k in label.split(" ")]
categorical_labels[i, list_indices] = 1
print(categorical_labels.shape )<normalization>
%%time
m_lgb = lgb.train(params, train_data, valid_sets = [fake_valid_data], verbose_eval=20 )
def shift_img(img, width_shift_range, height_shift_range):

 img_shape = img.get_shape()
if width_shift_range or height_shift_range:
if width_shift_range:
width_shift_range = tf.random_uniform([],
-width_shift_range * img_shape[1].value,
width_shift_range * img_shape[1].value)
 if height_shift_range:
height_shift_range = tf.random_uniform([],
-height_shift_range * img_shape[0].value,
height_shift_range * img_shape[0].value)
 output_img = tf.contrib.image.translate(img,
[width_shift_range, height_shift_range])
 return output_img
else:
return img
def rotate_img(img, angle_range):
rotate_angle = tf.random_uniform([], minval = angle_range[0]/180.0*3.1415, maxval=angle_range[1]/180.0*3.1415)
 out_img = tf.contrib.image.rotate(img, angles=rotate_angle)
 return out_img
def flip_img(horizontal_flip, img):
if horizontal_flip:
flip_prob = tf.random_uniform([], 0.0, 1.0)
 out_img = tf.cond(tf.less(flip_prob, 0.5),
lambda: tf.image.flip_left_right(img),
lambda: img)
 return out_img
else:
return img<create_dataframe>
m_lgb.save_model("model.lgb" )
<prepare_x_and_y><EOS> 
sub.id.nunique() , sub["id"].str.contains("evaluation$" ).sum()