def label_encoder(self):
"""
Takes the output_df and label encode any features in lbl_enc_feats list
"""
# Loop through each feature in lbl_enc_feats and label encode it
for feat in self.lbl_enc_feats:
le = LabelEncoder()
le.fit(self.output_df[feat])
self.output_df[feat] = le.transform(self.output_df[feat])
def create_features(
self,
train_df: cudf.DataFrame,
test_df: cudf.DataFrame,
):
with timer("load data"):
train = train_df.copy()
len_train = len(train)
test = test_df.copy()
with timer("concat train and test"):
total = cudf.concat([train, test], ignore_index=True)
with timer("label encoding"):
with timer("rating"):
rating_dict = {
"RP": 0,
"EC": 1,
"K-A": 2,
"E": 2,
"E10+": 3,
"T": 4,
"M": 5,
"AO": 5,
}
total["Rating"] = total["Rating"].replace(rating_dict).astype(
int)
with timer("other cat cols"):
cat_cols = [
"Name",
"Platform",
"Genre",
"Publisher",
"Developer",
]
for col in cat_cols:
le = LabelEncoder(handle_unknown="ignore")
le.fit(total[col])
total[col] = le.transform(total[col]).astype("category")
with timer("User_Score"):
total["User_Score"] = (total["User_Score"].replace(
to_replace="tbd", value=np.nan).astype(float))
with timer("Year_of_Release"):
total["Year_of_Release"] = total["Year_of_Release"].replace(
to_replace=2020.0, value=2017.0)
with timer("log_User_Count"):
total["log_User_Count"] = np.log1p(total["User_Count"].to_pandas())
with timer("end"):
basic_cols = [
"Name",
"Platform",
"Year_of_Release",
"Genre",
"Publisher",
"Critic_Score",
"Critic_Count",
"User_Score",
"User_Count",
"log_User_Count",
"Developer",
"Rating",
]
target_cols = [
"NA_Sales",
"EU_Sales",
"JP_Sales",
"Other_Sales",
"Global_Sales",
]
self.train = total[basic_cols +
target_cols].iloc[:len_train].reset_index(
drop=True)
self.test = total[basic_cols].iloc[len_train:].reset_index(
drop=True)