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""" | |
Public LB: 0.50456 | |
""" | |
from collections import Counter | |
import lightgbm as lgb | |
import ml_metrics | |
import numpy as np | |
import pandas as pd | |
from sklearn.feature_extraction import DictVectorizer | |
from sklearn.model_selection import KFold | |
from sklearn.model_selection import train_test_split | |
def parse_host_verifications(df): | |
raw_value_list = [] | |
for val in df['host_verifications'].tolist(): | |
values = eval(val) | |
if values is not None: | |
raw_value_list.append(Counter(values)) | |
else: | |
raw_value_list.append({}) | |
vectorizer = DictVectorizer(sparse=False) | |
X = vectorizer.fit_transform(raw_value_list) | |
for idx, col in enumerate(vectorizer.feature_names_): | |
df[f'host_verifications_{col}'] = X[:, idx] | |
def load_data(): | |
df_trn = pd.read_csv('./train.csv') | |
df_tst = pd.read_csv('./test.csv') | |
df = pd.concat([df_trn, df_tst], sort=False) | |
original_train_size = len(df_trn) | |
y_train = df.iloc[:original_train_size]['price'].values | |
# Parse date | |
df.loc[:, 'host_since_year'] = df.host_since.fillna('2020-01-01').apply( | |
lambda x: int(x.split('-')[0])) | |
df.loc[:, 'host_since_month'] = df.host_since.fillna('2020-01-01').apply( | |
lambda x: int(x.split('-')[1])) | |
df.loc[:, 'host_since_day'] = df.host_since.fillna('2020-01-01').apply( | |
lambda x: int(x.split('-')[2])) | |
# Parse host_verifications | |
parse_host_verifications(df) | |
# Baseline features | |
categorical_cols = [] | |
cols = [] | |
for col in df.columns: | |
if col in ['listing_id', 'price']: | |
continue | |
if pd.api.types.is_numeric_dtype(df[col]): | |
df[col] = df[col].fillna(df[col].mean()) | |
else: | |
df[col] = df[col].factorize()[0] | |
categorical_cols.append(col) | |
cols.append(col) | |
return df, df_trn, df_tst, y_train, cols, categorical_cols | |
def cv(params, fit_params): | |
df, df_trn, df_tst, y_train, cols, categorical_cols = load_data() | |
original_train_size = y_train.shape[0] | |
X_train = df.iloc[:original_train_size][cols].values | |
val_score_list = [] | |
kf = KFold(n_splits=3, random_state=11, shuffle=True) | |
for idx_valtrn, idx_valtst in kf.split(X_train): | |
X_valtrn, X_valtst = X_train[idx_valtrn], X_train[idx_valtst] | |
y_valtrn, y_valtst = y_train[idx_valtrn], y_train[idx_valtst] | |
lgb_valtrn = lgb.Dataset(X_valtrn, np.log1p(y_valtrn), | |
feature_name=cols, | |
categorical_feature=categorical_cols) | |
lgb_eval = lgb.Dataset(X_valtst, np.log1p(y_valtst), | |
reference=lgb_valtrn, | |
feature_name=cols, | |
categorical_feature=categorical_cols) | |
fit_params['valid_sets'] = lgb_eval | |
clf = lgb.train(params, lgb_valtrn, **fit_params) | |
y_pred = np.expm1(clf.predict(X_valtst, | |
num_iteration=clf.best_iteration)) | |
val_score = ml_metrics.rmsle(y_pred, y_valtst) | |
print(f'RMSLE: {val_score:.6f}') | |
val_score_list.append(val_score) | |
avg_val_score = np.mean(val_score_list) | |
print(f'Avg-RMSLE: {avg_val_score:.6f}') | |
def main(params, fit_params): | |
df, df_trn, df_tst, y_train, cols, categorical_cols = load_data() | |
original_train_size = y_train.shape[0] | |
cols = [] | |
categorical_cols = [] | |
for col in df.columns: | |
if col in ['listing_id', 'price']: | |
continue | |
if pd.api.types.is_numeric_dtype(df[col]): | |
df[col] = df[col].fillna(df[col].mean()) | |
else: | |
df[col] = df[col].factorize()[0] | |
categorical_cols.append(col) | |
cols.append(col) | |
X_train = df.iloc[:original_train_size][cols].values | |
X_test = df.iloc[original_train_size:][cols].values | |
# Early stopping のための validation split を作成 | |
X_valtrn, X_valtst, y_valtrn, y_valtst = train_test_split( | |
X_train, y_train, test_size=0.1, random_state=11) | |
lgb_valtrn = lgb.Dataset(X_valtrn, np.log1p(y_valtrn), | |
feature_name=cols, | |
categorical_feature=categorical_cols) | |
lgb_eval = lgb.Dataset(X_valtst, np.log1p(y_valtst), | |
reference=lgb_valtrn, | |
feature_name=cols, | |
categorical_feature=categorical_cols) | |
fit_params['valid_sets'] = lgb_eval | |
clf = lgb.train(params, lgb_valtrn, **fit_params) | |
y_pred = np.expm1(clf.predict(X_valtst, num_iteration=clf.best_iteration)) | |
print('RMSLE: {:.6f}'.format(ml_metrics.rmsle(y_pred, y_valtst))) | |
y_pred = np.expm1(clf.predict(X_test, num_iteration=clf.best_iteration)) | |
df_tst.loc[:, 'price'] = y_pred | |
df_tst[['listing_id', 'price']].to_csv('./baseline_v2.csv', index=False) | |
if __name__ == '__main__': | |
# LightGBM parameters | |
# https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst#core-parameters | |
params = { | |
'task': 'train', | |
'boosting_type': 'gbdt', | |
'objective': 'regression', | |
'metric': 'rmse', | |
# 'num_leaves' : 60, | |
# 'learning_rate' : 0.1, | |
# 'feature_fraction' : 1.0, | |
# 'bagging_fraction' : 1.0, | |
'verbose': -1, | |
} | |
# https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst#learning-control-parameters | |
fit_params = { | |
'num_boost_round': 8, | |
'verbose_eval': 8, | |
'early_stopping_rounds': 3, | |
} | |
cv(params, fit_params) | |
main(params, fit_params) |
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