def ensemble(train_x=None, train_y=None, test_x=None, ids=None):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
preds_train = []
preds_test = []
for i in range(5):
params["random_seed"] = i
params["depth"] = 5 + i
pred_train, pred_test = predict_cv(params,
train_x,
train_y,
test_x,
seed=i + 100)
preds_train.append(pred_train)
preds_test.append(pred_test)
learn_lgb.output_metrics(train_y, pred_train)
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
learn_lgb.output_metrics(train_y, pred_train)
learn_lgb.output_metrics(np.expm1(train_y), np.expm1(pred_train))
return pred_train, pred_test
def em(complement=True):
train_x, train_y, test_x, ids = data_frame.main(complement=complement)
embeded_lgb_feature = feature_selection.null_importance(train_x,
train_y,
test_x,
ids,
create=False)
if "categoryId_TE_mean" not in embeded_lgb_feature:
embeded_lgb_feature.append("categoryId_TE_mean")
if "ratings_disabled" not in embeded_lgb_feature:
embeded_lgb_feature.append("ratings_disabled")
train_x = train_x[embeded_lgb_feature]
test_x = test_x[embeded_lgb_feature]
train_x, train_y, test_x = pseudo.get_pseudo_data_set(train_x,
train_y,
test_x,
threshold=0.3)
pred_train, pred_test = ensemble(train_x, train_y, test_x, ids)
sub = pd.DataFrame()
sub["id"] = ids
sub['y'] = np.expm1(pred_test)
sub.to_csv(f'./data/output/test_cat_complement_{complement}.csv',
index=False)
sub = pd.DataFrame()
sub['y'] = np.expm1(pred_train)
sub.to_csv(f'./data/output/train_cat_complement_{complement}.csv',
index=False)
def ensemble_diff_category_TE(train_x=None,
train_y=None,
test_x=None,
ids=None):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
train_y = train_y - train_x.categoryId_TE_mean
preds_train = []
preds_test = []
for i in range(5):
params["random_state"] = i
# params["num_leaves"] -= 1
pred_train, pred_test = main(train_x, train_y, test_x, ids, i)
preds_train.append(pred_train)
preds_test.append(pred_test)
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
train_y = train_y + train_x.categoryId_TE_mean
pred_train = pred_train + train_x.categoryId_TE_mean
pred_test = pred_test + test_x.categoryId_TE_mean
output_metrics(train_y, pred_train)
output_metrics(np.expm1(train_y), np.expm1(pred_train))
return pred_train, pred_test
def ensemble_div_period(train_x=None, train_y=None, test_x=None, ids=None):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
train_y = train_y / train_x.period.apply(np.log1p)
preds_train = []
preds_test = []
for i in range(5):
params["random_state"] = i
params["num_leaves"] -= 1
pred_train, pred_test = main(train_x, train_y, test_x, ids, i)
preds_train.append(pred_train)
preds_test.append(pred_test)
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
train_y = train_y * train_x.period.apply(np.log1p)
pred_train = pred_train * train_x.period.apply(np.log1p)
pred_test = pred_test * test_x.period.apply(np.log1p)
output_metrics(train_y, pred_train)
output_metrics(np.expm1(train_y), np.expm1(pred_train))
sub = pd.DataFrame()
sub["id"] = ids
sub['y'] = np.expm1(pred_test)
sub.to_csv('./data/output/lgb.csv', index=False)
return pred_train, pred_test
def ensemble(train_x=None, train_y=None, test_x=None, ids=None):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
preds_train = []
preds_test = []
for i in range(5):
params["random_state"] = i
params["num_leaves"] += 1
pred_train, pred_test = main(train_x, train_y, test_x, ids, i)
preds_train.append(pred_train)
preds_test.append(pred_test)
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
output_metrics(train_y, pred_train)
output_metrics(np.expm1(train_y), np.expm1(pred_train))
# sub = pd.DataFrame()
# sub["id"] = ids
# sub['y'] = np.expm1(pred_test)
#
# sub.to_csv('./data/output/test_lgb.csv', index=False)
#
# sub = pd.DataFrame()
# sub['y'] = np.expm1(pred_train)
#
# sub.to_csv('./data/output/train_lgb.csv', index=False)
return pred_train, pred_test
def em(complement=True):
train_x, train_y, test_x, ids = data_frame.main(complement=complement)
# embeded_lgb_feature = feature_selection.null_importance(train_x, train_y, test_x, ids, create=True)
embeded_lgb_feature = feature_selection.main(train_x, train_y, test_x, ids)
if "categoryId_TE_mean" not in embeded_lgb_feature:
embeded_lgb_feature.append("categoryId_TE_mean")
if "ratings_disabled" not in embeded_lgb_feature:
embeded_lgb_feature.append("ratings_disabled")
train_x = train_x[embeded_lgb_feature]
test_x = test_x[embeded_lgb_feature]
train_x, train_y, test_x = pseudo.get_pseudo_data_set(train_x,
train_y,
test_x,
threshold=0.3)
preds_train = []
preds_test = []
# 通常
pred_train, pred_test = ensemble(train_x, train_y, test_x, ids)
preds_train.append(pred_train)
preds_test.append(pred_test)
# categoryIdのTEの差分
pred_train, pred_test = ensemble_diff_category_TE(train_x, train_y, test_x,
ids)
preds_train.append(pred_train)
preds_test.append(pred_test)
# 期間で除算
pred_train, pred_test = ensemble_div_period(train_x, train_y, test_x, ids)
preds_train.append(pred_train)
preds_test.append(pred_test)
# 平均をとる
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
output_metrics(train_y, pred_train)
output_metrics(np.expm1(train_y), np.expm1(pred_train))
sub = pd.DataFrame()
sub["id"] = ids
sub['y'] = np.expm1(pred_test)
sub.to_csv(f'./data/output/test_lgb_complement_{complement}.csv',
index=False)
sub = pd.DataFrame()
sub['y'] = np.expm1(pred_train)
sub.to_csv(f'./data/output/train_lgb_complement_{complement}.csv',
index=False)
return pred_train, pred_test, preds_train, preds_test
def select_k_best(train_x=None,
train_y=None,
test_x=None,
ids=None,
seed=22,
k=300):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
selector = SelectKBest(score_func=f_regression, k=5)
selector.fit(train_x, train_y)
mask = selector.get_support()
return selector.transform(train_x), selector.transform(test_x)
def ensemble():
train_x, train_y, test_x, ids = data_frame.main()
preds_train = []
preds_test = []
for i in range(5):
pred_train, pred_test = main(train_x, train_y, test_x, ids, i)
preds_train.append(pred_train)
preds_test.append(pred_test)
pred_train = np.mean(preds_train, axis=0)
pred_test = np.mean(preds_test, axis=0)
learn_lgb.output_metrics(train_y, pred_train)
learn_lgb.output_metrics(np.expm1(train_y), np.expm1(pred_train))
sub = pd.DataFrame()
sub["id"] = ids
sub['y'] = np.expm1(pred_test)
sub.to_csv('./data/output/ridge.csv', index=False)
def em2():
train_x, train_y, test_x, ids = data_frame.main(complement=True)
pred_train_a, pred_test_a, _, _ = em(complement=True)
pred_train_b, pred_test_b, _, _ = em(complement=False)
# 平均をとる
pred_train = np.mean([pred_train_a, pred_train_b], axis=0)
pred_test = np.mean([pred_test_a, pred_test_b], axis=0)
output_metrics(train_y, pred_train)
output_metrics(np.expm1(train_y), np.expm1(pred_train))
sub = pd.DataFrame()
sub["id"] = ids
sub['y'] = np.expm1(pred_test)
sub.to_csv('./data/output/test_lgb.csv', index=False)
sub = pd.DataFrame()
sub['y'] = np.expm1(pred_train)
sub.to_csv('./data/output/train_lgb.csv', index=False)
def main(train_x=None, train_y=None, test_x=None, ids=None, seed=22):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
lgbr = LGBMRegressor(n_estimators=500,
learning_rate=0.1,
num_leaves=86,
subsample_freq=1,
subsample=0.9,
feature_fraction=0.2,
bagging_seed=11,
metrics="rmse",
reg_alpha=0.1,
reg_lambda=0.1,
random_state=0)
embeded_lgb_selector = SelectFromModel(lgbr, threshold='1.25*median')
embeded_lgb_selector.fit(train_x, train_y)
embeded_lgb_support = embeded_lgb_selector.get_support()
embeded_lgb_feature = train_x.loc[:, embeded_lgb_support].columns.tolist()
print(str(len(embeded_lgb_feature)), 'selected features')
return embeded_lgb_feature
def main(train_x=None, train_y=None, test_x=None, ids=None, seed=22):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
drop_null = set(test_x.keys()[test_x.isna().any()].to_list() +
train_x.keys()[train_x.isna().any()].to_list())
drop_list = ["publishedAt", "categoryId", "collection_date"
] + list(drop_null)
train_x = train_x.drop(drop_list, axis=1)
test_x = test_x.drop(drop_list, axis=1)
# train_x, test_x = preprocess(train_x, test_x)
pred_train, pred_test = predict_cv(train_x, train_y, test_x, seed=seed)
learn_lgb.output_metrics(train_y, pred_train)
# sub = pd.DataFrame()
# sub["id"] = ids
# sub['y'] = np.expm1(pred_test)
#
# sub.to_csv('./data/output/ridge.csv', index=False)
return pred_train, pred_test
def main(train_x=None, train_y=None, test_x=None, ids=None, seed=22):
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
# embeded_lgb_feature = feature_selection.null_importance(train_x, train_y, test_x, ids)
# if "categoryId_TE_mean" not in embeded_lgb_feature:
# embeded_lgb_feature.append("categoryId_TE_mean")
# if "ratings_disabled" not in embeded_lgb_feature:
# embeded_lgb_feature.append("ratings_disabled")
# train_x = train_x[embeded_lgb_feature]
# test_x = test_x[embeded_lgb_feature]
pred_train, pred_test = predict_cv(params, train_x, train_y, test_x, seed)
# output_metrics(train_y, pred_train)
# output_metrics(np.expm1(train_y), np.expm1(pred_train))
# sub = pd.DataFrame()
# sub["id"] = ids
# sub['y'] = np.expm1(pred_test)
#
# sub.to_csv('./data/output/lgb.csv', index=False)
return pred_train, pred_test
def null_importance(train_x=None,
train_y=None,
test_x=None,
ids=None,
seed=22,
create=False):
# 閾値を設定
THRESHOLD = 40
if not create:
print(f"Create {create}")
actual_importance = pd.read_csv("./data/null_importance.csv")
imp_features = []
for feature, score in zip(actual_importance["feature"],
actual_importance["score"]):
if score >= THRESHOLD:
imp_features.append(feature)
print(str(len(imp_features)), 'selected features')
return imp_features
if train_x is None:
train_x, train_y, test_x, ids = data_frame.main()
# 実際の目的変数でモデルを学習し、特徴量の重要度を含むデータフレームを作成
actual_importance = get_feature_importances(train_x,
train_y,
shuffle=False)
# 目的変数をシャッフルした状態でモデルを学習し、特徴量の重要度を含むデータフレームを作成
N_RUNS = 100
null_importance = pd.DataFrame()
for i in range(N_RUNS):
imp_df = get_feature_importances(train_x,
train_y,
shuffle=True,
seed=i)
imp_df["run"] = i + 1
null_importance = pd.concat([null_importance, imp_df])
# 実データにおいて特徴量の重要度が高かった上位5位を表示
# for feature in actual_importance["feature"][:5]:
# display_distributions(actual_importance, null_importance, feature)
score_list = []
# 閾値を超える特徴量を取得
imp_features = []
for feature in actual_importance["feature"]:
actual_value = actual_importance.query(
f"feature=='{feature}'")["importance"].values
null_value = null_importance.query(
f"feature=='{feature}'")["importance"].values
percentage = (null_value < actual_value).sum() / null_value.size * 100
score_list.append(percentage)
if percentage >= THRESHOLD:
imp_features.append(feature)
actual_importance["score"] = score_list
actual_importance.to_csv("./data/null_importance.csv", index=False)
print(str(len(imp_features)), 'selected features')
return imp_features
