-
Notifications
You must be signed in to change notification settings - Fork 0
/
stacked_regressor.py
164 lines (128 loc) · 5.6 KB
/
stacked_regressor.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
from __future__ import division
from optparse import OptionParser
import pandas as pd
import pickle
import psutil
import json
import os
import gc
import numpy as np
from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import classification_report, \
confusion_matrix, \
roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from utils.stacking import StackingAveragedModels
from utils.metrics import rmse
import lightgbm as lgb
import xgboost as xgb
from config import XGB_PARAMS
parser = OptionParser()
parser.add_option("-p", dest="path", help="Path to the csv.")
(options, args) = parser.parse_args()
if not options.path:
parser.error("You must pass -p argument")
common_path = os.path.join('logs', 'stacked_regressor')
if not os.path.exists(common_path):
os.makedirs(common_path)
data = pd.read_csv(options.path)
# by default, select the last column to be the target and other columns to be the source data.
# Note that there is no preprocessing here. You need to implement your own
label_column = data.columns[-1]
x = data.drop(labels=[label_column], axis=1)
y = data.iloc[:, -1]
# by default, check if the label contains numbers. If don't, so automatically label encode it
if y.dtype.name == "object":
lbl = LabelEncoder()
lbl.fit(y.values)
y = lbl.transform(y.values)
# save to use later
with open(os.path.join(common_path, 'target_encoder.pickle'), 'wb') as f:
pickle.dump(lbl, f)
else:
y = y.values
# check if there is any column that contains categorical features. If yes, so label encode it
# and dump the LabelEncoder to use later
try:
for column in x.dtypes[data.dtypes == "object"].index:
# Use LabelEncoder to transform categorical into numerical
lbl = LabelEncoder()
lbl.fit(list(x[column].values))
x[column] = lbl.transform(list(x[column].values))
# pickle the encoder to use later
with open(os.path.join(common_path, column + '.pickle'), 'wb') as f:
pickle.dump(lbl, f)
except:
# probably x is a series
pass
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
#Validation function
n_folds = 5
def rmsle_cv(model):
kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(x_train)
rmse = np.sqrt(-cross_val_score(model, x_train, y_train, scoring="neg_mean_squared_error", cv=kf))
return rmse
# Base models
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))
ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))
KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
max_depth=4, max_features='sqrt',
min_samples_leaf=15, min_samples_split=10,
loss='huber', random_state=5)
model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,
learning_rate=0.05, max_depth=3,
min_child_weight=1.7817, n_estimators=2200,
reg_alpha=0.4640, reg_lambda=0.8571,
subsample=0.5213, silent=1,
random_state=7, nthread=-1)
model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,
learning_rate=0.05, n_estimators=720,
max_bin=55, bagging_fraction=0.8,
bagging_freq=5, feature_fraction=0.2319,
feature_fraction_seed=9, bagging_seed=9,
min_data_in_leaf=6, min_sum_hessian_in_leaf=11)
stacked_averaged_models = StackingAveragedModels(base_models=(ENet, GBoost, KRR), meta_model=lasso)
# training step
print("Traininf stacked model...")
stacked_averaged_models.fit(x_train, y_train)
print("Training xgb model...")
model_xgb.fit(x_train, y_train)
print("Training lgb model..")
model_lgb.fit(x_train, y_train)
# save models
with open(os.path.join(common_path, 'xgb_regressor.pickle'), 'wb') as f:
pickle.dump(model_xgb,f)
with open(os.path.join(common_path, 'lgb_regressor.pickle'), 'wb') as f:
pickle.dump(model_lgb,f)
stacked_averaged_models.save(common_path, ["enet, gboost, krr"], "lasso")
# eval step
print("Predicting...")
stacked_train_pred = stacked_averaged_models.predict(x_test)
xgb_train_pred = model_xgb.predict(x_test)
lgb_train_pred = model_lgb.predict(x_test)
print("Stacked RMSE:", rmse(y_test, stacked_train_pred))
print("XGBRegressor RMSE:", rmse(y_test, xgb_train_pred))
print("LGBRegressor RMSE:", rmse(y_test, lgb_train_pred))
print("Averaged score:", rmse(y_test, stacked_train_pred*0.65 + xgb_train_pred+0.2 + lgb_train_pred*0.15))
result = pd.DataFrame(
data=[
rmse(y_test, stacked_train_pred),
rmse(y_test, xgb_train_pred),
rmse(y_test, lgb_train_pred),
rmse(y_test, stacked_train_pred*0.65 + xgb_train_pred+0.2 + lgb_train_pred*0.15)
],
columns=[
'stacked-rsme',
'xgbregressor-rmse',
'lgbregressor-rmse',
'averaged-rmse'
]
)
result.to_csv(os.path.join(common_path, 'result.csv'), index=False, encoding='utf-8')