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model_stacking.py
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#!/usr/bin/env python
# -- coding:utf-8 --
from xgboost import XGBRegressor
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.svm import LinearSVR
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
def dfs(l, r, feature_file, folder):
if l == r:
return pd.read_csv(folder + '/' + feature_file[l] + '.csv')
mid = (l + r) / 2
l_frame = dfs(l, mid, feature_file, folder)
r_frame = dfs(mid + 1, r, feature_file, folder)
return pd.merge(l_frame, r_frame, how='left')
def calculate(test_set):
return np.mean(
test_set[test_set.y != -1].apply(
lambda x: ((x['predictY'] - x['y']) / x['y']) ** 2,
axis=1))
def diff1(x):
if x['_3day_exists_avg'] is None:
return None
return x['_3day_exists_avg'] - x['_7day_exists_avg']
def diff2(x):
if x['_7day_avg'] is None:
return None
return x['_7day_avg'] - x['_30day_avg']
def run(feature_files, training_dates, feature_set_folder):
train_set1 = pd.concat(
[dfs(0, len(feature_files), feature_files + ['y'], 'dataset/' + date) for date in training_dates])
train_set = train_set1[train_set1.time_diff > 15]
test_set = train_set1[train_set1.time_diff <= 15]
train_set = train_set.fillna(-1, downcast='infer')
test_set = test_set.fillna(-1, downcast='infer')
train_set['y_log'] = train_set['y'].apply(lambda x: np.log(1 + x))
test_set['y_log'] = test_set['y'].apply(lambda x: np.log(1 + x))
feature_set = filter(lambda x: x not in ['y', 'time', 'province', 'market', 'name', 'type', 'y_log'],
train_set.columns)
scaler = StandardScaler()
scaler.fit(train_set[feature_set].as_matrix())
#
# # model1
# model1 = LinearRegression(normalize=True)
# model1.fit(scaler.transform(train_set[feature_set].as_matrix()), train_set['y'].as_matrix(),
# sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
# )
# print zip(feature_set, model1.coef_)
# test_set['predictY'] = model1.predict(scaler.transform(test_set[feature_set].as_matrix()))
# test_set.to_csv('result/' + feature_set_folder + '/model1_offline_stacking1.csv')
#
# # model2
# model2 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=6, colsample_bytree=0.7, subsample=0.7,
# colsample_bylevel=0.7)
# model2.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
# sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
# )
# test_set['predictY'] = model2.predict(test_set[feature_set].as_matrix())
# test_set.to_csv('result/' + feature_set_folder + '/model2_offline_stacking1.csv')
#
# # model3
# model3 = LinearSVR(tol=1e-7)
# model3.fit(scaler.transform(train_set[feature_set].as_matrix()), train_set['y'].as_matrix(),
# sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
# )
# test_set['predictY'] = model3.predict(scaler.transform(test_set[feature_set].as_matrix()))
# test_set.to_csv('result/' + feature_set_folder + '/model3_offline.csv')
# model4
model4 = RandomForestRegressor(n_estimators=1000, max_depth=7, max_features=0.2, max_leaf_nodes=100)
model4.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=np.array(map(lambda x: 1.0 / x / x, train_set['y'].as_matrix()))
)
test_set['predictY'] = model4.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model4_offline_stacking1.csv')
# model5
model5 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=6, colsample_bytree=0.7, subsample=0.7,
colsample_bylevel=0.7, seed=10000)
model5.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model5.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model5_offline_stacking1.csv')
# model5
model6 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=5, colsample_bytree=0.7, subsample=0.7,
colsample_bylevel=0.7)
model6.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model6.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model6_offline_stacking1.csv')
pass
def submit(feature_files, training_dates, feature_set_folder):
train_set1 = pd.concat(
[dfs(0, len(feature_files), feature_files + ['y'], 'dataset/' + date) for date in training_dates])
train_set = train_set1[train_set1.time_diff > 15]
test_set = train_set1[train_set1.time_diff <= 15]
train_set = train_set.fillna(-1, downcast='infer')
test_set = test_set.fillna(-1, downcast='infer')
train_set['y_log'] = train_set['y'].apply(lambda x: np.log(1 + x))
test_set['y_log'] = test_set['y'].apply(lambda x: np.log(1 + x))
feature_set = filter(lambda x: x not in ['y', 'time', 'province', 'market', 'name', 'type', 'y_log'],
train_set.columns)
scaler = StandardScaler()
scaler.fit(train_set[feature_set].as_matrix())
#
# model1
model1 = LinearRegression(normalize=True)
model1.fit(scaler.transform(train_set[feature_set].as_matrix()), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
print zip(feature_set, model1.coef_)
test_set['predictY'] = model1.predict(scaler.transform(test_set[feature_set].as_matrix()))
test_set.to_csv('result/' + feature_set_folder + '/model1_online_stacking1.csv')
print test_set
# model2
model2 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=6, colsample_bytree=0.7, subsample=0.7,
colsample_bylevel=0.7)
model2.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model2.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model2_online_stacking1.csv')
# model3
model3 = LinearSVR(tol=1e-7)
model3.fit(scaler.transform(train_set[feature_set].as_matrix()), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model3.predict(scaler.transform(test_set[feature_set].as_matrix()))
test_set.to_csv('result/' + feature_set_folder + '/model3_offline.csv')
# model4
model4 = RandomForestRegressor(n_estimators=1000, max_depth=7, max_features=0.2, max_leaf_nodes=100)
model4.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=np.array(map(lambda x: 1.0 / x / x, train_set['y'].as_matrix()))
)
test_set['predictY'] = model4.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model4_online_stacking1.csv')
# model5
model5 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=6, colsample_bytree=0.7, subsample=0.7,
colsample_bylevel=0.7, seed=10000)
model5.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model5.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model5_online_stacking1.csv')
# model6
model6 = XGBRegressor(n_estimators=600, learning_rate=0.01, max_depth=5, colsample_bytree=0.7, subsample=0.7,
colsample_bylevel=0.7)
model6.fit(train_set[feature_set].as_matrix(), train_set['y'].as_matrix(),
sample_weight=map(lambda x: 1.0 / x / x, train_set['y'].as_matrix())
)
test_set['predictY'] = model6.predict(test_set[feature_set].as_matrix())
test_set.to_csv('result/' + feature_set_folder + '/model6_online_stacking1.csv')
pass
# run(['v1', 'v3', 'v11'], ['2016-05-01', '2016-04-25'], 'feature_set1')
# run(['v1', 'v3', 'v11', 'v14'], ['2016-05-01', '2016-04-25'], 'feature_set9')
# submit(['v1', 'v3', 'v11', 'v14'], ['2016-06-01', '2016-05-25'], 'feature_set9')
# run(['v1', 'v2', 'v3', 'v11'], ['2016-05-01', '2016-04-25'], 'feature_set4')
# run(['v1', 'v2', 'v3', 'xxv11'], ['2016-05-01', '2016-04-25', '2016-04-20', '2016-04-15'], 'feature_set6')
# run(['v1', 'v2', 'v3', 'v10'], ['2016-05-01', '2016-04-25'], 'feature_set3')
# submit(['v1', 'v14', 'v3', 'v11'], ['2016-06-01', '2016-05-25'], 'feature_set9')
submit(['v1', 'v3', 'v11'], ['2016-06-01', '2016-05-25'], 'feature_set1')
# submit(['v1', 'v2', 'v3', 'v11', 'v14'], ['2016-06-01', '2016-05-25'], 'feature_set6')
f = pd.read_csv('result/feature_set1/model1_offline.csv')
print calculate(f)