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from gen_feat import make_train_set
from gen_feat import make_test_set
from gen_feat import get_labels_8
from sklearn.cross_validation import train_test_split
import xgboost as xgb
from sklearn.ensemble import GradientBoostingClassifier as gbdt
from sklearn.linear_model import LogisticRegression as lg
from gen_feat import report
from numpy import float32
import numpy as np
from sklearn.preprocessing import Imputer
def getKey(item):
return item[1]
def logistic_make_submission():
train_start_date = '2016-03-10'
train_end_date = '2016-04-11'
test_start_date = '2016-04-11'
test_end_date = '2016-04-16'
sub_start_date = '2016-03-15'
sub_end_date = '2016-04-16'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
X_train, X_test, y_train, y_test = train_test_split(training_data.values, label.values, test_size=0.2, random_state=0)
y_train = list(map(int, y_train))
# print(np.any(np.isnan(X_train)))
# print(np.all(np.isfinite(X_train)))
clf = lg() # 使用类，参数全是默认的
clf.fit(X_train,y_train)
sub_user_index, sub_trainning_data = make_test_set(sub_start_date, sub_end_date)
y_hat = clf.predict(sub_trainning_data.values)
sub_user_index['label'] = y_hat
pred = sub_user_index[sub_user_index['label'] == 1]
pred = pred[['user_id', 'sku_id']]
pred = pred.groupby('user_id').first().reset_index()
pred['user_id'] = pred['user_id'].astype(int)
pred.to_csv('../sub/submissionLOG508.csv', index=False, index_label=False)
def gbdt_make_submission():
train_start_date = '2016-03-10'
train_end_date = '2016-04-11'
test_start_date = '2016-04-11'
test_end_date = '2016-04-16'
sub_start_date = '2016-03-15'
sub_end_date = '2016-04-16'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
training_data = training_data.fillna(0)
print(training_data.info())
X_train, X_test, y_train, y_test = train_test_split(training_data.values, label.values, test_size=0.2, random_state=0)
# X_train = X_train.astype(int)
y_train = list(map(int, y_train))
param = {'n_estimators': 1200, 'max_depth': 3, 'subsample': 1.0,
'learning_rate': 0.01, 'min_samples_leaf': 1,'random_state': 3,'max_features':0.8}
clf = gbdt(param)
clf.fit(X_train,y_train)
sub_user_index, sub_trainning_data = make_test_set(sub_start_date, sub_end_date)
sub_trainning_data = sub_trainning_data.fillna(0)
y_hat = clf.predict(sub_trainning_data.values)
sub_user_index['label'] = y_hat
pred = sub_user_index[sub_user_index['label'] == 1]
pred = pred[['user_id', 'sku_id']]
pred = pred.groupby('user_id').first().reset_index()
pred['user_id'] = pred['user_id'].astype(int)
pred.to_csv('../sub/submissionGBDT508.csv', index=False, index_label=False)
def gbdt_cv():
train_start_date = '2016-03-05'
train_end_date = '2016-04-06'
test_start_date = '2016-04-06'
test_end_date = '2016-04-11'
sub_start_date = '2016-03-10'
sub_end_date = '2016-04-11'
sub_test_start_date = '2016-04-11'
sub_test_end_date = '2016-04-16'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
X_train, X_test, y_train, y_test = train_test_split(training_data, label, test_size=0.2, random_state=0)
param = {'n_estimators': 1200, 'max_depth': 3, 'subsample': 1.0,
'learning_rate': 0.01, 'min_samples_leaf': 1,'random_state': 3,'max_features':0.8}
clf = gbdt(param)
clf.fit(X_train,y_train)
sub_user_index, sub_trainning_date, sub_label = make_train_set(sub_start_date, sub_end_date,
sub_test_start_date, sub_test_end_date) # use this data to see the offline score
test = sub_trainning_date.values
y = clf.predict(test)
pred = sub_user_index.copy()
y_true = get_labels_8(sub_test_start_date, sub_test_end_date) # during the test date, real label for cate 8
# y_true = sub_user_index.copy()
pred['label'] = y # add the new column which is the predict label for the test date
ans = []
for i in range(0,30):
pred = sub_user_index.copy()
pred['label'] = y
pred = pred[pred.label >= i / 100]
# print(pred)
rep = report(pred, y_true)
print('%s : score:%s' %(i/100,rep))
ans.append([i / 100, rep])
print('ans:%s' %ans)
threshold = sorted(ans, key=getKey, reverse=True)[0][0]
bestscore = sorted(ans, key=getKey, reverse=True)[0][1]
print('best threshold:%s' % threshold)
print('best score:%s' % bestscore)
def xgboost_make_submission():
train_start_date = '2016-03-10'
train_end_date = '2016-04-11'
test_start_date = '2016-04-11'
test_end_date = '2016-04-16'
sub_start_date = '2016-03-15'
sub_end_date = '2016-04-16'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
X_train, X_test, y_train, y_test = train_test_split(training_data.values, label.values, test_size=0.2, random_state=0) # select some features
dtrain=xgb.DMatrix(X_train, label=y_train)
dtest=xgb.DMatrix(X_test, label=y_test) # don't use these
param = {'learning_rate' : 0.05, 'n_estimators': 1000, 'max_depth': 5,
'min_child_weight': 1, 'gamma': 0, 'subsample': 1, 'colsample_bytree': 0.8,
'scale_pos_weight': 1, 'eta': 0.05, 'silent': 1, 'objective': 'binary:logistic'}
num_round = 20
param['nthread'] = 5
#param['eval_metric'] = "auc"
plst = param.items()
plst = list(plst)
plst += [('eval_metric', 'auc')]
evallist = [(dtest, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst, dtrain, num_round, evallist)
# make test data
sub_user_index, sub_trainning_data = make_test_set(sub_start_date, sub_end_date)
sub_trainning_data = xgb.DMatrix(sub_trainning_data.values) # predict this subdata,the DMatrix Object is array
y_hat = bst.predict(sub_trainning_data)
sub_user_index['label'] = y_hat
pred = sub_user_index[sub_user_index['label'] >= 0.05]
pred = pred[['user_id', 'sku_id']]
pred = pred.groupby('user_id').first().reset_index()
pred['user_id'] = pred['user_id'].astype(int)
pred.to_csv('../sub/submission424.csv', index=False, index_label=False)
def xgboost_cv2():
train_start_date = '2016-03-05'
train_end_date = '2016-04-06'
test_start_date = '2016-04-06'
test_end_date = '2016-04-11'
sub_start_date = '2016-03-10'
sub_end_date = '2016-04-11'
sub_test_start_date = '2016-04-11'
sub_test_end_date = '2016-04-16'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
X_train, X_test, y_train, y_test = train_test_split(training_data, label, test_size=0.2, random_state=0)
dtrain = xgb.DMatrix(X_train.values, label=y_train)
dtest = xgb.DMatrix(X_test.values, label=y_test)
param = {'learning_rate' : 0.05, 'n_estimators': 1000, 'max_depth':10,
'min_child_weight': 1, 'gamma': 0, 'subsample': 0.8, 'colsample_bytree': 0.8,
'scale_pos_weight': 1, 'eta': 0.05, 'silent': 1, 'objective': 'binary:logistic','eval_metric':'auc'}
num_round = 300
param['nthread'] = 5
# param['eval_metric'] = "auc"
# plst = param.items()
# plst += [('eval_metric', 'logloss')]
evallist = [(dtest, 'eval'), (dtrain, 'train')]
bst = xgb.train(param, dtrain, num_round, evallist)
sub_user_index, sub_trainning_date, sub_label = make_train_set(sub_start_date, sub_end_date,
sub_test_start_date, sub_test_end_date) # use this data to see the offline score
test = xgb.DMatrix(sub_trainning_date.values)
y = bst.predict(test)
pred = sub_user_index.copy()
y_true = get_labels_8(sub_test_start_date, sub_test_end_date) # during the test date, real label for cate 8
# y_true = sub_user_index.copy()
pred['label'] = y # add the new column which is the predict label for the test date
# print(pred[(pred.label >= 0.12)].shape)
# print("y_true:")
# print(y_true)
# pred = pred[(pred.label >= 0.35)]
# print(len(pred))
# print(pred)
ans = []
for i in range(0,30):
pred = sub_user_index.copy()
pred['label'] = y
pred = pred[pred.label >= i / 100]
# print(pred)
rep = report(pred, y_true)
print('%s : score:%s' %(i/100,rep))
ans.append([i / 100, rep])
print('ans:%s' %ans)
threshold = sorted(ans, key=getKey, reverse=True)[0][0]
bestscore = sorted(ans, key=getKey, reverse=True)[0][1]
print('best threshold:%s' % threshold)
print('best score:%s' % bestscore)
def xgboost_cv():
train_start_date = '2016-03-05'
train_end_date = '2016-04-06'
test_start_date = '2016-04-11'
test_end_date = '2016-04-16'
sub_start_date = '2016-02-05'
sub_end_date = '2016-03-05'
sub_test_start_date = '2016-03-05'
sub_test_end_date = '2016-03-10'
user_index, training_data, label = make_train_set(train_start_date, train_end_date, test_start_date, test_end_date)
X_train, X_test, y_train, y_test = train_test_split(training_data, label, test_size=0.2, random_state=0)
dtrain=xgb.DMatrix(X_train, label=y_train)
dtest=xgb.DMatrix(X_test, label=y_test)
param = {'max_depth': 10, 'eta': 0.05, 'silent': 1, 'objective': 'binary:logistic'}
num_round = 4000
param['nthread'] = 4
# param['eval_metric'] = "auc"
plst = param.items()
plst = list(plst)
plst += [('eval_metric', 'logloss')]
evallist = [(dtest, 'eval'), (dtrain, 'train')]
bst=xgb.train( plst, dtrain, num_round, evallist)
sub_user_index, sub_trainning_date, sub_label = make_train_set(sub_start_date, sub_end_date,
sub_test_start_date, sub_test_end_date)
test = xgb.DMatrix(sub_trainning_date)
y = bst.predict(test)
pred = sub_user_index.copy()
y_true = sub_user_index.copy()
pred['label'] = y
y_true['label'] = sub_label
report(pred, y_true)
# report(y, y_true)
if __name__ == '__main__':
# xgboost_cv2()
logistic_make_submission()
# gbdt_make_submission()
# xgboost_make_submission()
# train_start_date = '2016-03-10'
# train_end_date = '2016-04-11'
# test_start_date = '2016-04-11'
# test_end_date = '2016-04-16'
# sub_start_date = '2016-03-15'
# sub_end_date = '2016-04-16'
# sub_user_index, sub_trainning_data = make_test_set(sub_start_date, sub_end_date)