def xgbTuning(self, pX, change = 3):
w = self.getWeight(self.y)
dm = xgb.DMatrix(pX, self.y, weight=w)
best_auc = 0
n = pX.shape[0]
best_params = None
for i in range(change):
randp = np.random.random_sample(3)
param = {
'bst:eta': randp[0],
'max_depth': int(3+6*randp[1]) ,
'nthread':4,
'silent':1,
'alpha':randp[2],
'eval_metric':'auc',
'objective': 'binary:logistic'
}
m = xgb.cv(param, dm, metrics='auc', nfold=3, num_boost_round = 50,early_stopping_rounds=5)
auc = m['test-auc-mean'].max()
if auc > best_auc :
print 'xgb:' + str(auc)
best_auc = auc
best_params = param
Xtrain, Xtest, ytrain, ytest = train_test_split(pX, self.y, test_size=.33)
trainw = self.getWeight(ytrain)
testw = self.getWeight(ytest)
dtrain = xgb.DMatrix(Xtrain, label = ytrain, feature_names=Xtrain.columns, weight = trainw)
dtest = xgb.DMatrix(Xtest, label = ytest, feature_names=Xtest.columns, weight = testw)
evallist = [(dtrain, 'train'), (dtest, 'eval')]
booster = xgb.train(best_params, dtrain, evals=evallist, num_boost_round=100,early_stopping_rounds=10)
rounds = booster.attr("best_iteration")
best_auc = booster.attr("best_score")
return float(best_auc), xgb.train(best_params, dtrain, num_boost_round=int(rounds))
def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=1000):
param = {}
param['objective'] = 'multi:softprob'
param['eta'] = 0.1
param['max_depth'] = 6
param['silent'] = 1
param['num_class'] = 3
param['eval_metric'] = "mlogloss"
param['min_child_weight'] = 1
param['subsample'] = 0.7
param['colsample_bytree'] = 0.7
param['seed'] = seed_val
num_rounds = num_rounds
plst = list(param.items())
xgtrain = xgb.DMatrix(train_X, label=train_y)
if test_y is not None:
xgtest = xgb.DMatrix(test_X, label=test_y)
watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=20)
else:
xgtest = xgb.DMatrix(test_X)
model = xgb.train(plst, xgtrain, num_rounds)
pred_test_y = model.predict(xgtest)
return pred_test_y, model
def hyperopt_obj(self,param,train_X,train_y):
# 5-fold crossvalidation error
#ret = xgb.cv(param,dtrain,num_boost_round=param['num_round'])
kf = KFold(n_splits = 3)
errors = []
r2 = []
int_params = ['max_depth','num_round']
for item in int_params:
param[item] = int(param[item])
for train_ind,test_ind in kf.split(train_X):
train_valid_x,train_valid_y = train_X[train_ind],train_y[train_ind]
test_valid_x,test_valid_y = train_X[test_ind],train_y[test_ind]
dtrain = xgb.DMatrix(train_valid_x,label = train_valid_y)
dtest = xgb.DMatrix(test_valid_x)
pred_model = xgb.train(param,dtrain,num_boost_round=int(param['num_round']))
pred_test = pred_model.predict(dtest)
errors.append(mean_squared_error(test_valid_y,pred_test))
r2.append(r2_score(test_valid_y,pred_test))
all_dtrain = xgb.DMatrix(train_X,label = train_y)
print('training score:')
pred_model = xgb.train(param,all_dtrain,num_boost_round= int(param['num_round']))
all_dtest = xgb.DMatrix(train_X)
pred_train = pred_model.predict(all_dtest)
print(str(r2_score(train_y,pred_train)))
print(np.mean(r2))
print('\n')
return {'loss':np.mean(errors),'status': STATUS_OK}
def fit(self, X, y):
X = self.build_matrix(X, y)
param = {
'silent': 1 if self.silent else 0,
'use_buffer': int(self.use_buffer),
'num_round': self.num_round,
'ntree_limit': self.ntree_limit,
'nthread': self.nthread,
'booster': self.booster,
'eta': self.eta,
'gamma': self.gamma,
'max_depth': self.max_depth,
'min_child_weight': self.min_child_weight,
'subsample': self.subsample,
'colsample_bytree': self.colsample_bytree,
'max_delta_step': self.max_delta_step,
'l': self.l,
'alpha': self.alpha,
'lambda_bias': self.lambda_bias,
'objective': self.objective,
'eval_metric': self.eval_metric,
'seed': self.seed
}
if self.num_class is not None:
param['num_class']= self.num_class
watchlist = [(X,'train')]
if self.early_stopping_rounds > 0:
self.bst = xgb.train(param, X, self.num_round, watchlist, early_stopping_rounds=self.early_stopping_rounds)
else:
self.bst = xgb.train(param, X, self.num_round, watchlist)
return self
def test_predict(self):
iterations = 10
np.random.seed(1)
test_num_rows = [10, 1000, 5000]
test_num_cols = [10, 50, 500]
for num_rows in test_num_rows:
for num_cols in test_num_cols:
dtrain = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
dval = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
dtest = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows / 2))
watchlist = [(dtrain, 'train'), (dval, 'validation')]
res = {}
param = {
"objective": "binary:logistic",
"predictor": "gpu_predictor",
'eval_metric': 'auc',
}
bst = xgb.train(param, dtrain, iterations, evals=watchlist, evals_result=res)
assert self.non_decreasing(res["train"]["auc"])
gpu_pred_train = bst.predict(dtrain, output_margin=True)
gpu_pred_test = bst.predict(dtest, output_margin=True)
gpu_pred_val = bst.predict(dval, output_margin=True)
param["predictor"] = "cpu_predictor"
bst_cpu = xgb.train(param, dtrain, iterations, evals=watchlist)
cpu_pred_train = bst_cpu.predict(dtrain, output_margin=True)
cpu_pred_test = bst_cpu.predict(dtest, output_margin=True)
cpu_pred_val = bst_cpu.predict(dval, output_margin=True)
np.testing.assert_allclose(cpu_pred_train, gpu_pred_train, rtol=1e-5)
np.testing.assert_allclose(cpu_pred_val, gpu_pred_val, rtol=1e-5)
np.testing.assert_allclose(cpu_pred_test, gpu_pred_test, rtol=1e-5)
def train_predict(self,X_train,y_train,X_test,base_train_prediction,base_test_prediction):
xgmat_train = xgb.DMatrix(X_train, label=y_train,missing=-999)
test_size = X_test.shape[0]
param = {}
param['objective'] = 'binary:logistic'
param['bst:eta'] = self.eta
param['colsample_bytree']=1
param['min_child_weight']=self.min_child_weight
param['bst:max_depth'] = self.depth
param['eval_metric'] = 'auc'
param['silent'] = 1
param['nthread'] = self.threads
plst = list(param.items())
watchlist = [ (xgmat_train,'train') ]
num_round = self.num_round
xgmat_test = xgb.DMatrix(X_test,missing=-999)
if self.boost_from_exist_prediction:
# train xgb with existing predictions
# see more at https://github.com/tqchen/xgboost/blob/master/demo/guide-python/boost_from_prediction.py
xgmat_train.set_base_margin(base_train_prediction)
xgmat_test.set_base_margin(base_test_prediction)
bst = xgb.train(param, xgmat_train, self.exist_num_round, watchlist )
else:
bst = xgb.train( plst, xgmat_train, num_round, watchlist )
ypred = bst.predict(xgmat_test)
return ypred
def test_custom_objective(self):
param = {'max_depth':2, 'eta':1, 'silent':1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# test custom_objective in training
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
# test maximize parameter
def neg_evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels == (preds > 0.0))) / len(labels)
bst2 = xgb.train(param, dtrain, num_round, watchlist, logregobj, neg_evalerror, maximize=True)
preds2 = bst2.predict(dtest)
err2 = sum(1 for i in range(len(preds2)) if int(preds2[i]>0.5)!=labels[i]) / float(len(preds2))
assert err == err2
def test_fast_histmaker(self):
variable_param = {'tree_method': ['hist'],
'max_depth': [2, 8],
'max_bin': [2, 256],
'grow_policy': ['depthwise', 'lossguide'],
'max_leaves': [64, 0],
'verbosity': [0]}
for param in parameter_combinations(variable_param):
result = run_suite(param)
assert_results_non_increasing(result, 1e-2)
# hist must be same as exact on all-categorial data
dpath = 'demo/data/'
ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
ag_param = {'max_depth': 2,
'tree_method': 'hist',
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': 'auc'}
hist_res = {}
exact_res = {}
xgb.train(ag_param, ag_dtrain, 10,
[(ag_dtrain, 'train'), (ag_dtest, 'test')],
evals_result=hist_res)
ag_param["tree_method"] = "exact"
xgb.train(ag_param, ag_dtrain, 10,
[(ag_dtrain, 'train'), (ag_dtest, 'test')],
evals_result=exact_res)
assert hist_res['train']['auc'] == exact_res['train']['auc']
assert hist_res['test']['auc'] == exact_res['test']['auc']
def run_benchmark(args):
print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
tmp = time.time()
X, y = make_classification(args.rows, n_features=args.columns, random_state=7)
if args.sparsity < 1.0:
X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
print ("DMatrix Start")
dtrain = xgb.DMatrix(X_train, y_train, nthread=-1)
dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
param = {'objective': 'binary:logistic'}
if args.params is not '':
param.update(ast.literal_eval(args.params))
param['tree_method'] = args.tree_method
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
def run_benchmark(args, gpu_algorithm, cpu_algorithm):
print("Generating dataset: {} rows * {} columns".format(args.rows,args.columns))
tmp = time.time()
X, y = make_classification(args.rows, n_features=args.columns, random_state=7)
print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
print ("DMatrix Start")
# omp way
dtrain = xgb.DMatrix(X, y, nthread=-1)
# non-omp way
#dtrain = xgb.DMatrix(X, y)
print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
param = {'objective': 'binary:logistic',
'max_depth': 6,
'silent': 0,
'n_gpus': 1,
'gpu_id': 0,
'eval_metric': 'auc'}
param['tree_method'] = gpu_algorithm
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations)
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
param['silent'] = 1
param['tree_method'] = cpu_algorithm
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations)
print ("Time: %s seconds" % (str(time.time() - tmp)))
def fit(self, X, y,num_boost_round=None):
num_boost_round = num_boost_round or self.num_boost_round
self.label2num = dict((label, i) for i, label in enumerate(sorted(set(y))))
early_stopping = False
if early_stopping == True:
xg_train,xg_validate,xg_train_y,xg_validate_y = train_test_split(X,y,test_size=0.2)
print self.params
if self.params["objective"] == "binary:logistic":
print "binary:logistic"
dtrain = xgb.DMatrix(xg_train, label=xg_train_y)
dvalid = xgb.DMatrix(xg_validate, label=xg_validate_y)
else:
dtrain = xgb.DMatrix(X, label=[self.label2num[label] for label in xg_train_y])
dvalid = xgb.DMatrix(X, label=[self.label2num[label] for label in xg_validate_y])
#evallist = [(dtrain,'train')]
watchlist = [(dtrain,'train'),(dvalid,'val')]
self.clf = xgb.train(self.params, dtrain, num_boost_round,watchlist,early_stopping_rounds=80)
else:
xg_train,xg_train_y = X,y
if self.params["objective"] == "binary:logistic":
print "binary:logistic"
dtrain = xgb.DMatrix(xg_train, label=xg_train_y)
watchlist = [(dtrain,'train')]
self.clf = xgb.train(self.params, dtrain, num_boost_round,watchlist)
else:
dtrain = xgb.DMatrix(X, label=[self.label2num[label] for label in xg_train_y])
watchlist = [(dtrain,'train')]
self.clf = xgb.train(self.params, dtrain, num_boost_round,watchlist)
def test_multi_predict(self):
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
n = 1000
X, y = make_regression(n, random_state=rng)
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=123)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test)
params = {}
params["tree_method"] = "gpu_hist"
params['predictor'] = "gpu_predictor"
bst_gpu_predict = xgb.train(params, dtrain)
params['predictor'] = "cpu_predictor"
bst_cpu_predict = xgb.train(params, dtrain)
predict0 = bst_gpu_predict.predict(dtest)
predict1 = bst_gpu_predict.predict(dtest)
cpu_predict = bst_cpu_predict.predict(dtest)
assert np.allclose(predict0, predict1)
assert np.allclose(predict0, cpu_predict)
def fit(self, X, y):
#data
data = X
label = y
skf = cross_validation.StratifiedKFold(label, n_folds=10)
for train_index, val_index in skf:
dtrain = xgb.DMatrix(data[train_index], label=label[train_index])
dval = xgb.DMatrix(data[val_index], label=label[val_index])
break
#set params
plst = self.param.items()
plst += [('eval_metric', 'merror')]
evallist = [ (dtrain,'train'), (dval,'eval')]
#train
if self.bst == None:
num_round = 100000
self.bst = xgb.train(plst, dtrain, num_round, evals=evallist, early_stopping_rounds=100)
self.best_score_ = 1-self.bst.best_score
self.best_params_= self.bst.best_iteration
#refit
dtrain = xgb.DMatrix(data, label=label)
num_round = self.best_params_
self.bst = xgb.train(plst, dtrain, num_round, evals=evallist)
def XgbTrain(self, submitfile):
offset = 5000
X_train, y_train = self.dataMat, self.labelMat
X_test = self.testData
xgtest = xgb.DMatrix(X_test)
xgtrain_train = xgb.DMatrix(X_train[offset:,:], label=y_train[offset:])
xgtrain_val = xgb.DMatrix(X_train[:offset,:], label=y_train[:offset])
watchlist = [(xgtrain_train, 'train'),(xgtrain_val, 'val')]
model = xgb.train(self.params_best, xgtrain_train, self.num_rounds_best, watchlist,early_stopping_rounds=self.early_stopping_rounds_best)
preds1 = model.predict(xgtest)
X_train = X_train[::-1,:]
y_train = y_train[::-1]
xgtrain_train = xgb.DMatrix(X_train[offset:,:], label=y_train[offset:])
xgtrain_val = xgb.DMatrix(X_train[:offset,:], label=y_train[:offset])
watchlist = [(xgtrain_train, 'train'),(xgtrain_val, 'val')]
model = xgb.train(self.params_best, xgtrain_train, self.num_rounds_best, watchlist, early_stopping_rounds=self.early_stopping_rounds_best)
preds2 = model.predict(xgtest)
preds = preds1 + preds2
#preds = pd.DataFrame({"Id": self.testid, "Hazard": preds})
if submitfile!='':
writer=csv.writer(open(submitfile,'wb'))
writer.writerow(['ID','Hazard'])
for i in range(len(preds)):
line = [self.testid[i], preds[i]]
writer.writerow(line)
def train_predict(self,train_x,train_y,test_x):
xgmat_train = xgb.DMatrix(train_x, label=train_y, missing=-9999)
test_size = test_x.shape[0]
params = {
'booster':'gbtree',
'objective':'binary:logistic',
'silent':self.silent,
'eta':self.eta,
'gamma':self.gamma,
'max_depth':self.max_depth,
'min_chile_weitght':self.min_chile_weight,
'subsample':self.subsample,
'lambda':self.lambda_,
'scale_pos_weight':self.scale_pos_weight,
"colsample_bytree": self.colsample_bytree,
'eval_metirc':'auc',
'seed':2014,
'nthread':self.threads
}
watchlist = [ (xgmat_train,'train') ]
num_round = self.num_boost_round
bst = xgb.train( params, xgmat_train, num_round, watchlist )
xgmat_test = xgb.DMatrix(test_x,missing=-9999)
if self.exist_prediction:
tmp_train = bst.predict(xgmat_train, output_margin=True)
tmp_test = bst.predict(xgmat_test, output_margin=True)
xgmat_train.set_base_margin(tmp_train)
xgmat_test.set_base_margin(tmp_test)
bst = xgb.train(params, xgmat_train, self.exist_num_boost_round, watchlist )
ypred = bst.predict(xgmat_test)
return ypred
def run(train_matrix,test_matrix):
params = {'booster': 'gbtree',
#'objective': 'multi:softmax',
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'gamma': 1,
'min_child_weight': 1.5,
'max_depth': 5,
'lambda': 10,
'subsample': 0.7,
'colsample_bytree': 0.7,
'colsample_bylevel': 0.7,
'eta': 0.03,
'tree_method': 'exact',
'seed': 2017,
'nthread': 12,
"num_class":3
}
num_round = 10000
early_stopping_rounds = 50
watchlist = [(train_matrix, 'train'),
(test_matrix, 'eval')
]
if test_matrix:
model = xgb.train(params, train_matrix, num_boost_round=num_round, evals=watchlist,
early_stopping_rounds=early_stopping_rounds
)
pred_test_y = model.predict(test_matrix,ntree_limit=model.best_iteration)
return pred_test_y, model
else:
model = xgb.train(params, train_matrix, num_boost_round=num_round
)
return model
def run_benchmark(args, gpu_algorithm, cpu_algorithm):
print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
tmp = time.time()
X, y = make_classification(args.rows, n_features=args.columns, random_state=7)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
print ("DMatrix Start")
# omp way
dtrain = xgb.DMatrix(X_train, y_train, nthread=-1)
dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
param = {'objective': 'binary:logistic',
'max_depth': 6,
'silent': 0,
'n_gpus': 1,
'gpu_id': 0,
'eval_metric': 'error',
'debug_verbose': 0,
}
param['tree_method'] = gpu_algorithm
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
param['silent'] = 1
param['tree_method'] = cpu_algorithm
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
print ("Time: %s seconds" % (str(time.time() - tmp)))
def evalModelOHE(train_data, eval_data, train_labels, eval_labels):
params = {}
# params["objective"] = "reg:linear"
# params["eta"] = 0.05
# params["min_child_weight"] = 8
# params["subsample"] = 0.7
# params["colsample_bytree"] = 0.7
# params["scale_pos_weight"] = 1.0
# params["silent"] = 1
# params["max_depth"] = 8
# params["max_delta_step"]=2
params["objective"] = "reg:linear"
params["eta"] = 0.013
params["min_child_weight"] = 6
params["subsample"] = 0.51
params["colsample_bytree"] = 0.6
params["scale_pos_weight"] = 1.0
params["silent"] = 1
params["max_depth"] = 10
params["max_delta_step"]=1
plst = list(params.items())
xgtrain = xgb.DMatrix(train_data,label=train_labels)
xgeval = xgb.DMatrix(eval_data,label=eval_labels)
evallist = [(xgeval,'eval'), (xgtrain,'train')]
xgb.train(plst, xgtrain, num_boost_round=5000, evals=evallist,feval=evalerror)
def train(self, X, Y, getApproxError=False):
dtrain = xgb.DMatrix(X, label=Y)
self.bst = xgb.train(self.param, dtrain, self.nRounds)
if getApproxError:
e = 0.0
c = 0.0
kf = KFold(Y.shape[0], n_folds=4)
for train_index, test_index in kf:
XTrain = X[train_index, :]
XTest = X[test_index, :]
YTrain = Y[train_index]
YTest = Y[test_index]
dtrain2 = xgb.DMatrix(XTrain, label=YTrain)
bst = xgb.train(self.param, dtrain2, self.nRounds)
dtest = xgb.DMatrix(XTest)
probs = bst.predict(dtest)
ypred =numpy.argmax(probs, axis=1)
error = float(numpy.sum(ypred != YTest))
e += error
c += float(len(YTest))
e/=c
return e
def xgboost_model(train, test, num_round, params):
"""
Takes in: training set, test set, number of estimators, params is a list
Returns: predictions in correct format
"""
X = train.as_matrix(train.columns[:-1]).astype(float)
y = train.as_matrix(["cost"])[:, 0].astype(float)
ylog1p = np.log1p(y)
X_test = test.as_matrix(test.columns[:-1]).astype(float)
xgb_train = xgb.DMatrix(X, label=ylog1p)
xgb_test = xgb.DMatrix(X_test)
# Round 1
bst1 = xgb.train(params, xgb_train, num_round)
y_pred1 = bst1.predict(xgb_test)
# Round 2
# num_round2 = 2000
# bst2 = xgb.train(params, xgb_train, 2000)
# y_pred2 = bst2.predict(xgb_test)
# Power Train
ypower3 = np.power(y, 1 / 47.0)
xgb_train3 = xgb.DMatrix(X, label=ypower3)
xst3 = xgb.train(params, xgb_train3, num_round)
y_predp3 = xst3.predict(xgb_test)
p = 0.5
y_pred = p * np.expm1(y_pred1) + (1 - p) * np.power(y_predp3, 47.0)
return y_pred
def xgb_features(X,y,Xtest,params=None,random_state=0,n_folds=4,early_stop=20,eval_with_gini=False):
try:
if params['objective'] == 'reg:logistic':
yt = MinMaxScaler().fit_transform(y*1.)
else:
yt = y
skf = StratifiedKFold(yt, n_folds=n_folds,shuffle=True,random_state=random_state)
ypred_test = np.zeros(Xtest.shape[0])
ypred_train =np.zeros(X.shape[0])
seed = random_state;
dtest = xgb.DMatrix(data=Xtest)
for train_index, test_index in skf:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = yt[train_index], yt[test_index]
dtrain = xgb.DMatrix(data=X_train,label=y_train)
dvalid = xgb.DMatrix(data=X_test,label=y_test)
evallist = [(dtrain,'train'),(dvalid,'valid')]
num_round = 5000
params['seed'] = seed+1
seed+=1
plst = params.items()
if eval_with_gini:
bst = xgb.train( plst, dtrain, num_round,evallist,early_stopping_rounds=early_stop,feval=evalerror)
else :
bst = xgb.train( plst, dtrain, num_round,evallist,early_stopping_rounds=early_stop)
ypred = bst.predict(dtest,ntree_limit=bst.best_iteration)
ypred_valid = bst.predict(dvalid)
print ("\tcross validation gini score %s: %f"%(params['objective'],gini(y_test,ypred_valid)))
ypred_test += ypred
ypred_train[test_index] = ypred_valid
except KeyboardInterrupt:
ypred_test = np.zeros(Xtest.shape[0]);
ypred_train = np.zeros(X.shape[0]);
return ypred_train, ypred_test
return ypred_train, ypred_test*1./n_folds
def xgboost_pred(train,labels,test):
params = {}
params["objective"] = "reg:linear"
params["eta"] = 0.01
params["min_child_weight"] = 25
params["subsample"] = 0.8
params["colsample_bytree"] = 0.85
params["scale_pos_weight"] = 1.0
params["silent"] = 1
params["max_depth"] = 10
plst = list(params.items())
#Using 8000 rows for early stopping.
offset = 8000
num_rounds = 5000
xgtest = xgb.DMatrix(test)
#create a train and validation dmatrices
xgtrain = xgb.DMatrix(train[offset:,:], label=labels[offset:])
xgval = xgb.DMatrix(train[:offset,:], label=labels[:offset])
# xgtrain = xgb.DMatrix(train, label=labels)
#xgval = xgb.DMatrix(train, label=labels)
#train using early stopping and predict
watchlist = [(xgtrain, 'train'),(xgval, 'val')]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50)
preds_valid = model.predict(xgval)
valid_gini=Gini(np.array(labels[:offset,]), preds_valid[:,])
print valid_gini
#model = xgb.train(plst, xgtrain, 1000)
preds1 = model.predict(xgtest)
#reverse train and labels and use different 5k for early stopping.
# this adds very little to the score but it is an option if you are concerned about using all the data.
train = train[::-1,:]
labels = np.log(labels[::-1])
xgtrain = xgb.DMatrix(train[offset:,:], label=labels[offset:])
xgval = xgb.DMatrix(train[:offset,:], label=labels[:offset])
# xgtrain = xgb.DMatrix(train, label=labels)
watchlist = [(xgtrain, 'train'),(xgval, 'val')]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50)
preds_valid = model.predict(xgval)
valid_gini=Gini(np.array(labels[:offset,]), preds_valid[:,])
print valid_gini
# model = xgb.train(plst, xgtrain, 1000)
preds2 = model.predict(xgtest)
#combine predictions
#since the metric only cares about relative rank we don't need to average
preds = preds1*0.4 + preds2*0.6
return preds
def model(self):
x = self.train[self.features].values
x_test = self.test[self.features].values
# TODO compute from no. of rows & features
k_folds = 10
n_times = 1
dx = xgb.DMatrix(x, label=self.y, missing = float('nan'))
dtest = xgb.DMatrix(x_test, missing = float('nan') )
# setup parameters for xgboost
param = {}
param['objective'] = 'multi:softmax'
param['eval_metric'] = 'mlogloss'
param['num_class'] = self.noOfClasses
param['eta'] = 0.3
param['max_depth'] = 4
param['silent'] = 1
# param['subsample'] = 0.5
# param['colsample_bytree'] = 0.6
scores = []
iters = []
for n in range(n_times):
print '---------------- ' + str(n+1)
skf = StratifiedKFold(self.y, n_folds=k_folds, shuffle=True)
# for train_index, validation_index in skf:
for validation_index, train_index in skf:
x_train, x_validate = x[train_index], x[validation_index]
y_train, y_validate = self.y[train_index], self.y[validation_index]
dtrain = xgb.DMatrix(x_train, label=y_train, missing = float('nan') )
dval = xgb.DMatrix(x_validate, label=y_validate, missing = float('nan') ) #
watchlist = [ (dtrain,'train'), (dval, 'test') ]
num_round = 500
clf = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=30, verbose_eval=False) #
scores.append(np.absolute(clf.best_score))
iters.append(clf.best_iteration)
print('\n=========== overall eval metric for ' + str(k_folds) + ' folds ===========')
print(' '.join(self.features) + '\n')
print(param)
print('XGBoost classifier = ' + str(1-np.mean(scores)) + ' +/- ' + str(round(np.std(scores)*100, 2)) + '%' )
n_rounds_max = np.max(iters) + 1
xgb_clf = xgb.train(param, dx, n_rounds_max)
self.predictions = xgb_clf.predict(dtest)
print self.predictions
submission = pd.DataFrame({ self.id_col: self.test[self.id_col],
self.y.name: self.predictions })
submission[self.y.name] = submission[self.y.name]
submission.to_csv(os.path.join(self.directory, 'submission.csv' ), index=False)
def learn_and_predict_xgb(self, dataset='train'):
'''
Use xgboost to do work
'''
#predictors = ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Titles", "FamilyId"]
predictors = self.PREDICTORS
if dataset == 'train':
param_dist = {'max_depth': sp_randint(3, 10),
'learning_rate': [0.01, 0.03, 0.1, 0.3, 1.0],
'gamma': [0, 0.1, 0.2, 0.3],
'subsample': [.1, .2, .3, .4, 0.5],
'colsample_bytree': [.4, .5],
'objective': ['binary:logistic'],
'n_estimators': sp_randint(20, 150),
}
clf = xgb.XGBClassifier()
#random_search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=500, cv=3)
#random_search.fit(self.train_df[predictors], self.train_df['Survived'])
#report(random_search.grid_scores_)
params = {'max_depth': 6, 'learning_rate': 0.1, 'colsample_bytree': 0.5, 'n_estimators': 54, 'subsample': .3, 'gamma': 0, 'objective':'binary:logistic', 'eval_metric': 'auc'} #0.845, cv=3
bst = xgb.train(params, self.DMatrix_train)
predictions = pd.Series(bst.predict(self.DMatrix_train))
predictions[predictions >= .5] = 1
predictions[predictions < .5] = 0
predictions = [int(x) for x in predictions.tolist()]
train_model = pd.DataFrame({
'PassengerId': self.train_df['PassengerId'],
'Survived': predictions,
})
train_model.to_csv('./xgb_train.csv', index=False)
else:
params = {'max_depth': 6, 'learning_rate': 0.1, 'colsample_bytree': 0.5, 'n_estimators': 54, 'subsample': .3, 'gamma': 0, 'objective':'binary:logistic', 'eval_metric': 'auc'} #0.845, cv=3
bst = xgb.train(params, self.DMatrix_train)
#clf = xgb.XGBClassifier(params)
#clf.fit(self.train_df[predictors], self.train_df['Survived'], verbose=True)
#print(self.test_df[predictors])
predictions = pd.Series(bst.predict(self.DMatrix_test))
predictions_proba = predictions.copy()
predictions[predictions >= .5] = 1
predictions[predictions < .5] = 0
predictions = [int(x) for x in predictions.tolist()]
print(predictions)
submission = pd.DataFrame({
'PassengerId': self.test_df['PassengerId'],
'Survived': predictions
})
submission.to_csv("xgboost_845.csv", index=False)
submission_proba = pd.DataFrame({
'PassengerId': self.test_df['PassengerId'],
'Survived': predictions_proba,
})
submission_proba.to_csv("xgboost_845_soft.csv", index=False)
def predict(self, X, y, X_test, stage):
np.random.seed(self.seed)
n_train = X.shape[0]
kf = KFold(n_train, n_folds=self.n_fold, shuffle=True)
param = {}
param['objective'] = self.obj
param['eval_metric'] = self.eval_metric
param['num_class'] = self.num_class
param['nthread'] = self.nthread
param['silent'] = self.silent
param['eta'] = self.eta
param['colsample_bytree'] = self.colsample_bytree
param['subsample'] = self.subsample
param['max_depth'] = self.max_depth
param['max_delta_step'] = self.max_delta_step
param['gamma'] = self.gamma
param['alpha'] = self.alpha
param['lambda'] = self.param_lambda
num_round = 10000
best_score = []
best_iter = []
y_pred_sum = np.zeros((X_test.shape[0], self.num_class))
if stage=='base':
meta_feat = np.zeros((n_train+X_test.shape[0], self.num_class))
xg_test = xgb.DMatrix(X_test)
i = 0
for train, val in kf:
i += 1
print(i)
X_train, X_val, y_train, y_val = X[train], X[val], y[train], y[val]
xg_train = xgb.DMatrix(X_train, y_train)
xg_val = xgb.DMatrix(X_val, y_val)
evallist = [(xg_train,'train'), (xg_val,'eval')]
## CV sets
# train
bst = xgb.train(param, xg_train, num_round, evallist, early_stopping_rounds=100)
best_score += [bst.best_score]
best_iter += [bst.best_iteration]
# predict
if stage=='base':
meta_feat[val, :] = bst.predict(xg_val, ntree_limit=bst.best_iteration)
else:
y_pred = bst.predict(xg_test, ntree_limit=bst.best_iteration)
y_pred_sum = y_pred_sum+y_pred
print(np.mean(best_score), np.std(best_score))
## test set
if stage=='base':
# train
xg_train = xgb.DMatrix(X, y)
evallist = [(xg_train,'train')]
bst = xgb.train(param, xg_train, int(np.mean(best_iter)), evallist)
# predict
meta_feat[n_train:, :] = bst.predict(xg_test)
return meta_feat
else:
y_pred = y_pred_sum/self.n_fold
return y_pred
def doclassify(self, type='normal'): # Boosting
if type == 'split':
dtrainmis = xgb.DMatrix(array(self.misstrain), array(self.misstrain_y), missing=NAN)
dtest = xgb.DMatrix(array(self.normaltest), missing=NAN)
dtestmis = xgb.DMatrix(array(self.misstest), missing=NAN)
param = {'bst:max_depth':10, 'bst:eta':0.02, 'silent':1, 'objective':'binary:logistic', 'subsample':0.8,"colsample_bytree": 0.68,"booster": "gbtree"}
param['nthread'] = 4
param['eval_metric'] = 'logloss'
evallist = [(dtrainmis, 'train')]
num_round = 320
bstmis = xgb.train(param, dtrainmis, num_round, evallist,)
dtrain = xgb.DMatrix(array(self.normaltrain), array(self.normaltrain_y))
num_round = 366
evallist = [(dtrain, 'train')]
bst = xgb.train(param, dtrain, num_round, evallist,)
ypredmis = bstmis.predict(dtestmis)
ypred = bst.predict(dtest)
result = []
output1 = list(ypredmis)
output2 = list(ypred)
for i in self.test:
if dp.List_dataProcess().check_missing(i):
result.append(output1.pop(0))
else:
result.append(output2.pop(0))
print len(output1)
print len(output2)
writer(self.id, result)
if type == 'normal':
dtrain = xgb.DMatrix(array(self.train_x), array(self.train_y))
dtest = xgb.DMatrix(array(self.test))
param = {'bst:max_depth':10, 'bst:eta':0.02, 'silent':1, 'objective':'binary:logistic', 'subsample':0.9,"colsample_bytree": 0.68,"booster": "gbtree"}
param['nthread'] = 4
param['eval_metric'] = 'logloss'
evallist = [(dtrain, 'train')]
num_round = 300
bst = xgb.train(param, dtrain, num_round, evallist,)
ypred = bst.predict(dtest)
writer(self.id, ypred)
acc = 0.0
for i in range(10000):
if array(self.train_y)[len(self.train_y)-10000+i] == 1 and ypred[i] > 0.35:
acc += 1
if array(self.train_y)[len(self.train_y)-10000+i] == 0 and ypred[i] <= 0.35:
acc += 1
print "Accuracy : ", acc/10000
fpr, tpr, thresholds = metrics.roc_curve(self.train_y[-10000:], ypred, pos_label=1)
for i in range(len(fpr)):
plt.plot(fpr[i], tpr[i], "b*")
plt.plot(fpr, tpr)
plt.title(val)
plt.show()
print "AUC : ", metrics.auc(fpr, tpr)
print thresholds
def train(X, y, available_devices):
dtrain = xgb.dask.create_worker_dmatrix(X, y)
local_device = available_devices[xgb.rabit.get_rank()]
# Specify the GPU algorithm and device for this worker
params = {"tree_method": "gpu_hist", "gpu_id": local_device}
print("Worker {} starting training on {} rows".format(xgb.rabit.get_rank(), dtrain.num_row()))
start = time.time()
xgb.train(params, dtrain, num_boost_round=500)
end = time.time()
print("Worker {} finished training in {:0.2f}s".format(xgb.rabit.get_rank(), end - start))
def fit(self, X, y, X_valid=None, y_valid=None, sample_weight=None):
"""Fit training dafa.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
The input samples.
y : array-like, shape=(n_samples,)
X_valid : array-like or sparse matrix, shape=(n_valid_samples, n_features)
The validation samples.
y_valid : array-like, shape=(n_valid_samples,)
sample_weight : array-like, shape = [n_samples], optional
Returns
-------
self : object
Returns self.
"""
X, y = self._ready_to_fit(X, y)
xgb_params = self.get_xgb_params()
# xgboost accepts dense, csc, csr
if isinstance(X, sp.sparse.coo_matrix):
X = X.tocsc()
if sample_weight is not None:
xg_train = xgb.DMatrix(X, label=y, weight=sample_weight)
else:
xg_train = xgb.DMatrix(X, label=y)
watchlist = [ (xg_train,'train')]
if not (X_valid is None):
if isinstance(X_valid, sp.sparse.coo_matrix):
X_valid = X_valid.tocsc()
if sample_weight is not None:
xg_valid = xgb.DMatrix(X_valid, label=y_valid, weight=sample_weight)
else:
xg_valid = xgb.DMatrix(X_valid, label=y_valid)
watchlist = [ (xg_train,'train'), (xg_valid, 'valid') ]
if self.verbose:
# with watchlist
self.bst_ = xgb.train(params=xgb_params, dtrain=xg_train, num_boost_round=int(self.n_iter), evals=watchlist, early_stopping_rounds=int(self.early_stopping_rounds))
else:
# without watchlist
# early stopping is not available
self.bst_ = xgb.train(params=xgb_params, dtrain=xg_train, num_boost_round=int(self.n_iter))
return self
def train(train_X, train_Y, validation_X, validation_Y, feature_names):
train_X = array(train_X);
train_Y = array(train_Y);
validation_X = array(validation_X);
validation_Y = array(validation_Y);
print(type(train_X));
dtrain = xgb.DMatrix( train_X, label=train_Y, missing=float('NaN'));
dvalidation = xgb.DMatrix( validation_X, label=validation_Y,missing=float('NaN'))
#Clean up data
del train_X, validation_X, train_Y, validation_Y;
#Track metrics on the watchlist
watchlist = [ (dtrain,'train'), (dvalidation, 'validation') ]
parameters_to_try = generateParams();
best_params = None;
overall_best_metric = 0;
overall_best_nrounds = 0;
for i in range(0, len(parameters_to_try)):
param = parameters_to_try[i];
num_round = 1000;
classifier = xgb.train(param,dtrain,num_round,evals=watchlist,early_stopping_rounds=100);
metric = classifier.best_score;
itr = classifier.best_iteration;
print("\n Metric : " + str(metric) + " for Params " + str(param) + " occurs at " + str(itr));
if metric > overall_best_metric:
overall_best_metric = metric;
best_params = copy.copy(param);
overall_best_nrounds = itr;
print("\n Training the model on the entire training set with the best params")
bst = xgb.train(best_params, dtrain, 1+overall_best_nrounds);
print("\n\n Overall Best AUC : " + str(overall_best_metric) + " for Params " + str(best_params) + " occurs at " + str(overall_best_nrounds));
feature_imp = bst.get_fscore();
print("Feature Importance ... ");
for w in sorted(feature_imp, key=feature_imp.get, reverse=True):
print( str(feature_names[int(w.replace("f",""))]) + " : " + str(feature_imp[w]) );
return bst;
def buildXGB(self):
'''
train_shfl = train.iloc[np.random.permutation(len(train))]
X = train_shfl.as_matrix(train_shfl.columns[:-1]).astype(float)
y = train_shfl.as_matrix(['cost'])[:,0].astype(float)
ylog1p = np.log1p(y).astype(float)
'''
X = self.train.as_matrix(self.train.columns[:-1]).astype(float)
y = self.train.as_matrix(['cost'])[:,0].astype(float)
ylog1p = np.log1p(y).astype(float)
# cost is still last column
X_test = self.test.as_matrix(self.test.columns[:-1]).astype(float)
xgb_train = xgb.DMatrix(X, label = ylog1p)
xgb_test = xgb.DMatrix(X_test)
#Train multiple times
# Round 1
num_round1 = 4000
self.bst1 = xgb.train(self.params, xgb_train, num_round1)
y_pred1 = self.bst1.predict(xgb_test)
# Round 2
num_round2 = 2000
self.bst2 = xgb.train(self.params, xgb_train, num_round2)
y_pred2 = self.bst2.predict(xgb_test)
#Power Train
#ypower2 = np.power(y,1/5.0)
ypower3 = np.power(y,1/20.0)
#xgb_train2 = xgb.DMatrix(X, label = ypower2)
xgb_train3 = xgb.DMatrix(X, label = ypower3)
#self.xst2 = xgb.train(self.params, xgb_train2, self.num_round)
#y_predp2 = self.xst2.predict(xgb_test)
self.xst3 = xgb.train(self.params, xgb_train3, self.num_round)
y_predp3 = self.xst3.predict(xgb_test)
#y_power = (np.power(y_predp2,5.0) + np.power(y_predp3,10.0))/2.0
y_power = np.power(y_predp3,20.0)
self.y_pred = (np.expm1(0.75*y_pred1+0.25*y_pred2) + y_power)/2.0
#self.y_pred = 0.35*np.expm1(0.75*y_pred1+0.25*y_pred2) + 0.65*y_power
print
print "================================================================"
print "================ Finished with Prediction ==================="
print "================================================================"
train_data = xgb.DMatrix(result_path + 'train.sparse')
test_data = xgb.DMatrix(result_path + 'test.sparse')
param = {
'bst:max_depth': 10,
'bst:min_child_weight': 5,
'bst:eta': 0.5,
'silent': 0,
'objective': 'binary:logistic',
'eval_metric': 'logloss'
}
param['nthread'] = 4
plst = param.items()
evallist = [(train_data, 'train')]
num_round = 30
bst = xgb.train(plst, train_data, num_round, evallist)
bst.dump_model(result_path + 'dump.raw.txt')
ypred = bst.predict(test_data)
output = open(result_path + 'submission.csv', 'w')
output.write('Id,Predicted\n')
for p in ypred:
output.write('{0},{1}\n'.format('anything', p))
output.close()
'base_score': y_mean, # base prediction = mean(target)
'silent': 1
}
# prepare dict of params for xgboost to run with
# NOTE: Make sure that the class is labeled 'class' in the data file
dtrain = xgb.DMatrix(train.drop('y', axis=1), y_train)
dtest = xgb.DMatrix(test)
num_boost_rounds = 1350
# train model
model = xgb.train(
dict(xgb_params, silent=0),
dtrain,
num_boost_round=num_boost_rounds,
# early_stopping_rounds=50,
verbose_eval=10)
y_pred = model.predict(dtest)
'''Train the stacked models then predict the test data'''
# Stacking with average
en = make_pipeline(RobustScaler(), PCA(n_components=12),
ElasticNet(alpha=0.001, l1_ratio=0.1))
rf = RandomForestRegressor(n_estimators=250,
n_jobs=4,
min_samples_split=25,
min_samples_leaf=25,
max_depth=3)
def run_training_continuation(self, xgb_params_01, xgb_params_02,
xgb_params_03):
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
digits_2class = load_digits(n_class=2)
digits_5class = load_digits(n_class=5)
X_2class = digits_2class['data']
y_2class = digits_2class['target']
X_5class = digits_5class['data']
y_5class = digits_5class['target']
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
gbdt_01 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
gbdt_02 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=0)
gbdt_02.save_model('xgb_tc.model')
gbdt_02a = xgb.train(xgb_params_01,
dtrain_2class,
num_boost_round=10,
xgb_model=gbdt_02)
gbdt_02b = xgb.train(xgb_params_01,
dtrain_2class,
num_boost_round=10,
xgb_model="xgb_tc.model")
ntrees_02a = len(gbdt_02a.get_dump())
ntrees_02b = len(gbdt_02b.get_dump())
assert ntrees_02a == 10
assert ntrees_02b == 10
res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
assert res1 == res2
res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
gbdt_03 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=3)
gbdt_03.save_model('xgb_tc.model')
gbdt_03a = xgb.train(xgb_params_01,
dtrain_2class,
num_boost_round=7,
xgb_model=gbdt_03)
gbdt_03b = xgb.train(xgb_params_01,
dtrain_2class,
num_boost_round=7,
xgb_model="xgb_tc.model")
ntrees_03a = len(gbdt_03a.get_dump())
ntrees_03b = len(gbdt_03b.get_dump())
assert ntrees_03a == 10
assert ntrees_03b == 10
os.remove('xgb_tc.model')
res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
assert res1 == res2
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class, num_boost_round=3)
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration +
1) * self.num_parallel_tree
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
gbdt_04 = xgb.train(xgb_params_02,
dtrain_2class,
num_boost_round=7,
xgb_model=gbdt_04)
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration +
1) * self.num_parallel_tree
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class, num_boost_round=7)
assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration +
1) * self.num_parallel_tree
gbdt_05 = xgb.train(xgb_params_03,
dtrain_5class,
num_boost_round=3,
xgb_model=gbdt_05)
assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration +
1) * self.num_parallel_tree
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(dtrain_5class,
ntree_limit=gbdt_05.best_ntree_limit)
np.testing.assert_almost_equal(res1, res2)
X = format_input_for_network(X_train, N_FEAT)
X_eval = format_input_for_network(X_val, N_FEAT)
callback = tf.keras.callbacks.EarlyStopping(monitor='loss',
patience=PATIENCE,
restore_best_weights=True)
# Create our baseline model: a gradient boosted tree.
dtrain = xgb.DMatrix(X_train, label=y_train, silent=True)
param = {'max_depth': 2, 'eta': 1, 'objective': 'reg:squarederror'}
param['nthread'] = 4
param['eval_metric'] = 'rmse'
dtest = xgb.DMatrix(X_val, label=y_val, silent=True)
evallist = [(dtest, 'eval'), (dtrain, 'train')]
bst = xgb.train(param,
dtrain,
PATIENCE,
evallist,
early_stopping_rounds=10,
verbose_eval=10)
ypred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
#For ease of use and reproducability I have saved the model,
# but here is the code to create it from scratch.
'''
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(N_STEPS, N_FEAT), dropout=0.25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mae')
# fit model
model.fit(X, y_train, epochs=200, verbose=1, callbacks=[callback])
'scale_pos_weight': 1,
'colsample_bytree': 0.8,
'eval_metric': 'logloss',
'nthread': 8,
'sample_type': 'uniform',
'normalize_type': 'forest',
'random_state': 1
}
plst = params.items()
evallist = [(xgb_train, 'train'), (xgb_val, 'eval')]
num_round = 500
bst = xgb.train(plst,
xgb_train,
num_round,
evals=evallist,
early_stopping_rounds=10)
#
# bst = xgb.cv(params=params, dtrain=xgb_train, nfold=5, metrics='logloss', verbose_eval=2, early_stopping_rounds=10)
# bst.save_model(out_path + 'xgb.model')
feat_importance = bst.get_fscore()
print(feat_importance)
with open(out_path + 'feat_importance.csv', 'w') as fo:
for k in feat_importance.keys():
fo.write(str(k) + ',')
fo.write('\n')
# 'scale_pos_weight': 10,
'eval_metric': 'auc',
'subsample': 0.76,
'colsample_bytree': 0.95,
# 'n_estimators': 5000,
# 'eta_decay': 0.5,
'seed': 1,
# 'min_child_weight': 0.8,
}
rounds = 10000
bst = xgb.train(param,
dtrain,
rounds,
early_stopping_rounds=300,
evals=evals,
evals_result=gpu_res,
verbose_eval=True)
# pickle.dump(bst, open(_dir+"/models/model.1", "wb"))
# bst = get_pickled(_dir, "models/model.1")
# trained = xgb.train(param, dtrain, 782)
# xgb.train(param, dtrain)
# imp = xgb.plot_importance(bst)
# scores = bst.get_score()
#
# scores_sort = sorted(scores.items(), key=lambda kv: kv[1])
# this is log likelihood loss
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0 - preds)
return grad, hess
# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make builtin evaluation metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the builtin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain):
labels = dtrain.get_label()
# return a pair metric_name, result. The metric name must not contain a colon (:)
# since preds are margin(before logistic transformation, cutoff at 0)
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst = xgb.train(param,
dtrain,
num_round,
watchlist,
obj=logregobj,
feval=evalerror)
tr_angle_mat.append(0)
tr_input_arr = np.array(tr_input_mat)
tr_angle_arr = np.array(tr_angle_mat)
dtrain = xgb.DMatrix(tr_input_arr, label=tr_angle_arr)
param = {
'max_depth': 6,
'eta': 0.2,
'subsumble': 0.5,
'silent': 1,
'objective': 'binary:logistic'
}
watchlist = [(dtrain, 'train')]
num_round = 3000 #10 #3000 # 1000
bst = xgb.train(param, dtrain, num_round, watchlist)
bst.dump_model('./dump.raw.txt')
bst.save_model('./hoge.model')
### training end
# trade
portfolio = 1000000
LONG = 1
SHORT = 2
NOT_HAVE = 3
pos_kind = NOT_HAVE
HALF_SPREAD = 0.0015
SONKIRI_RATE = 0.05
RIKAKU_PIPS = 0.60
def fit(self, x_train, y_train, x_valid=None, y_valid=None):
# xgb_start = time.time()
best_model = None
best_round = None
best_score = {}
if self.cv_folds is not None:
log.logger.info('CrossValidation。。。。')
d_train = xgb.DMatrix(x_train, label=y_train)
cv_result = self._kfold(d_train)
print('cv_result %s' % cv_result)
print('type_cv_result %s' % type(cv_result))
# min_rmse = pd.Series(cv_result['test-rmse-mean']).min()
# self.best_score['min_test-rmse-mean'] = min_rmse
# self.best_round = cv_result[cv_result['test-rmse-mean'].isin([min_rmse])].index[0]
best_score['min_test-rmse-mean'] = pd.Series(
cv_result['test-rmse-mean']).min()
best_round = pd.Series(cv_result['test-rmse-mean']).idxmin()
best_model = xgb.train(self.xgb_params, d_train, best_round)
elif self.ts_cv_folds is not None:
log.logger.info('TimeSeriesCrossValidation。。。。')
# 时间序列k_fold
bst_score = 0
details = []
scores = []
tscv = TimeSeriesSplit(n_splits=self.ts_cv_folds)
if self.xgb_params['objective'] is not 'reg:linear':
log.logger.error('Objective ERROR........')
exit()
for n_fold, (tr_idx, val_idx) in enumerate(tscv.split(x_train)):
print(f'the {n_fold} training start ...')
tr_x, tr_y, val_x, val_y = x_train.iloc[tr_idx], y_train[
tr_idx], x_train.iloc[val_idx], y_train[val_idx]
d_train = xgb.DMatrix(tr_x, label=tr_y)
d_valid = xgb.DMatrix(val_x, label=val_y)
watchlist = [(d_train, "train"), (d_valid, "valid")]
xgb_model = xgb.train(
params=self.xgb_params,
dtrain=d_train,
num_boost_round=self.num_boost_round,
evals=watchlist,
early_stopping_rounds=self.early_stop_round)
details.append((xgb_model.best_score, xgb_model.best_iteration,
xgb_model))
if xgb_model.best_score > bst_score:
bst_score = xgb_model.best_score
best_model = xgb_model
best_round = xgb_model.best_iteration
else:
best_model = xgb_model
best_round = xgb_model.best_iteration
scores.append(xgb_model.best_score)
best_score['avg_score'] = np.mean(scores)
else:
log.logger.info('NonCrossValidation。。。。')
if x_valid is None and y_valid is None:
# 注意这里的shift
# x_train, x_valid, y_train, y_valid = train_test_sp(x_train, y_train, test_size=0.2, shift=0)
d_train = xgb.DMatrix(x_train, label=y_train)
watchlist = [(d_train, "train")]
else:
d_train = xgb.DMatrix(x_train, label=y_train)
d_valid = xgb.DMatrix(x_valid, label=y_valid)
watchlist = [(d_train, "train"), (d_valid, "valid")]
best_model = xgb.train(params=self.xgb_params,
dtrain=d_train,
num_boost_round=self.num_boost_round,
evals=watchlist,
verbose_eval=5,
early_stopping_rounds=self.early_stop_round)
best_round = best_model.best_iteration
best_score['best_score'] = best_model.best_score
# print('spend time :' + str((time.time() - xgb_start)) + '(s)')
return best_score, best_round, best_model
# 训练函数参数设定
params = {
'objective': 'multi:softmax',
'eta': 0.1,
'max_depth': 9,
'eval_metric': 'merror',
'seed': 0,
'missing': -999,
'num_class':num_class,
'silent' : 1
}
#从数据中去掉多余的列
feature=[x for x in train1.columns if x not in ['user_id','label','shop_id','time_stamp','mall_id','wifi_infos']]
df_train_1 = df_train[feature] #训练数据对null值进行填充-100
df_train_1 = df_train_1.where(df_train_1.notnull(), -100)
xgbtrain = xgb.DMatrix(df_train_1, df_train['label'])
df_test_1 = df_test[feature] #测试数据对null值进行填充-100
df_test_1 = df_test_1.where(df_test_1.notnull(), -100)
xgbtest = xgb.DMatrix(df_test_1)
watchlist = [ (xgbtrain,'train'), (xgbtrain, 'test') ]
num_rounds=100
model = xgb.train(params, xgbtrain, num_rounds, watchlist, early_stopping_rounds=15) #训练模型
df_test['label']=model.predict(xgbtest) #预测
df_test['shop_id']=df_test['label'].apply(lambda x:lbl.inverse_transform(int(x))) #对标签1-N转化为shop_id
r=df_test[['row_id','shop_id']] #选出r
result=pd.concat([result,r])
j = j + 1
print j
result['row_id']=result['row_id'].astype('int')
result.to_csv('sub.csv',index=False)
def fit(self, data_x, data_y, num_class):
if self.special_objective is None:
# get the parameter list
self.para_dict = {
'max_depth': self.max_depth,
'eta': self.eta,
'silent': self.silent_mode,
'objective': self.objective_func,
'num_class': num_class,
'eval_metric': self.eval_metric,
'booster': self.booster
}
else:
# get the parameter list, without stating the objective function
self.para_dict = {
'max_depth': self.max_depth,
'eta': self.eta,
'silent': self.silent_mode,
'eval_metric': self.eval_metric,
'booster': self.booster
}
# make sure data is in [nData * nSample] format
assert len(data_x.shape) == 2
# check if data length is the same
if data_x.shape[0] != data_y.shape[0]:
raise ValueError(
'The numbner of instances for x and y data should be the same!'
)
# data_x is in [nData*nDim]
nData = data_x.shape[0]
nDim = data_x.shape[1]
# split the data into train and validation
holistic_ind = np.random.permutation(nData)
train_ind = holistic_ind[0:nData * 3 // 4]
valid_ind = holistic_ind[nData * 3 // 4:nData]
# indexing and get the data
train_data = data_x[train_ind]
train_label = data_y[train_ind]
valid_data = data_x[valid_ind]
valid_label = data_y[valid_ind]
# marixilize the data and train the estimator
dtrain = xgb.DMatrix(train_data, label=train_label)
dvalid = xgb.DMatrix(valid_data, label=valid_label)
self.eval_list = [(dvalid, 'valid'), (dtrain, 'train')]
if self.special_objective is None:
# fit the classfifier
self.boosting_model = xgb.train(self.para_dict,
dtrain,
self.num_round,
self.eval_list,
verbose_eval=False)
elif self.special_objective == 'weighted':
# fit the classfifier
self.boosting_model = xgb.train(self.para_dict,
dtrain,
self.num_round,
self.eval_list,
weighted_binary_cross_entropy,
evalerror,
verbose_eval=False)
elif self.special_objective == 'focal':
# fit the classfifier
self.boosting_model = xgb.train(self.para_dict,
dtrain,
self.num_round,
self.eval_list,
focal_binary_object,
evalerror,
verbose_eval=False)
else:
raise ValueError(
'The input special objective mode not recognized! Could only be \'weighted\' or \'focal\', but got '
+ str(self.special_objective))
'nthread': 20,
'seed': 42,
'silent': 0
}
if online == False:
Dtrain = xgb.DMatrix(train[features],
train[target],
feature_names=features)
Dtest = xgb.DMatrix(test[features],
test[target],
feature_names=features)
watchlist = [(Dtrain, 'train'), (Dtest, 'val')]
clf = xgb.train(xgb_pars,
Dtrain,
num_boost_round=450,
verbose_eval=1,
evals=watchlist)
#xx = clf.predict(Dtrain, output_margin=False, ntree_limit=0, pred_leaf=True)
train['lgb_predict'] = clf.predict(Dtrain)
print('train log_loss',
log_loss(train[target], train['lgb_predict']))
test['lgb_predict'] = clf.predict(Dtest)
print('test log_loss', log_loss(test[target], test['lgb_predict']))
"""
xgb_feature = clf.predict(Dtrain,pred_leaf=True)
xgb_feature = pd.DataFrame(xgb_feature,columns=["xgb_{}".format(i+1) for i in range(xgb_feature.shape[1])])
xgb_feature['instance_id'] = train['instance_id']
def xgb_train(train_df, test_df, mode, params, num_boost_round,
early_stopping):
if mode == "train":
train = train_df.values[:, 1:-1]
train_target = train_df.values[:, -1]
# 5-fold
kf = KFold(n_splits=5, shuffle=True)
trainEorror = 0
error = 0
for train_index, valid_index in kf.split(train):
x_train, x_valid = train[train_index], train[valid_index]
y_train, y_valid = train_target[train_index], train_target[
valid_index]
dtrain = xgb.DMatrix(x_train, y_train)
dvalid = xgb.DMatrix(x_valid, y_valid)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
gbm = xgb.train(params,
dtrain,
num_boost_round,
evals=watchlist,
early_stopping_rounds=early_stopping,
verbose_eval=True)
print("validating")
tranHat = gbm.predict(xgb.DMatrix(x_train))
trainEorror += rmsep(y_train, tranHat)
yhat = gbm.predict(xgb.DMatrix(x_valid))
error += rmsep(y_valid, yhat)
print('rmse:{:.6f}'.format(error / 5.0))
else:
train = train_df.values[:, 1:-1]
train_target = train_df.values[:, -1]
kf = KFold(n_splits=5, shuffle=True)
result = np.zeros(2000)
dtest = test_df.values[:, 1:]
dtest = xgb.DMatrix(dtest)
for train_index, valid_index in kf.split(train):
x_train, x_valid = train[train_index], train[valid_index]
y_train, y_valid = train_target[train_index], train_target[
valid_index]
dtrain = xgb.DMatrix(x_train, y_train)
watchlist = [(dtrain, 'train')]
gbm = xgb.train(
params,
dtrain,
num_boost_round=num_boost_round,
evals=watchlist,
early_stopping_rounds=early_stopping,
)
result += gbm.predict(dtest)
result = result / 5.0
return result
def xgb_model_val():
# 'is_first_get_coupon','user_hour_count_label','context_timestamp_rank_desc_label' user_item_brand_count user_diff_shop_count
train_set = pd.read_csv('data/ftrain.csv', sep=",")
validate_set = pd.read_csv('data/fvalidate.csv', sep=",")
train_x = train_set.drop([
'instance_id', 'context_id', 'item_city_id', 'item_id', 'user_id',
'item_brand_id', 'shop_id', 'user_gender_id', 'user_occupation_id',
'is_trade', 'context_timestamp', 'context_page_id',
'context_timestamp_and_dates', 'dates', 'day', 'hour',
'item_category_list', 'item_property_list',
'predict_category_property', 'is_first_get_coupon',
'context_timestamp_rank_desc_label', 'category',
'user_shop_count_istrade_rate', 'user_item_brand_count',
'user_istrade_rate', 'user_diff_shop_count',
'user_count_minus_user_count_istrade',
'user_item_count_minus_user_item_istrade', 'user_count_istrade',
'user_and_user_occupation_count_label',
'predict_property_jiaoji_item_property',
'user_shop_count_minus_user_shop_istrade'
],
axis=1)
train_y = train_set['is_trade']
# # 相关性分析
# pearson_analysis_feature(train_x,train_y)
# return
val_x = validate_set.drop([
'instance_id', 'context_id', 'item_city_id', 'item_id', 'user_id',
'item_brand_id', 'shop_id', 'user_gender_id', 'user_occupation_id',
'is_trade', 'context_timestamp', 'context_page_id',
'context_timestamp_and_dates', 'dates', 'day', 'hour',
'item_category_list', 'item_property_list',
'predict_category_property', 'is_first_get_coupon',
'context_timestamp_rank_desc_label', 'category',
'user_shop_count_istrade_rate', 'user_item_brand_count',
'user_istrade_rate', 'user_diff_shop_count',
'user_count_minus_user_count_istrade',
'user_item_count_minus_user_item_istrade', 'user_count_istrade',
'user_and_user_occupation_count_label',
'predict_property_jiaoji_item_property',
'user_shop_count_minus_user_shop_istrade'
],
axis=1)
val_y = validate_set['is_trade']
xgb_train = xgb.DMatrix(train_x, label=train_y)
xgb_val = xgb.DMatrix(val_x, label=val_y)
params = {
'booster': 'gbtree',
'objective': 'binary:logistic', # 二分类的问题
# 'gamma':0.1, # 用于控制是否后剪枝的参数,越大越保守，一般0.1、0.2这样子。
'max_depth': 5, # 构建树的深度，越大越容易过拟合
# 'lambda':2, # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。
'subsample': 0.8, # 随机采样训练样本
'colsample_bytree': 0.8, # 生成树时进行的列采样
'min_child_weight': 3,
# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言
# ，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。
# 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。
'silent': 0, # 设置成1则没有运行信息输出，最好是设置为0.
'eta': 0.03, # 如同学习率
'nthread': 30, # cpu 线程数
'eval_metric': 'logloss' # 评价方式
}
plst = list(params.items())
num_rounds = 500 # 迭代次数
watchlist = [(xgb_train, 'train'), (xgb_val, 'val')]
# early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
model = xgb.train(plst, xgb_train, num_rounds, watchlist)
importance = model.get_fscore()
#-----------------------important of feature start-----------------------------------------
importance = sorted(importance.items(),
key=operator.itemgetter(1),
reverse=True)
df = pd.DataFrame(importance, columns=['feature', 'fscore'])
df['fscore'] = df['fscore'] / df['fscore'].sum()
print(df)
outfile.close()
features = [x for x in train_data.columns]
ceate_feature_map(features)
import xgboost as xgb
from xgboost import plot_importance
print ('start running ....')
dtrain = xgb.DMatrix(x_train,label=y_train)
dval = xgb.DMatrix(x_val,label=y_val)
param = {'learning_rate' : 0.1,
'n_estimators': 1000,
'max_depth': 4,
'min_child_weight': 7,
'gamma': 0,
'subsample': 0.8,
'colsample_bytree': 0.8,
'eta': 0.05,
'silent': 1,
}
num_round =100
plst = list(param.items())
plst += [('eval_metric', 'rmse')]
evallist = [(dval, 'eval'), (dtrain, 'train')]
bst=xgb.train(plst,dtrain,num_round,evallist,early_stopping_rounds=50)
dtest = xgb.DMatrix(test_data)
y3 = bst.predict(dtest)
plot_importance(bst)
plt.show()
importance = bst.get_fscore(fmap='xgb.fmap')
importance = sorted(importance.items(), key=operator.itemgetter(1))
params = {
'booster': 'gbtree',
'objective': 'binary:logistic',
'eval_metric': 'auc',
'max_depth': 4,
'lambda': 10,
'subsample': 0.75,
'colsample_bytree': 0.75,
'min_child_weight': 2,
'eta': 0.025,
'seed': 0,
'nthread': 8,
'silent': 1
}
watchlist = [(dtrain, 'train')]
bst = xgb.train(params, dtrain, num_boost_round=100, evals=watchlist)
ypred = bst.predict(dtest)
# 设置阈值, 输出一些评价指标
y_pred = (ypred >= 0.5) * 1
print('AUC: %.4f' % metrics.roc_auc_score(test_y, ypred))
print('Accuracy: %.4f' % metrics.accuracy_score(test_y, y_pred))
print('Recall: %.4f' % metrics.recall_score(test_y, y_pred))
print('F1-score: %.4f' % metrics.f1_score(test_y, y_pred))
print('Precesion: %.4f' % metrics.precision_score(test_y, y_pred))
# metrics.confusion_matrix(test_y,y_pred)
def make_one_step(_df_in, _it):
_df = _df_in.copy()
_ratio = 4
# _x_train, _y_train, _x_val, _y_val = _dataframe
_y_train = _df.pop('notified')
_sample_set = get_pickled(data_features_prefix, features_storm_set)
if _sample_set is None:
_sample_set = set()
_sample = take_sample_dim(_df, _ratio)
_sample = persist_sample(_df, _sample, _sample_set, _ratio)
# remove_columns(_df, _sample)
# _cat_df = pd.DataFrame({col: _df[col].astype('category').cat.codes for col in _df},
# index=_df.index)
_dummies_df = pd.get_dummies(_df, drop_first=True)
_dummies_df['notified'] = _y_train
# _df_positives = get_rows(_dummies_df, 'notified', 1)
# _df_negatives = get_rows(_dummies_df, 'notified', 0)
train_indexes, valid_indexes = get_pickled(_dir,
"train_valid_shuffled_indexes")
_train_df = _dummies_df.iloc[train_indexes]
_valid_df = _dummies_df.iloc[valid_indexes]
# _train_df, _valid_df = get_split_train_valid(_df_positives, _df_negatives, 0.8)
_y_train = _train_df.pop('notified')
_y_valid = _valid_df.pop('notified')
_tmp = time.time()
_gpu_res = {}
_dtrain = xgb.DMatrix(_train_df.values,
label=_y_train.values,
missing=-999)
_dval = xgb.DMatrix(_valid_df.values, label=_y_valid.values, missing=-999)
_evals = [(_dval, 'valid')]
_param = {
'objective': 'binary:logistic', # Specify multiclass classification
'num_class': 1, # Number of possible output classes
'tree_method': 'gpu_exact', # Use GPU accelerated algorithm
'scale_pos_weight': 16.32,
'gpu_id': 1,
# 'scale_pos_weight': 1,
# 'scale_pos_weight': 10,
'eval_metric': 'auc',
'subsample': 0.8,
'colsample_bytree': 0.9,
# 'n_estimators': 5000,
# 'eta_decay': 0.5,
'seed': 1,
# 'min_child_weight': 0.8,
}
_rounds = 10000
_bst = None
try:
_bst = xgb.train(_param,
_dtrain,
_rounds,
early_stopping_rounds=300,
evals=_evals,
evals_result=_gpu_res,
verbose_eval=True)
except Exception:
_bst = xgb.train(_param,
_dtrain,
_rounds,
early_stopping_rounds=300,
evals=_evals,
evals_result=_gpu_res,
verbose_eval=True)
print("GPU Training Time: %s seconds" % (str(time.time() - _tmp)))
_predicted = _bst.predict(_dval)
_auc = metrics.roc_auc_score(_y_valid, _predicted)
# auc = trainClassifier(clf, xtrain, ytrain, xtest, ytest)
write_chosen_solution(_sample, _auc)
print("Iteration: {} AUC: {} sample: {}".format(_it, _auc, _sample))
i = 1
def xgboost_euro(conn, num, df):
# 이거를 num만큼 반복
for j in range(1, num+1):
print("{}번째".format(j))
df_xg = df
# 1일 추가하고 주말제거하기
today = date.today() + relativedelta(days=+j)
if getDay(today.year, today.month, today.day) == 'Sat' or getDay(today.year, today.month, today.day) == 'Sun':
continue
else:
df_xg = df_xg.append({"Date": pd.Timestamp(
today), "euro_close": float('nan')}, ignore_index=True)
# 하루씩 추가하면서 예측하고 그 다음날을 추가해서 1주일, 2주일, 1달 이렇게 빼서 확인해보기
# XGBOOST
# extract the date feature
df_xg['day'] = df_xg.Date.dt.day
df_xg['dayofweek'] = df_xg.Date.dt.dayofweek
df_xg['dayofyear'] = df_xg.Date.dt.dayofyear
df_xg['week'] = df_xg.Date.dt.week
df_xg['month'] = df_xg.Date.dt.month
df_xg['year'] = df_xg.Date.dt.year
df_xg = df_xg.drop('Date', axis=1)
# 시계열 데이터에서 이전데이터를 현재 데이터에 넣으면 좀 더 정확한 학습이 가능
# 이것을 lag(지연 데이터라고 표현)
# lag 데이터를 만들어보도록 함
for i in range(1, 6):
df_xg['lag'+str(i)] = df_xg.euro_close.shift(i).fillna(0)
X = df_xg.drop('euro_close', axis=1)
y = df_xg.euro_close
X_train, X_test = X[:-1], X[-1:]
y_train, y_test = y[:-1], y[-1:]
# convert data to xgb matrix form
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test)
# bayesian hyper parameter tuning
# define the params
def xgb_evaluate(max_depth, gamma, colsample_bytree):
params = {'eval_metric': 'rmse',
'max_depth': int(max_depth),
'subsample': 0.8,
'eta': 0.1,
'gamma': gamma,
'colsample_bytree': colsample_bytree}
cv_result = xgb.cv(params, dtrain, num_boost_round=250, nfold=3)
return -1.0 * cv_result['test-rmse-mean'].iloc[-1]
# run optimizer
xgb_bo = BayesianOptimization(xgb_evaluate, {'max_depth': (3, 7),
'gamma': (0, 1),
'colsample_bytree': (0.3, 0.9)})
# define iter points
xgb_bo.maximize(init_points=10, n_iter=15, acq='ei')
# get the best parameters
params = xgb_bo.max['params']
params['max_depth'] = int(round(params['max_depth']))
# train the data
model = xgb.train(params, dtrain, num_boost_round=200)
# predict the test data
predictions = model.predict(dtest)
lenv_ = len(df_xg)
df_xg.euro_close[lenv_-1] = predictions
df_xg = df_xg.drop(['day', 'dayofweek', 'dayofyear', 'week', 'month',
'year', 'day', 'lag1', 'lag2', 'lag3', 'lag4', 'lag5'], axis=1)
if getDay(today.year, today.month, today.day) == 'Sat' or getDay(today.year, today.month, today.day) == 'Sun':
continue
else:
df = df.append({"Date": pd.Timestamp(today),
"euro_close": predictions[0]}, ignore_index=True)
euro_close = float(predictions[0])
print(today)
print(type(today))
print(euro_close)
print(type(euro_close))
xgboost_EURO(conn, today, euro_close)
xgboost_EURO_remove(conn)
return
def xg_train_wrapper(parser):
xgdata, data_test, params, params_t, params_other = conf_parser(
parser.conf)
'''x,y = load_svmlight_file(xgdata)
x = x.todense()
test_x,test_y = load_svmlight_file(data_test)
test_x = test_x.todense()'''
df_train = pd.read_csv(xgdata)
y = df_train['label'].values
x_columns = [
item for item in df_train.columns if item not in ['label', 'id']
]
x = df_train[x_columns].as_matrix()
df_test = pd.read_csv(data_test)
test_y = df_test['label'].values
x_test_columns = [
item for item in df_test.columns if item not in ['label', 'id']
]
test_x = df_test[x_test_columns].as_matrix()
x_train, x_val, y_train, y_val = train_test_split(x,
y,
test_size=0.3,
random_state=42)
dtrain = xgb.DMatrix(x_train, label=y_train)
dval = xgb.DMatrix(x_val, y_val)
dtrain_whole = xgb.DMatrix(x, label=y)
watchlist = [(dtrain, 'train'), (dval, 'eval')]
watchlist_whole = [(dtrain_whole, 'eval')]
scale_pos_weight = get_negative_positive_ratio(y)
params['scale_pos_weight'] = scale_pos_weight
custom_feval = set_custom_eval_metirc(params_other['eval_metric'])
log = log_class.log_class('grid_search_xgb', params_other['log_dir'])
log.add('scale_pos_weight:' + str(scale_pos_weight))
log.add('eval_metric:' + params_other['eval_metric'])
print(params)
num_round = tune_num_boost_round(params,
dtrain,
params_other['num_round'],
log,
watchlist,
eval_metric=params_other['eval_metric'],
feval=custom_feval,
ascend=params_other['ascend'])
params_t = [
dict(max_depth=params_t['max_depth']),
dict(subsample=params_t['subsample']),
dict(min_child_weight=params_t['min_child_weight']),
dict(colsample_bytree=params_t['colsample_bytree']),
dict(colsample_bylevel=params_t['colsample_bylevel']),
dict(max_delta_step=params_t['max_delta_step']),
dict(gamma=params_t['gamma'])
]
for param_t in params_t:
k = param_t.keys()[0]
values = param_t[k]
if (k == 'num_round'):
continue
log.add("=====" + str(k) + "=======" + str(values))
print('========== ', k, ' ========== ', values)
result = []
if (len(values) == 1):
params[k] = values[0]
continue
for v in values:
print('**** for : %s ****\n' % (str(v)))
log.add("**** for :" + str(v) + "****")
params[k] = v
if (custom_feval == None):
params['eval_metric'] = params_other['eval_metric']
result_df = xgb.cv(
params=params,
dtrain=dtrain_whole,
num_boost_round=num_round,
nfold=params_other['cv'],
# metrics=params_other['eval_metric'],
feval=custom_feval,
stratified=True,
verbose_eval=False,
show_stdv=False,
shuffle=True,
early_stopping_rounds=100)
result_df = result_df[[
'test-' + params_other['eval_metric'] + '-mean'
]]
assert result_df.columns[0] == 'test-' + params_other[
'eval_metric'] + '-mean', 'choose the correct column\n'
result_np = result_df.as_matrix()
result.append(float(result_np[-1][0]))
print(zip(values, result))
if (params_other['ascend'] == 1):
loc = max(enumerate(result), key=lambda x: x[1])[0]
else:
loc = min(enumerate(result), key=lambda x: x[1])[0]
params[k] = values[loc]
print('%s : %s\n' % (k, params[k]))
log.add(k)
log.add(str(params[k]))
num_round = tune_num_boost_round(params,
dtrain_whole,
params_other['num_round'],
log,
watchlist_whole,
eval_metric=params_other['eval_metric'],
feval=custom_feval,
ascend=params_other['ascend'])
model = xgb.train(params,
dtrain_whole,
num_round,
watchlist_whole,
feval=custom_feval)
pprint.pprint(params)
time_str = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
if not os.path.isdir('./models'):
os.mkdir('./models')
dataname_model_path = os.path.join('./models', params_other['log_dir'])
if not os.path.isdir(dataname_model_path):
os.mkdir(dataname_model_path)
model.save_model(dataname_model_path + '/' + time_str + '.xgmodel')
print('saved : %s' % (dataname_model_path + '/' + time_str + '.xgmodel'))
predict_test(model, test_x, test_y, log)
y_test = np.array(YY[0:start].reshape(1, -1)[0])
x_test = np.array(XX.iloc[0:start, selected])
dtrain = xgb.DMatrix(x2, label=y2)
dtest = xgb.DMatrix(x_test, label=y_test)
param = {
'max_depth': 20,
'eta': 1,
'silent': 1,
'objective': 'binary:logistic'
}
evallist = [(dtrain, 'train')]
num_round = 10
feature_names = dict(np.array([range(0, XX.shape[1]), np.array(XX.columns)]).T)
bst = xgb.train(param, dtrain, num_round, evallist)
#bst.save_model('0001.model')
#bst.dump_model('dump.raw.txt')
pred01 = bst.predict(dtrain)
Y = y2
pred = []
for cutoff in np.linspace(0.001, 0.999, 2000):
pred0[pred0 < cutoff] = 0
pred0[pred0 != 0] = 1
pred.append(accuracy_score(np.array(Y), pred0))
thre = np.linspace(0.001, 0.999, 2000)[np.where(pred == np.max(pred))[0][0]]
pred01[pred01 >= thre] = 1
'objective': 'binary:logistic',
'eta': 0.1,
'colsample_bytree': 0.886,
'min_child_weight': 2,
'max_depth': 10,
'subsample': 0.886,
'alpha': 10,
'gamma': 30,
'lambda': 50,
'verbose_eval': True,
'nthread': 8,
'eval_metric': 'auc',
'scale_pos_weight': 10,
'seed': 201703,
'missing': -1
}
xgbtrain = xgb.DMatrix(train_feat[predictors], train_feat['label'])
xgbtest = xgb.DMatrix(test_feat[predictors])
model = xgb.train(params, xgbtrain, num_boost_round=120)
del train_feat, xgbtrain
gc.collect()
test_feat.loc[:, 'pred'] = model.predict(xgbtest)
result = reshape(test_feat)
test = pd.read_csv(test_path)
result = pd.merge(test[['orderid']], result, on='orderid', how='left')
result.fillna('0', inplace=True)
result.to_csv('result.csv', index=False, header=False)
print('一共用时{}秒'.format(time.time() - t0))
'min_child_weight': 1,
'gamma': 0.1,
'subsample': 0.9,
'colsample_bytree': 0.9,
'reg_alpha': 2,
'reg_lambda': 0.1,
'objective': 'multi:softmax',
'nthread': 8,
'scale_pos_weight': 1
}
plst = params.items()
num_rounds = 500
model = xgb.train(plst, dtrain, num_rounds)
ans = model.predict(dtest)
y_test1 = np.asarray(y_test)
cm1 = pd.crosstab(y_test, ans, rownames=['Actual'], colnames=['Predicted'])
print(cm1)
plt.figure(num=1, figsize=(12, 8))
plot_importance(model)
plt.show()
plt.savefig('xgbmodel3.png')
#fit model on all training data
xgb2.fit(x_train, y_train)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
import pandas as pd
import xgboost as xgb
from sklearn.metrics import mean_squared_error
training = pd.read_csv("Titanic Training.csv")
test = pd.read_csv("Titanic Test.csv")
X_train = training.drop(columns="Survived").to_numpy()
y_train = training["Survived"].to_numpy().reshape(y_train.shape[0], 1)
m = y_train.shape[0]
D = np.ones((m, 1)) / m
X_test = test.drop(columns="PassengerId").to_numpy()
test_ID = test["PassengerId"]
D_train = xgb.DMatrix(X_train, label=y_train)
D_test = xgb.DMatrix(X_test, label=None)
parameters = {"max_depth": 10, "num_class": 2}
steps = 100
model = xgb.train(parameters, D_train, steps)
predictions_train = model.predict(D_train).reshape(X_train.shape[0], 1)
predictions_test = model.predict(D_test).reshape(X_test.shape[0], 1)
check_train = np.equal(predictions_train, y_train) * 1
correct_train = np.sum(check_train)
accuracy_train = 100 * (correct_train / y_train.shape[0])
error_train = 100 - accuracy_train
results_df = pd.concat([test_ID, pd.DataFrame(predictions_test)], axis=1)
results_df = results_df.rename(columns={0: "Survived"})
def fit_log(self, records, plan_size):
tic = time.time()
# filter data, only pick the data with a same task
data = []
for inp, res in records:
if inp.task.name == self.task.name and \
inp.config.template_key == self.task.config_space.template_key:
data.append((inp, res))
logger.debug("XGB load %d entries from history log file", len(data))
# extract feature
self._reset_pool(self.space, self.target, self.task)
pool = self._get_pool()
if self.fea_type == 'itervar':
feature_extract_func = _extract_itervar_feature_log
elif self.fea_type == 'knob':
feature_extract_func = _extract_knob_feature_log
elif self.fea_type == 'curve':
feature_extract_func = _extract_curve_feature_log
else:
raise RuntimeError("Invalid feature type: " + self.fea_type)
res = pool.map(feature_extract_func, data)
# filter out feature with different shapes
fea_len = len(self._get_feature([0])[0])
xs, ys = [], []
for x, y in res:
if len(x) == fea_len:
xs.append(x)
ys.append(y)
if len(xs) < 500: # no enough samples
return False
xs, ys = np.array(xs), np.array(ys)
x_train = xs
y_train = ys
y_max = np.max(y_train)
y_train = y_train / max(y_max, 1e-8)
index = np.random.permutation(len(x_train))
dtrain = xgb.DMatrix(x_train[index], y_train[index])
plan_size *= 2
self.bst = xgb.train(
self.xgb_params,
dtrain,
num_boost_round=400,
callbacks=[
custom_callback(stopping_rounds=100,
metric='tr-a-recall@%d' % plan_size,
evals=[(dtrain, 'tr')],
maximize=True,
fevals=[
xgb_average_recalln_curve_score(plan_size),
],
verbose_eval=self.log_interval)
])
logger.debug("XGB train: %.2f\tobs: %d", time.time() - tic, len(xs))
return True
'colsample_bytree': 0.3,
'max_depth': 10,
'subsample': 0.8,
'lambda': 0.5,
'nthread': -1,
'booster': 'gbtree',
'silent': 1,
#'eval_metric': 'rmsle',
'objective': 'reg:linear'
}
# You could try to train with more epoch
model = xgb.train(xgb_pars,
dtrain,
6000,
watchlist,
feval=rmsle_eval,
early_stopping_rounds=50,
maximize=False,
verbose_eval=10)
print('Modeling RMSLE %.5f' % model.best_score)
t1 = dt.datetime.now()
print('Training time: %i seconds' % (t1 - t0).seconds)
print('4. ---> Submission ... ')
ytest = model.predict(dtest)
print('Test shape OK.') if test.shape[0] == ytest.shape[0] else print('Oops')
test['trip_duration'] = np.exp(ytest) - 1
#test['trip_duration'] = ytest
subfn = "base2__val_" + str(model.best_score) + "__rnd_" + str(
model.best_iteration) + "csv.gz"
def train_xgboost(df_preds, df_preds2):
df_preds = df_preds.drop(['Filename'], axis=1)
df_preds = df_preds[['DenseNet121_Predictions','InceptionV3_Predictions','ResNet50_Predictions','Vgg_Predictions','sex', 'localization', 'age','dx']]
X,y = df_preds.iloc[:,:-1],df_preds.iloc[:,-1]
print(X.head())
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=None, random_state=50 ,shuffle=True)
X_train.to_csv("X_train.csv", index=False)
X_test.to_csv("X_test.csv", index=False)
params = {"objective":"multi:softmax", "num_class":7 ,'colsample_bytree': 0.3,'learning_rate': 0.001,
'max_depth': 100, 'alpha': 10, "n_estimators":100}
xg_class = xgb.XGBClassifier(objective ='mult:softmax', num_class=7, colsample_bytree = 0.03, learning_rate = 0.1,
max_depth = 1000, alpha = 1000, n_estimators = 1000)
xg_class.fit(X_train,y_train)
df_preds2 = df_preds2.drop(['Filename'], axis=1)
df_preds2 = df_preds2[['DenseNet121_Predictions','InceptionV3_Predictions','ResNet50_Predictions','Vgg_Predictions','sex', 'localization', 'age','dx']]
X,y = df_preds2.iloc[:,:-1],df_preds2.iloc[:,-1]
print(X.head())
data_dmatrix = xgb.DMatrix(data=X,label=y)
X_test = X
y_test = y
preds = xg_class.predict(X_test)
cm = confusion_matrix(y_test, preds)
cm_plot_labels = ['akiec', 'bcc', 'bkl', 'df', 'mel','nv', 'vasc']
plot_confusion_matrix(cm, cm_plot_labels, "xgboost")
print("Balanced Accuracy: " + str(balanced_accuracy_score(y_test, preds)))
print("Weighted Recall: " + str(recall_score(y_test, preds, average='weighted')))
print("Class Recall: " + str(recall_score(y_test, preds, average=None)))
print("Weighted Precision: " + str(precision_score(y_test, preds, average='weighted')))
print("Class Precision: " + str(precision_score(y_test, preds, average=None)))
print("Mean f1 score " + str(f1_score(y_test, preds, average='weighted')))
print("Class f1 score " + str(f1_score(y_test, preds, average=None)))
file = open("xgboost_results_Original.txt","w+")
file.write("Balanced Accuracy: " + str(balanced_accuracy_score(y_test, preds)) + "\n")
file.write("Weighted Recall: " + str(recall_score(y_test, preds, average='weighted')) + "\n")
file.write("Class Recall: " + str(recall_score(y_test, preds, average=None)) + "\n")
file.write("Weighted Precision: " + str(precision_score(y_test, preds, average='weighted')) + "\n")
file.write("Class Precision: " + str(precision_score(y_test, preds, average=None)) + "\n")
file.write("Mean f1 score " + str(f1_score(y_test, preds, average='weighted')))
file.write("Class f1 score " + str(f1_score(y_test, preds, average=None)))
file.close()
xg_class = xgb.train(params=params, dtrain=data_dmatrix, num_boost_round=10)
ax = xgb.plot_importance(xg_class)
fig = ax.figure
fig.set_size_inches(20,20)
random_state=42,
stratify=y)
preds = np.ones(y_test.shape[0])
# Get from cross validation
params = {
'booster': 'gbtree',
'objective': 'reg:logistic',
'colsample_bytree': 0.2,
'min_child_weight': 4,
'subsample': 1,
'learning_rate': 0.1,
'max_depth': 6,
'gamma': 0.05
}
training_data = xgb.DMatrix(X_train, y_train)
model = xgb.train(params, training_data, 230, feval=mcc_eval, maximize=True)
preds = model.predict(xgb.DMatrix(X_test))
# pick the best threshold out-of-fold
thresholds = np.linspace(0.01, 0.99, 99)
mcc = np.array([matthews_corrcoef(y_test, preds > thr) for thr in thresholds])
plt.plot(thresholds, mcc)
plt.show()
best_threshold = thresholds[mcc.argmax()]
print(mcc.max())
print(best_threshold)
model = xgb.train(params,
xgb.DMatrix(X, y),
230,
'min_split_loss': 0,
'max_depth': 6,
'min_child_weight': 1,
'max_delta_step': 0,
'subsample': 1,
'colsample_bytree': 1,
'colsample_bylevel': 1,
'reg_lambda': 1,
'reg_alpha': 0,
'grow_policy': 'depthwise',
'max_leaves': 0,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'seed': 7
}
history = {} # This will record rmse score of training and test set
eval_list = [(Train, "Training"), (Validation, "Validation")]
clf = xgb.train(params,
Train,
num_boost_round=119,
evals=eval_list,
obj=None,
feval=None,
maximize=False,
early_stopping_rounds=40,
evals_result=history)
prediction = clf.predict(xgb.DMatrix(x_test))
submission = pd.DataFrame({
"card_id": main_test["card_id"].values,
"target": np.ravel(prediction)
})
}
dtrain = xgb.DMatrix(x_train, y_train)
# cross-validation
cv_result = xgb.cv(xgb_params,
dtrain,
nfold=5,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=1,
show_stdv=False)
num_boost_rounds = len(cv_result)
print(num_boost_rounds)
# train model
model = xgb.train(dict(xgb_params, silent=1),
dtrain,
num_boost_round=num_boost_rounds)
res = []
for i in range(3):
x_test['month_logerror'] = round(
traingroupedMonth.ix[9 + int(i)]['logerror'], 6)
x_test['quarter_logerror'] = round(traingroupedQuarter.ix[3]['logerror'],
6)
test_set = sc.transform(x_test)
dtest = xgb.DMatrix(test_set)
pred = model.predict(dtest)
res.append(pred)
output = pd.DataFrame({
'ParcelId': properties['parcelid'].astype(np.int32),
x_train, x_valid, y_train, y_valid = train_test_split(x_train,
y_train,
test_size=0.2,
random_state=4242)
#XGBoost算法
params = {}
params['objective'] = 'binary:logistic'
params['eva_metric'] = 'logloss'
params['eta'] = 0.02
params['max_depth'] = 4
d_train = xgb.DMatrix(x_train, label=y_train)
d_valid = xgb.DMatrix(x_valid, label=y_valid)
watch_list = [(d_train, 'train'), (d_valid, 'valid')]
bst = xgb.train(params,
d_train,
400,
watch_list,
early_stopping_rounds=50,
verbose_eval=10)
d_test = xgb.DMatrix(x_test)
p_test = bst.predict(d_test)
sub = pd.DataFrame()
sub['test_id'] = df_test['test_id']
sub['is_duplicate'] = p_test
sub.to_csv('simple_xgb.csv', index=False)
