def test_prediction():
model = Regressor(estimator=LinearRegression, parameters={}, dataset=RealDataset)
output = model.predict()
assert len(output.shape) == 1
assert model.dataset.X_test.shape[0] == output.shape[0]
# Retrieve cached object
output = model.predict()
assert len(output.shape) == 1
assert model.dataset.X_test.shape[0] == output.shape[0]
def test_prediction():
model = Regressor(estimator=LinearRegression,
parameters={},
dataset=RealDataset)
output = model.predict()
assert len(output.shape) == 1
assert model.dataset.X_test.shape[0] == output.shape[0]
# Retrieve cached object
output = model.predict()
assert len(output.shape) == 1
assert model.dataset.X_test.shape[0] == output.shape[0]
from heamy.estimator import Regressor
from heamy.pipeline import ModelsPipeline
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
data = load_boston()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=2)
# create dataset
Data = Dataset(X_train,y_train,X_test)
# initialize RandomForest & LinearRegression
RfModel = Regressor(dataset=Data, estimator=RandomForestRegressor, parameters={'n_estimators': 50},name='rf')
LRModel = Regressor(dataset=Data, estimator=LinearRegression, parameters={'normalize': True},name='lr')
# Stack two models
# Returns new dataset with out-of-fold predictions
Pipeline = ModelsPipeline(RfModel,LRModel)
StackModel = Pipeline.stack(k=10,seed=2)
# Train LinearRegression on stacked data (second stage)
Stacker = Regressor(dataset=StackModel, estimator=LinearRegression)
Results = Stacker.predict()
# Validate results using 10 fold cross-validation
Results = Stacker.validate(k=10,scorer=mean_absolute_error)
estimator=xgb_feature3,
name='xgb3',
use_cache=False)
model_lgb = Regressor(dataset=lgb_dataset,
estimator=lgb_feature,
name='lgb',
use_cache=False)
model_gbdt = Regressor(dataset=xgb_dataset,
estimator=gbdt_model,
name='gbdt',
use_cache=False)
pipeline = ModelsPipeline(model_xgb, model_xgb2, model_xgb3, model_lgb,
model_gbdt)
stack_ds = pipeline.stack(k=5,
seed=111,
add_diff=False,
full_test=True)
stacker = Regressor(dataset=stack_ds,
estimator=LinearRegression,
parameters={'fit_intercept': False})
predict_result = stacker.predict()
ans = pd.read_csv('../AI_risk_test_V3.0/test_list.csv',
parse_dates=['appl_sbm_tm'])
ans['PROB'] = predict_result
ans = ans.drop(['appl_sbm_tm'], axis=1)
minmin, maxmax = min(ans['PROB']), max(ans['PROB'])
ans['PROB'] = ans['PROB'].map(lambda x: (x - minmin) /
(maxmax - minmin))
ans['PROB'] = ans['PROB'].map(lambda x: '%.4f' % x)
ans.to_csv('./ans_stacking.csv', index=None)
estimator=RandomForestRegressor,
parameters={'n_estimators': 50},
name='rf')
model_lr = Regressor(dataset=dataset,
estimator=LinearRegression,
parameters={'normalize': True},
name='lr')
# Stack two models
# Returns new dataset with out-of-fold predictions
pipeline = ModelsPipeline(model_rf, model_lr)
stack_ds = pipeline.stack(k=10, seed=111)
# Train LinearRegression on stacked data (second stage)
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression)
results = stacker.predict()
# Validate results using 10 fold cross-validation
results = stacker.validate(k=10, scorer=mean_absolute_error)
#blend
# load boston dataset from sklearn
from sklearn.datasets import load_boston
data = load_boston()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=111)
# create dataset
dataset = Dataset(X_train, y_train, X_test)
model_ext = Regressor(dataset=dataset,
estimator=ext,
parameters=params_ext,
name='ext')
model_rcv = Regressor(dataset=dataset,
estimator=rcv,
parameters=params_rcv,
name='rcv')
#model_lascv = Regressor(dataset=dataset, estimator=lascv, parameters=params_lascv,name='lascv')
pipeline = ModelsPipeline(model_rf, model_rcv, model_ext)
stack_ds = pipeline.stack(k=5, seed=111)
stacker = Regressor(dataset=stack_ds,
estimator=Lasso,
parameters=params_las)
y_pre = stacker.predict()
y_pre_last = np.append(y_pre, y_pre)
y_pre_last[10] * 1.08
###
#loss_gbrt = Evaluation([y_pre_gbrt],[y_test])
output(fw, i + 1, y_pre_last)
'''
if loss_gbrt>0.015:
output(fw_gbrt,i+1,y_pre_rf)
fw_gbrt.write(str(i+1)+',gbrt,'+str(loss_gbrt)+'\n')
'''
'''
plt.scatter(xday[-364:-14],y_train)
plt.scatter(xday[-14:],y_test,color = 'green')
plt.plot(xday[-14:],y_pre_rf,color = 'red')
path = "d://tianchi_koubei/fig/rf_train/"+str(i+1)+'.png'
# create dataset
dataset = Dataset(X_train,y_train,X_test)
# initialize RandomForest & LinearRegression
model_rf = Regressor(dataset=dataset, estimator=RandomForestRegressor, parameters={'n_estimators': 50},name='rf')
model_lr = Regressor(dataset=dataset, estimator=LinearRegression, parameters={'normalize': True},name='lr')
# Stack two models
# Returns new dataset with out-of-fold predictions
pipeline = ModelsPipeline(model_rf,model_lr)
stack_ds = pipeline.stack(k=10,seed=111)
# Train LinearRegression on stacked data (second stage)
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression)
results = stacker.predict()
# Validate results using 10 fold cross-validation
results = stacker.validate(k=10,scorer=mean_absolute_error)
#blend
# load boston dataset from sklearn
from sklearn.datasets import load_boston
data = load_boston()
X, y = data['data'], data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=111)
# create dataset
dataset = Dataset(X_train,y_train,X_test)
'custom_metric': ['MAE'],
'random_seed': seed,
'logging_level': 'Silent'
},
name='cr')
]
# pipelineを定義、2nd levelデータセットの作成
pipeline = ModelsPipeline(*models)
stack_ds = pipeline.stack(k=10, seed=seed)
# modelを作ってvalidation
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression)
y_trues, y_preds = stacker.validate(k=10)
# 精度出力
# X_testを使ってpredict
y_pred = stacker.predict()
print(y_pred)
sum = 0
buy = 0
for i, yosoku in enumerate(x_test):
if stacker.predict(x_test[i]) < 0.3:
if t_test[i] == 0:
sum += z_test[i]
buy += 1
print(sum)
print(buy)
print(sum / buy)
#model_rf2 = Regressor(dataset=dataset, estimator=rf2, parameters=params_rf2,name='rf2')
model_rcv = Regressor(dataset=dataset, estimator=rcv, parameters=params_rcv,name='rcv')
#model_gbrt = Regressor(dataset=dataset, estimator=gbrt, parameters=params_gbrt,name='gbrt')
#model_lascv = Regressor(dataset=dataset, estimator=lascv, parameters=params_lascv,name='lascv')
model_br = Regressor(dataset=dataset, estimator=br, parameters=params_br,name='br')
model_knn = Regressor(dataset=dataset, estimator=knn, parameters=params_knn,name='knn')
#blending = pipeline.blend(proportion=0.3,seed=111)
params_las = {'alpha':1.7}
params_rcv2 = {'cv':5,'normalize':True,'gcv_mode':'auto','scoring':'neg_mean_squared_error'}
params_lascv = {'max_iter':500,'cv':8}
pipeline = ModelsPipeline(model_rf1,model_knn)
stack_ds = pipeline.stack(k=5,seed=111)
stacker = Regressor(dataset=stack_ds,estimator=LassoCV, parameters=params_lascv)
y_pre = stacker.predict()
pipeline2 = ModelsPipeline(model_rf1,model_knn)
stack_ds2 = pipeline2.blend(seed=111)
blending = Regressor(dataset=stack_ds2,estimator=LassoCV, parameters=params_lascv)
y_pre2 = blending.predict()
blending_pre.append(y_pre2)
#print(y_pre)
#y_pre = pipeline.blend()
#print(y_pre)
###
#loss_stack = Evaluation([y_pre],[y_test])
stacking_pre.append(y_pre)
