def test_cls():
save_dir = './data/eval_exps/soln-ml'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
time_limit = 60
print('==> Start to evaluate with Budget %d' % time_limit)
ensemble_method = 'stacking'
eval_type = 'holdout'
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1,
stratify=y)
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
clf = Classifier(time_limit=time_limit,
output_dir=save_dir,
ensemble_method=ensemble_method,
enable_meta_algorithm_selection=False,
ensemble_size=4,
evaluation=eval_type,
metric='acc')
clf.fit(train_data)
print(clf.summary())
pred = clf.predict(test_data)
print(accuracy_score(test_data.data[1], pred))
shutil.rmtree(save_dir)
def test_rgs():
time_limit = 60
print('==> Start to evaluate with Budget %d' % time_limit)
ensemble_method = 'stacking'
eval_type = 'holdout'
boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1)
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
save_dir = './data/eval_exps/soln-ml'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
rgs = Regressor(metric='mse',
ensemble_method=ensemble_method,
enable_meta_algorithm_selection=False,
ensemble_size=4,
evaluation=eval_type,
time_limit=time_limit,
output_dir=save_dir)
rgs.fit(train_data)
print(rgs.summary())
pred = rgs.predict(test_data)
print(mean_squared_error(test_data.data[1], pred))
shutil.rmtree(save_dir)
def evaluate():
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1)
try:
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
clf = Classifier(dataset_name='iris',
time_limit=150,
output_dir=save_dir,
ensemble_method=ensemble_method,
evaluation=eval_type,
metric='acc')
clf.fit(train_data)
clf.refit()
pred = clf.predict(test_data)
print('final score', clf.score(test_data))
except Exception as e:
return False
return True
def main():
time_limit = 60
print('==> Start to evaluate with Budget %d' % time_limit)
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1,
stratify=y)
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
save_dir = './data/eval_exps/soln-ml'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
add_classifier(UserDefinedDecisionTree)
clf = Classifier(time_limit=time_limit,
output_dir=save_dir,
enable_meta_algorithm_selection=False,
include_algorithms=['UserDefinedDecisionTree'],
ensemble_method=None,
metric='acc')
_start_time = time.time()
clf.fit(train_data)
print(clf.summary())
pred = clf.predict(test_data)
print(accuracy_score(test_data.data[1], pred))
shutil.rmtree(save_dir)
def load_data(dataset,
data_dir='./',
datanode_returned=False,
preprocess=True,
task_type=None):
dm = DataManager()
if task_type is None:
data_path = data_dir + 'data/datasets/%s.csv' % dataset
elif task_type in CLS_TASKS:
data_path = data_dir + 'data/cls_datasets/%s.csv' % dataset
elif task_type in RGS_TASKS:
data_path = data_dir + 'data/rgs_datasets/%s.csv' % dataset
else:
raise ValueError("Unknown task type %s" % str(task_type))
# Load train data.
if dataset in [
'higgs', 'amazon_employee', 'spectf', 'usps', 'vehicle_sensIT',
'codrna'
]:
label_column = 0
elif dataset in ['rmftsa_sleepdata(1)']:
label_column = 1
else:
label_column = -1
if dataset in ['spambase', 'messidor_features']:
header = None
else:
header = 'infer'
if dataset in ['winequality_white', 'winequality_red']:
sep = ';'
else:
sep = ','
train_data_node = dm.load_train_csv(
data_path,
label_col=label_column,
header=header,
sep=sep,
na_values=["n/a", "na", "--", "-", "?"])
if preprocess:
pipeline = FEPipeline(fe_enabled=False,
metric='acc',
task_type=task_type)
train_data = pipeline.fit_transform(train_data_node)
else:
train_data = train_data_node
if datanode_returned:
return train_data
else:
X, y = train_data.data
feature_types = train_data.feature_types
return X, y, feature_types
def evaluate_fe_pipeline():
from mindware.utils.data_manager import DataManager
dm = DataManager()
# file_path = "data/proprocess_data.csv"
file_path = 'data/a9a/dataset_183_adult.csv'
dm.load_train_csv(file_path)
pipeline = FEPipeline(fe_enabled=True).fit(dm)
train_data = pipeline.transform(dm)
print(train_data)
print(train_data.data)
def load_tabular_data(self, data_path):
self.data_manager = DataManager()
train_data_node = self.data_manager.load_train_csv(
data_path,
label_col=self.label_column,
header=self.header,
sep=self.sep,
na_values=list(self.nan_values))
task_type = REGRESSION if self.is_regression else CLASSIFICATION
self._process_pipeline = FEPipeline(fe_enabled=False,
metric='acc',
task_type=task_type)
return self._process_pipeline.fit_transform(train_data_node)
def evaluate_data_manager():
# from data_manager import DataManager
# dm = DataManager()
# train_df = dm.load_train_csv("data/proprocess_data.csv")
# print(train_df)
# print(dm.feature_types)
# print(dm.missing_flags)
from mindware.utils.data_manager import DataManager
import numpy as np
X = np.array([[1, 2, 3, 4], [1, 'asfd', 2, 1.4]])
y = [1, 2]
dm = DataManager(X, y)
print(dm.feature_types)
print(dm.missing_flags)
def main():
tmp_dir = './data/eval_exps/soln-ml'
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
time_limit = 60
print('==> Start new AutoML task with budget - %d' % time_limit)
ensemble_method = 'ensemble_selection'
eval_type = 'holdout'
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1,
stratify=y)
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
clf = Classifier(time_limit=time_limit,
output_dir=tmp_dir,
ensemble_method=ensemble_method,
enable_meta_algorithm_selection=False,
ensemble_size=10,
optimizer='random_search',
evaluation=eval_type,
metric='acc',
n_jobs=1)
clf.fit(train_data, tree_id=2)
print(clf.summary())
pred = clf.predict(test_data)
print(accuracy_score(test_data.data[1], pred))
shutil.rmtree(tmp_dir)
time_limit = args.time_limit
eval_type = args.eval_type
n_jobs = args.n_jobs
ensemble_method = args.ens_method
if ensemble_method == 'none':
ensemble_method = None
print('==> Start to evaluate with Budget %d' % time_limit)
boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=1)
dm = DataManager(X_train, y_train)
train_data = dm.get_data_node(X_train, y_train)
test_data = dm.get_data_node(X_test, y_test)
save_dir = './data/eval_exps/soln-ml'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
rgs = Regressor(metric='mse',
dataset_name='boston',
ensemble_method=ensemble_method,
evaluation=eval_type,
time_limit=time_limit,
output_dir=save_dir,
random_state=1,
n_jobs=n_jobs)
parser.add_argument('--ens_method', default='ensemble_selection',
choices=['none', 'bagging', 'blending', 'stacking', 'ensemble_selection'])
parser.add_argument('--n_jobs', type=int, default=1)
args = parser.parse_args()
time_limit = args.time_limit
eval_type = args.eval_type
n_jobs = args.n_jobs
ensemble_method = args.ens_method
if ensemble_method == 'none':
ensemble_method = None
print('==> Start to evaluate with Budget %d' % time_limit)
dm = DataManager()
train_node = dm.load_train_csv("train_dataset.csv", label_col=-1, header='infer', na_values=['nan', '?'])
test_node = dm.load_test_csv("test_dataset.csv", header='infer', has_label=True)
from mindware.components.utils.constants import REGRESSION
pipeline = FEPipeline(fe_enabled=False, task_type=REGRESSION)
train_data = pipeline.fit_transform(train_node)
test_data = pipeline.transform(test_node)
save_dir = './data/eval_exps/soln-ml'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
rgs = Regressor(metric='mse',
ensemble_method=ensemble_method,
evaluation=eval_type,