def test_estimator():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
from alphaml.datasets.cls_dataset.dataset_loader import load_data
from alphaml.utils.constants import MAX_INT
rep_num = args.rep
run_count = args.run_count
datasets = args.datasets.split(',')
print(rep_num, run_count, datasets)
for dataset in datasets:
dataset_id = dataset.split('_')[0]
result_dir = 'data/' + dataset_id
if not os.path.exists(result_dir):
os.mkdir(result_dir)
task_format = dataset + '_est_%d'
X, y, _ = load_data(dataset)
dm = DataManager(X, y)
seed = np.random.random_integers(MAX_INT)
for optimizer in ['smbo']:
cls = Classifier(include_models=['gradient_boosting'],
optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
task_name=task_format)
print(cls.predict(X))
def test_hyperspace():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
from alphaml.datasets.cls_dataset.dataset_loader import load_data
from alphaml.utils.constants import MAX_INT
try:
for dataset in datasets:
for run_id in range(start_run, rep_num):
X, y, _ = load_data(dataset)
dm = DataManager(X, y)
seed = np.random.random_integers(MAX_INT)
for update_mode in [2, 3]:
task_format = dataset + '_mode_%d_%d' % (update_mode,
run_id)
cls = Classifier(optimizer='ts_smbo',
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
task_name=task_format,
update_mode=update_mode)
print(cls.predict(X))
except Exception as e:
print(e)
print('Exit!')
def evaluate_c():
rep_num = 10
run_count = 500
start_id = args.start_runid
datasets = args.datasets.split(',')
task_id = 'exp5_eval_c'
print(rep_num, run_count, datasets, task_id)
for dataset in datasets:
# Make directories.
dataset_id = dataset.split('_')[0]
save_dir = "data/%s/" % dataset_id
if not os.path.exists(save_dir):
os.makedirs(save_dir)
result = dict()
seeds = get_seeds(dataset, start_id + rep_num)
for run_id in range(start_id, start_id + rep_num):
seed = seeds[run_id]
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y)
dm = DataManager(X_train, y_train)
# Test each optimizer algorithm:
for p in [1, 4, 10, 14, 16, 20]:
task_name = dataset + '_%s_%d_%d_%d' % (task_id, run_count,
run_id, p)
mode = 3
optimizer = 'mono_smbo'
print('Test %s optimizer => %s' % (optimizer, task_name))
# Construct the AutoML classifier.
cls = Classifier(optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
task_name=task_name,
update_mode=mode,
param=p)
acc = cls.score(X_test, y_test)
key_id = '%s_%d_%d_%d_%s' % (dataset, run_count, run_id, p,
optimizer)
result[key_id] = acc
# Display and save the test result.
print(result)
with open(
'data/%s/%s_test_%s_%d_%d_%d.pkl' %
(dataset_id, dataset, task_id, run_count, rep_num, start_id),
'wb') as f:
pickle.dump(result, f)
def test_auto():
from sklearn.datasets import load_breast_cancer
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
X, y = load_breast_cancer(return_X_y=True)
# Classifier(exclude_models=['libsvm_svc']).fit(DataManager(X, y))
for _ in range(5):
Classifier(include_models=['adaboost', 'gradient_boosting', 'random_forest'], optimizer='ts_smac').fit(DataManager(X, y))
for _ in range(5):
Classifier(include_models=['adaboost', 'gradient_boosting', 'random_forest'], optimizer='smac').fit(DataManager(X, y))
def test_cash_module():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
import random
from sklearn.metrics import roc_auc_score
result = []
for i in range(1):
import xlrd
sheet = xlrd.open_workbook("lyqdata.xlsx")
sheet = sheet.sheet_by_index(0)
nrows = sheet.nrows
X_train = []
y_train = []
for i in range(2, nrows):
X_train.append(sheet.row_values(i, start_colx=1))
y_train.append(int(sheet.cell_value(i, 0)))
dm = DataManager(X_train, y_train)
cls = Classifier(
# include_models=['liblinear_svc', 'libsvm_svc', 'random_forest', 'logistic_regression', 'mlp'],
include_models=['mlp'],
optimizer='smbo',
cross_valid=False,
ensemble_method='ensemble_selection',
ensemble_size=args.ensemble_size,
save_dir='data/save_models'
)
cls.fit(dm, metric='auc', runcount=args.run_count)
sheet = xlrd.open_workbook("lyqtestdata.xlsx")
sheet = sheet.sheet_by_index(0)
nrows = sheet.nrows
X_test = []
y_test = []
for i in range(1, nrows):
X_test.append(sheet.row_values(i, start_colx=1))
y_test.append(int(sheet.cell_value(i, 0)))
pred = cls.predict_proba(X_test)
result.append(roc_auc_score(y_test, pred[:, 1:2]))
print(result)
import pickle
with open('result.pkl', 'wb') as f:
pickle.dump(result, f)
def test_cash_module():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
import random
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
result = []
for i in range(1):
import xlrd
sheet = xlrd.open_workbook("ybai_Keratoconus_TJ_20190425.xlsx")
sheet = sheet.sheet_by_index(0)
nrows = sheet.nrows
X_train = []
y_train = []
for i in range(1, nrows):
X_train.append(sheet.row_values(i, start_colx=1))
y_train.append(int(sheet.cell_value(i, 0)))
encoder = OneHotEncoder()
encoder.fit(np.reshape(y_train, (len(y_train), 1)))
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, stratify=y_train)
dm = DataManager(X_train, y_train)
cls = Classifier(
# include_models=['liblinear_svc', 'libsvm_svc', 'xgboost', 'random_forest', 'logistic_regression', 'mlp'],
optimizer='smbo',
ensemble_method='bagging',
ensemble_size=args.ensemble_size,
)
cls.fit(dm, metric='auc', runcount=args.run_count)
pred = cls.predict_proba(X_test)
print(pred)
y_test = encoder.transform(np.reshape(y_test, (len(y_test), 1))).toarray()
result.append(roc_auc_score(y_test, pred))
print(result)
import pickle
with open('result.pkl', 'wb') as f:
pickle.dump(result, f)
def test_cash_module():
df = pd.read_csv("data/cls_data/santander/train.csv")
df = df.drop(columns=["ID"])
cls = Classifier(include_models=['xgboost', 'random_forest', 'decision_tree'],
optimizer='baseline',
ensemble_method='ensemble_selection',
ensemble_size=30,
).fit(df, metric='auc', runcount=1)
df = pd.read_csv("data/cls_data/santander/test.csv")
data = df.values
df = df.drop(columns=["ID"])
pred2 = cls.predict(df)
print(pred2)
import csv
with open('data/cls_data/santander/submission.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['ID', 'TARGET'])
for i in range(len(pred2)):
line = [int(data[i, 0]), pred2[i]]
writer.writerow(line)
def test_no_free_lunch():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
from alphaml.datasets.cls_dataset.dataset_loader import load_data
for dataset in datasets:
seeds = get_seeds(dataset, rep_num)
for run_id in range(rep_num):
seed = seeds[run_id]
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
dm = DataManager(X_train, y_train)
for algo in algo_list:
for optimizer in ['smbo']:
task_format = dataset + '_' + algo + '_%d_%d'
cls = Classifier(
include_models=[algo], optimizer=optimizer, seed=seed).fit(
dm, metric='accuracy', runcount=run_count, task_name=task_format % (run_count, run_id))
print(cls.predict(X))
def test_hyperspace():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
from alphaml.datasets.cls_dataset.dataset_loader import load_data
from alphaml.utils.constants import MAX_INT
try:
for dataset in datasets:
for run_id in range(start_run, rep_num):
X, y, _ = load_data(dataset)
dm = DataManager(X, y)
seed = np.random.random_integers(MAX_INT)
for n_est in [1, 2, 4, 8, 12]:
algos = algo_list[:n_est]
task_format = dataset + '_hp_%d_%d' % (n_est, run_id)
cls = Classifier(
include_models=algos, optimizer='smbo', seed=seed).fit(
dm, metric='accuracy', runcount=run_count, task_name=task_format)
print(cls.predict(X))
except Exception as e:
print(e)
print('Exit!')
def test_claim():
from alphaml.engine.components.data_manager import DataManager
from alphaml.estimators.classifier import Classifier
from alphaml.datasets.cls_dataset.dataset_loader import load_data
from alphaml.utils.constants import MAX_INT
perfs_list = list()
for dataset in datasets:
for run_id in range(rep_num):
X, y, _ = load_data(dataset)
dm = DataManager(X, y)
seed = np.random.random_integers(MAX_INT)
task_format = dataset + '_claim_%d'
for optimizer in ['smbo']:
cls = Classifier(optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
task_name=task_format % run_id)
print(cls.predict(X))
file_id = 'data/%s/%s_claim_%d_%s.data' % (dataset, dataset,
run_id, 'smac')
with open(file_id, 'rb') as f:
data = pickle.load(f)
best_id = np.argmax(data['perfs'])
best_value = data['perfs'][best_id]
if data['perfs'].count(best_value) > 1:
stats = dict()
for conf, perf in zip(data['configs'], data['perfs']):
if perf == best_value:
est = conf['estimator']
if est not in stats:
stats[est] = 0
stats[est] += 1
tmp_id = np.argmax(stats.values())
best_estimator = list(stats.keys())[tmp_id]
print('=' * 20, best_value, stats)
else:
best_estimator = data['configs'][best_id]['estimator']
print('=' * 20, data['perfs'][best_id],
data['configs'][best_id])
run_cnts = len([
item for item in data['configs']
if item['estimator'] == best_estimator
])
task_format = dataset + '_claim_single_%d'
cls = Classifier(include_models=[best_estimator],
optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_cnts,
task_name=task_format % run_id)
print(cls.predict(X))
file_id = 'data/%s/%s_claim_single_%d_%s.data' % (
dataset, dataset, run_id, 'smac')
with open(file_id, 'rb') as f:
data_s = pickle.load(f)
print('=' * 20 + 'single', max(data_s['perfs']))
perfs_list.append((data['perfs'], data_s['perfs']))
for item in perfs_list:
item1, item2 = item
print(len(item1), max(item1), len(item2), max(item2))
print('=' * 50)
print(perfs_list)
def test_cash_module():
rep_num = args.rep
run_count = args.run_count
start_id = args.start_runid
datasets = args.datasets.split(',')
optimizer_algos = args.opt_algo.split(',')
task_id = args.task_id
print(rep_num, run_count, datasets, optimizer_algos, task_id)
result = dict()
for dataset in datasets:
dataset_id = dataset.split('_')[0]
result_dir = 'data/' + dataset_id
if not os.path.exists(result_dir):
os.mkdir(result_dir)
seeds = get_seeds(dataset, rep_num)
for run_id in range(start_id, rep_num):
task_name = dataset + '_%s_%d_%d' % (task_id, run_count, run_id)
seed = seeds[run_id]
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y)
dm = DataManager(X_train, y_train)
# Test each optimizer algorithm:
for optimizer in optimizer_algos:
# Parse the parameters for each optimizer.
mode = 2
eta, r = 2, 2
if optimizer.startswith('baseline'):
optimizer, mode = optimizer.split('_')
mode = 1 if mode == 'rand' else 2
if optimizer.startswith('sh'):
if len(optimizer.split('_')) == 2:
optimizer, eta = optimizer.split('_')
eta = float(eta)
else:
raise ValueError('Wrong SH params!')
if optimizer.startswith('rl'):
if len(optimizer.split('_')) == 3:
_, mode, eta = optimizer.split('_')
mode = int(mode)
optimizer = 'rl_smbo'
else:
raise ValueError('Wrong SH params!')
if optimizer.startswith('ts_smbo'):
mode = 1
if len(optimizer.split('_')) == 3:
_, _, mode = optimizer.split('_')
mode = int(mode)
optimizer = 'ts_smbo'
if optimizer.startswith('mcmc_ts'):
_, _, mode, eta, r = optimizer.split('_')
mode = int(mode)
eta = int(eta)
r = int(r)
optimizer = 'mcmc_ts_smbo'
if optimizer.startswith('ucb_smbo'):
mode = 1
if len(optimizer.split('_')) == 3:
_, _, mode = optimizer.split('_')
mode = int(mode)
optimizer = 'ucb_smbo'
if optimizer.startswith('mono_smbo'):
mode = 2
if len(optimizer.split('_')) == 4:
_, _, mode, r = optimizer.split('_')
mode, r = int(mode), int(r)
eta = 10
optimizer = 'mono_smbo'
print('Test %s optimizer => %s' % (optimizer, task_name))
# Construct the AutoML classifier.
cls = Classifier(optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
task_name=task_name,
update_mode=mode,
eta=eta,
r=r,
param=eta)
acc = cls.score(X_test, y_test)
key_id = '%s_%d_%d_%s' % (dataset, run_count, run_id,
optimizer)
result[key_id] = acc
# Display and save the test result.
print(result)
with open(
'data/%s/%s_test_result_%s_%d_%d_%d.pkl' %
(dataset_id, dataset_id, task_id, run_count, rep_num,
start_id), 'wb') as f:
pickle.dump(result, f)
from time import time
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser()
parser.add_argument("--generated_feature", type=int, default=1)
parser.add_argument("--dataset", type=str)
args = parser.parse_args()
x, y, c = load_data(args.dataset)
dm = DataManager(x, y)
lr = LogisticRegression()
lr.fit(dm.train_X, dm.train_y)
y_pred = lr.predict(dm.val_X)
print("original lr accu:", accuracy_score(dm.val_y, y_pred), flush=True)
if args.generated_feature > 0:
af = AutoFeature("accuracy", "auto_cross")
af.fit(dm, args.generated_feature)
dm = af.transform(dm)
clf = Classifier()
start_time = time()
clf.fit(dm, metric="accuracy", runcount=50)
print("alphaml time:", time() - start_time)
print("dataset:", args.dataset)
print("generated data:", args.generated_feature, ", alphaml score:",
clf.score(dm.val_X, dm.val_y))
def test_exp4_runtime():
rep_num = args.rep
run_count = args.run_count
B = args.B
if B > 0:
run_count = 0
start_id = args.start_runid
datasets = args.datasets.split(',')
print(rep_num, run_count, datasets)
task_id = "exp4_runtime"
for dataset in datasets:
dataset_id = dataset.split('_')[0]
result_dir = 'data/' + dataset_id
if not os.path.exists(result_dir):
os.mkdir(result_dir)
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42,
stratify=y)
dm = DataManager(X_train, y_train)
runcount_dict = dict()
tpe_runcount = 0.
optimizer_algos = ['mono_smbo_4', 'smbo', 'tpe']
# optimizer_algos = ['mono_smbo_3_0']
# Test each optimizer algorithm:
assert optimizer_algos[-1] == 'tpe'
for opt_algo in optimizer_algos:
# if algo is tpe, we need to estimate its runcount in one hour.
if opt_algo != 'tpe':
runcount_dict[opt_algo] = list()
else:
count_list = list()
for key in runcount_dict.keys():
count_list.append(np.mean(runcount_dict[key]))
assert len(count_list) > 0
tpe_runcount = np.min(count_list)
print('=' * 50, tpe_runcount)
result = dict()
mode, eta = None, None
# Parse the parameters for each optimizer.
if opt_algo.startswith('mono_smbo'):
mode = 2
if len(opt_algo.split('_')) == 3:
_, _, mode = opt_algo.split('_')
mode = int(mode)
eta = 10
optimizer = 'mono_smbo'
else:
optimizer = opt_algo
print('Test optimizer: %s' % optimizer)
seeds = get_seeds(dataset, rep_num)
for run_id in range(start_id, rep_num):
if B > 0:
task_name = dataset + '_%s_%d_%d_%d' % (task_id, B,
run_count, run_id)
else:
task_name = dataset + '_%s_%d_%d' % (task_id, run_count,
run_id)
seed = seeds[run_id]
runcount_const = run_count if opt_algo != 'tpe' else tpe_runcount
# Construct the AutoML classifier.
cls = Classifier(optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=runcount_const,
runtime=B,
task_name=task_name,
update_mode=mode,
param=eta)
# Test the CASH performance on test set.
cash_test_acc = cls.score(X_test, y_test)
# Load CASH intermediate infos.
if optimizer == 'smbo':
file_id = 'smac'
elif optimizer == 'tpe':
file_id = 'hyperopt'
elif optimizer == 'mono_smbo':
file_id = 'mm_bandit_%d_smac' % mode
else:
raise ValueError('Invalid optimizer!')
tmp_task_id = '%s_%d' % (task_id, B) if B > 0 else task_id
tmp_configs, tmp_perfs = load_infos(dataset, tmp_task_id,
run_count, run_id, file_id)
if opt_algo != 'tpe':
runcount_dict[opt_algo].append(len(tmp_configs))
model_infos = (tmp_configs, tmp_perfs)
ensemble_size = 50
task_type = type_of_target(dm.train_y)
if optimizer == 'tpe':
task_type = 'hyperopt_' + task_type
metric = accuracy_score
ensemble_model = EnsembleSelection(model_infos,
ensemble_size,
task_type,
metric,
n_best=20)
ensemble_model.fit(dm)
ens_val_pred = ensemble_model.predict(dm.val_X)
ens_val_acc = accuracy_score(ens_val_pred, dm.val_y)
ens_pred = ensemble_model.predict(X_test)
ens_test_acc = accuracy_score(ens_pred, y_test)
key_id = '%s_%d_%d_%s' % (dataset, run_count, run_id,
optimizer)
result[key_id] = [cash_test_acc, ens_val_acc, ens_test_acc]
print(result)
# Save the test result.
with open(
'data/%s/%s_test_result_%s_%s_%d_%d_%d.pkl' %
(dataset_id, dataset, opt_algo, task_id, run_count, rep_num,
start_id), 'wb') as f:
pickle.dump(result, f)
def test_exp2_evaluation():
rep_num = args.rep
run_count = args.run_count
start_id = args.start_runid
datasets = args.datasets.split(',')
print(rep_num, run_count, datasets)
task_id = "exp_2_evaluation"
for dataset in datasets:
dataset_id = dataset.split('_')[0]
result_dir = 'data/' + dataset_id
if not os.path.exists(result_dir):
os.mkdir(result_dir)
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42,
stratify=y)
dm = DataManager(X_train, y_train)
# optimizer_algos = ['cmab_ts', 'rl_1_0.3', 'rl_2_1', 'rl_3_0']
optimizer_algos = ['cmab_ts', 'rl_2_1', 'rl_3_0']
# Test each optimizer algorithm:
for opt_algo in optimizer_algos:
result = dict()
mode, eta = None, None
# Parse the parameters for each optimizer.
if opt_algo.startswith('rl'):
if len(opt_algo.split('_')) == 3:
_, mode, eta = opt_algo.split('_')
mode = int(mode)
optimizer = 'rl_smbo'
eta = float(eta)
else:
raise ValueError('Wrong params!')
else:
optimizer = opt_algo
print('Test optimizer: %s' % optimizer)
seeds = get_seeds(dataset, rep_num)
for run_id in range(start_id, rep_num):
task_name = dataset + '_%s_%d_%d' % (task_id, run_count,
run_id)
seed = seeds[run_id]
# Construct the AutoML classifier.
cls = Classifier(optimizer=optimizer,
seed=seed).fit(dm,
metric='accuracy',
runcount=run_count,
runtime=None,
task_name=task_name,
update_mode=mode,
param=eta)
# Test the CASH performance on test set.
cash_test_acc = cls.score(X_test, y_test)
key_id = '%s_%d_%d_%s' % (dataset, run_count, run_id,
optimizer)
result[key_id] = [cash_test_acc]
print(result)
# Save the test result.
with open(
'data/%s/%s_test_result_%s_%s_%d_%d_%d.pkl' %
(dataset_id, dataset, opt_algo, task_id, run_count, rep_num,
start_id), 'wb') as f:
pickle.dump(result, f)
df["Sex"] = df["Sex"].replace(["male", "female"], [0, 1])
df.drop(columns="Ticket", axis=1, inplace=True)
for i in range(df.shape[0]):
if df["Cabin"][i] == "C23 C25 C27":
df["Cabin"][i] = 0
else:
df["Cabin"][i] = 1
df["Cabin"] = df["Cabin"].astype("float")
df = pd.get_dummies(df)
x = df.values
x_train = x[:train_size]
x_test = x[train_size:]
dm = DataManager()
dm.train_X = x_train
dm.train_y = y_train
clf = Classifier(optimizer="smbo")
clf.fit(dm, metric="accuracy", runcount=200)
submission = pd.read_csv(home_path + "/datasets/titanic/gender_submission.csv")
submission["Survived"] = clf.predict(x_test)
submission.to_csv(home_path + "/datasets/titanic/xgboost.csv", index=False)
def evaluate_k():
algo_list = [
'xgboost', 'liblinear_svc', 'gradient_boosting', 'decision_tree',
'passive_aggressive', 'qda', 'random_forest', 'sgd', 'extra_trees',
'lda', 'gaussian_nb', 'libsvm_svc', 'logistic_regression', 'adaboost',
'k_nearest_neighbors'
]
rep_num = args.rep
run_count = args.run_count
start_id = args.start_runid
datasets = args.datasets.split(',')
task_id = 'exp5_eval_k'
print(rep_num, run_count, datasets, task_id)
for dataset in datasets:
# Make directories.
dataset_id = dataset.split('_')[0]
save_dir = "data/%s/" % dataset_id
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Dataset partition.
X, y, _ = load_data(dataset)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42,
stratify=y)
dm = DataManager(X_train, y_train)
# opt_algos = ['mono_smbo_3_0', 'smbo', 'baseline_2', 'tpe']
opt_algos = ['mono_smbo_3_0', 'smbo', 'baseline_2']
for algo in opt_algos:
result = dict()
seeds = get_seeds(dataset, rep_num)
for run_id in range(start_id, rep_num):
seed = seeds[run_id]
# Test each optimizer algorithm:
for n_est in [15, 12, 8, 4, 2, 1]:
algos = algo_list[:n_est]
task_name = dataset + '_%s_%d_%d_%d' % (task_id, run_count,
run_id, n_est)
mode, param = 3, None
if algo.startswith('mono_smbo'):
optimizer = 'mono_smbo'
mode, param = 3, 10
elif algo.startswith('baseline'):
optimizer = 'baseline'
mode = 2
else:
optimizer = algo
print('Test %s optimizer => %s' % (optimizer, task_name))
# Construct the AutoML classifier.
cls = Classifier(optimizer=optimizer,
seed=seed,
include_models=algos).fit(
dm,
metric='accuracy',
runcount=run_count,
task_name=task_name,
update_mode=mode,
param=param)
acc = cls.score(X_test, y_test)
key_id = '%s_%d_%d_%d_%s' % (dataset, run_count, n_est,
run_id, optimizer)
result[key_id] = acc
# Display and save the test result.
print(result)
with open(
'data/%s/%s_test_%s_%d_%d_%d.pkl' %
(dataset_id, dataset, algo, run_count, rep_num, start_id),
'wb') as f:
pickle.dump(result, f)
