def test_raw_decision_function():
from EvoDAG.model import EvoDAGE
m = EvoDAGE(n_estimators=3, n_jobs=2, time_limit=4)
m.fit(X, cl)
pr = m.raw_decision_function(X)
default_nargs()
print(pr.shape)
assert pr.shape[1] == np.unique(cl).shape[0] * len(m._m.models)
time_spent = time.time() - start
vprint(verbose, "[+] Remaining time after building model %5.2f sec" % (time_budget-time_spent))
if time_spent >= time_budget:
vprint(verbose, "[-] Sorry, time budget exceeded, skipping this task")
execution_success = False
continue
time_predict_value = time_to_predict(D)
time_budget = time_budget - time_spent # Remove time spent so far
start = time.time() # Reset the counter
time_spent = 0 # Initialize time spent learning
M.time_limit = time_budget * time_predict_value * 0.9
vprint(verbose, "[+] Time budget to train the model %5.2f sec" % M._time_limit)
Xtest = None
if D.info['test_num'] < 1000:
Xtest = np.array([x.hy.full_array() for x in read_data(test_fname)]).T
M.fit(X, y, test_set=Xtest)
# log_reg = LogisticRegression(random_state=0, class_weight='balanced')
# log_reg.fit(M.raw_decision_function(X), y)
vprint(verbose, "=========== " + basename.capitalize() + " Training cycle " + " ================")
vprint(verbose, "[+] Fitting success, time spent so far %5.2f sec" % (time.time() - start))
vprint(verbose, "[+] Size of trained model %5.2f bytes" % data_io.total_size(M))
# Make predictions
# -----------------
if os.path.isfile(valid_fname):
Y_valid = M.predict_proba(read_data(valid_fname))[:, 1]
# Y_valid = log_reg.predict_proba(M.raw_decision_function(read_data(valid_fname)))[:, 1]
else:
Y_valid = None
if Xtest is None:
Xtest = read_data(test_fname)
Y_test = M.predict_proba(Xtest)[:, 1]