def CF_Boosted_Trees(model,
Data,
config,
name,
location,
states=3): # train_data, test_data
"""
Input:
train_data ... list-of-lists
"""
train_data, test_data = dat.PrepareCrossFold(Data.H_alpha)
train_labels, test_labels = dat.PrepareCrossFold(Data.labels)
K = len(train_data)
score_tab = Scoring.ScoringTable(location=location,
name=name,
n_states=states)
feat = FE.Features(config=config, normalize=True)
start = time()
for cross in range(K):
clf = copy(model)
train_matrix = feat.fit_transform(Data=train_data[cross])
test_matrix = feat.fit_transform_batch(Data=test_data[cross])
target = dat.merge_labels(train_labels[cross])
# print(np.shape(train_matrix), np.shape(target))
# print(np.shape(test_matrix), np.shape(test_labels[cross]))
pred = train_and_predict(model=clf,
train=train_matrix,
test=test_matrix,
labels=target,
unsupervised=False)
score = Scoring.score(states=pred,
results=test_labels[cross],
unsupervised=False,
pocet_stavu=states)
# print("score", score)
score_tab.add(scores=score)
print("{} section done. Time taken from start {}".format(
cross + 1,
time() - start))
score_tab.save_table()
config["n_estimators"] = model.n_estimators
with open(location + name + '_config.pickle', 'wb') as f:
pickle.dump(config, f)
return score_tab.return_table()
def test_PrepareCrossFold(Data, vypis=False):
train, test = Datasets.PrepareCrossFold(Data.H_alpha)
tmp1, tmp2 = Datasets.PrepareCrossFold(Data.data)
shodujiSe = True
for m, n in enumerate(train):
for i, j in enumerate(n):
YN = np.allclose(j, tmp1[m][i][1])
if YN == False:
shodujiSe = False
if vypis:
print(f'{m} Souhlasí {i+1} signál? ', YN)
print("Shodují se všechny? ", shodujiSe)
