def PrincipalComponentAnalysis(model, train_data, test_data, n_comp, configg,
model_nm, dataset_nm):
model_name = f"{model_nm}_{dataset_nm}_dataset.csv"
table = CL.ScoringTable(name=model_name, location=valid_path)
for cross in range(10):
start = time()
train_matrix = CL.prepare_features(data=train_data[cross],
config=configg)
test_matrix = CL.prepare_features(data=test_data[cross],
config=configg)
labels = d.merge_labels(d.get_layer(train_data[cross], 2))
clf = copy(model)
scale = StandardScaler()
train_matrix = scale.fit_transform(X=train_matrix, y=labels)
test_matrix = scale.transform(X=test_matrix)
pca = PCA(n_components=n_comp)
train_matrix = pca.fit_transform(X=train_matrix)
test_matrix = pca.transform(X=test_matrix)
states = CL.train_and_predict(clf,
train_matrix,
test_matrix, [],
labels,
unsupervised=False,
HMMmodified=False)
[a, m, f, f_a, p, r] = CL.score(states,
test_data[cross][0][2],
unsupervised=False)
kombinace = "pca"
params = "pca"
table.add(scores=[a, m, f, f_a, p, r],
n_estim=clf.n_estimators,
configg={
"Komb": kombinace,
"Param": params
})
print(cross + 1, '. -> ', round(time() - start), "seconds")
table.save_table()
return table.return_table()
