def initData():
initialization = InitDataSet()
doas = initialization.get_dataset_as_doas()
encoding = Encoding('./../../data_to_be_saved/alphabet_3.txt')
# SAU 1 SAU 2
# 1 th
mark_bursts_regions_one_threshold(doas)
# diff th
# mark_bursts_regions(doas)
# remove_bursted_trials_when_segment(doas)
# doas_train, doas_test, ind_test = train_test_doa(doas, 0.2)
# sa imi fac propriul test and train
doas_train, doas_test, ind_test = train_test_doa_check_trials(doas, 0.2)
train_data = ExtractData(doas_train, [channel], ['light', 'medium', 'deep'], [segment], ['all'])
test_data = ExtractData(doas_test, [channel], ['light', 'medium', 'deep'], [segment], ['all'])
# doar fft features
X_train, y_train = obtain_TESPAR_A_FFT_features(train_data)
x_test, y_test = obtain_TESPAR_A_FFT_features(test_data)
return X_train, y_train, x_test, y_test
def get_data(self):
# initialization = InitDataSet()
# doas = initialization.get_dataset_as_doas()
# dataset, channels, levels, segment, orientation
# split_data = SplitData(self.doas, self.channels, self.level, self.segment, self.orientation)
split_data = ExtractData(self.doas, self.channels, self.level,
self.segment, self.orientation)
self.X = []
self.X_validate = []
for i in range(len(split_data.result.arrays)):
for j in range(len(split_data.result.arrays[i].array_data)):
self.X.append(split_data.result.arrays[i].array_data[j])
for j in range(len(split_data.result.arrays[i].array_validate)):
self.X_validate.append(
split_data.result.arrays[i].array_validate[j])
return self.X, self.X_validate
for run in range(run_nr):
print('************************RUN ' + str(run) +
'************************')
# firstly split the input into train test
doas_train, doas_test, ind_test = train_test_doa(doas, 0.2)
np.savetxt(write_file, np.array(ind_test), fmt="%s", newline=' ')
write_file.write('\n')
for ind_segment, segment in enumerate(segments):
for channel in range(len(all_channels)):
print("start running for channel " + str(all_channels[channel]) +
' ' + segment + '\n')
# SplitData(self, doas, channels, levels, segment, orientation):
train_data = ExtractData(doas_train, [all_channels[channel]],
['light', 'deep'], [segment], ['all'])
test_data = ExtractData(doas_test, [all_channels[channel]],
['light', 'deep'], [segment], ['all'])
X_train, y_train = obtain_features_labels(train_data, encoding)
x_test, y_test = obtain_features_labels(test_data, encoding)
model = DecisionTreeClassifier(random_state=99,
criterion='gini',
max_depth=2)
model.fit(X_train, y_train)
predictions = model.predict(x_test)
report = classification_report(y_test,
predictions,
output_dict=True)
good_channels_spont = [1, 3, 5, 12, 14]
run_nr = 5
channel = 2
segment = 'stimulus'
print('test for overfitting RF 0.2')
for run in range(run_nr):
# firstly split the input into train test
doas_train, doas_test = train_test_doa_remake_balanced(doas)
#
print()
train_data = ExtractData(doas_train, [2], ['light', 'medium', 'deep'],
['spontaneous', 'stimulus'], ['all'])
test_data = ExtractData(doas_test, [2], ['light', 'medium', 'deep'],
['spontaneous', 'stimulus'], ['all'])
X_train, y_train = obtain_concatenate_segments_fft(train_data)
x_test, y_test = obtain_concatenate_segments_fft(test_data)
# classify with parameters tunning
model = RandomForestClassifier(n_estimators=5000,
max_depth=5,
min_samples_split=5,
min_samples_leaf=10)
# model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions_train = model.predict(X_train)
predictions_test = model.predict(x_test)
f1scores = [[[] for i in range(channels_range - 1)]
for j in range(len(segments))]
for run in range(run_nr):
# firstly split the input into train test
doas_train, doas_test = train_test_doa_remake_balanced(doas)
# np.savetxt(write_file, np.array(ind_test), fmt="%s", newline=' ')
# write_file.write('\n')
for ind_segment, segment in enumerate(segments):
for channel in range(len(all_channels)):
print("start running for channel " + str(channel) + '\n')
# SplitData(self, doas, channels, levels, segment, orientation):
train_data = ExtractData(doas_train, [all_channels[channel]],
['medium', 'deep'],
['spontaneous', 'stimulus'], ['all'])
test_data = ExtractData(doas_test, [all_channels[channel]],
['medium', 'deep'],
['spontaneous', 'stimulus'], ['all'])
X_train, y_train = obtain_concatenate_segments_fft(train_data)
x_test, y_test = obtain_concatenate_segments_fft(test_data)
model = RandomForestClassifier(n_estimators=5000,
max_depth=5,
min_samples_split=5,
min_samples_leaf=10)
model.fit(X_train, y_train)
predictions = model.predict(x_test)
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32
]
encoding = Encoding('./../../data_to_be_saved/alphabet_3.txt')
data_dir = os.path.join('../..', '..')
initialization = InitDataSet()
doas = initialization.get_dataset_as_doas()
mark_bursts_regions(doas)
for segment in segments:
for channel in all_channels:
print("start running for channel " + str(channel) + ' ' + segment)
data = ExtractData(doas, [channel], ['light', 'medium', 'deep'],
[segment], ['all'])
X, y = obtain_TESPAR_A_FFT_features(data, encoding)
model = RandomForestClassifier(n_estimators=5000,
max_depth=5,
min_samples_split=5,
min_samples_leaf=10)
skf = StratifiedKFold(n_splits=10)
skf.get_n_splits(X, y)
results = cross_validate(model,
X,
y,
scoring=['accuracy', 'f1_weighted'],