def select_multisurf(X, y, percentile=10):
num = math.ceil(X.shape[0] * percentile / 100)
selector = MultiSURF(n_features_to_select=num,
discrete_threshold=3,
n_jobs=-1)
selector.fit(X, y)
return selector
def fit(self, X, y=None, **kwargs):
X, y = self.check_X_y(X, y)
self.check_params(X, y)
selector = MultiSURF(n_features_to_select=self.num_features, )
selector.fit(X, y)
_support = selector.top_features_[:self.num_features]
self.support = self.check_support(_support)
return self
def multisurf_fs(X_df,X_train_all,X_test_all,y_train):
'''MultiSURF for feature selection'''
fs = MultiSURF(discrete_threshold = 1000, n_jobs=1)
fs.fit(X_train_all, y_train)
feature_scores = fs.feature_importances_
feature_ids = np.where(feature_scores>=0)[0]
selected_features = np.array(X_df.columns[feature_ids])
#New X_train and X_test matrices
X_train = X_train_all[:,feature_ids]
X_test = X_test_all[:,feature_ids]
return selected_features, feature_scores, X_train, X_test
def job(experiment_path,cv):
job_start_time = time.time()
file = open(experiment_path+'/phase1pickle', 'rb')
phase1_pickle = pickle.load(file)
file.close()
cv_info = phase1_pickle[0]
learning_iterations = phase1_pickle[3]
N = phase1_pickle[4]
nu = phase1_pickle[5]
attribute_tracking_method = phase1_pickle[6]
random_state = phase1_pickle[7]
class_label = phase1_pickle[8]
feature_selection_sample_size = phase1_pickle[10]
rule_compaction_method = phase1_pickle[11]
data_headers = phase1_pickle[1][2]
train_data_features = cv_info[cv][0]
train_data_phenotypes = cv_info[cv][1]
train_instance_labels = cv_info[cv][2]
train_group_labels = cv_info[cv][3]
test_data_features = cv_info[cv][4]
test_data_phenotypes = cv_info[cv][5]
test_instance_labels = cv_info[cv][6]
test_group_labels = cv_info[cv][7]
inst_label = cv_info[cv][8]
group_label = cv_info[cv][9]
# Create CV directory
if not os.path.exists(experiment_path + '/CV_' + str(cv)):
os.mkdir(experiment_path + '/CV_' + str(cv))
#MultiSURF Feature Scoring
merged = np.insert(train_data_features, train_data_features.shape[1], train_data_phenotypes, 1)
rb_sample = np.random.choice(merged.shape[0], min(feature_selection_sample_size,merged.shape[0]), replace=False)
new_data = []
for i in rb_sample:
new_data.append(merged[i])
new_data = np.array(new_data)
data_featuresR = np.delete(new_data, -1, axis=1)
data_phenotypesR = new_data[:, -1]
featureimportance_model = MultiSURF()
featureimportance_model.fit(data_featuresR, data_phenotypesR)
scores = featureimportance_model.feature_importances_
# Train ExSTraCS Model
model = ExSTraCS(learning_iterations=learning_iterations, N=N, nu=nu,attribute_tracking_method=attribute_tracking_method,
rule_compaction=rule_compaction_method,random_state=random_state,do_correct_set_subsumption=False,expert_knowledge=scores)
model.fit(train_data_features, train_data_phenotypes)
outfile = open(experiment_path + '/CV_' + str(cv) + '/model', 'wb')
pickle.dump(model, outfile)
outfile.close()
# Export Testing Accuracy for each instance
predicted_data_phenotypes = model.predict(test_data_features)
equality = np.equal(predicted_data_phenotypes, test_data_phenotypes)
with open(experiment_path + '/CV_' + str(cv) + '/instTestingAccuracy.csv', mode='w') as file:
writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow([inst_label, 'isCorrect'])
for i in range(len(test_instance_labels)):
writer.writerow([test_instance_labels[i], 1 if equality[i] else 0])
file.close()
# Export Aggregate Testing Accuracy
outfile = open(experiment_path + '/CV_' + str(cv) + '/testingAccuracy.txt', mode='w')
outfile.write(str(model.score(test_data_features, test_data_phenotypes)))
outfile.close()
# Save train and testing datasets into csvs
with open(experiment_path + '/CV_' + str(cv) + '/trainDataset.csv', mode='w') as file:
writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow(list(data_headers) + [class_label, inst_label, group_label])
for i in range(len(train_instance_labels)):
writer.writerow(list(train_data_features[i]) + [train_data_phenotypes[i]] + [train_instance_labels[i]] + [
train_group_labels[i]])
file.close()
with open(experiment_path + '/CV_' + str(cv) + '/testDataset.csv', mode='w') as file:
writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow(list(data_headers) + [class_label, inst_label, group_label])
for i in range(len(test_instance_labels)):
writer.writerow(list(test_data_features[i]) + [test_data_phenotypes[i]] + [test_instance_labels[i]] + [
test_group_labels[i]])
file.close()
# Get AT Scores for each instance
AT_scores = model.get_attribute_tracking_scores(instance_labels=np.array(train_instance_labels))
# Normalize AT Scores
normalized_AT_scores = []
for i in range(len(AT_scores)):
normalized = AT_scores[i][1]
max_score = max(normalized)
for j in range(len(normalized)):
if max_score != 0:
normalized[j] /= max_score
else:
normalized[j] = 0
normalized_AT_scores.append(list(normalized))
# Save Normalized AT Scores
with open(experiment_path + '/CV_' + str(cv) + '/normalizedATScores.csv', mode='w') as file:
writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
writer.writerow([inst_label] + list(data_headers))
for i in range(len(train_instance_labels)):
writer.writerow([train_instance_labels[i]] + normalized_AT_scores[i])
file.close()
# Save Runtime
runtime_file = open(experiment_path + '/CV_' + str(cv) + '/runtime.txt', 'w')
runtime_file.write(str(time.time() - job_start_time))
runtime_file.close()
# Print completion
print('CV '+str(cv) + " phase 1 complete")