self.coef_ = self.feature_importances_ ############################################################################## # #folder to read feartures from output_folder = "/home/ubuntu/Documents/Thesis_work/results/1_min_features/aftdb/" output_folder_aftdb = "/home/ubuntu/Documents/Thesis_work/results/1_min_features/aftdb/" output_folder_afpdb_patient = "/home/ubuntu/Documents/Thesis_work/results/1_min_features/afpdb_patient/" output_folder_afpdb_normal = "/home/ubuntu/Documents/Thesis_work/results/1_min_features/afpdb_normal/" ### read values from text files ###### ## read these once #global_vocab=rw.read_features_frm_file(output_folder_afpdb_normal,"global_vocab_pickle.txt") global_vocab = rw.read_features_frm_file(output_folder, "global_vocab_pickle.txt") # ##################### change key value pairs of global vocab #################### inv_global_vocab = dict(zip(global_vocab.values(), global_vocab.keys())) print inv_global_vocab[ 3] # confirm that this is the std_dev_features and del is from dictionary del inv_global_vocab[3] ## read features from different databases # all_features_aftdb = rw.read_features_frm_file(output_folder_aftdb, "all_features_pickle.txt") all_features_afpdb_patient = rw.read_features_frm_file( output_folder_afpdb_patient, "all_features_pickle.txt") all_features_afpdb_normal = rw.read_features_frm_file( output_folder_afpdb_normal, "all_features_pickle.txt")
def get_features_from_folder(output_folder): ### read values from text files ###### all_features=rw.read_features_frm_file(output_folder,"all_features_pickle.txt") rec_name_array=rw.read_features_frm_file(output_folder,"rec_name_array_pickle.txt") return all_features,rec_name_array
from sklearn import cross_validation import wfdb_setup as ws; import process_rr as pr; import data_cleaning as dc; import graphs import read_write as rw; import non_linear_measures as nlm; import classification_functions as cl ############################################################################## output_folder="/home/ubuntu/Documents/Thesis_work/results/19_oct_results/non_linear/sodp_analysis/non_linear_features_edges_changed/" #output_folder="/home/ubuntu/Documents/Thesis_work/results/19_oct_results/afpdb_test_records/" ### read values from text files ###### all_features=rw.read_features_frm_file(output_folder,"all_features_pickle.txt") rw.write_value(all_features,output_folder,"list_of_list_before_cleaning","w") global_vocab=rw.read_features_frm_file(output_folder,"global_vocab_pickle.txt") rec_name_array=rw.read_features_frm_file(output_folder,"rec_name_array_pickle.txt") ##################### change key value pairs of global vocab #################### inv_global_vocab = dict(zip(global_vocab.values(), global_vocab.keys())) #print type(inv_global_vocab.values()) all_features_list=inv_global_vocab.values() np.savetxt(output_folder+"all_features_list.txt",all_features_list,fmt="%s",delimiter=',',newline='\n') #generate class labels y=np.array(cl.generate_labels(rec_name_array)) print ("label array is: " + str(y))
from sklearn import cross_validation import wfdb_setup as ws import process_rr as pr import data_cleaning as dc import graphs import read_write as rw import non_linear_measures as nlm import classification_functions as cl ############################################################################## output_folder = "/home/ubuntu/Documents/Thesis_work/results/19_oct_results/non_linear/sodp_analysis/non_linear_features_edges_changed/" #output_folder="/home/ubuntu/Documents/Thesis_work/results/19_oct_results/afpdb_test_records/" ### read values from text files ###### all_features = rw.read_features_frm_file(output_folder, "all_features_pickle.txt") rw.write_value(all_features, output_folder, "list_of_list_before_cleaning", "w") global_vocab = rw.read_features_frm_file(output_folder, "global_vocab_pickle.txt") rec_name_array = rw.read_features_frm_file(output_folder, "rec_name_array_pickle.txt") ##################### change key value pairs of global vocab #################### inv_global_vocab = dict(zip(global_vocab.values(), global_vocab.keys())) #print type(inv_global_vocab.values()) all_features_list = inv_global_vocab.values() np.savetxt(output_folder + "all_features_list.txt", all_features_list, fmt="%s", delimiter=',',
def get_features_from_folder(output_folder): ### read values from text files ###### all_features = rw.read_features_frm_file(output_folder, "all_features_pickle.txt") rec_name_array = rw.read_features_frm_file(output_folder, "rec_name_array_pickle.txt") return all_features, rec_name_array
self.coef_ = self.feature_importances_ ############################################################################## # #folder to read feartures from output_folder="/home/ubuntu/Documents/Thesis_work/results/1_min_features/aftdb/" output_folder_aftdb="/home/ubuntu/Documents/Thesis_work/results/1_min_features/aftdb/" output_folder_afpdb_patient="/home/ubuntu/Documents/Thesis_work/results/1_min_features/afpdb_patient/" output_folder_afpdb_normal="/home/ubuntu/Documents/Thesis_work/results/1_min_features/afpdb_normal/" ### read values from text files ###### ## read these once #global_vocab=rw.read_features_frm_file(output_folder_afpdb_normal,"global_vocab_pickle.txt") global_vocab=rw.read_features_frm_file(output_folder,"global_vocab_pickle.txt") # ##################### change key value pairs of global vocab #################### inv_global_vocab = dict(zip(global_vocab.values(), global_vocab.keys())) print inv_global_vocab[3] # confirm that this is the std_dev_features and del is from dictionary del inv_global_vocab[3] ## read features from different databases # all_features_aftdb=rw.read_features_frm_file(output_folder_aftdb,"all_features_pickle.txt") all_features_afpdb_patient=rw.read_features_frm_file(output_folder_afpdb_patient,"all_features_pickle.txt") all_features_afpdb_normal=rw.read_features_frm_file(output_folder_afpdb_normal,"all_features_pickle.txt") # all_features_nsrdb=rw.read_features_frm_file(output_folder_nsrdb,"all_features_pickle.txt") # all_features_afdb=rw.read_features_frm_file(output_folder_afdb,"all_features_pickle.txt") #all_features=rw.read_features_frm_file(output_folder,"all_features_pickle.txt")