notebooks/decision_tree.ipynb

@@ -167,6 +167,20 @@
     "test_accuracy = accuracy_score(test_y, test_pred)\n",
     "print(f'Testgenauigkeit: {test_accuracy}')\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Validierungsgenauigkeit des Modells liegt bei 75,5%, was darauf hinweist, dass das Modell in etwa drei Vierteln der Fälle korrekte Vorhersagen auf den Validierungsdaten macht. Dies zeigt eine recht solide Leistung, deutet jedoch auch darauf hin, dass es noch Verbesserungspotenzial gibt, insbesondere bei der Verfeinerung des Modells, um die Fehlerquote zu senken"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mit einer Testgenauigkeit von 79% klassifiziert das Modell die Testdaten überwiegend korrekt. Dieses Ergebnis ist ein Indikator dafür, dass das Modell eine gute Generalisierungsfähigkeit aufweist und zuverlässig auf neuen, unbekannten Daten agieren kann. "
+   ]
   }
  ],
  "metadata": {

scripts/data_helper.py

@@ -13,7 +13,7 @@ import cv2 as cv
 TODO create overall description
 """
 
-def load_data(only_demographic:bool=False, path_settings:str="../settings.json"):
+def load_data(only_demographic:bool=False, only_diagnosis_ids=False, path_settings:str="../settings.json"):
     """
     Loads data from pickle files based on the specified settings.
 
@@ -28,6 +28,10 @@ def load_data(only_demographic:bool=False, path_settings:str="../settings.json")
     path_data = settings["data_path"] 
     labels = settings["labels"]
 
+    if only_diagnosis_ids:
+        with open(f'{path_data}/diagnosis.pkl', 'rb') as f:
+            return pickle.load(f)
+
     data = {}
     if only_demographic:
         data = {'age': [], 'diag': [], 'gender': []}

scripts/feature_extraction.py

@@ -5,6 +5,7 @@ import math
 import time
 from multiprocessing import Pool
 import sqlite3
+import random
 
 def get_y_value(ecg_cleaned, indecies):
     """
@@ -213,7 +214,6 @@ def extract_features_parallel(data_dict, num_processes, sampling_rate=500, used_
     c = conn.cursor()
     # get unique data
     data_dict = exclude_already_extracted(data_dict, conn)
-
     for label, data in data_dict.items():
         print(f"Extracting features for {label} with {len(data)} data entries.")
         with Pool(processes=num_processes) as pool:
@@ -239,7 +239,7 @@ def extract_features_parallel(data_dict, num_processes, sampling_rate=500, used_
 
 
 
-def extract_features(data_dict, sampling_rate=500, used_channels=[0, 1, 2, 3, 4, 5]):
+def extract_features(data_dict, sampling_rate=500, used_channels=[0, 1, 2, 3, 4, 5], limit=1000):
     """
     Extracts the features from the data.
     Args:
@@ -266,6 +266,8 @@ def extract_features(data_dict, sampling_rate=500, used_channels=[0, 1, 2, 3, 4,
         print("No last file in DB")
 
     for label, data in data_dict.items():
+        # get limit amount of radom samples out of data
+        data = random.sample(data, min(len(data), limit))
         print(f"Extracting features for {label} with {len(data)} data entries.")
         for data_idx, record in enumerate(data):
             # Skip the records that are already in the database

scripts/generate_data.py

@@ -30,7 +30,7 @@ def get_diagnosis_ids(record):
     list_diagnosis = [int(x.strip()) for x in diagnosis.split(',')]
     return list_diagnosis
 
-def generate_raw_data(path_to_data, settings, max_counter=100_000):
+def generate_raw_data(path_to_data, settings, max_counter=100_000, only_ids=False):
     """
     Generates the raw data from the WFDB records.
     Args:
@@ -43,6 +43,9 @@ def generate_raw_data(path_to_data, settings, max_counter=100_000):
     failed_records = []
     categories = settings["labels"]
 
+    if only_ids:
+        diag_dict = {}
+    else:
         diag_dict = {k: [] for k in categories.keys()}
     # Loop through the records
     for dir_th in os.listdir(path_to_data):
@@ -60,6 +63,9 @@ def generate_raw_data(path_to_data, settings, max_counter=100_000):
                     record = wfdb.rdrecord(path_to_100_records + '/' + record_name)
                     # Get the diagnosis
                     diagnosis = np.array(get_diagnosis_ids(record))
+                    if only_ids: 
+                        diag_dict[record_name] = diagnosis
+                    else:
                         # check if diagnosis is a subset of one of the categories
                         for category_name, category_codes in categories.items():
                             # if any of the diagnosis codes is in the category_codes
@@ -83,7 +89,7 @@ def generate_raw_data(path_to_data, settings, max_counter=100_000):
             break
     return diag_dict
 
-def write_data(data_dict, path='./data', file_prefix=''):
+def write_data(data_dict, path='./data', file_prefix='', only_ids=False):
     """
     Writes the data to a pickle file.
     Args:
@@ -93,6 +99,13 @@ def write_data(data_dict, path='./data', file_prefix=''):
     # if path not exists create it
     if not os.path.exists(path):
             os.makedirs(path)
+
+    if only_ids:
+        # write to pickle
+        print(f"Writing diagnosis IDs to pickle with {len(data_dict)} data entries.")
+        with open(f'{path}/{file_prefix}.pkl', 'wb') as f:
+            pickle.dump(data_dict, f)
+        return
     # write to pickle
     for cat_name, data in data_dict.items():
         print(f"Writing {cat_name} to pickle with {len(data)} data entries.")
@@ -114,7 +127,7 @@ def generate_feature_data(input_data_path, settings, parallel=False, split_ratio
         split_ratio = settings['split_ratio']
     print(list(os.listdir(input_data_path)))
     for file in os.listdir(input_data_path):
-        if file.endswith(".pkl"):
+        if file.endswith(".pkl") and not file.startswith("diagnosis"):
             print(f"Reading {file}")
             with open(f'{input_data_path}/{file}', 'rb') as f:
                 data = pickle.load(f)
@@ -127,13 +140,14 @@ def generate_feature_data(input_data_path, settings, parallel=False, split_ratio
                 print(f"Using {max_processes} processes to extract features.")
                 feature_extraction.extract_features_parallel(data_dict, num_processes=max_processes)
             else:
-                feature_extraction.extract_features(data_dict)
+                print(f"For even distribution of data, the limit is set to the smallest size: 1000.")
+                feature_extraction.extract_features(data_dict, limit=1000)
     # Split the data
     feature_extraction.split_and_shuffle_data(split_ratio=split_ratio)
 
 
 
-def main(gen_data=True, gen_features=True, split_ratio=None, parallel=False, settings_path='./settings.json', num_process_files=-1):
+def main(gen_data=True, gen_features=True, gen_diag_ids=True, split_ratio=None, parallel=False, settings_path='./settings.json', num_process_files=-1):
     """
     Main function to generate the data.
     Args:
@@ -159,6 +173,11 @@ def main(gen_data=True, gen_features=True, split_ratio=None, parallel=False, set
     if gen_features:
         feature_data_dict = generate_feature_data(settings["data_path"], settings, split_ratio=split_ratio, parallel=parallel)
         ret_data = feature_data_dict
+    if gen_diag_ids:
+        raw_data_dir = settings["wfdb_path"] + '/WFDBRecords'
+        data_dict = generate_raw_data(raw_data_dir, settings, max_counter=num_process_files, only_ids=True)
+        write_data(data_dict, path=settings["data_path"], file_prefix='diagnosis', only_ids=True)
+        ret_data = data_dict
 
     return ret_data
         
@@ -178,6 +197,7 @@ if __name__ == '__main__':
     # SB, AFIB, GSVT, SR
     # new GSVT, AFIB, SR, SB
     # Generate the data
-    main(gen_data=True, gen_features=False, num_process_files=100_000)
-    #main(gen_data=False, gen_features=True, split_ratio=[0.8, 0.1, 0.1], parallel=False, num_process_files=100_000)
+    #main(gen_data=True, gen_features=False, gen_diag_ids=False, num_process_files=100_000)
+    #main(gen_data=False, gen_features=True, gen_diag_ids=False, split_ratio=[0.8, 0.1, 0.1])
+    main(gen_data=False, gen_features=False, gen_diag_ids=True)
     print("Data generation completed.")

settings.json

@@ -1,8 +1,8 @@
 {
     "wfdb_path_comment": "Path to the WFDB data. This is the folder where the WFDB data is stored.",
-    "wfdb_path": "C:/Users/arman/PycharmProjects/pythonProject/DSA/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0",
+    "wfdb_path": "C:/Studium/dsa/large_12_ecg_data/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0",
     "data_path_comment": "Path to the data folder. This is the folder where the genereated data is stored.",
-    "data_path": "C:/Users/arman/PycharmProjects/pythonProject/DSA/DSA_SS24/data",
+    "data_path": "C:/Studium/dsa/data",
     "labels_comment": "Labels for the different classes. The labels are the SNOMED CT codes.",
     "labels": {
         "GSVT":  [426761007, 713422000, 233896004, 233897008, 713422000],