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Don't load all recordings into dataframe, process one by one #6

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48 changes: 25 additions & 23 deletions train_12ECG_classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,37 +17,18 @@ def train_12ECG_classifier(input_directory, output_directory):
header_files.append(g)

classes = get_classes(input_directory, header_files)
num_classes = len(classes)
num_files = len(header_files)
recordings = list()
headers = list()

for i in range(num_files):
recording, header = load_challenge_data(header_files[i])
recordings.append(recording)
headers.append(header)

# Train model.
print('Training model...')

features = list()
labels = list()

num_files = len(header_files)
for i in range(num_files):
recording = recordings[i]
header = headers[i]

tmp = get_12ECG_features(recording, header)
features.append(tmp)

for l in header:
if l.startswith('#Dx:'):
labels_act = np.zeros(num_classes)
arrs = l.strip().split(' ')
for arr in arrs[1].split(','):
class_index = classes.index(arr.rstrip()) # Only use first positive index
labels_act[class_index] = 1
labels.append(labels_act)
feats, labs = process_single_recording(header_files[i], classes)
features.append(feats)
labels.append(labs)

features = np.array(features)
labels = np.array(labels)
Expand All @@ -67,6 +48,7 @@ def train_12ECG_classifier(input_directory, output_directory):
filename = os.path.join(output_directory, 'finalized_model.sav')
joblib.dump(final_model, filename, protocol=0)


# Load challenge data.
def load_challenge_data(header_file):
with open(header_file, 'r') as f:
Expand All @@ -76,6 +58,7 @@ def load_challenge_data(header_file):
recording = np.asarray(x['val'], dtype=np.float64)
return recording, header


# Find unique classes.
def get_classes(input_directory, filenames):
classes = set()
Expand All @@ -87,3 +70,22 @@ def get_classes(input_directory, filenames):
for c in tmp:
classes.add(c.strip())
return sorted(classes)


# Process a single sample.
def process_single_recording(header_file, classes):

recording, header = load_challenge_data(header_file)

feats = get_12ECG_features(recording, header)
num_classes = len(classes)

for l in header:
if l.startswith('#Dx:'):
labels_act = np.zeros(num_classes)
arrs = l.strip().split(' ')
for arr in arrs[1].split(','):
class_index = classes.index(arr.rstrip()) # Only use first positive index
labels_act[class_index] = 1

return feats, labels_act