def fit(cnf, predict, per_patient, features_file, n_iter, blend_cnf, test_dir):
config = util.load_module(cnf).config
image_files = data.get_image_files(config.get('train_dir'))
names = data.get_names(image_files)
labels = data.get_labels(names).astype(np.float32)[:, np.newaxis]
if features_file is not None:
runs = {'run': [features_file]}
else:
runs = data.parse_blend_config(yaml.load(open(blend_cnf)))
scalers = {run: StandardScaler() for run in runs}
tr, te = data.split_indices(image_files, labels)
y_preds = []
for i in range(n_iter):
print("iteration {} / {}".format(i + 1, n_iter))
for run, files in runs.items():
print("fitting features for run {}".format(run))
X = data.load_features(files)
X = scalers[run].fit_transform(X)
X = data.per_patient_reshape(X) if per_patient else X
est = get_estimator(X.shape[1], image_files, labels,
eval_size=0.0 if predict else 0.1)
est.fit(X, labels)
if not predict:
y_pred = est.predict(X[te]).ravel()
y_preds.append(y_pred)
y_pred = np.mean(y_preds, axis=0)
y_pred = np.clip(np.round(y_pred).astype(int),
np.min(labels), np.max(labels))
print("kappa after run {}, iter {}: {}".format(
run, i, util.kappa(labels[te], y_pred)))
print("confusion matrix")
print(confusion_matrix(labels[te], y_pred))
else:
X = data.load_features(files, test=True)
X = scalers[run].transform(X)
X = data.per_patient_reshape(X) if per_patient else X
y_pred = est.predict(X).ravel()
y_preds.append(y_pred)
if predict:
y_pred = np.mean(y_preds, axis=0)
y_pred = np.clip(np.round(y_pred),
np.min(labels), np.max(labels)).astype(int)
submission_filename = util.get_submission_filename()
image_files = data.get_image_files(test_dir or config.get('test_dir'))
names = data.get_names(image_files)
image_column = pd.Series(names, name='image')
level_column = pd.Series(y_pred, name='level')
predictions = pd.concat([image_column, level_column], axis=1)
print("tail of predictions file")
print(predictions.tail())
predictions.to_csv(submission_filename, index=False)
print("saved predictions to {}".format(submission_filename))
def fit(cnf, predict, per_patient, features_file, n_iter, blend_cnf, test_dir):
config = util.load_module(cnf).config
image_files = data.get_image_files(config.get('train_dir'))
names = data.get_names(image_files)
labels = data.get_labels(names).astype(np.float32)[:, np.newaxis]
if features_file is not None:
runs = {'run': [features_file]}
else:
runs = data.parse_blend_config(yaml.load(open(blend_cnf)))
scalers = {run: StandardScaler() for run in runs}
tr, te = data.split_indices(image_files, labels)
y_preds = []
for i in range(n_iter):
print("iteration {} / {}".format(i + 1, n_iter))
for run, files in list(runs.items()):
print("fitting features for run {}".format(run))
X = data.load_features(files)
X = scalers[run].fit_transform(X)
X = data.per_patient_reshape(X) if per_patient else X
est = get_estimator(X.shape[1], image_files, labels,
eval_size=0.0 if predict else 0.1)
est.fit(X, labels)
if not predict:
y_pred = est.predict(X[te]).ravel()
y_preds.append(y_pred)
y_pred = np.mean(y_preds, axis=0)
y_pred = np.clip(np.round(y_pred).astype(int),
np.min(labels), np.max(labels))
print("kappa after run {}, iter {}: {}".format(
run, i, util.kappa(labels[te], y_pred)))
print("confusion matrix")
print(confusion_matrix(labels[te], y_pred))
else:
X = data.load_features(files, test=True)
X = scalers[run].transform(X)
X = data.per_patient_reshape(X) if per_patient else X
y_pred = est.predict(X).ravel()
y_preds.append(y_pred)
if predict:
y_pred = np.mean(y_preds, axis=0)
y_pred = np.clip(np.round(y_pred),
np.min(labels), np.max(labels)).astype(int)
submission_filename = util.get_submission_filename()
image_files = data.get_image_files(test_dir or config.get('test_dir'))
names = data.get_names(image_files)
image_column = pd.Series(names, name='image')
level_column = pd.Series(y_pred, name='level')
predictions = pd.concat([image_column, level_column], axis=1)
print("tail of predictions file")
print(predictions.tail())
predictions.to_csv(submission_filename, index=False)
print("saved predictions to {}".format(submission_filename))
def split(X, y, eval_size):
if eval_size:
tr, te = data.split_indices(files, labels, eval_size)
return X[tr], X[te], y[tr], y[te]
else:
return X, X[len(X):], y, y[len(y):]
def split(X, y, eval_size):
if eval_size:
tr, te = data.split_indices(files, labels, eval_size)
return X[tr], X[te], y[tr], y[te]
else:
return X, X[len(X):], y, y[len(y):]
#def fit(cnf, predict, per_patient, features_file, n_iter, blend_cnf, test_dir):
config = util.load_module(cnf).config
image_files = data.get_image_files(config.get('train_dir'))
names = data.get_names(image_files)
labels = data.get_labels(names).astype(np.float32)[:, np.newaxis]
if features_file is not None:
runs = {'run': [features_file]}
else:
runs = data.parse_blend_config(yaml.load(open(blend_cnf)))
scalers = {run: StandardScaler() for run in runs}
tr, te = data.split_indices(image_files, labels)
y_preds = []
for i in range(n_iter):
print("iteration {} / {}".format(i + 1, n_iter))
for run, files in runs.items():
print("fitting features for run {}".format(run))
X = data.load_features(files)
X = scalers[run].fit_transform(X)
X = data.per_patient_reshape(X) if per_patient else X
est = get_estimator(X.shape[1], image_files, labels,
eval_size=0.0 if predict else 0.1)
est.fit(X, labels)
if not predict:
y_pred = est.predict(X[te]).ravel()
y_preds.append(y_pred)
