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 __init__(self,
val_split=0.1,
test_split=0.1,
label_col='20d_ret',
features_dir=None):
features_dir = features_dir or FEATURES_DIR
X, y, dates = load_features(features_dir, label_col=label_col)
self.X = X
self.y = y
self.dates = pd.to_datetime(dates)
self.val_split, self.test_split = val_split, test_split
self.X_train, self.X_val, self.X_test = None, None, None
self.y_train, self.y_val, self.y_test = None, None, None
print("Processing data...")
self._process_data()
print("Done")
import json
import numpy as np
import pylab as pl
from sklearn import neighbors
from data import load_features, load_mfccs
# Load and divide data
##songs, prop_dict = load_mfccs()
songs, prop_dict = load_features()
training_set = dict([e for e in prop_dict.items()[0:30]])
testing_set = dict([e for e in prop_dict.items()[30:]])
# Train k-NN
fo = open('data/drums.genres.json', 'r')
genres = json.loads(fo.read())
cmap = {'pop': 0, 'rock': 1, 'reggae': 2, 'jazz': 3, 'classical': 4}
classes = [cmap[genres[k]] for k in training_set.keys()]
fo.close()
X = np.array([p for k, p in training_set.items()])
Y = np.array(classes)
n_neighbors = 9
clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance').fit(X, Y)
# Test k-NN
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))
def fit(cnf, exp_run_folder, classifier, features_file, n_iter, blend_cnf,
test_dir, fold):
config = util.load_module(cnf).config
config.cnf[
'fold'] = fold # <-- used to change the directories for weights_best, weights_epoch and weights_final
config.cnf['exp_run_folder'] = exp_run_folder
folds = yaml.load(open('folds/' + data.settings['protocol'] + '.yml'))
f0, f1 = fold.split('x')
train_list = folds['Fold_' + f0][int(f1) - 1]
test_list = folds['Fold_' + f0][0 if f1 == '2' else 1]
image_files = data.get_image_files(config.get('train_dir'), train_list)
names = data.get_names(image_files)
labels = data.get_labels(names,
label_file='folds/' + data.settings['protocol'] +
'.csv').astype(np.int32)[:, np.newaxis]
if features_file is not None:
runs = {'run': [features_file]}
else:
runs = {
run: [
os.path.join(exp_run_folder + '/data/features', f)
for f in files
]
for run, files in yaml.load(open(blend_cnf)).items()
}
scalers = {run: StandardScaler() for run in runs}
y_preds = []
y_preds_proba = []
for i in range(n_iter):
print("iteration {} / {}".format(i + 1, n_iter))
for run, files in runs.items():
files = [
f.replace('f0xf1.npy', '{}.npy'.format(fold)) for f in files
]
if classifier is None:
X_test = data.load_features(files, test=True)
if data.settings['protocol'] != 'protocol3':
y_pred_proba = X_test
y_proba = []
for i in range(0, len(X_test)):
y_proba.append(
y_pred_proba[i][1]) #using score from the positive
y_pred = np.clip(np.round(y_proba), 0, 1).astype(int)
else:
y_pred_proba = est.predict_proba(X)
else:
print("fitting features for run {}".format(run))
X_train = data.load_features(files)
l2Norm = np.linalg.norm(X_train, axis=1)
X_train = np.divide(X_train.T, l2Norm).T
est = estimator(data.settings['protocol'],
classifier,
X_train.shape[1],
image_files,
X_train,
labels,
run,
fold,
eval_size=0.1)
open(
exp_run_folder +
"/best_estimator_fold_{}.txt".format(fold),
"w").write(str(est))
X_test = data.load_features(files, test=True)
l2Norm = np.linalg.norm(X_test, axis=1)
X_test = np.divide(X_test.T, l2Norm).T
if data.settings['protocol'] != 'protocol3':
y_pred = est.predict(X_test).ravel()
y_pred_proba = est.predict_proba(X_test).ravel()
y_proba = []
for i in range(0, 2 * len(X_test), 2):
y_proba.append(
y_pred_proba[i +
1]) #using score from the positive
else:
y_pred_binary = est.predict(X_test)
y_pred = preprocessing.LabelBinarizer().fit([0, 1, 2])
y_pred = y_pred.inverse_transform(y_pred_binary)
y_proba = est.predict_proba(X_test)
image_files = data.get_image_files(test_dir or config.get('test_dir'),
test_list)
names = data.get_names(image_files)
labels = data.get_labels(
names, label_file='folds/' + data.settings['protocol'] +
'.csv').astype(np.int32)[:, np.newaxis] # , per_patient=per_patient
image_column = pd.Series(names, name='image')
labels_column = pd.Series(np.squeeze(labels), name='true')
level_column = pd.Series(y_pred, name='pred')
if data.settings['protocol'] != 'protocol3':
proba_column = pd.Series(y_proba, name='proba')
predictions = pd.concat(
[image_column, labels_column, level_column, proba_column], axis=1)
else:
proba_label_0 = pd.Series(y_proba[:, 0], name='proba_label_0')
proba_label_1 = pd.Series(y_proba[:, 1], name='proba_label_1')
proba_label_2 = pd.Series(y_proba[:, 2], name='proba_label_2')
predictions = pd.concat([
image_column, labels_column, level_column, proba_label_0,
proba_label_1, proba_label_2
],
axis=1)
predictions.to_csv(exp_run_folder +
"/ranked_list_fold_{}.csv".format(fold),
sep=';')
print("tail of predictions")
print(predictions.tail())
acc = len(filter(lambda
(l, y): l == y, zip(labels, y_pred))) / float(len(labels))
print("accuracy: {}".format(acc))
print("confusion matrix")
print(confusion_matrix(labels, y_pred))
if data.settings['protocol'] != 'protocol3':
auc = calc_auc(y_proba, labels, exp_run_folder, classifier, fold)
print("AUC: {}".format(auc))
average_precision = average_precision_score(labels, y_proba)
print("average precision: {}".format(average_precision))
c_matrix = confusion_matrix(labels, y_pred)
print("sensitivity: {}".format(c_matrix[1][1] /
(c_matrix[1][1] + c_matrix[0][1])))
print("specificity: {}".format(c_matrix[0][0] /
(c_matrix[0][0] + c_matrix[1][0])))
else:
y_test = label_binarize(labels, classes=[0, 1, 2])
auc = roc_auc_score(y_test, y_proba, average='macro')
print("AUC: {}".format(auc))
average_precision = average_precision_score(y_test,
y_proba,
average="macro")
print("mean average precision: {}".format(average_precision))
results = pd.concat([
pd.Series(exp_run_folder, name='folder'),
pd.Series(fold, name='fold'),
pd.Series(auc, name='auc'),
pd.Series(average_precision, name='ap'),
pd.Series(acc, name='acc')
],
axis=1)
with open('results.csv', 'a') as f:
results.to_csv(f, header=False)
