def report_md_breakdown(metadata):
"""Prints a breakdown of metadata info to logger
Parameters
----------
metadata : array_like
List of dicts of the metadata
"""
n_samples = len(metadata) # Find number of samples in metadata
# Find the adipose shell ID of each sample
adi_ids = np.array([md['phant_id'].split('F')[0] for md in metadata])
unique_adi_ids = np.unique(adi_ids) # Find the unique adi IDs
logger.info('\tAdipose Fractions:')
for adi_id in unique_adi_ids:
# Find fraction samples with this adipose ID
frac_here = np.sum(adi_ids == adi_id) / n_samples
logger.info('\t\t%s:\t%.2f%%' % (adi_id, 100 * frac_here))
# Get tumor presence and print to logger
tum_presence = get_class_labels(metadata)
logger.info('\tTumor Fraction:\t%.2f'
% (np.sum(tum_presence) * 100 / n_samples))
# Get BI-RADS class of each sample
birads_classes = np.array([md['birads'] for md in metadata])
unique_birads = np.unique(birads_classes)
logger.info('\tBI-RADS Fractions:')
for birads_class in unique_birads:
# Find fraction of samples with this BI-RADS class
frac_here = np.sum(birads_classes == birads_class) / n_samples
logger.info('\t\tClass %d:\t%.2f' % (birads_class, 100 * frac_here))
# Get the indices of the samples with the target Adi ID
tar_idxs = np.array(
[this_adi_id in [adi_id] for this_adi_id in adi_ids])
# Use the samples with this adipose ID as the test set,
# all others assigned to train set
test_data = g2_d[tar_idxs, :, :]
train_data = g2_d[~tar_idxs, :, :]
test_md = g2_md[tar_idxs]
train_md = g2_md[~tar_idxs]
# Perform data augmentation on the training set here
train_data, train_md = full_aug(train_data, train_md)
# Get class labels for train/test sets here
test_labels = get_class_labels(test_md)
train_labels = get_class_labels(train_md)
test_labels = to_categorical(test_labels)
train_labels = to_categorical(train_labels)
# Resize data for use with keras
test_data = resize_features_for_keras(test_data)
train_data = resize_features_for_keras(train_data)
# Init arrays for storing results for this test set
aucs_here = np.zeros([
__N_RUNS,
])
accs_here = np.zeros([
__N_RUNS,
])
g1_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/test_fd.pickle'))
g1_md = load_pickle(
os.path.join(__DATA_DIR, 'g1-train-test/test_md.pickle'))
# Convert data to time domain, take magnitude, apply window
g1_d = correct_g1_ini_ant_ang(g1_d)
g1_d = np.abs(to_td(g1_d))
g2_d = np.abs(to_td(g2_d))
# Perform data augmentation
g2_d, g2_md = full_aug(g2_d, g2_md)
g2_d = resize_features_for_keras(g2_d)
g1_d = resize_features_for_keras(g1_d)
g2_labels = to_categorical(get_class_labels(g2_md))
g1_labels = to_categorical(get_class_labels(g1_md))
n_runs = 20
# Init arrays for storing performance metrics
auc_scores = np.zeros([
n_runs,
])
accs = np.zeros([
n_runs,
])
sens = np.zeros([
n_runs,
])
spec = np.zeros([
metadata = load_pickle(os.path.join(__DATA_DIR, 'g1_metadata.pickle'))
# Init vars for train/test fractions
tr_frac = 0
te_frac = 0
# Until the train tumor fraction is approx 50%
while not (0.45 <= tr_frac <= 0.55):
# Shuffle the arrays
[fd_data, metadata] = shuffle_arrs([fd_data, metadata])
# Split data/metadata into train and test sets
tr_data = fd_data[:125, :, :]
tr_md = metadata[:125]
te_data = fd_data[125:, :, :]
te_md = metadata[125:]
# Get the train/test class labels to determine tumor fraction
cv_labels = get_class_labels(tr_md)
test_labels = get_class_labels(te_md)
# Get tumor fraction in the training set
tr_frac = np.sum(cv_labels) / len(cv_labels)
# Save train/test sets as .pickles
save_pickle(te_data, os.path.join(__OUT_DIR, 'test_fd.pickle'))
save_pickle(te_md, os.path.join(__OUT_DIR, 'test_md.pickle'))
save_pickle(tr_data, os.path.join(__OUT_DIR, 'train_fd.pickle'))
save_pickle(tr_md, os.path.join(__OUT_DIR, 'train_md.pickle'))
val_data = g1_tr_d
val_md = g1_tr_md
# Correct the initial antenna position used in G1 scans
val_data = correct_g1_ini_ant_ang(val_data)
# Preprocess data, take magnitude and apply time-window, augment
# training dataset
val_data = np.abs(to_td(val_data))
val_data = resize_features_for_keras(val_data)
train_data = np.abs(to_td(train_data))
train_data, train_md = full_aug(train_data, train_md)
train_data = resize_features_for_keras(train_data)
# Get the validation and train set class labels and make categorical
val_labels = get_class_labels(val_md)
val_labels = to_categorical(val_labels)
train_labels = get_class_labels(train_md)
train_labels = to_categorical(train_labels)
# Create arrays for storing the AUC on the train and validation
# sets for this regularization parameter after training with
# correct labels
train_set_aucs = np.zeros([__N_RUNS, __N_EPOCHS])
val_set_aucs = np.zeros([__N_RUNS, __N_EPOCHS])
train_set_loss = np.zeros([__N_RUNS, __N_EPOCHS])
val_set_loss = np.zeros([__N_RUNS, __N_EPOCHS])
for run_idx in range(__N_RUNS): # For each CV-fold
