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train_eval.py
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train_eval.py
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import argparse
import glob
import os
import matplotlib.pyplot as plt
from sklearn import metrics, base
from sklearn.model_selection import GridSearchCV
from data_loader import MarkerExpressionDataset
from utils import load_ymal, save_yaml, maybe_create_path, double_print, eval_results, get_model
from visualization import plot_table, plot_feature_distribution
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=False, default='default',
help='config name. default: \'default\'')
parser.add_argument('-s', '--sub_setting', type=str, default='default',
help='sub setting name for current method config')
parser.add_argument('-r', '--retrain', action='store_true',
help='if set, override the existing saved model')
parser.add_argument('-o', '--overwrite_config', action='store_true',
help='if set, ignore existing config file in exp path and use that in config folder')
args = parser.parse_args()
def train_eval(config, exp_path):
dataset = MarkerExpressionDataset(config)
if dataset.data_clean is not None:
with open(os.path.join(exp_path, 'dirty_data.txt'), 'w') as f:
f.write('---data clean method: %s---\n' % dataset.data_clean)
for marker, item in dataset.outlier_samples.items():
f.write('marker %s:\n' % marker)
for class_id in dataset.classes:
f.write('class %s:\n' % class_id)
for sample_id in item.keys():
if item[sample_id]['class'] == class_id:
f.write('\t%s\n' % sample_id)
if dataset.feature_selection is not None or dataset.feature_transformation is not None:
with open(os.path.join(exp_path, 'feature_selection_and_transformation.txt'), 'w') as f:
if dataset.feature_selection is not None:
f.write('---feature selection method: %s---\n' % dataset.feature_selection['method'])
if 'kwargs' in dataset.feature_selection:
f.write('---feature selection kwargs: %s---\n' % str(dataset.feature_selection['kwargs']))
if dataset.feature_transformation is not None:
f.write('---feature transformation method: %s---\n' % dataset.feature_transformation['method'])
if 'kwargs' in dataset.feature_transformation:
f.write('---feature transformation kwargs: %s---\n' % str(dataset.feature_transformation['kwargs']))
for marker in dataset.markers:
f.write('marker %s:\n' % marker)
if dataset.fs_metric_params is not None:
f.write('---feature selection and transformation kwargs: %s---\n'
% str(dataset.fs_metric_params[marker]))
if dataset.feature_selection is not None:
features = dataset.features
feature_index = 0
f.write('---selected features---\n')
if dataset.feature_selection['method'] == 'custom':
support_flags = dataset.feature_selection['selection'][marker]
else:
support_flags = dataset.feature_selector[marker].get_support()
for flag in support_flags:
f.write('%s:\t%s\n' % (features[feature_index], flag))
feature_index = (feature_index + 1) % len(features)
if dataset.feature_transformation is not None:
components = dataset.feature_transformer[marker].components_
f.write('---feature transformation components---:\n%s' % components.tolist())
# if 'feature_mean' in config:
# feature_mean = config['feature_mean']
# coefficients = np.abs(feature_mean*components.sum(axis=0)).\
# reshape([len(dataset.features), -1]).sum(axis=0)
# else:
# coefficients = np.abs(components.sum(axis=0)).reshape([len(dataset.features), -1]).sum(axis=0)
# coefficients = coefficients / coefficients.sum()
#
# f.write('---feature transformation coefficients---:\n%s' % coefficients.tolist())
threshold = config.get('threshold', 'roc_optimal')
metrics_names = ['sensitivity', 'specificity', 'roc_auc_score']
metrics_avg_names = ['roc_auc_score_avg', 'roc_auc_score_avg_std']
fig, ax = plt.subplots(9, len(dataset.markers), squeeze=False, figsize=(6 * len(dataset.markers), 40))
metrics_file = open(os.path.join(exp_path, 'metrics.txt'), 'w')
metrics_fig_filename = os.path.join(exp_path, 'conf_mat.png')
best_params = dict()
all_marker_train_metrics = []
all_marker_test_metrics = []
for i, marker in enumerate(dataset.markers):
model = get_model(config)
if 'model_kwargs_search' in config:
# parameter search
print('parameter search for marker %s...' % marker)
all_x, all_y, cv_index = dataset.get_all_data(marker)
best_model = GridSearchCV(model,
param_grid=config['model_kwargs_search'],
cv=cv_index,
scoring='roc_auc_ovr')
best_model.fit(all_x, all_y)
best_params[marker] = best_model.best_params_
print('search done')
else:
best_model = model
best_params[marker] = config['model_kwargs']
# run train and test
train_xs = []
train_ys = []
train_ys_score = []
test_xs = []
test_ys = []
test_ys_score = []
for fold_i, (train_x, train_y, test_x, test_y) in enumerate(dataset.get_split_data(marker)):
model = base.clone(model)
model.set_params(**best_params[marker])
model.fit(train_x, train_y)
# model.classes_ = dataset.classes
train_xs += train_x
train_ys += train_y
test_xs += test_x
test_ys += test_y
train_y_score = model.predict_proba(train_x).tolist()
train_ys_score += train_y_score
test_y_score = model.predict_proba(test_x).tolist()
test_ys_score += test_y_score
# model_filename = os.path.join(exp_path, 'model', '%s_%s_fold_%d.pkl'
# % (config['model'], marker, fold_i))
# maybe_create_path(os.path.dirname(model_filename))
# with open(model_filename, 'wb') as f:
# pickle.dump(model, f)
train_metrics = eval_results(train_ys, train_ys_score,
labels=dataset.classes, average='macro',
threshold=threshold, num_fold=dataset.num_fold)
test_metrics = eval_results(test_ys, test_ys_score,
labels=dataset.classes, average='macro',
threshold=train_metrics['used_threshold'], num_fold=dataset.num_fold)
all_marker_train_metrics.append(train_metrics)
all_marker_test_metrics.append(test_metrics)
# print metrics to console and file
double_print('marker: %s' % marker, metrics_file)
double_print('metrics on training set:', metrics_file)
for j, class_j in enumerate(dataset.classes):
log_str = '[class: %s. threshold: %1.1f] ' % (class_j, 100 * train_metrics['used_threshold'][j])
for metrics_name in metrics_names:
log_str += '%s: %1.1f. ' % (metrics_name, train_metrics[metrics_name][j])
double_print(log_str, metrics_file)
for metrics_name in metrics_avg_names:
double_print('%s: %1.1f' % (metrics_name, train_metrics[metrics_name]), metrics_file)
double_print('metrics on test set:', metrics_file)
for j, class_j in enumerate(dataset.classes):
log_str = '[class: %s. threshold: %1.1f] ' % (class_j, 100 * test_metrics['used_threshold'][j])
for metrics_name in metrics_names:
log_str += '%s: %1.1f. ' % (metrics_name, test_metrics[metrics_name][j])
double_print(log_str, metrics_file)
for metrics_name in metrics_avg_names:
double_print('%s: %1.1f' % (metrics_name, test_metrics[metrics_name]), metrics_file)
# generate figure
current_ax = ax[0, i]
dataset.plot_data_clean_distribution(current_ax, marker)
current_ax.set_title('data cleaning on marker %s' % marker)
current_ax = ax[1, i]
contour_flag = len(train_xs[0]) == 2
# dup_reduced = list(tuple(tuple([train_xs[j] + [train_ys[j]] for j in range(len(train_xs))])))
# dup_reduced_train_xs = [item[:-1] for item in dup_reduced]
# dup_reduced_train_ys = [item[-1] for item in dup_reduced]
# dup_reduced_train_ys_str = [str(item) for item in dup_reduced_train_ys]
dup_reduced_train_xs = train_x + test_x
dup_reduced_train_ys = train_y + test_y
dup_reduced_train_ys_str = [str(item) for item in dup_reduced_train_ys]
classes_str = [str(item) for item in dataset.classes]
plot_feature_distribution(dup_reduced_train_xs, ax=current_ax, t_sne=True,
hue=dup_reduced_train_ys_str, hue_order=classes_str,
style=dup_reduced_train_ys_str, style_order=classes_str,
# x_lim='box', y_lim='box',
x_lim='min_max_extend', y_lim='min_max_extend',
contour=contour_flag, z_generator=best_model.predict)
current_ax.set_title('%s trained on whole set' % marker)
current_ax = ax[2, i]
metrics.ConfusionMatrixDisplay(train_metrics['conf_mat'], display_labels=dataset.classes).plot(ax=current_ax)
current_ax.set_title('%s on train set of all folds' % marker)
current_ax = ax[3, i]
for j in range(len(dataset.classes)):
roc_curve = train_metrics['roc_curve'][j]
roc_auc_score = train_metrics['roc_auc_score'][j]
class_id = dataset.classes[j]
sen = train_metrics['sensitivity'][j] / 100
spe = train_metrics['specificity'][j] / 100
metrics.RocCurveDisplay(fpr=roc_curve[0], tpr=roc_curve[1],
roc_auc=roc_auc_score, estimator_name='class %s' % class_id).plot(ax=current_ax)
current_ax.scatter(1 - spe, sen)
current_ax = ax[4, i]
table_val_list = [dataset.classes, [100 * item for item in train_metrics['used_threshold']]]
row_labels = ['cls', 'thr']
for metrics_name in metrics_names:
table_val_list.append(train_metrics[metrics_name])
row_labels.append(metrics_name[:min(3, len(metrics_name))])
additional_text = []
for metrics_name in metrics_avg_names:
additional_text.append('%s: %1.1f' % (metrics_name, train_metrics[metrics_name]))
additional_text.append(best_params[marker])
plot_table(table_val_list, row_labels, ax=current_ax, additional_text=additional_text)
current_ax = ax[5, i]
contour_flag = len(train_xs[0]) == 2
test_y_str = [str(item) for item in test_y]
classes_str = [str(item) for item in dataset.classes]
plot_feature_distribution(test_x, ax=current_ax, t_sne=True,
hue=test_y_str, hue_order=classes_str,
style=test_y_str, style_order=classes_str,
# x_lim='box', y_lim='box',
x_lim='min_max_extend', y_lim='min_max_extend',
contour=contour_flag, z_generator=model.predict)
current_ax.set_title('%s on test set of the last fold' % marker)
current_ax = ax[6, i]
metrics.ConfusionMatrixDisplay(test_metrics['conf_mat'], display_labels=dataset.classes).plot(ax=current_ax)
current_ax.set_title('%s on test set of all folds' % marker)
current_ax = ax[7, i]
for j in range(len(dataset.classes)):
roc_curve = test_metrics['roc_curve'][j]
roc_auc_score = test_metrics['roc_auc_score'][j]
class_id = dataset.classes[j]
sen = test_metrics['sensitivity'][j] / 100
spe = test_metrics['specificity'][j] / 100
metrics.RocCurveDisplay(fpr=roc_curve[0], tpr=roc_curve[1],
roc_auc=roc_auc_score, estimator_name='class %s' % class_id).plot(ax=current_ax)
current_ax.scatter(1 - spe, sen)
current_ax = ax[8, i]
table_val_list = [dataset.classes, [100 * item for item in test_metrics['used_threshold']]]
row_labels = ['cls', 'thr']
for metrics_name in metrics_names:
table_val_list.append(test_metrics[metrics_name])
row_labels.append(metrics_name[:min(3, len(metrics_name))])
additional_text = []
for metrics_name in metrics_avg_names:
additional_text.append('%s: %1.1f' % (metrics_name, test_metrics[metrics_name]))
plot_table(table_val_list, row_labels, ax=current_ax, additional_text=additional_text)
for metrics_name in metrics_avg_names:
all_marker_values = [item[metrics_name] for item in all_marker_train_metrics]
double_print('overall train %s: %1.1f' % (metrics_name, sum(all_marker_values) / len(all_marker_values)),
metrics_file)
for metrics_name in metrics_avg_names:
all_marker_values = [item[metrics_name] for item in all_marker_test_metrics]
double_print('overall test %s: %1.1f' % (metrics_name, sum(all_marker_values) / len(all_marker_values)),
metrics_file)
metrics_file.close()
save_yaml(os.path.join(exp_path, 'best_params.yaml'), best_params)
fig.savefig(metrics_fig_filename, bbox_inches='tight', pad_inches=1)
if __name__ == '__main__':
exp_path = os.path.join('exp', args.config, args.sub_setting)
if not args.overwrite_config and os.path.exists(os.path.join(exp_path, 'config.yaml')):
config = load_ymal(os.path.join(exp_path, 'config.yaml'))
else:
config = load_ymal(os.path.join('config', args.config + '.yaml'))
save_yaml(os.path.join(exp_path, 'config.yaml'), config)
if not args.retrain:
if os.path.exists(os.path.join(exp_path, 'model')) \
or len(glob.glob(os.path.join(exp_path, 'model', '*.pkl'))) != 0:
raise FileExistsError('there are already models saved in %s.' % exp_path)
maybe_create_path(exp_path)
train_eval(config, exp_path)