def main():
cv_dir = sys.argv[1]
r = eval_funcs.average_results(cv_dir)
eval_funcs.print_eval_classifier(r)
def main(cfg, rep, fold, output_path, print_results=True, return_model=False):
K.clear_session()
dataset_path = cfg['task_path']
train_test_path = cfg['splits_path']
# load dataset
df = pd.read_csv(dataset_path)
# create output
Y = np_utils.to_categorical(df[cfg['target_col']])
# load train/test split
data = np.load(train_test_path)
train_sets = data['train_sets']
valid_sets = data['valid_sets']
test_sets = data['test_sets']
# create training and testing data frames
train_ix = train_sets[rep, fold, :]
valid_ix = valid_sets[rep, fold, :]
test_ix = test_sets[rep, fold, :]
print("Dataset size: %d, Total: %d" %
(df.shape[0], np.sum(train_ix + valid_ix + test_ix)))
if not cfg.get("early_stopping", True):
train_ix = train_ix + valid_ix
if cfg.get("train_on_full_dataset", False):
print(colored("******** TRAINING ON FULL DATASET ***********", "red"))
train_ix = np.ones_like(train_ix)
if cfg.get("test_on_full_dataset", False):
print(colored("******** TESTING ON FULL DATASET ***********", "green"))
test_ix = np.ones_like(test_ix)
train_df = df.iloc[train_ix]
valid_df = df.iloc[valid_ix]
test_df = df.iloc[test_ix]
print("Train size: %d, valid: %d, test: %d" %
(train_df.shape[0], valid_df.shape[0], test_df.shape[0]))
train_Y = Y[train_ix, :]
valid_Y = Y[valid_ix, :]
test_Y = Y[test_ix, :]
if cfg.get("bootstrap_training", False):
print(colored("******** BOOTSTRAPPING TRAINING ***********", "blue"))
rix = rng.choice(train_df.shape[0], train_df.shape[0], replace=True)
train_df = train_df.iloc[rix]
train_Y = train_Y[rix, :]
fsets, feature_labels = load_features(cfg)
#
# NN definition
#
input_node = layers.Input(shape=(len(feature_labels), ),
name='input_features')
if cfg['type'] == 'orm':
linear_layer = layers.Dense(1, activation='linear')
latent_variable = linear_layer(input_node)
ordinal_layer = OrdinalLayer(Y.shape[1])
output_node = ordinal_layer(latent_variable)
else:
output_layer = layers.Dense(Y.shape[1], activation='softmax')
output_node = output_layer(input_node)
if cfg['balanced_loss']:
loss = weighted_categorical_xentropy
else:
loss = 'categorical_crossentropy'
model = keras.models.Model(inputs=input_node, outputs=output_node)
model.compile(cfg['optimizer'], loss)
if cfg.get("trained_model_path", None) is not None:
if cfg.get("add_repfold_to_trained_model_path", True):
cfg["trained_model_path"] = "%s_%d_%d" % (
cfg["trained_model_path"], rep, fold)
# train
if cfg.get("train_model", True):
train_iterator = create_data_iterator(train_df, train_Y, fsets, cfg)
valid_iterator = create_data_iterator(valid_df, valid_Y, fsets, cfg)
if cfg.get("early_stopping", True):
callbacks = [
keras.callbacks.EarlyStopping(monitor='val_loss',
patience=cfg['patience'],
restore_best_weights=True)
]
else:
callbacks = []
if cfg['epochs'] > 0:
model.fit_generator(train_iterator(),
steps_per_epoch=np.ceil(train_df.shape[0] /
cfg['batch_size']),
epochs=cfg['epochs'],
verbose=cfg['verbose'],
validation_data=valid_iterator(),
validation_steps=np.ceil(valid_df.shape[0] /
cfg['batch_size']),
callbacks=callbacks)
if cfg.get("trained_model_path", None) is not None:
print("Saving model to %s" % cfg['trained_model_path'])
model.save_weights(cfg["trained_model_path"])
if cfg.get("save_tjs", False):
tfjs.converters.save_keras_model(model, cfg["tjs_path"])
else:
print("Loading from %s" % cfg['trained_model_path'])
model.load_weights(cfg["trained_model_path"]).expect_partial()
if return_model:
return model, fsets
test_iterator = create_data_iterator(test_df, test_Y, fsets, cfg, False)
preds = model.predict(test_iterator(),
steps=np.ceil(test_df.shape[0] / cfg['batch_size']))
y_target = np.argmax(test_Y, axis=1)
ix = np.sum(np.isnan(preds), axis=1) > 0
print("Nan: %d" % np.sum(ix))
print(test_df[ix])
r, cm = eval_funcs.eval_classifier(y_target, preds)
if print_results:
eval_funcs.print_eval_classifier(r)
if cfg['type'] == 'orm':
np.savez(output_path,
preds=preds,
y_target=y_target,
cfg=cfg,
r=r,
cm=cm,
rep=rep,
biases=linear_layer.get_weights()[1],
weights=linear_layer.get_weights()[0],
thresholds=ordinal_layer.get_thresholds(),
labels=feature_labels,
fold=fold)
else:
weights_list = output_layer.get_weights()
np.savez(output_path,
preds=preds,
y_target=y_target,
cfg=cfg,
r=r,
cm=cm,
rep=rep,
weights=weights_list[0],
biases=weights_list[1],
labels=feature_labels,
fold=fold)
return None
def main(cfg, rep, fold, output_path, print_results=True):
dataset_path = cfg['task_path']
train_test_path = cfg['splits_path']
# load dataset
df = pd.read_csv(dataset_path)
# create output
Y = np_utils.to_categorical(df[cfg['target_col']])
# read features
all_features, labels = read_features(cfg)
# load train/test split
data = np.load(train_test_path)
train_sets = data['train_sets']
valid_sets = data['valid_sets']
test_sets = data['test_sets']
# create training and testing data frames
train_ix = train_sets[rep, fold,:]
valid_ix = valid_sets[rep,fold,:]
test_ix = test_sets[rep,fold,:]
if not cfg.get("early_stopping", True):
train_ix = train_ix + valid_ix
if cfg.get("train_on_full_dataset", False):
print(colored("******** TRAINING ON FULL DATASET ***********", "red"))
train_ix = train_ix + test_ix
if cfg.get("test_on_full_dataset", False):
print(colored("******** TESTING ON FULL DATASET ***********", "green"))
test_ix = train_ix + test_ix + valid_ix
train_df = df.iloc[train_ix]
valid_df = df.iloc[valid_ix]
test_df = df.iloc[test_ix]
train_Y = Y[train_ix,:]
valid_Y = Y[valid_ix,:]
test_Y = Y[test_ix, :]
F = all_features[df['id'], :]
train_F = F[train_ix, :]
valid_F = F[valid_ix, :]
test_F = F[test_ix, :]
if cfg.get("bootstrap_training", False):
print(colored("******** BOOTSTRAPPING TRAINING ***********", "blue"))
rix = rng.choice(train_df.shape[0], train_df.shape[0], replace=True)
train_df = train_df.iloc[rix]
train_Y = train_Y[rix,:]
train_F = train_F[rix,:]
# ordinal model
input_node = layers.Input(shape=(F.shape[1],))
if cfg['type'] == 'orm':
linear_layer = layers.Dense(1, activation='linear')
latent_variable = linear_layer(input_node)
ordinal_layer = OrdinalLayer(train_Y.shape[1])
output_node = ordinal_layer(latent_variable)
else:
output_layer = layers.Dense(Y.shape[1], activation='softmax')
output_node = output_layer(input_node)
if cfg['balanced_loss']:
loss = weighted_categorical_xentropy
else:
loss = 'categorical_crossentropy'
model = keras.models.Model(inputs=input_node, outputs=output_node)
model.compile(cfg['optimizer'], loss=loss)
if cfg.get("train_model", True):
# setup early stopping
if cfg.get("early_stopping", True):
callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss',
patience=cfg['patience'], restore_best_weights=True)]
else:
callbacks = []
# train
model.fit(
x=train_F,
y=train_Y,
batch_size=int(cfg['batch_size_p'] * train_Y.shape[0]),
epochs=cfg['epochs'],
verbose=cfg['verbose'],
validation_data=(valid_F, valid_Y),
callbacks=callbacks)
if cfg.get("trained_model_path", None) is not None:
print("Saving model")
model.save_weights(cfg["trained_model_path"])
else:
model.load_weights(cfg["trained_model_path"]).expect_partial()
preds = model.predict(test_F)
y_target = np.argmax(test_Y, axis=1)
r, cm = eval_funcs.eval_classifier(y_target, preds)
if print_results:
eval_funcs.print_eval_classifier(r)
if cfg['type'] == 'orm':
np.savez(output_path,
preds = preds,
y_target = y_target,
cfg = cfg,
r=r,
cm=cm,
rep=rep,
biases=linear_layer.get_weights()[1],
weights=linear_layer.get_weights()[0],
thresholds=ordinal_layer.get_thresholds(),
labels=labels,
fold=fold)
else:
weights_list = output_layer.get_weights()
np.savez(output_path,
preds = preds,
y_target = y_target,
cfg = cfg,
r=r,
cm=cm,
rep=rep,
weights=weights_list[0],
biases=weights_list[1],
labels=labels,
fold=fold)
def main(cfg, rep, fold, output_path, print_results=True, return_model=False):
dataset_path = cfg['task_path']
train_test_path = cfg['splits_path']
# load dataset
df = pd.read_csv(dataset_path)
# load input features
single_gene_spec = [s for s in cfg['spec'] if not s['pairwise']]
pairwise_gene_spec = [s for s in cfg['spec'] if s['pairwise']]
single_fsets, single_fsets_shapes = feature_loader.load_feature_sets(single_gene_spec, False)
pairwise_fsets, pairwise_fsets_shapes = feature_loader.load_feature_sets(pairwise_gene_spec, False)
# create output
Y = np_utils.to_categorical(df[cfg['target_col']])
# load train/test split
data = np.load(train_test_path)
train_sets = data['train_sets']
valid_sets = data['valid_sets']
test_sets = data['test_sets']
# create training and testing data frames
train_ix = train_sets[rep, fold,:]
valid_ix = valid_sets[rep,fold,:]
test_ix = test_sets[rep,fold,:]
if cfg.get("train_on_full_dataset", False):
print(colored("******** TRAINING ON FULL DATASET ***********", "red"))
train_ix = train_ix + test_ix
if cfg.get("test_on_full_dataset", False):
print(colored("******** TESTING ON FULL DATASET ***********", "green"))
test_ix = train_ix + test_ix + valid_ix
train_df = df.iloc[train_ix]
valid_df = df.iloc[valid_ix]
test_df = df.iloc[test_ix]
train_Y = Y[train_ix,:]
valid_Y = Y[valid_ix,:]
test_Y = Y[test_ix, :]
#
# NN definition
#
# single gene features
inputs_a = nn_arch.create_input_nodes(single_gene_spec, single_fsets_shapes, base_name="a")
inputs_b = nn_arch.create_input_nodes(single_gene_spec, single_fsets_shapes, base_name="b")
single_gene_arch = nn_arch.create_input_architecture(output_size=cfg['embedding_size'],
output_activation=cfg['embedding_activation'],
name='single_input', spec=single_gene_spec)
output_a = single_gene_arch(inputs_a, name="input_a")
output_b = single_gene_arch(inputs_b, name="input_b")
# pairwise features
inputs_ab = nn_arch.create_input_nodes(pairwise_gene_spec, pairwise_fsets_shapes, base_name="ab")
pairwise_gene_arch = nn_arch.create_input_architecture(output_size=cfg['embedding_size'],
output_activation=cfg['embedding_activation'],
name='pairwise_input', spec=pairwise_gene_spec)
output_ab = pairwise_gene_arch(inputs_ab, name="input_ab")
merged = nn_arch.concatenate([output_a + output_b, output_ab], name="preoutput")
output_node = layers.Dense(Y.shape[1], activation='softmax')(merged)
if cfg['balanced_loss']:
loss = weighted_categorical_xentropy
else:
loss = 'categorical_crossentropy'
model = keras.models.Model(inputs=inputs_a + inputs_b + inputs_ab, outputs=output_node)
opt = tf.keras.optimizers.Nadam(learning_rate=cfg.get('learning_rate', 0.001))
model.compile(opt, loss)
model.outputs[0]._uses_learning_phase = True
# train
if cfg.get("train_model", True):
# create data iterators (necessary because some feature sets are too large to put in ram)
train_iterator = create_data_iterator(train_df, train_Y, single_fsets, pairwise_fsets, cfg, cfg['scramble'])
valid_iterator = create_data_iterator(valid_df, valid_Y, single_fsets, pairwise_fsets, cfg, False)
callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=cfg['patience'], restore_best_weights=True)]
model.fit_generator(train_iterator(),
steps_per_epoch=np.ceil(train_df.shape[0] / cfg['batch_size']),
epochs=cfg['epochs'],
verbose=cfg['verbose'],
validation_data=valid_iterator(),
validation_steps=np.ceil(valid_df.shape[0] / cfg['batch_size']),
callbacks=callbacks)
if cfg.get("trained_model_path", None) is not None:
print("Saving model")
model.save_weights(cfg["trained_model_path"])
else:
model.load_weights(cfg["trained_model_path"]).expect_partial()
if return_model:
return model, [FeatureTransform(single_fsets, pairwise_fsets)]
test_F = feature_transform(test_df, single_fsets, pairwise_fsets)
preds = model.predict(test_F)
y_target = np.argmax(test_Y, axis=1)
r, cm = eval_funcs.eval_classifier(y_target, preds)
if print_results:
eval_funcs.print_eval_classifier(r)
np.savez(output_path,
preds = preds,
y_target = y_target,
cfg = cfg,
r=r,
cm=cm,
rep=rep,
fold=fold)
def main(cfg, rep, fold, output_path, print_results=True):
dataset_path = cfg['task_path']
train_test_path = cfg['splits_path']
# load dataset
df = pd.read_csv(dataset_path)
# create output
Y = keras.utils.to_categorical(df[cfg['target_col']])
print(Y.shape)
# load train/test split
data = np.load(train_test_path)
train_sets = data['train_sets']
valid_sets = data['valid_sets']
test_sets = data['test_sets']
# create training and testing data frames
train_ix = train_sets[rep, fold,:]
valid_ix = valid_sets[rep,fold,:]
test_ix = test_sets[rep,fold,:]
if cfg.get("train_on_full_dataset", False):
print(colored("******** TRAINING ON FULL DATASET ***********", "red"))
train_ix = train_ix + test_ix
if cfg.get("test_on_full_dataset", False):
print(colored("******** TESTING ON FULL DATASET ***********", "green"))
test_ix = train_ix + test_ix + valid_ix
train_df = df.iloc[train_ix]
train_genes = set(train_df['a_id']) | set(train_df['b_id'])
valid_df = df.iloc[valid_ix]
valid_genes = set(valid_df['a_id']) | set(valid_df['b_id'])
print("in Valid but not train: %d" % len(valid_genes - train_genes))
test_df = df.iloc[test_ix]
train_X = [np.array(train_df['a_id']), np.array(train_df['b_id'])]
valid_X = [np.array(valid_df['a_id']), np.array(valid_df['b_id'])]
test_X = [np.array(test_df['a_id']), np.array(test_df['b_id'])]
train_Y = Y[train_ix,:]
valid_Y = Y[valid_ix,:]
print("Validation sums:")
print(np.sum(valid_Y, axis=0))
test_Y = Y[test_ix, :]
#
# NN definition
#
# single gene features
n_genes = np.maximum(np.max(df['a_id']), np.max(df['b_id'])) + 1
emb = tf.keras.layers.Embedding(n_genes, cfg['embedding_size'])
input_a = tf.keras.layers.Input(shape=(1,))
input_b = tf.keras.layers.Input(shape=(1,))
embd_a = emb(input_a)
embd_b = emb(input_b)
merged = embd_a + embd_b
merged = tf.squeeze(merged, axis=1)
output_node = layers.Dense(Y.shape[1], activation='softmax')(merged)
if cfg['balanced_loss']:
loss = weighted_categorical_xentropy
else:
loss = 'categorical_crossentropy'
model = keras.models.Model(inputs=[input_a, input_b], outputs=output_node)
model.compile(cfg['optimizer'], loss)
print(model.summary())
#exit()
model.outputs[0]._uses_learning_phase = True
# train
if cfg.get("train_model", True):
callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=cfg['patience'], restore_best_weights=True)]
print("Batch size: %d" % int(cfg['batch_size_p'] * train_Y.shape[0]))
model.fit(
train_X,
train_Y,
epochs=cfg['epochs'],
verbose=cfg['verbose'],
validation_data=(valid_X, valid_Y),
batch_size=int(cfg['batch_size_p'] * train_Y.shape[0]),
callbacks=callbacks
)
if cfg.get("trained_model_path", None) is not None:
print("Saving model")
model.save_weights(cfg["trained_model_path"])
else:
model.load_weights(cfg["trained_model_path"]).expect_partial()
preds = model.predict(test_X)
y_target = np.argmax(test_Y, axis=1)
r, cm = eval_funcs.eval_classifier(y_target, preds)
if print_results:
eval_funcs.print_eval_classifier(r)
np.savez(output_path,
preds = preds,
y_target = y_target,
cfg = cfg,
r=r,
cm=cm,
rep=rep,
fold=fold)
def main(cfg, rep, fold, output_path, print_results=True):
dataset_path = cfg['task_path']
train_test_path = cfg['splits_path']
# load dataset
df = pd.read_csv(dataset_path)
# create output
Y = np_utils.to_categorical(df[cfg['target_col']])
# load input features
single_gene_spec = [s for s in cfg['spec'] if not s['pairwise']]
single_fsets, single_fsets_shapes = feature_loader.load_feature_sets(
single_gene_spec, scramble=False)
# load train/test split
data = np.load(train_test_path)
train_sets = data['train_sets']
valid_sets = data['valid_sets']
test_sets = data['test_sets']
# create training and testing data frames
train_ix = train_sets[rep, fold, :]
valid_ix = valid_sets[rep, fold, :]
test_ix = test_sets[rep, fold, :]
if cfg.get("train_on_full_dataset", False):
print(colored("******** TRAINING ON FULL DATASET ***********", "red"))
train_ix = train_ix + test_ix
if cfg.get("test_on_full_dataset", False):
print(colored("******** TESTING ON FULL DATASET ***********", "green"))
test_ix = train_ix + test_ix + valid_ix
train_df = df.iloc[train_ix]
valid_df = df.iloc[valid_ix]
test_df = df.iloc[test_ix]
train_Y = Y[train_ix, :]
valid_Y = Y[valid_ix, :]
test_Y = Y[test_ix, :]
# setup feature sets
train_fsets = [
single_fsets[i][train_df['id'], :] for i in range(len(single_fsets))
]
if cfg['scramble']:
rix = rng.permutation(train_df.shape[0])
train_fsets = [f[rix, :] for f in train_fsets]
valid_fsets = [
single_fsets[i][valid_df['id'], :] for i in range(len(single_fsets))
]
test_fsets = [
single_fsets[i][test_df['id'], :] for i in range(len(single_fsets))
]
#
# NN definition
#
# single gene features
inputs_a = nn_arch.create_input_nodes(single_gene_spec,
single_fsets_shapes,
base_name="a")
single_gene_arch = nn_arch.create_input_architecture(
output_size=cfg['embedding_size'],
output_activation=cfg['embedding_activation'],
name='single_input',
spec=single_gene_spec)
output_a = single_gene_arch(inputs_a, name="input_a")
output_node = layers.Dense(Y.shape[1], activation='softmax')(output_a)
if cfg['balanced_loss']:
loss = weighted_categorical_xentropy
else:
loss = 'categorical_crossentropy'
model = keras.models.Model(inputs=inputs_a, outputs=output_node)
opt = tf.keras.optimizers.Nadam(
learning_rate=cfg.get('learning_rate', 0.001))
model.compile(opt, loss=loss)
if cfg.get("train_model", True):
# setup early stopping
callbacks = [
keras.callbacks.EarlyStopping(monitor='val_loss',
patience=cfg['patience'],
restore_best_weights=True)
]
# train
model.fit(x=train_fsets,
y=train_Y,
batch_size=int(cfg['batch_size_p'] * train_Y.shape[0]),
epochs=cfg['epochs'],
verbose=cfg['verbose'],
validation_data=(valid_fsets, valid_Y),
callbacks=callbacks)
if cfg.get("trained_model_path", None) is not None:
print("Saving model")
model.save_weights(cfg["trained_model_path"])
else:
model.load_weights(cfg["trained_model_path"]).expect_partial()
preds = model.predict(test_fsets)
y_target = np.argmax(test_Y, axis=1)
r, cm = eval_funcs.eval_classifier(y_target, preds)
if print_results:
eval_funcs.print_eval_classifier(r)
np.savez(output_path,
preds=preds,
y_target=y_target,
cfg=cfg,
r=r,
cm=cm,
rep=rep,
fold=fold)