def main(model, use_z, fraction, max_epoch=300, epoch_checkpoint=0):
filter_func = filter_func_dict[fraction]
n_latent_layer = 2
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
genes = None
genes_name = None
# genes=np.load("/media/hag007/Data/dlproj/cache_global/datasets/vemurafenib_resveratrol_olaparib/genes.npy", allow_pickle=True)
# genes_name="vemurafenib_resveratrol_olaparib"
dataset_names = constants.ALL_DATASET_NAMES
dataset = datasets.Dataset(dataset_names=dataset_names,
data_type=constants.DATA_TYPE)
dataloader_ctor = datasets.DataLoader(dataset, 0.2, 0.2)
testloader = dataloader_ctor.test_loader()
dataset_mask = datasets.DatasetMask(dataset_names=dataset_names,
data_type=constants.DATA_TYPE,
filter_func=filter_func)
dataloader_ctor_mask = datasets.DataLoader(dataset_mask, 0.2, 0, 2)
trainloader = dataloader_ctor_mask.train_loader()
validationloader = dataloader_ctor_mask.valid_loader()
encoder = Encoder(n_latent_layer=n_latent_layer)
decoder = Decoder(n_latent_layer=n_latent_layer)
classifier = Classifier(
n_input_layer=n_latent_layer,
n_classes=(len(constants.DATASETS_FILES) if genes_name is None else
genes_name.count("_") + 1)) # * 2
path_format_to_save = os.path.join(
constants.CACHE_GLOBAL_DIR, constants.DATA_TYPE,
"model_{}_{}_{}_{{}}".format(fraction, model, "z" if use_z else "mu"))
PATH_ENCODER = os.path.join(path_format_to_save, "ENC_mdl")
PATH_DECODER = os.path.join(path_format_to_save, "DEC_mdl")
PATH_CLASSIFIER = os.path.join(path_format_to_save, "CLS_mdl")
load_model = epoch_checkpoint > 0
if load_model and os.path.exists(PATH_ENCODER.format(epoch_checkpoint)):
encoder.load_state_dict(
torch.load(PATH_ENCODER.format(epoch_checkpoint)))
encoder.eval()
decoder.load_state_dict(
torch.load(PATH_DECODER.format(epoch_checkpoint)))
decoder.eval()
classifier.load_state_dict(
torch.load(PATH_CLASSIFIER.format(epoch_checkpoint)))
classifier.eval()
else:
epoch_checkpoint = 0
lr_vae = 3e-4
lr_cls = 3e-4
parameters = list(encoder.parameters()) + list(decoder.parameters())
optimizer_vae = optim.Adam(parameters, lr=lr_vae)
optimizer_cls = optim.Adam(list(encoder.parameters()) +
list(classifier.parameters()),
lr=lr_cls)
log_interval = 100
min_encoder = None
min_decoder = None
min_classifier = None
min_epoch = -1
min_val_loss = 10e10
train_losses = []
val_losses = []
for cur_epoch in np.arange(epoch_checkpoint, max_epoch + 1):
if model == constants.MODEL_FULL:
mdl = train_full
elif model == constants.MODEL_CLS:
mdl = train_cls
elif model == constants.MODEL_VAE:
mdl = train_vae
else:
raise
factor_vae = 1
factor_cls = 1
train_loss, validation_loss, = mdl(cur_epoch, encoder, decoder,
classifier, factor_vae, factor_cls,
optimizer_vae, optimizer_cls,
trainloader, validationloader,
device, log_interval)
train_losses.append(['{:.2f}'.format(a) for a in train_loss])
val_losses.append(['{:.2f}'.format(a) for a in validation_loss])
if min_val_loss > sum(validation_loss):
min_encoder = copy.deepcopy(encoder)
min_decoder = copy.deepcopy(decoder)
min_classifier = copy.deepcopy(classifier)
min_epoch = cur_epoch
min_val_loss = sum(validation_loss)
print("min_val_loss: {} (epoch n={})".format(min_epoch, min_val_loss))
if (cur_epoch) % 50 == 0 and cur_epoch != epoch_checkpoint:
try:
os.makedirs(path_format_to_save.format(cur_epoch))
except:
pass
if min_encoder is not None:
torch.save(min_encoder.state_dict(),
PATH_ENCODER.format(cur_epoch) + "_min")
torch.save(min_decoder.state_dict(),
PATH_DECODER.format(cur_epoch) + "_min")
torch.save(min_classifier.state_dict(),
PATH_CLASSIFIER.format(cur_epoch) + "_min")
open(
os.path.join(path_format_to_save.format(cur_epoch),
"min_epoch.txt"),
"w").write("{}_{}".format(min_val_loss, min_epoch))
plot(
min_encoder if model != constants.MODEL_CLS or use_z else
torch.nn.Sequential(min_encoder, min_classifier),
testloader, device, "_min", dataset_names,
path_format_to_save.format(cur_epoch))
torch.save(encoder.state_dict(), PATH_ENCODER.format(cur_epoch))
torch.save(decoder.state_dict(), PATH_DECODER.format(cur_epoch))
torch.save(classifier.state_dict(),
PATH_CLASSIFIER.format(cur_epoch))
plot(
encoder if model != constants.MODEL_CLS or use_z else
torch.nn.Sequential(encoder, classifier), testloader, device,
"", dataset_names, path_format_to_save.format(cur_epoch))
if model == constants.MODEL_FULL:
open(
os.path.join(path_format_to_save.format(cur_epoch),
"train_1_losses.txt"),
"w").write("\n".join([a[0] for a in train_losses]))
open(
os.path.join(path_format_to_save.format(cur_epoch),
"train_2_losses.txt"),
"w").write("\n".join([a[1] for a in train_losses]))
open(
os.path.join(path_format_to_save.format(cur_epoch),
"val_1_losses.txt"),
"w").write("\n".join([a[0] for a in val_losses]))
open(
os.path.join(path_format_to_save.format(cur_epoch),
"val_2_losses.txt"),
"w").write("\n".join([a[1] for a in val_losses]))
else:
open(
os.path.join(path_format_to_save.format(cur_epoch),
"train_losses.txt"),
"w").write("\n".join([a[0] for a in train_losses]))
open(
os.path.join(path_format_to_save.format(cur_epoch),
"val_losses.txt"),
"w").write("\n".join([a[0] for a in val_losses]))
train_losses = []
val_losses = []
def main(model, use_z, fraction, epoch_checkpoint=300, suffix=""):
n_latent_layer = 2
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
genes = None
genes_name = None
# genes=np.load("/media/hag007/Data/dlproj/cache_global/datasets/vemurafenib_resveratrol_olaparib/genes.npy", allow_pickle=True)
# genes_name="vemurafenib_resveratrol_olaparib"
dataset_names = constants_cmap.ALL_DATASET_NAMES # [a for i, a in enumerate(constants.DATASETS_NAMES) if constants.DATASETS_DICT[a]]
dataset = datasets.Dataset(dataset_names, constants_cmap.DATA_TYPE)
dataloader_ctor = datasets.DataLoader(dataset, 0.2, 0.2)
testloader = dataloader_ctor.test_loader()
dataloader_ctor_mask = datasets.DataLoader(dataset, 0.2, 0, 2)
trainloader = dataloader_ctor_mask.train_loader()
validationloader = dataloader_ctor_mask.valid_loader()
encoder = Encoder(n_latent_layer=n_latent_layer)
decoder = Decoder(n_latent_layer=n_latent_layer)
classifier = Classifier(
n_input_layer=n_latent_layer,
n_classes=(len(constants_cmap.DATASETS_FILES)
if genes_name is None else genes_name.count("_") + 1))
path_format_to_save = os.path.join(
constants_cmap.CACHE_GLOBAL_DIR, constants_cmap.DATA_TYPE,
"model_{}_{}_{}_{{}}".format(fraction, model, "z" if use_z else "mu"))
PATH_ENCODER = os.path.join(path_format_to_save, "ENC_mdl")
PATH_DECODER = os.path.join(path_format_to_save, "DEC_mdl")
if model != constants_cmap.MODEL_VAE:
PATH_CLASSIFIER = os.path.join(path_format_to_save, "CLS_mdl")
load_model = True
if load_model and os.path.exists(
PATH_ENCODER.format(epoch_checkpoint) + suffix):
encoder.load_state_dict(
torch.load(PATH_ENCODER.format(epoch_checkpoint) + suffix))
decoder.load_state_dict(
torch.load(PATH_DECODER.format(epoch_checkpoint) + suffix))
encoder.eval()
decoder.eval()
if model != constants_cmap.MODEL_VAE:
classifier.load_state_dict(
torch.load(PATH_CLASSIFIER.format(epoch_checkpoint) + suffix))
classifier.eval()
with torch.no_grad():
extract_latent_dimension(
encoder if model != constants_cmap.MODEL_CLS else
torch.nn.Sequential(encoder, classifier), trainloader, device,
suffix + "_train", path_format_to_save.format(epoch_checkpoint))
extract_latent_dimension(
encoder if model != constants_cmap.MODEL_CLS else
torch.nn.Sequential(encoder, classifier), validationloader,
device, suffix + "_validation",
path_format_to_save.format(epoch_checkpoint))
extract_latent_dimension(
encoder if model != constants_cmap.MODEL_CLS else
torch.nn.Sequential(encoder, classifier), testloader, device,
suffix + "_test", path_format_to_save.format(epoch_checkpoint))
def main(model, use_z, fraction, epoch_checkpoint=300, suffix=""):
n_latent_layer = 2
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
genes = None
genes_name = None
# genes=np.load("/media/hag007/Data/dlproj/cache_global/datasets/vemurafenib_resveratrol_olaparib/genes.npy", allow_pickle=True)
# genes_name="vemurafenib_resveratrol_olaparib"
dataset_names_new = constants.NEW_DATASETS_NAMES
dataset = datasets.Dataset(dataset_names_new, constants.DATA_TYPE)
dataloader_ctor = datasets.DataLoader(dataset, 0.0, 0.0)
testloader = dataloader_ctor.train_loader()
dataset_names = constants.DATASETS_NAMES
dataset = datasets.Dataset(dataset_names, constants.DATA_TYPE)
dataloader_ctor = datasets.DataLoader(dataset, 0.2, 0.2)
trainloader = dataloader_ctor.train_loader()
test_original_loader = dataloader_ctor.test_loader()
encoder = Encoder(n_latent_layer=n_latent_layer)
decoder = Decoder(n_latent_layer=n_latent_layer)
classifier = Classifier(
n_input_layer=n_latent_layer,
n_classes=(len(constants.DATASETS_FILES)
if genes_name is None else genes_name.count("_") + 1))
path_format_to_save = os.path.join(
constants.CACHE_GLOBAL_DIR, constants.DATA_TYPE,
"model_{}_{}_{}_{{}}".format(fraction, model, "z" if use_z else "mu"))
PATH_ENCODER = os.path.join(path_format_to_save, "ENC_mdl")
PATH_DECODER = os.path.join(path_format_to_save, "DEC_mdl")
PATH_CLASSIFIER = os.path.join(path_format_to_save, "CLS_mdl")
load_model = True
if load_model and os.path.exists(
PATH_ENCODER.format(epoch_checkpoint) + suffix):
encoder.load_state_dict(
torch.load(PATH_ENCODER.format(epoch_checkpoint) + suffix))
encoder.eval()
decoder.load_state_dict(
torch.load(PATH_DECODER.format(epoch_checkpoint) + suffix))
decoder.eval()
if model != constants.MODEL_VAE:
classifier.load_state_dict(
torch.load(PATH_CLASSIFIER.format(epoch_checkpoint) + suffix))
classifier.eval()
with torch.no_grad():
data_train = tensor([])
labels_train = tensor([]).long()
for batch_idx, (data, label) in enumerate(trainloader):
data_train = torch.cat((data_train, data), 0)
labels_train = torch.cat((labels_train, label), 0)
data_test_original = tensor([])
labels_test = tensor([]).long()
for batch_idx, (data, label) in enumerate(test_original_loader):
data_original_test = torch.cat((data_test_original, data), 0)
labels_original_test = torch.cat((labels_test, label), 0)
data_test = tensor([])
labels_test = tensor([]).long()
for batch_idx, (data, label) in enumerate(testloader):
data_test = torch.cat((data_test, data), 0)
labels_test = torch.cat((labels_test, label), 0)
n_labels = len(dataset_names)
X_train = encoder(data_train)[0].cpu().numpy()
y_train = labels_train.cpu().numpy()
X_original_test = encoder(data_original_test)[0].cpu().numpy()
y_original_test = labels_original_test.cpu().numpy()
X_test = encoder(data_test)[0].cpu().numpy()
y_test = labels_test.cpu().numpy() + n_labels
y_original = knn(X_train, y_train, X_original_test, y_original_test)
ys = []
fractions = [0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99]
for fraction in fractions:
new_X_train, new_X_test, new_y_train, new_y_test = train_test_split(
X_test, y_test, test_size=1 - fraction)
ys.append(
knn(np.vstack((X_train, new_X_train)),
np.concatenate((y_train, new_y_train)), new_X_test,
new_y_test))
plt.plot(fractions, ys, label="knn score of new labels")
plt.plot([0.01, 0.99], [y_original, y_original],
label="knn score of original labels")
plt.xlabel("test fraction")
plt.ylabel("knn score")
plt.legend()
plt.savefig(
os.path.join(constants.OUTPUT_GLOBAL_DIR, "new_label_plot.png"))
path_to_save = path_format_to_save.format(epoch_checkpoint)
plt.subplots(figsize=(20, 20))
colormap = cm.jet
patches_tcga = plot_bu(encoder, trainloader, device, suffix + "_tcga",
path_to_save, constants.DATASETS_NAMES,
colormap, 'yellow')
plt.legend(handles=patches_tcga)
plt.savefig(
os.path.join(path_to_save,
"zs_scatter{}.png".format(suffix + "_tcga")))
colormap = cm.terrain
patches_new = plot_bu(encoder, testloader, device, suffix + "_new",
path_format_to_save.format(epoch_checkpoint),
constants.NEW_DATASETS_NAMES, colormap, 'blue')
plt.legend(handles=patches_tcga + patches_new)
plt.savefig(
os.path.join(path_to_save,
"zs_scatter{}.png".format(suffix + "_new")))
def main(model, use_z, fraction, epoch_checkpoint=300, suffix=""):
filter_func = filter_func_dict[fraction]
n_latent_layer = 2
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
genes = None
genes_name = None
# genes=np.load("/media/hag007/Data/dlproj/cache_global/datasets/vemurafenib_resveratrol_olaparib/genes.npy", allow_pickle=True)
# genes_name="vemurafenib_resveratrol_olaparib"
dataset_names = constants.ALL_DATASET_NAMES
dataset = datasets.Dataset(dataset_names, "tcga")
dataloader_ctor = datasets.DataLoader(dataset, 0.0, 0.0)
trainloader = dataloader_ctor.train_loader()
encoder = Encoder(n_latent_layer=n_latent_layer)
decoder = Decoder(n_latent_layer=n_latent_layer)
classifier = Classifier(n_input_layer=n_latent_layer,
n_classes=len(dataset_names))
path_format_to_save = os.path.join(
constants.CACHE_GLOBAL_DIR, constants.DATA_TYPE,
"model_{}_{}_{}_{{}}".format(fraction, model, "z" if use_z else "mu"))
PATH_ENCODER = os.path.join(path_format_to_save, "ENC_mdl")
PATH_DECODER = os.path.join(path_format_to_save, "DEC_mdl")
PATH_CLASSIFIER = os.path.join(path_format_to_save, "CLS_mdl")
load_model = True
if load_model and os.path.exists(
PATH_ENCODER.format(epoch_checkpoint) + suffix):
encoder.load_state_dict(
torch.load(PATH_ENCODER.format(epoch_checkpoint) + suffix))
encoder.eval()
decoder.load_state_dict(
torch.load(PATH_DECODER.format(epoch_checkpoint) + suffix))
decoder.eval()
if model != constants.MODEL_VAE:
classifier.load_state_dict(
torch.load(PATH_CLASSIFIER.format(epoch_checkpoint) + suffix))
classifier.eval()
with torch.no_grad():
path_to_save = path_format_to_save.format(epoch_checkpoint)
# plt.subplots(figsize=(20,20))
# colormap = cm.jet
# patches_tcga=plot(encoder, trainloader, device, suffix + "_tcga", path_to_save, constants.DATASETS_NAMES, colormap, 'yellow')
# # plt.legend(handles=patches_tcga)
# # plt.savefig(os.path.join(path_to_save, "zs_scatter{}.png".format(suffix + "_tcga_diff")))
# plt.clf()
plt.subplots(figsize=(20, 20))
colormap = cm.jet
patches_tcga = plot_median_diff(encoder, trainloader, device,
suffix + "_tcga", path_to_save,
dataset_names, epoch_checkpoint,
colormap, 'yellow')
plt.legend(handles=patches_tcga)
plt.savefig(
os.path.join(
path_to_save,
"zs_scatter{}_{}.png".format(suffix + "_tcga_diff",
time.time())))