num_pos_va = np.array((y_class_va == +1).sum(0)).flatten()
num_neg_va = np.array((y_class_va == -1).sum(0)).flatten()
num_regr_va = np.bincount(y_regr_va.indices, minlength=y_regr.shape[1])
batch_size = int(np.ceil(args.batch_ratio * idx_tr.shape[0]))
num_int_batches = 1
if args.internal_batch_max is not None:
if args.internal_batch_max < batch_size:
num_int_batches = int(np.ceil(batch_size / args.internal_batch_max))
batch_size = int(np.ceil(batch_size / num_int_batches))
vprint(f"#internal batch size: {batch_size}")
dataset_tr = sc.ClassRegrSparseDataset(x=ecfp[idx_tr],
y_class=y_class_tr,
y_regr=y_regr_tr,
y_censor=y_censor_tr)
dataset_va = sc.ClassRegrSparseDataset(x=ecfp[idx_va],
y_class=y_class_va,
y_regr=y_regr_va,
y_censor=y_censor_va)
loader_tr = DataLoader(dataset_tr,
batch_size=batch_size,
num_workers=8,
pin_memory=True,
collate_fn=dataset_tr.collate,
shuffle=True)
loader_va = DataLoader(dataset_va,
batch_size=batch_size,
num_workers=4,
def train():
if torch.cuda.is_available():
nvmlInit()
multiprocessing.set_start_method('fork', force=True)
parser = argparse.ArgumentParser(
description="Training a multi-task model.")
parser.add_argument("--x",
help="Descriptor file (matrix market, .npy or .npz)",
type=str,
default=None)
parser.add_argument("--y_class",
"--y",
"--y_classification",
help="Activity file (matrix market, .npy or .npz)",
type=str,
default=None)
parser.add_argument("--y_regr",
"--y_regression",
help="Activity file (matrix market, .npy or .npz)",
type=str,
default=None)
parser.add_argument(
"--y_censor",
help="Censor mask for regression (matrix market, .npy or .npz)",
type=str,
default=None)
parser.add_argument(
"--weights_class",
"--task_weights",
"--weights_classification",
help=
"CSV file with columns task_id, training_weight, aggregation_weight, task_type (for classification tasks)",
type=str,
default=None)
parser.add_argument(
"--weights_regr",
"--weights_regression",
help=
"CSV file with columns task_id, training_weight, censored_weight, aggregation_weight, aggregation_weight, task_type (for regression tasks)",
type=str,
default=None)
parser.add_argument(
"--censored_loss",
help="Whether censored loss is used for training (default 1)",
type=int,
default=1)
parser.add_argument("--folding",
help="Folding file (npy)",
type=str,
required=True)
parser.add_argument("--fold_va",
help="Validation fold number",
type=int,
default=0)
parser.add_argument("--fold_te",
help="Test fold number (removed from dataset)",
type=int,
default=None)
parser.add_argument("--batch_ratio",
help="Batch ratio",
type=float,
default=0.02)
parser.add_argument("--internal_batch_max",
help="Maximum size of the internal batch",
type=int,
default=None)
parser.add_argument(
"--normalize_loss",
help=
"Normalization constant to divide the loss (default uses batch size)",
type=float,
default=None)
parser.add_argument(
"--normalize_regression",
help="Set this to 1 if the regression tasks should be normalized",
type=int,
default=0)
parser.add_argument(
"--normalize_regr_va",
help=
"Set this to 1 if the regression tasks in validation fold should be normalized together with training folds",
type=int,
default=0)
parser.add_argument(
"--inverse_normalization",
help=
"Set this to 1 if the regression tasks in validation fold should be inverse normalized at validation time",
type=int,
default=0)
parser.add_argument("--hidden_sizes",
nargs="+",
help="Hidden sizes of trunk",
default=[],
type=int,
required=True)
parser.add_argument(
"--last_hidden_sizes",
nargs="+",
help=
"Hidden sizes in the head (if specified , class and reg heads have this dimension)",
default=None,
type=int)
#parser.add_argument("--middle_dropout", help="Dropout for layers before the last", type=float, default=0.0)
#parser.add_argument("--last_dropout", help="Last dropout", type=float, default=0.2)
parser.add_argument("--weight_decay",
help="Weight decay",
type=float,
default=0.0)
parser.add_argument("--last_non_linearity",
help="Last layer non-linearity (depecrated)",
type=str,
default="relu",
choices=["relu", "tanh"])
parser.add_argument("--middle_non_linearity",
"--non_linearity",
help="Before last layer non-linearity",
type=str,
default="relu",
choices=["relu", "tanh"])
parser.add_argument("--input_transform",
help="Transformation to apply to inputs",
type=str,
default="none",
choices=["binarize", "none", "tanh", "log1p"])
parser.add_argument("--lr", help="Learning rate", type=float, default=1e-3)
parser.add_argument("--lr_alpha",
help="Learning rate decay multiplier",
type=float,
default=0.3)
parser.add_argument("--lr_steps",
nargs="+",
help="Learning rate decay steps",
type=int,
default=[10])
parser.add_argument("--input_size_freq",
help="Number of high importance features",
type=int,
default=None)
parser.add_argument("--fold_inputs",
help="Fold input to a fixed set (default no folding)",
type=int,
default=None)
parser.add_argument("--epochs",
help="Number of epochs",
type=int,
default=20)
parser.add_argument(
"--pi_zero",
help="Reference class ratio to be used for calibrated aucpr",
type=float,
default=0.1)
parser.add_argument(
"--min_samples_class",
help=
"Minimum number samples in each class and in each fold for AUC calculation (only used if aggregation_weight is not provided in --weights_class)",
type=int,
default=5)
parser.add_argument("--min_samples_auc",
help="Obsolete: use 'min_samples_class'",
type=int,
default=None)
parser.add_argument(
"--min_samples_regr",
help=
"Minimum number of uncensored samples in each fold for regression metric calculation (only used if aggregation_weight is not provided in --weights_regr)",
type=int,
default=10)
parser.add_argument("--dev",
help="Device to use",
type=str,
default="cuda:0")
parser.add_argument("--run_name",
help="Run name for results",
type=str,
default=None)
parser.add_argument(
"--output_dir",
help="Output directory, including boards (default 'models')",
type=str,
default="models")
parser.add_argument("--prefix",
help="Prefix for run name (default 'run')",
type=str,
default='run')
parser.add_argument(
"--verbose",
help="Verbosity level: 2 = full; 1 = no progress; 0 = no output",
type=int,
default=2,
choices=[0, 1, 2])
parser.add_argument("--save_model",
help="Set this to 0 if the model should not be saved",
type=int,
default=1)
parser.add_argument(
"--save_board",
help="Set this to 0 if the TensorBoard should not be saved",
type=int,
default=1)
parser.add_argument(
"--profile",
help="Set this to 1 to output memory profile information",
type=int,
default=0)
parser.add_argument(
"--mixed_precision",
help=
"Set this to 1 to run in mixed precision mode (vs single precision)",
type=int,
default=0)
parser.add_argument("--eval_train",
help="Set this to 1 to calculate AUCs for train data",
type=int,
default=0)
parser.add_argument("--enable_cat_fusion",
help="Set this to 1 to enable catalogue fusion",
type=int,
default=0)
parser.add_argument(
"--eval_frequency",
help=
"The gap between AUC eval (in epochs), -1 means to do an eval at the end.",
type=int,
default=1)
#hybrid model features
parser.add_argument(
"--regression_weight",
help=
"between 0 and 1 relative weight of regression loss vs classification loss",
type=float,
default=0.5)
parser.add_argument(
"--scaling_regularizer",
help=
"L2 regularizer of the scaling layer, if inf scaling layer is switched off",
type=float,
default=np.inf)
parser.add_argument(
"--class_feature_size",
help=
"Number of leftmost features used from the output of the trunk (default: use all)",
type=int,
default=-1)
parser.add_argument(
"--regression_feature_size",
help=
"Number of rightmost features used from the output of the trunk (default: use all)",
type=int,
default=-1)
parser.add_argument(
"--last_hidden_sizes_reg",
nargs="+",
help=
"Hidden sizes in the regression head (overwritten by last_hidden_sizes)",
default=None,
type=int)
parser.add_argument(
"--last_hidden_sizes_class",
nargs="+",
help=
"Hidden sizes in the classification head (overwritten by last_hidden_sizes)",
default=None,
type=int)
parser.add_argument(
"--dropouts_reg",
nargs="+",
help=
"List of dropout values used in the regression head (needs one per last hidden in reg head, ignored if last_hidden_sizes_reg not specified)",
default=[],
type=float)
parser.add_argument(
"--dropouts_class",
nargs="+",
help=
"List of dropout values used in the classification head (needs one per last hidden in class head, ignored if no last_hidden_sizes_class not specified)",
default=[],
type=float)
parser.add_argument("--dropouts_trunk",
nargs="+",
help="List of dropout values used in the trunk",
default=[],
type=float,
required=True)
args = parser.parse_args()
if (args.last_hidden_sizes
is not None) and ((args.last_hidden_sizes_class is not None) or
(args.last_hidden_sizes_reg is not None)):
raise ValueError(
"Head specific and general last_hidden_sizes argument were both specified!"
)
if (args.last_hidden_sizes is not None):
args.last_hidden_sizes_class = args.last_hidden_sizes
args.last_hidden_sizes_reg = args.last_hidden_sizes
if args.last_hidden_sizes_reg is not None:
assert len(args.last_hidden_sizes_reg) == len(
args.dropouts_reg
), "Number of hiddens and number of dropout values specified must be equal in the regression head!"
if args.last_hidden_sizes_class is not None:
assert len(args.last_hidden_sizes_class) == len(
args.dropouts_class
), "Number of hiddens and number of dropout values specified must be equal in the classification head!"
if args.hidden_sizes is not None:
assert len(args.hidden_sizes) == len(
args.dropouts_trunk
), "Number of hiddens and number of dropout values specified must be equal in the trunk!"
def vprint(s=""):
if args.verbose:
print(s)
vprint(args)
if args.class_feature_size == -1:
args.class_feature_size = args.hidden_sizes[-1]
if args.regression_feature_size == -1:
args.regression_feature_size = args.hidden_sizes[-1]
assert args.regression_feature_size <= args.hidden_sizes[
-1], "Regression feature size cannot be larger than the trunk output"
assert args.class_feature_size <= args.hidden_sizes[
-1], "Classification feature size cannot be larger than the trunk output"
assert args.regression_feature_size + args.class_feature_size >= args.hidden_sizes[
-1], "Unused features in the trunk! Set regression_feature_size + class_feature_size >= trunk output!"
#if args.regression_feature_size != args.hidden_sizes[-1] or args.class_feature_size != args.hidden_sizes[-1]:
# raise ValueError("Hidden spliting not implemented yet!")
if args.run_name is not None:
name = args.run_name
else:
name = f"sc_{args.prefix}_h{'.'.join([str(h) for h in args.hidden_sizes])}_ldo_r{'.'.join([str(d) for d in args.dropouts_reg])}_wd{args.weight_decay}"
name += f"_lr{args.lr}_lrsteps{'.'.join([str(s) for s in args.lr_steps])}_ep{args.epochs}"
name += f"_fva{args.fold_va}_fte{args.fold_te}"
if args.mixed_precision == 1:
name += f"_mixed_precision"
vprint(f"Run name is '{name}'.")
if args.profile == 1:
assert (
args.save_board == 1
), "Tensorboard should be enabled to be able to profile memory usage."
if args.save_board:
tb_name = os.path.join(args.output_dir, "boards", name)
writer = SummaryWriter(tb_name)
else:
writer = Nothing()
assert args.input_size_freq is None, "Using tail compression not yet supported."
if (args.y_class is None) and (args.y_regr is None):
raise ValueError(
"No label data specified, please add --y_class and/or --y_regr.")
ecfp = sc.load_sparse(args.x)
y_class = sc.load_sparse(args.y_class)
y_regr = sc.load_sparse(args.y_regr)
y_censor = sc.load_sparse(args.y_censor)
if (y_regr is None) and (y_censor is not None):
raise ValueError("y_censor provided please also provide --y_regr.")
if y_class is None:
y_class = scipy.sparse.csr_matrix((ecfp.shape[0], 0))
if y_regr is None:
y_regr = scipy.sparse.csr_matrix((ecfp.shape[0], 0))
if y_censor is None:
y_censor = scipy.sparse.csr_matrix(y_regr.shape)
folding = np.load(args.folding)
assert ecfp.shape[0] == folding.shape[
0], "x and folding must have same number of rows"
## Loading task weights
tasks_class = sc.load_task_weights(args.weights_class,
y=y_class,
label="y_class")
tasks_regr = sc.load_task_weights(args.weights_regr,
y=y_regr,
label="y_regr")
## Input transformation
ecfp = sc.fold_transform_inputs(ecfp,
folding_size=args.fold_inputs,
transform=args.input_transform)
print(f"count non zero:{ecfp[0].count_nonzero()}")
num_pos = np.array((y_class == +1).sum(0)).flatten()
num_neg = np.array((y_class == -1).sum(0)).flatten()
num_class = np.array((y_class != 0).sum(0)).flatten()
if (num_class != num_pos + num_neg).any():
raise ValueError(
"For classification all y values (--y_class/--y) must be 1 or -1.")
num_regr = np.bincount(y_regr.indices, minlength=y_regr.shape[1])
assert args.min_samples_auc is None, "Parameter 'min_samples_auc' is obsolete. Use '--min_samples_class' that specifies how many samples a task needs per FOLD and per CLASS to be aggregated."
if tasks_class.aggregation_weight is None:
## using min_samples rule
fold_pos, fold_neg = sc.class_fold_counts(y_class, folding)
n = args.min_samples_class
tasks_class.aggregation_weight = ((fold_pos >= n).all(0) &
(fold_neg >= n)).all(0).astype(
np.float64)
if tasks_regr.aggregation_weight is None:
if y_censor.nnz == 0:
y_regr2 = y_regr.copy()
y_regr2.data[:] = 1
else:
## only counting uncensored data
y_regr2 = y_censor.copy()
y_regr2.data = (y_regr2.data == 0).astype(np.int32)
fold_regr, _ = sc.class_fold_counts(y_regr2, folding)
del y_regr2
tasks_regr.aggregation_weight = (
fold_regr >= args.min_samples_regr).all(0).astype(np.float64)
vprint(f"Input dimension: {ecfp.shape[1]}")
vprint(f"#samples: {ecfp.shape[0]}")
vprint(f"#classification tasks: {y_class.shape[1]}")
vprint(f"#regression tasks: {y_regr.shape[1]}")
vprint(
f"Using {(tasks_class.aggregation_weight > 0).sum()} classification tasks for calculating aggregated metrics (AUCROC, F1_max, etc)."
)
vprint(
f"Using {(tasks_regr.aggregation_weight > 0).sum()} regression tasks for calculating metrics (RMSE, Rsquared, correlation)."
)
if args.fold_te is not None and args.fold_te >= 0:
## removing test data
assert args.fold_te != args.fold_va, "fold_va and fold_te must not be equal."
keep = folding != args.fold_te
ecfp = ecfp[keep]
y_class = y_class[keep]
y_regr = y_regr[keep]
y_censor = y_censor[keep]
folding = folding[keep]
normalize_inv = None
if args.normalize_regression == 1 and args.normalize_regr_va == 1:
y_regr, mean_save, var_save = sc.normalize_regr(y_regr)
fold_va = args.fold_va
idx_tr = np.where(folding != fold_va)[0]
idx_va = np.where(folding == fold_va)[0]
y_class_tr = y_class[idx_tr]
y_class_va = y_class[idx_va]
y_regr_tr = y_regr[idx_tr]
y_regr_va = y_regr[idx_va]
y_censor_tr = y_censor[idx_tr]
y_censor_va = y_censor[idx_va]
if args.normalize_regression == 1 and args.normalize_regr_va == 0:
y_regr_tr, mean_save, var_save = sc.normalize_regr(y_regr_tr)
if args.inverse_normalization == 1:
normalize_inv = {}
normalize_inv["mean"] = mean_save
normalize_inv["var"] = var_save
num_pos_va = np.array((y_class_va == +1).sum(0)).flatten()
num_neg_va = np.array((y_class_va == -1).sum(0)).flatten()
num_regr_va = np.bincount(y_regr_va.indices, minlength=y_regr.shape[1])
pos_rate = num_pos_va / (num_pos_va + num_neg_va)
pos_rate_ref = args.pi_zero
pos_rate = np.clip(pos_rate, 0, 0.99)
cal_fact_aucpr = pos_rate * (1 - pos_rate_ref) / (pos_rate_ref *
(1 - pos_rate))
#import ipdb; ipdb.set_trace()
batch_size = int(np.ceil(args.batch_ratio * idx_tr.shape[0]))
num_int_batches = 1
if args.internal_batch_max is not None:
if args.internal_batch_max < batch_size:
num_int_batches = int(np.ceil(batch_size /
args.internal_batch_max))
batch_size = int(np.ceil(batch_size / num_int_batches))
vprint(f"#internal batch size: {batch_size}")
tasks_cat_id_list = None
select_cat_ids = None
if tasks_class.cat_id is not None:
tasks_cat_id_list = [[x, i] for i, x in enumerate(tasks_class.cat_id)
if str(x) != 'nan']
tasks_cat_ids = [
i for i, x in enumerate(tasks_class.cat_id) if str(x) != 'nan'
]
select_cat_ids = np.array(tasks_cat_ids)
cat_id_size = len(tasks_cat_id_list)
else:
cat_id_size = 0
dataset_tr = sc.ClassRegrSparseDataset(x=ecfp[idx_tr],
y_class=y_class_tr,
y_regr=y_regr_tr,
y_censor=y_censor_tr,
y_cat_columns=select_cat_ids)
dataset_va = sc.ClassRegrSparseDataset(x=ecfp[idx_va],
y_class=y_class_va,
y_regr=y_regr_va,
y_censor=y_censor_va,
y_cat_columns=select_cat_ids)
loader_tr = DataLoader(dataset_tr,
batch_size=batch_size,
num_workers=8,
pin_memory=True,
collate_fn=dataset_tr.collate,
shuffle=True)
loader_va = DataLoader(dataset_va,
batch_size=batch_size,
num_workers=4,
pin_memory=True,
collate_fn=dataset_va.collate,
shuffle=False)
args.input_size = dataset_tr.input_size
args.output_size = dataset_tr.output_size
args.class_output_size = dataset_tr.class_output_size
args.regr_output_size = dataset_tr.regr_output_size
args.cat_id_size = cat_id_size
dev = torch.device(args.dev)
net = sc.SparseFFN(args).to(dev)
loss_class = torch.nn.BCEWithLogitsLoss(reduction="none")
loss_regr = sc.censored_mse_loss
if not args.censored_loss:
loss_regr = functools.partial(loss_regr, censored_enabled=False)
tasks_class.training_weight = tasks_class.training_weight.to(dev)
tasks_regr.training_weight = tasks_regr.training_weight.to(dev)
tasks_regr.censored_weight = tasks_regr.censored_weight.to(dev)
vprint("Network:")
vprint(net)
reporter = None
h = None
if args.profile == 1:
torch_gpu_id = torch.cuda.current_device()
if "CUDA_VISIBLE_DEVICES" in os.environ:
ids = list(
map(int,
os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",")))
nvml_gpu_id = ids[torch_gpu_id] # remap
else:
nvml_gpu_id = torch_gpu_id
h = nvmlDeviceGetHandleByIndex(nvml_gpu_id)
if args.profile == 1:
##### output saving #####
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
reporter = MemReporter(net)
with open(f"{args.output_dir}/memprofile.txt", "w+") as profile_file:
with redirect_stdout(profile_file):
profile_file.write(f"\nInitial model detailed report:\n\n")
reporter.report()
optimizer = torch.optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
scheduler = MultiStepLR(optimizer,
milestones=args.lr_steps,
gamma=args.lr_alpha)
num_prints = 0
scaler = torch.cuda.amp.GradScaler()
for epoch in range(args.epochs):
t0 = time.time()
sc.train_class_regr(net,
optimizer,
loader=loader_tr,
loss_class=loss_class,
loss_regr=loss_regr,
dev=dev,
weights_class=tasks_class.training_weight *
(1 - args.regression_weight) * 2,
weights_regr=tasks_regr.training_weight *
args.regression_weight * 2,
censored_weight=tasks_regr.censored_weight,
normalize_loss=args.normalize_loss,
num_int_batches=num_int_batches,
progress=args.verbose >= 2,
reporter=reporter,
writer=writer,
epoch=epoch,
args=args,
scaler=scaler,
nvml_handle=h)
if args.profile == 1:
with open(f"{args.output_dir}/memprofile.txt",
"a+") as profile_file:
profile_file.write(
f"\nAfter epoch {epoch} model detailed report:\n\n")
with redirect_stdout(profile_file):
reporter.report()
t1 = time.time()
eval_round = (args.eval_frequency > 0) and ((epoch + 1) %
args.eval_frequency == 0)
last_round = epoch == args.epochs - 1
if eval_round or last_round:
results_va = sc.evaluate_class_regr(net,
loader_va,
loss_class,
loss_regr,
tasks_class=tasks_class,
tasks_regr=tasks_regr,
dev=dev,
progress=args.verbose >= 2,
normalize_inv=normalize_inv,
cal_fact_aucpr=cal_fact_aucpr)
# import ipdb; ipdb.set_trace()
for key, val in results_va["classification_agg"].items():
writer.add_scalar(key + "/va", val, epoch)
for key, val in results_va["regression_agg"].items():
writer.add_scalar(key + "/va", val, epoch)
if args.eval_train:
results_tr = sc.evaluate_class_regr(net,
loader_tr,
loss_class,
loss_regr,
tasks_class=tasks_class,
tasks_regr=tasks_regr,
dev=dev,
progress=args.verbose >= 2)
for key, val in results_tr["classification_agg"].items():
writer.add_scalar(key + "/tr", val, epoch)
for key, val in results_tr["regression_agg"].items():
writer.add_scalar(key + "/tr", val, epoch)
else:
results_tr = None
if args.verbose:
## printing a new header every 20 lines
header = num_prints % 20 == 0
num_prints += 1
sc.print_metrics_cr(epoch, t1 - t0, results_tr, results_va,
header)
scheduler.step()
#print("DEBUG data for hidden spliting")
#print (f"Classification mask: Sum = {net.classmask.sum()}\t Uniques: {np.unique(net.classmask)}")
#print (f"Regression mask: Sum = {net.regmask.sum()}\t Uniques: {np.unique(net.regmask)}")
#print (f"overlap: {(net.regmask * net.classmask).sum()}")
writer.close()
vprint()
if args.profile == 1:
multiplexer = sc.create_multiplexer(tb_name)
# sc.export_scalars(multiplexer, '.', "GPUmem", "testcsv.csv")
data = sc.extract_scalars(multiplexer, '.', "GPUmem")
vprint(f"Peak GPU memory used: {sc.return_max_val(data)}MB")
vprint("Saving performance metrics (AUCs) and model.")
##### model saving #####
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_file = f"{args.output_dir}/{name}.pt"
out_file = f"{args.output_dir}/{name}.json"
if args.save_model:
torch.save(net.state_dict(), model_file)
vprint(f"Saved model weights into '{model_file}'.")
results_va["classification"]["num_pos"] = num_pos_va
results_va["classification"]["num_neg"] = num_neg_va
results_va["regression"]["num_samples"] = num_regr_va
if results_tr is not None:
results_tr["classification"]["num_pos"] = num_pos - num_pos_va
results_tr["classification"]["num_neg"] = num_neg - num_neg_va
results_tr["regression"]["num_samples"] = num_regr - num_regr_va
stats = None
if args.normalize_regression == 1:
stats = {}
stats["mean"] = mean_save
stats["var"] = np.array(var_save)[0]
sc.save_results(out_file,
args,
validation=results_va,
training=results_tr,
stats=stats)
vprint(
f"Saved config and results into '{out_file}'.\nYou can load the results by:\n import sparsechem as sc\n res = sc.load_results('{out_file}')"
)
def predict():
parser = argparse.ArgumentParser(description="Using trained model to make predictions.")
parser.add_argument("--x", help="Descriptor file (matrix market, .npy or .npz)", type=str, required=True)
parser.add_argument("--y_class", "--y", "--y_classification", help="Sparse pattern file for classification, optional. If provided returns predictions for given locations only (matrix market, .npy or .npz)", type=str, default=None)
parser.add_argument("--y_regr", "--y_regression", help="Sparse pattern file for regression, optional. If provided returns predictions for given locations only (matrix market, .npy or .npz)", type=str, default=None)
parser.add_argument("--folding", help="Folds for rows of y, optional. Needed if only one fold should be predicted.", type=str, required=False)
parser.add_argument("--predict_fold", help="One or more folds, integer(s). Needed if --folding is provided.", nargs="+", type=int, required=False)
parser.add_argument("--outprefix", help="Prefix for output files, '-class.npy', '-regr.npy' will be appended.", type=str, required=True)
parser.add_argument("--conf", help="Model conf file (.json or .npy)", type=str, required=True)
parser.add_argument("--model", help="Pytorch model file (.pt)", type=str, required=True)
parser.add_argument("--batch_size", help="Batch size (default 4000)", type=int, default=4000)
parser.add_argument("--last_hidden", help="If set to 1 returns last hidden layer instead of Yhat", type=int, default=0)
parser.add_argument("--dropout", help="If set to 1 enables dropout for evaluation", type=int, default=0)
parser.add_argument("--inverse_normalization", help="If set to 1 enables inverse normalization given means and variances from config file", type=int, default=0)
parser.add_argument("--weights_class", "--task_weights", "--weights_classification", help="CSV file with columns task_id, training_weight, aggregation_weight, task_type (for classification tasks)", type=str, default=None)
parser.add_argument("--dev", help="Device to use (default cuda:0)", type=str, default="cuda:0")
parser.add_argument("--num_workers", help="Number of workers for DataLoader", type=int, default=4)
args = parser.parse_args()
print(args)
results_loaded = sc.load_results(args.conf, two_heads=True)
conf = results_loaded["conf"]
if args.inverse_normalization == 1:
stats = results_loaded["stats"]
x = sc.load_sparse(args.x)
x = sc.fold_transform_inputs(x, folding_size=conf.fold_inputs, transform=conf.input_transform)
print(f"Input dimension: {x.shape[1]}")
print(f"#samples: {x.shape[0]}")
## error checks for --y_class, --y_regr, --folding and --predict_fold
if args.last_hidden:
assert args.y_class is None, "Cannot use '--last_hidden 1' with sparse predictions ('--y_class' or '--y_regr' is specified)."
if args.y_class is None and args.y_regr is None:
assert args.predict_fold is None, "To use '--predict_fold' please specify '--y_class' and/or '--y_regr'."
assert args.folding is None, "To use '--folding' please specify '--y_class' and/or '--y_regr'."
else:
if args.predict_fold is None:
assert args.folding is None, "If --folding is given please also specify --predict_fold."
if args.folding is None:
assert args.predict_fold is None, "If --predict_fold is given please also specify --folding."
res = types.SimpleNamespace(task_id=None, training_weight=None, aggregation_weight=None, task_type=None, censored_weight=torch.FloatTensor(), cat_id=None)
if args.weights_class is not None:
tasks_class = pd.read_csv(args.weights_class)
if "catalog_id" in tasks_class:
res.cat_id = tasks_class.catalog_id.values
tasks_cat_id_list = None
select_cat_ids = None
if res.cat_id is not None:
tasks_cat_id_list = [[x,i] for i,x in enumerate(res.cat_id) if str(x) != 'nan']
tasks_cat_ids = [i for i,x in enumerate(res.cat_id) if str(x) != 'nan']
select_cat_ids = np.array(tasks_cat_ids)
cat_id_size = len(tasks_cat_id_list)
else:
cat_id_size = 0
dev = args.dev
net = sc.SparseFFN(conf).to(dev)
state_dict = torch.load(args.model, map_location=torch.device(dev))
if conf.model_type == "federated":
state_dict_new = OrderedDict()
state_dict_new["net.0.net_freq.weight"] = state_dict["0.0.net_freq.weight"]
state_dict_new["net.0.net_freq.bias"] = state_dict["0.0.net_freq.bias"]
state_dict_new["net.2.net.2.weight"] = state_dict["1.net.2.weight"]
state_dict_new["net.2.net.2.bias"] = state_dict["1.net.2.bias"]
state_dict = state_dict_new
net.load_state_dict(state_dict)
print(f"Model weights: '{args.model}'")
print(f"Model config: '{args.conf}'.")
y_class = sc.load_check_sparse(args.y_class, (x.shape[0], conf.class_output_size))
y_regr = sc.load_check_sparse(args.y_regr, (x.shape[0], conf.regr_output_size))
if args.folding is not None:
folding = np.load(args.folding) if args.folding else None
assert folding.shape[0] == x.shape[0], f"Folding has {folding.shape[0]} rows and X has {x.shape[0]}. Must be equal."
keep = np.isin(folding, args.predict_fold)
y_class = sc.keep_row_data(y_class, keep)
y_regr = sc.keep_row_data(y_regr, keep)
dataset_te = sc.ClassRegrSparseDataset(x=x, y_class=y_class, y_regr=y_regr)
loader_te = DataLoader(dataset_te, batch_size=args.batch_size, num_workers = args.num_workers, pin_memory=True, collate_fn=dataset_te.collate)
if args.last_hidden:
## saving only hidden layer
out = sc.predict_hidden(net, loader_te, dev=dev, dropout=args.dropout, progress=True)
filename = f"{args.outprefix}-hidden.npy"
np.save(filename, out.numpy())
print(f"Saved (numpy) matrix of hiddens to '{filename}'.")
else:
if args.y_class is None and args.y_regr is None:
class_out, regr_out = sc.predict_dense(net, loader_te, dev=dev, dropout=args.dropout, progress=True, y_cat_columns=select_cat_ids)
else:
class_out, regr_out = sc.predict_sparse(net, loader_te, dev=dev, dropout=args.dropout, progress=True, y_cat_columns=select_cat_ids)
if args.inverse_normalization == 1:
regr_out = sc.inverse_normalization(regr_out, mean=np.array(stats["mean"]), variance=np.array(stats["var"]), array=True)
if net.class_output_size > 0:
np.save(f"{args.outprefix}-class.npy", class_out)
print(f"Saved prediction matrix (numpy) for classification to '{args.outprefix}-class.npy'.")
if net.regr_output_size > 0:
np.save(f"{args.outprefix}-regr.npy", regr_out)
print(f"Saved prediction matrix (numpy) for regression to '{args.outprefix}-regr.npy'.")
net.load_state_dict(state_dict)
print(f"Model weights: '{args.model}'")
print(f"Model config: '{args.conf}'.")
print(f"FedModel config: '{args.fedconf}'.")
y_class = sc.load_check_sparse(args.y_class, (x.shape[0], conf.class_output_size))
y_regr = sc.load_check_sparse(args.y_regr, (x.shape[0], conf.regr_output_size))
if args.folding is not None:
folding = np.load(args.folding) if args.folding else None
assert folding.shape[0] == x.shape[0], f"Folding has {folding.shape[0]} rows and X has {x.shape[0]}. Must be equal."
keep = np.isin(folding, args.predict_fold)
y_class = sc.keep_row_data(y_class, keep)
y_regr = sc.keep_row_data(y_regr, keep)
dataset_te = sc.ClassRegrSparseDataset(x=x, y_class=y_class, y_regr=y_regr)
loader_te = DataLoader(dataset_te, batch_size=args.batch_size, num_workers = 4, pin_memory=True, collate_fn=dataset_te.collate)
if args.last_hidden:
## saving only hidden layer
out = sc.predict_hidden(net, loader_te, dev=dev, dropout=args.dropout, progress=True)
filename = f"{args.outprefix}-hidden.npy"
np.save(filename, out.numpy())
print(f"Saved (numpy) matrix of hiddens to '{filename}'.")
else:
if args.y_class is None and args.y_regr is None:
class_out, regr_out = sc.predict_dense(net, loader_te, dev=dev, dropout=args.dropout, progress=True)
else:
class_out, regr_out = sc.predict_sparse(net, loader_te, dev=dev, dropout=args.dropout, progress=True)
if net.class_output_size > 0: