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from typing import Iterable
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import pytorch_lightning as pl
from pricePrediction import config
from pricePrediction.ArgParser_base import ArgParseable
from pricePrediction.config import USE_FEATURES_NET
from pricePrediction.nets.FDS_imbalance import FDS
class PricePredictorModule(pl.LightningModule, ArgParseable):
DESIRED_PARAMS_TO_ASK= ['n_layers', 'hidden_size_node', 'hidden_size_edges', 'fcc_hidden_size', 'use_fds', 'lr',
'dropout', 'gnn_class', 'weight_decay', 'training_loss']
def __init__(
self,
n_layers: int = config.N_LAYERS,
hidden_size_node: int = config.N_HIDDEN_NODE,
hidden_size_edges: int = config.N_HIDDEN_EDGE,
gnn_class="GNN_PNAConv",
towers: int = 5,
fcc_hidden_size: int = 25,
dropout: float = 0,
use_fds: bool = False,
training_loss : str= "l2", # l1, l2 or huber
last_layer_size: int = 1,
nodes_n_features: int = None,
edges_n_features: int = None,
deg: Iterable[float] = None,
lr: float = config.LEARNING_RATE,
b1: float = 0.5,
b2: float = 0.999,
weight_decay=1e-8,
data_hparams =None,
logs_only_in_epoch = False
):
'''
:param str encodedDir: The directory where the dataset has been prepared as lmdb files. defaults to %(config.ENCODED_DIR)s
:param str deg_fname: The file containing the statistics about the nodes degree. By default is automatically searched within
encodedDir defaults to None
:param int n_layers: The number of layers.
:param int hidden_size_node: Channels for nodes.
:param int hidden_size_edges: Channels for edges.
:param str gnn_class: Name of the model type.
:param int towers: Only for gnn_class="GNN_PNAConv", The number of towers of the model.
:param int fcc_hidden_size: Size of fully connected.
:param float dropout: Dropout rate.
:param bool use_fds: Use feature density smoothing for data imbalance
:param str training_loss: Training loss: "l2" "l1" or "huber"
:param int last_layer_size: The number of neurons for last layer. Set it to 1 for regression
:param int nodes_n_features: The number of features used to encode a node
:param int edges_n_features: The number of features used to encode an edge
:param Iterable deg: The degree count of the dataset
:param float lr: Learning rate
:param float b1: Adam optimizer b1
:param float b2: Adam optimizer b1
'''
super().__init__()
self.save_hyperparameters() #copies __init__() kwargs to self.hparams
deg = self.hparams.deg
if isinstance(deg, list):
deg = torch.tensor(deg[:], dtype=torch.long)
else:
deg = deg.clone().detach()
self.hparams.deg = deg
if gnn_class == "GNN_PNAConv":
from pricePrediction.nets.basicArchitectures import GNN_PNAConv as GNN
elif gnn_class == "GNN_AttentiveFP":
from pricePrediction.nets.basicArchitectures import GNN_AttentiveFP as GNN
elif gnn_class == "QdolarAR":
assert USE_FEATURES_NET
from pricePrediction.nets.basicArchitectures import QdolarAR as GNN
else:
raise ValueError("Error, gnn_class not supported")
self.net = GNN(** self.hparams)
self.dropoutLayer = nn.Dropout(self.hparams.dropout)
self.final_layer = nn.Linear(self.net.fcc_hidden_size, 1)
if use_fds:
self.fds = FDS(self.net.latent_size, max_val=10, start_update=1)
else:
self.fds = None
if training_loss=="l1":
self.trainLossF = F.l1_loss
elif training_loss=="l2":
self.trainLossF = F.mse_loss
elif training_loss=="huber":
self.trainLossF = F.smooth_l1_loss
else:
raise ValueError("Loss option not recognized")
if logs_only_in_epoch: #TODO: check if this prevents multi-gpu hang
self.logs_kwargs = dict(on_step=False, on_epoch=True, sync_dist=True)
else:
self.logs_kwargs = dict(on_step=True, on_epoch=False, sync_dist=False)
def compute_training_loss(self, y_pred, y, w=None):
loss = self.trainLossF(y_pred, y, reduction='none')
if w is not None:
loss *= w
loss = torch.mean(loss)
return loss
def forward(self, g):
return self.compute_y_pred( g, training=False)
def compute_y_pred(self, g, y=None, training=True):
x = self.net(g.x, g.edge_index, g.edge_attr, g.batch)
if training and self.fds is not None:
self.fds.smooth(x, y, self.current_epoch)
x = self.dropoutLayer(x)
y_pred = self.final_layer(x)
return y_pred.view(-1)
def resolve_batch(self, batch):
if isinstance(batch, list):
return batch[0]
else:
return batch
def training_step(self, batch, batch_idx):
graphs = self.resolve_batch(batch)
y_pred = self.compute_y_pred(graphs, training=True)
loss = self.compute_training_loss(y_pred, graphs.y, graphs.w)
loss_l1 = F.l1_loss(y_pred, graphs.y)
self.log('loss', loss, **self.logs_kwargs)
self.log('loss_l1', loss_l1, prog_bar=True, **self.logs_kwargs)
tqdm_dict = {'loss': loss.detach()}
output = OrderedDict({
'loss': loss,
'targets': graphs.y,
'preds': y_pred.detach(),
'progress_bar': tqdm_dict,
'log': tqdm_dict
})
return output
def _validation_step(self, graphs, batch_idx):
with torch.no_grad():
y_pred = self.compute_y_pred(graphs, training=False)
loss = self.compute_training_loss(y_pred, graphs.y, graphs.w)
loss_l1 = F.l1_loss(y_pred, graphs.y)
return y_pred, loss, loss_l1
def validation_step(self, batch, batch_idx):
graphs = self.resolve_batch(batch)
y_pred, loss, loss_l1 = self._validation_step(graphs, batch_idx)
# tensorboard = self.logger.experiment
# tensorboard.add_histogram("val_price", graphs.y)
# tensorboard.add_histogram("val_preds", y_pred)
self.log('val_loss', loss, prog_bar=True, sync_dist=True) #rank_zero_only=True
self.log('val_lossL1', loss_l1, prog_bar=True, sync_dist=True)
return loss
def training_epoch_end(self, outputs ):
# tensorboard = self.logger.experiment
# tensorboard.add_histogram("train_price", y)
# tensorboard.add_histogram("train_preds", torch.cat([ x["preds"] for x in outputs]))
# tensorboard.add_histogram("train_price", torch.cat([ x["targets"] for x in outputs]))
print("Epoch %d done"%self.current_epoch)
def test_step(self, batch, batch_idx):
graphs = self.resolve_batch(batch)
y_pred, loss, loss_l1 = self._validation_step(graphs, batch_idx)
tensorboard = self.logger.experiment
# tensorboard.add_histogram("test_price", graphs.y)
# tensorboard.add_histogram("test_preds", y_pred)
self.log('test_loss', loss, prog_bar=True, sync_dist=True)
self.log('test_lossL1', loss_l1, prog_bar=True, sync_dist=True)
return loss
def configure_optimizers(self):
lr = self.hparams.lr
b1 = self.hparams.b1
b2 = self.hparams.b2
opt = torch.optim.Adam(self.parameters(), lr=lr, betas=(b1, b2), weight_decay=self.hparams.weight_decay)
# for param_group in opt.param_groups:
# print(param_group["lr"])
conf = {
'optimizer': opt,
'scheduler': torch.optim.lr_scheduler.ReduceLROnPlateau(opt, verbose=True, cooldown=2,
patience=config.PATIENT_REDUCE_LR_PLATEAU_N_EPOCHS, ),
'monitor': 'val_loss'
}
return conf
if __name__ == "__main__":
from torch_geometric.data import Batch
degree =[0, 41130, 117278, 70152, 3104]
gnn_class = "QdolarAR" #"GNN_PNAConv"# "GNN_AttentiveFP"
if gnn_class != "QdolarAR":
from pricePrediction.preprocessData.smilesToGraph import smiles_to_graph
else:
from pricePrediction.preprocessData.smilesToDescriptors import smiles_to_graph
g = smiles_to_graph("CCCCCCCCCCC")
nodes_n_features = g.x.shape[1]
edges_n_features = g.edge_attr.shape[1]
net = PricePredictorModule(nodes_n_features=nodes_n_features, edges_n_features=edges_n_features,
deg=degree, lr=1e-3, gnn_class=gnn_class)
print( net )
graphs = [smiles_to_graph(smi) for smi in ["CCCCCO", "CCCCCNCCCCN"]]
print(graphs[0])
print( net(Batch.from_data_list(graphs)))
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