load csv file

[QUEST2179]

Dear Developer,

try to mimic beta_lactamase.py to load csv file directly, but got the following error. could you please help? Thanks!

File "C:\Users\18482\work\torchdrug-master\torchdrug\tasks\property_prediction.py", line 66, in preprocess if not math.isnan(sample[task]): TypeError: must be real number, not str

I included beta_csv.py for your troubleshooting.

import os from torch.utils import data as torch_data from torchdrug import data, utils from torchdrug.core import Registry as R import pandas as pd from collections import defaultdict

@R.register("datasets.beta_csv") @utils.copy_args(data.ProteinDataset.load_sequence, ignore=("target_fields")) class beta_csv(data.ProteinDataset): """ Qualitative data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons.

Statistics:
    - #Molecule: 1,478
    - #Classification task: 2

Parameters:
    path (str): path to store the dataset
    verbose (int, optional): output verbose level
    **kwargs
"""

def __init__(self,  path, verbose=1, **kwargs):
    path = os.path.expanduser(path)
    if not os.path.exists(path):
        os.makedirs(path)
    self.path = path

    sequences = []
    num_samples = []
    targets = defaultdict(list)
    target_fields = ["scaled_effect1"]

    csv_files = ['protein-datasets/beta_lactamase/beta_lactamase_test.csv',
                'protein-datasets/beta_lactamase/beta_lactamase_train.csv',
                'protein-datasets/beta_lactamase/beta_lactamase_valid.csv',]
    for csv_file in csv_files:
        df = pd.read_csv(csv_file)
        print(df.head())
        sequences.extend(df['Sequences'].values)
        if target_fields:
            for field in target_fields:
                targets[field].extend(df['Sequences'].values)
        num_samples.append(df.shape[0])
    print(num_samples)
    self.load_sequence(sequences, targets, attributes=None, verbose=verbose, **kwargs)
    self.num_samples = num_samples

beta_lactamase_test.csv looks like this Unnamed: 0 Sequences Targets 0 0 MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLAARVGYIE... 1.011182 1 1 MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIE... 1.003127 2 2 MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIE... -0.008031 3 3 MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIE... 0.621368 4 4 MSIQHFRVALIPFFAAFCLPVFAHPETLVKVKDAEDQLGARVGYIE... 1.005303

[Oxer11]

Hi, this is because your targets are str and not transformed to float. One way to deal with this is to use the utils.literal_eval function for transformation as in data.MoleculeDataset. load_csv.

[QUEST2179]

Thanks, I will try your suggestion.

[QUEST2179]

Another error on save model.

with open("beta_cnn.json", "w") as fout:
    json.dump(solver.config_dict(), fout)

raise TypeError(f'Object of type {o.__class__.__name__} '

TypeError: Object of type TruncateProtein is not JSON serializable

solver.save("beta_cnn.pth") works though.

[Oxer11]

Hi, could you share more contexts, e.g., how you define your solver and dataset? Everything works well for me.

[QUEST2179]

Just follow your BetaLactamase example on your webpage.

def testPropertyPrediction(): from torchdrug import models

model = models.ProteinCNN(input_dim=21,
                        hidden_dims=[1024, 1024],
                        kernel_size=5, padding=2, readout="max")

from torchdrug import transforms

truncate_transform = transforms.TruncateProtein(max_length=200, random=False)
protein_view_transform = transforms.ProteinView(view="residue")
transform = transforms.Compose([truncate_transform, protein_view_transform])

from torchdrug import datasets

dataset = datasets.BetaLactamase("protein-datasets/", atom_feature=None, bond_feature=None, residue_feature="default", transform=transform)
train_set, valid_set, test_set = dataset.split()
print("The label of first sample: ", dataset[0][dataset.target_fields[0]])
print("train samples: %d, valid samples: %d, test samples: %d" % (len(train_set), len(valid_set), len(test_set)))

from torchdrug import tasks

task = tasks.PropertyPrediction(model, task= ('scaled_effect1'), #dataset.tasks,
                                criterion="mse", metric=("mae", "rmse", "spearmanr"),
                                normalization=False, num_mlp_layer=2)
import torch
from torchdrug import core

optimizer = torch.optim.Adam(task.parameters(), lr=1e-4)
solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0], batch_size=64)
solver.train(num_epoch=10)
solver.evaluate("valid")

import json

with open("beta_cnn.json", "w") as fout:
    json.dump(solver.config_dict(), fout)
solver.save("beta_cnn.pth")

got output: mean absolute error [scaled_effect1]: 0.303814 root mean squared error [scaled_effect1]: 0.331703 spearmanr [scaled_effect1]: 0.442122

Name Version Build Channel

pytorch 1.13.1 py3.7_cuda11.6_cudnn8_0 pytorch pytorch-cuda 11.6 h867d48c_1 pytorch

[Oxer11]

Thanks for raising this issue. It seems to be a bug that the config_dict() function fails to deal with list arguments in transforms.Compose. This has been fixed in https://github.com/DeepGraphLearning/torchdrug/commit/b50884877f8e1185d7500cc9207cc7b3782fb028.

[QUEST2179]

Sorry this fix doesn't work. I still get the same error.

[Oxer11]

It works for me. Maybe you need to clone the latest repo to fetch the commit and remember to install from the source code.

[QUEST2179]

sorry I didn't pay much attention to the exact error message, use your latest repo, the error message has changed to the following

raise TypeError(f'Object of type {o.__class__.__name__} '

TypeError: Object of type range is not JSON serializable

I print out solver.config_dict(), it had 3 occurrences of range.

{'class': 'core.Engine', 'task': {'class': 'tasks.PropertyPrediction', 'model': {'class': 'models.ProteinConvolutionalNetwork', 'input_dim': 21, 'hidden_dims': [1024, 1024], 'kernel_size': 5, 'stride': 1, 'padding': 2, 'activation': 'relu', 'short_cut': False, 'concat_hidden': False, 'readout': 'max'}, 'task': 'scaled_effect1', 'criterion': 'mse', 'metric': ('mae', 'rmse', 'spearmanr'), 'num_mlp_layer': 2, 'normalization': False, 'num_class': None, 'mlp_batch_norm': False, 'mlp_dropout': 0, 'graph_construction_model': None, 'verbose': 0}, 'train_set': {'class': 'dataset.Subset', 'dataset': {'class': 'datasets.BetaLactamase', 'path': 'protein-datasets/', 'verbose': 1, 'atom_feature': None, 'bond_feature': None, 'residue_feature': 'default', 'transform': {'class': 'transforms.Compose', 'transforms': [{'class': 'transforms.TruncateProtein', 'max_length': 200, 'random': False, 'keys': 'graph'}, {'class': 'transforms.ProteinView', 'view': 'residue', 'keys': 'graph'}]}}, 'indices': range(0, 4158)}, 'valid_set': {'class': 'dataset.Subset', 'dataset': {'class': 'datasets.BetaLactamase', 'path': 'protein-datasets/', 'verbose': 1, 'atom_feature': None, 'bond_feature': None, 'residue_feature': 'default', 'transform': {'class': 'transforms.Compose', 'transforms': [{'class': 'transforms.TruncateProtein', 'max_length': 200, 'random': False, 'keys': 'graph'}, {'class': 'transforms.ProteinView', 'view': 'residue', 'keys': 'graph'}]}}, 'indices': range(4158, 4678)}, 'test_set': {'class': 'dataset.Subset', 'dataset': {'class': 'datasets.BetaLactamase', 'path': 'protein-datasets/', 'verbose': 1, 'atom_feature': None, 'bond_feature': None, 'residue_feature': 'default', 'transform': {'class': 'transforms.Compose', 'transforms': [{'class': 'transforms.TruncateProtein', 'max_length': 200, 'random': False, 'keys': 'graph'}, {'class': 'transforms.ProteinView', 'view': 'residue', 'keys': 'graph'}]}}, 'indices': range(4678, 5198)}, 'optimizer': {'class': 'optim.Adam', 'lr': 0.0001, 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False, 'foreach': None, 'maximize': False, 'capturable': False, 'differentiable': False, 'fused': False}, 'scheduler': None, 'gpus': [0], 'batch_size': 64, 'gradient_interval': 1, 'num_worker': 0, 'logger': 'logging', 'log_interval': 100}

[Oxer11]

Yes, you're right. The problem is caused by range, while the behavior of config_dict() is correct. In this case, I would suggest to manually convert range into list when dumping as json.