MPNN Value Error

Description:

• DeepChem Version: 2.1.1
• Platform: MacOS 10.14.2

Encountered a dimensionality error when the model tried to reshape pair features. My sample code:

import deepchem 
from deepchem.molnet.load_function.hiv_datasets import load_hiv

hiv_tasks, (train_wv, valid_wv, test_wv), transformers = load_hiv(featurizer='Weave')
model = deepchem.models.MPNNModel(
		len(hiv_tasks),
		mode="classification")
model.fit(train_wv)

The error I got:

2019-02-05 23:02:55.096153: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:112: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/threading.py", line 916, in _bootstrap_inner
    self.run()
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py", line 1373, in _enqueue_batch
    for feed_dict in generator:
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/site-packages/deepchem/models/tensorgraph/models/graph_models.py", line 954, in default_generator
    (n_atoms * n_atoms, self.n_pair_feat)))
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 279, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "/Users/iron4dam/anaconda3/envs/rdkit/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 51, in _wrapfunc
    return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 36414 into shape (2601,8)

[vsomnath]

The script below ran on my laptop with no errors, but I was able to train with only a single sample, otherwise my TensorFlow was getting killed.

import deepchem
from deepchem.molnet.load_function.hiv_datasets import load_hiv
import numpy as np

hiv_tasks, (train_wv, valid_wv, test_wv), transformers = load_hiv(featurizer='Weave')

X = train_wv.X[:1]
y = train_wv.y[:1]

new_dataset = deepchem.data.NumpyDataset(X, y)
model = deepchem.models.MPNNModel(
  len(hiv_tasks),
  n_pair_feat=14,
  n_atom_feat=75,
  n_hidden=75,
  T=1, M=1,
  mode="classification")

metric = deepchem.metrics.Metric(deepchem.metrics.roc_auc_score, np.mean, mode="classification")
model.fit(new_dataset, nb_epoch=10)

scores = model.evaluate(test_wv, [metric], transformers)
print(scores['mean-roc_auc_score'])

[vsomnath]

Nope, I have never used the MPNN model before. To be honest, I haven’t gone through its paper as well. Also, could you close this issue? - Feel free to reopen if necessary?