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Models #3

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Mar 12, 2021
Merged

Models #3

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06b9b91
Completed code to evaluated pdb
whitead Feb 22, 2021
8b627e6
Serialization is successful and can generate reports
whitead Feb 24, 2021
0ea0119
Added training history as output
whitead Mar 5, 2021
42cb1fa
Added baseline model
whitead Mar 12, 2021
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129 changes: 127 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,131 @@
# Graph neural network for predicting NMR chemical shifts


## TODO
## Model Performance

* get initial layer
| | N | baseline |
|:------------|-----:|:--------------------|
| Mol-H-r | 307 | 0.9591749434360993 |
| Mol-H-rmsd | 307 | 0.39710393617916234 |
| P-C-r | 6701 | 0.864163 |
| P-H-r | 7747 | 0.72265 |
| P-N-r | 7640 | 0.890842 |
| P-CA-r | 8305 | 0.97374 |
| P-CB-r | 6827 | 0.990706 |
| P-CD-r | 739 | 0.996123 |
| P-CD1-r | 961 | 0.999515 |
| P-CD2-r | 609 | 0.999223 |
| P-CE-r | 340 | 0.991736 |
| P-CE1-r | 261 | 0.958121 |
| P-CE2-r | 173 | 0.943739 |
| P-CE3-r | 37 | -0.215088 |
| P-CG-r | 1674 | 0.998763 |
| P-CG1-r | 589 | 0.93124 |
| P-CG2-r | 839 | 0.829016 |
| P-CH2-r | 43 | 0.158363 |
| P-CZ-r | 125 | 0.984575 |
| P-CZ2-r | 45 | 0.311805 |
| P-CZ3-r | 37 | 0.164961 |
| P-HA-r | 5565 | 0.839377 |
| P-HA2-r | 462 | 0.495514 |
| P-HA3-r | 449 | 0.262298 |
| P-HB-r | 960 | 0.958713 |
| P-HB2-r | 3427 | 0.901358 |
| P-HB3-r | 3255 | 0.901234 |
| P-HD1-r | 383 | 0.44733 |
| P-HD11-r | 753 | 0.615756 |
| P-HD12-r | 753 | 0.585852 |
| P-HD13-r | 753 | 0.609181 |
| P-HD2-r | 1043 | 0.988991 |
| P-HD21-r | 428 | 0.617599 |
| P-HD22-r | 428 | 0.651927 |
| P-HD23-r | 428 | 0.605888 |
| P-HD3-r | 637 | 0.95089 |
| P-HE-r | 93 | 0.396258 |
| P-HE1-r | 413 | 0.879142 |
| P-HE2-r | 561 | 0.98963 |
| P-HE3-r | 293 | 0.985685 |
| P-HG-r | 389 | 0.810401 |
| P-HG1-r | 11 | 0.0653286 |
| P-HG11-r | 350 | 0.572609 |
| P-HG12-r | 350 | 0.498696 |
| P-HG13-r | 350 | 0.558426 |
| P-HG2-r | 1317 | 0.867619 |
| P-HG21-r | 936 | 0.689592 |
| P-HG22-r | 936 | 0.674086 |
| P-HG23-r | 936 | 0.662057 |
| P-HG3-r | 1200 | 0.856177 |
| P-HH-r | 1 | nan |
| P-HH2-r | 51 | 0.217372 |
| P-HZ-r | 134 | 0.407285 |
| P-HZ2-r | 54 | 0.419415 |
| P-HZ3-r | 45 | 0.318577 |
| P-ND1-r | 9 | 0.184443 |
| P-ND2-r | 173 | 0.320299 |
| P-NE-r | 88 | 0.0135033 |
| P-NE1-r | 64 | 0.0998792 |
| P-NE2-r | 149 | 0.972614 |
| P-NH1-r | 3 | -0.914066 |
| P-NH2-r | 3 | -0.276087 |
| P-NZ-r | 1 | nan |
| P-C-rmsd | 6701 | 1.22819 |
| P-H-rmsd | 7747 | 0.279766 |
| P-N-rmsd | 7640 | 6.65505 |
| P-CA-rmsd | 8305 | 1.3298 |
| P-CB-rmsd | 6827 | 3.10571 |
| P-CD-rmsd | 739 | 10.3192 |
| P-CD1-rmsd | 961 | 2.74597 |
| P-CD2-rmsd | 609 | 4.35399 |
| P-CE-rmsd | 340 | 1.14623 |
| P-CE1-rmsd | 261 | 4.69154 |
| P-CE2-rmsd | 173 | 4.82229 |
| P-CE3-rmsd | 37 | 3.0327 |
| P-CG-rmsd | 1674 | 1.63828 |
| P-CG1-rmsd | 589 | 1.558 |
| P-CG2-rmsd | 839 | 1.87753 |
| P-CH2-rmsd | 43 | 1.95861 |
| P-CZ-rmsd | 125 | 4.32496 |
| P-CZ2-rmsd | 45 | 1.22984 |
| P-CZ3-rmsd | 37 | 1.99567 |
| P-HA-rmsd | 5565 | 0.0903255 |
| P-HA2-rmsd | 462 | 0.119584 |
| P-HA3-rmsd | 449 | 0.234069 |
| P-HB-rmsd | 960 | 0.103812 |
| P-HB2-rmsd | 3427 | 0.10552 |
| P-HB3-rmsd | 3255 | 0.117287 |
| P-HD1-rmsd | 383 | 0.114696 |
| P-HD11-rmsd | 753 | 0.0699893 |
| P-HD12-rmsd | 753 | 0.0744762 |
| P-HD13-rmsd | 753 | 0.0711484 |
| P-HD2-rmsd | 1043 | 0.105893 |
| P-HD21-rmsd | 428 | 0.0737762 |
| P-HD22-rmsd | 428 | 0.0689306 |
| P-HD23-rmsd | 428 | 0.0764191 |
| P-HD3-rmsd | 637 | 0.0869007 |
| P-HE-rmsd | 93 | 0.422132 |
| P-HE1-rmsd | 413 | 0.376196 |
| P-HE2-rmsd | 561 | 0.0861489 |
| P-HE3-rmsd | 293 | 0.0855213 |
| P-HG-rmsd | 389 | 0.118694 |
| P-HG1-rmsd | 11 | 10.3704 |
| P-HG11-rmsd | 350 | 0.0504736 |
| P-HG12-rmsd | 350 | 0.0552385 |
| P-HG13-rmsd | 350 | 0.0516929 |
| P-HG2-rmsd | 1317 | 0.0654069 |
| P-HG21-rmsd | 936 | 0.0634577 |
| P-HG22-rmsd | 936 | 0.0650697 |
| P-HG23-rmsd | 936 | 0.0679991 |
| P-HG3-rmsd | 1200 | 0.0775636 |
| P-HH-rmsd | 1 | 4.07231 |
| P-HH2-rmsd | 51 | 0.0862706 |
| P-HZ-rmsd | 134 | 0.147387 |
| P-HZ2-rmsd | 54 | 0.13507 |
| P-HZ3-rmsd | 45 | 0.083249 |
| P-ND1-rmsd | 9 | 1576.13 |
| P-ND2-rmsd | 173 | 6.56618 |
| P-NE-rmsd | 88 | 231.589 |
| P-NE1-rmsd | 64 | 4.51713 |
| P-NE2-rmsd | 149 | 13.9975 |
| P-NH1-rmsd | 3 | 5.76985 |
| P-NH2-rmsd | 3 | 0.91028 |
| P-NZ-rmsd | 1 | 165.069 |
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4 changes: 2 additions & 2 deletions nmrgnn/__init__.py
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Expand Up @@ -8,7 +8,7 @@
from .metrics import *
from .losses import *

custom_things = [MeanSquaredLogartihmicErrorNames, NameRMSD, NameCorr, MPLayer,
RBFExpansion, EdgeFCBlock, MPBlock, FCBlock, corr_loss]
custom_things = [NameRMSD, NameCorr, MPLayer, NameLoss, NameCount,
RBFExpansion, EdgeFCBlock, MPBlock, FCBlock]
custom_objects = {o.__name__: o for o in custom_things}
del custom_things
1 change: 1 addition & 0 deletions nmrgnn/layers.py
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Expand Up @@ -38,6 +38,7 @@ def call(self, inputs):
# m -> atom atom feature output
reduced = tf.einsum('ijn,ijl,lmn,i->im', edges,
sliced_features, self.w, inv_degree)
# TODO break it up to reduce memory
out = self.activation(reduced)
# output -> N x D number of atoms x node feature dimension
if self.mpl_regularizer is not None:
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29 changes: 5 additions & 24 deletions nmrgnn/losses.py
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@@ -1,12 +1,6 @@
import tensorflow as tf
import numpy as np


class MeanSquaredLogartihmicErrorNames(tf.keras.losses.MeanSquaredLogarithmicError):
def call(self, y_true, y_pred):
return super().call(y_true[:, 0], y_pred)


def corr_coeff(x, y, w = None):
if w is None:
w = tf.ones_like(x)
Expand All @@ -20,31 +14,18 @@ def corr_coeff(x, y, w = None):
cor = tf.math.divide_no_nan(cov, m * tf.math.sqrt(tf.clip_by_value((xm2 - xm**2) * (ym2 - ym**2), 0, 1e32)))
return cor

def corr_loss(labels, predictions, sample_weight = None, s=1e-3):
'''
Mostly correlation, with small squared diff
'''
x = predictions
y = labels[:,0]
w = labels[:,-1]
l2 = tf.math.divide_no_nan(tf.reduce_sum( w * tf.math.abs( y - x) ), tf.reduce_sum(w))
#loss = tf.keras.losses.mean_squared_logarithmic_error(y, x) * w
#l2 = tf.reduce_mean(loss)
return s * l2 + (1 - corr_coeff(x, y, w))



class NameLoss:
class NameLoss(tf.keras.losses.Loss):
'''Compute L2 loss * s + corr_loss * (1 - s) for specific atom name'''

def __init__(self, label_idx, s=1.):
def __init__(self, label_idx, s=1., name='name-loss', reduction='none'):
super(NameLoss, self).__init__(name=name, reduction=reduction)
self.label_idx = label_idx
self.ln = np.array(label_idx, dtype=np.int32)
self.s = s

def get_config(self):
config = {'label_idx': self.label_idx, 's': self.s}
return config
base_config = super(NameLoss, self).get_config()
return dict(list(base_config.items()) + list(config.items()))

def call(self, y_true, y_pred, sample_weight=None):
# mask diff by which predictions match the label
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