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update chars to the libr format to fix LectinOracle_flex #28

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Aug 27, 2023
Merged

update chars to the libr format to fix LectinOracle_flex #28

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24 changes: 12 additions & 12 deletions build/lib/glycowork/ml/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -291,14 +291,14 @@ def forward(self, prot, nodes, edge_index, batch, inference = False):
# Fully connected part
h_n = self.act1_n(self.bn1_n(self.fc1_n(h_n)))

x1 = self.fc2(self.dp1(h_n))
x2 = self.fc2(self.dp1(h_n))
x3 = self.fc2(self.dp1(h_n))
x4 = self.fc2(self.dp1(h_n))
x5 = self.fc2(self.dp1(h_n))
x6 = self.fc2(self.dp1(h_n))
x7 = self.fc2(self.dp1(h_n))
x8 = self.fc2(self.dp1(h_n))
x1 = self.fc2_n(self.dp1(h_n))
x2 = self.fc2_n(self.dp1(h_n))
x3 = self.fc2_n(self.dp1(h_n))
x4 = self.fc2_n(self.dp1(h_n))
x5 = self.fc2_n(self.dp1(h_n))
x6 = self.fc2_n(self.dp1(h_n))
x7 = self.fc2_n(self.dp1(h_n))
x8 = self.fc2_n(self.dp1(h_n))

out = self.sigmoid(torch.mean(torch.stack([x1, x2, x3, x4, x5, x6, x7, x8]), dim = 0))

Expand Down Expand Up @@ -345,25 +345,25 @@ def prep_model(model_type, num_classes, libr = None,
model = SweetNet(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'sparse'))
if trained:
model.load_state_dict(torch.load(trained_SweetNet))
model.load_state_dict(torch.load(trained_SweetNet, map_location = device))
model = model.to(device)
elif model_type == 'LectinOracle':
model = LectinOracle(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_LectinOracle))
model.load_state_dict(torch.load(trained_LectinOracle, map_location = device))
model = model.to(device)
elif model_type == 'LectinOracle_flex':
model = LectinOracle_flex(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_LectinOracle_flex))
model.load_state_dict(torch.load(trained_LectinOracle_flex, map_location = device))
model = model.to(device)
elif model_type == 'NSequonPred':
model = NSequonPred()
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_NSequonPred))
model.load_state_dict(torch.load(trained_NSequonPred, map_location = device))
model = model.to(device)
else:
print("Invalid Model Type")
Expand Down
7 changes: 4 additions & 3 deletions build/lib/glycowork/motif/tokenization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,9 @@
from glycowork.motif.processing import min_process_glycans, canonicalize_iupac
from glycowork.motif.graph import compare_glycans, glycan_to_nxGraph, graph_to_string

chars = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T',
'V', 'W', 'Y', 'X', 'Z'] + ['z']
chars = {'A':1, 'B':2, 'C':3, 'D':4, 'E':5, 'F':6, 'G':7, 'H':8, 'I':9, 'J':10, 'K':11,
'L':12, 'M':13, 'N':14, 'P':15, 'Q':16, 'R':17, 'S':18, 'T':19,
'V':20, 'W':21, 'Y':22, 'X':23, 'Z':24, 'z':25}

io = pkg_resources.resource_stream(__name__, "mz_to_composition.csv")
mapping_file = pd.read_csv(io)
Expand All @@ -35,7 +36,7 @@ def constrain_prot(proteins, libr = None):
if libr is None:
libr = chars
# Check whether any character is not in libr and replace it with a 'z' placeholder character
forbidden = [k for k in set(list(''.join(proteins))) if k not in libr]
forbidden = [k for k in set(list(''.join(proteins))) if k not in libr.keys()]
for k in forbidden:
proteins = [j.replace(k, 'z') for j in proteins]
return proteins
Expand Down
10 changes: 5 additions & 5 deletions glycowork.egg-info/PKG-INFO
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: glycowork
Version: 0.8.0
Version: 0.8.1
Summary: Package for processing and analyzing glycans
Home-page: https://github.com/BojarLab/glycowork
Author: Daniel Bojar
Expand Down Expand Up @@ -70,10 +70,10 @@ alternative: <br>
Note that we have optional extra installs for specialized use (even
further instructions can be found in the `Examples` tab), such as: <br>
*deep learning* <br> `pip install glycowork[ml]` <br> *drawing glycan
images with GlycoDraw* <br> `pip install glycowork[draw]` <br>
*analyzing atomic/chemical properties of glycans* <br>
`pip install glycowork[chem]` <br> *everything* <br>
`pip install glycowork[all]` <br>
images with GlycoDraw (see install instructions in the `Examples` tab)*
<br> `pip install glycowork[draw]` <br> *analyzing atomic/chemical
properties of glycans* <br> `pip install glycowork[chem]` <br>
*everything* <br> `pip install glycowork[all]` <br>

## Data & Models

Expand Down
24 changes: 12 additions & 12 deletions glycowork/ml/models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -291,14 +291,14 @@ def forward(self, prot, nodes, edge_index, batch, inference = False):
# Fully connected part
h_n = self.act1_n(self.bn1_n(self.fc1_n(h_n)))

x1 = self.fc2(self.dp1(h_n))
x2 = self.fc2(self.dp1(h_n))
x3 = self.fc2(self.dp1(h_n))
x4 = self.fc2(self.dp1(h_n))
x5 = self.fc2(self.dp1(h_n))
x6 = self.fc2(self.dp1(h_n))
x7 = self.fc2(self.dp1(h_n))
x8 = self.fc2(self.dp1(h_n))
x1 = self.fc2_n(self.dp1(h_n))
x2 = self.fc2_n(self.dp1(h_n))
x3 = self.fc2_n(self.dp1(h_n))
x4 = self.fc2_n(self.dp1(h_n))
x5 = self.fc2_n(self.dp1(h_n))
x6 = self.fc2_n(self.dp1(h_n))
x7 = self.fc2_n(self.dp1(h_n))
x8 = self.fc2_n(self.dp1(h_n))

out = self.sigmoid(torch.mean(torch.stack([x1, x2, x3, x4, x5, x6, x7, x8]), dim = 0))

Expand Down Expand Up @@ -345,25 +345,25 @@ def prep_model(model_type, num_classes, libr = None,
model = SweetNet(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'sparse'))
if trained:
model.load_state_dict(torch.load(trained_SweetNet))
model.load_state_dict(torch.load(trained_SweetNet, map_location = device))
model = model.to(device)
elif model_type == 'LectinOracle':
model = LectinOracle(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_LectinOracle))
model.load_state_dict(torch.load(trained_LectinOracle, map_location = device))
model = model.to(device)
elif model_type == 'LectinOracle_flex':
model = LectinOracle_flex(len(libr), num_classes = num_classes)
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_LectinOracle_flex))
model.load_state_dict(torch.load(trained_LectinOracle_flex, map_location = device))
model = model.to(device)
elif model_type == 'NSequonPred':
model = NSequonPred()
model = model.apply(lambda module: init_weights(module, mode = 'xavier'))
if trained:
model.load_state_dict(torch.load(trained_NSequonPred))
model.load_state_dict(torch.load(trained_NSequonPred, map_location = device))
model = model.to(device)
else:
print("Invalid Model Type")
Expand Down
7 changes: 4 additions & 3 deletions glycowork/motif/tokenization.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,9 @@
from glycowork.motif.processing import min_process_glycans, canonicalize_iupac
from glycowork.motif.graph import compare_glycans, glycan_to_nxGraph, graph_to_string

chars = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T',
'V', 'W', 'Y', 'X', 'Z'] + ['z']
chars = {'A':1, 'B':2, 'C':3, 'D':4, 'E':5, 'F':6, 'G':7, 'H':8, 'I':9, 'J':10, 'K':11,
'L':12, 'M':13, 'N':14, 'P':15, 'Q':16, 'R':17, 'S':18, 'T':19,
'V':20, 'W':21, 'Y':22, 'X':23, 'Z':24, 'z':25}

io = pkg_resources.resource_stream(__name__, "mz_to_composition.csv")
mapping_file = pd.read_csv(io)
Expand All @@ -35,7 +36,7 @@ def constrain_prot(proteins, libr = None):
if libr is None:
libr = chars
# Check whether any character is not in libr and replace it with a 'z' placeholder character
forbidden = [k for k in set(list(''.join(proteins))) if k not in libr]
forbidden = [k for k in set(list(''.join(proteins))) if k not in libr.keys()]
for k in forbidden:
proteins = [j.replace(k, 'z') for j in proteins]
return proteins
Expand Down
2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

setuptools.setup(
name="glycowork",
version="0.8.0",
version="0.8.1",
author="Daniel Bojar",
author_email="daniel.bojar@gu.se",
description="Package for processing and analyzing glycans",
Expand Down