Merge branch 'master' into dqc-ci-fix

GreatRSingh · Aug 5, 2024 · f707a6c · f707a6c
2 parents 6963adb + 3241eef
commit f707a6c
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diff --git a/deepchem/models/tests/test_graph_conv.py b/deepchem/models/tests/test_graph_conv.py
@@ -10,10 +10,10 @@
 from deepchem.metrics import Metric, roc_auc_score, mean_absolute_error
 from deepchem.molnet import load_bace_classification, load_delaney
 from deepchem.utils.data_utils import download_url, get_data_dir
-from deepchem.models.torch_models import GraphConvModel
 
 try:
     import torch
+    from deepchem.models.torch_models import GraphConvModel
     has_torch = True
 except:
     has_torch = False

diff --git a/deepchem/models/torch_models/tests/test_prot_bert.py b/deepchem/models/torch_models/tests/test_prot_bert.py
@@ -31,7 +31,7 @@ def test_prot_bert_finetuning(protein_classification_dataset):
     model_path = 'Rostlab/prot_bert'
 
     model = ProtBERT(task='classification',
-                     HG_model_path=model_path,
+                     model_path=model_path,
                      n_tasks=1,
                      cls_name="LogReg")
     loss = model.fit(protein_classification_dataset, nb_epoch=1)
@@ -43,7 +43,7 @@ def test_prot_bert_finetuning(protein_classification_dataset):
     assert prediction.shape == (protein_classification_dataset.y.shape[0], 2)
 
     model = ProtBERT(task='classification',
-                     HG_model_path=model_path,
+                     model_path=model_path,
                      n_tasks=1,
                      cls_name="FFN")
     loss = model.fit(protein_classification_dataset, nb_epoch=1)
@@ -74,7 +74,7 @@ def forward(self, x):
 
     custom_torch_CNN_network = SimpleCNN()
     model = ProtBERT(task='classification',
-                     HG_model_path=model_path,
+                     model_path=model_path,
                      n_tasks=1,
                      cls_name="custom",
                      classifier_net=custom_torch_CNN_network)
@@ -93,13 +93,13 @@ def test_protbert_load_from_pretrained(tmpdir):
     finetune_model_dir = os.path.join(tmpdir, 'finetune')
     model_path = 'Rostlab/prot_bert'
     pretrain_model = ProtBERT(task='mlm',
-                              HG_model_path=model_path,
+                              model_path=model_path,
                               n_tasks=1,
                               model_dir=pretrain_model_dir)
     pretrain_model.save_checkpoint()
 
     finetune_model = ProtBERT(task='classification',
-                              HG_model_path=model_path,
+                              model_path=model_path,
                               n_tasks=1,
                               cls_name="LogReg",
                               model_dir=finetune_model_dir)
@@ -125,15 +125,15 @@ def test_protbert_load_from_pretrained(tmpdir):
 def test_protbert_save_reload(tmpdir):
     model_path = 'Rostlab/prot_bert'
     model = ProtBERT(task='classification',
-                     HG_model_path=model_path,
+                     model_path=model_path,
                      n_tasks=1,
                      cls_name="FFN",
                      model_dir=tmpdir)
     model._ensure_built()
     model.save_checkpoint()
 
     model_new = ProtBERT(task='classification',
-                         HG_model_path=model_path,
+                         model_path=model_path,
                          n_tasks=1,
                          cls_name="FFN",
                          model_dir=tmpdir)
@@ -164,7 +164,7 @@ def test_protbert_overfit():
                      "../../tests/assets/example_protein_classification.csv"))
     model_path = 'Rostlab/prot_bert'
     finetune_model = ProtBERT(task='classification',
-                              HG_model_path=model_path,
+                              model_path=model_path,
                               n_tasks=1,
                               cls_name="FFN",
                               batch_size=1,

diff --git a/examples/tutorials/Compiling_DeepChem_Torch_Models.ipynb b/examples/tutorials/Compiling_DeepChem_Torch_Models.ipynb
diff --git a/examples/tutorials/Introduction_to_ProtBERT.ipynb b/examples/tutorials/Introduction_to_ProtBERT.ipynb
diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md
@@ -76,6 +76,8 @@ tutorials discuss about using DeepChem for specific applications.
 * [1 Protein Deep Learning](Protein_Deep_Learning.ipynb)
 * [2 Antibody Design via Protein Language Models and Directed Evolution](DeepChem_AntibodyTutorial_Simplified.ipynb)
 * [3 Protein Structure Prediction with ESMFold](Protein_Structure_Prediction_with_ESMFold.ipynb)
+* [4 Introduction to ProtBERT](Introduction_to_ProtBERT.ipynb)
+
 ### Protein Ligand Modeling
 * [1 Modeling Protein Ligand Interactions](Modeling_Protein_Ligand_Interactions.ipynb)
 * [2 Modeling Protein Ligand Interactions with Atomic Convolutions](Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb)

diff --git a/examples/tutorials/crystallization_tendency_regression.ipynb b/examples/tutorials/crystallization_tendency_regression.ipynb
diff --git a/examples/tutorials/website-render-order/12-polymer-science.csv b/examples/tutorials/website-render-order/12-polymer-science.csv
@@ -1,2 +1,3 @@
 Title,File Name
-An Introduction to the Polymers and Their Representation, An_Introduction_to_the_Polymers_and_Their_Representation.ipynb 
+An Introduction to the Polymers and Their Representation, An_Introduction_to_the_Polymers_and_Their_Representation.ipynb 
+Utilizing Deepchem Molecular Models for Crystalization Tendency Regression of Polymers, crystallization_tendency_regression.ipynb
diff --git a/examples/tutorials/website-render-order/3-modeling-proteins.csv b/examples/tutorials/website-render-order/3-modeling-proteins.csv
@@ -1,4 +1,5 @@
 Title,File Name
 Protein Deep Learning,Protein_Deep_Learning.ipynb
 Antibody Design via Protein Language Models and Directed Evolution,DeepChem_AntibodyTutorial_Simplified.ipynb
-Protein Structure Prediction with ESMFold, Protein_Structure_Prediction_with_ESMFold.ipynb
+Protein Structure Prediction with ESMFold, Protein_Structure_Prediction_with_ESMFold.ipynb
+Introduction to ProtBERT, Introduction_to_ProtBERT.ipynb