1.8.1 - Release

README.md

@@ -16,7 +16,7 @@ The ATOM Modeling PipeLine (AMPL) extends the functionality of DeepChem and supp
 
 Check out our new tutorial series that walks through AMPL's end-to-end modeling pipeline to build a machine learning model! View them in our [docs](https://ampl.readthedocs.io/en/latest/) or as Jupyter notebooks in our [repo](https://github.com/ATOMScience-org/AMPL/tree/master/atomsci/ddm/examples/tutorials).
 
-![Static Badge](https://img.shields.io/badge/Announcement-1.8.0-blue)
+![Static Badge](https://img.shields.io/badge/Announcement-1.8.1-blue)
 
 In addition to our written tutorials, we now provide a series of video tutorials on our YouTube channel, [ATOMScience-org](https://www.youtube.com/channel/UCOF6zZ7ltGwopYCoOGIFM-w). These videos are created to assist users in exploring and leveraging AMPL's robust capabilities. We provided a playlist for easy streamlined Learning:
 

VERSION

@@ -1 +1 @@
-1.8.0
+1.8.1

atomsci/ddm/__init__.py

@@ -4,10 +4,10 @@
 #except TypeError:
 #    pass
 try:
-    from importlib.metadata import version, PackageNotFoundError
+    from importlib.metadata import PackageNotFoundError, version
 except ImportError:
     try:
-        from importlib_metadata import version, PackageNotFoundError
+        from importlib_metadata import PackageNotFoundError, version
     except ImportError:
         version = None
         PackageNotFoundError = Exception

atomsci/ddm/docs/source/conf.py

@@ -28,7 +28,7 @@
 # import atomsci.ddm
 #version = atomsci.ddm.__version__
 # The short X.Y version
-version = '1.8.0'
+version = '1.8.1'
 copyright = f'{datetime.datetime.now().year}, {author}'
 
 # The full version, including alpha/beta/rc tags

atomsci/ddm/docs/source/ddm.utils.rst

@@ -76,6 +76,14 @@ ddm.utils.model\_version\_utils module
    :undoc-members:
    :show-inheritance:
 
+ddm.utils.patch\_model\_dataset\_key module
+-------------------------------------------
+
+.. automodule:: ddm.utils.patch_model_dataset_key
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 ddm.utils.pubchem\_utils module
 -------------------------------
 
@@ -109,6 +117,33 @@ ddm.utils.struct\_utils module
    :undoc-members:
    :show-inheritance:
 
+
+ddm.utils.thresholded\_spearmanr module
+---------------------------------------
+
+.. automodule:: ddm.utils.thresholded_spearmanr
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+ddm.utils.query\_by\_committee module
+------------------------------------
+
+.. automodule:: ddm.utils.query_by_committee
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+ddm.utils.query\_by\_committee\_utils module
+--------------------------------------------
+
+.. automodule:: ddm.utils.query_by_committee_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Module contents
 ---------------
 

atomsci/ddm/docs/source/utils.rst

@@ -84,6 +84,14 @@ utils.model\_version\_utils module
    :undoc-members:
    :show-inheritance:
 
+utils.patch\_model\_dataset\_key module
+---------------------------------------
+
+.. automodule:: utils.patch_model_dataset_key
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 utils.pubchem\_utils module
 ---------------------------
 
@@ -117,6 +125,32 @@ utils.struct\_utils module
    :undoc-members:
    :show-inheritance:
 
+utils.thresholded\_spearmanr module
+-----------------------------------
+
+.. automodule:: utils.thresholded_spearmanr
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+utils.query\_by\_committee module
+---------------------------------
+
+.. automodule:: utils.query_by_committee
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+utils.query\_by\_committee\_utils module
+----------------------------------------
+
+.. automodule:: utils.query_by_committee_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Module contents
 ---------------
 

atomsci/ddm/examples/BSEP/predict_bsep_inhibition.py

@@ -1,18 +1,20 @@
 #!/usr/bin/env python
-# coding: utf-8
 
 # Script for running example BSEP inhibition classification models against user-provided data.
 
-import sys
+import argparse
 import os
+import sys
+
 import pandas as pd
 from sklearn import metrics
-import argparse
 
 from atomsci.ddm.pipeline import predict_from_model as pfm
+
 # from atomsci.ddm.pipeline import model_pipeline as mp
 # from atomsci.ddm.pipeline import parameter_parser as parse
 from atomsci.ddm.pipeline.perf_data import negative_predictive_value
+
 # from atomsci.ddm.utils.struct_utils import base_smiles_from_smiles
 
 # =====================================================================================================
@@ -21,13 +23,13 @@ def predict_activity(args):
     input_df = pd.read_csv(args.input_file, index_col=False)
     colnames = set(input_df.columns.values)
     if args.id_col not in colnames:
-        input_df['compound_id'] = ['compound_%.6d' % i for i in range(input_df.shape[0])]
+        input_df['compound_id'] = [f'compound_{i:06d}' for i in range(input_df.shape[0])]
         args.id_col = 'compound_id'
     if args.smiles_col not in colnames:
         raise ValueError('smiles_col parameter not specified or column not in input file.')
-    model_files = dict(random = 'bsep_classif_random_split.tar.gz', scaffold = 'bsep_classif_scaffold_split.tar.gz')
+    model_files = {'random': 'bsep_classif_random_split.tar.gz', 'scaffold': 'bsep_classif_scaffold_split.tar.gz'}
     if args.model_type not in model_files:
-        raise ValueError("model_type %s is not a recognizied value." % args.model_type)
+        raise ValueError(f"model_type {args.model_type} is not a recognizied value.")
     if args.external_training_data is not None:
         data_file = os.path.join(os.getcwd(), args.external_training_data)
     else:
@@ -48,13 +50,13 @@ def predict_activity(args):
 
     # Write predictions to output file
     pred_df.to_csv(args.output_file, index=False)
-    print("Wrote predictions to file %s" % args.output_file)
+    print(f"Wrote predictions to file {args.output_file}")
 
     # If measured activity values are provided, print some performance metrics
     if args.response_col is not None:
-        actual_vals = pred_df['%s_actual' % args.response_col].values
-        pred_classes = pred_df['%s_pred' % args.response_col].values
-        pred_probs = pred_df['%s_prob' % args.response_col].values
+        actual_vals = pred_df[f'{args.response_col}_actual'].values
+        pred_classes = pred_df[f'{args.response_col}_pred'].values
+        pred_probs = pred_df[f'{args.response_col}_prob'].values
         conf_matrix = metrics.confusion_matrix(actual_vals, pred_classes)
         roc_auc = metrics.roc_auc_score(actual_vals, pred_probs)
         prc_auc = metrics.average_precision_score(actual_vals, pred_probs)
@@ -65,19 +67,19 @@ def predict_activity(args):
         mcc = metrics.matthews_corrcoef(actual_vals, pred_classes)
         ncorrect = sum(actual_vals == pred_classes)
         print("Performance metrics:\n")
-        print("%d out of %d predictions correct." % (ncorrect, pred_df.shape[0]))
-        print("Accuracy: %.3f" % accuracy)
-        print("Precision: %.3f" % precision)
-        print("Recall: %.3f" % recall)
-        print("NPV: %.3f" % npv)
-        print("ROC AUC: %.3f" % roc_auc)
-        print("PRC AUC: %.3f" % prc_auc)
-        print("Matthews correlation coefficient: %.3f" % mcc)
+        print(f"{ncorrect} out of {pred_df.shape[0]} predictions correct.")
+        print(f"Accuracy: {accuracy:.3f}")
+        print(f"Precision: {precision:.3f}")
+        print(f"Recall: {recall:.3f}")
+        print(f"NPV: {npv:.3f}")
+        print(f"ROC AUC: {roc_auc:.3f}")
+        print(f"PRC AUC: {prc_auc:.3f}")
+        print(f"Matthews correlation coefficient: {mcc:.3f}")
         print("Confusion matrix:")
         print("\t\tpredicted activity")
         print("actual\nactivity\t0\t1\n")
-        print("   0\t\t%d\t%d" % (conf_matrix[0][0], conf_matrix[0][1]))
-        print("   1\t\t%d\t%d" % (conf_matrix[1][0], conf_matrix[1][1]))
+        print(f"   0\t\t{conf_matrix[0][0]}\t{conf_matrix[0][1]}")
+        print(f"   1\t\t{conf_matrix[1][0]}\t{conf_matrix[1][1]}")
 
 
 

atomsci/ddm/examples/data_curation/MultiplesourceCurn/sourceCuration/custom_data_curation.py

@@ -1,17 +1,17 @@
 import matplotlib
+
 matplotlib.use('Agg')
-import pandas as pd
+import json
 import os
+from os import path
 
-import json
+import numpy as np
+import pandas as pd
+from target_data_curation import AMPLDataset
 
 #import custom_config as cc
 import atomsci.ddm.utils.pubchem_utils as pu
-from os import path
-from target_data_curation import AMPLDataset
 from atomsci.ddm.utils import data_curation_functions as dcf
-import numpy as np
-
 
 # Using data_curation_functions
 # Initial dataset downloaded org/record/173258#.XcXWHZJKhhE
@@ -185,7 +185,7 @@ def add_base_smiles_col(self):
          if not path.exists(self.smiles_file) :
             print("download from PubChem ")
             ## smiles are stored in 'smiles' column in returned dataframe
-            save_smiles_df,fail_lst,discard_lst=pu.download_smiles(myList)
+            save_smiles_df,_fail_lst,_discard_lst=pu.download_smiles(myList)
             save_smiles_df.to_csv(ofile, index=False)
          else :
             print("retrieve SMILES from predownloaded file",self.smiles_file)
@@ -243,42 +243,44 @@ def __init__(self, parser=None, sec = None, raw_target_lst = None, dataset = Non
 
         """
         if dataset is not None and df is not None :
-           self.set_columns(dataset.data_source_name)
-           self.df = df
+            self.set_columns(dataset.data_source_name)
+            self.df = df
         else :
-           self.set_columns(sec)
-           mapgn=parser.check_get(sec,'target_mapping')
-           self.target_dict = json.load(open(mapgn))
-           end_point_lst=parser.check_get(sec,"end_points").split(',')
-           end_point_lst =  [ x.lower() for x in end_point_lst ]
-
-           filename=parser.check_get(sec,'activity_csv')
-           dc=json.load(open(filename))
-           target_lst=[]
-
-           for val in raw_target_lst :
-              target_lst.append( self.target_dict[val] )
-
-           df_lst=[]
-           for kv in raw_target_lst :
-              tmp_df_lst=[]
-              for cid in dc[kv].keys() :
-                 lst=dc[kv][cid]['pAct']
-                 for it in range(len(lst)) :
-                     row={ self.id_col : cid, self.value_col : dc[kv][cid]['pAct'][it], self.relation_col : dc[kv][cid]['relation'][it],
-                           self.smiles_col : dc[kv][cid]['smiles'], self.standard_col : dc[kv][cid]['type'][it], self.units : dc[kv][cid]['units'][it], self.assay_id : dc[kv][cid]['assay_id'][it]   }
-                     tmp_df_lst.append(row)
-              df=pd.DataFrame(tmp_df_lst)
-              df[self.target_id_col] = self.target_dict[kv]
-              df = df.dropna(subset=[self.value_col,self.standard_col,self.id_col])
-              df_lst.append(df)
-
-           self.df = pd.concat(df_lst)
-
-           ## do we need to do any other filter/checks here, like units?
-           self.df=self.df[(self.df.units.str.lower() == 'nm')]
-           self.df.standard_type = self.df.standard_type.str.lower()
-           self.df=self.df[self.df.standard_type.isin( end_point_lst ) ]
+            self.set_columns(sec)
+            mapgn = parser.check_get(sec, 'target_mapping')
+            with open(mapgn) as f:
+                self.target_dict = json.load(f)
+            end_point_lst = parser.check_get(sec, "end_points").split(',')
+            end_point_lst = [x.lower() for x in end_point_lst]
+
+            filename = parser.check_get(sec, 'activity_csv')
+            with open(filename) as f:
+                dc = json.load(f)
+            target_lst = []
+
+            for val in raw_target_lst :
+                target_lst.append( self.target_dict[val] )
+
+            df_lst=[]
+            for kv in raw_target_lst :
+                tmp_df_lst=[]
+                for cid in dc[kv] :
+                    lst=dc[kv][cid]['pAct']
+                    for it in range(len(lst)) :
+                        row={ self.id_col : cid, self.value_col : dc[kv][cid]['pAct'][it], self.relation_col : dc[kv][cid]['relation'][it],
+                            self.smiles_col : dc[kv][cid]['smiles'], self.standard_col : dc[kv][cid]['type'][it], self.units : dc[kv][cid]['units'][it], self.assay_id : dc[kv][cid]['assay_id'][it]   }
+                        tmp_df_lst.append(row)
+                df=pd.DataFrame(tmp_df_lst)
+                df[self.target_id_col] = self.target_dict[kv]
+                df = df.dropna(subset=[self.value_col,self.standard_col,self.id_col])
+                df_lst.append(df)
+
+            self.df = pd.concat(df_lst)
+
+            ## do we need to do any other filter/checks here, like units?
+            self.df=self.df[(self.df.units.str.lower() == 'nm')]
+            self.df.standard_type = self.df.standard_type.str.lower()
+            self.df=self.df[self.df.standard_type.isin( end_point_lst ) ]
 
     def filter_task(self, target_id):
         """when the gene target label isn't standardized, need to return the gene target mapping"""
@@ -289,7 +291,7 @@ def convert_dtype(x):
         return 0.0
     try:
         return float(x)   
-    except Exception:        
+    except (ValueError, TypeError):        
         return 0.0
 
 class GPCRChEMBLActivityDump(AMPLDataset):

atomsci/ddm/examples/data_curation/MultiplesourceCurn/sourceCuration/target_data_curation.py

@@ -1,22 +1,24 @@
 import matplotlib
+
 matplotlib.use('Agg')
+import argparse
+import configparser
+
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
+import seaborn as sns
+from matplotlib.backends.backend_pdf import PdfPages
+from rdkit import Chem
+from rdkit.Chem import Descriptors
 
+import atomsci.ddm.pipeline.chem_diversity as cd
+import atomsci.ddm.pipeline.diversity_plots as dp
+from atomsci.ddm.utils import curate_data
 
 #import custom_config
 from atomsci.ddm.utils import struct_utils as su
-from atomsci.ddm.utils import curate_data
-import atomsci.ddm.pipeline.diversity_plots as dp
-import atomsci.ddm.pipeline.chem_diversity as cd
-from rdkit.Chem import Descriptors
-from rdkit import Chem
-import seaborn as sns
-import matplotlib.pyplot as plt
-from matplotlib.backends.backend_pdf import PdfPages
-import numpy as np
-import argparse
 
-import configparser
 
 class CustomConfigParser(configparser.ConfigParser) :
    def __init__(self):
@@ -31,7 +33,7 @@ def check_get(self,section,keyval) :
       except configparser.NoOptionError :
           try :
               rval = super().get(self.def_sec,keyval)
-          except Exception:
+          except (configparser.NoOptionError, configparser.NoSectionError):
               rval = None
       return rval
 
@@ -109,7 +111,7 @@ def make_filename_by_index(self, index):
         """
         r = self.df.iloc[index]
 
-        return '%s_%s.csv'%(r[self.target_name_col], r[self.standard_col])
+        return f'{r[self.target_name_col]}_{r[self.standard_col]}.csv'
 
     def iterrows(self):
         """Accessor function for self.sum_df.iterrows()