use dance logger

dance/datasets/multimodality.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import pickle
 
@@ -7,6 +6,7 @@
 import scanpy as sc
 import torch
 
+from dance import logger
 from dance.transforms.preprocess import lsiTransformer
 from dance.utils.download import download_file, unzip_file
 
@@ -183,7 +183,7 @@ def __init__(self, subtask, data_dir="./data"):
 
     def preprocess(self, kind='feature_selection', selection_threshold=10000):
         if kind == 'pca':
-            logging.info('Preprocessing method not supported.')
+            logger.info('Preprocessing method not supported.')
             return self
         elif kind == 'feature_selection':
             if self.modalities[0].shape[1] > selection_threshold:
@@ -193,9 +193,9 @@ def preprocess(self, kind='feature_selection', selection_threshold=10000):
                 for i in [0, 2]:
                     self.modalities[i] = self.modalities[i][:, self.modalities[i].var['highly_variable']]
         else:
-            logging.info('Preprocessing method not supported.')
+            logger.info('Preprocessing method not supported.')
             return self
-        logging.info('Preprocessing done.')
+        logger.info('Preprocessing done.')
         return self
 
 
@@ -294,9 +294,9 @@ def preprocess(self, kind='pca', pkl_path=None, selection_threshold=10000):
                     self.modalities[i] = self.modalities[i][:, self.modalities[i].var['highly_variable']]
                     self.modalities[i + 2] = self.modalities[i + 2][:, self.modalities[i + 2].var['highly_variable']]
         else:
-            logging.info('Preprocessing method not supported.')
+            logger.info('Preprocessing method not supported.')
             return self
-        logging.info('Preprocessing done.')
+        logger.info('Preprocessing done.')
         self.preprocessed = True
         return self
 
@@ -373,7 +373,7 @@ def preprocess(self, kind='aux', pretrained_folder='.', selection_threshold=1000
                     # cell types, batch labels, cell cycle
                     self.nb_cell_types, self.nb_batches, self.nb_phases = pickle.load(f)
                 self.preprocessed = True
-                logging.info('Preprocessing done.')
+                logger.info('Preprocessing done.')
                 return self
 
             ##########################################
@@ -466,8 +466,8 @@ def preprocess(self, kind='aux', pretrained_folder='.', selection_threshold=1000
                 'AURKA', 'PSRC1', 'ANLN', 'LBR', 'CKAP5', 'CENPE', 'CTCF', \
                 'NEK2', 'G2E3', 'GAS2L3', 'CBX5', 'CENPA']
 
-            logging.info('Data loading and pca done', mod1_pca.shape, mod2_pca.shape)
-            logging.info('Start to calculate cell_cycle score. It may roughly take an hour.')
+            logger.info('Data loading and pca done', mod1_pca.shape, mod2_pca.shape)
+            logger.info('Start to calculate cell_cycle score. It may roughly take an hour.')
 
             cell_type_labels = self.test_sol.obs['cell_type'].to_numpy()  #mod1_obs['cell_type']
             batch_ids = mod1_obs['batch']
@@ -523,10 +523,10 @@ def preprocess(self, kind='aux', pretrained_folder='.', selection_threshold=1000
                                                 n_top_genes=selection_threshold)
                     self.modalities[i] = self.modalities[i][:, self.modalities[i].var['highly_variable']]
         else:
-            logging.info('Preprocessing method not supported.')
+            logger.info('Preprocessing method not supported.')
             return self
         self.preprocessed = True
-        logging.info('Preprocessing done.')
+        logger.info('Preprocessing done.')
         return self
 
     def get_preprocessed_data(self):

dance/modules/multi_modality/predict_modality/babel.py

@@ -8,7 +8,6 @@
 multiomic profiles at single-cell resolution." Proceedings of the National Academy of Sciences 118, no. 15 (2021).
 
 """
-import logging
 import math
 from typing import Callable, List, Tuple, Union
 
@@ -18,6 +17,7 @@
 from torch.utils.data import DataLoader
 
 import dance.utils.loss as loss_functions
+from dance import logger
 
 REDUCE_LR_ON_PLATEAU_PARAMS = {
     "mode": "min",
@@ -317,7 +317,7 @@ def __init__(
                 self.final_activations["act1"] = final_activations
             else:
                 raise ValueError(f"Unrecognized type for final_activation: {type(final_activations)}")
-        logging.info(f"ChromDecoder with {len(self.final_activations)} output activations")
+        logger.info(f"ChromDecoder with {len(self.final_activations)} output activations")
 
         self.final_decoders = nn.ModuleList()  # List[List[Module]]
         for n in self.num_outputs:

dance/transforms/graph/scmogcn_graph.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import pickle
 from collections import defaultdict
@@ -9,8 +8,9 @@
 import torch
 from sklearn.decomposition import TruncatedSVD
 
+from dance import logger
 from dance.data.base import Data
-from dance.typing import Optional, Tuple, Union
+from dance.typing import Union
 
 from ..base import BaseTransform
 
@@ -76,9 +76,8 @@ def create_pathway_graph(gex_features: scipy.sparse.spmatrix, gene_names: Union[
 
     pk_path = f'pw_{subtask}_{pathway_weight}.pkl'
     if os.path.exists(pk_path):
-        logging.warning(
-            'Pathway file exist. Load pickle file by default. Auguments "--pathway_weight" and "--pathway_path" will not take effect.'
-        )
+        logger.warning("Pathway file exist. Load pickle file by default. "
+                       "Auguments '--pathway_weight' and '--pathway_path' will not take effect.")
         uu, vv, ee = pickle.load(open(pk_path, 'rb'))
     else:
         # Load Original Pathway File

examples/multi_modality/predict_modality/babel.py

@@ -7,14 +7,13 @@
 import mudata
 import torch
 
+from dance import logger
 from dance.data import Data
 from dance.datasets.multimodality import ModalityPredictionDataset
 from dance.modules.multi_modality.predict_modality.babel import BabelWrapper
 from dance.utils import set_seed
 
 if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
-
     OPTIMIZER_DICT = {
         "adam": torch.optim.Adam,
         "rmsprop": torch.optim.RMSprop,
@@ -52,13 +51,12 @@
         os.makedirs(os.path.dirname(args.outdir))
 
     # Specify output log file
-    logger = logging.getLogger()
     fh = logging.FileHandler(f"{args.outdir}/training_{args.subtask}_{args.rnd_seed}.log", "w")
     fh.setLevel(logging.INFO)
     logger.addHandler(fh)
 
     for arg in vars(args):
-        logging.info(f"Parameter {arg}: {getattr(args, arg)}")
+        logger.info(f"Parameter {arg}: {getattr(args, arg)}")
 
     # Construct data object
     mod1 = anndata.concat((dataset.modalities[0], dataset.modalities[2]))