readme.md

README.md

@@ -138,16 +138,7 @@ yarl                      1.9.4
 
 ```
 #单卡运行
-python geneformer/classifier.py \
-                --Classifierclassifier="cell"\
-                --cell_state_dict = {"state_key": "disease", "states": "all"}\
-                --filter_data=filter_data_dict\
-                --training_args=training_args\
-                --max_ncells=None\
-                --freeze_layers = 2 \
-                --num_crossval_splits = 1\
-                --forward_batch_size=200\
-                --nproc=1 
+python  train.py
 
 
 #详情请参考 Geneformer/examples/cell_classification.ipynb

test_cell_classifier.py

@@ -196,4 +196,3 @@ for organ in organ_list:
         pickle.dump(predictions, fp)
     trainer.save_metrics("eval",predictions.metrics)
     trainer.save_model(output_dir)
\ No newline at end of file
-

train.py

 import datetime
+import pickle
 from geneformer import Classifier
-
+import os 
 current_date = datetime.datetime.now()
 datestamp = f"{str(current_date.year)[-2:]}{current_date.month:02d}{current_date.day:02d}{current_date.hour:02d}{current_date.minute:02d}{current_date.second:02d}"
 datestamp_min = f"{str(current_date.year)[-2:]}{current_date.month:02d}{current_date.day:02d}"
 
-output_prefix = "cm_classifier_test"
+output_prefix = "tf_dosage_sens_test"
 output_dir = f"/path/to/output_dir/{datestamp}"
-# !mkdir $output_dir
+os.makedirs(output_dir)
 
+with open("/path/to/Genecorpus-30M/dosage_sensitivity_TFs.pickle", "rb") as fp:
+    gene_class_dict = pickle.load(fp)
 
-filter_data_dict={"cell_type":["Cardiomyocyte1","Cardiomyocyte2","Cardiomyocyte3"]}
-training_args = {
-    "num_train_epochs": 0.9,
-    "learning_rate": 0.000804,
-    "lr_scheduler_type": "polynomial",
-    "warmup_steps": 1812,
-    "weight_decay":0.258828,
-    "per_device_train_batch_size": 12,
-    "seed": 73,
-}
-cc = Classifier(classifier="cell",
-                cell_state_dict = {"state_key": "disease", "states": "all"},
-                filter_data=filter_data_dict,
-                training_args=training_args,
-                max_ncells=None,
-                freeze_layers = 2,
-                num_crossval_splits = 1,
+cc = Classifier(classifier="gene",
+                gene_class_dict = gene_class_dict,
+                max_ncells = 10_000,
+                freeze_layers = 4,
+                num_crossval_splits = 5,
                 forward_batch_size=200,
                 nproc=16)
 
 
-# previously balanced splits with prepare_data and validate functions
-# argument attr_to_split set to "individual" and attr_to_balance set to ["disease","lvef","age","sex","length"]
-train_ids = ["1447", "1600", "1462", "1558", "1300", "1508", "1358", "1678", "1561", "1304", "1610", "1430", "1472", "1707", "1726", "1504", "1425", "1617", "1631", "1735", "1582", "1722", "1622", "1630", "1290", "1479", "1371", "1549", "1515"]
-eval_ids = ["1422", "1510", "1539", "1606", "1702"]
-test_ids = ["1437", "1516", "1602", "1685", "1718"]
-
-train_test_id_split_dict = {"attr_key": "individual",
-                            "train": train_ids+eval_ids,
-                            "test": test_ids}
-
-# Example input_data_file: https://huggingface.co/datasets/ctheodoris/Genecorpus-30M/tree/main/example_input_files/cell_classification/disease_classification/human_dcm_hcm_nf.dataset
-cc.prepare_data(input_data_file="/path/to/Genecorpus-30M/genecorpus_30M_2048.dataset",
+cc.prepare_data(input_data_file="/path/to/Genecorpus-30M/dosage_sensitive_tfs",
                 output_directory=output_dir,
-                output_prefix=output_prefix,
-                split_id_dict=train_test_id_split_dict)
-
+                output_prefix=output_prefix)
 
-train_valid_id_split_dict = {"attr_key": "individual",
-                            "train": train_ids,
-                            "eval": eval_ids}
-
-# 6 layer Geneformer: https://huggingface.co/ctheodoris/Geneformer/blob/main/model.safetensors
 all_metrics = cc.validate(model_directory="/home/Geneformer",
-                          prepared_input_data_file=f"{output_dir}/{output_prefix}_labeled_train.dataset",
+                          prepared_input_data_file=f"{output_dir}/{output_prefix}_labeled.dataset",
                           id_class_dict_file=f"{output_dir}/{output_prefix}_id_class_dict.pkl",
                           output_directory=output_dir,
-                          output_prefix=output_prefix,
-                          split_id_dict=train_valid_id_split_dict)
-                          # to optimize hyperparameters, set n_hyperopt_trials=100 (or alternative desired # of trials)
+                          output_prefix=output_prefix)
+
+