fix(splitting): created davis splits #113

.gitignore

@@ -222,4 +222,6 @@ results/model_checkpoints/*/*.model
 results/model_media/*/train_log/*
 results/model_media/*/train_set_pred/*
 results/model_media/*/test_set_pred/*
-results/model_media/test_set_pred
+results/model_media/test_set_pred
+
+splits/*.csv

playground.py

@@ -1,92 +1,55 @@
-#%% now based on this test set we can create the splits that will be used for all models
-# 5-fold cross validation + test set
-import pandas as pd
-from src import cfg
-from src.train_test.splitting import balanced_kfold_split
-from src.utils.loader import Loader
-
-test_df = pd.read_csv('/home/jean/projects/data/splits/davis_test_genes_oncoG.csv')
-test_prots = set(test_df.prot_id)
-
-db = Loader.load_dataset(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/nomsa_binary_original_binary/full/')
-
-#%%
-train, val, test = balanced_kfold_split(db,
-                k_folds=5, test_split=0.1, val_split=0.1, 
-                test_prots=test_prots, random_seed=0, verbose=True
-                )
-
-
-#%%
-db.save_subset_folds(train, 'train')
-db.save_subset_folds(val, 'val')
-db.save_subset(test, 'test')
-
-#%%
+# %%
 import os
 from src.train_test.splitting import resplit
 from src import cfg
 
 csv_files = {}
 for split in ['test'] + [f'val{i}' for i in range(5)]:
-    csv_files[split] = f'{cfg.DATA_ROOT}/splits/davis/davis_{split}.csv'
+    csv_files[split] = f'./splits/davis_{split}.csv'
+    assert os.path.exists(csv_files[split]), csv_files[split]
 
+print(csv_files)
 
+#%%
+for d in os.listdir(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/'):
+    if len(d.split('_')) < 4:
+        print('skipping:', d)
+        continue
+    print('resplitting:', d)
+    resplit(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/{d}', split_files=csv_files)
 
-db = resplit(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/nomsa_binary_original_binary', 
-        split_files=csv_files)
 
 #%% Checking for overlap
 import pandas as pd
 
-# Define file paths
-file_paths = {
-    'test': 'test/cleaned_XY.csv',
-    'val0': 'val0/cleaned_XY.csv',
-    'val1': 'val1/cleaned_XY.csv',
-    'val2': 'val2/cleaned_XY.csv',
-    'val3': 'val3/cleaned_XY.csv',
-    'val4': 'val4/cleaned_XY.csv',
-    'train0': 'train0/cleaned_XY.csv',
-    'train1': 'train1/cleaned_XY.csv',
-    'train2': 'train2/cleaned_XY.csv',
-    'train3': 'train3/cleaned_XY.csv',
-    'train4': 'train4/cleaned_XY.csv'
-}
-file_paths = {name: f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/nomsa_binary_original_binary/{path}' for name, path in file_paths.items()}
 
-# Load CSV files into dataframes
-dataframes = {name: pd.read_csv(path) for name, path in file_paths.items()}
-
-# Function to check for overlap
-def check_overlap(df1, df2, name1, name2):
-    overlap = df1.merge(df2, on='prot_id', how='inner')
-    if not overlap.empty:
-        print(f'Overlap found between {name1} and {name2}')
-        print(overlap)
-    else:
-        print(f'No overlap between {name1} and {name2}')
-
-# Check for overlaps
-# Test should not overlap with any other CSV
-for name in file_paths:
-    if name != 'test':
-        check_overlap(dataframes['test'], dataframes[name], 'test', name)
-
-# valX should not overlap with corresponding trainX
-for i in range(5):
-    val_name = f'val{i}'
-    train_name = f'train{i}'
-    check_overlap(dataframes[val_name], dataframes[train_name], val_name, train_name)
-
-# Note: Overlaps between val0 and train1, val1 and train2, etc. are allowed as they are different folds
-
-
-
-#%%
-from src.utils.loader import Loader
+for d in os.listdir(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/'):
+    if len(d.split('_')) < 4:
+        print('skipping:', d)
+        continue
+    # Define file paths
+    file_paths = {
+        'test': 'test/cleaned_XY.csv',
+        'val0': 'val0/cleaned_XY.csv',
+        'train0': 'train0/cleaned_XY.csv',
+    }
+    file_paths = {name: f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/{d}/{path}' for name, path in file_paths.items()}    
+    count = 0
+    print(f"\n{'-'*10}{d}{'-'*10}")
+    for k, v in file_paths.items():
+        df = pd.read_csv(v)
+        print(f"{k:>12}: {len(df):>6d}")
+        count += len(df)
+
+    print(f'            = {count:>6d}')
+
+    df = f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/{d}/full/cleaned_XY.csv'
+    df = pd.read_csv(df)
+    # print(f'            = {count:>6d}')
+    print(f'Dataset Size: {len(df):>6d}')
+
+
 
-db_train = Loader.load_dataset(f'{cfg.DATA_ROOT}/DavisKibaDataset/davis/nomsa_binary_original_binary/train0/')
 
 # %%
 ########################################################################

src/train_test/splitting.py

@@ -325,7 +325,8 @@ def resplit(dataset:str|BaseDataset, split_files:dict|str=None, **kwargs):
         if not os.path.exists(f):
             raise ValueError(f'{f} does not exist')
 
-    # Getting indices for each split based on db.df    
+    # Getting indices for each split based on db.df
+    split_files = split_files.copy()
     test_prots = set(pd.read_csv(split_files['test'])['prot_id'])
     test_idxs = [i for i in range(len(dataset.df)) if dataset.df.iloc[i]['prot_id'] in test_prots]
     dataset.save_subset(test_idxs, 'test')