Pin compatible package versions

Author: shenwanxiang

compass/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.5.2"
+__version__ = "2.5.3"
 
 from .main import PreTrainer, FineTuner, loadcompass
 

compass/projector/projector.py

@@ -118,8 +118,8 @@ def _genes_to_idxs(genes):
         # Move 'Reference' to the end
         noref = CELLPATHWAY[CELLPATHWAY.index != "Reference"]
         ref = CELLPATHWAY[CELLPATHWAY.index == "Reference"]
-        CELLPATHWAY = noref._append(ref)
-
+        CELLPATHWAY = pd.concat([noref, ref])
+        
         self.GENESET = GENESET
         self.CELLPATHWAY = CELLPATHWAY
 

compass/utils/loader.py

@@ -37,7 +37,7 @@ def loadcompass(file, **kwargs):
         file = temp_file_path
 
     # Load the model
-    model = torch.load(file, **kwargs)
+    model = torch.load(file, weights_only=False, **kwargs)
 
     # Clean up wandb settings if present
     if hasattr(model, "with_wandb") and model.with_wandb:

compass/utils/scorer.py

@@ -7,7 +7,10 @@
 
 
 def score(y_true, y_prob, y_pred):
-
+    y_true = y_true.astype(int)
+    y_prob = y_prob.astype(float)
+    y_pred = y_pred.astype(int)
+    
     select = ~y_true.isna()
     y_prob = y_prob[select]
     y_true = y_true[select]
@@ -28,12 +31,16 @@ def score(y_true, y_prob, y_pred):
 
 
 def score2(y_true, y_prob, y_pred):
-
+   
+    y_true = y_true.astype(int)
+    y_prob = y_prob.astype(float)
+    y_pred = y_pred.astype(int)
+    
     select = ~y_true.isna()
     y_prob = y_prob[select]
     y_true = y_true[select]
     y_pred = y_pred[select]  # .map({'NR':0, 'R':1})
-
+    
     if len(y_true.unique()) == 1:
         roc = np.nan
         prc = np.nan
@@ -62,6 +69,11 @@ def score3(y_true, y_prob, y_pred):
     tuple: roc, prc, f1, acc, mcc, fpr, fnr
     """
     # Filter non-NA values
+
+    y_true = y_true.astype(int)
+    y_prob = y_prob.astype(float)
+    y_pred = y_pred.astype(int)
+    
     select = ~y_true.isna()
     y_prob = y_prob[select]
     y_true = y_true[select]

paper/00_pretrain/run_scripts/00_pretraining.ipynb

@@ -6,11 +6,19 @@
    "id": "26c64883-0781-4fc3-9d5c-a2de7ce6d480",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/mamba_envs/envs/compass_tc2/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using COMPASS version: 2.4\n"
+      "Using COMPASS version: 2.5.3\n"
      ]
     }
    ],
@@ -74,11 +82,19 @@
    "id": "3f073864-d64b-44c8-b3d0-b9ebbc7f71f9",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "missing_keys: []\n",
+      "unexpected_keys: []\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|#################################################################################| 71/71 [00:02<00:00, 34.29it/s]\n"
+      "100%|#############################################################################################################################################| 71/71 [00:02<00:00, 34.32it/s]\n"
      ]
     }
    ],
@@ -203,7 +219,10 @@
     "                               'SD':'NR', 'CR':'R',\n",
     "                               'NR':'NR', 'R':'R'})).join(pred_testy)\n",
     "\n",
-    "    y_true, y_prob, y_pred = dfp['R'], dfp[1], dfp[[0, 1]].idxmax(axis=1)\n",
+    "    y_true = dfp['R'].astype(int)\n",
+    "    y_prob = dfp[1].astype(float)\n",
+    "    y_pred = dfp[[0, 1]].idxmax(axis=1).astype(int)\n",
+    "\n",
     "    fig = plot_performance(y_true, y_prob, y_pred)\n",
     "    fig.suptitle('cohort to cohort transfer: train: %s, test: %s' % (train_cohort_name, test_cohort), fontsize=16)\n",
     "    fig.savefig(os.path.join(work_dir, 'CTCT_train_%s_test_%s.jpg' % (train_cohort_name, test_cohort)))\n",
@@ -231,7 +250,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5313570569404332, 0.5572519522353203)"
+       "(np.float64(0.5307582081409932), np.float64(0.5572519522353203))"
       ]
      },
      "execution_count": 7,
@@ -245,29 +264,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "a528e043-7142-42c8-bafa-29defed5f280",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5302643465917933, 0.5583392651130374)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(0.5302643465917933, 0.5583392651130374)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ccc086ed-f292-4e89-8ebe-185824565c9d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fa71f74b-779c-43a4-9cf8-5dbe4eb82b11",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -286,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.20"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,