Refactor source code with new organization name and update README

README.md

@@ -18,8 +18,8 @@ from huggingface_hub import hf_hub_download
 import joblib
 
 # Define parameters
-REPO_ID = "HUBII-Platform/ECG2HRV"
-FILENAME = "feature-extractor.joblib"
+REPO_ID = "hubii-world/ECG2HRV"
+FILENAME = "ECG2HRV.joblib"
 
 # Load the model
 model = joblib.load(
@@ -28,6 +28,7 @@ model = joblib.load(
 ```
 Example usage of the model:
 ```python
-# TBD: Execute the model with the input data
-result = model.forward(...)
+# ecg should be a 1D numpy array with the ECG signal
+hrv_features = model(input_data=ecg, frequency=100.0)
+# returns hrv_features in a dictionary with the feature names as keys
 ```

notebooks/01_Model_Deployment_Research.ipynb

@@ -40,7 +40,9 @@
    "source": [
     "import timm\n",
     "import torch\n",
-    "from pipeline_wrapper import MyPipeline"
+    "from transformers import pipeline, AutoTokenizer, AutoModel\n",
+    "\n",
+    "from src.deprecated.pipeline_wrapper import MyPipeline"
    ],
    "metadata": {
     "collapsed": false,
@@ -248,7 +250,6 @@
    ],
    "source": [
     "# Example with pipeline\n",
-    "from transformers import pipeline\n",
     "checkpoint = \"facebook/bart-base\"\n",
     "feature_extractor = pipeline(\"feature-extraction\", framework=\"pt\",model=checkpoint)\n",
     "text = \"Transformers is an awesome library!\""
@@ -366,7 +367,6 @@
    ],
    "source": [
     "# Example with AutoModel\n",
-    "from transformers import AutoTokenizer, AutoModel\n",
     "model = AutoModel.from_pretrained('HUBII-Platform/ECG2HRV')"
    ],
    "metadata": {
@@ -398,25 +398,23 @@
    }
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [],
+   "cell_type": "markdown",
    "source": [
-    "from huggingface_hub import hf_hub_download\n",
-    "import joblib\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "from src.model import ECG2HRV"
+    "**Instantiate model and save the model as a joblib file in the huggingface repository**"
    ],
    "metadata": {
     "collapsed": false
    }
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
    "source": [
-    "**Instantiate model and save the model as a joblib file in the huggingface repository**"
+    "import joblib\n",
+    "import numpy as np\n",
+    "\n",
+    "from src.ecg2hrv import ECG2HRV"
    ],
    "metadata": {
     "collapsed": false
@@ -507,7 +505,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "**Test if the model can be loaded from the hub and used**"
+    "**Test the model loaded from the hub with random ecg**"
    ],
    "metadata": {
     "collapsed": false
@@ -515,12 +513,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [],
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "ECG2HRV.joblib:   0%|          | 0.00/39.0 [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "aef3c2ac2c9a4d91a392ec8091d4c779"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "from huggingface_hub import hf_hub_download\n",
+    "import joblib\n",
+    "\n",
     "# Load from hub\n",
-    "REPO_ID = \"HUBII-Platform/ECG2HRV\"\n",
-    "FILENAME = \"feature-extractor.joblib\"\n",
+    "REPO_ID = \"hubii-world/ECG2HRV\"\n",
+    "FILENAME = \"ECG2HRV.joblib\"\n",
     "\n",
     "model = joblib.load(\n",
     "    hf_hub_download(repo_id=REPO_ID, filename=FILENAME)\n",
@@ -529,21 +543,22 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-21T11:36:52.302912800Z",
-     "start_time": "2024-02-21T11:36:52.145834500Z"
+     "end_time": "2024-02-21T16:26:49.913818400Z",
+     "start_time": "2024-02-21T16:26:49.506802900Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 8,
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'rmssd': tensor(1.), 'mean_rr': tensor(3.), 'sdnn': tensor(1.), 'mean_hr': tensor(20000.)}\n"
-     ]
+     "data": {
+      "text/plain": "[{'HRV_MeanNN': 413.4782608695652,\n  'HRV_SDNN': 100.97743652790477,\n  'HRV_SDANN1': nan,\n  'HRV_SDNNI1': nan,\n  'HRV_SDANN2': nan,\n  'HRV_SDNNI2': nan,\n  'HRV_SDANN5': nan,\n  'HRV_SDNNI5': nan,\n  'HRV_RMSSD': 92.78518690551262,\n  'HRV_SDSD': 94.96410805236795,\n  'HRV_CVNN': 0.24421462041449105,\n  'HRV_CVSD': 0.22440160870944167,\n  'HRV_MedianNN': 400.0,\n  'HRV_MadNN': 118.60799999999999,\n  'HRV_MCVNN': 0.29651999999999995,\n  'HRV_IQRNN': 150.0,\n  'HRV_SDRMSSD': 1.0882926455785953,\n  'HRV_Prc20NN': 320.0,\n  'HRV_Prc80NN': 490.0,\n  'HRV_pNN50': 52.17391304347826,\n  'HRV_pNN20': 69.56521739130434,\n  'HRV_MinNN': 310.0,\n  'HRV_MaxNN': 640.0,\n  'HRV_HTI': 5.75,\n  'HRV_TINN': 0.0}]"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -553,8 +568,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-21T11:37:12.239246200Z",
-     "start_time": "2024-02-21T11:37:12.219556200Z"
+     "end_time": "2024-02-21T16:26:58.064981500Z",
+     "start_time": "2024-02-21T16:26:58.041072600Z"
     }
    }
   },

src/ecg2hrv.py

@@ -1,7 +1,5 @@
 import numpy as np
-import pandas as pd
 import neurokit2 as nk
-import torch
 
 from src.feature_extractor import FeatureExtractor
 

src/feature_extractor.py

@@ -13,5 +13,5 @@ class FeatureExtractor(ABC):
         pass
 
     @abstractmethod
-    def normalize_features(self, features, baseline_features=None):
+    def normalize_features(self, features, baseline_features, normalization_method=None):
         pass