predict.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refer to the sample data in the repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"sample_data.csv\", header=None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column names and formats in order: \n",
    "\n",
    "Maxillary arch length discrepancy: float\n",
    "\n",
    "Mandibular arch length discrepancy: float\n",
    "\n",
    "Curve of Spee: float\n",
    "\n",
    "Sex (Is Male): True (Male), False (Female)\n",
    "\n",
    "Lip Posture (Is Competent): True (Competent), False (Non-Competent)\n",
    "\n",
    "SNA: float\n",
    "\n",
    "SNB: float\n",
    "\n",
    "ANB: float\n",
    "\n",
    "Wits Appraisal float\n",
    "\n",
    "GoGnSN: float\n",
    "\n",
    "FMA: float\n",
    "\n",
    "Sum of the angles: float\n",
    "\n",
    "ANS-Me: float\n",
    "\n",
    "U1-NA (mm): float\n",
    "\n",
    "U1-NA (angle): float\n",
    "\n",
    "U1-FH: float\n",
    "\n",
    "U1-PP: float\n",
    "\n",
    "L1-NB (mm): float\n",
    "\n",
    "L1-NB (angle): float\n",
    "\n",
    "IMPA: float\n",
    "\n",
    "L1-A-Pg: float\n",
    "\n",
    "Interincisal angle: float\n",
    "\n",
    "Overjet: float\n",
    "\n",
    "Overbite: float\n",
    "\n",
    "Upper lip to E line: float\n",
    "\n",
    "Lower lip to E line: float\n",
    "\n",
    "Z Angle: float\n",
    "\n",
    "Maxillary sulcus depth: float\n",
    "\n",
    "Mandibulary sulcus depth: float\n",
    "\n",
    "Upper lip thickness: float\n",
    "\n",
    "Lower lip thickness: float\n",
    "\n",
    "Nasolabial angle: float\n",
    "\n",
    "Facial convexity angle: float\n",
    "\n",
    "Upper incisor exposure: float\n",
    "\n",
    "Angle’s molar classification Class I: binary (1 or 0)\n",
    "\n",
    "Angle’s molar classification Class II Div I: binary (1 or 0)\n",
    "\n",
    "Angle’s molar classification Class II Div II: binary (1 or 0)\n",
    "\n",
    "Angle’s molar classification Class III: binary (1 or 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "0 -0.9 -3.5  2.0  True  True  87.1  78.0  6.9 -0.9  69.6  ...  111.1  15.5   \n",
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    "data"
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  {
   "attachments": {},
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   "source": [
    "Choose the model type you want to make predictions:\n",
    "\n",
    "**Gradient Boosted Trees**:        \"lgbm\"\n",
    "\n",
    "**Random Forest:**                 \"rf\"\n",
    "\n",
    "**Support Vector Machines:**       \"svm\"\n",
    "\n",
    "**Logistic Regression:**           \"lr\"\n",
    "\n",
    "**Voting Ensemble:**               \"voting\"\n",
    "\n",
    "**Stacking Ensemble:**             \"stacking\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_type = \"lgbm\"\n",
    "\n",
    "models = joblib.load(f\"models/{model_type}/model.pkl\")\n",
    "\n",
    "preds = []\n",
    "for model in models:\n",
    "    preds.append(model.predict(data) if model_type == \"lgbm\" else model.predict_proba(data)[:, 1]) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extraction probability:**\n",
    "\n",
    "The outputs represent the extraction probabilities for the given data points. The output probabilities over 0.5 (50%) are identified as extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30734043])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(preds, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not Extraction'], dtype='<U14')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vectorize({False: \"Not Extraction\", True: \"Extraction\"}.get)(np.mean(preds, axis=0) > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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