GlioML-Main-EnhancedFeature_mutaionCountsNOTmatter-trainModelOnly.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa55bed2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:21.659638Z",
     "start_time": "2023-09-26T08:28:20.234131Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from autogluon.tabular import TabularDataset, TabularPredictor\n",
    "from autogluon.common.utils.utils import setup_outputdir\n",
    "from autogluon.core.utils.loaders import load_pkl\n",
    "from autogluon.core.utils.savers import save_pkl\n",
    "from autogluon.tabular import FeatureMetadata\n",
    "import os.path\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from random import sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41b72ee4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:21.675639Z",
     "start_time": "2023-09-26T08:28:21.660641Z"
    }
   },
   "outputs": [],
   "source": [
    "class MultilabelPredictor():\n",
    "    \"\"\" Tabular Predictor for predicting multiple columns in table.\n",
    "        Creates multiple TabularPredictor objects which you can also use individually.\n",
    "        You can access the TabularPredictor for a particular label via: `multilabel_predictor.get_predictor(label_i)`\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        labels : List[str]\n",
    "            The ith element of this list is the column (i.e. `label`) predicted by the ith TabularPredictor stored in this object.\n",
    "        path : str, default = None\n",
    "            Path to directory where models and intermediate outputs should be saved.\n",
    "            If unspecified, a time-stamped folder called \"AutogluonModels/ag-[TIMESTAMP]\" will be created in the working directory to store all models.\n",
    "            Note: To call `fit()` twice and save all results of each fit, you must specify different `path` locations or don't specify `path` at all.\n",
    "            Otherwise files from first `fit()` will be overwritten by second `fit()`.\n",
    "            Caution: when predicting many labels, this directory may grow large as it needs to store many TabularPredictors.\n",
    "        problem_types : List[str], default = None\n",
    "            The ith element is the `problem_type` for the ith TabularPredictor stored in this object.\n",
    "        eval_metrics : List[str], default = None\n",
    "            The ith element is the `eval_metric` for the ith TabularPredictor stored in this object.\n",
    "        consider_labels_correlation : bool, default = True\n",
    "            Whether the predictions of multiple labels should account for label correlations or predict each label independently of the others.\n",
    "            If True, the ordering of `labels` may affect resulting accuracy as each label is predicted conditional on the previous labels appearing earlier in this list (i.e. in an auto-regressive fashion).\n",
    "            Set to False if during inference you may want to individually use just the ith TabularPredictor without predicting all the other labels.\n",
    "        kwargs :\n",
    "            Arguments passed into the initialization of each TabularPredictor.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    multi_predictor_file = 'multilabel_predictor.pkl'\n",
    "\n",
    "    def __init__(self, labels, path=None, problem_types=None, eval_metrics=None, consider_labels_correlation=False, **kwargs):\n",
    "        if (problem_types is not None) and (len(problem_types) != len(labels)):\n",
    "            raise ValueError(\"If provided, `problem_types` must have same length as `labels`\")\n",
    "        if (eval_metrics is not None) and (len(eval_metrics) != len(labels)):\n",
    "            raise ValueError(\"If provided, `eval_metrics` must have same length as `labels`\")\n",
    "        self.path = setup_outputdir(path, warn_if_exist=False)\n",
    "        self.labels = labels\n",
    "        self.consider_labels_correlation = consider_labels_correlation\n",
    "        self.predictors = {}  # key = label, value = TabularPredictor or str path to the TabularPredictor for this label\n",
    "        if eval_metrics is None:\n",
    "            self.eval_metrics = {}\n",
    "        else:\n",
    "            self.eval_metrics = {labels[i] : eval_metrics[i] for i in range(len(labels))}\n",
    "        problem_type = None\n",
    "        eval_metric = None\n",
    "        for i in range(len(labels)):\n",
    "            label = labels[i]\n",
    "            path_i = self.path + \"Predictor_\" + label\n",
    "            if problem_types is not None:\n",
    "                problem_type = problem_types[i]\n",
    "            if eval_metrics is not None:\n",
    "                eval_metric = eval_metrics[i]\n",
    "            self.predictors[label] = TabularPredictor(label=label, problem_type=problem_type, eval_metric=eval_metric, path=path_i, **kwargs)\n",
    "\n",
    "    def fit(self, train_data, tuning_data=None, final = False, **kwargs):\n",
    "        \"\"\" Fits a separate TabularPredictor to predict each of the labels.\n",
    "\n",
    "            Parameters\n",
    "            ----------\n",
    "            train_data, tuning_data : str or autogluon.tabular.TabularDataset or pd.DataFrame\n",
    "                See documentation for `TabularPredictor.fit()`.\n",
    "            kwargs :\n",
    "                Arguments passed into the `fit()` call for each TabularPredictor.\n",
    "        \"\"\"\n",
    "        if isinstance(train_data, str):\n",
    "            train_data = TabularDataset(train_data)\n",
    "        if tuning_data is not None and isinstance(tuning_data, str):\n",
    "            tuning_data = TabularDataset(tuning_data)\n",
    "        train_data_og = train_data.copy()\n",
    "        if tuning_data is not None:\n",
    "            tuning_data_og = tuning_data.copy()\n",
    "        else:\n",
    "            tuning_data_og = None\n",
    "        save_metrics = len(self.eval_metrics) == 0\n",
    "        start = time.time()\n",
    "        for i in range(len(self.labels)):\n",
    "            label = self.labels[i]\n",
    "            predictor = self.get_predictor(label)\n",
    "            if not self.consider_labels_correlation:\n",
    "                labels_to_drop = [l for l in self.labels if l != label]\n",
    "            else:\n",
    "                labels_to_drop = [self.labels[j] for j in range(i+1, len(self.labels))]\n",
    "            train_data = train_data_og.drop(labels_to_drop, axis=1)\n",
    "            if tuning_data is not None:\n",
    "                tuning_data = tuning_data_og.drop(labels_to_drop, axis=1)\n",
    "            print(f\"Fitting TabularPredictor for label: {label} ...{i / len(self.labels) * 100}%\")\n",
    "            print(f\"{(time.time() - start) / 60} minutes\")\n",
    "            if (final):\n",
    "                predictor.fit(train_data=train_data[train_data[label] > float('-inf')]\n",
    "                              , tuning_data = tuning_data\n",
    "                              ,presets = 'best_quality'\n",
    "                              ,num_bag_folds = 5,num_bag_sets = 2\n",
    "                              ,feature_generator=None\n",
    "                              , **kwargs)\n",
    "            else:\n",
    "                predictor.fit(train_data=train_data[train_data[label] > float('-inf')]\n",
    "                              , tuning_data = tuning_data\n",
    "                              ,presets = 'medium_quality'\n",
    "                              ,feature_generator=None\n",
    "                              #,presets = 'best_quality'\n",
    "                              #,num_bag_folds = 5,num_bag_sets = 2\n",
    "                              , **kwargs)\n",
    "            self.predictors[label] = predictor.path\n",
    "            if save_metrics:\n",
    "                self.eval_metrics[label] = predictor.eval_metric\n",
    "        self.save()\n",
    "\n",
    "    def predict(self, data, **kwargs):\n",
    "        \"\"\" Returns DataFrame with label columns containing predictions for each label.\n",
    "\n",
    "            Parameters\n",
    "            ----------\n",
    "            data : str or autogluon.tabular.TabularDataset or pd.DataFrame\n",
    "                Data to make predictions for. If label columns are present in this data, they will be ignored. See documentation for `TabularPredictor.predict()`.\n",
    "            kwargs :\n",
    "                Arguments passed into the predict() call for each TabularPredictor.\n",
    "        \"\"\"\n",
    "        return self._predict(data, as_proba=False, **kwargs)\n",
    "\n",
    "    def predict_proba(self, data, **kwargs):\n",
    "        \"\"\" Returns dict where each key is a label and the corresponding value is the `predict_proba()` output for just that label.\n",
    "\n",
    "            Parameters\n",
    "            ----------\n",
    "            data : str or autogluon.tabular.TabularDataset or pd.DataFrame\n",
    "                Data to make predictions for. See documentation for `TabularPredictor.predict()` and `TabularPredictor.predict_proba()`.\n",
    "            kwargs :\n",
    "                Arguments passed into the `predict_proba()` call for each TabularPredictor (also passed into a `predict()` call).\n",
    "        \"\"\"\n",
    "        return self._predict(data, as_proba=True, **kwargs)\n",
    "\n",
    "    def evaluate(self, data, **kwargs):\n",
    "        \"\"\" Returns dict where each key is a label and the corresponding value is the `evaluate()` output for just that label.\n",
    "\n",
    "            Parameters\n",
    "            ----------\n",
    "            data : str or autogluon.tabular.TabularDataset or pd.DataFrame\n",
    "                Data to evalate predictions of all labels for, must contain all labels as columns. See documentation for `TabularPredictor.evaluate()`.\n",
    "            kwargs :\n",
    "                Arguments passed into the `evaluate()` call for each TabularPredictor (also passed into the `predict()` call).\n",
    "        \"\"\"\n",
    "        data = self._get_data(data)\n",
    "        eval_dict = {}\n",
    "        for label in self.labels:\n",
    "            print(f\"Evaluating TabularPredictor for label: {label} ...\")\n",
    "            predictor = self.get_predictor(label)\n",
    "            \n",
    "            eval_dict[label] = predictor.evaluate(data[data[label] > float('-inf')], **kwargs)\n",
    "            if self.consider_labels_correlation:\n",
    "                data[label] = predictor.predict(data, **kwargs)\n",
    "        return eval_dict\n",
    "\n",
    "    def save(self):\n",
    "        \"\"\" Save MultilabelPredictor to disk. \"\"\"\n",
    "        for label in self.labels:\n",
    "            if not isinstance(self.predictors[label], str):\n",
    "                self.predictors[label] = self.predictors[label].path\n",
    "        save_pkl.save(path=self.path+self.multi_predictor_file, object=self)\n",
    "        print(f\"MultilabelPredictor saved to disk. Load with: MultilabelPredictor.load('{self.path}')\")\n",
    "\n",
    "    @classmethod\n",
    "    def load(cls, path):\n",
    "        \"\"\" Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. \"\"\"\n",
    "        path = os.path.expanduser(path)\n",
    "        if path[-1] != os.path.sep:\n",
    "            path = path + os.path.sep\n",
    "        return load_pkl.load(path=path+cls.multi_predictor_file)\n",
    "\n",
    "    def get_predictor(self, label):\n",
    "        \"\"\" Returns TabularPredictor which is used to predict this label. \"\"\"\n",
    "        predictor = self.predictors[label]\n",
    "        if isinstance(predictor, str):\n",
    "            return TabularPredictor.load(path=predictor)\n",
    "        return predictor\n",
    "\n",
    "    def _get_data(self, data):\n",
    "        if isinstance(data, str):\n",
    "            return TabularDataset(data)\n",
    "        return data.copy()\n",
    "\n",
    "    def _predict(self, data, as_proba=False, **kwargs):\n",
    "        data = self._get_data(data)\n",
    "        if as_proba:\n",
    "            predproba_dict = {}\n",
    "        for i,label in enumerate(self.labels):\n",
    "            print(f\"Predicting with TabularPredictor for label: {label} ...{i / len(self.labels) * 100}%\")\n",
    "            predictor = self.get_predictor(label)\n",
    "            if as_proba:\n",
    "                predproba_dict[label] = predictor.predict_proba(data, as_multiclass=True, **kwargs)\n",
    "            data[label] = predictor.predict(data, **kwargs)\n",
    "        if not as_proba:\n",
    "            return data[self.labels]\n",
    "        else:\n",
    "            return predproba_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3ae94b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:21.707639Z",
     "start_time": "2023-09-26T08:28:21.675639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "187\n",
      "197\n",
      "1616\n",
      "665\n",
      "280\n",
      "53\n"
     ]
    }
   ],
   "source": [
    "#1 Load my ccl's ssGSEA signature\n",
    "myCCLSignature = []\n",
    "for name in ['sample.c2.cp.biocarta.gct',\n",
    "             'sample.c2.cp.kegg.gct',\n",
    "             'sample.c2.cp.pid.gct',\n",
    "             'sample.c2.cp.reactome.gct',\n",
    "             'sample.c2.cp.wiki.gct',\n",
    "             'sample.c6.gct',\n",
    "             'sample.hallmark.gct']:    \n",
    "    with open(name, mode ='r')as file:\n",
    "        csvFile = csv.reader(file)\n",
    "        CCLSignature = list(csvFile)[2:]\n",
    "        print(len(CCLSignature))\n",
    "    for i, row in enumerate(CCLSignature):\n",
    "        temp = CCLSignature[i][0].split('\\t')\n",
    "        if i > 0:\n",
    "            CCLSignature[i] = [temp[0]] + [float(d) for d in temp[2:]]\n",
    "        else:\n",
    "            CCLSignature[i] = [temp[0]] + temp[2:]\n",
    "    if not myCCLSignature:\n",
    "        myCCLSignature += CCLSignature\n",
    "    else:\n",
    "        myCCLSignature += CCLSignature[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1ebef18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:22.410641Z",
     "start_time": "2023-09-26T08:28:21.708640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293\n",
      "187\n",
      "197\n",
      "1616\n",
      "665\n",
      "280\n",
      "53\n"
     ]
    }
   ],
   "source": [
    "#2 Load CCLE ssGSEA signature\n",
    "CCLECCLSignature = []\n",
    "for name in ['ccle.c2.cp.biocarta.gct',\n",
    "             'ccle.c2.cp.kegg.gct',\n",
    "             'ccle.c2.cp.pid.gct',\n",
    "             'ccle.c2.cp.reactome.gct',\n",
    "             'ccle.c2.cp.wiki.gct',\n",
    "             'ccle.c6.gct',\n",
    "             'ccle.hallmark.gct']:\n",
    "    with open(name, mode ='r')as file:\n",
    "        csvFile = csv.reader(file)\n",
    "        CCLSignature = list(csvFile)[2:]\n",
    "        print(len(CCLSignature))\n",
    "    for i, row in enumerate(CCLSignature):\n",
    "        temp = CCLSignature[i][0].split('\\t')\n",
    "        CCLSignature[i] = [temp[0]] + temp[2:]\n",
    "    if not CCLECCLSignature:\n",
    "        CCLECCLSignature += CCLSignature\n",
    "    else:\n",
    "        CCLECCLSignature += CCLSignature[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74b85673",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:22.426644Z",
     "start_time": "2023-09-26T08:28:22.411644Z"
    }
   },
   "outputs": [],
   "source": [
    "############new "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6136f3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:22.474638Z",
     "start_time": "2023-09-26T08:28:22.427641Z"
    }
   },
   "outputs": [],
   "source": [
    "#2.1 load model\n",
    "modelMetaData = pd.read_csv('Model.csv', header=0)\n",
    "modelMetaData = modelMetaData[['ModelID','Age','Sex']]\n",
    "sex_mapping = {'Male': 1, 'Female': 0}\n",
    "modelMetaData['Sex'] = modelMetaData['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07fb2549",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:22.522638Z",
     "start_time": "2023-09-26T08:28:22.475638Z"
    }
   },
   "outputs": [],
   "source": [
    "metaDataDict = {}\n",
    "for index, row in modelMetaData.iterrows():\n",
    "    key = row['ModelID'] \n",
    "    value = [row['Age'], row['Sex']] \n",
    "    if key not in metaDataDict:\n",
    "        metaDataDict[key] = value\n",
    "    else:\n",
    "        raise Exception(\"duplicated Id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b62ef3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:27.326709Z",
     "start_time": "2023-09-26T08:28:22.523639Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_8340\\2322606452.py:2: DtypeWarning: Columns (49,50,51,53) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  mutationData = pd.read_csv('OmicsSomaticMutations.csv')\n"
     ]
    }
   ],
   "source": [
    "#2.2 load Mutation\n",
    "mutationData = pd.read_csv('OmicsSomaticMutations.csv')\n",
    "mutationData = mutationData[['ModelID','HugoSymbol']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b80e741d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:27.342761Z",
     "start_time": "2023-09-26T08:28:27.327710Z"
    }
   },
   "outputs": [],
   "source": [
    "candidateKeyList = [\n",
    "    'MGMT', 'IDH1', 'IDH2', 'EGFR',\n",
    "    'TTN', 'MAPRE3', 'TP53', 'PIK3C2B', 'CIC', 'LRP2', 'LRP1', 'NRXN2', 'TEAD2', 'MYH3', 'NOTCH1', 'TFE3', 'PIK3R1', 'FRMD4A', 'PRCC', 'CHD3', 'BAG6', 'GLYR1', 'ADAM23', 'MSH6', 'ATRX',\n",
    "    'MUC16', 'PTEN', 'NF1', 'OBSN', 'FLG', 'RYR2', 'MUC17',\n",
    "    'BRAF', 'CDKN2A', 'CDKN2B', 'TERT', 'MYC'\n",
    "]\n",
    "candidateGeneMutationCount = {key: 0 for key in candidateKeyList}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a560b348",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:46.665733Z",
     "start_time": "2023-09-26T08:28:27.345762Z"
    }
   },
   "outputs": [],
   "source": [
    "mutationDataDict = {}\n",
    "for index, row in mutationData.iterrows():\n",
    "    key = row['ModelID']\n",
    "    if key not in mutationDataDict:\n",
    "        mutationDataDict[key] = candidateGeneMutationCount.copy()\n",
    "    gene = row['HugoSymbol']\n",
    "    if gene in candidateGeneMutationCount:\n",
    "        mutationDataDict[key][row['HugoSymbol']] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc7b0a4b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:46.681761Z",
     "start_time": "2023-09-26T08:28:46.666735Z"
    }
   },
   "outputs": [],
   "source": [
    "#############new end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce386d0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:46.776761Z",
     "start_time": "2023-09-26T08:28:46.682762Z"
    }
   },
   "outputs": [],
   "source": [
    "#3 Load CTRP cclName to AUC map\n",
    "cclToAUCdict = collections.defaultdict(list)\n",
    "with open('CTRP_CCL_AUC.gct', mode ='r') as file:\n",
    "    csvFile = csv.reader(file)\n",
    "    CTRPCCLAUC = list(csvFile)\n",
    "    CTRPCCLAUC = [''.join(sub).split('\\t') for sub in CTRPCCLAUC]\n",
    "    cclNames = CTRPCCLAUC[3][4:]\n",
    "\n",
    "for i,cclName in enumerate(cclNames):\n",
    "    cclToAUCdict[cclName] = [float( '-inf' if sub[4+i] == 'NaN' else sub[4+i]) for sub in CTRPCCLAUC[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfc80f9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:46.808761Z",
     "start_time": "2023-09-26T08:28:46.777761Z"
    }
   },
   "outputs": [],
   "source": [
    "#4 Load ccleID to ctrpName map\n",
    "CCLEidToCTRPNameDict = collections.defaultdict(str)\n",
    "CCLEidToDiseaseName = collections.defaultdict(str)\n",
    "with open('sample_info.csv', mode ='r') as file:\n",
    "    csvFile = csv.reader(file)\n",
    "    mapInfos = list(csvFile)\n",
    "    for mapInfo in mapInfos[1:]:\n",
    "        CCLEidToCTRPNameDict[mapInfo[0]] = mapInfo[2]  \n",
    "        CCLEidToDiseaseName[mapInfo[0]] = mapInfo[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8996ddbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:46.824761Z",
     "start_time": "2023-09-26T08:28:46.809762Z"
    }
   },
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    result = df.copy()\n",
    "    for feature_name in df.columns:\n",
    "        max_value = df[feature_name].max()\n",
    "        min_value = df[feature_name].min()\n",
    "        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc53a03e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:47.856779Z",
     "start_time": "2023-09-26T08:28:46.825762Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIOCARTA_GRANULOCYTES_PATHWAY</th>\n",
       "      <th>BIOCARTA_LYM_PATHWAY</th>\n",
       "      <th>BIOCARTA_BLYMPHOCYTE_PATHWAY</th>\n",
       "      <th>BIOCARTA_CARM_ER_PATHWAY</th>\n",
       "      <th>BIOCARTA_LAIR_PATHWAY</th>\n",
       "      <th>BIOCARTA_VDR_PATHWAY</th>\n",
       "      <th>BIOCARTA_MTA3_PATHWAY</th>\n",
       "      <th>BIOCARTA_GABA_PATHWAY</th>\n",
       "      <th>BIOCARTA_EGFR_SMRTE_PATHWAY</th>\n",
       "      <th>BIOCARTA_MONOCYTE_PATHWAY</th>\n",
       "      <th>...</th>\n",
       "      <th>HALLMARK_COAGULATION</th>\n",
       "      <th>HALLMARK_IL2_STAT5_SIGNALING</th>\n",
       "      <th>HALLMARK_BILE_ACID_METABOLISM</th>\n",
       "      <th>HALLMARK_PEROXISOME</th>\n",
       "      <th>HALLMARK_ALLOGRAFT_REJECTION</th>\n",
       "      <th>HALLMARK_SPERMATOGENESIS</th>\n",
       "      <th>HALLMARK_KRAS_SIGNALING</th>\n",
       "      <th>HALLMARK_KRAS_SIGNALING_UP</th>\n",
       "      <th>HALLMARK_KRAS_SIGNALING_DN</th>\n",
       "      <th>HALLMARK_PANCREAS_BETA_CELLS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>THP1_d3_B.TPM</th>\n",
       "      <td>0.991211</td>\n",
       "      <td>0.985352</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.876465</td>\n",
       "      <td>0.993652</td>\n",
       "      <td>0.595703</td>\n",
       "      <td>0.475098</td>\n",
       "      <td>0.492432</td>\n",
       "      <td>0.594238</td>\n",
       "      <td>0.986328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675781</td>\n",
       "      <td>0.810059</td>\n",
       "      <td>0.680176</td>\n",
       "      <td>0.774902</td>\n",
       "      <td>0.928711</td>\n",
       "      <td>0.793457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.880859</td>\n",
       "      <td>0.337402</td>\n",
       "      <td>0.670410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D_1.TPM</th>\n",
       "      <td>0.160278</td>\n",
       "      <td>0.161499</td>\n",
       "      <td>0.059387</td>\n",
       "      <td>0.379395</td>\n",
       "      <td>0.045349</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089844</td>\n",
       "      <td>0.554199</td>\n",
       "      <td>0.363525</td>\n",
       "      <td>0.187866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025284</td>\n",
       "      <td>0.065186</td>\n",
       "      <td>0.221802</td>\n",
       "      <td>0.194458</td>\n",
       "      <td>0.019119</td>\n",
       "      <td>0.555664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037323</td>\n",
       "      <td>0.343262</td>\n",
       "      <td>0.248169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRId7CWa.quant.TPM</th>\n",
       "      <td>0.438232</td>\n",
       "      <td>0.443604</td>\n",
       "      <td>0.540527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.506836</td>\n",
       "      <td>0.303955</td>\n",
       "      <td>0.583496</td>\n",
       "      <td>0.429932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.927246</td>\n",
       "      <td>0.736328</td>\n",
       "      <td>0.484619</td>\n",
       "      <td>0.130737</td>\n",
       "      <td>0.436523</td>\n",
       "      <td>0.053558</td>\n",
       "      <td>0.354004</td>\n",
       "      <td>0.771484</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.720703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H7.TPM</th>\n",
       "      <td>0.553711</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.427979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.724609</td>\n",
       "      <td>0.403809</td>\n",
       "      <td>0.853027</td>\n",
       "      <td>0.493896</td>\n",
       "      <td>0.511230</td>\n",
       "      <td>0.531738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.822754</td>\n",
       "      <td>0.816406</td>\n",
       "      <td>0.113708</td>\n",
       "      <td>0.248291</td>\n",
       "      <td>0.477051</td>\n",
       "      <td>0.189209</td>\n",
       "      <td>0.544434</td>\n",
       "      <td>0.707520</td>\n",
       "      <td>0.644043</td>\n",
       "      <td>0.258057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THP1_d3_A.TPM</th>\n",
       "      <td>0.992676</td>\n",
       "      <td>0.992676</td>\n",
       "      <td>0.973633</td>\n",
       "      <td>0.795898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548340</td>\n",
       "      <td>0.615723</td>\n",
       "      <td>0.447266</td>\n",
       "      <td>0.474854</td>\n",
       "      <td>0.989258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691406</td>\n",
       "      <td>0.826660</td>\n",
       "      <td>0.710938</td>\n",
       "      <td>0.877930</td>\n",
       "      <td>0.928223</td>\n",
       "      <td>0.791504</td>\n",
       "      <td>0.947754</td>\n",
       "      <td>0.933105</td>\n",
       "      <td>0.476562</td>\n",
       "      <td>0.819824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3284 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0                   BIOCARTA_GRANULOCYTES_PATHWAY  BIOCARTA_LYM_PATHWAY  \\\n",
       "Name                                                                      \n",
       "THP1_d3_B.TPM                            0.991211              0.985352   \n",
       "2D_1.TPM                                 0.160278              0.161499   \n",
       "TRId7CWa.quant.TPM                       0.438232              0.443604   \n",
       "H7.TPM                                   0.553711              0.562500   \n",
       "THP1_d3_A.TPM                            0.992676              0.992676   \n",
       "\n",
       "0                   BIOCARTA_BLYMPHOCYTE_PATHWAY  BIOCARTA_CARM_ER_PATHWAY  \\\n",
       "Name                                                                         \n",
       "THP1_d3_B.TPM                           0.984375                  0.876465   \n",
       "2D_1.TPM                                0.059387                  0.379395   \n",
       "TRId7CWa.quant.TPM                      0.540527                  0.000000   \n",
       "H7.TPM                                  0.427979                  1.000000   \n",
       "THP1_d3_A.TPM                           0.973633                  0.795898   \n",
       "\n",
       "0                   BIOCARTA_LAIR_PATHWAY  BIOCARTA_VDR_PATHWAY  \\\n",
       "Name                                                              \n",
       "THP1_d3_B.TPM                    0.993652              0.595703   \n",
       "2D_1.TPM                         0.045349              1.000000   \n",
       "TRId7CWa.quant.TPM               0.506836              0.303955   \n",
       "H7.TPM                           0.724609              0.403809   \n",
       "THP1_d3_A.TPM                    1.000000              0.548340   \n",
       "\n",
       "0                   BIOCARTA_MTA3_PATHWAY  BIOCARTA_GABA_PATHWAY  \\\n",
       "Name                                                               \n",
       "THP1_d3_B.TPM                    0.475098               0.492432   \n",
       "2D_1.TPM                         0.089844               0.554199   \n",
       "TRId7CWa.quant.TPM               0.583496               0.429932   \n",
       "H7.TPM                           0.853027               0.493896   \n",
       "THP1_d3_A.TPM                    0.615723               0.447266   \n",
       "\n",
       "0                   BIOCARTA_EGFR_SMRTE_PATHWAY  BIOCARTA_MONOCYTE_PATHWAY  \\\n",
       "Name                                                                         \n",
       "THP1_d3_B.TPM                          0.594238                   0.986328   \n",
       "2D_1.TPM                               0.363525                   0.187866   \n",
       "TRId7CWa.quant.TPM                     1.000000                   0.700195   \n",
       "H7.TPM                                 0.511230                   0.531738   \n",
       "THP1_d3_A.TPM                          0.474854                   0.989258   \n",
       "\n",
       "0                   ...  HALLMARK_COAGULATION  HALLMARK_IL2_STAT5_SIGNALING  \\\n",
       "Name                ...                                                       \n",
       "THP1_d3_B.TPM       ...              0.675781                      0.810059   \n",
       "2D_1.TPM            ...              0.025284                      0.065186   \n",
       "TRId7CWa.quant.TPM  ...              0.927246                      0.736328   \n",
       "H7.TPM              ...              0.822754                      0.816406   \n",
       "THP1_d3_A.TPM       ...              0.691406                      0.826660   \n",
       "\n",
       "0                   HALLMARK_BILE_ACID_METABOLISM  HALLMARK_PEROXISOME  \\\n",
       "Name                                                                     \n",
       "THP1_d3_B.TPM                            0.680176             0.774902   \n",
       "2D_1.TPM                                 0.221802             0.194458   \n",
       "TRId7CWa.quant.TPM                       0.484619             0.130737   \n",
       "H7.TPM                                   0.113708             0.248291   \n",
       "THP1_d3_A.TPM                            0.710938             0.877930   \n",
       "\n",
       "0                   HALLMARK_ALLOGRAFT_REJECTION  HALLMARK_SPERMATOGENESIS  \\\n",
       "Name                                                                         \n",
       "THP1_d3_B.TPM                           0.928711                  0.793457   \n",
       "2D_1.TPM                                0.019119                  0.555664   \n",
       "TRId7CWa.quant.TPM                      0.436523                  0.053558   \n",
       "H7.TPM                                  0.477051                  0.189209   \n",
       "THP1_d3_A.TPM                           0.928223                  0.791504   \n",
       "\n",
       "0                   HALLMARK_KRAS_SIGNALING  HALLMARK_KRAS_SIGNALING_UP  \\\n",
       "Name                                                                      \n",
       "THP1_d3_B.TPM                      1.000000                    0.880859   \n",
       "2D_1.TPM                           0.000000                    0.037323   \n",
       "TRId7CWa.quant.TPM                 0.354004                    0.771484   \n",
       "H7.TPM                             0.544434                    0.707520   \n",
       "THP1_d3_A.TPM                      0.947754                    0.933105   \n",
       "\n",
       "0                   HALLMARK_KRAS_SIGNALING_DN  HALLMARK_PANCREAS_BETA_CELLS  \n",
       "Name                                                                          \n",
       "THP1_d3_B.TPM                         0.337402                      0.670410  \n",
       "2D_1.TPM                              0.343262                      0.248169  \n",
       "TRId7CWa.quant.TPM                    0.968750                      0.720703  \n",
       "H7.TPM                                0.644043                      0.258057  \n",
       "THP1_d3_A.TPM                         0.476562                      0.819824  \n",
       "\n",
       "[5 rows x 3284 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction data \n",
    "predictData = pd.DataFrame(data = myCCLSignature).transpose()\n",
    "new_header = predictData.iloc[0] \n",
    "predictData = predictData[1:] \n",
    "predictData.columns = new_header \n",
    "predictData = predictData.apply(pd.to_numeric, errors='ignore')\n",
    "predictData = predictData.set_index(['Name'])\n",
    "predictData = normalize(predictData)\n",
    "predictData = predictData.astype('float16')\n",
    "predictData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06a614de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.000779Z",
     "start_time": "2023-09-26T08:28:47.857782Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 26 entries, THP1_d3_B.TPM to HCd7CWa.quant.TPM\n",
      "Columns: 3284 entries, BIOCARTA_GRANULOCYTES_PATHWAY to HALLMARK_PANCREAS_BETA_CELLS\n",
      "dtypes: float16(3284)\n",
      "memory usage: 168.5 KB\n"
     ]
    }
   ],
   "source": [
    "predictData.info(memory_usage = 'deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6935a1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.159779Z",
     "start_time": "2023-09-26T08:28:48.001781Z"
    }
   },
   "outputs": [],
   "source": [
    "# Prepare train set\n",
    "trainData = pd.DataFrame(data = CCLECCLSignature).transpose()\n",
    "new_header = trainData.iloc[0] \n",
    "trainData = trainData[1:] \n",
    "trainData.columns = new_header "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52014506",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.776779Z",
     "start_time": "2023-09-26T08:28:48.160780Z"
    }
   },
   "outputs": [
    {
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       "      <td>-1523.257437</td>\n",
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       "      <td>-1527.711407</td>\n",
       "      <td>3270.470271</td>\n",
       "      <td>-351.167918</td>\n",
       "      <td>-3621.638189</td>\n",
       "      <td>-2433.575233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000327</th>\n",
       "      <td>-3781.416152</td>\n",
       "      <td>-2313.928569</td>\n",
       "      <td>-4325.047772</td>\n",
       "      <td>5313.421286</td>\n",
       "      <td>-2977.444387</td>\n",
       "      <td>5864.860635</td>\n",
       "      <td>4989.815628</td>\n",
       "      <td>754.096070</td>\n",
       "      <td>2148.453587</td>\n",
       "      <td>-1585.842870</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.972973</td>\n",
       "      <td>2174.382638</td>\n",
       "      <td>1877.876002</td>\n",
       "      <td>5461.676880</td>\n",
       "      <td>-374.525043</td>\n",
       "      <td>-1131.652527</td>\n",
       "      <td>2941.386241</td>\n",
       "      <td>-475.140822</td>\n",
       "      <td>-3416.527063</td>\n",
       "      <td>-998.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000461</th>\n",
       "      <td>-3736.974891</td>\n",
       "      <td>-1104.574918</td>\n",
       "      <td>-4948.954189</td>\n",
       "      <td>4512.332186</td>\n",
       "      <td>-2696.549331</td>\n",
       "      <td>6554.031705</td>\n",
       "      <td>4377.850873</td>\n",
       "      <td>-550.037643</td>\n",
       "      <td>3431.112552</td>\n",
       "      <td>-1386.421102</td>\n",
       "      <td>...</td>\n",
       "      <td>694.296581</td>\n",
       "      <td>2594.017464</td>\n",
       "      <td>-367.008470</td>\n",
       "      <td>4193.268771</td>\n",
       "      <td>312.949334</td>\n",
       "      <td>-1275.923638</td>\n",
       "      <td>3834.275855</td>\n",
       "      <td>-95.907985</td>\n",
       "      <td>-3930.183840</td>\n",
       "      <td>-1971.890027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000792</th>\n",
       "      <td>-2075.019365</td>\n",
       "      <td>1703.994143</td>\n",
       "      <td>-1812.061451</td>\n",
       "      <td>4141.948015</td>\n",
       "      <td>448.719648</td>\n",
       "      <td>6202.852062</td>\n",
       "      <td>4702.842570</td>\n",
       "      <td>-1424.053292</td>\n",
       "      <td>2439.967871</td>\n",
       "      <td>2108.238933</td>\n",
       "      <td>...</td>\n",
       "      <td>1771.136924</td>\n",
       "      <td>3038.181715</td>\n",
       "      <td>-129.839290</td>\n",
       "      <td>4039.550791</td>\n",
       "      <td>949.878642</td>\n",
       "      <td>-1170.344231</td>\n",
       "      <td>3728.075095</td>\n",
       "      <td>-368.635379</td>\n",
       "      <td>-4096.710474</td>\n",
       "      <td>-1964.779113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3284 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0           BIOCARTA_GRANULOCYTES_PATHWAY  BIOCARTA_LYM_PATHWAY  \\\n",
       "Name                                                              \n",
       "ACH-001113                   -4026.645913          -2439.765461   \n",
       "ACH-000242                   -3937.716661          -2621.452770   \n",
       "ACH-000327                   -3781.416152          -2313.928569   \n",
       "ACH-000461                   -3736.974891          -1104.574918   \n",
       "ACH-000792                   -2075.019365           1703.994143   \n",
       "\n",
       "0           BIOCARTA_BLYMPHOCYTE_PATHWAY  BIOCARTA_CARM_ER_PATHWAY  \\\n",
       "Name                                                                 \n",
       "ACH-001113                  -4667.433695               4245.632425   \n",
       "ACH-000242                  -4241.404568               4712.036616   \n",
       "ACH-000327                  -4325.047772               5313.421286   \n",
       "ACH-000461                  -4948.954189               4512.332186   \n",
       "ACH-000792                  -1812.061451               4141.948015   \n",
       "\n",
       "0           BIOCARTA_LAIR_PATHWAY  BIOCARTA_VDR_PATHWAY  \\\n",
       "Name                                                      \n",
       "ACH-001113           -2634.558718           6679.038788   \n",
       "ACH-000242           -3189.755692           7318.928946   \n",
       "ACH-000327           -2977.444387           5864.860635   \n",
       "ACH-000461           -2696.549331           6554.031705   \n",
       "ACH-000792             448.719648           6202.852062   \n",
       "\n",
       "0           BIOCARTA_MTA3_PATHWAY  BIOCARTA_GABA_PATHWAY  \\\n",
       "Name                                                       \n",
       "ACH-001113            5122.943551             233.525589   \n",
       "ACH-000242            5045.464864           -1759.629780   \n",
       "ACH-000327            4989.815628             754.096070   \n",
       "ACH-000461            4377.850873            -550.037643   \n",
       "ACH-000792            4702.842570           -1424.053292   \n",
       "\n",
       "0           BIOCARTA_EGFR_SMRTE_PATHWAY  BIOCARTA_MONOCYTE_PATHWAY  ...  \\\n",
       "Name                                                                ...   \n",
       "ACH-001113                  3240.420783               -1373.036419  ...   \n",
       "ACH-000242                  3165.594813               -1523.257437  ...   \n",
       "ACH-000327                  2148.453587               -1585.842870  ...   \n",
       "ACH-000461                  3431.112552               -1386.421102  ...   \n",
       "ACH-000792                  2439.967871                2108.238933  ...   \n",
       "\n",
       "0           HALLMARK_COAGULATION  HALLMARK_IL2_STAT5_SIGNALING  \\\n",
       "Name                                                             \n",
       "ACH-001113            -63.480372                   2013.491371   \n",
       "ACH-000242            503.868738                   2278.095889   \n",
       "ACH-000327             -9.972973                   2174.382638   \n",
       "ACH-000461            694.296581                   2594.017464   \n",
       "ACH-000792           1771.136924                   3038.181715   \n",
       "\n",
       "0           HALLMARK_BILE_ACID_METABOLISM  HALLMARK_PEROXISOME  \\\n",
       "Name                                                             \n",
       "ACH-001113                      90.175905          4123.227172   \n",
       "ACH-000242                    1099.536405          4735.463738   \n",
       "ACH-000327                    1877.876002          5461.676880   \n",
       "ACH-000461                    -367.008470          4193.268771   \n",
       "ACH-000792                    -129.839290          4039.550791   \n",
       "\n",
       "0           HALLMARK_ALLOGRAFT_REJECTION  HALLMARK_SPERMATOGENESIS  \\\n",
       "Name                                                                 \n",
       "ACH-001113                   -741.313831               -557.117109   \n",
       "ACH-000242                    245.521668              -1527.711407   \n",
       "ACH-000327                   -374.525043              -1131.652527   \n",
       "ACH-000461                    312.949334              -1275.923638   \n",
       "ACH-000792                    949.878642              -1170.344231   \n",
       "\n",
       "0           HALLMARK_KRAS_SIGNALING  HALLMARK_KRAS_SIGNALING_UP  \\\n",
       "Name                                                              \n",
       "ACH-001113              2439.206531                 -861.816581   \n",
       "ACH-000242              3270.470271                 -351.167918   \n",
       "ACH-000327              2941.386241                 -475.140822   \n",
       "ACH-000461              3834.275855                  -95.907985   \n",
       "ACH-000792              3728.075095                 -368.635379   \n",
       "\n",
       "0           HALLMARK_KRAS_SIGNALING_DN  HALLMARK_PANCREAS_BETA_CELLS  \n",
       "Name                                                                  \n",
       "ACH-001113                -3301.023113                  -1722.977475  \n",
       "ACH-000242                -3621.638189                  -2433.575233  \n",
       "ACH-000327                -3416.527063                   -998.619342  \n",
       "ACH-000461                -3930.183840                  -1971.890027  \n",
       "ACH-000792                -4096.710474                  -1964.779113  \n",
       "\n",
       "[5 rows x 3284 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter valid ID\n",
    "validSet = set()\n",
    "for name in trainData['Name']:\n",
    "    if CCLEidToCTRPNameDict[name] in cclToAUCdict:\n",
    "        validSet.add(name)\n",
    "trainData = trainData.loc[trainData['Name'].isin(validSet)]\n",
    "trainData = trainData.reset_index(drop = True)\n",
    "trainData = trainData.set_index(['Name'])\n",
    "trainData = trainData.apply(pd.to_numeric)\n",
    "trainData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90ce4112",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.791799Z",
     "start_time": "2023-09-26T08:28:48.776779Z"
    }
   },
   "outputs": [],
   "source": [
    "if not all(trainData.columns == predictData.columns):\n",
    "    raise Exception(\"Column do not match!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da4c8b21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.807799Z",
     "start_time": "2023-09-26T08:28:48.791799Z"
    }
   },
   "outputs": [],
   "source": [
    "###new\n",
    "trainData['Age'] = trainData.index.map(lambda x: metaDataDict[x][0])\n",
    "trainData['Sex'] = trainData.index.map(lambda x: metaDataDict[x][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "18ed8251",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:48.839798Z",
     "start_time": "2023-09-26T08:28:48.808800Z"
    }
   },
   "outputs": [],
   "source": [
    "#counts not matter\n",
    "for geneName in candidateKeyList:\n",
    "    trainData[geneName] = trainData.index.map(lambda x: 1 if x in mutationDataDict and mutationDataDict[x][geneName] > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5770b06f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:55.998930Z",
     "start_time": "2023-09-26T08:28:48.840798Z"
    }
   },
   "outputs": [],
   "source": [
    "trainData = normalize(trainData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "794fb296",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.062941Z",
     "start_time": "2023-09-26T08:28:55.999931Z"
    }
   },
   "outputs": [],
   "source": [
    "trainData = trainData.astype('float16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "258d3930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.078942Z",
     "start_time": "2023-09-26T08:28:56.063943Z"
    }
   },
   "outputs": [],
   "source": [
    "### new end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53f9ca95",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.506982Z",
     "start_time": "2023-09-26T08:28:56.079943Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#labels data\n",
    "labelsDataOriginal = pd.DataFrame(columns = [sub[1] for sub in CTRPCCLAUC[7:]])\n",
    "for name in trainData.index:\n",
    "    labelsDataOriginal.loc[len(labelsDataOriginal.index)] = cclToAUCdict[CCLEidToCTRPNameDict[name]]\n",
    "labelsDataOriginal = labelsDataOriginal.set_index(trainData.index)\n",
    "labelsDataOriginal = labelsDataOriginal.astype('float16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "651b49a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.522941Z",
     "start_time": "2023-09-26T08:28:56.507945Z"
    }
   },
   "outputs": [],
   "source": [
    "#add high priority at the begining. e.g dasatinib\n",
    "labels = list(labelsDataOriginal.columns)\n",
    "labels = ['lovastatin','1S3R-RSL-3','daporinad','docetaxel','erlotinib','JW-55','selumetinib','trametinib'] + labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "062064dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.538941Z",
     "start_time": "2023-09-26T08:28:56.523941Z"
    }
   },
   "outputs": [],
   "source": [
    "#constants\n",
    "problem_types = ['regression'] \n",
    "eval_metrics = ['mean_squared_error']\n",
    "time_limit = 60 * 60 * 24\n",
    "tops = [100,300,600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b6fece86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.681941Z",
     "start_time": "2023-09-26T08:28:56.540942Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 636 entries, ACH-001113 to ACH-000052\n",
      "Columns: 3323 entries, BIOCARTA_GRANULOCYTES_PATHWAY to MYC\n",
      "dtypes: float16(3323)\n",
      "memory usage: 4.1 MB\n"
     ]
    }
   ],
   "source": [
    "trainData.info(memory_usage = 'deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ffada6c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:28:56.697941Z",
     "start_time": "2023-09-26T08:28:56.682943Z"
    }
   },
   "outputs": [],
   "source": [
    "skipped = []\n",
    "valScoreReuslt = [['Component','WeightedEnsemble_L2', 'NeuralNetFastAI','LightGBMLarge','LightGBM','NeuralNetTorch','RandomForestMSE','ExtraTreesMSE','LightGBMXT','CatBoost','KNeighborsDist','KNeighborsUnif','XGBoost']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a029b871",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:29:00.617942Z",
     "start_time": "2023-09-26T08:28:56.698942Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"lovastatin/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_lovastatin\\Predictor_lovastatin\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"1S3R-RSL-3/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_1S3R-RSL-3\\Predictor_1S3R-RSL-3\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"daporinad/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_daporinad\\Predictor_daporinad\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.509298       0.399004  261.396693                0.000999           0.222000            2       True         12\n",
      "1              CatBoost  -2.540507       0.113001  138.389594                0.113001         138.389594            1       True          6\n",
      "2         ExtraTreesMSE  -2.569937       0.049001    8.682175                0.049001           8.682175            1       True          7\n",
      "3        NeuralNetTorch  -2.591213       0.210001   16.094831                0.210001          16.094831            1       True         10\n",
      "4            LightGBMXT  -2.604052       0.015000    8.665190                0.015000           8.665190            1       True          3\n",
      "5       RandomForestMSE  -2.678971       0.048000   49.272099                0.048000          49.272099            1       True          5\n",
      "6              LightGBM  -2.686994       0.015000   13.542809                0.015000          13.542809            1       True          4\n",
      "7               XGBoost  -2.734427       0.021001   52.787874                0.021001          52.787874            1       True          9\n",
      "8         LightGBMLarge  -2.755209       0.015000   42.926129                0.015000          42.926129            1       True         11\n",
      "9        KNeighborsDist  -2.837966       0.077000    0.494002                0.077000           0.494002            1       True          2\n",
      "10       KNeighborsUnif  -2.851416       0.074001    0.496023                0.074001           0.496023            1       True          1\n",
      "11      NeuralNetFastAI  -2.855761       0.024003    2.311075                0.024003           2.311075            1       True          8\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -5.350350       0.196003  99.122645                0.000000           0.093004            2       True         12\n",
      "1              CatBoost  -5.493732       0.054001  85.215226                0.054001          85.215226            1       True          6\n",
      "2            LightGBMXT  -5.504962       0.012996   3.196713                0.012996           3.196713            1       True          3\n",
      "3              LightGBM  -5.649751       0.016001   6.369317                0.016001           6.369317            1       True          4\n",
      "4        NeuralNetTorch  -5.686353       0.094004   2.773295                0.094004           2.773295            1       True         10\n",
      "5         ExtraTreesMSE  -5.789898       0.033003   3.856138                0.033003           3.856138            1       True          7\n",
      "6       RandomForestMSE  -5.977260       0.032001  21.165754                0.032001          21.165754            1       True          5\n",
      "7               XGBoost  -6.346647       0.016001  27.439166                0.016001          27.439166            1       True          9\n",
      "8         LightGBMLarge  -6.614501       0.017001  22.815809                0.017001          22.815809            1       True         11\n",
      "9       NeuralNetFastAI  -6.802337       0.019001   1.475089                0.019001           1.475089            1       True          8\n",
      "10       KNeighborsDist  -6.999009       0.062002   0.323010                0.062002           0.323010            1       True          2\n",
      "11       KNeighborsUnif  -7.023834       0.055001   0.321013                0.055001           0.321013            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"docetaxel/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_docetaxel\\Predictor_docetaxel\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"erlotinib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_erlotinib\\Predictor_erlotinib\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2 -10.145541       0.153007  32.573600                0.000000           0.092001            2       True         12\n",
      "1        NeuralNetTorch -10.423170       0.088003   2.791130                0.088003           2.791130            1       True         10\n",
      "2            LightGBMXT -10.866522       0.017000   2.191098                0.017000           2.191098            1       True          3\n",
      "3         ExtraTreesMSE -10.999194       0.033003   3.382121                0.033003           3.382121            1       True          7\n",
      "4       RandomForestMSE -11.023846       0.033004  16.877604                0.033004          16.877604            1       True          5\n",
      "5              CatBoost -11.076791       0.052001  63.413526                0.052001          63.413526            1       True          6\n",
      "6               XGBoost -11.142275       0.016001  25.004794                0.016001          25.004794            1       True          9\n",
      "7              LightGBM -11.166608       0.012001   4.148665                0.012001           4.148665            1       True          4\n",
      "8         LightGBMLarge -11.929843       0.013001  18.592245                0.013001          18.592245            1       True         11\n",
      "9       NeuralNetFastAI -12.108142       0.016000   1.303554                0.016000           1.303554            1       True          8\n",
      "10       KNeighborsUnif -12.813021       0.053000   0.322526                0.053000           0.322526            1       True          1\n",
      "11       KNeighborsDist -12.871141       0.062002   0.319012                0.062002           0.319012            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2 -10.339091       0.102002   8.821841                0.000000           0.106002            2       True         12\n",
      "1        NeuralNetTorch -10.479342       0.077000   2.141088                0.077000           2.141088            1       True         10\n",
      "2       NeuralNetFastAI -10.768546       0.013002   2.906128                0.013002           2.906128            1       True          8\n",
      "3         ExtraTreesMSE -12.921575       0.033002   2.262089                0.033002           2.262089            1       True          7\n",
      "4            LightGBMXT -13.000643       0.014999   1.058024                0.014999           1.058024            1       True          3\n",
      "5              CatBoost -13.012551       0.051999  61.530680                0.051999          61.530680            1       True          6\n",
      "6       RandomForestMSE -13.132779       0.033002   6.978152                0.033002           6.978152            1       True          5\n",
      "7              LightGBM -13.573839       0.010002   1.238026                0.010002           1.238026            1       True          4\n",
      "8        KNeighborsUnif -13.768034       0.046294   0.306007                0.046294           0.306007            1       True          1\n",
      "9        KNeighborsDist -13.787963       0.059000   0.307040                0.059000           0.307040            1       True          2\n",
      "10              XGBoost -14.104269       0.010003  13.485028                0.010003          13.485028            1       True          9\n",
      "11        LightGBMLarge -14.589101       0.012000   3.668622                0.012000           3.668622            1       True         11\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.053063       0.450005  263.248682                0.000000           0.221003            2       True         12\n",
      "1              CatBoost  -2.175513       0.125003  175.075354                0.125003         175.075354            1       True          6\n",
      "2        NeuralNetTorch  -2.185080       0.210005   30.468600                0.210005          30.468600            1       True         10\n",
      "3              LightGBM  -2.214333       0.017000   13.275415                0.017000          13.275415            1       True          4\n",
      "4            LightGBMXT  -2.227479       0.017000    6.157381                0.017000           6.157381            1       True          3\n",
      "5         LightGBMLarge  -2.231185       0.017001   43.559871                0.017001          43.559871            1       True         11\n",
      "6       RandomForestMSE  -2.241829       0.048001   60.991504                0.048001          60.991504            1       True          5\n",
      "7         ExtraTreesMSE  -2.271065       0.048001    9.168281                0.048001           9.168281            1       True          7\n",
      "8        KNeighborsDist  -2.355120       0.080996    0.648439                0.080996           0.648439            1       True          2\n",
      "9        KNeighborsUnif  -2.369173       0.077002    0.645030                0.077002           0.645030            1       True          1\n",
      "10      NeuralNetFastAI  -2.604173       0.023001    2.500120                0.023001           2.500120            1       True          8\n",
      "11              XGBoost  -2.709697       0.020997   50.914812                0.020997          50.914812            1       True          9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"JW-55/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_JW-55\\Predictor_JW-55\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"selumetinib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_selumetinib\\Predictor_selumetinib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"trametinib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_trametinib\\Predictor_trametinib\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.431670       0.087002   10.433913                0.001000           0.222003            2       True         12\n",
      "1               XGBoost  -0.495153       0.010000    7.895186                0.010000           7.895186            1       True          9\n",
      "2        KNeighborsUnif  -0.526800       0.051001    0.605008                0.051001           0.605008            1       True          1\n",
      "3        KNeighborsDist  -0.526941       0.061002    0.608006                0.061002           0.608006            1       True          2\n",
      "4        NeuralNetTorch  -0.557458       0.202000    7.226160                0.202000           7.226160            1       True         10\n",
      "5         LightGBMLarge  -0.558285       0.014000    3.592611                0.014000           3.592611            1       True         11\n",
      "6              LightGBM  -0.561369       0.015000    1.708719                0.015000           1.708719            1       True          4\n",
      "7       RandomForestMSE  -0.585348       0.048002   10.216761                0.048002          10.216761            1       True          5\n",
      "8       NeuralNetFastAI  -0.604016       0.015001    2.639063                0.015001           2.639063            1       True          8\n",
      "9         ExtraTreesMSE  -0.607577       0.049001    3.070060                0.049001           3.070060            1       True          7\n",
      "10             CatBoost  -0.627309       0.125002  111.307412                0.125002         111.307412            1       True          6\n",
      "11           LightGBMXT  -0.651865       0.013999    1.843050                0.013999           1.843050            1       True          3\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -4.011662       0.346001  217.282265                0.000000           0.222003            2       True         12\n",
      "1        NeuralNetTorch  -4.093807       0.202999   29.520150                0.202999          29.520150            1       True         10\n",
      "2              CatBoost  -4.514339       0.125002  164.764643                0.125002         164.764643            1       True          6\n",
      "3            LightGBMXT  -4.526015       0.017001    8.627240                0.017001           8.627240            1       True          3\n",
      "4              LightGBM  -4.533627       0.018000   22.775469                0.018000          22.775469            1       True          4\n",
      "5         ExtraTreesMSE  -4.813393       0.048001    9.292211                0.048001           9.292211            1       True          7\n",
      "6       RandomForestMSE  -4.854536       0.048001   44.275998                0.048001          44.275998            1       True          5\n",
      "7               XGBoost  -4.999560       0.021000   51.391741                0.021000          51.391741            1       True          9\n",
      "8       NeuralNetFastAI  -5.129377       0.023000    2.670094                0.023000           2.670094            1       True          8\n",
      "9        KNeighborsDist  -5.186127       0.080002    0.645003                0.080002           0.645003            1       True          2\n",
      "10       KNeighborsUnif  -5.188501       0.081003    0.642011                0.081003           0.642011            1       True          1\n",
      "11        LightGBMLarge  -5.251635       0.018000   67.141911                0.018000          67.141911            1       True         11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"zebularine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_zebularine\\Predictor_zebularine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"azacitidine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_azacitidine\\Predictor_azacitidine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"nelarabine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_nelarabine\\Predictor_nelarabine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"myricetin/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_myricetin\\Predictor_myricetin\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -5.782718       0.300003   44.834750                0.001000           0.221002            2       True         12\n",
      "1        NeuralNetTorch  -6.133147       0.181001   17.997886                0.181001          17.997886            1       True         10\n",
      "2            LightGBMXT  -6.365355       0.015000    3.628204                0.015000           3.628204            1       True          3\n",
      "3       NeuralNetFastAI  -6.560314       0.021000    5.012711                0.021000           5.012711            1       True          8\n",
      "4         ExtraTreesMSE  -6.726813       0.049001    5.140593                0.049001           5.140593            1       True          7\n",
      "5       RandomForestMSE  -6.836134       0.048001   19.252557                0.048001          19.252557            1       True          5\n",
      "6              CatBoost  -6.867235       0.126001  180.154669                0.126001         180.154669            1       True          6\n",
      "7        KNeighborsDist  -7.101219       0.067001    0.626004                0.067001           0.626004            1       True          2\n",
      "8        KNeighborsUnif  -7.141677       0.059002    0.623525                0.059002           0.623525            1       True          1\n",
      "9         LightGBMLarge  -7.312137       0.015000   17.348942                0.015000          17.348942            1       True         11\n",
      "10              XGBoost  -7.757205       0.014000   28.790722                0.014000          28.790722            1       True          9\n",
      "11             LightGBM  -8.005491       0.016000    4.776140                0.016000           4.776140            1       True          4\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.929070       0.108001  22.455906                0.001000           0.094000            2       True         12\n",
      "1              LightGBM  -0.941624       0.014000   4.419158                0.014000           4.419158            1       True          4\n",
      "2         LightGBMLarge  -0.947778       0.008001  15.417693                0.008001          15.417693            1       True         11\n",
      "3        NeuralNetTorch  -0.969540       0.084999   2.525055                0.084999           2.525055            1       True         10\n",
      "4              CatBoost  -0.969757       0.062002  64.989564                0.062002          64.989564            1       True          6\n",
      "5            LightGBMXT  -0.981038       0.011000   1.957038                0.011000           1.957038            1       True          3\n",
      "6       NeuralNetFastAI  -0.988360       0.016001   1.273019                0.016001           1.273019            1       True          8\n",
      "7       RandomForestMSE  -0.989631       0.031000  27.773137                0.031000          27.773137            1       True          5\n",
      "8         ExtraTreesMSE  -0.994661       0.031000   3.804521                0.031000           3.804521            1       True          7\n",
      "9        KNeighborsUnif  -1.097667       0.052999   0.234960                0.052999           0.234960            1       True          1\n",
      "10              XGBoost  -1.098220       0.017000  21.282360                0.017000          21.282360            1       True          9\n",
      "11       KNeighborsDist  -1.100621       0.071000   0.233001                0.071000           0.233001            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.170001       0.011001   1.859299                0.000000           0.093001            2       True         12\n",
      "1            LightGBMXT  -1.170001       0.011001   1.766298                0.011001           1.766298            1       True          3\n",
      "2              LightGBM  -1.187547       0.014001   3.462009                0.014001           3.462009            1       True          4\n",
      "3              CatBoost  -1.191630       0.061004  39.901024                0.061004          39.901024            1       True          6\n",
      "4         LightGBMLarge  -1.193609       0.008000  13.543645                0.008000          13.543645            1       True         11\n",
      "5        NeuralNetTorch  -1.200484       0.091000   2.393105                0.091000           2.393105            1       True         10\n",
      "6       NeuralNetFastAI  -1.227443       0.014000   1.264035                0.014000           1.264035            1       True          8\n",
      "7       RandomForestMSE  -1.276949       0.031000  25.712888                0.031000          25.712888            1       True          5\n",
      "8         ExtraTreesMSE  -1.304637       0.032001   3.731033                0.032001           3.731033            1       True          7\n",
      "9               XGBoost  -1.340070       0.017000  20.558374                0.017000          20.558374            1       True          9\n",
      "10       KNeighborsDist  -1.448391       0.060999   0.228999                0.060999           0.228999            1       True          2\n",
      "11       KNeighborsUnif  -1.452452       0.052001   0.230000                0.052001           0.230000            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.854217       0.098004   3.896394                0.000000           0.095998            2       True         12\n",
      "1        NeuralNetTorch  -0.857791       0.083000   2.488392                0.083000           2.488392            1       True         10\n",
      "2            LightGBMXT  -0.884396       0.009001   1.856543                0.009001           1.856543            1       True          3\n",
      "3       NeuralNetFastAI  -0.891320       0.015003   1.312004                0.015003           1.312004            1       True          8\n",
      "4              CatBoost  -0.894902       0.062003  38.914034                0.062003          38.914034            1       True          6\n",
      "5         ExtraTreesMSE  -0.903101       0.032003   4.119418                0.032003           4.119418            1       True          7\n",
      "6         LightGBMLarge  -0.904610       0.008998  15.389916                0.008998          15.389916            1       True         11\n",
      "7              LightGBM  -0.905597       0.010999   3.821153                0.010999           3.821153            1       True          4\n",
      "8       RandomForestMSE  -0.909740       0.032962  23.394968                0.032962          23.394968            1       True          5\n",
      "9               XGBoost  -0.966078       0.018001  22.501716                0.018001          22.501716            1       True          9\n",
      "10       KNeighborsDist  -1.055238       0.058999   0.236001                0.058999           0.236001            1       True          2\n",
      "11       KNeighborsUnif  -1.059119       0.054000   0.236996                0.054000           0.236996            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K64610608/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K64610608\\Predictor_BRD-K64610608\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ML334 diastereomer/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_ML334 diastereomer\\Predictor_ML334 diastereomer\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K09344309/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K09344309\\Predictor_BRD-K09344309\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"isonicotinohydroxamic acid/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_isonicotinohydroxamic acid\\Predictor_isonicotinohydroxamic acid\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"QS-11/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_QS-11\\Predictor_QS-11\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.206328       0.064000  39.494820                0.000000           0.095000            2       True         12\n",
      "1              CatBoost  -1.206328       0.064000  39.399821                0.064000          39.399821            1       True          6\n",
      "2            LightGBMXT  -1.208622       0.015002   1.877018                0.015002           1.877018            1       True          3\n",
      "3              LightGBM  -1.213405       0.012000   3.710302                0.012000           3.710302            1       True          4\n",
      "4         LightGBMLarge  -1.215868       0.009999  15.008539                0.009999          15.008539            1       True         11\n",
      "5       NeuralNetFastAI  -1.227280       0.016000   1.328050                0.016000           1.328050            1       True          8\n",
      "6       RandomForestMSE  -1.249941       0.031960  24.181132                0.031960          24.181132            1       True          5\n",
      "7         ExtraTreesMSE  -1.295859       0.032002   3.603010                0.032002           3.603010            1       True          7\n",
      "8        NeuralNetTorch  -1.323286       0.086003   2.609076                0.086003           2.609076            1       True         10\n",
      "9               XGBoost  -1.367819       0.018000  22.146543                0.018000          22.146543            1       True          9\n",
      "10       KNeighborsDist  -1.512150       0.067012   0.234003                0.067012           0.234003            1       True          2\n",
      "11       KNeighborsUnif  -1.526194       0.107090   0.246018                0.107090           0.246018            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.756754       0.163999  70.289678                0.001000           0.093000            2       True         12\n",
      "1              CatBoost  -0.775313       0.048999  50.604990                0.048999          50.604990            1       True          6\n",
      "2         LightGBMLarge  -0.783928       0.008002   7.104624                0.008002           7.104624            1       True         11\n",
      "3              LightGBM  -0.786117       0.009001   2.087005                0.009001           2.087005            1       True          4\n",
      "4               XGBoost  -0.790398       0.014000  16.498140                0.014000          16.498140            1       True          9\n",
      "5        NeuralNetTorch  -0.791350       0.087000   1.833534                0.087000           1.833534            1       True         10\n",
      "6            LightGBMXT  -0.793119       0.009999   1.148019                0.009999           1.148019            1       True          3\n",
      "7         ExtraTreesMSE  -0.793809       0.032000   2.848007                0.032000           2.848007            1       True          7\n",
      "8       NeuralNetFastAI  -0.805561       0.013000   1.260014                0.013000           1.260014            1       True          8\n",
      "9       RandomForestMSE  -0.809061       0.033005  18.936069                0.033005          18.936069            1       True          5\n",
      "10       KNeighborsUnif  -0.959053       0.050000   0.228001                0.050000           0.228001            1       True          1\n",
      "11       KNeighborsDist  -0.963493       0.055000   0.228001                0.055000           0.228001            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.810656       0.141999    7.094290                0.000000           0.093000            2       True         12\n",
      "1       NeuralNetFastAI  -0.818802       0.008000    1.702208                0.008000           1.702208            1       True          8\n",
      "2        NeuralNetTorch  -0.868138       0.076000    1.192018                0.076000           1.192018            1       True         10\n",
      "3         ExtraTreesMSE  -1.125293       0.032000    1.218003                0.032000           1.218003            1       True          7\n",
      "4               XGBoost  -1.132852       0.008001    3.900059                0.008001           3.900059            1       True          9\n",
      "5        KNeighborsDist  -1.156172       0.049998    0.207005                0.049998           0.207005            1       True          2\n",
      "6        KNeighborsUnif  -1.167580       0.042001    0.213999                0.042001           0.213999            1       True          1\n",
      "7            LightGBMXT  -1.185162       0.007002    0.573003                0.007002           0.573003            1       True          3\n",
      "8       RandomForestMSE  -1.186868       0.032000    2.906024                0.032000           2.906024            1       True          5\n",
      "9              CatBoost  -1.244441       0.028999  109.040585                0.028999         109.040585            1       True          6\n",
      "10             LightGBM  -1.272093       0.006000    0.646003                0.006000           0.646003            1       True          4\n",
      "11        LightGBMLarge  -1.284190       0.011000    1.041020                0.011000           1.041020            1       True         11\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.934001       0.178001  36.883674                0.001000           0.092998            2       True         12\n",
      "1         ExtraTreesMSE  -0.945481       0.032001   2.527007                0.032001           2.527007            1       True          7\n",
      "2       RandomForestMSE  -0.951474       0.032000  15.535039                0.032000          15.535039            1       True          5\n",
      "3       NeuralNetFastAI  -0.957661       0.014000   1.237004                0.014000           1.237004            1       True          8\n",
      "4              CatBoost  -0.958107       0.048002  40.849708                0.048002          40.849708            1       True          6\n",
      "5         LightGBMLarge  -0.970298       0.011000   6.370199                0.011000           6.370199            1       True         11\n",
      "6        NeuralNetTorch  -0.977443       0.082000   1.815050                0.082000           1.815050            1       True         10\n",
      "7            LightGBMXT  -0.980885       0.010000   1.175630                0.010000           1.175630            1       True          3\n",
      "8              LightGBM  -0.985586       0.010999   1.916529                0.010999           1.916529            1       True          4\n",
      "9               XGBoost  -1.035316       0.017000  15.676577                0.017000          15.676577            1       True          9\n",
      "10       KNeighborsDist  -1.127289       0.057996   0.225004                0.057996           0.225004            1       True          2\n",
      "11       KNeighborsUnif  -1.138444       0.053054   0.226004                0.053054           0.226004            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.138803       0.051002  40.450017                0.000000           0.093003            2       True         12\n",
      "1              CatBoost  -1.236719       0.032001  33.023208                0.032001          33.023208            1       True          6\n",
      "2       NeuralNetFastAI  -1.294709       0.011001   1.749071                0.011001           1.749071            1       True          8\n",
      "3               XGBoost  -1.297058       0.008001   5.584736                0.008001           5.584736            1       True          9\n",
      "4         LightGBMLarge  -1.318501       0.009000   1.773052                0.009000           1.773052            1       True         11\n",
      "5        NeuralNetTorch  -1.348916       0.091000   1.328042                0.091000           1.328042            1       True         10\n",
      "6              LightGBM  -1.359230       0.013002   0.719049                0.013002           0.719049            1       True          4\n",
      "7            LightGBMXT  -1.369861       0.007000   0.614019                0.007000           0.614019            1       True          3\n",
      "8        KNeighborsDist  -1.426360       0.047999   0.214007                0.047999           0.214007            1       True          2\n",
      "9         ExtraTreesMSE  -1.431308       0.031001   1.435040                0.031001           1.435040            1       True          7\n",
      "10       KNeighborsUnif  -1.463402       0.042001   0.211006                0.042001           0.211006            1       True          1\n",
      "11      RandomForestMSE  -1.509930       0.032001   4.478131                0.032001           4.478131            1       True          5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"brivanib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_brivanib\\Predictor_brivanib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD8958/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD8958\\Predictor_BRD8958\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K34099515/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K34099515\\Predictor_BRD-K34099515\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"A-804598/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_A-804598\\Predictor_A-804598\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.713957       0.180006  48.484635                0.000000           0.093001            2       True         12\n",
      "1        NeuralNetTorch  -1.762634       0.091002   2.961422                0.091002           2.961422            1       True         10\n",
      "2       RandomForestMSE  -1.766092       0.030000  20.926117                0.030000          20.926117            1       True          5\n",
      "3         LightGBMLarge  -1.770866       0.012001  22.846858                0.012001          22.846858            1       True         11\n",
      "4               XGBoost  -1.806727       0.018002  22.346346                0.018002          22.346346            1       True          9\n",
      "5              LightGBM  -1.811143       0.011001   3.917173                0.011001           3.917173            1       True          4\n",
      "6         ExtraTreesMSE  -1.813821       0.032001   3.667107                0.032001           3.667107            1       True          7\n",
      "7              CatBoost  -1.825359       0.062004  50.844674                0.062004          50.844674            1       True          6\n",
      "8            LightGBMXT  -1.825512       0.012000   2.215599                0.012000           2.215599            1       True          3\n",
      "9       NeuralNetFastAI  -1.875427       0.015006   1.273037                0.015006           1.273037            1       True          8\n",
      "10       KNeighborsDist  -1.960841       0.059001   0.237008                0.059001           0.237008            1       True          2\n",
      "11       KNeighborsUnif  -1.964784       0.055001   0.238008                0.055001           0.238008            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.282020       0.235003  65.973388                0.001000           0.094999            2       True         12\n",
      "1       RandomForestMSE  -1.297566       0.032002  23.269405                0.032002          23.269405            1       True          5\n",
      "2              CatBoost  -1.298534       0.061001  39.458616                0.061001          39.458616            1       True          6\n",
      "3              LightGBM  -1.303820       0.011000   3.627154                0.011000           3.627154            1       True          4\n",
      "4            LightGBMXT  -1.305368       0.015001   1.816355                0.015001           1.816355            1       True          3\n",
      "5         LightGBMLarge  -1.319221       0.012000  14.209375                0.012000          14.209375            1       True         11\n",
      "6         ExtraTreesMSE  -1.332384       0.031000   3.670064                0.031000           3.670064            1       True          7\n",
      "7       NeuralNetFastAI  -1.351351       0.016000   1.318003                0.016000           1.318003            1       True          8\n",
      "8        KNeighborsUnif  -1.389428       0.052999   0.233007                0.052999           0.233007            1       True          1\n",
      "9        KNeighborsDist  -1.391674       0.059000   0.232005                0.059000           0.232005            1       True          2\n",
      "10       NeuralNetTorch  -1.408095       0.088001   2.917361                0.088001           2.917361            1       True         10\n",
      "11              XGBoost  -1.537292       0.021000  22.259578                0.021000          22.259578            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.697377       0.094001   7.499816                0.000000           0.096001            2       True         12\n",
      "1        NeuralNetTorch  -1.729860       0.086001   1.708240                0.086001           1.708240            1       True         10\n",
      "2               XGBoost  -1.820406       0.008000   5.695575                0.008000           5.695575            1       True          9\n",
      "3            LightGBMXT  -1.837588       0.014001   4.765051                0.014001           4.765051            1       True          3\n",
      "4       NeuralNetFastAI  -1.872146       0.013003   2.138439                0.013003           2.138439            1       True          8\n",
      "5         ExtraTreesMSE  -1.925324       0.032000   1.351014                0.032000           1.351014            1       True          7\n",
      "6       RandomForestMSE  -1.937522       0.032001   4.236552                0.032001           4.236552            1       True          5\n",
      "7              CatBoost  -1.942480       0.031017  23.653910                0.031017          23.653910            1       True          6\n",
      "8         LightGBMLarge  -1.989145       0.007000   1.685019                0.007000           1.685019            1       True         11\n",
      "9              LightGBM  -1.994488       0.013001   0.660008                0.013001           0.660008            1       True          4\n",
      "10       KNeighborsUnif  -2.106403       0.044020   0.211002                0.044020           0.211002            1       True          1\n",
      "11       KNeighborsDist  -2.128351       0.049996   0.212001                0.049996           0.212001            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.364276       0.129005  67.491440                0.000000           0.093001            2       True         12\n",
      "1              CatBoost  -0.367773       0.049003  65.290361                0.049003          65.290361            1       True          6\n",
      "2              LightGBM  -0.396698       0.014000   2.140023                0.014000           2.140023            1       True          4\n",
      "3         LightGBMLarge  -0.401044       0.008000   7.384900                0.008000           7.384900            1       True         11\n",
      "4            LightGBMXT  -0.403748       0.010001   1.279036                0.010001           1.279036            1       True          3\n",
      "5       RandomForestMSE  -0.413040       0.032000  15.432183                0.032000          15.432183            1       True          5\n",
      "6         ExtraTreesMSE  -0.415160       0.031000   2.847031                0.031000           2.847031            1       True          7\n",
      "7       NeuralNetFastAI  -0.437081       0.013000   1.221060                0.013000           1.221060            1       True          8\n",
      "8        NeuralNetTorch  -0.441653       0.080002   2.108078                0.080002           2.108078            1       True         10\n",
      "9               XGBoost  -0.445372       0.013001  15.811841                0.013001          15.811841            1       True          9\n",
      "10       KNeighborsUnif  -0.472339       0.051000   0.229003                0.051000           0.229003            1       True          1\n",
      "11       KNeighborsDist  -0.475983       0.057999   0.228006                0.057999           0.228006            1       True          2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"erismodegib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_erismodegib\\Predictor_erismodegib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"abiraterone/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_abiraterone\\Predictor_abiraterone\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ifosfamide/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_ifosfamide\\Predictor_ifosfamide\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"temozolomide/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_temozolomide\\Predictor_temozolomide\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-A05715709/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-A05715709\\Predictor_BRD-A05715709\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K48477130/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K48477130\\Predictor_BRD-K48477130\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.544195       0.303056  107.930695                0.001000           0.221003            2       True         12\n",
      "1              CatBoost  -0.548631       0.115048   90.226167                0.115048          90.226167            1       True          6\n",
      "2            LightGBMXT  -0.553497       0.015000    5.231876                0.015000           5.231876            1       True          3\n",
      "3              LightGBM  -0.553664       0.014001   12.576824                0.014001          12.576824            1       True          4\n",
      "4         LightGBMLarge  -0.557429       0.015000   41.060748                0.015000          41.060748            1       True         11\n",
      "5         ExtraTreesMSE  -0.558129       0.048001    9.419322                0.048001           9.419322            1       True          7\n",
      "6       RandomForestMSE  -0.568746       0.048015   63.249099                0.048015          63.249099            1       True          5\n",
      "7        NeuralNetTorch  -0.581317       0.215024   19.654470                0.215024          19.654470            1       True         10\n",
      "8       NeuralNetFastAI  -0.583602       0.023001    3.277665                0.023001           3.277665            1       True          8\n",
      "9               XGBoost  -0.594708       0.018001   47.657871                0.018001          47.657871            1       True          9\n",
      "10       KNeighborsUnif  -0.607650       0.150007    1.629036                0.150007           1.629036            1       True          1\n",
      "11       KNeighborsDist  -0.608346       0.098003    0.492007                0.098003           0.492007            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.785356       0.286996   9.220113                0.000000           0.220002            2       True         12\n",
      "1        NeuralNetTorch  -0.890320       0.216003   5.700036                0.216003           5.700036            1       True         10\n",
      "2              CatBoost  -0.899422       0.056000  37.045566                0.056000          37.045566            1       True          6\n",
      "3              LightGBM  -0.902776       0.013001   1.485015                0.013001           1.485015            1       True          4\n",
      "4        KNeighborsDist  -0.906195       0.055000   0.439005                0.055000           0.439005            1       True          2\n",
      "5        KNeighborsUnif  -0.913356       0.049001   0.440004                0.049001           0.440004            1       True          1\n",
      "6         LightGBMLarge  -0.914108       0.012999   3.196033                0.012999           3.196033            1       True         11\n",
      "7            LightGBMXT  -0.921405       0.013000   1.417014                0.013000           1.417014            1       True          3\n",
      "8         ExtraTreesMSE  -0.934956       0.049014   2.678024                0.049014           2.678024            1       True          7\n",
      "9       NeuralNetFastAI  -0.938651       0.015993   2.861070                0.015993           2.861070            1       True          8\n",
      "10      RandomForestMSE  -0.951367       0.049001   7.522092                0.049001           7.522092            1       True          5\n",
      "11              XGBoost  -1.009992       0.007998   8.324136                0.007998           8.324136            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.835600       0.224002  25.168349                0.001000           0.219002            2       True         12\n",
      "1         LightGBMLarge  -0.843475       0.014001  11.709473                0.014001          11.709473            1       True         11\n",
      "2              LightGBM  -0.866460       0.013001   3.784072                0.013001           3.784072            1       True          4\n",
      "3            LightGBMXT  -0.872784       0.013000   2.313555                0.013000           2.313555            1       True          3\n",
      "4              CatBoost  -0.878248       0.077001  67.495735                0.077001          67.495735            1       True          6\n",
      "5        NeuralNetTorch  -0.899268       0.209002  13.239874                0.209002          13.239874            1       True         10\n",
      "6         ExtraTreesMSE  -0.901452       0.048019   5.062069                0.048019           5.062069            1       True          7\n",
      "7       RandomForestMSE  -0.906071       0.048030  28.084048                0.048030          28.084048            1       True          5\n",
      "8               XGBoost  -0.925175       0.011000  28.699547                0.011000          28.699547            1       True          9\n",
      "9       NeuralNetFastAI  -0.989440       0.023000   4.933112                0.023000           4.933112            1       True          8\n",
      "10       KNeighborsDist  -1.080913       0.067001   0.461022                0.067001           0.461022            1       True          2\n",
      "11       KNeighborsUnif  -1.085220       0.059000   0.463021                0.059000           0.463021            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.547931       0.250004  66.672004                0.001000           0.219002            2       True         12\n",
      "1            LightGBMXT  -0.551867       0.017001   5.682717                0.017001           5.682717            1       True          3\n",
      "2        NeuralNetTorch  -0.555526       0.214002  19.378885                0.214002          19.378885            1       True         10\n",
      "3              LightGBM  -0.558147       0.014000  12.160780                0.014000          12.160780            1       True          4\n",
      "4              CatBoost  -0.559014       0.109004  76.214753                0.109004          76.214753            1       True          6\n",
      "5         LightGBMLarge  -0.561178       0.014000  42.390343                0.014000          42.390343            1       True         11\n",
      "6         ExtraTreesMSE  -0.567309       0.049001   9.886173                0.049001           9.886173            1       True          7\n",
      "7       RandomForestMSE  -0.576244       0.047999  86.624154                0.047999          86.624154            1       True          5\n",
      "8               XGBoost  -0.596042       0.018001  41.391399                0.018001          41.391399            1       True          9\n",
      "9       NeuralNetFastAI  -0.599679       0.024000   2.295061                0.024000           2.295061            1       True          8\n",
      "10       KNeighborsDist  -0.647706       0.076000   0.494005                0.076000           0.494005            1       True          2\n",
      "11       KNeighborsUnif  -0.650836       0.072001   0.495005                0.072001           0.495005            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.392952       0.215001  34.895126                0.001000           0.218001            2       True         12\n",
      "1        NeuralNetTorch  -0.395992       0.184000   9.360707                0.184000           9.360707            1       True         10\n",
      "2            LightGBMXT  -0.400977       0.013000   2.025035                0.013000           2.025035            1       True          3\n",
      "3              CatBoost  -0.402703       0.073527  62.316544                0.073527          62.316544            1       True          6\n",
      "4              LightGBM  -0.403671       0.013000   3.262544                0.013000           3.262544            1       True          4\n",
      "5         LightGBMLarge  -0.408695       0.013000   9.648140                0.013000           9.648140            1       True         11\n",
      "6         ExtraTreesMSE  -0.432667       0.049000   4.648108                0.049000           4.648108            1       True          7\n",
      "7       NeuralNetFastAI  -0.440734       0.020000   3.816553                0.020000           3.816553            1       True          8\n",
      "8       RandomForestMSE  -0.449435       0.048001  27.491143                0.048001          27.491143            1       True          5\n",
      "9               XGBoost  -0.451615       0.010000  21.499864                0.010000          21.499864            1       True          9\n",
      "10       KNeighborsUnif  -0.543769       0.058001   0.459003                0.058001           0.459003            1       True          1\n",
      "11       KNeighborsDist  -0.545498       0.063000   0.461022                0.063000           0.461022            1       True          2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"CAY10594/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_CAY10594\\Predictor_CAY10594\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"WP1130/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_WP1130\\Predictor_WP1130\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"tamoxifen/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_tamoxifen\\Predictor_tamoxifen\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"importazole/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_importazole\\Predictor_importazole\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ML006/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_ML006\\Predictor_ML006\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.662496       0.033000   6.445130                0.000000           0.220001            2       True         12\n",
      "1            LightGBMXT  -0.671377       0.013000   2.341070                0.013000           2.341070            1       True          3\n",
      "2              LightGBM  -0.687250       0.014001   3.393537                0.014001           3.393537            1       True          4\n",
      "3              CatBoost  -0.692999       0.074000  65.255523                0.074000          65.255523            1       True          6\n",
      "4         LightGBMLarge  -0.695231       0.013000  10.004632                0.013000          10.004632            1       True         11\n",
      "5        NeuralNetTorch  -0.704347       0.178000  10.226312                0.178000          10.226312            1       True         10\n",
      "6       RandomForestMSE  -0.727263       0.049001  22.896980                0.049001          22.896980            1       True          5\n",
      "7         ExtraTreesMSE  -0.731398       0.048000   4.571260                0.048000           4.571260            1       True          7\n",
      "8       NeuralNetFastAI  -0.765989       0.020000   3.884059                0.020000           3.884059            1       True          8\n",
      "9               XGBoost  -0.809115       0.010000  21.458301                0.010000          21.458301            1       True          9\n",
      "10       KNeighborsDist  -0.875232       0.062001   0.461003                0.062001           0.461003            1       True          2\n",
      "11       KNeighborsUnif  -0.883184       0.056034   0.463002                0.056034           0.463002            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.432388       0.218003  81.132239                0.000000           0.221002            2       True         12\n",
      "1              CatBoost  -0.452021       0.124001  77.723626                0.124001          77.723626            1       True          6\n",
      "2            LightGBMXT  -0.452754       0.017000   4.790585                0.017000           4.790585            1       True          3\n",
      "3       NeuralNetFastAI  -0.455652       0.023000   2.544585                0.023000           2.544585            1       True          8\n",
      "4        NeuralNetTorch  -0.464708       0.207001  19.800479                0.207001          19.800479            1       True         10\n",
      "5         ExtraTreesMSE  -0.465427       0.048003   9.253067                0.048003           9.253067            1       True          7\n",
      "6         LightGBMLarge  -0.467193       0.017001  35.791867                0.017001          35.791867            1       True         11\n",
      "7              LightGBM  -0.467804       0.015969  10.638122                0.015969          10.638122            1       True          4\n",
      "8        KNeighborsUnif  -0.469057       0.071001   0.643027                0.071001           0.643027            1       True          1\n",
      "9       RandomForestMSE  -0.470947       0.048001  61.109271                0.048001          61.109271            1       True          5\n",
      "10       KNeighborsDist  -0.475798       0.078001   0.645006                0.078001           0.645006            1       True          2\n",
      "11              XGBoost  -0.523506       0.020000  40.436590                0.020000          40.436590            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.564690       0.184024  103.644129                0.001000           0.224001            2       True         12\n",
      "1            LightGBMXT  -0.601376       0.016000    5.873306                0.016000           5.873306            1       True          3\n",
      "2               XGBoost  -0.601597       0.019000   43.550928                0.019000          43.550928            1       True          9\n",
      "3       RandomForestMSE  -0.604482       0.063024   53.488355                0.063024          53.488355            1       True          5\n",
      "4         ExtraTreesMSE  -0.608458       0.049010    9.385920                0.049010           9.385920            1       True          7\n",
      "5              CatBoost  -0.621135       0.114001  210.187756                0.114001         210.187756            1       True          6\n",
      "6        NeuralNetTorch  -0.622962       0.202995   15.136678                0.202995          15.136678            1       True         10\n",
      "7              LightGBM  -0.627470       0.015003   17.516975                0.015003          17.516975            1       True          4\n",
      "8         LightGBMLarge  -0.650916       0.015000   52.637807                0.015000          52.637807            1       True         11\n",
      "9        KNeighborsUnif  -0.693784       0.085000    0.507539                0.085000           0.507539            1       True          1\n",
      "10       KNeighborsDist  -0.697926       0.086517    0.496002                0.086517           0.496002            1       True          2\n",
      "11      NeuralNetFastAI  -0.737705       0.030000    2.524028                0.030000           2.524028            1       True          8\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.142429       0.158001  121.551837                0.000000           0.220001            2       True         12\n",
      "1       NeuralNetFastAI  -1.175009       0.047002    2.975576                0.047002           2.975576            1       True          8\n",
      "2              CatBoost  -1.179798       0.111000  118.356261                0.111000         118.356261            1       True          6\n",
      "3              LightGBM  -1.210233       0.014000   13.676600                0.014000          13.676600            1       True          4\n",
      "4         LightGBMLarge  -1.210731       0.014000   42.454611                0.014000          42.454611            1       True         11\n",
      "5            LightGBMXT  -1.210991       0.015002    6.366329                0.015002           6.366329            1       True          3\n",
      "6         ExtraTreesMSE  -1.236625       0.064998   11.092854                0.064998          11.092854            1       True          7\n",
      "7       RandomForestMSE  -1.238714       0.048009   67.896173                0.048009          67.896173            1       True          5\n",
      "8        KNeighborsUnif  -1.335950       0.077999    0.500001                0.077999           0.500001            1       True          1\n",
      "9        KNeighborsDist  -1.344420       0.090000    0.500001                0.090000           0.500001            1       True          2\n",
      "10       NeuralNetTorch  -1.355899       0.188020   13.382766                0.188020          13.382766            1       True         10\n",
      "11              XGBoost  -1.394679       0.019002   48.318152                0.019002          48.318152            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.333254       0.243007  127.756915                0.001000           0.226006            2       True         12\n",
      "1         LightGBMLarge  -2.488613       0.018001   42.687855                0.018001          42.687855            1       True         11\n",
      "2       NeuralNetFastAI  -2.509430       0.044000    4.262219                0.044000           4.262219            1       True          8\n",
      "3            LightGBMXT  -2.528270       0.017000    5.842355                0.017000           5.842355            1       True          3\n",
      "4              CatBoost  -2.537192       0.180006   80.580835                0.180006          80.580835            1       True          6\n",
      "5              LightGBM  -2.538844       0.017001   11.762830                0.017001          11.762830            1       True          4\n",
      "6        NeuralNetTorch  -2.550390       0.219007   18.884309                0.219007          18.884309            1       True         10\n",
      "7       RandomForestMSE  -2.619592       0.064019   46.020907                0.064019          46.020907            1       True          5\n",
      "8         ExtraTreesMSE  -2.625595       0.048001    9.415908                0.048001           9.415908            1       True          7\n",
      "9        KNeighborsDist  -2.759814       0.185399    0.686598                0.185399           0.686598            1       True          2\n",
      "10       KNeighborsUnif  -2.764624       0.156005    1.984059                0.156005           1.984059            1       True          1\n",
      "11              XGBoost  -2.812136       0.020999   48.778624                0.020999          48.778624            1       True          9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"AM-580/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_AM-580\\Predictor_AM-580\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"CD-1530/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_CD-1530\\Predictor_CD-1530\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"silmitasertib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_silmitasertib\\Predictor_silmitasertib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"PRL-3 inhibitor I/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_PRL-3 inhibitor I\\Predictor_PRL-3 inhibitor I\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"NPC-26/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_NPC-26\\Predictor_NPC-26\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"betulinic acid/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_betulinic acid\\Predictor_betulinic acid\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.902132       0.085024  122.658003                0.001000           0.224003            2       True         12\n",
      "1              LightGBM  -0.907339       0.015998   12.193335                0.015998          12.193335            1       True          4\n",
      "2            LightGBMXT  -0.916592       0.018001    5.705662                0.018001           5.705662            1       True          3\n",
      "3       RandomForestMSE  -0.917220       0.046025   65.714716                0.046025          65.714716            1       True          5\n",
      "4              CatBoost  -0.926992       0.179003   79.432176                0.179003          79.432176            1       True          6\n",
      "5         LightGBMLarge  -0.928520       0.017018   44.538182                0.017018          44.538182            1       True         11\n",
      "6       NeuralNetFastAI  -0.956459       0.027999    2.818066                0.027999           2.818066            1       True          8\n",
      "7         ExtraTreesMSE  -0.960355       0.047001   10.076238                0.047001          10.076238            1       True          7\n",
      "8        NeuralNetTorch  -1.027338       0.199003   20.115597                0.199003          20.115597            1       True         10\n",
      "9               XGBoost  -1.102170       0.022000   44.525949                0.022000          44.525949            1       True          9\n",
      "10       KNeighborsUnif  -1.215623       0.087002    0.678030                0.087002           0.678030            1       True          1\n",
      "11       KNeighborsDist  -1.218846       0.093004    0.673031                0.093004           0.673031            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.678089       0.434011  212.352066                0.000000           0.223002            2       True         12\n",
      "1        NeuralNetTorch  -2.700500       0.216007   20.197520                0.216007          20.197520            1       True         10\n",
      "2         ExtraTreesMSE  -2.770789       0.048001    9.425189                0.048001           9.425189            1       True          7\n",
      "3              LightGBM  -2.792152       0.018001   14.753215                0.018001          14.753215            1       True          4\n",
      "4       RandomForestMSE  -2.806528       0.047000   56.839841                0.047000          56.839841            1       True          5\n",
      "5              CatBoost  -2.807898       0.179005  128.595958                0.179005         128.595958            1       True          6\n",
      "6            LightGBMXT  -2.810795       0.016000    6.454156                0.016000           6.454156            1       True          3\n",
      "7       NeuralNetFastAI  -2.952622       0.025002    2.874053                0.025002           2.874053            1       True          8\n",
      "8         LightGBMLarge  -2.970414       0.017000   44.772755                0.017000          44.772755            1       True         11\n",
      "9               XGBoost  -2.999031       0.020999   48.582371                0.020999          48.582371            1       True          9\n",
      "10       KNeighborsDist  -3.286598       0.089954    0.679057                0.089954           0.679057            1       True          2\n",
      "11       KNeighborsUnif  -3.288619       0.079672    0.680044                0.079672           0.680044            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.283229       0.251997  23.330155                0.001000           0.223001            2       True         12\n",
      "1        NeuralNetTorch  -2.292101       0.225001  20.253109                0.225001          20.253109            1       True         10\n",
      "2         ExtraTreesMSE  -2.359151       0.049001   9.428319                0.049001           9.428319            1       True          7\n",
      "3            LightGBMXT  -2.377788       0.016999   6.026276                0.016999           6.026276            1       True          3\n",
      "4       NeuralNetFastAI  -2.390193       0.025995   2.854045                0.025995           2.854045            1       True          8\n",
      "5              CatBoost  -2.417122       0.194175  88.752271                0.194175          88.752271            1       True          6\n",
      "6         LightGBMLarge  -2.427667       0.018000  41.049068                0.018000          41.049068            1       True         11\n",
      "7              LightGBM  -2.428279       0.017041  12.029808                0.017041          12.029808            1       True          4\n",
      "8       RandomForestMSE  -2.464714       0.049001  48.411447                0.049001          48.411447            1       True          5\n",
      "9               XGBoost  -2.546554       0.020001  47.396878                0.020001          47.396878            1       True          9\n",
      "10       KNeighborsDist  -2.584479       0.085004   0.677994                0.085004           0.677994            1       True          2\n",
      "11       KNeighborsUnif  -2.601046       0.083004   0.676051                0.083004           0.676051            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.934934       0.101882   6.536206                0.000000           0.221002            2       True         12\n",
      "1            LightGBMXT  -1.941239       0.017002   5.641864                0.017002           5.641864            1       True          3\n",
      "2              CatBoost  -1.947105       0.176001  80.129608                0.176001          80.129608            1       True          6\n",
      "3         LightGBMLarge  -1.957514       0.017003  41.867221                0.017003          41.867221            1       True         11\n",
      "4              LightGBM  -1.965221       0.016000  12.669801                0.016000          12.669801            1       True          4\n",
      "5       NeuralNetFastAI  -2.048685       0.025000   2.873069                0.025000           2.873069            1       True          8\n",
      "6       RandomForestMSE  -2.068717       0.047999  57.717006                0.047999          57.717006            1       True          5\n",
      "7         ExtraTreesMSE  -2.090022       0.048001   9.023616                0.048001           9.023616            1       True          7\n",
      "8        NeuralNetTorch  -2.107510       0.205023  16.866292                0.205023          16.866292            1       True         10\n",
      "9               XGBoost  -2.254375       0.033007  46.476377                0.033007          46.476377            1       True          9\n",
      "10       KNeighborsUnif  -2.278050       0.084879   0.673341                0.084879           0.673341            1       True          1\n",
      "11       KNeighborsDist  -2.285932       0.082514   0.676776                0.082514           0.676776            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.619538       0.285004  77.554665                0.001000           0.222001            2       True         12\n",
      "1       NeuralNetFastAI  -0.625283       0.036000   2.921573                0.036000           2.921573            1       True          8\n",
      "2        NeuralNetTorch  -0.633755       0.224002  20.481389                0.224002          20.481389            1       True         10\n",
      "3         ExtraTreesMSE  -0.651659       0.048000  10.207183                0.048000          10.207183            1       True          7\n",
      "4            LightGBMXT  -0.658134       0.016999   5.850890                0.016999           5.850890            1       True          3\n",
      "5       RandomForestMSE  -0.661916       0.049000  88.333780                0.049000          88.333780            1       True          5\n",
      "6         LightGBMLarge  -0.661961       0.017000  47.429156                0.017000          47.429156            1       True         11\n",
      "7              CatBoost  -0.667691       0.180001  81.026881                0.180001          81.026881            1       True          6\n",
      "8              LightGBM  -0.671758       0.016000  13.166221                0.016000          13.166221            1       True          4\n",
      "9               XGBoost  -0.708900       0.024003  53.929701                0.024003          53.929701            1       True          9\n",
      "10       KNeighborsDist  -0.783321       0.086487   0.676739                0.086487           0.676739            1       True          2\n",
      "11       KNeighborsUnif  -0.792317       0.078003   0.671525                0.078003           0.671525            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.459335       0.246001  22.242938                0.000000           0.225001            2       True         12\n",
      "1       NeuralNetFastAI  -1.467263       0.027000   2.807053                0.027000           2.807053            1       True          8\n",
      "2        NeuralNetTorch  -1.470465       0.219001  19.210884                0.219001          19.210884            1       True         10\n",
      "3              CatBoost  -1.495647       0.177001  82.681646                0.177001          82.681646            1       True          6\n",
      "4              LightGBM  -1.499564       0.017001  11.903807                0.017001          11.903807            1       True          4\n",
      "5         LightGBMLarge  -1.510383       0.017000  45.010291                0.017000          45.010291            1       True         11\n",
      "6            LightGBMXT  -1.513004       0.018001   5.522341                0.018001           5.522341            1       True          3\n",
      "7         ExtraTreesMSE  -1.541987       0.049002   9.371109                0.049002           9.371109            1       True          7\n",
      "8       RandomForestMSE  -1.567108       0.047001  62.378141                0.047001          62.378141            1       True          5\n",
      "9               XGBoost  -1.675477       0.024000  46.672600                0.024000          46.672600            1       True          9\n",
      "10       KNeighborsDist  -1.996331       0.080002   0.682003                0.080002           0.682003            1       True          2\n",
      "11       KNeighborsUnif  -2.004710       0.085001   0.671660                0.085001           0.671660            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"salermide/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_salermide\\Predictor_salermide\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-M00053801/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-M00053801\\Predictor_BRD-M00053801\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"AA-COCF3/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_AA-COCF3\\Predictor_AA-COCF3\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"CI-976/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_CI-976\\Predictor_CI-976\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"pifithrin-alpha/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_pifithrin-alpha\\Predictor_pifithrin-alpha\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"purmorphamine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_purmorphamine\\Predictor_purmorphamine\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.529341       0.253527  53.852543                0.000000           0.226000            2       True         12\n",
      "1            LightGBMXT  -1.553419       0.018000   6.772412                0.018000           6.772412            1       True          3\n",
      "2        NeuralNetTorch  -1.575069       0.217527  30.355789                0.217527          30.355789            1       True         10\n",
      "3              LightGBM  -1.598445       0.018000  16.498343                0.018000          16.498343            1       True          4\n",
      "4              CatBoost  -1.609810       0.182001  95.974026                0.182001          95.974026            1       True          6\n",
      "5         ExtraTreesMSE  -1.627831       0.048000  10.059652                0.048000          10.059652            1       True          7\n",
      "6         LightGBMLarge  -1.639493       0.017000  47.290586                0.017000          47.290586            1       True         11\n",
      "7       NeuralNetFastAI  -1.687597       0.035999   2.910593                0.035999           2.910593            1       True          8\n",
      "8       RandomForestMSE  -1.693124       0.048000  93.447855                0.048000          93.447855            1       True          5\n",
      "9        KNeighborsUnif  -1.808143       0.073000   0.670030                0.073000           0.670030            1       True          1\n",
      "10       KNeighborsDist  -1.815606       0.082920   0.680217                0.082920           0.680217            1       True          2\n",
      "11              XGBoost  -2.016263       0.024000  53.371276                0.024000          53.371276            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.610099       0.222998  20.270512                0.001000           0.225000            2       True         12\n",
      "1        NeuralNetTorch  -2.633360       0.205997  16.266946                0.205997          16.266946            1       True         10\n",
      "2            LightGBMXT  -2.706747       0.016001   3.778566                0.016001           3.778566            1       True          3\n",
      "3              CatBoost  -2.736683       0.163001  87.711525                0.163001          87.711525            1       True          6\n",
      "4         LightGBMLarge  -2.748514       0.017000  22.786332                0.017000          22.786332            1       True         11\n",
      "5              LightGBM  -2.768521       0.016999   6.644090                0.016999           6.644090            1       True          4\n",
      "6       NeuralNetFastAI  -2.797914       0.027006   2.819555                0.027006           2.819555            1       True          8\n",
      "7         ExtraTreesMSE  -2.801711       0.049001   7.096800                0.049001           7.096800            1       True          7\n",
      "8       RandomForestMSE  -2.806638       0.046000  31.083613                0.046000          31.083613            1       True          5\n",
      "9               XGBoost  -2.966935       0.019001  41.017131                0.019001          41.017131            1       True          9\n",
      "10       KNeighborsDist  -3.201764       0.077000   0.664001                0.077000           0.664001            1       True          2\n",
      "11       KNeighborsUnif  -3.210162       0.084028   0.654557                0.084028           0.654557            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.071488       0.439008  174.144605                0.000000           0.222001            2       True         12\n",
      "1              CatBoost  -2.077315       0.156001  158.965821                0.156001         158.965821            1       True          6\n",
      "2         LightGBMLarge  -2.150034       0.021000   20.249755                0.021000          20.249755            1       True         11\n",
      "3            LightGBMXT  -2.165032       0.015999    3.709032                0.015999           3.709032            1       True          3\n",
      "4        NeuralNetTorch  -2.179021       0.196001   14.309761                0.196001          14.309761            1       True         10\n",
      "5              LightGBM  -2.229455       0.015000    6.758734                0.015000           6.758734            1       True          4\n",
      "6       NeuralNetFastAI  -2.243034       0.023994    2.800079                0.023994           2.800079            1       True          8\n",
      "7         ExtraTreesMSE  -2.244683       0.048000    6.193249                0.048000           6.193249            1       True          7\n",
      "8       RandomForestMSE  -2.252059       0.047000   33.297900                0.047000          33.297900            1       True          5\n",
      "9        KNeighborsUnif  -2.368959       0.087007    0.647023                0.087007           0.647023            1       True          1\n",
      "10       KNeighborsDist  -2.391745       0.081025    0.655002                0.081025           0.655002            1       True          2\n",
      "11              XGBoost  -2.413587       0.014000   36.847859                0.014000          36.847859            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.348212       0.559021  164.780928                0.001000           0.223573            2       True         12\n",
      "1              CatBoost  -2.363706       0.182001   87.422470                0.182001          87.422470            1       True          6\n",
      "2         LightGBMLarge  -2.375789       0.018001   46.408872                0.018001          46.408872            1       True         11\n",
      "3            LightGBMXT  -2.378356       0.018001    6.428614                0.018001           6.428614            1       True          3\n",
      "4         ExtraTreesMSE  -2.410236       0.046999    9.674686                0.046999           9.674686            1       True          7\n",
      "5              LightGBM  -2.423950       0.018000   13.534166                0.018000          13.534166            1       True          4\n",
      "6        NeuralNetTorch  -2.424621       0.218001   20.370327                0.218001          20.370327            1       True         10\n",
      "7       NeuralNetFastAI  -2.500847       0.028994    2.951562                0.028994           2.951562            1       True          8\n",
      "8       RandomForestMSE  -2.510699       0.048000   62.906872                0.048000          62.906872            1       True          5\n",
      "9               XGBoost  -2.678253       0.021001   52.582413                0.021001          52.582413            1       True          9\n",
      "10       KNeighborsDist  -2.948723       0.093019    0.681000                0.093019           0.681000            1       True          2\n",
      "11       KNeighborsUnif  -2.949597       0.099002    0.680042                0.099002           0.680042            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.078106       0.342003  42.617450                0.000000           0.222003            2       True         12\n",
      "1              LightGBM  -1.095304       0.017000  13.658973                0.017000          13.658973            1       True          4\n",
      "2            LightGBMXT  -1.110569       0.019000   6.508105                0.019000           6.508105            1       True          3\n",
      "3              CatBoost  -1.121936       0.183003  91.397392                0.183003          91.397392            1       True          6\n",
      "4         ExtraTreesMSE  -1.123219       0.048001  10.236704                0.048001          10.236704            1       True          7\n",
      "5        NeuralNetTorch  -1.128645       0.223003  21.546835                0.223003          21.546835            1       True         10\n",
      "6       RandomForestMSE  -1.134466       0.047001  67.716770                0.047001          67.716770            1       True          5\n",
      "7         LightGBMLarge  -1.146514       0.019002  47.557447                0.019002          47.557447            1       True         11\n",
      "8       NeuralNetFastAI  -1.173183       0.029997   2.952080                0.029997           2.952080            1       True          8\n",
      "9        KNeighborsUnif  -1.259192       0.083000   0.681534                0.083000           0.681534            1       True          1\n",
      "10              XGBoost  -1.263241       0.021001  59.663037                0.021001          59.663037            1       True          9\n",
      "11       KNeighborsDist  -1.270774       0.087000   0.671010                0.087000           0.671010            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.855220       0.368002   85.316165                0.000000           0.223010            2       True         12\n",
      "1         ExtraTreesMSE  -0.876632       0.048000   10.638422                0.048000          10.638422            1       True          7\n",
      "2        NeuralNetTorch  -0.877668       0.213002   24.901735                0.213002          24.901735            1       True         10\n",
      "3         LightGBMLarge  -0.881456       0.018000   48.871467                0.018000          48.871467            1       True         11\n",
      "4              CatBoost  -0.897012       0.181002  120.866610                0.181002         120.866610            1       True          6\n",
      "5            LightGBMXT  -0.897247       0.017000    6.199148                0.017000           6.199148            1       True          3\n",
      "6              LightGBM  -0.898838       0.017000   13.538754                0.017000          13.538754            1       True          4\n",
      "7       NeuralNetFastAI  -0.920731       0.029999    2.994049                0.029999           2.994049            1       True          8\n",
      "8       RandomForestMSE  -0.953840       0.048999   66.094073                0.048999          66.094073            1       True          5\n",
      "9        KNeighborsDist  -1.003718       0.089000    0.681532                0.089000           0.681532            1       True          2\n",
      "10       KNeighborsUnif  -1.005984       0.089004    0.679103                0.089004           0.679103            1       True          1\n",
      "11              XGBoost  -1.180428       0.022000   50.230713                0.022000          50.230713            1       True          9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BIBR-1532/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BIBR-1532\\Predictor_BIBR-1532\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"niclosamide/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_niclosamide\\Predictor_niclosamide\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"bardoxolone methyl/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_bardoxolone methyl\\Predictor_bardoxolone methyl\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"hyperforin/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_hyperforin\\Predictor_hyperforin\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD4132/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD4132\\Predictor_BRD4132\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.503179       0.274999  87.240847                0.001000           0.224068            2       True         12\n",
      "1       NeuralNetFastAI  -1.515538       0.030000   3.026053                0.030000           3.026053            1       True          8\n",
      "2        NeuralNetTorch  -1.523907       0.205998  19.651502                0.205998          19.651502            1       True         10\n",
      "3              LightGBM  -1.525863       0.017000  14.324477                0.017000          14.324477            1       True          4\n",
      "4            LightGBMXT  -1.536998       0.017002   5.943661                0.017002           5.943661            1       True          3\n",
      "5              CatBoost  -1.541860       0.179001  87.163982                0.179001          87.163982            1       True          6\n",
      "6         LightGBMLarge  -1.546676       0.018000  44.415284                0.018000          44.415284            1       True         11\n",
      "7         ExtraTreesMSE  -1.554797       0.047001  10.000569                0.047001          10.000569            1       True          7\n",
      "8       RandomForestMSE  -1.584241       0.048001  71.505260                0.048001          71.505260            1       True          5\n",
      "9               XGBoost  -1.653049       0.021000  50.014747                0.021000          50.014747            1       True          9\n",
      "10       KNeighborsUnif  -1.809030       0.096995   0.686025                0.096995           0.686025            1       True          1\n",
      "11       KNeighborsDist  -1.815024       0.090000   0.681021                0.090000           0.681021            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.689736       0.381030   50.237635                0.000000           0.226910            2       True         12\n",
      "1        NeuralNetTorch  -0.710680       0.224025   21.846920                0.224025          21.846920            1       True         10\n",
      "2              LightGBM  -0.711868       0.019000   15.721330                0.019000          15.721330            1       True          4\n",
      "3         ExtraTreesMSE  -0.714044       0.049000   11.755450                0.049000          11.755450            1       True          7\n",
      "4            LightGBMXT  -0.723435       0.017999    7.364601                0.017999           7.364601            1       True          3\n",
      "5              CatBoost  -0.724420       0.185001  123.679090                0.185001         123.679090            1       True          6\n",
      "6       RandomForestMSE  -0.725940       0.048000   82.241538                0.048000          82.241538            1       True          5\n",
      "7       NeuralNetFastAI  -0.736856       0.026995    3.066025                0.026995           3.066025            1       True          8\n",
      "8         LightGBMLarge  -0.760889       0.018001   52.279261                0.018001          52.279261            1       True         11\n",
      "9               XGBoost  -0.814772       0.026000   51.435326                0.026000          51.435326            1       True          9\n",
      "10       KNeighborsUnif  -0.833329       0.089004    0.687026                0.089004           0.687026            1       True          1\n",
      "11       KNeighborsDist  -0.835688       0.094001    0.682000                0.094001           0.682000            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.164006       0.300002  140.485098                0.000000           0.225001            2       True         12\n",
      "1        NeuralNetTorch  -2.213434       0.226001   24.819631                0.226001          24.819631            1       True         10\n",
      "2            LightGBMXT  -2.292967       0.021000   26.865153                0.021000          26.865153            1       True          3\n",
      "3         ExtraTreesMSE  -2.321091       0.049002    9.579999                0.049002           9.579999            1       True          7\n",
      "4       RandomForestMSE  -2.323994       0.048001   48.733483                0.048001          48.733483            1       True          5\n",
      "5              LightGBM  -2.364411       0.017001   20.541792                0.017001          20.541792            1       True          4\n",
      "6               XGBoost  -2.377259       0.025954   52.711341                0.025954          52.711341            1       True          9\n",
      "7              CatBoost  -2.406379       0.186003  128.582322                0.186003         128.582322            1       True          6\n",
      "8         LightGBMLarge  -2.421890       0.021002   85.481757                0.021002          85.481757            1       True         11\n",
      "9       NeuralNetFastAI  -2.477070       0.032000    3.093557                0.032000           3.093557            1       True          8\n",
      "10       KNeighborsDist  -2.658394       0.087001    0.682003                0.087001           0.682003            1       True          2\n",
      "11       KNeighborsUnif  -2.671562       0.084001    0.706785                0.084001           0.706785            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.335612       0.290992  105.406745                0.001001           0.223001            2       True         12\n",
      "1              LightGBM  -1.379492       0.027996   73.773764                0.027996          73.773764            1       True          4\n",
      "2            LightGBMXT  -1.382528       0.019000    8.691696                0.019000           8.691696            1       True          3\n",
      "3              CatBoost  -1.422642       0.176996  129.026660                0.176996         129.026660            1       True          6\n",
      "4       RandomForestMSE  -1.428439       0.049001   55.513357                0.049001          55.513357            1       True          5\n",
      "5        NeuralNetTorch  -1.455966       0.215000   19.641199                0.215000          19.641199            1       True         10\n",
      "6         ExtraTreesMSE  -1.460793       0.048000    8.735133                0.048000           8.735133            1       True          7\n",
      "7       NeuralNetFastAI  -1.470044       0.027996    3.077086                0.027996           3.077086            1       True          8\n",
      "8         LightGBMLarge  -1.537830       0.017000   50.329439                0.017000          50.329439            1       True         11\n",
      "9               XGBoost  -1.563427       0.021002   50.956080                0.021002          50.956080            1       True          9\n",
      "10       KNeighborsDist  -1.687286       0.086007    0.675019                0.086007           0.675019            1       True          2\n",
      "11       KNeighborsUnif  -1.693159       0.105001    0.669028                0.105001           0.669028            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.931346       0.414002  191.497153                0.000000           0.221001            2       True         12\n",
      "1        NeuralNetTorch  -1.942123       0.217000   23.846868                0.217000          23.846868            1       True         10\n",
      "2              CatBoost  -2.080175       0.181001  161.838159                0.181001         161.838159            1       True          6\n",
      "3       NeuralNetFastAI  -2.097798       0.026001    2.870055                0.026001           2.870055            1       True          8\n",
      "4         ExtraTreesMSE  -2.106904       0.047000   10.332716                0.047000          10.332716            1       True          7\n",
      "5            LightGBMXT  -2.131178       0.016000    5.591126                0.016000           5.591126            1       True          3\n",
      "6              LightGBM  -2.139256       0.018001   12.181194                0.018001          12.181194            1       True          4\n",
      "7       RandomForestMSE  -2.147927       0.048000   59.734119                0.048000          59.734119            1       True          5\n",
      "8         LightGBMLarge  -2.187509       0.020043   41.027470                0.020043          41.027470            1       True         11\n",
      "9               XGBoost  -2.284132       0.020000   46.577626                0.020000          46.577626            1       True          9\n",
      "10       KNeighborsUnif  -2.442593       0.082003    0.674019                0.082003           0.674019            1       True          1\n",
      "11       KNeighborsDist  -2.445117       0.081001    0.673020                0.081001           0.673020            1       True          2\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.484465       0.318002  24.545677                0.001000           0.223001            2       True         12\n",
      "1        NeuralNetTorch  -1.518812       0.209001  20.704602                0.209001          20.704602            1       True         10\n",
      "2       NeuralNetFastAI  -1.557246       0.026000   2.948071                0.026000           2.948071            1       True          8\n",
      "3              CatBoost  -1.603222       0.179019  87.640613                0.179019          87.640613            1       True          6\n",
      "4       RandomForestMSE  -1.632547       0.049001  56.196526                0.049001          56.196526            1       True          5\n",
      "5              LightGBM  -1.633477       0.017000  11.741223                0.017000          11.741223            1       True          4\n",
      "6            LightGBMXT  -1.635990       0.018000   6.173609                0.018000           6.173609            1       True          3\n",
      "7         LightGBMLarge  -1.656097       0.018000  39.829871                0.018000          39.829871            1       True         11\n",
      "8         ExtraTreesMSE  -1.666414       0.049000   9.705143                0.049000           9.705143            1       True          7\n",
      "9               XGBoost  -1.749584       0.022001  48.672435                0.022001          48.672435            1       True          9\n",
      "10       KNeighborsUnif  -1.784674       0.082001   0.670003                0.082001           0.670003            1       True          1\n",
      "11       KNeighborsDist  -1.787689       0.081999   0.673024                0.081999           0.673024            1       True          2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"regorafenib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_regorafenib\\Predictor_regorafenib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"sorafenib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_sorafenib\\Predictor_sorafenib\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K51490254/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K51490254\\Predictor_BRD-K51490254\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"EX-527/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_EX-527\\Predictor_EX-527\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"bendamustine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_bendamustine\\Predictor_bendamustine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"PYR-41/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_PYR-41\\Predictor_PYR-41\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"B02/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_B02\\Predictor_B02\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.637469       0.250001  153.781924                0.001000           0.223001            2       True         12\n",
      "1              CatBoost  -1.645103       0.184001   98.471835                0.184001          98.471835            1       True          6\n",
      "2            LightGBMXT  -1.666960       0.017999    5.304045                0.017999           5.304045            1       True          3\n",
      "3       NeuralNetFastAI  -1.667346       0.027000    2.798080                0.027000           2.798080            1       True          8\n",
      "4         LightGBMLarge  -1.686371       0.019000   40.175310                0.019000          40.175310            1       True         11\n",
      "5              LightGBM  -1.691936       0.019000   12.113698                0.019000          12.113698            1       True          4\n",
      "6         ExtraTreesMSE  -1.708318       0.048000    8.925128                0.048000           8.925128            1       True          7\n",
      "7       RandomForestMSE  -1.735818       0.048000   54.321332                0.048000          54.321332            1       True          5\n",
      "8        NeuralNetTorch  -1.748038       0.218001   21.114227                0.218001          21.114227            1       True         10\n",
      "9               XGBoost  -1.864404       0.022001   44.514323                0.022001          44.514323            1       True          9\n",
      "10       KNeighborsDist  -2.045432       0.082000    0.677006                0.082000           0.677006            1       True          2\n",
      "11       KNeighborsUnif  -2.047827       0.077001    0.678020                0.077001           0.678020            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.445930       0.478000  118.574812                0.000998           0.221001            2       True         12\n",
      "1              CatBoost  -1.483232       0.181001   96.597263                0.181001          96.597263            1       True          6\n",
      "2        NeuralNetTorch  -1.495464       0.215001   21.081530                0.215001          21.081530            1       True         10\n",
      "3            LightGBMXT  -1.528289       0.017000    5.688114                0.017000           5.688114            1       True          3\n",
      "4       RandomForestMSE  -1.549333       0.048016   57.391579                0.048016          57.391579            1       True          5\n",
      "5         ExtraTreesMSE  -1.549992       0.048000    9.292249                0.048000           9.292249            1       True          7\n",
      "6        KNeighborsDist  -1.552325       0.081000    0.675018                0.081000           0.675018            1       True          2\n",
      "7        KNeighborsUnif  -1.553862       0.077000    0.674021                0.077000           0.674021            1       True          1\n",
      "8       NeuralNetFastAI  -1.569266       0.027002    2.823099                0.027002           2.823099            1       True          8\n",
      "9              LightGBM  -1.582105       0.016999   12.584709                0.016999          12.584709            1       True          4\n",
      "10        LightGBMLarge  -1.589595       0.017000   43.544134                0.017000          43.544134            1       True         11\n",
      "11              XGBoost  -1.625510       0.021000   45.262929                0.021000          45.262929            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.011189       0.252002  92.133721                0.000000           0.222001            2       True         12\n",
      "1        NeuralNetTorch  -1.030641       0.208002  44.448203                0.208002          44.448203            1       True         10\n",
      "2            LightGBMXT  -1.085866       0.017001   6.218021                0.017001           6.218021            1       True          3\n",
      "3       NeuralNetFastAI  -1.088635       0.027000   2.907035                0.027000           2.907035            1       True          8\n",
      "4       RandomForestMSE  -1.106460       0.047000  72.387124                0.047000          72.387124            1       True          5\n",
      "5              CatBoost  -1.109549       0.179001  96.087701                0.179001          96.087701            1       True          6\n",
      "6              LightGBM  -1.111993       0.017000  11.904212                0.017000          11.904212            1       True          4\n",
      "7         ExtraTreesMSE  -1.121485       0.048000  10.213704                0.048000          10.213704            1       True          7\n",
      "8         LightGBMLarge  -1.123008       0.017000  44.556482                0.017000          44.556482            1       True         11\n",
      "9               XGBoost  -1.184694       0.020998  46.408548                0.020998          46.408548            1       True          9\n",
      "10       KNeighborsDist  -1.194255       0.082001   0.675017                0.082001           0.675017            1       True          2\n",
      "11       KNeighborsUnif  -1.199980       0.081002   0.672022                0.081002           0.672022            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.405707       0.322002  96.405343                0.001000           0.225001            2       True         12\n",
      "1              CatBoost  -1.407760       0.178000  80.232151                0.178000          80.232151            1       True          6\n",
      "2            LightGBMXT  -1.410071       0.016001   5.559601                0.016001           5.559601            1       True          3\n",
      "3              LightGBM  -1.422028       0.016000  11.380261                0.016000          11.380261            1       True          4\n",
      "4         LightGBMLarge  -1.433238       0.016001  40.299649                0.016001          40.299649            1       True         11\n",
      "5       NeuralNetFastAI  -1.433858       0.027998   2.816239                0.027998           2.816239            1       True          8\n",
      "6         ExtraTreesMSE  -1.434689       0.048000   9.709568                0.048000           9.709568            1       True          7\n",
      "7       RandomForestMSE  -1.443364       0.048000  67.244032                0.048000          67.244032            1       True          5\n",
      "8        NeuralNetTorch  -1.526880       0.211001  22.194539                0.211001          22.194539            1       True         10\n",
      "9        KNeighborsDist  -1.576773       0.079000   0.679022                0.079000           0.679022            1       True          2\n",
      "10       KNeighborsUnif  -1.583603       0.083001   0.674026                0.083001           0.674026            1       True          1\n",
      "11              XGBoost  -1.779639       0.021999  49.524224                0.021999          49.524224            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.838317       0.509029  127.498064                0.000000           0.222001            2       True         12\n",
      "1        NeuralNetTorch  -0.848664       0.211000   20.602029                0.211000          20.602029            1       True         10\n",
      "2              CatBoost  -0.854037       0.176024   91.528804                0.176024          91.528804            1       True          6\n",
      "3              LightGBM  -0.868651       0.016000   11.604741                0.016000          11.604741            1       True          4\n",
      "4       NeuralNetFastAI  -0.872825       0.026000    2.865087                0.026000           2.865087            1       True          8\n",
      "5            LightGBMXT  -0.873046       0.016998    5.186576                0.016998           5.186576            1       True          3\n",
      "6         ExtraTreesMSE  -0.878751       0.049001    8.597136                0.049001           8.597136            1       True          7\n",
      "7       RandomForestMSE  -0.879698       0.048014   51.535386                0.048014          51.535386            1       True          5\n",
      "8         LightGBMLarge  -0.930445       0.016000   37.479320                0.016000          37.479320            1       True         11\n",
      "9        KNeighborsUnif  -0.961274       0.079001    0.670018                0.079001           0.670018            1       True          1\n",
      "10       KNeighborsDist  -0.962285       0.080005    0.675402                0.080005           0.675402            1       True          2\n",
      "11              XGBoost  -1.050906       0.020000   41.307872                0.020000          41.307872            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.382396       0.407001  156.679000                0.000000           0.222001            2       True         12\n",
      "1              CatBoost  -0.386663       0.180001  132.594460                0.180001         132.594460            1       True          6\n",
      "2       NeuralNetFastAI  -0.397849       0.027000    2.814068                0.027000           2.814068            1       True          8\n",
      "3            LightGBMXT  -0.401792       0.017008    5.253051                0.017008           5.253051            1       True          3\n",
      "4         LightGBMLarge  -0.402139       0.016000   40.635181                0.016000          40.635181            1       True         11\n",
      "5       RandomForestMSE  -0.403190       0.048000   76.824454                0.048000          76.824454            1       True          5\n",
      "6              LightGBM  -0.406958       0.015999   11.691788                0.015999          11.691788            1       True          4\n",
      "7         ExtraTreesMSE  -0.408174       0.046999   10.105125                0.046999          10.105125            1       True          7\n",
      "8        NeuralNetTorch  -0.415742       0.200001   21.048471                0.200001          21.048471            1       True         10\n",
      "9               XGBoost  -0.443752       0.021001   43.148155                0.021001          43.148155            1       True          9\n",
      "10       KNeighborsDist  -0.465219       0.081001    0.673999                0.081001           0.673999            1       True          2\n",
      "11       KNeighborsUnif  -0.466751       0.081998    0.669551                0.081998           0.669551            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ML031/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_ML031\\Predictor_ML031\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"tipifarnib-P1/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_tipifarnib-P1\\Predictor_tipifarnib-P1\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD9876/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD9876\\Predictor_BRD9876\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"parthenolide/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_parthenolide\\Predictor_parthenolide\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"tandutinib/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_tandutinib\\Predictor_tandutinib\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.745806       0.296025  164.495542                0.000000           0.220003            2       True         12\n",
      "1              LightGBM  -0.779884       0.017001   12.595275                0.017001          12.595275            1       True          4\n",
      "2              CatBoost  -0.780311       0.181002  107.650745                0.181002         107.650745            1       True          6\n",
      "3       RandomForestMSE  -0.792656       0.048001   61.693693                0.048001          61.693693            1       True          5\n",
      "4         ExtraTreesMSE  -0.793507       0.048000    9.056642                0.048000           9.056642            1       True          7\n",
      "5        KNeighborsUnif  -0.794104       0.080022    0.672025                0.080022           0.672025            1       True          1\n",
      "6        KNeighborsDist  -0.798334       0.082001    0.671025                0.082001           0.671025            1       True          2\n",
      "7            LightGBMXT  -0.799217       0.017000    5.631608                0.017000           5.631608            1       True          3\n",
      "8         LightGBMLarge  -0.804855       0.018001   43.357494                0.018001          43.357494            1       True         11\n",
      "9        NeuralNetTorch  -0.831561       0.217001   20.586557                0.217001          20.586557            1       True         10\n",
      "10      NeuralNetFastAI  -0.879760       0.027960    2.831641                0.027960           2.831641            1       True          8\n",
      "11              XGBoost  -0.969417       0.021000   43.918470                0.021000          43.918470            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.947286       0.312008  34.571151                0.000000           0.220001            2       True         12\n",
      "1            LightGBMXT  -0.959377       0.017002   5.152477                0.017002           5.152477            1       True          3\n",
      "2              CatBoost  -0.988898       0.175001  98.226583                0.175001          98.226583            1       True          6\n",
      "3        NeuralNetTorch  -0.990139       0.198002  18.114953                0.198002          18.114953            1       True         10\n",
      "4              LightGBM  -0.992332       0.017000  10.417720                0.017000          10.417720            1       True          4\n",
      "5         ExtraTreesMSE  -1.004750       0.049001   8.808204                0.049001           8.808204            1       True          7\n",
      "6       RandomForestMSE  -1.014270       0.048000  50.756352                0.048000          50.756352            1       True          5\n",
      "7       NeuralNetFastAI  -1.028076       0.029000   2.879586                0.029000           2.879586            1       True          8\n",
      "8        KNeighborsDist  -1.060940       0.080003   0.666001                0.080003           0.666001            1       True          2\n",
      "9        KNeighborsUnif  -1.061122       0.079995   0.667546                0.079995           0.667546            1       True          1\n",
      "10        LightGBMLarge  -1.066400       0.015997  33.172615                0.015997          33.172615            1       True         11\n",
      "11              XGBoost  -1.244184       0.020000  38.608831                0.020000          38.608831            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.271709       0.282964  103.567183                0.000000           0.222001            2       True         12\n",
      "1              CatBoost  -1.304232       0.179001   99.766053                0.179001          99.766053            1       True          6\n",
      "2         ExtraTreesMSE  -1.335812       0.048000   10.111631                0.048000          10.111631            1       True          7\n",
      "3            LightGBMXT  -1.339599       0.017003    5.873436                0.017003           5.873436            1       True          3\n",
      "4       NeuralNetFastAI  -1.342490       0.027001    2.906072                0.027001           2.906072            1       True          8\n",
      "5       RandomForestMSE  -1.352829       0.049001   67.509301                0.049001          67.509301            1       True          5\n",
      "6              LightGBM  -1.364604       0.016999   13.071247                0.016999          13.071247            1       True          4\n",
      "7         LightGBMLarge  -1.450001       0.017000   46.385764                0.017000          46.385764            1       True         11\n",
      "8        KNeighborsUnif  -1.456012       0.076962    0.673057                0.076962           0.673057            1       True          1\n",
      "9        KNeighborsDist  -1.466499       0.082000    0.673004                0.082000           0.673004            1       True          2\n",
      "10       NeuralNetTorch  -1.505940       0.204014   27.160620                0.204014          27.160620            1       True         10\n",
      "11              XGBoost  -1.600370       0.020001   45.092495                0.020001          45.092495            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.715947       0.126002  59.724481                0.000000           0.220001            2       True         12\n",
      "1              LightGBM  -2.805904       0.017000  11.466269                0.017000          11.466269            1       True          4\n",
      "2            LightGBMXT  -2.819265       0.017000   5.623117                0.017000           5.623117            1       True          3\n",
      "3         LightGBMLarge  -2.832089       0.017001  39.816476                0.017001          39.816476            1       True         11\n",
      "4              CatBoost  -2.833405       0.179000  75.418849                0.179000          75.418849            1       True          6\n",
      "5       NeuralNetFastAI  -2.897901       0.027000   2.819064                0.027000           2.819064            1       True          8\n",
      "6        NeuralNetTorch  -2.901542       0.204021  21.657452                0.204021          21.657452            1       True         10\n",
      "7        KNeighborsUnif  -2.912292       0.089003   0.672540                0.089003           0.672540            1       True          1\n",
      "8        KNeighborsDist  -2.914729       0.081999   0.678021                0.081999           0.678021            1       True          2\n",
      "9         ExtraTreesMSE  -2.981888       0.049001   9.650713                0.049001           9.650713            1       True          7\n",
      "10      RandomForestMSE  -3.030057       0.048000  56.545758                0.048000          56.545758            1       True          5\n",
      "11              XGBoost  -3.167879       0.019999  47.365670                0.019999          47.365670            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.251209       0.259017  106.588717                0.000000           0.220001            2       True         12\n",
      "1              LightGBM  -2.257584       0.016000   12.422170                0.016000          12.422170            1       True          4\n",
      "2         ExtraTreesMSE  -2.295063       0.064001    8.997026                0.064001           8.997026            1       True          7\n",
      "3            LightGBMXT  -2.315741       0.017000    5.201568                0.017000           5.201568            1       True          3\n",
      "4              CatBoost  -2.319656       0.179016   84.949520                0.179016          84.949520            1       True          6\n",
      "5       RandomForestMSE  -2.338986       0.048001   60.102490                0.048001          60.102490            1       True          5\n",
      "6        NeuralNetTorch  -2.340980       0.216001   19.813365                0.216001          19.813365            1       True         10\n",
      "7       NeuralNetFastAI  -2.382733       0.029001    2.910147                0.029001           2.910147            1       True          8\n",
      "8         LightGBMLarge  -2.395032       0.017002   44.237103                0.017002          44.237103            1       True         11\n",
      "9               XGBoost  -2.648147       0.022000   55.156101                0.022000          55.156101            1       True          9\n",
      "10       KNeighborsDist  -2.650772       0.080002    0.670002                0.080002           0.670002            1       True          2\n",
      "11       KNeighborsUnif  -2.664780       0.076000    0.669023                0.076000           0.669023            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.939737       0.056997   61.419822                0.001000           0.220002            2       True         12\n",
      "1            LightGBMXT  -0.943028       0.017000    5.765668                0.017000           5.765668            1       True          3\n",
      "2              LightGBM  -0.996754       0.015998   11.926744                0.015998          11.926744            1       True          4\n",
      "3       RandomForestMSE  -1.000629       0.047997   51.102622                0.047997          51.102622            1       True          5\n",
      "4         ExtraTreesMSE  -1.006525       0.048001    8.993100                0.048001           8.993100            1       True          7\n",
      "5               XGBoost  -1.029931       0.022998   43.507408                0.022998          43.507408            1       True          9\n",
      "6         LightGBMLarge  -1.046586       0.018001   51.556404                0.018001          51.556404            1       True         11\n",
      "7              CatBoost  -1.059641       0.126520  154.596909                0.126520         154.596909            1       True          6\n",
      "8       NeuralNetFastAI  -1.074565       0.024001    2.615074                0.024001           2.615074            1       True          8\n",
      "9        NeuralNetTorch  -1.188679       0.212002   22.081201                0.212002          22.081201            1       True         10\n",
      "10       KNeighborsDist  -1.228739       0.079000    0.646006                0.079000           0.646006            1       True          2\n",
      "11       KNeighborsUnif  -1.232291       0.072000    0.641006                0.072000           0.641006            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"CID-5951923/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_CID-5951923\\Predictor_CID-5951923\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"DBeQ/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_DBeQ\\Predictor_DBeQ\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"LE-135/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_LE-135\\Predictor_LE-135\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD-K24690302/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD-K24690302\\Predictor_BRD-K24690302\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"ML203/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_ML203\\Predictor_ML203\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"dexamethasone/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_dexamethasone\\Predictor_dexamethasone\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"BRD8899/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_BRD8899\\Predictor_BRD8899\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.850798       0.138024  49.396889                0.000000           0.223000            2       True         12\n",
      "1         LightGBMLarge  -0.856643       0.018000  35.408751                0.018000          35.408751            1       True         11\n",
      "2              LightGBM  -0.858260       0.018001  10.579348                0.018001          10.579348            1       True          4\n",
      "3              CatBoost  -0.862169       0.123001  74.989357                0.123001          74.989357            1       True          6\n",
      "4            LightGBMXT  -0.864297       0.018000   4.859056                0.018000           4.859056            1       True          3\n",
      "5       NeuralNetFastAI  -0.867792       0.023005   2.535788                0.023005           2.535788            1       True          8\n",
      "6         ExtraTreesMSE  -0.875676       0.048000   9.001209                0.048000           9.001209            1       True          7\n",
      "7       RandomForestMSE  -0.893283       0.048000  52.914004                0.048000          52.914004            1       True          5\n",
      "8        NeuralNetTorch  -0.940800       0.202001  21.475057                0.202001          21.475057            1       True         10\n",
      "9               XGBoost  -0.978231       0.019001  43.845287                0.019001          43.845287            1       True          9\n",
      "10       KNeighborsDist  -0.987388       0.079018   0.650003                0.079018           0.650003            1       True          2\n",
      "11       KNeighborsUnif  -0.990209       0.076003   0.644528                0.076003           0.644528            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.607799       0.243002  70.675109                0.000000           0.221001            2       True         12\n",
      "1            LightGBMXT  -1.646954       0.018000   7.793204                0.018000           7.793204            1       True          3\n",
      "2       NeuralNetFastAI  -1.674749       0.025001   2.675103                0.025001           2.675103            1       True          8\n",
      "3              CatBoost  -1.687675       0.134991  96.024490                0.134991          96.024490            1       True          6\n",
      "4               XGBoost  -1.693664       0.021001  41.610217                0.021001          41.610217            1       True          9\n",
      "5         ExtraTreesMSE  -1.705268       0.049001   9.715641                0.049001           9.715641            1       True          7\n",
      "6        NeuralNetTorch  -1.730911       0.204001  21.050688                0.204001          21.050688            1       True         10\n",
      "7         LightGBMLarge  -1.755095       0.018000  36.270381                0.018000          36.270381            1       True         11\n",
      "8              LightGBM  -1.764185       0.016999  11.288291                0.016999          11.288291            1       True          4\n",
      "9       RandomForestMSE  -1.786962       0.064000  72.675480                0.064000          72.675480            1       True          5\n",
      "10       KNeighborsDist  -1.790278       0.082001   0.651021                0.082001           0.651021            1       True          2\n",
      "11       KNeighborsUnif  -1.792295       0.151005   0.660045                0.151005           0.660045            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.730255       0.484999  193.665660                0.001000           0.221001            2       True         12\n",
      "1        NeuralNetTorch  -0.763768       0.201001   23.118647                0.201001          23.118647            1       True         10\n",
      "2              CatBoost  -0.775330       0.127997  106.362880                0.127997         106.362880            1       True          6\n",
      "3              LightGBM  -0.776553       0.017000   11.627217                0.017000          11.627217            1       True          4\n",
      "4       RandomForestMSE  -0.776831       0.048000   60.519726                0.048000          60.519726            1       True          5\n",
      "5         ExtraTreesMSE  -0.794402       0.047000    9.242668                0.047000           9.242668            1       True          7\n",
      "6            LightGBMXT  -0.801709       0.018000    4.938056                0.018000           4.938056            1       True          3\n",
      "7         LightGBMLarge  -0.823659       0.018000   36.703079                0.018000          36.703079            1       True         11\n",
      "8       NeuralNetFastAI  -0.827478       0.024000    2.611068                0.024000           2.611068            1       True          8\n",
      "9               XGBoost  -0.857175       0.019999   44.139766                0.019999          44.139766            1       True          9\n",
      "10       KNeighborsDist  -0.896163       0.079999    0.650501                0.079999           0.650501            1       True          2\n",
      "11       KNeighborsUnif  -0.897816       0.076001    0.647025                0.076001           0.647025            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.786314       0.336002  131.870460                0.000000           0.223001            2       True         12\n",
      "1              CatBoost  -0.800766       0.124001  105.326901                0.124001         105.326901            1       True          6\n",
      "2        NeuralNetTorch  -0.806987       0.189001   23.762518                0.189001          23.762518            1       True         10\n",
      "3            LightGBMXT  -0.828472       0.017000    5.253113                0.017000           5.253113            1       True          3\n",
      "4              LightGBM  -0.832521       0.016000   11.798170                0.016000          11.798170            1       True          4\n",
      "5         LightGBMLarge  -0.851165       0.019003   38.134078                0.019003          38.134078            1       True         11\n",
      "6       NeuralNetFastAI  -0.858312       0.023000    2.558039                0.023000           2.558039            1       True          8\n",
      "7         ExtraTreesMSE  -0.875951       0.048000    9.657628                0.048000           9.657628            1       True          7\n",
      "8       RandomForestMSE  -0.881041       0.049001   68.929010                0.049001          68.929010            1       True          5\n",
      "9               XGBoost  -1.049094       0.020000   42.260799                0.020000          42.260799            1       True          9\n",
      "10       KNeighborsDist  -1.064280       0.084002    0.650002                0.084002           0.650002            1       True          2\n",
      "11       KNeighborsUnif  -1.065333       0.076999    0.650022                0.076999           0.650022            1       True          1\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.211884       0.089000  23.774431                0.001000           0.222999            2       True         12\n",
      "1       NeuralNetFastAI  -1.212025       0.023000   2.543551                0.023000           2.543551            1       True          8\n",
      "2              LightGBM  -1.252349       0.017000  10.979271                0.017000          10.979271            1       True          4\n",
      "3            LightGBMXT  -1.252798       0.018000   5.034075                0.018000           5.034075            1       True          3\n",
      "4              CatBoost  -1.254749       0.124001  77.896147                0.124001          77.896147            1       True          6\n",
      "5        NeuralNetTorch  -1.255760       0.202001  20.689427                0.202001          20.689427            1       True         10\n",
      "6         ExtraTreesMSE  -1.256762       0.048001  10.028609                0.048001          10.028609            1       True          7\n",
      "7         LightGBMLarge  -1.261198       0.018000  38.011796                0.018000          38.011796            1       True         11\n",
      "8       RandomForestMSE  -1.270679       0.048000  75.237823                0.048000          75.237823            1       True          5\n",
      "9               XGBoost  -1.451984       0.020000  44.850925                0.020000          44.850925            1       True          9\n",
      "10       KNeighborsUnif  -1.546240       0.076999   0.649005                0.076999           0.649005            1       True          1\n",
      "11       KNeighborsDist  -1.550194       0.079998   0.648021                0.079998           0.648021            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.717464       0.083000   26.669196                0.000000           0.226001            2       True         12\n",
      "1            LightGBMXT  -1.722532       0.018000    6.375182                0.018000           6.375182            1       True          3\n",
      "2              LightGBM  -1.795332       0.016999   10.870733                0.016999          10.870733            1       True          4\n",
      "3         ExtraTreesMSE  -1.800943       0.048001    9.197281                0.048001           9.197281            1       True          7\n",
      "4              CatBoost  -1.814706       0.129001  116.865942                0.129001         116.865942            1       True          6\n",
      "5       NeuralNetFastAI  -1.820057       0.025000    2.665114                0.025000           2.665114            1       True          8\n",
      "6         LightGBMLarge  -1.885190       0.019000   37.168197                0.019000          37.168197            1       True         11\n",
      "7       RandomForestMSE  -1.999587       0.048000   63.566687                0.048000          63.566687            1       True          5\n",
      "8        NeuralNetTorch  -2.022896       0.213001   22.297048                0.213001          22.297048            1       True         10\n",
      "9        KNeighborsUnif  -2.329584       0.105004    0.663026                0.105004           0.663026            1       True          1\n",
      "10       KNeighborsDist  -2.336853       0.089018    0.652033                0.089018           0.652033            1       True          2\n",
      "11              XGBoost  -2.381597       0.030039   42.176322                0.030039          42.176322            1       True          9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"KH-CB19/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_KH-CB19\\Predictor_KH-CB19\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"MK-0752/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_MK-0752\\Predictor_MK-0752\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"SB-431542/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_SB-431542\\Predictor_SB-431542\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"Repligen 136/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_Repligen 136\\Predictor_Repligen 136\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"OSI-930/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_OSI-930\\Predictor_OSI-930\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.749555       0.224000  23.440138                0.000000           0.223001            2       True         12\n",
      "1        NeuralNetTorch  -0.764719       0.200001  20.655554                0.200001          20.655554            1       True         10\n",
      "2       NeuralNetFastAI  -0.764961       0.023999   2.561583                0.023999           2.561583            1       True          8\n",
      "3            LightGBMXT  -0.774412       0.018000   5.068881                0.018000           5.068881            1       True          3\n",
      "4              CatBoost  -0.775554       0.123001  76.314495                0.123001          76.314495            1       True          6\n",
      "5         LightGBMLarge  -0.781090       0.024002  37.019292                0.024002          37.019292            1       True         11\n",
      "6              LightGBM  -0.788410       0.017000  10.841631                0.017000          10.841631            1       True          4\n",
      "7         ExtraTreesMSE  -0.803011       0.048001   9.830710                0.048001           9.830710            1       True          7\n",
      "8       RandomForestMSE  -0.813227       0.048000  85.318192                0.048000          85.318192            1       True          5\n",
      "9        KNeighborsDist  -0.896355       0.088001   0.649003                0.088001           0.649003            1       True          2\n",
      "10       KNeighborsUnif  -0.897461       0.078514   0.647010                0.078514           0.647010            1       True          1\n",
      "11              XGBoost  -1.004049       0.021000  40.254213                0.021000          40.254213            1       True          9\n",
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.512734       0.213002  21.423465                0.001000           0.220001            2       True         12\n",
      "1              LightGBM  -0.516719       0.017001   5.751626                0.017001           5.751626            1       True          4\n",
      "2        NeuralNetTorch  -0.518060       0.195001  15.451838                0.195001          15.451838            1       True         10\n",
      "3         LightGBMLarge  -0.530386       0.017000  18.738428                0.017000          18.738428            1       True         11\n",
      "4              CatBoost  -0.532945       0.133022  73.230350                0.133022          73.230350            1       True          6\n",
      "5            LightGBMXT  -0.534644       0.017000   3.186032                0.017000           3.186032            1       True          3\n",
      "6       NeuralNetFastAI  -0.554580       0.021000   2.483051                0.021000           2.483051            1       True          8\n",
      "7       RandomForestMSE  -0.625661       0.048000  41.673047                0.048000          41.673047            1       True          5\n",
      "8         ExtraTreesMSE  -0.628619       0.048000   6.759074                0.048000           6.759074            1       True          7\n",
      "9               XGBoost  -0.630503       0.015996  28.845090                0.015996          28.845090            1       True          9\n",
      "10       KNeighborsUnif  -0.942516       0.078999   0.638026                0.078999           0.638026            1       True          1\n",
      "11       KNeighborsDist  -0.953690       0.076000   0.636033                0.076000           0.636033            1       True          2\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.557048       0.106001    3.539109                0.000000           0.225039            2       True         12\n",
      "1       NeuralNetFastAI  -0.562794       0.025000    2.663074                0.025000           2.663074            1       True          8\n",
      "2              CatBoost  -0.603813       0.125001  125.858732                0.125001         125.858732            1       True          6\n",
      "3         ExtraTreesMSE  -0.607703       0.048001    9.655165                0.048001           9.655165            1       True          7\n",
      "4       RandomForestMSE  -0.610737       0.048000   64.281036                0.048000          64.281036            1       True          5\n",
      "5            LightGBMXT  -0.612229       0.018000    4.957970                0.018000           4.957970            1       True          3\n",
      "6         LightGBMLarge  -0.615858       0.018000   38.568418                0.018000          38.568418            1       True         11\n",
      "7              LightGBM  -0.617933       0.017000   10.770675                0.017000          10.770675            1       True          4\n",
      "8        NeuralNetTorch  -0.622684       0.202031   19.813926                0.202031          19.813926            1       True         10\n",
      "9        KNeighborsDist  -0.657055       0.081001    0.650995                0.081001           0.650995            1       True          2\n",
      "10       KNeighborsUnif  -0.658553       0.082002    0.647038                0.082002           0.647038            1       True          1\n",
      "11              XGBoost  -0.666285       0.021000   46.356517                0.021000          46.356517            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.829360       0.289527  210.752419                0.000000           0.223004            2       True         12\n",
      "1              LightGBM  -0.838669       0.019001   23.363438                0.019001          23.363438            1       True          4\n",
      "2            LightGBMXT  -0.856948       0.045001  123.633687                0.045001         123.633687            1       True          3\n",
      "3               XGBoost  -0.867621       0.021001   42.016523                0.021001          42.016523            1       True          9\n",
      "4       RandomForestMSE  -0.871225       0.065021   66.962018                0.065021          66.962018            1       True          5\n",
      "5         LightGBMLarge  -0.882662       0.020000   49.686059                0.020000          49.686059            1       True         11\n",
      "6        NeuralNetTorch  -0.886804       0.204524   21.515768                0.204524          21.515768            1       True         10\n",
      "7       NeuralNetFastAI  -0.887427       0.025000    2.726123                0.025000           2.726123            1       True          8\n",
      "8              CatBoost  -0.888078       0.128002   85.845697                0.128002          85.845697            1       True          6\n",
      "9         ExtraTreesMSE  -0.888233       0.047001   10.127269                0.047001          10.127269            1       True          7\n",
      "10       KNeighborsDist  -0.975955       0.082002    0.651381                0.082002           0.651381            1       True          2\n",
      "11       KNeighborsUnif  -0.981257       0.157005    0.652534                0.157005           0.652534            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.919420       0.145003   52.061862                0.000000           0.221003            2       True         12\n",
      "1            LightGBMXT  -0.929393       0.021001    5.908689                0.021001           5.908689            1       True          3\n",
      "2              CatBoost  -0.973040       0.124002  106.115457                0.124002         106.115457            1       True          6\n",
      "3         ExtraTreesMSE  -0.980727       0.048001    9.535149                0.048001           9.535149            1       True          7\n",
      "4         LightGBMLarge  -0.982721       0.020007   36.108399                0.020007          36.108399            1       True         11\n",
      "5       RandomForestMSE  -0.987789       0.048010   63.858567                0.048010          63.858567            1       True          5\n",
      "6              LightGBM  -1.005815       0.020008   11.572252                0.020008          11.572252            1       True          4\n",
      "7       NeuralNetFastAI  -1.009994       0.023000    2.573798                0.023000           2.573798            1       True          8\n",
      "8        NeuralNetTorch  -1.014727       0.193209   22.792400                0.193209          22.792400            1       True         10\n",
      "9        KNeighborsDist  -1.119604       0.081002    0.654006                0.081002           0.654006            1       True          2\n",
      "10       KNeighborsUnif  -1.121305       0.079001    0.649049                0.079001           0.649049            1       True          1\n",
      "11              XGBoost  -1.148443       0.020000   42.704367                0.020000          42.704367            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.245256       0.374006  121.870433                0.001000           0.225001            2       True         12\n",
      "1              CatBoost  -1.259409       0.129002   94.314011                0.129002          94.314011            1       True          6\n",
      "2            LightGBMXT  -1.272100       0.019000    5.041780                0.019000           5.041780            1       True          3\n",
      "3       NeuralNetFastAI  -1.278567       0.023000    2.642106                0.023000           2.642106            1       True          8\n",
      "4              LightGBM  -1.288507       0.020007    8.364732                0.020007           8.364732            1       True          4\n",
      "5         ExtraTreesMSE  -1.290687       0.047000    8.436201                0.047000           8.436201            1       True          7\n",
      "6         LightGBMLarge  -1.295677       0.020000   28.917243                0.020000          28.917243            1       True         11\n",
      "7       RandomForestMSE  -1.307260       0.053004   50.567314                0.053004          50.567314            1       True          5\n",
      "8        NeuralNetTorch  -1.341499       0.202003   19.647536                0.202003          19.647536            1       True         10\n",
      "9        KNeighborsDist  -1.600056       0.078000    0.651031                0.078000           0.651031            1       True          2\n",
      "10       KNeighborsUnif  -1.602375       0.134003    0.649008                0.134003           0.649008            1       True          1\n",
      "11              XGBoost  -1.758965       0.019000   36.029218                0.019000          36.029218            1       True          9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"tacrolimus/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_tacrolimus\\Predictor_tacrolimus\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"cytarabine hydrochloride/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_cytarabine hydrochloride\\Predictor_cytarabine hydrochloride\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"clofarabine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_clofarabine\\Predictor_clofarabine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"3-Cl-AHPC/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_3-Cl-AHPC\\Predictor_3-Cl-AHPC\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"CD-437/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_CD-437\\Predictor_CD-437\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"gemcitabine/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_gemcitabine\\Predictor_gemcitabine\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"mitomycin/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_mitomycin\\Predictor_mitomycin\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.486832       0.257002  71.866255                0.000000           0.224001            2       True         12\n",
      "1              LightGBM  -0.490033       0.020000  10.389867                0.020000          10.389867            1       True          4\n",
      "2            LightGBMXT  -0.494882       0.020000   4.818611                0.020000           4.818611            1       True          3\n",
      "3       NeuralNetFastAI  -0.500353       0.024000   2.521036                0.024000           2.521036            1       True          8\n",
      "4         LightGBMLarge  -0.501343       0.020001  34.614185                0.020001          34.614185            1       True         11\n",
      "5              CatBoost  -0.502891       0.124002  75.491446                0.124002          75.491446            1       True          6\n",
      "6         ExtraTreesMSE  -0.561073       0.048000   9.484198                0.048000           9.484198            1       True          7\n",
      "7        NeuralNetTorch  -0.567042       0.193001  24.117166                0.193001          24.117166            1       True         10\n",
      "8       RandomForestMSE  -0.575577       0.048000  64.762766                0.048000          64.762766            1       True          5\n",
      "9        KNeighborsDist  -0.645059       0.079996   0.658009                0.079996           0.658009            1       True          2\n",
      "10       KNeighborsUnif  -0.647992       0.081005   0.654529                0.081005           0.654529            1       True          1\n",
      "11              XGBoost  -0.705770       0.020001  38.098313                0.020001          38.098313            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -4.713418       0.377003  219.927234                0.000000           0.221002            2       True         12\n",
      "1              CatBoost  -4.920203       0.126001  133.280240                0.126001         133.280240            1       True          6\n",
      "2       NeuralNetFastAI  -4.933073       0.023000    2.676553                0.023000           2.676553            1       True          8\n",
      "3        NeuralNetTorch  -4.999760       0.208001   41.298313                0.208001          41.298313            1       True         10\n",
      "4         LightGBMLarge  -5.209568       0.020001   42.451126                0.020001          42.451126            1       True         11\n",
      "5         ExtraTreesMSE  -5.229930       0.048000    9.439132                0.048000           9.439132            1       True          7\n",
      "6       RandomForestMSE  -5.266829       0.048001   56.992600                0.048001          56.992600            1       True          5\n",
      "7            LightGBMXT  -5.303160       0.020007    6.024127                0.020007           6.024127            1       True          3\n",
      "8              LightGBM  -5.478006       0.020010   12.775243                0.020010          12.775243            1       True          4\n",
      "9        KNeighborsDist  -5.594927       0.091187    0.657009                0.091187           0.657009            1       True          2\n",
      "10       KNeighborsUnif  -5.595982       0.087018    0.657025                0.087018           0.657025            1       True          1\n",
      "11              XGBoost  -5.960523       0.021000   47.049736                0.021000          47.049736            1       True          9\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -4.830415       0.292022   83.207649                0.001000           0.221001            2       True         12\n",
      "1        NeuralNetTorch  -5.070275       0.203001   23.945080                0.203001          23.945080            1       True         10\n",
      "2            LightGBMXT  -5.130867       0.020012    5.386817                0.020012           5.386817            1       True          3\n",
      "3              CatBoost  -5.151827       0.127001  139.461410                0.127001         139.461410            1       True          6\n",
      "4       RandomForestMSE  -5.191208       0.048004   48.222357                0.048004          48.222357            1       True          5\n",
      "5              LightGBM  -5.312572       0.021003   11.694787                0.021003          11.694787            1       True          4\n",
      "6         ExtraTreesMSE  -5.368260       0.048000    9.470589                0.048000           9.470589            1       True          7\n",
      "7       NeuralNetFastAI  -5.455697       0.025999    2.608167                0.025999           2.608167            1       True          8\n",
      "8               XGBoost  -5.499455       0.022001   45.512803                0.022001          45.512803            1       True          9\n",
      "9         LightGBMLarge  -5.586797       0.021007   39.351795                0.021007          39.351795            1       True         11\n",
      "10       KNeighborsDist  -5.689252       0.082001    0.655040                0.082001           0.655040            1       True          2\n",
      "11       KNeighborsUnif  -5.690751       0.077000    0.659018                0.077000           0.659018            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -2.251820       0.334999   77.846907                0.000000           0.222001            2       True         12\n",
      "1        NeuralNetTorch  -2.380821       0.210001   23.035452                0.210001          23.035452            1       True         10\n",
      "2         ExtraTreesMSE  -2.388351       0.048000    9.915654                0.048000           9.915654            1       True          7\n",
      "3               XGBoost  -2.401715       0.022000   51.367354                0.022000          51.367354            1       True          9\n",
      "4              LightGBM  -2.415443       0.020008   12.059253                0.020008          12.059253            1       True          4\n",
      "5       NeuralNetFastAI  -2.447049       0.024000    2.567573                0.024000           2.567573            1       True          8\n",
      "6            LightGBMXT  -2.453446       0.020013    7.318602                0.020013           7.318602            1       True          3\n",
      "7       RandomForestMSE  -2.453525       0.047000   50.973171                0.047000          50.973171            1       True          5\n",
      "8              CatBoost  -2.458896       0.126001  106.036801                0.126001         106.036801            1       True          6\n",
      "9        KNeighborsUnif  -2.500518       0.078999    0.654527                0.078999           0.654527            1       True          1\n",
      "10       KNeighborsDist  -2.501083       0.081000    0.655997                0.081000           0.655997            1       True          2\n",
      "11        LightGBMLarge  -2.752028       0.020001   62.272400                0.020001          62.272400            1       True         11\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.347605       0.350008   79.887458                0.000000           0.223001            2       True         12\n",
      "1        NeuralNetTorch  -1.378136       0.203001   20.903257                0.203001          20.903257            1       True         10\n",
      "2              LightGBM  -1.477298       0.020007   12.623652                0.020007          12.623652            1       True          4\n",
      "3         ExtraTreesMSE  -1.487464       0.047000    9.206102                0.047000           9.206102            1       True          7\n",
      "4              CatBoost  -1.496181       0.126001  111.477877                0.126001         111.477877            1       True          6\n",
      "5               XGBoost  -1.512063       0.022001   42.764500                0.022001          42.764500            1       True          9\n",
      "6            LightGBMXT  -1.545399       0.020006    6.682468                0.020006           6.682468            1       True          3\n",
      "7       NeuralNetFastAI  -1.554672       0.024000    2.715051                0.024000           2.715051            1       True          8\n",
      "8       RandomForestMSE  -1.556423       0.047999   47.243442                0.047999          47.243442            1       True          5\n",
      "9        KNeighborsDist  -1.590487       0.080999    0.657997                0.080999           0.657997            1       True          2\n",
      "10       KNeighborsUnif  -1.592391       0.077026    0.652003                0.077026           0.652003            1       True          1\n",
      "11        LightGBMLarge  -1.836627       0.020004   46.128476                0.020004          46.128476            1       True         11\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -6.370097       0.211998   27.416665                0.001000           0.221001            2       True         12\n",
      "1        NeuralNetTorch  -6.453115       0.187001   24.528111                0.187001          24.528111            1       True         10\n",
      "2       NeuralNetFastAI  -7.553713       0.023997    2.667553                0.023997           2.667553            1       True          8\n",
      "3            LightGBMXT  -7.752485       0.019000    6.178078                0.019000           6.178078            1       True          3\n",
      "4         ExtraTreesMSE  -8.080738       0.048001    8.575927                0.048001           8.575927            1       True          7\n",
      "5       RandomForestMSE  -8.433642       0.048000   43.552627                0.048000          43.552627            1       True          5\n",
      "6         LightGBMLarge  -8.456958       0.020000   57.073703                0.020000          57.073703            1       True         11\n",
      "7              CatBoost  -8.490118       0.129001  690.100052                0.129001         690.100052            1       True          6\n",
      "8              LightGBM  -8.525814       0.019007   11.862177                0.019007          11.862177            1       True          4\n",
      "9               XGBoost  -8.950951       0.021000   42.355926                0.021000          42.355926            1       True          9\n",
      "10       KNeighborsDist  -9.123238       0.075999    0.653001                0.075999           0.653001            1       True          2\n",
      "11       KNeighborsUnif  -9.129074       0.076001    0.649022                0.076001           0.649022            1       True          1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"PF-184/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_PF-184\\Predictor_PF-184\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"chlorambucil/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_chlorambucil\\Predictor_chlorambucil\"\n",
      "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"SN-38/GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_SN-38\\Predictor_SN-38\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -3.941422       0.342020  99.434999                0.001000           0.223001            2       True         12\n",
      "1        NeuralNetTorch  -4.069571       0.207002  22.963875                0.207002          22.963875            1       True         10\n",
      "2         ExtraTreesMSE  -4.194486       0.049001   9.306245                0.049001           9.306245            1       True          7\n",
      "3            LightGBMXT  -4.196229       0.020008   7.650127                0.020008           7.650127            1       True          3\n",
      "4       RandomForestMSE  -4.206760       0.048001  49.799441                0.048001          49.799441            1       True          5\n",
      "5              LightGBM  -4.229255       0.021013  11.880084                0.021013          11.880084            1       True          4\n",
      "6              CatBoost  -4.323685       0.126002  98.891400                0.126002          98.891400            1       True          6\n",
      "7       NeuralNetFastAI  -4.323962       0.023997   2.694037                0.023997           2.694037            1       True          8\n",
      "8               XGBoost  -4.364976       0.019999  44.717630                0.019999          44.717630            1       True          9\n",
      "9        KNeighborsDist  -4.764881       0.080001   0.657998                0.080001           0.657998            1       True          2\n",
      "10       KNeighborsUnif  -4.796888       0.085999   0.653524                0.085999           0.653524            1       True          1\n",
      "11        LightGBMLarge  -4.919683       0.020010  49.657317                0.020010          49.657317            1       True         11\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -0.855782       0.279058   77.387820                0.000000           0.222001            2       True         12\n",
      "1              LightGBM  -0.875206       0.020007   11.256783                0.020007          11.256783            1       True          4\n",
      "2         ExtraTreesMSE  -0.909296       0.049001    9.507610                0.049001           9.507610            1       True          7\n",
      "3       RandomForestMSE  -0.909435       0.049001   48.286939                0.049001          48.286939            1       True          5\n",
      "4        NeuralNetTorch  -0.910528       0.204001   20.343904                0.204001          20.343904            1       True         10\n",
      "5            LightGBMXT  -0.922022       0.019006    5.449438                0.019006           5.449438            1       True          3\n",
      "6               XGBoost  -0.948731       0.020000   42.930102                0.020000          42.930102            1       True          9\n",
      "7       NeuralNetFastAI  -0.949533       0.035051    2.635030                0.035051           2.635030            1       True          8\n",
      "8         LightGBMLarge  -0.965217       0.020007   41.392977                0.020007          41.392977            1       True         11\n",
      "9              CatBoost  -0.969421       0.127001  148.332038                0.127001         148.332038            1       True          6\n",
      "10       KNeighborsDist  -1.094200       0.079027    0.658002                0.079027           0.658002            1       True          2\n",
      "11       KNeighborsUnif  -1.096445       0.079997    0.656023                0.079997           0.656023            1       True          1\n",
      "                  model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0   WeightedEnsemble_L2  -1.335550       0.104024  699.836067                0.001000           0.228997            2       True         12\n",
      "1               XGBoost  -1.378275       0.023999   78.781741                0.023999          78.781741            1       True          9\n",
      "2         LightGBMLarge  -1.386298       0.024017  601.011766                0.024017         601.011766            1       True         11\n",
      "3         ExtraTreesMSE  -1.404355       0.048001   10.856752                0.048001          10.856752            1       True          7\n",
      "4              LightGBM  -1.409227       0.020008   16.692369                0.020008          16.692369            1       True          4\n",
      "5            LightGBMXT  -1.410955       0.024001    7.260220                0.024001           7.260220            1       True          3\n",
      "6       RandomForestMSE  -1.414208       0.064022   61.583364                0.064022          61.583364            1       True          5\n",
      "7              CatBoost  -1.437099       0.194006  124.413647                0.194006         124.413647            1       True          6\n",
      "8       NeuralNetFastAI  -1.466983       0.035000    3.121194                0.035000           3.121194            1       True          8\n",
      "9        KNeighborsDist  -1.599236       0.092030    0.686004                0.092030           0.686004            1       True          2\n",
      "10       KNeighborsUnif  -1.610333       0.193000    0.710026                0.193000           0.710026            1       True          1\n",
      "11       NeuralNetTorch  -1.719338       0.281003   33.460513                0.281003          33.460513            1       True         10\n",
      "             model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0  RandomForestMSE  -4.720793       0.079209   51.266673                0.079209          51.266673            1       True          5\n",
      "1    ExtraTreesMSE  -4.792356       0.049002    9.344443                0.049002           9.344443            1       True          7\n",
      "2       LightGBMXT  -4.831382       0.023000   38.451992                0.023000          38.451992            1       True          3\n",
      "3  NeuralNetFastAI  -4.854063       0.030002    3.353641                0.030002           3.353641            1       True          8\n",
      "4         CatBoost  -4.977974       0.205998  683.387262                0.205998         683.387262            1       True          6\n",
      "5         LightGBM  -5.041591       0.022002   31.770318                0.022002          31.770318            1       True          4\n",
      "6   KNeighborsDist  -5.175079       0.095000    1.946046                0.095000           1.946046            1       True          2\n",
      "7   KNeighborsUnif  -5.188632       0.223019    2.430072                0.223019           2.430072            1       True          1\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'WeightedEnsemble_L2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m temp \u001b[38;5;241m=\u001b[39m [label]\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m valScoreReuslt[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m---> 29\u001b[0m     temp\u001b[38;5;241m.\u001b[39mappend(\u001b[43mmodelScoreDict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[0;32m     30\u001b[0m valScoreReuslt \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [temp]\n",
      "\u001b[1;31mKeyError\u001b[0m: 'WeightedEnsemble_L2'"
     ]
    }
   ],
   "source": [
    "for label in labels:\n",
    "    if label == 'neuronal differentiation inducer III':\n",
    "        continue  # Skip the 'neuronal differentiation inducer III' label and continue with the next iteration\n",
    "#     try:\n",
    "#         if len(os.listdir(label)) > 0:\n",
    "#              continue\n",
    "#     except:\n",
    "#         print('Working on first time training of ' + label)\n",
    "    \n",
    "    labelData = labelsDataOriginal[[label]]    \n",
    "    trainDataSet = pd.concat([trainData, labelData], axis = 1)\n",
    "    \n",
    "    #first time training \n",
    "    save_path = label + '/' + 'GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Model_' + label \n",
    "    multi_predictor = MultilabelPredictor(labels=[label], problem_types=problem_types, eval_metrics=eval_metrics, path=save_path)\n",
    "    #multi_predictor.fit(trainDataSet, time_limit=time_limit)\n",
    "    multi_predictor = MultilabelPredictor.load(save_path) \n",
    "    try:\n",
    "        predictor = multi_predictor.get_predictor(label)\n",
    "        modleValScore = predictor.leaderboard()\n",
    "    except:\n",
    "        skipped.append(label)\n",
    "        continue\n",
    "    modelScoreDict = {}\n",
    "    for index, row in modleValScore.iterrows():\n",
    "        modelScoreDict[row['model']] = row['score_val']\n",
    "    temp = [label]\n",
    "    for model in valScoreReuslt[0][1:]:\n",
    "        temp.append(modelScoreDict[model])\n",
    "    valScoreReuslt += [temp]\n",
    "    \n",
    "    #result = multi_predictor.predict(predictData)\n",
    "    #result.to_csv(label + '/' + 'GiloML_predictDrugAUC_Full_Feature_Medium_Quality_Result_' + label+ '.csv')\n",
    "    \n",
    "#     #get feature importance\n",
    "#     predictor = multi_predictor.get_predictor(label)\n",
    "#     feature_importance = predictor.feature_importance(trainDataSet[trainDataSet[label] > float('-inf')].astype('float16'), num_shuffle_sets = 3)\n",
    "#     feature_importance.to_csv(label + '/' + \"GlioML_feature_importance_\" + label + \".csv\")\n",
    "    \n",
    "#     del labelData, trainDataSet, feature_importance, multi_predictor\n",
    "#     #only consider feature has positive contribution\n",
    "#     feature_importance = feature_importance[feature_importance['importance'] > 0]\n",
    "    \n",
    "#     #Evaluation\n",
    "#     evaaluateResult = []\n",
    "#     for top in tops:\n",
    "#         reducedfeatures = list(feature_importance[:top].index)\n",
    "#         trainDataR =  trainData[reducedfeatures]\n",
    "#         trainDataSetR = pd.concat([trainDataR,labelData],axis = 1)\n",
    "#         save_path_R = label + '/' + 'GlioML_ReducedFeature_' + str(top) + '_' + label + '_FeatureEvaluationModel'\n",
    "#         train, evaldata = train_test_split(trainDataSetR,test_size = 0.2,random_state=1112)\n",
    "#         predictor_single = MultilabelPredictor(labels=[label], problem_types=problem_types, eval_metrics=eval_metrics, path=save_path_R)\n",
    "#         predictor_single.fit(train, time_limit=time_limit)\n",
    "#         #predictor_single = MultilabelPredictor.load(save_path_R)\n",
    "#         evaluationData = predictor_single.evaluate(evaldata)\n",
    "#         evaaluateResult.append(evaluationData[label]['mean_squared_error'])\n",
    "#         if(top == 100):\n",
    "#             save_path_final = label + '/' + 'GiloML_predictDrugAUC_Top_100_Features_Best_Quality_Full_Data_Model_' + label\n",
    "#             predictor_single = MultilabelPredictor(labels = [label], problem_types = problem_types, eval_metrics = eval_metrics, path = save_path_final)\n",
    "#             predictor_single.fit(trainDataSetR, final = True, time_limit = time_limit)\n",
    "#             #result_final = predictor_single.predict(predictData[reducedfeatures])\n",
    "#             #result_final.to_csv(save_path_final + \"_Result.csv\")\n",
    "    \n",
    "#     maxPerformanceFeatureCount = tops[evaaluateResult.index(max(evaaluateResult))]\n",
    "#     evaaluateResult = [['Component Name','TOP100','TOP300','TOP600'], [label] + evaaluateResult]\n",
    "#     with open(label + '/' + 'GlioML_ReducedFeature_evalScore_' + label + '.csv', 'w', newline='') as csvfile:\n",
    "#         writer = csv.writer(csvfile)\n",
    "#         writer.writerows(evaaluateResult)\n",
    "    \n",
    "#     if maxPerformanceFeatureCount > 100: \n",
    "#         reducedfeatures = list(feature_importance[:maxPerformanceFeatureCount].index)\n",
    "#         trainDataR =  trainData[reducedfeatures]\n",
    "#         trainDataSetR = pd.concat([trainDataR,labelData],axis = 1)\n",
    "#         save_path_final = label + '/' + 'GiloML_predictDrugAUC_Top_' + str(maxPerformanceFeatureCount) +'_Features_Best_Quality_Full_Data_Model_' + label\n",
    "#         predictor_single = MultilabelPredictor(labels = [label], problem_types = problem_types, eval_metrics = eval_metrics, path = save_path_final)\n",
    "#         predictor_single.fit(trainDataSetR, final = True, time_limit = time_limit)\n",
    "#         #result_final = predictor_single.predict(predictData[reducedfeatures])\n",
    "#         #result_final.to_csv(save_path_final + \"_Result.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec81004b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:29:36.831681Z",
     "start_time": "2023-09-26T08:29:36.813042Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(valScoreReuslt).to_csv('modelValScore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "334f7567",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T08:29:34.166805Z",
     "start_time": "2023-09-26T08:29:34.149805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zebularine']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "73a7a944",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T09:25:46.097791Z",
     "start_time": "2023-09-26T09:25:46.088778Z"
    }
   },
   "outputs": [
    {
     "data": {
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       " 'BRD-K26531177',\n",
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       " 'neratinib',\n",
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       " 'BRD-K16147474',\n",
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       " 'BRD-K49290616',\n",
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       " 'BRD-K96431673',\n",
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       " 'epigallocatechin-3-monogallate',\n",
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       " 'CAY10576',\n",
       " 'BRD-K41334119',\n",
       " 'BRD-K27224038',\n",
       " 'BRD-K84807411',\n",
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       " 'BRD-K78574327',\n",
       " 'BRD-K42260513',\n",
       " 'IU1',\n",
       " 'LY-2157299',\n",
       " 'BCL-LZH-4',\n",
       " 'PF-4800567 hydrochloride',\n",
       " 'pandacostat',\n",
       " 'BRD6340',\n",
       " 'BEC',\n",
       " 'VAF-347',\n",
       " 'BRD-K34485477',\n",
       " 'BRD-K37390332',\n",
       " 'BRD-K44224150',\n",
       " 'procarbazine',\n",
       " 'necrostatin-1',\n",
       " 'sildenafil',\n",
       " 'cyclophosphamide',\n",
       " 'ML083',\n",
       " 'necrostatin-7',\n",
       " 'bexarotene',\n",
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       " 'daporinad',\n",
       " 'CAY10618',\n",
       " 'dacarbazine',\n",
       " 'fluorouracil',\n",
       " 'decitabine',\n",
       " 'PF-3758309',\n",
       " 'omacetaxine mepesuccinate',\n",
       " 'SNS-032',\n",
       " 'PHA-793887',\n",
       " 'leptomycin B',\n",
       " 'narciclasine',\n",
       " 'SR-II-138A',\n",
       " 'CR-1-31B',\n",
       " 'BMS-345541',\n",
       " 'dinaciclib',\n",
       " 'alvocidib',\n",
       " 'linifanib',\n",
       " 'MK-1775',\n",
       " 'AZD7762',\n",
       " 'N9-isopropylolomoucine',\n",
       " 'momelotinib',\n",
       " 'AT13387',\n",
       " 'SNX-2112',\n",
       " 'tanespimycin',\n",
       " 'NVP-231',\n",
       " 'ML311',\n",
       " 'GW-843682X',\n",
       " 'LY-2183240',\n",
       " 'BI-2536',\n",
       " 'GSK461364',\n",
       " 'triazolothiadiazine',\n",
       " 'vincristine',\n",
       " 'parbendazole',\n",
       " 'KX2-391',\n",
       " 'paclitaxel',\n",
       " 'SB-743921',\n",
       " 'rigosertib',\n",
       " 'BRD-K70511574',\n",
       " 'ceranib-2',\n",
       " 'nakiterpiosin',\n",
       " 'BRD-K66453893',\n",
       " 'SB-225002',\n",
       " 'CHM-1',\n",
       " 'FQI-1',\n",
       " 'FQI-2',\n",
       " 'ELCPK',\n",
       " 'docetaxel',\n",
       " 'tivantinib',\n",
       " 'YK 4-279',\n",
       " 'isoevodiamine',\n",
       " 'brefeldin A',\n",
       " 'ouabain',\n",
       " 'BRD-K30748066',\n",
       " 'CIL55A',\n",
       " 'MG-132',\n",
       " 'MLN2238',\n",
       " 'bortezomib',\n",
       " 'palmostatin B',\n",
       " 'pitstop2',\n",
       " 'darinaparsin',\n",
       " 'QW-BI-011',\n",
       " 'SJ-172550',\n",
       " 'IC-87114',\n",
       " 'BRD-K50799972',\n",
       " 'BRD-K55116708',\n",
       " 'fluvastatin',\n",
       " 'lovastatin',\n",
       " 'simvastatin',\n",
       " 'staurosporine',\n",
       " 'GSK-3 inhibitor IX',\n",
       " 'CHIR-99021',\n",
       " 'avicin D',\n",
       " 'CIL56',\n",
       " 'erastin',\n",
       " 'ML210',\n",
       " 'ML162',\n",
       " '1S3R-RSL-3',\n",
       " 'CIL41',\n",
       " 'CIL70',\n",
       " 'BRD-K01737880',\n",
       " 'VX-680',\n",
       " 'isoliquiritigenin',\n",
       " 'tigecycline',\n",
       " 'COL-3',\n",
       " 'BRD-K71781559',\n",
       " 'MLN2480',\n",
       " 'GDC-0879',\n",
       " 'dabrafenib',\n",
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       " 'ML320',\n",
       " 'BRD-K29313308',\n",
       " 'BRD-K88742110',\n",
       " 'BRD-A02303741',\n",
       " 'NSC 74859',\n",
       " 'AZD7545',\n",
       " 'PLX-4032',\n",
       " 'compound 1B',\n",
       " 'BRD-K99006945',\n",
       " 'fulvestrant',\n",
       " 'SKI-II',\n",
       " 'cimetidine',\n",
       " 'JW-480',\n",
       " 'itraconazole',\n",
       " 'pifithrin-mu',\n",
       " 'SB-525334',\n",
       " 'phloretin',\n",
       " 'valdecoxib',\n",
       " 'PF-750',\n",
       " 'ML239',\n",
       " 'MST-312',\n",
       " 'TW-37',\n",
       " 'gossypol',\n",
       " 'BRD1812',\n",
       " 'YM-155',\n",
       " 'BRD-K92856060',\n",
       " 'neopeltolide',\n",
       " 'KHS101',\n",
       " 'spautin-1',\n",
       " 'oligomycin A',\n",
       " 'BRD-K97651142',\n",
       " 'SR8278',\n",
       " 'GSK-J4']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "869ae1e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-26T02:01:19.122688Z",
     "start_time": "2023-09-26T02:01:19.113573Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22205832",
   "metadata": {},
   "outputs": [],
   "source": []
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