{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d1e7188-e3fa-43eb-bf79-23afc40b5a8d",
   "metadata": {},
   "source": [
    "# Sequential Forward Feature Selection\n",
    "\n",
    "In this tutorial we go through the **Sequential Forward Feature Selection**, given a set of initial features, used to selected few \"informative\" from a pool of features. More details on the SFFS method can be found here : [MLX Tend Feature Selection](https://rasbt.github.io/mlxtend/api_subpackages/mlxtend.feature_selection/#sequentialfeatureselector)"
   ]
  },
  {
   "attachments": {
    "FS_MF.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c16ebeda",
   "metadata": {},
   "source": [
    "![FS_MF.png](attachment:FS_MF.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc21f7f-7c5c-49df-90c2-f4919b6a78aa",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb4aadbb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "\n",
    "from Hive_ML.utilities.feature_utils import data_shuffling\n",
    "import json \n",
    "from importlib.resources import as_file, files\n",
    "import Hive_ML.configs\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5703db4b-9b28-44c4-ae1a-552f241cd655",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from Hive_ML.training.models import adab_tree, random_forest, knn, decicion_tree, lda, qda, naive, svm_kernel, logistic_regression, ridge, mlp\n",
    "\n",
    "MODELS = {\n",
    "    \"rf\": random_forest,\n",
    "    \"adab\": adab_tree,\n",
    "    \"lda\": lda,\n",
    "    \"qda\": qda,\n",
    "    \"logistic_regression\": logistic_regression,\n",
    "    \"knn\": knn,\n",
    "    \"naive\": naive,\n",
    "    \"decision_tree\": decicion_tree,\n",
    "    \"svm\": svm_kernel,\n",
    "    \"ridge\": ridge,\n",
    "    \"mlp\": mlp\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "094509b8-5082-43b6-b424-6e4c7a3b709f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn import preprocessing\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debb16f2-251a-4084-9b12-41ac4f60b5d3",
   "metadata": {},
   "source": [
    "## Load Configuration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31370573",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "config_file = \"Radiodynamics_config.json\"\n",
    "\n",
    "try:\n",
    "    with open(config_file) as json_file:\n",
    "        config_dict = json.load(json_file)\n",
    "except FileNotFoundError:\n",
    "    with as_file(files(Hive_ML.configs).joinpath(config_file)) as json_path:\n",
    "        with open(json_path) as json_file:\n",
    "            config_dict = json.load(json_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9938e8ed-06d2-48cf-89e0-2dc790e8e2a4",
   "metadata": {},
   "source": [
    "## Features Loading and Aggregation\n",
    "\n",
    "Radiomics Features are aggregagated across the 3D Sequences:\n",
    "\n",
    "$F\\mu_{p,i}=\\frac{1}{N} \\sum_{n=0}^N F_{n, p, i}$\n",
    "\n",
    "$F\\sigma_{p,i}= \\sqrt{\\frac{\\sum_{n=0}^N (F_{n, p, i} - F\\mu_{p,i})^2}{N}}$\n",
    "\n",
    "$F\\Delta_{p,i}=\\frac{1}{N} \\sum_{n=0}^N |F_{n, p, i}- F\\mu_{p,i}|$\n",
    "\n",
    "with $ p = patient_p, i = feature_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5773ea14-8d01-429b-8e61-04fa769da85a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_feature_set_details(feature_set):\n",
    "\n",
    "    n_sequences = feature_set[[\"Subject_ID\", \"Sequence_Number\"]].groupby(\"Subject_ID\").count()\n",
    "    n_sequences = max(n_sequences[\"Sequence_Number\"])\n",
    "    subjects_and_labels = feature_set[[\"Subject_ID\", \"Subject_Label\"]].drop_duplicates()\n",
    "\n",
    "    subject_ids = subjects_and_labels[\"Subject_ID\"].values\n",
    "    subject_labels = subjects_and_labels[\"Subject_Label\"].values\n",
    "\n",
    "    feature_list = []\n",
    "    for sequence in range(n_sequences):\n",
    "        sequence_feature_list = []\n",
    "        for subject in subject_ids:\n",
    "            feature_set_subject = feature_set[\n",
    "                (feature_set[\"Subject_ID\"] == subject) & (feature_set[\"Sequence_Number\"] == sequence)]\n",
    "            feature_set_subject = feature_set_subject.copy(deep=True)\n",
    "            feature_set_subject.drop(\"Subject_ID\", inplace=True, axis=1)\n",
    "            feature_set_subject.drop(\"Subject_Label\", inplace=True, axis=1)\n",
    "            feature_set_subject.drop(\"Sequence_Number\", inplace=True, axis=1)\n",
    "            if feature_set_subject.values.shape[0] > 0:\n",
    "                sequence_feature_list.append(feature_set_subject.values)\n",
    "            else:\n",
    "                nan_array = np.empty((1, feature_set_subject.values.shape[1]))\n",
    "                nan_array[:] = np.nan\n",
    "                sequence_feature_list.append(nan_array)\n",
    "        feature_list.append(sequence_feature_list)\n",
    "\n",
    "    feature_set_names = feature_set.copy(deep=True)\n",
    "    feature_set_names.drop(\"Subject_ID\", inplace=True, axis=1)\n",
    "    feature_set_names.drop(\"Subject_Label\", inplace=True, axis=1)\n",
    "    feature_set_names.drop(\"Sequence_Number\", inplace=True, axis=1)\n",
    "    feature_names = feature_set_names.columns\n",
    "    return feature_list, subject_ids, subject_labels, feature_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8dd15ead-7121-47e5-84ec-cbf383531a55",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_4D_feature_stats(feature_list):\n",
    "\n",
    "    feature_arrays = np.array(feature_list).squeeze(axis=-2)\n",
    "\n",
    "    mean_features = np.nanmean(feature_arrays, axis=0)\n",
    "    sum_features = np.nansum(feature_arrays, axis=0)\n",
    "    std_features = np.nanstd(feature_arrays, axis=0)\n",
    "\n",
    "    delta_features = np.absolute(np.subtract(feature_arrays, mean_features))\n",
    "    mean_delta_features = np.nanmean(delta_features, axis=0)\n",
    "\n",
    "    return mean_features, sum_features, std_features, mean_delta_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0a42801-7ef2-4378-a14c-f0354670b929",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_folder = \"Data/DCE_MRI_dataset\"\n",
    "feature_set_filename = str(Path(data_folder).joinpath(\"Perfusion_Radiomics.xlsx\"))\n",
    "feature_set = pd.read_excel(feature_set_filename, index_col=0)\n",
    "\n",
    "feature_set = feature_set.sort_values(by=['Subject_Label','Subject_ID','Sequence_Number'])\n",
    "\n",
    "feature_list, subject_ids, subject_labels, feature_names = get_feature_set_details(feature_set)\n",
    "mean_features, sum_features, std_features, mean_delta_features = get_4D_feature_stats(feature_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d68ea6-4101-48cb-8a5c-33d79ae8e825",
   "metadata": {},
   "source": [
    "## 3D Feature Array Flattening into 2D Array\n",
    "\n",
    "$ F_{n,p,i} $ to $ F_{p,i*n} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba529b0f-d8ab-4234-9f95-267e4ff5cf40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def flatten_4D_features(feature_list, feature_names):\n",
    "    feature_arrays = np.array(feature_list).squeeze(axis=-2)\n",
    "    flat_feature_array = np.zeros((feature_arrays.shape[1], 1))\n",
    "\n",
    "    if feature_arrays.shape[0] > 1:\n",
    "        T = feature_arrays.shape[0]\n",
    "        n_features = feature_arrays.shape[2]\n",
    "        for n in range(n_features):\n",
    "            for t in range(T):\n",
    "                flat_feature_array = np.hstack(\n",
    "                    [flat_feature_array, feature_arrays[t, :, n].reshape(feature_arrays.shape[1], 1)])\n",
    "\n",
    "        flatten_feature_names = []\n",
    "        for feature_name in feature_names:\n",
    "            for t in range(T):\n",
    "                flatten_feature_names.append(\"{}_{}\".format(t, feature_name))\n",
    "        return flat_feature_array[:, 1:], flatten_feature_names\n",
    "\n",
    "    else:\n",
    "        return feature_list, feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83364fb3-b679-4391-93a2-074b68e03471",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_list_flatten, feature_names_flatten = flatten_4D_features(feature_list, feature_names)\n",
    "\n",
    "label_set = np.array(subject_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a87e8d3-6ea3-4634-bec2-2ec7b5fc9619",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Features: (352,)\n",
      "#Labels: (80,)\n"
     ]
    }
   ],
   "source": [
    "print(\"# Features: {}\".format(feature_list_flatten.shape[1:]))\n",
    "print(\"#Labels: {}\".format(label_set.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1db891-464b-4205-849b-10e7bcc96f86",
   "metadata": {},
   "source": [
    "## Feature Filtering\n",
    "\n",
    "Features where > 50% of values are identical are removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcffd4a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding: 0_original_firstorder_10Percentile\n",
      "Excluding: 0_original_firstorder_90Percentile\n",
      "Excluding: 0_original_firstorder_Entropy\n",
      "Excluding: 3_original_firstorder_Entropy\n",
      "Excluding: 0_original_firstorder_Maximum\n",
      "Excluding: 0_original_firstorder_Minimum\n",
      "Excluding: 0_original_firstorder_Range\n",
      "Excluding: 0_original_firstorder_Uniformity\n",
      "Excluding: 3_original_firstorder_Uniformity\n",
      "Excluding: 0_original_glcm_Autocorrelation\n",
      "Excluding: 3_original_glcm_Autocorrelation\n",
      "Excluding: 0_original_glcm_ClusterProminence\n",
      "Excluding: 3_original_glcm_ClusterProminence\n",
      "Excluding: 0_original_glcm_ClusterShade\n",
      "Excluding: 3_original_glcm_ClusterShade\n",
      "Excluding: 0_original_glcm_ClusterTendency\n",
      "Excluding: 3_original_glcm_ClusterTendency\n",
      "Excluding: 0_original_glcm_Contrast\n",
      "Excluding: 3_original_glcm_Contrast\n",
      "Excluding: 0_original_glcm_Correlation\n",
      "Excluding: 3_original_glcm_Correlation\n",
      "Excluding: 0_original_glcm_DifferenceAverage\n",
      "Excluding: 3_original_glcm_DifferenceAverage\n",
      "Excluding: 0_original_glcm_DifferenceEntropy\n",
      "Excluding: 3_original_glcm_DifferenceEntropy\n",
      "Excluding: 0_original_glcm_DifferenceVariance\n",
      "Excluding: 3_original_glcm_DifferenceVariance\n",
      "Excluding: 0_original_glcm_Id\n",
      "Excluding: 3_original_glcm_Id\n",
      "Excluding: 0_original_glcm_Idm\n",
      "Excluding: 3_original_glcm_Idm\n",
      "Excluding: 0_original_glcm_Idmn\n",
      "Excluding: 3_original_glcm_Idmn\n",
      "Excluding: 0_original_glcm_Idn\n",
      "Excluding: 3_original_glcm_Idn\n",
      "Excluding: 0_original_glcm_Imc1\n",
      "Excluding: 3_original_glcm_Imc1\n",
      "Excluding: 0_original_glcm_Imc2\n",
      "Excluding: 3_original_glcm_Imc2\n",
      "Excluding: 0_original_glcm_InverseVariance\n",
      "Excluding: 3_original_glcm_InverseVariance\n",
      "Excluding: 0_original_glcm_JointAverage\n",
      "Excluding: 3_original_glcm_JointAverage\n",
      "Excluding: 0_original_glcm_JointEnergy\n",
      "Excluding: 3_original_glcm_JointEnergy\n",
      "Excluding: 0_original_glcm_JointEntropy\n",
      "Excluding: 3_original_glcm_JointEntropy\n",
      "Excluding: 0_original_glcm_MCC\n",
      "Excluding: 3_original_glcm_MCC\n",
      "Excluding: 0_original_glcm_MaximumProbability\n",
      "Excluding: 3_original_glcm_MaximumProbability\n",
      "Excluding: 0_original_glcm_SumAverage\n",
      "Excluding: 3_original_glcm_SumAverage\n",
      "Excluding: 0_original_glcm_SumEntropy\n",
      "Excluding: 3_original_glcm_SumEntropy\n",
      "Excluding: 0_original_glcm_SumSquares\n",
      "Excluding: 3_original_glcm_SumSquares\n",
      "Excluding: 0_original_glrlm_GrayLevelNonUniformityNormalized\n",
      "Excluding: 3_original_glrlm_GrayLevelNonUniformityNormalized\n",
      "Excluding: 0_original_glrlm_GrayLevelVariance\n",
      "Excluding: 3_original_glrlm_GrayLevelVariance\n",
      "Excluding: 0_original_glrlm_HighGrayLevelRunEmphasis\n",
      "Excluding: 3_original_glrlm_HighGrayLevelRunEmphasis\n",
      "Excluding: 0_original_glrlm_LowGrayLevelRunEmphasis\n",
      "Excluding: 3_original_glrlm_LowGrayLevelRunEmphasis\n",
      "Excluding: 0_original_glszm_GrayLevelNonUniformity\n",
      "Excluding: 3_original_glszm_GrayLevelNonUniformity\n",
      "Excluding: 0_original_glszm_GrayLevelNonUniformityNormalized\n",
      "Excluding: 3_original_glszm_GrayLevelNonUniformityNormalized\n",
      "Excluding: 0_original_glszm_GrayLevelVariance\n",
      "Excluding: 3_original_glszm_GrayLevelVariance\n",
      "Excluding: 0_original_glszm_HighGrayLevelZoneEmphasis\n",
      "Excluding: 3_original_glszm_HighGrayLevelZoneEmphasis\n",
      "Excluding: 0_original_glszm_LowGrayLevelZoneEmphasis\n",
      "Excluding: 3_original_glszm_LowGrayLevelZoneEmphasis\n",
      "Excluding: 0_original_glszm_SizeZoneNonUniformity\n",
      "Excluding: 3_original_glszm_SizeZoneNonUniformity\n",
      "Excluding: 0_original_glszm_SizeZoneNonUniformityNormalized\n",
      "Excluding: 0_original_glszm_ZoneEntropy\n",
      "Excluding: 0_original_glszm_ZoneVariance\n",
      "Excluding: 0_original_gldm_GrayLevelVariance\n",
      "Excluding: 3_original_gldm_GrayLevelVariance\n",
      "Excluding: 0_original_gldm_HighGrayLevelEmphasis\n",
      "Excluding: 3_original_gldm_HighGrayLevelEmphasis\n",
      "Excluding: 0_original_gldm_LowGrayLevelEmphasis\n",
      "Excluding: 3_original_gldm_LowGrayLevelEmphasis\n"
     ]
    }
   ],
   "source": [
    "#features = mean_features\n",
    "features = feature_list_flatten\n",
    "feature_names = feature_names_flatten\n",
    "\n",
    "n_features = features.shape[1]\n",
    "n_subjects = features.shape[0]\n",
    "\n",
    "filtered_feature_set = []\n",
    "filtered_feature_names = []\n",
    "features = np.nan_to_num(features)\n",
    "for feature in range(n_features):\n",
    "    exclude = False\n",
    "    for feature_val in np.unique(features[:, feature]):\n",
    "        if (np.count_nonzero(features[:, feature] == feature_val) / n_subjects) > 0.5:\n",
    "            exclude = True\n",
    "            print(\"Excluding:\", feature_names[feature])\n",
    "            break\n",
    "\n",
    "    if not exclude:\n",
    "        filtered_feature_set.append(list(features[:, feature]))\n",
    "        filtered_feature_names.append(feature_names[feature])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b9b0280c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Features: 266\n",
      "#Labels: (80,)\n"
     ]
    }
   ],
   "source": [
    "feature_set = np.vstack(filtered_feature_set).T\n",
    "feature_names = filtered_feature_names\n",
    "\n",
    "print(\"# Features: {}\".format(feature_set.shape[1]))\n",
    "print(\"#Labels: {}\".format(label_set.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "837fd905",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 266)\n",
      "(16, 266)\n"
     ]
    }
   ],
   "source": [
    "train_feature_set, train_label_set,test_feature_set, test_label_set = data_shuffling(feature_set, label_set, config_dict[\"random_seed\"])\n",
    "\n",
    "print(train_feature_set.shape)\n",
    "print(test_feature_set.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b626f1f-11fb-4d7d-bdac-f3f3b886d33f",
   "metadata": {},
   "source": [
    "### 3D Features\n",
    "\n",
    "This section has to be executed ONLY for 3D feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca11d6d1-2c4a-42b3-9b17-2573eb4ec5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 80, 88)\n",
      "(65, 4, 88)\n",
      "(15, 4, 88)\n"
     ]
    }
   ],
   "source": [
    "feature_set_3D = np.array(feature_list).squeeze(-2)\n",
    "print(feature_set_3D.shape)\n",
    "\n",
    "train_feature_set, train_label_set,test_feature_set, test_label_set = data_shuffling(np.swapaxes(feature_set_3D, 0, 1), label_set, config_dict[\"random_seed\"])\n",
    "\n",
    "\n",
    "print(train_feature_set.shape)\n",
    "print(test_feature_set.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481ea6d6-660f-41d6-b016-cb98ca679f69",
   "metadata": {},
   "source": [
    "## Experiment Folder Creation\n",
    "\n",
    "*experiment_name* and the type of *aggregation* are set here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d0a2624-b2cb-48cb-bded-9b4d13076b85",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "os.environ[\"ROOT_FOLDER\"] = \"Experiments\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e15ff51b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "experiment_name = \"Perfusion_Radiomics\"\n",
    "\n",
    "aggregation = \"Flat\"\n",
    "\n",
    "experiment_dir = Path(os.environ[\"ROOT_FOLDER\"]).joinpath(\n",
    "        experiment_name,config_dict[\"feature_selection\"],aggregation,\"FS\")\n",
    "experiment_dir.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5159abdb-2d0b-478e-9029-3a510a5fb16f",
   "metadata": {},
   "source": [
    "## Sequential Forward Feature Selection\n",
    "\n",
    "The maximum number of selected features is set in *config_dict.n_features*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d61eaa79-e94d-437d-993b-4e827913c775",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def prepare_features(feature_set, label_set, train_index, aggregation, val_index = None,test_feature_set=None,test_label_set=None):\n",
    "    x_val = None\n",
    "    y_val = None\n",
    "\n",
    "    if test_feature_set is not None:\n",
    "        x_train = feature_set\n",
    "        x_val = test_feature_set\n",
    "    else:\n",
    "        x_train = feature_set[train_index, :]\n",
    "        if val_index is not None:\n",
    "            x_val = feature_set[val_index, :]\n",
    "\n",
    "    if test_label_set is not None:\n",
    "        y_train = label_set\n",
    "        y_val = test_label_set\n",
    "    else:\n",
    "        y_train = label_set[train_index]\n",
    "        if val_index is not None:\n",
    "            y_val = label_set[val_index]\n",
    "\n",
    "    if len(x_train.shape) > 2:\n",
    "        for t in range(x_train.shape[1]):\n",
    "            min_max_norm = preprocessing.MinMaxScaler(feature_range=(0, 1))\n",
    "            x_train[:, t, :] = min_max_norm.fit_transform(x_train[:, t, :])\n",
    "            if x_val is not None:\n",
    "                x_val[:, t, :] = min_max_norm.transform(x_val[:, t, :])\n",
    "\n",
    "    if aggregation == \"Mean_Norm\":\n",
    "        x_train = np.nanmean(x_train, axis=1)\n",
    "        if x_val is not None:\n",
    "            x_val = np.nanmean(x_val, axis=1)\n",
    "    elif aggregation == \"SD_Norm\":\n",
    "        x_train = np.nanstd(x_train, axis=1)\n",
    "        if x_val is not None:\n",
    "            x_val = np.nanstd(x_val, axis=1)\n",
    "\n",
    "    return x_train, y_train, x_val, y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34b767b8-b9e3-4d49-9112-0fa37d6d6f5e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def feature_normalization(x_train, x_val=None, x_test=None):\n",
    "    min_max_norm = preprocessing.MinMaxScaler(feature_range=(0, 1))\n",
    "    x_train = min_max_norm.fit_transform(x_train)\n",
    "    if x_val is not None:\n",
    "        x_val = min_max_norm.transform(x_val)\n",
    "\n",
    "    if x_test is not None:\n",
    "        x_test = min_max_norm.transform(x_test)\n",
    "\n",
    "    return x_train, x_val, x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09d4fc06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "selected_features = {}\n",
    "\n",
    "models = config_dict[\"models\"]\n",
    "\n",
    "classifier = \"qda\"\n",
    "\n",
    "selected_features[classifier] = {}\n",
    "kf = StratifiedKFold(n_splits=config_dict[\"n_folds\"], random_state=config_dict[\"random_seed\"], shuffle=True)\n",
    "for fold, (train_index, _) in enumerate(kf.split(train_feature_set, train_label_set)):\n",
    "    \n",
    "    x_train, y_train,_,_ = prepare_features(train_feature_set, train_label_set, train_index, aggregation)\n",
    "\n",
    "    n_features = config_dict[\"n_features\"]\n",
    "    if n_features > x_train.shape[1]:\n",
    "        n_features = x_train.shape[1]\n",
    "\n",
    "    x_train, _, _ = feature_normalization(x_train)\n",
    "\n",
    "    clf = MODELS[classifier](**models[classifier],random_state=config_dict[\"random_seed\"])\n",
    "    sffs_model = SFS(clf,\n",
    "                     k_features=n_features,\n",
    "                     forward=True,\n",
    "                     floating=True,\n",
    "                     scoring='roc_auc',\n",
    "                     verbose=0,\n",
    "                     n_jobs=-1,\n",
    "                     cv=5)\n",
    "    df_features_x = []\n",
    "    for x_train_row in x_train:\n",
    "        df_row = {}\n",
    "        for idx, feature_name in enumerate(feature_names):\n",
    "            df_row[feature_name] = x_train_row[idx]\n",
    "        df_features_x.append(df_row)\n",
    "\n",
    "    df_features_x = pd.DataFrame.from_records(df_features_x)\n",
    "    sffs = sffs_model.fit(df_features_x, y_train)\n",
    "\n",
    "    sffs_features = sffs.subsets_\n",
    "    for key in sffs_features:\n",
    "        sffs_features[key]['cv_scores'] = sffs_features[key]['cv_scores'].tolist()\n",
    "\n",
    "    selected_features[classifier][fold] = sffs_features\n",
    "    with open(str(Path(experiment_dir).joinpath(f\"FS_summary_{classifier}_fold_{fold}.json\")), \"w\") as f:\n",
    "        json.dump(sffs_features, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb35de8c-6ea1-4f75-9130-6b1e3241a2c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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