analyses/PCC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c2fb7f7-4018-4328-81ed-149a2dd09b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (678, 360) y shape: (678,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib.pyplot import figure\n",
    "plt.style.use('seaborn')\n",
    "X = np.load('XbrainRegions.npy')\n",
    "y = np.load('YbrainRegions.npy')\n",
    "\n",
    "print(f'X shape: {X.shape} y shape: {y.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78909a52-1640-484c-9ab5-1be355c991cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3750x5000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def makePCC(X , y):\n",
    "    accHist = []\n",
    "    pcs = [i+1 for i in range(X.shape[1])]\n",
    "    \n",
    "    for i in range(X.shape[1]):\n",
    "        k = i+1\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = scaler.transform(X)\n",
    "\n",
    "        pca = PCA(n_components=k)\n",
    "        pca.fit(X_scaled) \n",
    "        X_pca = pca.transform(X_scaled)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.25)\n",
    "        logisticRegr = LogisticRegression()\n",
    "        logisticRegr.fit(X_train, y_train)\n",
    "        score = logisticRegr.score(X_test, y_test)\n",
    "        accHist.append(score)\n",
    "    figure(figsize=(15,20), dpi=250)\n",
    "    plt.bar(pcs , accHist)\n",
    "    plt.xlabel('PCs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.show()\n",
    "        \n",
    "        \n",
    "        \n",
    "makePCC(X,y)\n",
    "\n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc61c05e-2677-46bb-83f5-b55281decc6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc0f118-91da-4cc1-93ed-71ebb0b50898",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}