TumorTypePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "data_before_split = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n",
       "       3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,\n",
       "       8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,\n",
       "       3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,\n",
       "       1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_before_split.data[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 569, 30)\n",
      "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
      "        1.189e-01],\n",
      "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
      "        8.902e-02],\n",
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      "        8.758e-02],\n",
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      "        7.820e-02],\n",
      "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
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      "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
      "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
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      "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
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      "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
      "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'frame': None, 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
      "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
      "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
      "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
      "       'smoothness error', 'compactness error', 'concavity error',\n",
      "       'concave points error', 'symmetry error',\n",
      "       'fractal dimension error', 'worst radius', 'worst texture',\n",
      "       'worst perimeter', 'worst area', 'worst smoothness',\n",
      "       'worst compactness', 'worst concavity', 'worst concave points',\n",
      "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': 'breast_cancer.csv', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "#print(data_before_split.data[0, :])\n",
    "#x is the sample\n",
    "yes = np.array_split(data_before_split.data, data_before_split.data.shape[0])\n",
    "print((data_before_split.data - yes).shape)\n",
    "print(data_before_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_splitter(data, trainamt):\n",
    "    \"\"\"Returns training and test data sets based on the percentage of training set inputted.\"\"\"\n",
    "    copy_of = data\n",
    "    num_sample = int((data.data.shape)[0] * trainamt)\n",
    "    indices = np.arange(data.data.shape[0])\n",
    "    np.random.shuffle(indices)\n",
    "    indices = indices[:num_sample]\n",
    "    is_test = np.ones(data.data.shape[0]).astype(bool)\n",
    "    is_test[indices] = False\n",
    "    xtrain = data.data[~is_test]\n",
    "    ytrain = data.target[~indices]\n",
    "    xtest = data.data[is_test]\n",
    "    ytest = data.target[is_test]\n",
    "    return xtrain, ytrain, xtest, ytest\n",
    "split_data = data_splitter(data_before_split, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data = load_breast_cancer()\n",
    "indices = np.random.randint(data.data.shape[0], size=500)\n",
    "data.data[indices]\n",
    "is_training = np.zeros([569, ]) != 0\n",
    "is_training[indices] = True\n",
    "test = data.data[is_training]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " [0.1 0.9]\n",
      " [0.  1. ]\n",
      " [0.1 0.9]\n",
      " [1.  0. ]\n",
      " [0.  1. ]\n",
      " [1.  0. ]\n",
      " [0.1 0.9]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.1 0.9]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.1 0.9]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.2 0.8]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.  1. ]\n",
      " [0.5 0.5]\n",
      " [1.  0. ]\n",
      " [1.  0. ]\n",
      " [1.  0. ]\n",
      " [0.8 0.2]\n",
      " [1.  0. ]\n",
      " [0.  1. ]]\n"
     ]
    }
   ],
   "source": [
    "class Knn:\n",
    "    \"\"\"Knn \"\"\"\n",
    "    def __init__(self, k, threshold=0.5):\n",
    "        self.value = k\n",
    "        self.threshold = threshold\n",
    "    \n",
    "    def fit(self, xtrain, ytrain):\n",
    "        self.data = xtrain\n",
    "        self.target = ytrain\n",
    "    \n",
    "    def predict(self, xtest):\n",
    "        proba = self.predict_proba(xtest)\n",
    "        return (proba[:,1] > self.threshold).astype(np.float32)\n",
    "\n",
    "    def predict_proba(self, xtest):\n",
    "        \n",
    "        return_list = []\n",
    "        for x in xtest:\n",
    "            distances = (((self.data - x) ** 2).sum(axis=1))\n",
    "            indices = np.argsort(distances)[:self.value]\n",
    "            probability = self.target[indices].mean()\n",
    "            return_list.append(probability)\n",
    "        \n",
    "        return_array = np.array(return_list)\n",
    "        return np.stack([1- return_array, return_array], axis=1)\n",
    "            \n",
    "first_try = Knn(10)\n",
    "first_try.fit(data_before_split.data[290:], data_before_split.target[290:])\n",
    "print(first_try.predict_proba(data_before_split.data[290:]))\n",
    "#print(data_before_split.target[290:])\n",
    "#use argsort to speed up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def accuracy_check(prediction, ytest):\n",
    "    final = prediction == ytest\n",
    "    return final.astype(int).sum() / final.shape[0]\n",
    "        \n",
    "\n",
    "first_try = Knn(1)\n",
    "first_try.fit(data_before_split.data[290:], data_before_split.target[290:])\n",
    "pred = first_try.predict(data_before_split.data[290:])    \n",
    "accuracy_check(pred, data_before_split.target[290:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hs/9wxtjrjx2k718tlbtzyfl1dm0000gn/T/ipykernel_12961/3988325639.py:2: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  final = prediction == ytest\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'bool' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[326], line 12\u001b[0m\n\u001b[1;32m     10\u001b[0m     first_k\u001b[39m.\u001b[39mfit(ctrain, dtrain)\n\u001b[1;32m     11\u001b[0m     predtest \u001b[39m=\u001b[39m first_k\u001b[39m.\u001b[39mpredict(ctest)\n\u001b[0;32m---> 12\u001b[0m     input_array_list\u001b[39m.\u001b[39mappend(accuracy_check(predtest, dtest))\n\u001b[1;32m     13\u001b[0m     y_array_list\u001b[39m.\u001b[39mappend(nums)\n\u001b[1;32m     14\u001b[0m input_array \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(input_array_list)\n",
      "Cell \u001b[0;32mIn[160], line 3\u001b[0m, in \u001b[0;36maccuracy_check\u001b[0;34m(prediction, ytest)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maccuracy_check\u001b[39m(prediction, ytest):\n\u001b[1;32m      2\u001b[0m     final \u001b[39m=\u001b[39m prediction \u001b[39m==\u001b[39m ytest\n\u001b[0;32m----> 3\u001b[0m     \u001b[39mreturn\u001b[39;00m final\u001b[39m.\u001b[39;49mastype(\u001b[39mint\u001b[39m)\u001b[39m.\u001b[39msum() \u001b[39m/\u001b[39m final\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'bool' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "atrain, btrain, atest, btest = data_splitter(data_before_split, 0.5)\n",
    "y = 10\n",
    "ctrain, dtrain, ctest, dtest = data_before_split.data[:y], data_before_split.target[y:], data_before_split.data[:y], data_before_split.target[y:]\n",
    "number_of_k_in_graph = 50\n",
    "input_array_list = []\n",
    "y_array_list = []\n",
    "for nums in range(1, number_of_k_in_graph + 1):\n",
    "    first_k = Knn(nums)\n",
    "    first_k.fit(ctrain, dtrain)\n",
    "    predtest = first_k.predict(ctest)\n",
    "    input_array_list.append(accuracy_check(predtest, dtest))\n",
    "    y_array_list.append(nums)\n",
    "input_array = np.array(input_array_list)\n",
    "y_array = np.array(y_array_list)\n",
    "f = plt.figure()\n",
    "plt.scatter(y_array, input_array, color='red')\n",
    "plt.xlabel('K value')\n",
    "plt.ylabel('Accuracy of Prediction')\n",
    "plt.title('Plot of K-value Relationship with Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of Kn & Accuracy')"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "atrain, btrain, atest, btest = data_splitter(data_before_split, 0.5)\n",
    "number_of_k_in_graph = 100\n",
    "input_array_list = []\n",
    "y_array_list = []\n",
    "for nums in range(1, number_of_k_in_graph + 1):\n",
    "    neigh = KNeighborsClassifier(n_neighbors=nums)\n",
    "    neigh.fit(atrain, btrain)\n",
    "    pred = neigh.predict(atest) \n",
    "    input_array_list.append(accuracy_check(pred, btest))\n",
    "    y_array_list.append(nums)\n",
    "input_array = np.array(input_array_list)\n",
    "y_array = np.array(y_array_list)\n",
    "f = plt.figure()\n",
    "plt.scatter(y_array, input_array, color='red')\n",
    "plt.xlabel('K value')\n",
    "plt.ylabel('Accuracy of Prediction')\n",
    "plt.title('Plot of Kn & Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [0. 1.]]\n",
      "1.0\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "neb = KNeighborsClassifier(n_neighbors=1)\n",
    "c = 537\n",
    "neb.fit(data_before_split.data[:c], data_before_split.target[:c])\n",
    "print(neb.predict_proba(data_before_split.data[c:]))\n",
    "a = neb.predict(data_before_split.data[c:])\n",
    "print(accuracy_check(a,data_before_split.target[c:] ))\n",
    "print(a)\n",
    "print(data_before_split.target[c:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=0.8)\n",
    "\n",
    "my_k = Knn(10)\n",
    "my_k.fit(X_train, y_train)\n",
    "\n",
    "other_k = KNeighborsClassifier(n_neighbors=10)\n",
    "other_k.fit(X_train, y_train)\n",
    "\n",
    "#print(my_k.predict_proba(X_test) == other_k.predict_proba(X_test))\n",
    "my_pred = my_k.predict(X_test)\n",
    "sk_pred = other_k.predict(X_test)\n",
    "prediction_comparison = np.isclose(my_pred, sk_pred)\n",
    "print(prediction_comparison.all())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# def metrics(probability, threshold, label):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((pred==True) & (ytest==True)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0])"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tp(pred, ytest):\n",
    "    #the predicted as 1 and being actual 1 \n",
    "    return ((pred==True) & (ytest==True)).sum()\n",
    "\n",
    "def fp(pred, ytest):\n",
    "    #the false predictions that are 1\n",
    "    return ((pred==True) & (ytest==False)).sum()\n",
    "\n",
    "def tn(pred, ytest):\n",
    "    #the correct predictions that are 0\n",
    "    return ((pred==False) & (ytest==False)).sum()\n",
    "\n",
    "def fn(pred, ytest):\n",
    "    #the false predictions that are 0\n",
    "    return ((pred==False) & (ytest==True)).sum()\n",
    "\n",
    "def tpr(pred, label):\n",
    "    return tp(pred, label) / (tp(pred, label) + fn(pred, label))\n",
    "\n",
    "def fnr(pred, label):\n",
    "    return fn(pred, label) / (fn(pred, label) + tp(pred, label))\n",
    "\n",
    "def fpr(pred, label):\n",
    "    return fp(pred, label) / (fp(pred, label) + tn(pred, label))\n",
    "\n",
    "def accuracy(pred, ytest):\n",
    "    return (tp(pred, ytest) + tn(pred, ytest)) / ytest.shape[0]\n",
    "\n",
    "synthetic_pred = np.array([0, 1, 0, 1])\n",
    "synthetic_test = np.array([1, 1, 0, 0])\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # most used math library\n",
    "from sklearn.datasets import load_breast_cancer # sklearn is the most popular python library for data science.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = load_breast_cancer()\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(data.data, data.target, train_size=0.8)\n",
    "sk_ = Knn(10)\n",
    "sk_.fit(xtrain, ytrain)\n",
    "sk_pred = sk_.predict_proba(xtest)[:,1]\n",
    "\n",
    "idx = np.argsort(sk_pred)\n",
    "thresholds =  np.concatenate([[-1],(sk_pred[idx][1:] + sk_pred[idx][:-1]) / 2, [10] ])\n",
    "accuracies = []\n",
    "tpr_list = []\n",
    "fpr_list = []\n",
    "for t in thresholds:\n",
    "    pred = (sk_pred >= t).astype(np.float32)\n",
    "    accuracies.append(accuracy(pred, ytest))\n",
    "    tpr_list.append(tpr(pred, ytest))\n",
    "    fpr_list.append(fpr(pred, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate')"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure\n",
    "plt.plot(fpr_list, tpr_list, color='red')\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[536], line 15\u001b[0m\n\u001b[1;32m     13\u001b[0m     predtest \u001b[39m=\u001b[39m first_k\u001b[39m.\u001b[39mpredict(xtest1)\n\u001b[1;32m     14\u001b[0m     input_array_list\u001b[39m.\u001b[39mappend(nums)\n\u001b[0;32m---> 15\u001b[0m     y_array_list\u001b[39m.\u001b[39mappend(roc_auc_score(ytest1, sk_pred[:, \u001b[39m1\u001b[39;49m]))\n\u001b[1;32m     16\u001b[0m kvalues \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(input_array_list)\n\u001b[1;32m     17\u001b[0m scores \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(y_array_list)\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "xtrain1, xtest1, ytrain1, ytest1 = train_test_split(data.data, data.target, train_size=0.8)\n",
    "# sk_ = Knn(10)\n",
    "# sk_.fit(xtrain1, ytrain1)\n",
    "# sk_pred = sk_.predict_proba(xtest1)\n",
    "# score = roc_auc_score(ytest1, sk_pred[:, 1])\n",
    "f = plt.figure\n",
    "number_of_k = 20\n",
    "input_array_list = []\n",
    "y_array_list = []\n",
    "for nums in range(1, number_of_k_in_graph + 1):\n",
    "    first_k = Knn(nums)\n",
    "    first_k.fit(xtrain1, ytrain1)\n",
    "    predtest = first_k.predict(xtest1)\n",
    "    input_array_list.append(nums)\n",
    "    y_array_list.append(roc_auc_score(ytest1, sk_pred[:, 1]))\n",
    "kvalues = np.array(input_array_list)\n",
    "scores = np.array(y_array_list)\n",
    "plt.plot(kvalues, scores, color='red')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('ROC AUC Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "class Knn:\n",
    "    \"\"\"Knn \"\"\"\n",
    "    def __init__(self, k, threshold=0.5):\n",
    "        self.value = k\n",
    "        self.threshold = threshold\n",
    "    \n",
    "    def fit(self, xtrain, ytrain):\n",
    "        self.data = xtrain\n",
    "        self.target = ytrain\n",
    "    \n",
    "    def predict(self, xtest):\n",
    "        proba = self.predict_proba(xtest)\n",
    "        return (proba[:,1] > self.threshold).astype(np.float32)\n",
    "\n",
    "    def predict_proba(self, xtest):\n",
    "        \n",
    "        return_list = []\n",
    "        for x in xtest:\n",
    "            distances = (((self.data - x) ** 2).sum(axis=1))\n",
    "            indices = np.argsort(distances)[:self.value]\n",
    "            probability = self.target[indices].mean()\n",
    "            return_list.append(probability)\n",
    "        \n",
    "        return_array = np.array(return_list)\n",
    "        return np.stack([1- return_array, return_array], axis=1)\n",
    "            \n",
    "first_try = Knn(10)\n",
    "first_try.fit(data_before_split.data[290:], data_before_split.target[290:])\n",
    "print(first_try.predict_proba(data_before_split.data[290:]))\n",
    "#print(data_before_split.target[290:])\n",
    "#use argsort to speed up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation(data, trainamt, validamt, testamt):\n",
    "    xtrain_valid, ytrain_valid, testamt, testtarget = data_splitter(data, trainamt + validamt)\n",
    "    split = xtrain_valid.shape[0] // 5  \n",
    "    np.split(xtrain_valid, split)\n",
    "    np.split(ytrain_valid, split)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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