6001Test-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''numpy'''\n",
    "import numpy as np\n",
    "\n",
    "'''pandas'''\n",
    "import pandas as pd \n",
    "\n",
    "'''time'''\n",
    "import time\n",
    "\n",
    "'''seaborn'''\n",
    "import seaborn as sn\n",
    "\n",
    "'''sklearn'''\n",
    "from sklearn import neighbors, metrics \n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "'''matplotlib'''\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PRIMARY_KEY    REP_DATE  REP_TIME SEX       APD_RACE_DESC  \\\n",
      "0       201612402  01/01/2016      2355   F               WHITE   \n",
      "1       201620049  01/02/2016       123   M  HISPANIC OR LATINO   \n",
      "2       201620049  01/02/2016       123   M  HISPANIC OR LATINO   \n",
      "3       201620049  01/02/2016       123   F  HISPANIC OR LATINO   \n",
      "4       201612438  01/01/2016      2212   M               WHITE   \n",
      "...           ...         ...       ...  ..                 ...   \n",
      "9179  20163651685  12/30/2016      2119   F               WHITE   \n",
      "9180  20163641610  12/29/2016      2320   M  HISPANIC OR LATINO   \n",
      "9181  20163660456  12/31/2016       817   M               WHITE   \n",
      "9182  20163651875  12/30/2016      2354   M  HISPANIC OR LATINO   \n",
      "9183  20163660189  12/31/2016       211   F               BLACK   \n",
      "\n",
      "                               LOCATION PERSON_SEARCHED_DESC  \\\n",
      "0               BURNET RD / W BRAKER LN              YES = 1   \n",
      "1     W STASSNEY LN / EMERALD FOREST DR              YES = 1   \n",
      "2     W STASSNEY LN / EMERALD FOREST DR               NO = 2   \n",
      "3     W STASSNEY LN / EMERALD FOREST DR              YES = 1   \n",
      "4       200 BLOCK W ANDERSON LN SVRD EB               NO = 2   \n",
      "...                                 ...                  ...   \n",
      "9179                1906 W SLAUGHTER LN              YES = 1   \n",
      "9180                3900 S CONGRESS AVE              YES = 1   \n",
      "9181                  2100 S LAMAR BLVD              YES = 1   \n",
      "9182               2501 S IH 35 SVRD NB              YES = 1   \n",
      "9183            100 E BEN WHITE BLVD EB               NO = 2   \n",
      "\n",
      "                               REASON_FOR_STOP_DESC  SEARCH_BASED_ON_DESC  \\\n",
      "0                                  CALL FOR SERVICE  INCIDENTAL TO ARREST   \n",
      "1                                  CALL FOR SERVICE        PROBABLE CAUSE   \n",
      "2                                               NaN                   NaN   \n",
      "3                           VIOLATION OF PENAL CODE        PROBABLE CAUSE   \n",
      "4                                               NaN                   NaN   \n",
      "...                                             ...                   ...   \n",
      "9179  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST   \n",
      "9180  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS        PROBABLE CAUSE   \n",
      "9181  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST   \n",
      "9182  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST   \n",
      "9183  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS                   NaN   \n",
      "\n",
      "     SEARCH_DISC_DESC                                        RACE_KNOWN  \\\n",
      "0             NOTHING  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "1               OTHER  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "2                 NaN                                               NaN   \n",
      "3             NOTHING     YES - RACE OR ETHNICITY WAS KNOWN BEFORE STOP   \n",
      "4                 NaN                                               NaN   \n",
      "...               ...                                               ...   \n",
      "9179          NOTHING  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "9180            DRUGS  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "9181          NOTHING  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "9182          NOTHING  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "9183              NaN  NO - RACE OR ETHNICITY WAS NOT KNOWN BEFORE STOP   \n",
      "\n",
      "      X_COORDINATE  Y_COORDINATE   SECTOR  LOCAL_FIELD1  \n",
      "0          3119888      10115666  ADAM PD           7.0  \n",
      "1          3100814      10049567    FRANK           2.0  \n",
      "2          3100814      10049567    FRANK           2.0  \n",
      "3          3100814      10049567    FRANK           2.0  \n",
      "4          3125331      10098656      IDA           4.0  \n",
      "...            ...           ...      ...           ...  \n",
      "9179       3089803      10035918    FRANK           5.0  \n",
      "9180       3108121      10054692    DAVID           3.0  \n",
      "9181       3105636      10063425    DAVID           5.0  \n",
      "9182       3115068      10057440     HENR           3.0  \n",
      "9183       3108116      10054117    DAVID           3.0  \n",
      "\n",
      "[9184 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "'''Read and display data'''\n",
    "data = pd.read_csv(\"Datasets/2016-rp-arrests-1.csv\") #Load in the arrest data\n",
    "print(data) #Display the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     SEX       APD_RACE_DESC PERSON_SEARCHED_DESC  \\\n",
      "0      F               WHITE              YES = 1   \n",
      "1      M  HISPANIC OR LATINO              YES = 1   \n",
      "2      M  HISPANIC OR LATINO               NO = 2   \n",
      "3      F  HISPANIC OR LATINO              YES = 1   \n",
      "4      M               WHITE               NO = 2   \n",
      "...   ..                 ...                  ...   \n",
      "9179   F               WHITE              YES = 1   \n",
      "9180   M  HISPANIC OR LATINO              YES = 1   \n",
      "9181   M               WHITE              YES = 1   \n",
      "9182   M  HISPANIC OR LATINO              YES = 1   \n",
      "9183   F               BLACK               NO = 2   \n",
      "\n",
      "                               REASON_FOR_STOP_DESC  SEARCH_BASED_ON_DESC  \n",
      "0                                  CALL FOR SERVICE  INCIDENTAL TO ARREST  \n",
      "1                                  CALL FOR SERVICE        PROBABLE CAUSE  \n",
      "2                                               NaN                   NaN  \n",
      "3                           VIOLATION OF PENAL CODE        PROBABLE CAUSE  \n",
      "4                                               NaN                   NaN  \n",
      "...                                             ...                   ...  \n",
      "9179  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9180  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS        PROBABLE CAUSE  \n",
      "9181  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9182  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9183  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS                   NaN  \n",
      "\n",
      "[9184 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "'''Assigning attributes to use'''\n",
    "#data = data.head(n=100)\n",
    "\n",
    "dataColumns = [ \n",
    "    'SEX',\n",
    "    'APD_RACE_DESC',\n",
    "    'PERSON_SEARCHED_DESC',\n",
    "    'REASON_FOR_STOP_DESC',\n",
    "    'SEARCH_BASED_ON_DESC']\n",
    "\n",
    "selectedClass = ['APD_RACE_DESC']\n",
    "\n",
    "selectedData = data[dataColumns].values\n",
    "\n",
    "selectedData = pd.DataFrame(selectedData, columns = [dataColumns])\n",
    "print(selectedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     SEX       APD_RACE_DESC PERSON_SEARCHED_DESC  \\\n",
      "0      F               WHITE                 TRUE   \n",
      "1      M  HISPANIC OR LATINO                 TRUE   \n",
      "2      M  HISPANIC OR LATINO                FALSE   \n",
      "3      F  HISPANIC OR LATINO                 TRUE   \n",
      "4      M               WHITE                FALSE   \n",
      "...   ..                 ...                  ...   \n",
      "9179   F               WHITE                 TRUE   \n",
      "9180   M  HISPANIC OR LATINO                 TRUE   \n",
      "9181   M               WHITE                 TRUE   \n",
      "9182   M  HISPANIC OR LATINO                 TRUE   \n",
      "9183   F               BLACK                FALSE   \n",
      "\n",
      "                               REASON_FOR_STOP_DESC  SEARCH_BASED_ON_DESC  \n",
      "0                                  CALL FOR SERVICE  INCIDENTAL TO ARREST  \n",
      "1                                  CALL FOR SERVICE        PROBABLE CAUSE  \n",
      "2                                    NONE SPECIFIED        NONE SPECIFIED  \n",
      "3                           VIOLATION OF PENAL CODE        PROBABLE CAUSE  \n",
      "4                                    NONE SPECIFIED        NONE SPECIFIED  \n",
      "...                                             ...                   ...  \n",
      "9179  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9180  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS        PROBABLE CAUSE  \n",
      "9181  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9182  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS  INCIDENTAL TO ARREST  \n",
      "9183  VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS        NONE SPECIFIED  \n",
      "\n",
      "[9184 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#selectedData[['REASON_FOR_STOP_DESC']] = selectedData[['REASON_FOR_STOP_DESC']].fillna(value=\"NONE SPECIFIED\")\n",
    "\n",
    "selectedData = selectedData.replace(\"YES = 1\", \"TRUE\")\n",
    "selectedData = selectedData.replace(\"NO = 2\", \"FALSE\")\n",
    "selectedData.fillna(\"NONE SPECIFIED\", inplace=True)\n",
    "\n",
    "print(selectedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "APD_RACE_DESC\n"
     ]
    }
   ],
   "source": [
    "print(selectedClass[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['F' 'TRUE' 'CALL FOR SERVICE' 'INCIDENTAL TO ARREST']\n",
      " ['M' 'TRUE' 'CALL FOR SERVICE' 'PROBABLE CAUSE']\n",
      " ['M' 'FALSE' 'NONE SPECIFIED' 'NONE SPECIFIED']\n",
      " ...\n",
      " ['M' 'TRUE' 'VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS'\n",
      "  'INCIDENTAL TO ARREST']\n",
      " ['M' 'TRUE' 'VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS'\n",
      "  'INCIDENTAL TO ARREST']\n",
      " ['F' 'FALSE' 'VIOLATION OF TRANSPORTATION CODE/VEHICLE LAWS'\n",
      "  'NONE SPECIFIED']]            APD_RACE_DESC\n",
      "0                  WHITE\n",
      "1     HISPANIC OR LATINO\n",
      "2     HISPANIC OR LATINO\n",
      "3     HISPANIC OR LATINO\n",
      "4                  WHITE\n",
      "...                  ...\n",
      "9179               WHITE\n",
      "9180  HISPANIC OR LATINO\n",
      "9181               WHITE\n",
      "9182  HISPANIC OR LATINO\n",
      "9183               BLACK\n",
      "\n",
      "[9184 rows x 1 columns]\n",
      "(9184, 4) (9184, 1)\n",
      "{'ASIAN', 'HAWAIIAN/PACIFIC ISLANDER', 'BLACK', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HISPANIC OR LATINO', 'UNKNOWN', 'MIDDLE EASTERN', 'WHITE'}\n"
     ]
    }
   ],
   "source": [
    "dataColumns.remove(selectedClass[0])\n",
    "\n",
    "X = selectedData[dataColumns].values\n",
    "\n",
    "#Set the class\n",
    "y = selectedData[selectedClass]\n",
    "\n",
    "#X = selectedData.to_numpy()\n",
    "print(X, y)\n",
    "print(X.shape, y.shape)\n",
    "\n",
    "realYValues = y\n",
    "print(set(np.ravel(realYValues)))\n",
    "\n",
    "y = np.ravel(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ASIAN', 'HAWAIIAN/PACIFIC ISLANDER', 'BLACK', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HISPANIC OR LATINO', 'UNKNOWN', 'MIDDLE EASTERN', 'WHITE'}\n",
      "[[0 1 0 3]\n",
      " [1 1 0 6]\n",
      " [1 0 3 5]\n",
      " ...\n",
      " [1 1 9 3]\n",
      " [1 1 9 3]\n",
      " [0 0 9 5]] [7 4 4 ... 7 4 2]\n"
     ]
    }
   ],
   "source": [
    "'''Translate classification values into numerical values'''\n",
    "Le = LabelEncoder() #Use the LabelEncoder library\n",
    "for i in range(len(X[0])): #Iterate over instances of the data\n",
    "    X[:, i] = Le.fit_transform(X[:, i]) #Use fit_transform to convert values \n",
    "\n",
    "print(set(y))\n",
    "\n",
    "classificationValues = set(y)\n",
    "\n",
    "yRealValues, y = np.unique(y, return_inverse=True)\n",
    "\n",
    "y = np.ravel(y) #Put the classification into a single 1D array to avoid future warning and error messages\n",
    "print(X, y) #Ensure that the transformations have taken place correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Simple function to train the models from the data'''\n",
    "def trainModel(model):\n",
    "    model.fit(X_train, y_train) #Fit the model\n",
    "    prediction = model.predict(X_test) #Give the predictions for the y values\n",
    "    return round(metrics.accuracy_score(y_test, prediction), 3), classification_report(y_test, prediction) #Return the accuracy value and the report for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = neighbors.KNeighborsClassifier(n_neighbors=11, weights='uniform') #Define the KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) #Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainData(model):\n",
    "    start_time = time.time()  #Set the starting execution time\n",
    "    acc, rep = trainModel(model) #set knn accuracy and report values on uncleaned data\n",
    "    finish_time = round(time.time() - start_time, 3)\n",
    "    print(\"{} seconds to run for {}\".format(finish_time, model)) #Display the runtime\n",
    "    return acc, rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runAllModels():\n",
    "    knn_acc, knn_rep = trainData(knn)\n",
    "    log_acc, log_rep = trainData(LogisticRegression())\n",
    "    svc_acc, svc_rep = trainData(SVC())\n",
    "    return knn_acc, knn_rep, log_acc, log_rep, svc_acc, svc_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "def displayAllModels():\n",
    "    knn_acc, knn_rep, log_acc, log_rep, svc_acc, svc_rep = runAllModels()\n",
    "    print(\"\\nKNN\\n--------\\nAccuracy = {}\\n{}\".format(knn_acc, knn_rep)) #Display the accuracy and report for the entire dataset for knn\n",
    "    print(\"Logistic Regression\\n--------\\nAccuracy = {}\\n{}\".format(log_acc, log_rep)) #Display the accuracy and report for the entire dataset for logistic regression\n",
    "    print(\"SVC\\n--------\\nAccuracy = {}\\n{}\".format(svc_acc, svc_rep)) #Display the accuracy and report for the entire dataset for logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.141 seconds to run for KNeighborsClassifier(n_neighbors=11)\n",
      "0.364 seconds to run for LogisticRegression()\n",
      "2.4 seconds to run for SVC()\n",
      "\n",
      "KNN\n",
      "--------\n",
      "Accuracy = 0.412\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        18\n",
      "           2       0.26      0.03      0.05       456\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.39      0.70      0.50       659\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00        10\n",
      "           7       0.48      0.41      0.44       686\n",
      "\n",
      "    accuracy                           0.41      1837\n",
      "   macro avg       0.14      0.14      0.12      1837\n",
      "weighted avg       0.38      0.41      0.36      1837\n",
      "\n",
      "Logistic Regression\n",
      "--------\n",
      "Accuracy = 0.428\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        18\n",
      "           2       0.33      0.02      0.04       456\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.40      0.63      0.49       659\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00        10\n",
      "           7       0.47      0.52      0.50       686\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.15      0.15      0.13      1837\n",
      "weighted avg       0.40      0.43      0.37      1837\n",
      "\n",
      "SVC\n",
      "--------\n",
      "Accuracy = 0.428\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        18\n",
      "           2       0.35      0.06      0.10       456\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.40      0.61      0.49       659\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00        10\n",
      "           7       0.47      0.52      0.49       686\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.15      0.15      0.14      1837\n",
      "weighted avg       0.41      0.43      0.38      1837\n",
      "\n"
     ]
    }
   ],
   "source": [
    "displayAllModels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler() #Define which scaler to use\n",
    "scale_X = scaler.fit_transform(X) #Scale the entire dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(scale_X, y, test_size=0.2) #Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.125 seconds to run for KNeighborsClassifier(n_neighbors=11)\n",
      "0.301 seconds to run for LogisticRegression()\n",
      "2.422 seconds to run for SVC()\n",
      "\n",
      "KNN\n",
      "--------\n",
      "Accuracy = 0.396\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.29      0.26      0.27       405\n",
      "           4       0.39      0.55      0.46       675\n",
      "           5       0.00      0.00      0.00         9\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.49      0.34      0.40       718\n",
      "\n",
      "    accuracy                           0.40      1837\n",
      "   macro avg       0.17      0.17      0.16      1837\n",
      "weighted avg       0.40      0.40      0.39      1837\n",
      "\n",
      "Logistic Regression\n",
      "--------\n",
      "Accuracy = 0.415\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.36      0.05      0.09       405\n",
      "           4       0.38      0.64      0.48       675\n",
      "           5       0.00      0.00      0.00         9\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.47      0.44      0.45       718\n",
      "\n",
      "    accuracy                           0.42      1837\n",
      "   macro avg       0.17      0.16      0.15      1837\n",
      "weighted avg       0.40      0.42      0.37      1837\n",
      "\n",
      "SVC\n",
      "--------\n",
      "Accuracy = 0.426\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.39      0.05      0.09       405\n",
      "           4       0.39      0.66      0.49       675\n",
      "           5       0.00      0.00      0.00         9\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.49      0.44      0.47       718\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.18      0.16      0.15      1837\n",
      "weighted avg       0.42      0.43      0.38      1837\n",
      "\n"
     ]
    }
   ],
   "source": [
    "displayAllModels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val(model):\n",
    "    accuracies = cross_val_score(model, scale_X, y, cv=5, scoring='accuracy') #Cross validate the machine learning model\n",
    "    return \"Cross validated average scaled accuracy = {}\\nMaximum scaled accuracy = {}\\nMinimum scaled accuracy = {}\\n\".format(round(accuracies.mean(), 3), round(accuracies.max(), 3), round(accuracies.min(), 3)) #Return the maximum and average accuracy using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "def displayCrossValidation():\n",
    "    print(\"KNN\")\n",
    "    print(cross_val(knn)) #Display the cross validation score for knn using the entire dataset\n",
    "    print(\"\\nLogistic Regression\")\n",
    "    print(cross_val(LogisticRegression())) #Display the cross validation score for logistic regression using the entire dataset\n",
    "    print(\"\\nSVC\")\n",
    "    print(cross_val(SVC())) #Display the cross validation score for logistic regression using the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "Cross validated average scaled accuracy = 0.367\n",
      "Maximum scaled accuracy = 0.425\n",
      "Minimum scaled accuracy = 0.258\n",
      "\n",
      "\n",
      "Logistic Regression\n",
      "Cross validated average scaled accuracy = 0.429\n",
      "Maximum scaled accuracy = 0.446\n",
      "Minimum scaled accuracy = 0.402\n",
      "\n",
      "\n",
      "SVC\n",
      "Cross validated average scaled accuracy = 0.424\n",
      "Maximum scaled accuracy = 0.439\n",
      "Minimum scaled accuracy = 0.398\n",
      "\n"
     ]
    }
   ],
   "source": [
    "displayCrossValidation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Set the parameters for knn'''\n",
    "n_neighbors = range(1, 21, 2) #Sets a range for the number of neighbours to check against\n",
    "weights = ['uniform', 'distance'] #Checks the difference between distance and uniform usage of knn\n",
    "metric = ['euclidean', 'manhattan', 'minkowski'] #Set the knn metrics to check\n",
    "\n",
    "'''Set variables for grid search'''\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear'] #Sets the solvers used to optimise the algrorithm\n",
    "penalty = ['l1', 'l2'] #Used to penalise the hyperparamter\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01, 0.001] #Sets the strength of the penalty\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) #Set the cross validation parameters\n",
    "\n",
    "knn_grid = dict(n_neighbors=n_neighbors,weights=weights,metric=metric) #Set the parameters for knn\n",
    "log_grid = dict(solver=solvers, penalty=penalty, C=c_values) #Set the parameters for logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Functions to run grid search and random search'''\n",
    "def display_grid_search():\n",
    "    start_time = time.time() #Set the starting execution time\n",
    "    grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0) #Define the grid search\n",
    "    grid_result = grid_search.fit(X, y) #Fit the model\n",
    "    print(\"{} seconds to run\".format(round(time.time() - start_time, 3))) #Display the runtime \n",
    "    print(\"Best accuracy {} using {}\".format(round(grid_result.best_score_, 3), grid_result.best_params_)) #Display the best score and the best parameters for grid search\n",
    "def display_random_search():\n",
    "    start_time = time.time()  #Set the starting execution time\n",
    "    rand_search = RandomizedSearchCV(model, grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0) #Define the random search\n",
    "    rand_result = rand_search.fit(X, y) #Fit the model\n",
    "    print(\"{} seconds to run\".format(round(time.time() - start_time, 3))) #Display the runtime\n",
    "    print(\"Best accuracy {} using {}\".format(round(rand_result.best_score_, 3), rand_result.best_params_)) #Display the best score and the best parameters for random search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.684 seconds to run\n",
      "Best accuracy 0.401 using {'metric': 'euclidean', 'n_neighbors': 19, 'weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "'''Grid search with KNN'''\n",
    "grid = knn_grid #Set the grid as the ones defined for knn\n",
    "model = KNeighborsClassifier() #Set the model to knn\n",
    "display_grid_search()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.912 seconds to run\n",
      "Best accuracy 0.401 using {'weights': 'uniform', 'n_neighbors': 17, 'metric': 'minkowski'}\n"
     ]
    }
   ],
   "source": [
    "'''Random search with KNN'''\n",
    "grid = knn_grid #Set the grid as the ones defined for knn\n",
    "model = KNeighborsClassifier() #Set the model to knn\n",
    "display_random_search()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142.652 seconds to run\n",
      "Best accuracy 0.43 using {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n"
     ]
    }
   ],
   "source": [
    "'''Grid search with Logistic Regression'''\n",
    "grid = log_grid #Set the grid as the ones defined for logisic regression\n",
    "model = LogisticRegression() #Set the model to logistic regression\n",
    "display_grid_search()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.679 seconds to run\n",
      "Best accuracy 0.43 using {'solver': 'liblinear', 'penalty': 'l1', 'C': 100}\n"
     ]
    }
   ],
   "source": [
    "'''Random search with Logistic Regression'''\n",
    "grid = log_grid #Set the grid as the ones defined for logisic regression\n",
    "model = LogisticRegression() #Set the model to logistic regression\n",
    "display_random_search()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Plot a confusion matrix'''\n",
    "def c_m():\n",
    "    start_time = time.time()  #Set the starting execution time\n",
    "    y_pred = accpred() #Get the predicted values for y\n",
    "    matrix = confusion_matrix(y_test, y_pred) #Build a confusion matrix for the predicted and actual values\n",
    "    #real_vals = ['K', 'P', 'S', 'D', 'F', 'G', 'C', 'H', 'L', 'A', 'I', 'X', 'W', 'Z', 'U', 'B', 'J', 'V', 'O'] #Set the prediction values back to diagnosis\n",
    "    #real_vals = list(set(np.ravel(realYValues)))\n",
    "    real_vals = assignRealClasses()\n",
    "    df_cm = pd.DataFrame(matrix, columns=np.unique(real_vals), index = np.unique(real_vals)) #Set the columns as the real values\n",
    "    df_cm.index.name = 'Actual' #Label the x axis as Actual\n",
    "    df_cm.columns.name = 'Predicted' #Label the y axis as Predicted\n",
    "    plt.figure(figsize = (10,7)) #Set the size\n",
    "    sn.set(font_scale=1.4) #Label size\n",
    "    sn.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16}) #Font size\n",
    "    finish_time = round(time.time() - start_time, 3)\n",
    "    print(\"{} seconds to run\".format(finish_time)) #Display the runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accpred():\n",
    "    model.fit(X_train, y_train) #Fit the model\n",
    "    return model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "realClasses = {\n",
    "    0:'ASIAN', \n",
    "    1:'HAWAIIAN/PACIFIC ISLANDER', \n",
    "    2:'BLACK', \n",
    "    3:'AMERICAN INDIAN/ALASKAN NATIVE', \n",
    "    4:'HISPANIC OR LATINO', \n",
    "    5:'UNKNOWN', \n",
    "    6:'MIDDLE EASTERN', \n",
    "    7:'WHITE'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assignRealClasses():\n",
    "    real_vals = []\n",
    "    for i in set(y_test):\n",
    "        real_vals.append(realClasses[i])\n",
    "    return real_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 4, 5, 6, 7}\n"
     ]
    }
   ],
   "source": [
    "print(set(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "0.253 seconds to run\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Display the confusion matrix for knn'''\n",
    "model = knn #Set the model as knn\n",
    "print(\"KNN\")\n",
    "c_m()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "0.469 seconds to run\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Display the confusion matrix for logistic regession'''\n",
    "model = LogisticRegression() #Set the model as logistic regression\n",
    "print(\"Logistic Regression\")\n",
    "c_m()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Display the confusion matrix for logistic regession'''\n",
    "model = SVC() #Set the model as logistic regression\n",
    "print(\"SVC\")\n",
    "c_m()"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}