Austin Dataset Ethnicity Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "'''Remove warnings'''\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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 = [ #Select all columns needed \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'] #Assign the class column\n",
    "\n",
    "selectedData = data[dataColumns].values \n",
    "\n",
    "selectedData = pd.DataFrame(selectedData, columns = [dataColumns]) #Make into pandas dataframe\n",
    "print(selectedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\") #Making the data a bit cleaner\n",
    "selectedData = selectedData.replace(\"NO = 2\", \"FALSE\")\n",
    "selectedData.fillna(\"NONE SPECIFIED\", inplace=True) #I think that the absense might be useful so I'm keeping it in for this\n",
    "\n",
    "print(selectedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "APD_RACE_DESC\n"
     ]
    }
   ],
   "source": [
    "print(selectedClass[0]) #Checking the right class is selected after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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', 'MIDDLE EASTERN', 'UNKNOWN', 'WHITE', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HAWAIIAN/PACIFIC ISLANDER', 'BLACK', 'HISPANIC OR LATINO'}\n"
     ]
    }
   ],
   "source": [
    "dataColumns.remove(selectedClass[0]) #Remove the class from the columns needed for training\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ASIAN', 'MIDDLE EASTERN', 'UNKNOWN', 'WHITE', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HAWAIIAN/PACIFIC ISLANDER', 'BLACK', 'HISPANIC OR LATINO'}\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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = neighbors.KNeighborsClassifier(n_neighbors=19, weights='uniform') #Define the KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Just run all the models in order to get results for all of them'''\n",
    "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": 13,
   "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 SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.362 seconds to run for KNeighborsClassifier(n_neighbors=19)\n",
      "0.599 seconds to run for LogisticRegression()\n",
      "3.166 seconds to run for SVC()\n",
      "\n",
      "KNN\n",
      "--------\n",
      "Accuracy = 0.298\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.25      0.62      0.35       430\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.31      0.22      0.25       672\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.48      0.19      0.27       697\n",
      "\n",
      "    accuracy                           0.30      1837\n",
      "   macro avg       0.13      0.13      0.11      1837\n",
      "weighted avg       0.35      0.30      0.28      1837\n",
      "\n",
      "Logistic Regression\n",
      "--------\n",
      "Accuracy = 0.431\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.00      0.00      0.00       430\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.41      0.70      0.52       672\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.47      0.46      0.46       697\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.11      0.15      0.12      1837\n",
      "weighted avg       0.33      0.43      0.37      1837\n",
      "\n",
      "SVC\n",
      "--------\n",
      "Accuracy = 0.437\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00        20\n",
      "           2       0.37      0.23      0.29       430\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.43      0.58      0.49       672\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         8\n",
      "           7       0.47      0.45      0.46       697\n",
      "\n",
      "    accuracy                           0.44      1837\n",
      "   macro avg       0.16      0.16      0.16      1837\n",
      "weighted avg       0.42      0.44      0.42      1837\n",
      "\n"
     ]
    }
   ],
   "source": [
    "displayAllModels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.256 seconds to run for KNeighborsClassifier(n_neighbors=19)\n",
      "0.738 seconds to run for LogisticRegression()\n",
      "3.517 seconds to run for SVC()\n",
      "\n",
      "KNN\n",
      "--------\n",
      "Accuracy = 0.421\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      0.26      0.30       439\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.41      0.58      0.48       683\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         6\n",
      "           7       0.48      0.39      0.43       683\n",
      "\n",
      "    accuracy                           0.42      1837\n",
      "   macro avg       0.15      0.15      0.15      1837\n",
      "weighted avg       0.41      0.42      0.41      1837\n",
      "\n",
      "Logistic Regression\n",
      "--------\n",
      "Accuracy = 0.426\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      0.04      0.07       439\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.41      0.63      0.50       683\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         6\n",
      "           7       0.45      0.49      0.47       683\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.15      0.14      0.13      1837\n",
      "weighted avg       0.40      0.43      0.38      1837\n",
      "\n",
      "SVC\n",
      "--------\n",
      "Accuracy = 0.426\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       0.36      0.22      0.27       439\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.42      0.52      0.46       683\n",
      "           5       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         6\n",
      "           7       0.46      0.48      0.47       683\n",
      "\n",
      "    accuracy                           0.43      1837\n",
      "   macro avg       0.15      0.15      0.15      1837\n",
      "weighted avg       0.41      0.43      0.41      1837\n",
      "\n"
     ]
    }
   ],
   "source": [
    "displayAllModels() #Scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "Cross validated average scaled accuracy = 0.393\n",
      "Maximum scaled accuracy = 0.426\n",
      "Minimum scaled accuracy = 0.302\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": 20,
   "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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.4 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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.25 seconds to run\n",
      "Best accuracy 0.401 using {'weights': 'uniform', 'n_neighbors': 19, 'metric': 'euclidean'}\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154.813 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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.641 seconds to run\n",
      "Best accuracy 0.43 using {'solver': 'liblinear', 'penalty': 'l2', 'C': 10}\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": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Assign the real string data to the numerical values used for training data'''\n",
    "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 4, 5, 6, 7}\n"
     ]
    }
   ],
   "source": [
    "print(set(y_test)) #Check to see which values have been tested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "0.439 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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "0.513 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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n",
      "3.834 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 = SVC() #Set the model as logistic regression\n",
    "print(\"SVC\")\n",
    "c_m()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}