From 382b78f906c18efeff3c3fbe398af10c9b85a72f Mon Sep 17 00:00:00 2001
From: "Alastair Holland (hollan84)" <hollan84@coventry.ac.uk>
Date: Thu, 29 Apr 2021 23:42:37 +0100
Subject: [PATCH] Delete 6001Test-2.ipynb

---
 6001Test-2.ipynb | 954 -----------------------------------------------
 1 file changed, 954 deletions(-)
 delete mode 100644 6001Test-2.ipynb

diff --git a/6001Test-2.ipynb b/6001Test-2.ipynb
deleted file mode 100644
index d596617..0000000
--- a/6001Test-2.ipynb
+++ /dev/null
@@ -1,954 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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": 4,
-   "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": 5,
-   "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": 6,
-   "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": 7,
-   "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": 8,
-   "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",
-      "{'BLACK', 'WHITE', 'HAWAIIAN/PACIFIC ISLANDER', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HISPANIC OR LATINO', 'UNKNOWN', 'MIDDLE EASTERN', 'ASIAN'}\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": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'BLACK', 'WHITE', 'HAWAIIAN/PACIFIC ISLANDER', 'AMERICAN INDIAN/ALASKAN NATIVE', 'HISPANIC OR LATINO', 'UNKNOWN', 'MIDDLE EASTERN', 'ASIAN'}\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": 10,
-   "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": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn = neighbors.KNeighborsClassifier(n_neighbors=11, weights='uniform') #Define the KNN algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "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": 13,
-   "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": 14,
-   "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": 15,
-   "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": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.123 seconds to run for KNeighborsClassifier(n_neighbors=11)\n",
-      "0.361 seconds to run for LogisticRegression()\n",
-      "2.391 seconds to run for SVC()\n",
-      "\n",
-      "KNN\n",
-      "--------\n",
-      "Accuracy = 0.408\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         1\n",
-      "           1       0.00      0.00      0.00        24\n",
-      "           2       0.35      0.25      0.29       437\n",
-      "           4       0.40      0.54      0.46       675\n",
-      "           5       0.00      0.00      0.00         4\n",
-      "           6       0.00      0.00      0.00         8\n",
-      "           7       0.46      0.40      0.42       688\n",
-      "\n",
-      "    accuracy                           0.41      1837\n",
-      "   macro avg       0.17      0.17      0.17      1837\n",
-      "weighted avg       0.40      0.41      0.40      1837\n",
-      "\n",
-      "Logistic Regression\n",
-      "--------\n",
-      "Accuracy = 0.399\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         1\n",
-      "           1       0.00      0.00      0.00        24\n",
-      "           2       0.27      0.04      0.06       437\n",
-      "           4       0.39      0.64      0.48       675\n",
-      "           5       0.00      0.00      0.00         4\n",
-      "           6       0.00      0.00      0.00         8\n",
-      "           7       0.43      0.42      0.43       688\n",
-      "\n",
-      "    accuracy                           0.40      1837\n",
-      "   macro avg       0.16      0.16      0.14      1837\n",
-      "weighted avg       0.37      0.40      0.35      1837\n",
-      "\n",
-      "SVC\n",
-      "--------\n",
-      "Accuracy = 0.41\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         1\n",
-      "           1       0.00      0.00      0.00        24\n",
-      "           2       0.37      0.23      0.29       437\n",
-      "           4       0.40      0.53      0.46       675\n",
-      "           5       0.00      0.00      0.00         4\n",
-      "           6       0.00      0.00      0.00         8\n",
-      "           7       0.44      0.43      0.44       688\n",
-      "\n",
-      "    accuracy                           0.41      1837\n",
-      "   macro avg       0.17      0.17      0.17      1837\n",
-      "weighted avg       0.40      0.41      0.40      1837\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "displayAllModels()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "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": 18,
-   "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",
-      "2.327 seconds to run for SVC()\n",
-      "\n",
-      "KNN\n",
-      "--------\n",
-      "Accuracy = 0.323\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         1\n",
-      "           1       0.00      0.00      0.00        19\n",
-      "           2       0.27      0.66      0.38       420\n",
-      "           3       0.00      0.00      0.00         2\n",
-      "           4       0.35      0.30      0.32       673\n",
-      "           5       0.00      0.00      0.00        10\n",
-      "           6       0.00      0.00      0.00        12\n",
-      "           7       0.55      0.16      0.25       700\n",
-      "\n",
-      "    accuracy                           0.32      1837\n",
-      "   macro avg       0.15      0.14      0.12      1837\n",
-      "weighted avg       0.40      0.32      0.30      1837\n",
-      "\n",
-      "Logistic Regression\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        19\n",
-      "           2       0.40      0.07      0.11       420\n",
-      "           3       0.00      0.00      0.00         2\n",
-      "           4       0.39      0.60      0.47       673\n",
-      "           5       0.00      0.00      0.00        10\n",
-      "           6       0.00      0.00      0.00        12\n",
-      "           7       0.47      0.48      0.48       700\n",
-      "\n",
-      "    accuracy                           0.42      1837\n",
-      "   macro avg       0.16      0.14      0.13      1837\n",
-      "weighted avg       0.41      0.42      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        19\n",
-      "           2       0.37      0.21      0.27       420\n",
-      "           3       0.00      0.00      0.00         2\n",
-      "           4       0.40      0.48      0.44       673\n",
-      "           5       0.00      0.00      0.00        10\n",
-      "           6       0.00      0.00      0.00        12\n",
-      "           7       0.47      0.53      0.50       700\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": 19,
-   "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": 20,
-   "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": 21,
-   "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": 22,
-   "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": 23,
-   "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": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "38.202 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": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6.74 seconds to run\n",
-      "Best accuracy 0.399 using {'weights': 'uniform', 'n_neighbors': 15, '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": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "110.375 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": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53.307 seconds to run\n",
-      "Best accuracy 0.43 using {'solver': 'newton-cg', '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": 28,
-   "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": 29,
-   "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": 30,
-   "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": 31,
-   "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, 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",
-      "0.282 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",
-      "0.417 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",
-      "2.679 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()"
-   ]
-  }
- ],
- "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
-}