{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "879144d9",
   "metadata": {},
   "source": [
    "### Load MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd032860",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30da011c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit-learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f555050",
   "metadata": {},
   "outputs": [],
   "source": [
    "# common imports\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4de4331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import function to scikit-learn datasets\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "# load specified dataset (MNIST)\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n",
    "\n",
    "# print type of dataset\n",
    "type(mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5221963",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811db75a",
   "metadata": {},
   "source": [
    "### labels to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bcc19ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotting libraries\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc4b728f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert string labels to int\n",
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7113df3",
   "metadata": {},
   "source": [
    "### Prepare data for machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570f328e",
   "metadata": {},
   "source": [
    "### Identify Train Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80e1ca03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: 56000, (56000, 784)\n",
      "X_test: 14000, (14000, 784)\n",
      "y_train: 56000, (56000,)\n",
      "y_test: 14000, (14000,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=177013)\n",
    "\n",
    "print(f\"X_train: {len(X_train)}, {X_train.shape}\")\n",
    "print(f\"X_test: {len(X_test)}, {X_test.shape}\")\n",
    "print(f\"y_train: {len(y_train)}, {y_train.shape}\")\n",
    "print(f\"y_test: {len(y_test)}, {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade8a1f6",
   "metadata": {},
   "source": [
    "## Pipeline Declaration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc5896c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA, KernelPCA\n",
    "from sklearn.preprocessing import (StandardScaler, \n",
    "                                   MinMaxScaler, \n",
    "                                   MaxAbsScaler,\n",
    "                                   PowerTransformer,\n",
    "                                   Binarizer)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "n_neighbors = 3\n",
    "metric = 'euclidean'\n",
    "weights = 'distance'\n",
    "n95_components = 0.95\n",
    "n99_components = 0.99\n",
    "\n",
    "names = []\n",
    "classifiers = []\n",
    "accuracies = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e905584",
   "metadata": {},
   "source": [
    "# Crossvalidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbbb447c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# not this\n",
    "def cv_train(num,cv):\n",
    "    name = names[num]\n",
    "    clf = classifiers[num]\n",
    "    y_train_pred = cross_val_predict(clf, X_train, y_train, cv=cv, n_jobs=-1)\n",
    "    accuracy = accuracy_score(y_train, y_train_pred, normalize=True)*100\n",
    "    print(f\"Pipeline: {name} ({accuracy:.4f}%)\")\n",
    "    print(classification_report(y_train, y_train_pred))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a8240c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# also not this\n",
    "def cv_test(num):\n",
    "    name = names[num]\n",
    "    clf = classifiers[num]\n",
    "    y_test_pred = cross_val_predict(clf, X_test, y_test, cv=5, n_jobs=-1)\n",
    "    accuracy = accuracy_score(y_test, y_test_pred, normalize=True)*100\n",
    "    print(f\"Pipeline: {name} ({accuracy:.4f}%)\")\n",
    "    print(classification_report(y_test, y_test_pred))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f397cf42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# this\n",
    "def cv(num,cv_arg=10):\n",
    "    name = names[num]\n",
    "    clf = classifiers[num]\n",
    "    clf = clf.fit(X_train, y_train)\n",
    "    cv = cross_validate(clf, X_train, y_train, cv=cv_arg, n_jobs=-1)\n",
    "    # cv_clf = cv['estimator'][np.argmax(cv['test_score'])] # get the estimator where the max(test_score) on the cross validation\n",
    "    y_test_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_test_pred, normalize=True)*100\n",
    "    print(f\"Pipeline: {name} ({accuracy:.4f}%) ({cv['fit_time'][np.argmax(cv['test_score'])]:.4})\")\n",
    "    print(classification_report(y_test, y_test_pred))\n",
    "    print(pd.DataFrame.from_dict(cv))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a543706f",
   "metadata": {},
   "source": [
    "# Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45452ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "op_kNN = KNeighborsClassifier(n_neighbors=n_neighbors, n_jobs=-1, metric=metric, weights=weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03c01cd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: knn (baseline) (97.3071%) (0.1162)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.96      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.94      0.96      1411\n",
      "           9       0.96      0.97      0.96      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.125081    9.229141    0.972143\n",
      "1  0.123761    8.918457    0.971429\n",
      "2  0.125360    9.193326    0.971607\n",
      "3  0.119904    8.944645    0.970714\n",
      "4  0.244280    9.182126    0.972143\n",
      "5  0.233787    9.137941    0.972857\n",
      "6  0.123714    9.257920    0.975179\n",
      "7  0.116261    9.135718    0.972500\n",
      "8  0.116210    9.125259    0.975357\n",
      "9  0.141345    9.308920    0.973393\n"
     ]
    }
   ],
   "source": [
    "names.append('knn (baseline)')\n",
    "classifiers.append(Pipeline([\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "    \n",
    "accuracies.append(cv(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18f02d0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: scalar+knn (94.8000%) (1.146)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.95      0.93      0.94      1369\n",
      "           3       0.94      0.95      0.95      1415\n",
      "           4       0.94      0.93      0.94      1373\n",
      "           5       0.93      0.91      0.92      1257\n",
      "           6       0.96      0.97      0.97      1351\n",
      "           7       0.94      0.95      0.94      1422\n",
      "           8       0.96      0.91      0.93      1411\n",
      "           9       0.92      0.94      0.93      1449\n",
      "\n",
      "    accuracy                           0.95     14000\n",
      "   macro avg       0.95      0.95      0.95     14000\n",
      "weighted avg       0.95      0.95      0.95     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.758668    8.452150    0.949643\n",
      "1  0.885169    8.631167    0.945000\n",
      "2  1.160197    8.948529    0.949107\n",
      "3  1.353553    8.502009    0.942500\n",
      "4  0.912583    9.036203    0.948393\n",
      "5  0.833251    9.013008    0.945179\n",
      "6  0.886441    8.995505    0.943214\n",
      "7  0.935067    8.891803    0.946607\n",
      "8  1.146215    8.633170    0.951607\n",
      "9  1.234975    8.846080    0.947143\n"
     ]
    }
   ],
   "source": [
    "names.append('scalar+knn') \n",
    "classifiers.append(Pipeline([\n",
    "    ('standard', StandardScaler()),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(1,10)) # Pipeline: standard+knn (94.3714%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2e7ee09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: minmax+knn (97.3071%) (0.5789)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.96      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.94      0.96      1411\n",
      "           9       0.96      0.97      0.96      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  0.389049    8.747383    0.972143\n",
      "1  0.385594    9.063268    0.971429\n",
      "2  0.527658    8.926810    0.971786\n",
      "3  0.564983    8.949160    0.970714\n",
      "4  0.607976    9.087395    0.972143\n",
      "5  0.689083    8.948745    0.972857\n",
      "6  0.578933    8.950404    0.975179\n",
      "7  0.647489    9.248726    0.972679\n",
      "8  0.634490    9.124195    0.975179\n",
      "9  0.619997    8.861808    0.973393\n"
     ]
    }
   ],
   "source": [
    "names.append('minmax+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('minmax', MinMaxScaler()),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23ae34c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: standard+pca95+knn (95.3929%) (9.151)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1359\n",
      "           1       0.98      0.99      0.98      1594\n",
      "           2       0.96      0.94      0.95      1369\n",
      "           3       0.94      0.96      0.95      1415\n",
      "           4       0.95      0.94      0.95      1373\n",
      "           5       0.94      0.92      0.93      1257\n",
      "           6       0.96      0.97      0.97      1351\n",
      "           7       0.95      0.96      0.95      1422\n",
      "           8       0.97      0.92      0.94      1411\n",
      "           9       0.92      0.94      0.93      1449\n",
      "\n",
      "    accuracy                           0.95     14000\n",
      "   macro avg       0.95      0.95      0.95     14000\n",
      "weighted avg       0.95      0.95      0.95     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  7.882113    6.468349    0.953036\n",
      "1  8.902088    5.919568    0.950357\n",
      "2  8.750770    5.959131    0.953571\n",
      "3  7.767436    6.355160    0.945179\n",
      "4  9.307496    5.559806    0.952500\n",
      "5  7.845258    6.534233    0.950000\n",
      "6  8.874461    5.844108    0.948214\n",
      "7  7.704501    6.488686    0.950714\n",
      "8  9.151180    5.622928    0.955893\n",
      "9  7.257694    6.649508    0.953571\n"
     ]
    }
   ],
   "source": [
    "names.append('standard+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('standard', StandardScaler()),\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(3,10)) # Pipeline: standard+pca95+knn (95.0500%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cac23616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: minmax+pca95+knn (97.5500%) (8.162)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.98      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.97      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.98     14000\n",
      "   macro avg       0.98      0.98      0.98     14000\n",
      "weighted avg       0.98      0.98      0.98     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  8.170779    5.294063    0.973750\n",
      "1  8.088926    5.365682    0.972857\n",
      "2  6.857623    5.776273    0.974286\n",
      "3  7.262239    5.806243    0.973036\n",
      "4  8.057861    5.374735    0.974464\n",
      "5  7.276100    5.745563    0.975179\n",
      "6  8.202060    5.424418    0.975893\n",
      "7  8.114796    5.319392    0.975357\n",
      "8  8.162190    5.424424    0.976607\n",
      "9  7.437983    5.727555    0.975536\n"
     ]
    }
   ],
   "source": [
    "names.append('minmax+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('minmax', MinMaxScaler()),\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a57eb660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: standard+pca99+knn (94.9071%) (9.396)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.95      0.93      0.94      1369\n",
      "           3       0.94      0.95      0.95      1415\n",
      "           4       0.95      0.93      0.94      1373\n",
      "           5       0.93      0.91      0.92      1257\n",
      "           6       0.96      0.97      0.97      1351\n",
      "           7       0.94      0.95      0.95      1422\n",
      "           8       0.97      0.91      0.94      1411\n",
      "           9       0.92      0.94      0.93      1449\n",
      "\n",
      "    accuracy                           0.95     14000\n",
      "   macro avg       0.95      0.95      0.95     14000\n",
      "weighted avg       0.95      0.95      0.95     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  9.473285    6.515933    0.949821\n",
      "1  7.714788    7.746434    0.946786\n",
      "2  7.546448    7.823940    0.950179\n",
      "3  7.546017    7.754220    0.943036\n",
      "4  7.534473    7.836074    0.948750\n",
      "5  7.524864    7.952840    0.945893\n",
      "6  7.699588    7.758791    0.946071\n",
      "7  9.373224    6.639953    0.947679\n",
      "8  9.395972    6.550251    0.953214\n",
      "9  9.444257    6.667987    0.947679\n"
     ]
    }
   ],
   "source": [
    "names.append('standard+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('standard', StandardScaler()),\n",
    "    ('pca99', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(5,10)) # Pipeline: standard+pca99+knn (94.5357%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bcbedf38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: minmax+pca99+knn (97.3643%) (9.134)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.96      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  8.213089    6.006415    0.972679\n",
      "1  7.115110    6.500668    0.971429\n",
      "2  7.381336    6.424851    0.972143\n",
      "3  7.099746    6.442294    0.970179\n",
      "4  9.174922    5.636195    0.972679\n",
      "5  6.937273    6.640518    0.973214\n",
      "6  8.361929    6.048856    0.975536\n",
      "7  9.058427    5.660812    0.972857\n",
      "8  9.134326    5.704015    0.975714\n",
      "9  9.088895    5.631931    0.974286\n"
     ]
    }
   ],
   "source": [
    "names.append('minmax+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('minmax', MinMaxScaler()),\n",
    "    ('pca99', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5bc4f44b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: maxabs+pca95+knn (97.5500%) (8.568)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.98      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.97      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.98     14000\n",
      "   macro avg       0.98      0.98      0.98     14000\n",
      "weighted avg       0.98      0.98      0.98     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  7.831842    5.514181    0.973750\n",
      "1  7.772907    5.701575    0.972857\n",
      "2  8.581123    5.059241    0.974286\n",
      "3  7.486752    5.769173    0.973036\n",
      "4  7.644667    5.623410    0.974464\n",
      "5  8.568252    5.032640    0.975179\n",
      "6  7.305880    5.804784    0.975893\n",
      "7  7.243364    5.869418    0.975357\n",
      "8  8.568058    5.175154    0.976607\n",
      "9  8.545392    5.035311    0.975536\n"
     ]
    }
   ],
   "source": [
    "names.append('maxabs+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('maxabs', MaxAbsScaler()),\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a901ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: maxabs+pca99+knn (97.3643%) (7.451)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.96      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  7.066843    6.724576    0.972679\n",
      "1  8.163119    5.973942    0.971429\n",
      "2  7.298925    6.530500    0.972143\n",
      "3  8.869127    5.643595    0.970179\n",
      "4  8.898886    5.403235    0.972679\n",
      "5  7.828445    6.313535    0.973214\n",
      "6  8.803470    5.529734    0.975536\n",
      "7  8.294126    5.839177    0.972857\n",
      "8  7.450544    6.585145    0.975714\n",
      "9  7.483754    6.628288    0.974286\n"
     ]
    }
   ],
   "source": [
    "names.append('maxabs+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('maxabs', MaxAbsScaler()),\n",
    "    ('pca99', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19e87457",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/jupyterhub/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:3237: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: power+pca95+knn (94.9786%) (39.14)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1359\n",
      "           1       0.98      0.98      0.98      1594\n",
      "           2       0.95      0.94      0.95      1369\n",
      "           3       0.94      0.94      0.94      1415\n",
      "           4       0.96      0.93      0.95      1373\n",
      "           5       0.94      0.91      0.93      1257\n",
      "           6       0.95      0.98      0.96      1351\n",
      "           7       0.94      0.95      0.94      1422\n",
      "           8       0.96      0.92      0.94      1411\n",
      "           9       0.92      0.94      0.93      1449\n",
      "\n",
      "    accuracy                           0.95     14000\n",
      "   macro avg       0.95      0.95      0.95     14000\n",
      "weighted avg       0.95      0.95      0.95     14000\n",
      "\n",
      "    fit_time  score_time  test_score\n",
      "0  38.802822    5.871142    0.946429\n",
      "1  37.150519    6.993097    0.945357\n",
      "2  37.138519    7.017011    0.948929\n",
      "3  38.357648    6.108760    0.945714\n",
      "4  37.175505    7.018548    0.949107\n",
      "5  37.249800    6.882046    0.945357\n",
      "6  37.172034    6.747881    0.949643\n",
      "7  38.738593    5.878512    0.948571\n",
      "8  39.135663    5.579420    0.956607\n",
      "9  37.060059    6.904209    0.949821\n"
     ]
    }
   ],
   "source": [
    "names.append('power+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('power', PowerTransformer()),\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(9,10)) # likes to die # Pipeline: power+pca95+knn (94.3714%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6146ccb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/jupyterhub/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:3237: RuntimeWarning: divide by zero encountered in log\n",
      "  loglike = -n_samples / 2 * np.log(x_trans.var())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: power+pca99+knn (94.9786%) (37.94)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1359\n",
      "           1       0.98      0.98      0.98      1594\n",
      "           2       0.95      0.94      0.95      1369\n",
      "           3       0.94      0.94      0.94      1415\n",
      "           4       0.96      0.93      0.95      1373\n",
      "           5       0.94      0.91      0.93      1257\n",
      "           6       0.95      0.98      0.96      1351\n",
      "           7       0.94      0.95      0.94      1422\n",
      "           8       0.96      0.92      0.94      1411\n",
      "           9       0.92      0.94      0.93      1449\n",
      "\n",
      "    accuracy                           0.95     14000\n",
      "   macro avg       0.95      0.95      0.95     14000\n",
      "weighted avg       0.95      0.95      0.95     14000\n",
      "\n",
      "    fit_time  score_time  test_score\n",
      "0  39.081897    5.799132    0.946429\n",
      "1  39.650227    5.733288    0.945357\n",
      "2  36.740629    6.834802    0.948929\n",
      "3  39.349449    5.738063    0.945714\n",
      "4  38.051169    6.654780    0.949107\n",
      "5  38.139323    6.575838    0.945357\n",
      "6  36.967650    6.827009    0.949643\n",
      "7  37.961230    6.632579    0.948571\n",
      "8  37.944791    6.445367    0.956607\n",
      "9  39.280427    5.751522    0.949821\n"
     ]
    }
   ],
   "source": [
    "names.append('power+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('power', PowerTransformer()),\n",
    "    ('pca99', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(10,10)) # likes to die # Pipeline: power+pca99+knn (94.6429%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "66a7637b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: bin+pca95+knn (96.8286%) (8.674)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99      1359\n",
      "           1       0.96      0.99      0.98      1594\n",
      "           2       0.98      0.97      0.98      1369\n",
      "           3       0.96      0.96      0.96      1415\n",
      "           4       0.98      0.95      0.97      1373\n",
      "           5       0.97      0.94      0.95      1257\n",
      "           6       0.97      0.99      0.98      1351\n",
      "           7       0.97      0.97      0.97      1422\n",
      "           8       0.97      0.95      0.96      1411\n",
      "           9       0.94      0.97      0.95      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  8.605724    5.490393    0.967679\n",
      "1  7.290196    6.624226    0.969107\n",
      "2  7.169216    6.591240    0.968571\n",
      "3  7.225861    6.645801    0.965357\n",
      "4  7.196922    6.595732    0.969643\n",
      "5  7.294749    6.646243    0.966429\n",
      "6  8.720850    5.419779    0.969821\n",
      "7  8.711336    5.597409    0.967679\n",
      "8  7.221519    6.624207    0.970536\n",
      "9  8.673788    5.737247    0.970714\n"
     ]
    }
   ],
   "source": [
    "names.append('bin+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('bin', Binarizer()),\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(11,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4fadaac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: bin+pca99+knn (96.6571%) (7.146)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      1359\n",
      "           1       0.96      0.99      0.98      1594\n",
      "           2       0.98      0.97      0.97      1369\n",
      "           3       0.96      0.96      0.96      1415\n",
      "           4       0.98      0.95      0.97      1373\n",
      "           5       0.97      0.94      0.95      1257\n",
      "           6       0.96      0.99      0.97      1351\n",
      "           7       0.97      0.97      0.97      1422\n",
      "           8       0.97      0.94      0.96      1411\n",
      "           9       0.93      0.97      0.95      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  6.961178    6.772386    0.965536\n",
      "1  8.445867    6.193574    0.967143\n",
      "2  9.256319    5.771667    0.966607\n",
      "3  7.188147    6.795888    0.965179\n",
      "4  9.336459    5.739643    0.966964\n",
      "5  7.107809    6.823560    0.964821\n",
      "6  9.127543    5.848893    0.968393\n",
      "7  9.106971    5.873384    0.966429\n",
      "8  7.145829    6.698655    0.969107\n",
      "9  8.202323    6.141933    0.968750\n"
     ]
    }
   ],
   "source": [
    "names.append('bin+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('bin', Binarizer()),\n",
    "    ('pca99', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d15fb11c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: pca95+knn (97.5500%) (7.416)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.98      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.97      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.98     14000\n",
      "   macro avg       0.98      0.98      0.98     14000\n",
      "weighted avg       0.98      0.98      0.98     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  7.440297    5.405928    0.973750\n",
      "1  7.141649    5.609115    0.972857\n",
      "2  6.829969    5.687647    0.974286\n",
      "3  8.291485    5.057073    0.973036\n",
      "4  8.281738    4.872021    0.974464\n",
      "5  6.882516    5.804941    0.975179\n",
      "6  8.275357    5.190776    0.975893\n",
      "7  8.244917    5.013079    0.975357\n",
      "8  7.416139    5.446140    0.976607\n",
      "9  6.752497    5.906509    0.975536\n"
     ]
    }
   ],
   "source": [
    "names.append('pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('pca95', PCA(n_components=n95_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2db8577b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: pca99+knn (97.3643%) (6.88)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1359\n",
      "           1       0.97      0.99      0.98      1594\n",
      "           2       0.98      0.96      0.97      1369\n",
      "           3       0.97      0.97      0.97      1415\n",
      "           4       0.98      0.97      0.97      1373\n",
      "           5       0.96      0.96      0.96      1257\n",
      "           6       0.98      0.99      0.98      1351\n",
      "           7       0.97      0.98      0.97      1422\n",
      "           8       0.99      0.95      0.97      1411\n",
      "           9       0.96      0.97      0.97      1449\n",
      "\n",
      "    accuracy                           0.97     14000\n",
      "   macro avg       0.97      0.97      0.97     14000\n",
      "weighted avg       0.97      0.97      0.97     14000\n",
      "\n",
      "   fit_time  score_time  test_score\n",
      "0  8.113361    5.777437    0.972857\n",
      "1  6.950958    6.503708    0.971429\n",
      "2  8.775186    5.424367    0.972143\n",
      "3  8.805392    5.562713    0.970179\n",
      "4  6.980318    6.581474    0.972679\n",
      "5  8.412863    5.525903    0.973214\n",
      "6  7.188666    6.495338    0.975536\n",
      "7  6.802638    6.664372    0.972857\n",
      "8  6.879545    6.603721    0.975714\n",
      "9  7.031381    6.631324    0.974286\n"
     ]
    }
   ],
   "source": [
    "names.append('pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('pca99', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "accuracies.append(cv(14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5702428",
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append('kpca+pca95+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('kpca', KernelPCA(n_jobs=-1)),\n",
    "    ('pca', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "# accuracies.append(cv(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ee57cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append('kpca+pca99+knn')\n",
    "classifiers.append(Pipeline([\n",
    "    ('kpca', KernelPCA(n_jobs=-1)),\n",
    "    ('pca', PCA(n_components=n99_components)),\n",
    "    ('knn', op_kNN)\n",
    "]))\n",
    "\n",
    "# accuracies.append(cv(16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbbc930",
   "metadata": {},
   "source": [
    "# Auswertung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5d609aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum accuracy (97.55%) for ['minmax+pca95+knn', 'maxabs+pca95+knn', 'pca95+knn']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Maximum accuracy ({max(accuracies):.6}%) for {[names[n] for n in np.where(accuracies==max(accuracies))[0]]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "234f14bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: knn (baseline)       (97.3071%)\n",
      "name: scalar+knn           (94.8000%)\n",
      "name: minmax+knn           (97.3071%)\n",
      "name: standard+pca95+knn   (95.3929%)\n",
      "name: minmax+pca95+knn     (97.5500%)\n",
      "name: standard+pca99+knn   (94.9071%)\n",
      "name: minmax+pca99+knn     (97.3643%)\n",
      "name: maxabs+pca95+knn     (97.5500%)\n",
      "name: maxabs+pca99+knn     (97.3643%)\n",
      "name: power+pca95+knn      (94.9786%)\n",
      "name: power+pca99+knn      (94.9786%)\n",
      "name: bin+pca95+knn        (96.8286%)\n",
      "name: bin+pca99+knn        (96.6571%)\n",
      "name: pca95+knn            (97.5500%)\n",
      "name: pca99+knn            (97.3643%)\n"
     ]
    }
   ],
   "source": [
    "for n, a in zip(names, accuracies):\n",
    "    print(f\"name: {n:20} ({a:.4f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ddb6f2",
   "metadata": {},
   "source": [
    "Default n=3\\\n",
    "name: knn (baseline)       (97.2143%)\\\n",
    "name: scalar+knn           (94.6286%)\\\n",
    "name: minmax+knn           (97.2143%)\\\n",
    "name: standard+pca95+knn   (95.1429%)\\\n",
    "name: minmax+pca95+knn     (97.4357%)\\\n",
    "name: standard+pca99+knn   (94.7214%)\\\n",
    "name: minmax+pca99+knn     (97.2714%)\\\n",
    "name: maxabs+pca95+knn     (97.4357%)\\\n",
    "name: maxabs+pca99+knn     (97.2714%)\\\n",
    "name: power+pca95+knn      (94.8071%)\\\n",
    "name: power+pca99+knn      (94.8071%)\\\n",
    "name: bin+pca95+knn        (96.6643%)\\\n",
    "name: bin+pca99+knn        (96.5500%)\\\n",
    "name: pca95+knn            (97.4357%)\\\n",
    "name: pca99+knn            (97.2714%)\\"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde6e847",
   "metadata": {},
   "source": [
    "n=3 euclid distance\\\n",
    "name: knn (baseline)       (97.3071%)\\\n",
    "name: scalar+knn           (94.8000%)\\\n",
    "name: minmax+knn           (97.3071%)\\\n",
    "name: standard+pca95+knn   (95.3929%)\\\n",
    "name: minmax+pca95+knn     (97.5500%)\\\n",
    "name: standard+pca99+knn   (94.9071%)\\\n",
    "name: minmax+pca99+knn     (97.3643%)\\\n",
    "name: maxabs+pca95+knn     (97.5500%)\\\n",
    "name: maxabs+pca99+knn     (97.3643%)\\\n",
    "name: power+pca95+knn      (94.9786%)\\\n",
    "name: power+pca99+knn      (94.9786%)\\\n",
    "name: bin+pca95+knn        (96.8286%)\\\n",
    "name: bin+pca99+knn        (96.6571%)\\\n",
    "name: pca95+knn            (97.5500%)\\\n",
    "name: pca99+knn            (97.3643%)\\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c625bc3",
   "metadata": {},
   "source": [
    "# Hyper Parameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "24ff7ea2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 32 candidates, totalling 96 fits\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# grid_params = {\n",
    "#     'n_neighbors': [3, 5, 7 , 11],\n",
    "#     'weights': ['uniform', 'distance'],\n",
    "#     'metric': ['euclidean', 'manhattan', 'chebyshev', 'minkowski'],\n",
    "# }\n",
    "\n",
    "# gs = GridSearchCV(\n",
    "#     KNeighborsClassifier(n_jobs=-1),\n",
    "#     grid_params,\n",
    "#     cv = 3,\n",
    "#     verbose=1,\n",
    "#     n_jobs = -1\n",
    "# )\n",
    "\n",
    "# gs_results = gs.fit(X_train, y_train)\n",
    "# Results in gs_results.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b3b0eac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame.from_dict(gs_results.cv_results_).sort_values('rank_test_score').to_csv('./gs_result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b68589fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame.from_dict(gs_results.cv_results_)[['rank_test_score','mean_fit_time','param_n_neighbors', 'param_metric', 'param_weights', 'mean_test_score']].to_csv('./gs_result_filtered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b162e908",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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