Example_ML_restructured.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris experiments\n",
    "Several models trained and tested on the Iris dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "import matplotlib as plot\n",
    "import pandas as pd\n",
    "import sklearn as sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
    "data = pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "print(type(data))\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal-length  sepal-width  petal-length  petal-width        class\n",
      "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
      "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
      "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
      "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
      "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
      "       sepal-length  sepal-width  petal-length  petal-width\n",
      "count    150.000000   150.000000    150.000000   150.000000\n",
      "mean       5.843333     3.054000      3.758667     1.198667\n",
      "std        0.828066     0.433594      1.764420     0.763161\n",
      "min        4.300000     2.000000      1.000000     0.100000\n",
      "25%        5.100000     2.800000      1.600000     0.300000\n",
      "50%        5.800000     3.000000      4.350000     1.300000\n",
      "75%        6.400000     3.300000      5.100000     1.800000\n",
      "max        7.900000     4.400000      6.900000     2.500000\n"
     ]
    }
   ],
   "source": [
    "# head\n",
    "print(data.head(5))\n",
    "# descriptions\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class\n",
      "Iris-setosa        50\n",
      "Iris-versicolor    50\n",
      "Iris-virginica     50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# class distribution\n",
    "print(dataset.groupby('class').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of variables\n",
    "Histogram of variables in the dataset are plotted, first on dataset level, then per class.\n",
    "\n",
    "**Note**: the difference between the three classes is already apparent in the distribution of their features! (see histograms below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms\n",
    "data.hist()\n",
    "plot.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "iris_classes = pd.unique(data['class'])\n",
    "print(iris_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris-setosa\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris-versicolor\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris-virginica\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in iris_classes:\n",
    "    print(c)\n",
    "    data[data['class'] == c].hist(layout = (1,4))\n",
    "    plot.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO DO: Continue from here! \n",
    "# 1. Replace scatter matrix with scatter plot, with colour-coded categories\n",
    "# 2. Multivariate classifiers are cool, but bivariate are easier to understand. From the scatter plot it \n",
    "#    will become obvious that 2 out of three categories are similar to each other. One can define a new target using \n",
    "#    the third category alone.\n",
    "# 3. Visualize the algorithms below. E.g. plot misclassified data points for each model. Plot SVM line.\n",
    "# 4. Understand differences between bivariate and multivariate classifiers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# scatter plot matrix\n",
    "scatter_matrix(data)\n",
    "plot.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "...\n",
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "y = array[:,4]\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.951807 (0.052427)\n",
      "LDA: 0.976923 (0.035251)\n",
      "KNN: 0.951807 (0.052427)\n",
      "CART: 0.953205 (0.061888)\n",
      "NB: 0.952448 (0.062375)\n",
      "SVM: 0.984615 (0.030769)\n"
     ]
    }
   ],
   "source": [
    "...\n",
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
    "\tcv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tprint('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "...\n",
    "# Compare Algorithms\n",
    "plot.pyplot.boxplot(results, labels=names)\n",
    "plot.pyplot.title('Algorithm Comparison')\n",
    "plot.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.951807 (0.052427)\n",
      "LDA: 0.976923 (0.035251)\n",
      "KNN: 0.951807 (0.052427)\n",
      "CART: 0.953205 (0.061888)\n",
      "NB: 0.952448 (0.062375)\n",
      "SVM: 0.984615 (0.030769)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "# Load dataset\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
    "dataset = read_csv(url, names=names)\n",
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "y = array[:,4]\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)\n",
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
    "\tcv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tprint('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))\n",
    "# Compare Algorithms\n",
    "plot.pyplot.boxplot(results, labels=names)\n",
    "plot.pyplot.title('Algorithm Comparison')\n",
    "plot.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "...\n",
    "# Make predictions on validation dataset\n",
    "model = SVC(gamma='auto')\n",
    "model.fit(X_train, Y_train)\n",
    "predictions = model.predict(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-20-d80961afdf65>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-d80961afdf65>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    ....\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "....\n",
    "# Evaluate predictions\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n",
      "[[11  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  0  6]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        11\n",
      "Iris-versicolor       1.00      0.92      0.96        13\n",
      " Iris-virginica       0.86      1.00      0.92         6\n",
      "\n",
      "       accuracy                           0.97        30\n",
      "      macro avg       0.95      0.97      0.96        30\n",
      "   weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "# Load dataset\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\"\n",
    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
    "dataset = read_csv(url, names=names)\n",
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:4]\n",
    "y = array[:,4]\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)\n",
    "# Make predictions on validation dataset\n",
    "model = SVC(gamma='auto')\n",
    "model.fit(X_train, Y_train)\n",
    "predictions = model.predict(X_validation)\n",
    "# Evaluate predictions\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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