sitc/ml1/2_5_1_kNN_Model.ipynb

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    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
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   "metadata": {},
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
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   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
   ]
  },
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   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [kNN Model](#kNN-Model)\n",
    "* [Load data and preprocessing](#Load-data-and-preprocessing)\n",
    "* [Train classifier](#Train-classifier)\n",
    "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
    "    * [Precision, recall and f-score](#Precision,-recall-and-f-score)\n",
    "\t* [Confusion matrix](#Confusion-matrix)\n",
    "\t* [K-Fold validation](#K-Fold-validation)\n",
    "* [Tuning the algorithm](#Tuning-the-algorithm)\n",
    "* [References](#References)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to learn how to train a model, make predictions with that model and evaluate these predictions.\n",
    "\n",
    "The notebook uses the [kNN (k nearest neighbors) algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)."
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Loading data and preprocessing\n",
    "\n",
    "The first step is loading and preprocessing the data as explained in the previous notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# library for displaying plots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# display plots in the notebook \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First, we repeat the load and preprocessing steps\n",
    "\n",
    "# Load data\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Training and test spliting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_iris, y_iris = iris.data, iris.target\n",
    "\n",
    "# Test set will be the 25% taken randomly\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
    "\n",
    "# Preprocess: normalize\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual steps for creating a classifier are:\n",
    "1. Create classifier object\n",
    "2. Call *fit* to train the classifier\n",
    "3. Call *predict* to obtain predictions\n",
    "\n",
    "Once the model is created, the most relevant methods are:\n",
    "* model.fit(x_train, y_train): train the model\n",
    "* model.predict(x): predict\n",
    "* model.score(x, y): evaluate the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np\n",
    "\n",
    "# Create kNN model\n",
    "model = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "# Train the model using the training sets\n",
    "model.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Prediction \", model.predict(x_train))\n",
    "print(\"Expected \", y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate Accuracy in training\n",
    "\n",
    "from sklearn import metrics\n",
    "y_train_pred = model.predict(x_train)\n",
    "print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Now we evaluate error in testing\n",
    "y_test_pred = model.predict(x_test)\n",
    "print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to visualize the Nearest Neighbors classification. It will plot the decision boundaries for each class.\n",
    "\n",
    "We are going to import a function defined in the file [util_knn.py](files/util_knn.py) using the *magic command* **%run**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run util_knn.py\n",
    "\n",
    "plot_classification_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision, recall and f-score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
    "\n",
    "* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
    "* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
    "* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred, target_names=iris.target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful metric is the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(metrics.confusion_matrix(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see we classify well all the 'setosa' and 'versicolor' samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Fold validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
    "model = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('kNN', KNeighborsClassifier())\n",
    "])\n",
    "\n",
    "# create a k-fold cross validation iterator of k=10 folds\n",
    "cv = KFold(10, shuffle=True, random_state=33)\n",
    "\n",
    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
    "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import sem\n",
    "def mean_score(scores):\n",
    "    return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
    "print(mean_score(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we get an average accuracy of 0.940."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to tune the algorithm, and calculate which is the best value for the k hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_range = range(1, 21)\n",
    "accuracy = []\n",
    "for k in k_range:\n",
    "    m = KNeighborsClassifier(k)\n",
    "    m.fit(x_train, y_train)\n",
    "    y_test_pred = m.predict(x_test)\n",
    "    accuracy.append(metrics.accuracy_score(y_test, y_test_pred))\n",
    "plt.plot(k_range, accuracy)\n",
    "plt.xlabel('k value')\n",
    "plt.ylabel('Accuracy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [KNeighborsClassifier API scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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