impute_miss_labels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Create Feature Matrix With Missing Values\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix with categorical feature\n",
    "X = np.array([[0, 2.10, 1.45], \n",
    "              [1, 1.18, 1.33], \n",
    "              [0, 1.22, 1.27],\n",
    "              [0, -0.21, -1.19],\n",
    "              [np.nan, 0.87, 1.31],\n",
    "              [np.nan, -0.67, -0.22]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Fill Missing Values' Class With Most Frequent Class\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.  ,  2.1 ,  1.45],\n",
       "       [ 1.  ,  1.18,  1.33],\n",
       "       [ 0.  ,  1.22,  1.27],\n",
       "       [ 0.  , -0.21, -1.19],\n",
       "       [ 0.  ,  0.87,  1.31],\n",
       "       [ 0.  , -0.67, -0.22]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Imputer object\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "# Fill missing values with most frequent class\n",
    "imputer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' \n",
    "Method 2 >> Using K Nearest Neighbour\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix with categorical feature\n",
    "X = np.array([[0, 2.10, 1.45], \n",
    "              [1, 1.18, 1.33], \n",
    "              [0, 1.22, 1.27],\n",
    "              [1, -0.21, -1.19]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Create Feature Matrix With Missing Values\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature matrix with missing values in the categorical feature\n",
    "X_with_nan = np.array([[np.nan, 0.87, 1.31], \n",
    "                       [np.nan, -0.67, -0.22]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Train k-Nearest Neighbor Classifier\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train KNN learner\n",
    "clf = KNeighborsClassifier(3, weights='distance')\n",
    "trained_model = clf.fit(X[:,1:], X[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Predict Missing Values' Class\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.  ,  0.87,  1.31],\n",
       "       [ 1.  , -0.67, -0.22],\n",
       "       [ 0.  ,  2.1 ,  1.45],\n",
       "       [ 1.  ,  1.18,  1.33],\n",
       "       [ 0.  ,  1.22,  1.27],\n",
       "       [ 1.  , -0.21, -1.19]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict missing values' class\n",
    "imputed_values = trained_model.predict(X_with_nan[:,1:])\n",
    "\n",
    "# Join column of predicted class with their other features\n",
    "X_with_imputed = np.hstack((imputed_values.reshape(-1,1), X_with_nan[:,1:]))\n",
    "\n",
    "# Join two feature matrices\n",
    "np.vstack((X_with_imputed, X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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