MSc_ML_Lab_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ScemrRjDtBF"
      },
      "source": [
        "**Name : Belo Abhigyan**\n",
        "\n",
        "**Course : MSc Computer Science**\n",
        "\n",
        "**Roll Number 16**\n",
        "\n",
        "**Machine Learning Lab Assignment - 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaQjfcmlX5Jj"
      },
      "source": [
        "# **FIND-S ALGORITHM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "SCa3S7xyXvvK"
      },
      "outputs": [],
      "source": [
        "dict = {\n",
        "    'Sky':['sunny','sunny','rainy','sunny'],\n",
        "    'AirTemp':['warm','warm','cold','warm'],\n",
        "    'Humidity':['normal','high','high','high'],\n",
        "    'Wind':['strong','strong','strong','strong'],\n",
        "    'Water':['warm','warm','warm','cool'],\n",
        "    'Forecast':['same','same','change','change'],\n",
        "    'EnjoySport':['yes','yes','no','yes']\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "P4ZuAizZcZGG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "EGtt0xCUcdLP",
        "outputId": "4bcfc166-7e0d-45bc-f537-1848a0b89e9f"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sky</th>\n",
              "      <th>AirTemp</th>\n",
              "      <th>Humidity</th>\n",
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            "text/plain": [
              "     Sky AirTemp Humidity    Wind Water Forecast EnjoySport\n",
              "0  sunny    warm   normal  strong  warm     same        yes\n",
              "1  sunny    warm     high  strong  warm     same        yes\n",
              "2  rainy    cold     high  strong  warm   change         no\n",
              "3  sunny    warm     high  strong  cool   change        yes"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = pd.DataFrame(dict)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYMRd3TdgvsB",
        "outputId": "e7ae1301-ffac-44fd-9ada-a0cd8eb7a3c0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Sky', 'AirTemp', 'Humidity', 'Wind', 'Water', 'Forecast', 'EnjoySport']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features = list(dataset.columns)\n",
        "features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XkB6ll8eWhB6"
      },
      "source": [
        "Most Specific Hy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "kkXVCjvbc94R"
      },
      "outputs": [],
      "source": [
        "h0 = [0,0,0,0,0,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IodMJF8QeFx9",
        "outputId": "caf19c45-0037-4279-f954-223317f83d57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "General Hypothesis when considering only single datapoint :  ['sunny', 'warm', 'normal', 'strong', 'warm', 'same']\n"
          ]
        }
      ],
      "source": [
        "for i in range(0,len(dict[features[0]])):\n",
        "  if(dataset['EnjoySport'][i]=='yes'):\n",
        "    for j in range(len(h0)):\n",
        "      h0[j] = dataset[features[j]][i]\n",
        "    break\n",
        "print(\"General Hypothesis when considering only single datapoint : \",h0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCOBgB66csFj",
        "outputId": "63d6d7d5-c26f-4c4f-c8ff-30691defa43a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Most General Hypothesis :  ['sunny', 'warm', '?', 'strong', '?', '?']\n"
          ]
        }
      ],
      "source": [
        "for i in range(1,len(dict[features[0]])):\n",
        "  if(dataset['EnjoySport'][i]=='yes'):\n",
        "    for j in range(len(h0)):\n",
        "      if(h0[j] != dataset[features[j]][i]):\n",
        "        h0[j] = '?'\n",
        "\n",
        "print(\"Most General Hypothesis : \",h0)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-sB8ttoBlO1"
      },
      "source": [
        "# **Candidate Elimination Algorithm**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HcmOoOYBvIK"
      },
      "source": [
        "###**Seperating Concept Features from target**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uWM94jv5DSi",
        "outputId": "8e95d01f-2c2f-40dd-dac1-9560e64f0132"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['sunny' 'warm' 'normal' 'strong' 'warm' 'same']\n",
            " ['sunny' 'warm' 'high' 'strong' 'warm' 'same']\n",
            " ['rainy' 'cold' 'high' 'strong' 'warm' 'change']\n",
            " ['sunny' 'warm' 'high' 'strong' 'cool' 'change']]\n"
          ]
        }
      ],
      "source": [
        "concepts = np.array(dataset.iloc[:,0:-1])\n",
        "print(concepts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CO4HF-lyB9UD"
      },
      "source": [
        "### **Seperating Target**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUcOZbzt9jL5",
        "outputId": "032bf7df-c208-47a6-e620-6d3bf0688235"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['yes' 'yes' 'no' 'yes']\n"
          ]
        }
      ],
      "source": [
        "target = np.array(dataset.iloc[:,-1])\n",
        "print(target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvE24FjNCaQX"
      },
      "source": [
        "##**Applying Candidate Elimination Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Czmd7ps9oJF",
        "outputId": "9a37238e-719b-4d3b-8930-203890ab167d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Initialization of specific_h and general_h\n",
            "['sunny' 'warm' 'normal' 'strong' 'warm' 'same']\n",
            "[['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?']]\n",
            "\n",
            "Steps of Candidate Elimination Algorithm 1\n",
            "['sunny' 'warm' 'normal' 'strong' 'warm' 'same']\n",
            "[['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?']]\n",
            "\n",
            "Steps of Candidate Elimination Algorithm 2\n",
            "['sunny' 'warm' '?' 'strong' 'warm' 'same']\n",
            "[['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?']]\n",
            "\n",
            "Steps of Candidate Elimination Algorithm 3\n",
            "['sunny' 'warm' '?' 'strong' 'warm' 'same']\n",
            "[['sunny', '?', '?', '?', '?', '?'], ['?', 'warm', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', 'same']]\n",
            "\n",
            "Steps of Candidate Elimination Algorithm 4\n",
            "['sunny' 'warm' '?' 'strong' '?' '?']\n",
            "[['sunny', '?', '?', '?', '?', '?'], ['?', 'warm', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?']]\n"
          ]
        }
      ],
      "source": [
        "def learn(concepts, target):\n",
        "    \"\"\"\n",
        "    learn() function implements the learning method of the Candidate elimination algorithm.\n",
        "    Arguments:\n",
        "        concepts - a data frame with all the features\n",
        "        target - a data frame with corresponding output values\n",
        "    \"\"\"\n",
        "    # Initialise S0 with the first instance from concepts\n",
        "    # .copy() makes sure a new list is created instead of just pointing to the same memory location\n",
        "    specific_h = concepts[0].copy()\n",
        "    print(\"\\nInitialization of specific_h and general_h\")\n",
        "    print(specific_h)\n",
        "    general_h = [[\"?\" for i in range(len(specific_h))] for i in range(len(specific_h))]\n",
        "    print(general_h)\n",
        "    # The learning iterations\n",
        "    for i, h in enumerate(concepts):\n",
        "        # Checking if the hypothesis has a positive target\n",
        "        if target[i] == \"yes\":\n",
        "            for x in range(len(specific_h)):\n",
        "                # Change values in S & G only if values change\n",
        "                if h[x] != specific_h[x]:\n",
        "                    specific_h[x] = '?'\n",
        "                    general_h[x][x] = '?'\n",
        "        # Checking if the hypothesis has a positive target\n",
        "        if target[i] == \"no\":\n",
        "            for x in range(len(specific_h)):\n",
        "                # For negative hyposthesis change values only  in G\n",
        "                if h[x] != specific_h[x]:\n",
        "                    general_h[x][x] = specific_h[x]\n",
        "                else:\n",
        "                    general_h[x][x] = '?'\n",
        "        print(\"\\nSteps of Candidate Elimination Algorithm\",i+1)\n",
        "        print(specific_h)\n",
        "        print(general_h)\n",
        "    # find indices where we have empty rows, meaning those that are unchanged\n",
        "    indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]\n",
        "    for i in indices:\n",
        "        # remove those rows from general_h\n",
        "        general_h.remove(['?', '?', '?', '?', '?', '?'])\n",
        "    # Return final values\n",
        "    return specific_h, general_h\n",
        "s_final, g_final = learn(concepts, target)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rr5pqHMdDD_W",
        "outputId": "fd997b69-8193-47c8-a981-e0efe6c07e8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Final Specific_h:\n",
            "['sunny' 'warm' '?' 'strong' '?' '?']\n",
            "\n",
            "Final General_h:\n",
            "[['sunny', '?', '?', '?', '?', '?'], ['?', 'warm', '?', '?', '?', '?']]\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nFinal Specific_h:\", s_final, sep=\"\\n\")\n",
        "print(\"\\nFinal General_h:\", g_final, sep=\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SSqzw5RkDEVV"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}