Mod2_Lab2.ipynb

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        "<a href=\"https://colab.research.google.com/github/Harsha-070/FMML_IIITH/blob/main/Mod2_Lab2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Module 2: Appreciating, Interpreting and Visualizing Data\n",
        "## Lab 2: Principal Components Analysis (PCA)\n",
        "\n",
        "\n",
        "```\n",
        "Coordinator: Abhiroop Talasila\n",
        "```\n",
        "\n",
        "---"
      ]
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        "This lab is designed to give you an understanding of Principal Components\n",
        "Analysis (PCA). PCA has been called one of the most valuable results from applied linear algebra. PCA is used abundantly in all forms of analysis - from neuroscience to computer graphics - because it is a simple, non-parametric method of extracting relevant information from confusing data sets.\n",
        "\n",
        "Principal Component Analysis (PCA) is a linear dimensionality reduction technique that can be utilized for extracting information from a high-dimensional space by projecting it into a lower-dimensional sub-space. It tries to preserve the essential parts that have more variation of the data and remove the non-essential parts with fewer variation."
      ]
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        "PCA is generally used for 2 applications:\n",
        "1.   Visualization of high dimentional datasets.\n",
        "2.   Reducing number of features, thereby speeding up ML algorithms.\n",
        "\n",
        "\n",
        "We will be primarily focussing on the first application in this lab."
      ]
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import load_breast_cancer"
      ]
    },
    {
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        "id": "V4rN4KMblQ32"
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      "source": [
        "In this lab, we will be using the breast_cancer dataset. The data has 569 samples with thirty features, and each sample has a label associated with it (benign or malignant)."
      ]
    },
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Features: (569, 30)\n",
            "Target: (569, 1)\n"
          ]
        }
      ],
      "source": [
        "breast_data = load_breast_cancer().data\n",
        "print(\"Features:\", breast_data.shape)  ### 569 rows and 30 columns expected\n",
        "\n",
        "breast_labels = np.reshape(load_breast_cancer().target, (569,1))\n",
        "print(\"Target:\", breast_labels.shape) ### 569 rows and 1 target column expected"
      ]
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  label  \n",
              "0          0.4601                  0.11890    0.0  \n",
              "1          0.2750                  0.08902    0.0  \n",
              "2          0.3613                  0.08758    0.0  \n",
              "3          0.6638                  0.17300    0.0  \n",
              "4          0.2364                  0.07678    0.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th>mean radius</th>\n",
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              "      <th>mean symmetry</th>\n",
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              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
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              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
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              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
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              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
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              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
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              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "## Creating a Pandas dataframe for the dataset with the last column as the target variable\n",
        "\n",
        "final_breast_data = np.concatenate([breast_data, breast_labels],axis=1)\n",
        "breast_dataset = pd.DataFrame(final_breast_data)\n",
        "\n",
        "features = load_breast_cancer().feature_names\n",
        "features_labels = np.append(features,'label')\n",
        "breast_dataset.columns = features_labels\n",
        "breast_dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cx45prZ5o14p",
        "outputId": "38aad715-9799-4185-d7d0-00be9aac9c66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 30) (569,)\n"
          ]
        }
      ],
      "source": [
        "# Dividing the values into the features and labels for convenience later on\n",
        "\n",
        "X = breast_dataset.iloc[:,:30].values\n",
        "y = breast_dataset.iloc[:,30].values\n",
        "\n",
        "print(np.shape(X), np.shape(y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wi17hT6Jo2eZ"
      },
      "source": [
        "### Step By Step Computation Of PCA\n",
        "The below steps need to be followed to perform dimensionality reduction using PCA:\n",
        "\n",
        "1. Standardization of the data\n",
        "\n",
        "2. Computing the covariance matrix\n",
        "\n",
        "3. Calculating the eigenvectors and eigenvalues\n",
        "\n",
        "4. Computing the Principal Components\n",
        "\n",
        "5. Reducing the dimensions of the data set\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_4sempd71rH"
      },
      "source": [
        "### 1. Standardization of the data\n",
        "\n",
        "---\n",
        "It is a common practice to normalize your data before feeding it to any machine learning algorithm.\n",
        "\n",
        "To apply normalization, you will import StandardScaler module from the sklearn library and then apply scaling by doing fit_transform on the feature data. While applying StandardScaler, each feature of your data should be normally distributed such that it will scale the distribution to a mean of zero and a standard deviation of one.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EvyGi2YonOQt",
        "outputId": "51d64390-c26f-4f08-fda0-221f6b0f7e7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_std = StandardScaler().fit_transform(X)\n",
        "\n",
        "print(np.std(X_std))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBdgIviKxjxZ"
      },
      "source": [
        "Here's the data after scaling. Yes, we do loose the numerical meaning of the data, but that's okay. We are more interested in the relative positions of the data points with respect to each other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "2R2jYhvQxjxZ",
        "outputId": "be3f89c6-03f9-446d-e45e-86ad72ec1f1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0     1.097064     -2.073335        1.269934   0.984375         1.568466   \n",
              "1     1.829821     -0.353632        1.685955   1.908708        -0.826962   \n",
              "2     1.579888      0.456187        1.566503   1.558884         0.942210   \n",
              "3    -0.768909      0.253732       -0.592687  -0.764464         3.283553   \n",
              "4     1.750297     -1.151816        1.776573   1.826229         0.280372   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0          3.283515        2.652874             2.532475       2.217515   \n",
              "1         -0.487072       -0.023846             0.548144       0.001392   \n",
              "2          1.052926        1.363478             2.037231       0.939685   \n",
              "3          3.402909        1.915897             1.451707       2.867383   \n",
              "4          0.539340        1.371011             1.428493      -0.009560   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                2.255747  ...      1.886690      -1.359293         2.303601   \n",
              "1               -0.868652  ...      1.805927      -0.369203         1.535126   \n",
              "2               -0.398008  ...      1.511870      -0.023974         1.347475   \n",
              "3                4.910919  ...     -0.281464       0.133984        -0.249939   \n",
              "4               -0.562450  ...      1.298575      -1.466770         1.338539   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0    2.001237          1.307686           2.616665         2.109526   \n",
              "1    1.890489         -0.375612          -0.430444        -0.146749   \n",
              "2    1.456285          0.527407           1.082932         0.854974   \n",
              "3   -0.550021          3.394275           3.893397         1.989588   \n",
              "4    1.220724          0.220556          -0.313395         0.613179   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0              2.296076        2.750622                 1.937015  \n",
              "1              1.087084       -0.243890                 0.281190  \n",
              "2              1.955000        1.152255                 0.201391  \n",
              "3              2.175786        6.046041                 4.935010  \n",
              "4              0.729259       -0.868353                -0.397100  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
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              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.097064</td>\n",
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              "      <td>1.269934</td>\n",
              "      <td>0.984375</td>\n",
              "      <td>1.568466</td>\n",
              "      <td>3.283515</td>\n",
              "      <td>2.652874</td>\n",
              "      <td>2.532475</td>\n",
              "      <td>2.217515</td>\n",
              "      <td>2.255747</td>\n",
              "      <td>...</td>\n",
              "      <td>1.886690</td>\n",
              "      <td>-1.359293</td>\n",
              "      <td>2.303601</td>\n",
              "      <td>2.001237</td>\n",
              "      <td>1.307686</td>\n",
              "      <td>2.616665</td>\n",
              "      <td>2.109526</td>\n",
              "      <td>2.296076</td>\n",
              "      <td>2.750622</td>\n",
              "      <td>1.937015</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.829821</td>\n",
              "      <td>-0.353632</td>\n",
              "      <td>1.685955</td>\n",
              "      <td>1.908708</td>\n",
              "      <td>-0.826962</td>\n",
              "      <td>-0.487072</td>\n",
              "      <td>-0.023846</td>\n",
              "      <td>0.548144</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>-0.868652</td>\n",
              "      <td>...</td>\n",
              "      <td>1.805927</td>\n",
              "      <td>-0.369203</td>\n",
              "      <td>1.535126</td>\n",
              "      <td>1.890489</td>\n",
              "      <td>-0.375612</td>\n",
              "      <td>-0.430444</td>\n",
              "      <td>-0.146749</td>\n",
              "      <td>1.087084</td>\n",
              "      <td>-0.243890</td>\n",
              "      <td>0.281190</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.579888</td>\n",
              "      <td>0.456187</td>\n",
              "      <td>1.566503</td>\n",
              "      <td>1.558884</td>\n",
              "      <td>0.942210</td>\n",
              "      <td>1.052926</td>\n",
              "      <td>1.363478</td>\n",
              "      <td>2.037231</td>\n",
              "      <td>0.939685</td>\n",
              "      <td>-0.398008</td>\n",
              "      <td>...</td>\n",
              "      <td>1.511870</td>\n",
              "      <td>-0.023974</td>\n",
              "      <td>1.347475</td>\n",
              "      <td>1.456285</td>\n",
              "      <td>0.527407</td>\n",
              "      <td>1.082932</td>\n",
              "      <td>0.854974</td>\n",
              "      <td>1.955000</td>\n",
              "      <td>1.152255</td>\n",
              "      <td>0.201391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.768909</td>\n",
              "      <td>0.253732</td>\n",
              "      <td>-0.592687</td>\n",
              "      <td>-0.764464</td>\n",
              "      <td>3.283553</td>\n",
              "      <td>3.402909</td>\n",
              "      <td>1.915897</td>\n",
              "      <td>1.451707</td>\n",
              "      <td>2.867383</td>\n",
              "      <td>4.910919</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.281464</td>\n",
              "      <td>0.133984</td>\n",
              "      <td>-0.249939</td>\n",
              "      <td>-0.550021</td>\n",
              "      <td>3.394275</td>\n",
              "      <td>3.893397</td>\n",
              "      <td>1.989588</td>\n",
              "      <td>2.175786</td>\n",
              "      <td>6.046041</td>\n",
              "      <td>4.935010</td>\n",
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              "      <th>4</th>\n",
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              "      <td>-1.151816</td>\n",
              "      <td>1.776573</td>\n",
              "      <td>1.826229</td>\n",
              "      <td>0.280372</td>\n",
              "      <td>0.539340</td>\n",
              "      <td>1.371011</td>\n",
              "      <td>1.428493</td>\n",
              "      <td>-0.009560</td>\n",
              "      <td>-0.562450</td>\n",
              "      <td>...</td>\n",
              "      <td>1.298575</td>\n",
              "      <td>-1.466770</td>\n",
              "      <td>1.338539</td>\n",
              "      <td>1.220724</td>\n",
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              "      <td>-0.313395</td>\n",
              "      <td>0.613179</td>\n",
              "      <td>0.729259</td>\n",
              "      <td>-0.868353</td>\n",
              "      <td>-0.397100</td>\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1a5008a-e192-4fd6-80b3-90ab67eddc63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "X_std_df = pd.DataFrame(X_std)\n",
        "X_std_df.columns = features\n",
        "X_std_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiJuI5J89BtN"
      },
      "source": [
        "### 2. Computing the covariance matrix\n",
        "\n",
        "---\n",
        "\n",
        "Recall that covariance is always measured between 2 dimensions. If we have a data set with more than 2 dimensions, there is more than one covariance measurement that can be calculated. For example, from a 3 dimensional data set (dimensions x,y,z) you could calculate cov(x,y), cov(y,z) and cov(x,z). In fact, for an n-dimensional data set, you can calculate N combinatorial 2 different covariances.\n",
        "\n",
        "Here we have 30 different features, so we will have to compute 435 different covariances."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nxhkli7_-6A0",
        "outputId": "ae956a41-2656-41fc-e3c1-7f3620deecd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance matrix first 5 rows and columns:\n",
            "[[ 1.00176056  0.32435193  0.99961207  0.98909547  0.17088151]\n",
            " [ 0.32435193  1.00176056  0.33011322  0.32165099 -0.02342969]\n",
            " [ 0.99961207  0.33011322  1.00176056  0.98824361  0.20764309]\n",
            " [ 0.98909547  0.32165099  0.98824361  1.00176056  0.17734005]\n",
            " [ 0.17088151 -0.02342969  0.20764309  0.17734005  1.00176056]]\n"
          ]
        }
      ],
      "source": [
        "mean_vec = np.mean(X_std, axis=0) ## Computing feature wise means\n",
        "\n",
        "# Covariance matrix = i/(N-1) * X^T * X\n",
        "# where X is the normalized feature matrix and N is the number of data points (rows)\n",
        "\n",
        "cov_mat = 1/ (X_std.shape[0]-1) * (X_std - mean_vec).T.dot(X_std - mean_vec)\n",
        "\n",
        "print(\"Covariance matrix first 5 rows and columns:\")\n",
        "print(cov_mat[0:5, 0:5])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cov_mat.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xKnq5fz1QSw",
        "outputId": "20a966c8-01d5-48cf-ec23-ec0d06937b33"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzEbNDFPAnow"
      },
      "source": [
        "A perhaps simpler way to do this is by using the numpy's covariance module \"np.cov\". However note that it takes as input the feature matrix with features in different rows, so in our application, we would be taking a transpose of the feature matrix before applying np.cov()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nViIHCWuA6Yz",
        "outputId": "d731e259-508f-44ca-8f15-7eb03e644ab0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance matrix first 5 rows and columns:\n",
            "[[ 1.00176056  0.32435193  0.99961207  0.98909547  0.17088151]\n",
            " [ 0.32435193  1.00176056  0.33011322  0.32165099 -0.02342969]\n",
            " [ 0.99961207  0.33011322  1.00176056  0.98824361  0.20764309]\n",
            " [ 0.98909547  0.32165099  0.98824361  1.00176056  0.17734005]\n",
            " [ 0.17088151 -0.02342969  0.20764309  0.17734005  1.00176056]]\n"
          ]
        }
      ],
      "source": [
        "cov_mat_numpy = np.cov(X_std.T)\n",
        "\n",
        "print(\"Covariance matrix first 5 rows and columns:\")\n",
        "print(cov_mat_numpy[0:5, 0:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfaWc030BoMi"
      },
      "source": [
        "### 3. Calculating the eigenvectors and eigenvalues\n",
        "\n",
        "---\n",
        "\n",
        "Since the covariance matrix is square, we can calculate the eigenvectors and eigenvalues for this matrix. These are rather important, as they tell us useful information about our data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHiO8FVcC-V4"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sV770hitDDot"
      },
      "source": [
        "See how one of the eigenvectors goes through the middle of the points, like drawing a line of best fit? That eigenvector is showing us how these two data sets are related along that line. The second eigenvector gives us the other, less important, pattern in the data, that all the points follow the main line, but are off to the side of the main line by some amount.\n",
        "\n",
        "So, by this process of taking the eigenvectors of the covariance matrix, we have\n",
        "been able to extract lines that characterise the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "cF_4FPXYDTJt"
      },
      "outputs": [],
      "source": [
        "eig_vals, eig_vecs = np.linalg.eig(cov_mat)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eig_vals.shape, eig_vecs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHFRqZZu11Cr",
        "outputId": "4c476783-69bf-41a5-d62b-00a1fd6fa6cc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((30,), (30, 30))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsByKEhAFoUI"
      },
      "source": [
        "### 4. Computing the Principal Components\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "GOw0ivWwxjxi",
        "outputId": "3a70d060-a0ca-4b9a-95e0-47d9c5a92232"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance:\n",
            "[44.27202560752635, 18.971182044033085, 9.393163257431393, 6.602134915470151, 5.495768492346255, 4.0245220398833546, 2.250733712982508, 1.5887238000213262, 1.3896493745591088, 1.1689781894131472, 0.9797189875980173, 0.870537900737884, 0.8045249871967302, 0.5233657454926353, 0.31378321676273985, 0.26620933651523176, 0.19799679253242683, 0.17539594502263636, 0.1649253059225157, 0.10386467483387138, 0.0999096463700257, 0.09146467510543423, 0.08113612588991044, 0.06018335666716721, 0.05160423791652017, 0.02725879954774855, 0.0230015462505957, 0.005297792903809204, 0.0024960103246895395, 0.00044348274273613787]\n",
            "Cumulative explained variance:\n",
            "[ 44.27202561  63.24320765  72.63637091  79.23850582  84.73427432\n",
            "  88.75879636  91.00953007  92.59825387  93.98790324  95.15688143\n",
            "  96.13660042  97.00713832  97.81166331  98.33502905  98.64881227\n",
            "  98.91502161  99.1130184   99.28841435  99.45333965  99.55720433\n",
            "  99.65711397  99.74857865  99.82971477  99.88989813  99.94150237\n",
            "  99.96876117  99.99176271  99.99706051  99.99955652 100.        ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
        "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
        "\n",
        "tot = sum(eig_vals)\n",
        "var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\n",
        "print(\"Explained variance:\")\n",
        "print(var_exp)\n",
        "\n",
        "cum_var_exp = np.cumsum(var_exp)\n",
        "print(\"Cumulative explained variance:\")\n",
        "print(cum_var_exp)\n",
        "\n",
        "# Plotting the variance explained by each component and the cumulative variance explained\n",
        "\n",
        "plt.figure(figsize=(6 , 4))\n",
        "plt.bar(range(30), var_exp, alpha=0.5, align='center', label='individual explained variance')\n",
        "plt.step(range(30), cum_var_exp, where='mid', label='cumulative explained variance')\n",
        "plt.ylabel('Explained variance ratio')\n",
        "plt.xlabel('Principal components')\n",
        "plt.legend(loc='best')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioLsYwoRTUEN"
      },
      "source": [
        "So, the first principal component alone explains more than 40% variance in the dataset. Also note that considering all the 30 dimensions explains 100% of the variance in the dataset as expected.\n",
        "\n",
        "Choosing a suitable number of components for further visualizations is generally based on the cumulative sum of PCs. The first 2 PCs explain nearly 65% variance, and the first 3 explain close to 75% variance.\n",
        "\n",
        "Lets now try and reduce the dimensions of our dataset to the first 2 and 3 principal components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95DDFgWqWvgj"
      },
      "source": [
        "### 5. Reducing the dimensions of the data set\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhJ-ZblNVc9o",
        "outputId": "16bd4b4e-ac1a-481b-cc03-5e3f336d65ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 9.19283683  1.94858307 -1.12316616]\n",
            " [ 2.3878018  -3.76817174 -0.52929269]\n",
            " [ 5.73389628 -1.0751738  -0.55174759]\n",
            " ...\n",
            " [ 1.25617928 -1.90229671  0.56273053]\n",
            " [10.37479406  1.67201011 -1.87702933]\n",
            " [-5.4752433  -0.67063679  1.49044308]]\n"
          ]
        }
      ],
      "source": [
        "matrix_w = np.hstack((eig_pairs[0][1].reshape(30,1),\n",
        "                      eig_pairs[1][1].reshape(30,1),\n",
        "                      eig_pairs[2][1].reshape(30,1)))\n",
        "\n",
        "Y = X_std.dot(matrix_w)\n",
        "\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwr_GVbFVzvV"
      },
      "source": [
        "Now, that you have spent such a long time building this final matrix Y which has reduced each datapoint to 3 dimensions......\n",
        "\n",
        "All of this can also be done in 2 lines by using sklearn's PCA module."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoXYrRSnWBUW",
        "outputId": "3c712f24-e99b-4fe5-e145-bfc0f421fdc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 9.19283683  1.94858282 -1.12316658]\n",
            " [ 2.38780181 -3.76817198 -0.52929315]\n",
            " [ 5.73389628 -1.07517382 -0.55174773]\n",
            " ...\n",
            " [ 1.25617927 -1.90229661  0.56273071]\n",
            " [10.37479406  1.67201021 -1.87702911]\n",
            " [-5.47524329 -0.67063719  1.49044231]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "sklearn_pca = PCA(n_components=3)\n",
        "Y_sklearn = sklearn_pca.fit_transform(X_std)\n",
        "\n",
        "print(Y_sklearn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKmtUGVHW3xj"
      },
      "source": [
        "## Let's visualize our dataset in 2 and 3 dimensions now\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bb_MAW5bW_oZ",
        "outputId": "931d48ee-b81e-4525-b500-4d761400a7e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PC1        PC2       PC3   Label\n",
              "0  9.192837   1.948583 -1.123166  Benign\n",
              "1  2.387802  -3.768172 -0.529293  Benign\n",
              "2  5.733896  -1.075174 -0.551748  Benign\n",
              "3  7.122953  10.275589 -3.232790  Benign\n",
              "4  3.935302  -1.948072  1.389767  Benign"
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              "      <td>2.387802</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "final_df = pd.DataFrame(columns=[\"PC1\", \"PC2\", \"PC3\", \"Label\"])\n",
        "\n",
        "for i in range(len(Y)):\n",
        "\n",
        "    dicti = dict()\n",
        "\n",
        "    dicti[\"PC1\"] = Y[i, 0]\n",
        "    dicti[\"PC2\"] = Y[i, 1]\n",
        "    dicti[\"PC3\"] = Y[i, 2]\n",
        "    if (int(breast_labels[i][0]) == 0):\n",
        "      dicti[\"Label\"] = \"Benign\"\n",
        "    else:\n",
        "      dicti[\"Label\"] = \"Malignant\"\n",
        "\n",
        "    final_df = pd.concat([final_df, pd.DataFrame([dicti])], ignore_index=True)\n",
        "\n",
        "\n",
        "final_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "JUwZFQeYhjBQ"
      },
      "outputs": [],
      "source": [
        "import plotly.graph_objects as go\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9hirY45agVlX",
        "outputId": "5a4b9393-811d-4b7f-8770-e3c4b794c8ce"
      },
      "outputs": [
        {
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      "source": [
        "for_x = final_df.PC1.tolist()\n",
        "for_y = final_df.PC2.tolist()\n",
        "for_label = final_df.Label.tolist()\n",
        "for_hover = final_df.Label.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_label,\n",
        "                 title=\"Principal Component Axis\",\n",
        "                 color_discrete_map={\"Benign\": \"aqua\", \"Malignant\": \"yellow\"})\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis=dict(title = 'PC1', showgrid=True, ticks='inside', zeroline=True, mirror=True, showline=True, linecolor='white'),\n",
        "    yaxis=dict(title = 'PC2', showgrid=True, ticks='inside', zeroline=True, mirror=True, showline=True, linecolor='white'),\n",
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        "    font=dict(\n",
        "        family=\"Times New Roman\",\n",
        "        size=16,\n",
        "        color=\"Black\"))\n",
        "fig.update_traces(marker=dict(size=8,))\n",
        "\n",
        "fig.show(renderer = \"colab\")"
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      "metadata": {
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      "source": [
        "### Question:\n",
        "Referring to the plot above, can you reason why PCA can be a good candidate before training models for Machine learning?\n",
        "\n",
        "#### Answer:\n",
        "You can see that the first 2 principal components were able to differentiate the benign and malignant tumours in our breast cancer dataset. This motivates the use of the second application of PCA, which was to reduce the number of features in the dataset so that the machine learning algorithm can be trained without overfitting.\n",
        "\n",
        "#**Detailed explanation of how PCA helps in this context:**\n",
        "\n",
        "Differentiation: PCA effectively separates the two classes (benign and malignant tumors) along the first two principal components. This separation can make it easier for machine learning algorithms to learn decision boundaries, as the data is more spread out in the transformed space.\n",
        "\n",
        "Feature Reduction: By using PCA to reduce the number of features, you are simplifying the dataset. This can help in avoiding overfitting because it reduces the risk of the model learning noise in the data. Machine learning models can become overly complex and less generalizable if they have to deal with a high number of features, especially when the number of features is close to or exceeds the number of samples.\n",
        "\n",
        "Generalization: A simpler model with fewer features is more likely to generalize well to unseen data. By focusing on the most important variations in the data (captured by the principal components), you are creating a more robust model.\n",
        "\n",
        "Computational Efficiency: Training machine learning models on high-dimensional data can be computationally expensive and time-consuming. PCA reduces the dimensionality, making the training process more efficient.\n",
        "\n",
        "It's important to note that PCA should be applied carefully, and the number of principal components retained should be chosen thoughtfully. The trade-off is between dimensionality reduction and information loss. Retaining too few components may result in a loss of critical information, while retaining too many components may not effectively reduce dimensionality. Cross-validation and evaluation of model performance should guide the selection of the appropriate number of retained principal components.\n",
        "\n",
        "**-->** PCA is a valuable preprocessing technique that can enhance the performance and generalization of machine learning models while facilitating visualization and reducing the risk of overfitting, as demonstrated by the successful differentiation of benign and malignant tumors in your dataset.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Let us now try and visualize this in the first 3 Principal Components space."
      ]
    },
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      "execution_count": 18,
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        "id": "CQwwO1dDlw2q",
        "outputId": "50cc65dc-1df0-4368-dc2f-b35d2d73a2ae"
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      "outputs": [
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      ],
      "source": [
        "fig = px.scatter_3d(final_df, x='PC1', y='PC2', z='PC3', color='Label', title=\"Principal Component Axis\")\n",
        "fig.update_traces(marker=dict(size=6,))\n",
        "\n",
        "fig.show(renderer = \"colab\")"
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      "cell_type": "markdown",
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        "id": "W6sIxLqbparP"
      },
      "source": [
        "Feel free to explore this interactive plot by PLOTLY, which is also another great tool for visualizations. You can zoom, pan, rotate, turn and download this plot."
      ]
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      "source": [
        "### Some interesting references:\n",
        "\n",
        "1. https://builtin.com/data-science/step-step-explanation-principal-component-analysis\n",
        "\n",
        "2. http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf\n",
        "\n",
        "3. https://www.cs.cmu.edu/~elaw/papers/pca.pdf\n",
        "\n",
        "4. https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
      ]
    }
  ],
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