normal_and_tsne_visualization_of_mnist_data.ipynb

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      "name": "normal and tsne visualization of mnist data.ipynb",
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/nayanjha16/hello-world/blob/master/normal_and_tsne_visualization_of_mnist_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HlKaanjlngbd"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.manifold import TSNE"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = datasets.fetch_openml('mnist_784', version=1, return_X_y=True)"
      ],
      "metadata": {
        "id": "RNCq_soPnw9S"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract data & target from the dataset\n",
        "pixel_data, targets = data\n",
        "targets = targets.astype(int)"
      ],
      "metadata": {
        "id": "3UwZo-Bkn2Ic"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of Pixel data : {}\".format(pixel_data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vX23WSveoB3s",
        "outputId": "fa49831c-bbc0-4cfa-c8f1-e623dc34a575"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Pixel data : (70000, 784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_data"
      ],
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          "height": 424
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        "id": "DRgqfyIEoIn1",
        "outputId": "dbcb9afe-7aff-4f59-8541-5de83602ada4"
      },
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      "outputs": [
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc3b96d0-ff93-47b6-8002-8d5aad1a7a8a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc3b96d0-ff93-47b6-8002-8d5aad1a7a8a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(np.array(pixel_data.iloc[0]).reshape(28,28)) #as each row is nothing but the pixels of an image of the mnist dataset "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tNBrLwX8qrhJ",
        "outputId": "4be65117-a957-4f33-93a5-7659f25449ad"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6d1b2c6b90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zesIQlS5qu3F",
        "outputId": "866bfb9d-acae-4f66-f5e8-1a1ac924dcbf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#custmising the plotting a bit \n",
        "plt.figure( figsize=(7,7)) \n",
        "idx=1\n",
        "grid_data=np.array(pixel_data.iloc[idx]).reshape(28,28)\n",
        "plt.imshow(grid_data, interpolation=\"none\",cmap=\"gray\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "2pC-aMghsm-i",
        "outputId": "893910c5-6615-4be1-a121-c9cbd6e8cc51"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_data.iloc[:3000, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "iJgBblUOwwrw",
        "outputId": "06593592-e8c6-46ce-fe68-734b07f0d62b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
              "0        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "1        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "3        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "4        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "2995     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2996     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2997     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2998     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2999     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "      pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
              "0         0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "1         0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "2         0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "3         0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "4         0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "...       ...  ...       ...       ...       ...       ...       ...   \n",
              "2995      0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "2996      0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "2997      0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "2998      0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "2999      0.0  ...       0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "      pixel780  pixel781  pixel782  pixel783  pixel784  \n",
              "0          0.0       0.0       0.0       0.0       0.0  \n",
              "1          0.0       0.0       0.0       0.0       0.0  \n",
              "2          0.0       0.0       0.0       0.0       0.0  \n",
              "3          0.0       0.0       0.0       0.0       0.0  \n",
              "4          0.0       0.0       0.0       0.0       0.0  \n",
              "...        ...       ...       ...       ...       ...  \n",
              "2995       0.0       0.0       0.0       0.0       0.0  \n",
              "2996       0.0       0.0       0.0       0.0       0.0  \n",
              "2997       0.0       0.0       0.0       0.0       0.0  \n",
              "2998       0.0       0.0       0.0       0.0       0.0  \n",
              "2999       0.0       0.0       0.0       0.0       0.0  \n",
              "\n",
              "[3000 rows x 784 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9bf84dd-2fba-4434-b1a2-d21434fd5ced\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>pixel10</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "<p>3000 rows × 784 columns</p>\n",
              "</div>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Object of tSNE\n",
        "tsne = TSNE(n_components=2, random_state=42)"
      ],
      "metadata": {
        "id": "MNiyGoUdwYgq"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_transformed = tsne.fit_transform(pixel_data.iloc[:3000, :]) # Data upto 3000 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCSB-RYGwbul",
        "outputId": "47cba8e2-2ecc-481a-eb4c-2a25a7bf5c86"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_transformed[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6uypS-Owm3G",
        "outputId": "30cba70e-1897-4c8e-d88f-3c1cf9d540df"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 31.72277 , -16.391663], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the transformed data into dataframe\n",
        "tsne_df = pd.DataFrame(np.column_stack((x_transformed, targets[:3000])), columns=['X', 'Y', \"Targets\"])\n",
        "\n",
        "tsne_df.loc[:, \"Targets\"] = tsne_df.Targets.astype(int)"
      ],
      "metadata": {
        "id": "km2lN507wLwA"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tsne_df.head(10)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "g8eUKNN6oJzS",
        "outputId": "df6248f1-2e15-4703-e7ae-d51b52c5b57b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           X          Y  Targets\n",
              "0  31.722771 -16.391663        5\n",
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              "7  23.328159 -14.326128        3\n",
              "8 -53.283909  -5.448031        1\n",
              "9   2.919093  30.534452        4"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d4522289-1ed1-4637-ba72-b8db7f6fe69b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "g = sns.FacetGrid(data=tsne_df, hue='Targets', height=8)\n",
        "\n",
        "g.map(plt.scatter, 'X', 'Y').add_legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "iAeUE1UcwLOL",
        "outputId": "ab6b225a-9531-4526-c510-96a6bb53aafa"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 620.125x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reference link : -https://www.kaggle.com/code/venkatkrishnan/t-sne-decomposition-on-mnist-dataset"
      ],
      "metadata": {
        "id": "skSRp-ESxVyI"
      }
    }
  ]
}