Classify_Multiple_Videos.ipynb

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        "<a href=\"https://colab.research.google.com/github/pyratlib/pyrat/blob/main/Classify_Multiple_Videos.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": "markdown",
      "metadata": {
        "id": "C4lDGvoBhI17"
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      "source": [
        "# PyRAT example\n",
        "\n",
        "This notebook has examples of using the library to perform behavior classification of multiple videos.\n",
        "\n",
        "For more details: https://github.com/pyratlib/pyrat"
      ]
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        "id": "AjXtaVQBbXu3"
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      "source": [
        "#@title Installing PyRAT and downloading data.\n",
        "!pip install pyratlib\n",
        "!pip install wget\n",
        "!pip install unrar\n",
        "!git clone \n",
        "!wget https://zenodo.org/record/5883277/files/PyRAT_dataset.rar?download=1\n",
        "!unrar x \"/content/PyRAT_dataset.rar?download=1\"\n",
        "\n",
        "import pyratlib as rat\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy import signal\n",
        "from IPython.display import clear_output \n",
        "\n",
        "clear_output()"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "In this cell it divides a data into three, to simulate the input of three different videos for the function. It may be necessary for the inputs (len of tracking data) to be the same size."
      ],
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        "data = pd.read_csv('/content/PyRAT_dataset/t-SNE.csv')\n",
        "\n",
        "df = data.loc[np.where(data.iloc[:,([3,6, 9, 21])].values[2:].astype(np.float).mean(axis=1) > 0.6)]\n",
        "\n",
        "df_1 = pd.concat([df[:2],df[1000:2000]])\n",
        "df_2 = pd.concat([df[:2],df[4000:5000]])\n",
        "df_3 = pd.concat([df[:2],df[9000:10000]])\n",
        "\n",
        "df_dict = {'video1':df_1,\n",
        "           'video2':df_2,\n",
        "           'video3':df_3}"
      ],
      "execution_count": null,
      "outputs": [
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            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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        " ClassifyBehaviorMultiVideos()   \n",
        " \n",
        "    \"\"\"\n",
        "    Returns an array with the cluster by frame, an array with the embedding data in low-dimensional \n",
        "    space and the clusterization model.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    data : dict with DataFrames\n",
        "        The input tracking data concatenated.\n",
        "    bodyparts_list : list\n",
        "        List with name of body parts.\n",
        "    dimensions : int\n",
        "        Dimension of the embedded space.\n",
        "    distance : int\n",
        "        The linkage distance threshold above which, clusters will not be merged.\n",
        "    n_jobs : int, optional\n",
        "        The number of parallel jobs to run for neighbors search.\n",
        "    verbose : int, optional\n",
        "        Verbosity level.\n",
        "    perplexity : float, optional\n",
        "        The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity.\n",
        "    learning_rate : float, optional\n",
        "        t-SNE learning rate.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    cluster_df : df\n",
        "        Array with the cluster by frame/video.\n",
        "    cluster_coord : DataFrame\n",
        "        Embedding of the training data in low-dimensional space.\n",
        "    fitted_model : Obj\n",
        "        AgglomerativeClustering model.\n",
        "\n",
        "    See Also\n",
        "    --------\n",
        "    For more information and usage examples: https://github.com/pyratlib/pyrat\n",
        "    \n",
        "    Notes\n",
        "    -----\n",
        "    This function was developed based on DLC outputs and is able to support \n",
        "    matplotlib configurations.\"\"\""
      ],
      "metadata": {
        "id": "LRH_yLxTrDTz"
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      "source": [
        "df_cluster, df_coord, model = rat.ClassifyBehaviorMultiVideos(df_dict,['nose','earR','earL','tailBase'])"
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            "/usr/local/lib/python3.7/dist-packages/pyratlib/processing.py:2420: RuntimeWarning: invalid value encountered in true_divide\n",
            "  distances2[:, i, j] = distances2[:, i, j]/np.max(distances2[:, i, j])\n",
            "/usr/local/lib/python3.7/dist-packages/pyratlib/processing.py:2420: RuntimeWarning: invalid value encountered in true_divide\n",
            "  distances2[:, i, j] = distances2[:, i, j]/np.max(distances2[:, i, j])\n",
            "/usr/local/lib/python3.7/dist-packages/pyratlib/processing.py:2420: RuntimeWarning: invalid value encountered in true_divide\n",
            "  distances2[:, i, j] = distances2[:, i, j]/np.max(distances2[:, i, j])\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:986: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
            "  FutureWarning,\n"
          ]
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        "df_cluster.head()"
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            "text/plain": [
              "   video1  video2  video3\n",
              "0       7       2       6\n",
              "1       7       2       6\n",
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        "The 'x_n_samples' and 'y_n_components' are used to plot the cluster map. In this example the first 1000 points are related with the first video, and so on. Eg. df[video1] and df_coord[x_n_samples,y_n_components][:1000]"
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              "   x_n_samples  y_n_components  distance 0  distance 1  distance 2  \\\n",
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              "   distance 3  distance 4  distance 5  \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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-93db38a8-550a-4e15-8e55-f9bd58835f62 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-93db38a8-550a-4e15-8e55-f9bd58835f62');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rat.dendrogram(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "l5N4nntVmiFz",
        "outputId": "380e8f2f-f8ba-46d1-efce-f7117815ce12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}