diff --git a/README.md b/README.md index 6862e0e..7681cb6 100644 --- a/README.md +++ b/README.md @@ -107,8 +107,7 @@ Per users' request, we processed two non-anthropogenic datasets Explore the following tutorials that can be opened directly in Google Colab: -- [![Open Tutorial 1 in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb) Tutorial 1: Dataset in EasyTPP. - +- [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/iLampard/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb) Tutorial 1: Dataset in EasyTPP. ### End-to-end Example diff --git a/notebooks/easytpp_1_dataset.ipynb b/notebooks/easytpp_1_dataset.ipynb index 1a37128..75eba0e 100644 --- a/notebooks/easytpp_1_dataset.ipynb +++ b/notebooks/easytpp_1_dataset.ipynb @@ -1,3153 +1,3155 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "TmnzuOArbQk-" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/iLampard/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb)\n", + "\n", + "# Tutorial 1: Dataset in EasyTPP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "26Wvh9rZbTcg" + }, + "source": [ + "In this tutorial, we’ll explore the dataset-related functionalities in **EasyTPP**, an advanced library designed for temporal point process modeling. We will guide you through the installation process, data loading options, and configurations to set up a training pipeline effectively.\n", + "\n", + "\n", + "## Step 1: Install EasyTPP\n", + "First, let’s install the EasyTPP package. Run the following command to install the library in your Colab environment:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U-gIiMZqMPFy", + "outputId": "40a622f2-57c2-4742-f913-9d58302d4e0e" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# Tutorial 1: Dataset in EasyTPP" - ], - "metadata": { - "id": "TmnzuOArbQk-" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/ant-research/EasyTemporalPointProcess.git\n", + " Cloning https://github.com/ant-research/EasyTemporalPointProcess.git to /tmp/pip-req-build-ddqktd57\n", + " Running command git clone --filter=blob:none --quiet https://github.com/ant-research/EasyTemporalPointProcess.git /tmp/pip-req-build-ddqktd57\n", + " Resolved https://github.com/ant-research/EasyTemporalPointProcess.git to commit de2eef65c9ee66c1dff8dc12d8bef7de270db86f\n", + " Preparing metadata 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the latest release\n", + "# !pip install easy_tpp\n", + "\n", + "# or use the git main branch\n", + "!pip install git+https://github.com/ant-research/EasyTemporalPointProcess.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I5YUvAc7bngQ" + }, + "source": [ + "## Step 2: Loading Preprocessed Datasets\n", + "\n", + "EasyTPP provides two methods to load preprocessed datasets:\n", + "- [Google Drive](https://drive.google.com/drive/folders/1f8k82-NL6KFKuNMsUwozmbzDSFycYvz7): Download the dataset in pickle format.\n", + "- [HuggingFace](https://huggingface.co/easytpp): Load the dataset in JSON format from the HuggingFace repository.\n", + "\n", + "> Note: The pickle format from Google Drive will be deprecated in future releases, and we recommend using the JSON files from HuggingFace for better compatibility and performance.\n", + "\n", + "\n", + "### Option 1: Load Pickle Data Files (Deprecated Soon)\n", + "If you choose to use the pickle files, muanlly download the data files fromt he Google Drive mentioned above, place them under a data directory in your workspace, and specify the directory path in the configuration file.\n", + "\n", + "Here is an example configuration for loading a Taxi dataset in pickle format:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6zfSHKhDmFSS" + }, + "source": [ + "\n", + "\n", + "```\n", + "data:\n", + " taxi:\n", + " data_format: pickle\n", + " train_dir: ./data/taxi/train.pkl\n", + " valid_dir: ./data/taxi/dev.pkl\n", + " test_dir: ./data/taxi/test.pkl\n", + "```\n", + "\n", + "Then we can launch the train/evaluation pipeline process. See [experiment_config](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/configs/experiment_config.yaml) for the full example.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HHPDzqud2wJf" + }, + "source": [ + "\n", + "### Option 2: Load JSON Data Files (Recommended)\n", + "\n", + "To use JSON data files from HuggingFace - [EasyTPP Repo](https://huggingface.co/easytpp), simply replace `data_format: pickle` with `data_format: json` in the config file, and update the directory paths accordingly. This setup is recommended for newer versions of EasyTPP and provides better compatibility with various processing functions in the library.\n", + "\n", + "To activate this loading process in the train/evaluation pipeline, similarly, we put the directory of huggingface repo in the config file, e.g.,\n", + "\n", + "```\n", + "data:\n", + " taxi:\n", + " data_format: json\n", + " train_dir: easytpp/taxi\n", + " valid_dir: easytpp/taxi\n", + " test_dir: easytpp/taxi\n", + "```\n", + "\n", + "Note that we can also manually put the locally directory of json files in the config:\n", + "\n", + "```\n", + "data:\n", + " taxi:\n", + " data_format: json\n", + " train_dir: ./data/taxi/train.json\n", + " valid_dir: ./data/taxi/dev.json\n", + " test_dir: ./data/taxi/test.json\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6HJd1lZB33mP" + }, + "source": [ + "## Step 3: Exploring Datasets\n", + "\n", + "The EasyTPP library offers several functions to streamline dataset loading and preprocessing. Let’s go over a few key functionalities:\n", + "\n", + "### Dataset Properties\n", + "\n", + "We firstly use the official HuggingFace APIs to directly download and inspect the dataset.\n", + "\n", + "In this example, the `load_dataset` function is used to load the \"taxi\" dataset, which is relatively small and suited for quick testing. The dataset is automatically split into three parts: train, validation, and test, with each split containing structured information on the events." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "41522af32da04925bc328c4a9d7e3a82", + "375ac02a2d6b43f4a70feaf921c5377d", + "ae9d5f81adc343edb3f7de9dccb15f47", + "03b8f2a927ff406b88d8ff844f620403", + "a4ac525ccb134ccbab57aaaafc37045b", + "8cfe483c4a924e6992a81fc44274f240", + "5d9a76f49322447d9ff1c71ab3f9bee7", + "8525620ea0104a05b5ba633671a95873", + "65d7f89d96cb4e9188da3db1300076aa", + "36ca2862fb10458abb4ba13a9e75ecf2", + "722c07901b4c454bb68055971a445778", + "5848c90c9f2144b588aa88290b529151", + "925eca71c7c64811ab88b672364f440d", + "3b29ed8d36864982a47b3510a83f00af", + "2b100553e6fe4bc5ad83e17ae523a42e", + "28db861ff5f845baac00370fbcd6521b", + "e6a5ce28f9974ba3a12d5044aa2e07d4", + "9c6f4364de0f4cfe90e189916df4a040", + "cd76860acc1b41b997d6df7427596106", + "500c3e320fef4e9e906bccc844c4bf4e", + "cf4cf5ecb14f45539e14971c4e8a7b07", + "5ff53796dc0046f8b077a84836d4fc61", + "1d41b7f7b722466f89c74067a3ed783d", + "7f82d7dbe42e445198a1f2e0bc512ea6", + "e0ad14cb4708466584a0ff46ebf70539", + "0b921bafccf148dfaa76e94580800dfb", + "cccb52e989424990a2f6fcd881a4137c", + "f3ad7c86e9604c27837ea509677cda39", + "599d263b0edc42c48b732f22b6f4bb52", + "b9cf1a3cdf9544578a1a1ac73dd182b9", + "8522fc20649e40aeb1fc9aabbd3501b0", + "c986defd720346949d9dfb4a4822bb68", + "e2ed3c0ee1bf4beeb1f564f43031648d", + "3cdce1231011425e913007b372d1354a", + "cfa4572b597345f7a5c69780eb2e19ab", + "494232ab57db40e391e34a90f0120dd7", + "df95b675f0504e34ab3b2f17d09109c2", + "02f0c7450af24e849e4c573ff7e0f9d8", + "a8eee5c478d84a4b828970f5f9c55016", + "faaf164d7dc142c3bfe55aac535efbf4", + "78bfe063ac114e4398007a26053956eb", + "0829c53e563b48c0b84dbad6baef16e0", + "86b40e77e7a34386b0876859f151b49a", + "59ebcd3733c14e8b87a42dc7399cc5e4", + "6d1e4b19483343bfa1dbd713c2efbd1f", + "ba666b3bd47e4568afeaec47a7acb912", + "38c9c5e1bd814f5ea92047d6f5833506", + "76579ac830bd404ba5008c5315c323c3", + "63da81f729204fb8b2dc5cc12074e09b", + "0e2d3e97ceed4b44b55ea7f70047ec7e", + "9adf81c38d8d4cce873ebed60c5b208c", + "3d24235ee98a4daeaaaf85a7b5763f0b", + "c4312014f1a54feab2dc7c2b58a837c5", + "75a31d51a0f940aa835b47236b4761b7", + "9bc1dbb8d296432f8c79f67a3cd5ef80", + "441615ebaf0f4db699d5fd569ee1655a", + "21ebc80b5d3c4eca8f8935ffd1a3590c", + "f622d1072c63472db30cb6ec6f6e72ae", + "879c8911eb3b41febff0d2a33cde55b4", + "22e1b3631d324a119e35e53088209296", + "93fbb1bbc4304d758998c904955ece3e", + "6bb36084b56f4277ad35da260978d9bf", + "fb6be1690b3247ee96124fd59c4c001d", + "1da7223d069345d88624d9a5485e1752", + "dc0b2a2b34fc483abe00409859000d02", + "5cdeca479dec41e9ba78fef207360283", + "3d4156a02f7a436f9d678531bdcad532", + "04ac17403fdd480d985d1f7885196c46", + "0b0bc44093404ad8b396dbc7f86a6967", + "f9d8cff1f4b24cd4b1aea8693746a81e", + "9b8a0445912840c194aed0846228972f", + "db91bc99d2964fc69fb4a71abeb0c254", + "0d554c589be24338bdeda06958b77140", + "33336a9fd5aa42acb54a06486781fb8b", + "47d50654d080473b9360a49e246d70df", + "f12c68ae19424e8eb8cad1e105674f4c", + "1d5ef40cf0004c5caa90c04564c8facb" + ] }, + "id": "8sM6riIxQClw", + "outputId": "a5742b18-2ea9-408e-8ef4-dac9c6448cc5" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "In this tutorial, we’ll explore the dataset-related functionalities in **EasyTPP**, an advanced library designed for temporal point process modeling. We will guide you through the installation process, data loading options, and configurations to set up a training pipeline effectively.\n", - "\n", - "\n", - "## Step 1: Install EasyTPP\n", - "First, let’s install the EasyTPP package. Run the following command to install the library in your Colab environment:" - ], - "metadata": { - "id": "26Wvh9rZbTcg" - } + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "U-gIiMZqMPFy", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "40a622f2-57c2-4742-f913-9d58302d4e0e" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "41522af32da04925bc328c4a9d7e3a82", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting git+https://github.com/ant-research/EasyTemporalPointProcess.git\n", - " Cloning https://github.com/ant-research/EasyTemporalPointProcess.git to /tmp/pip-req-build-ddqktd57\n", - " Running command git clone --filter=blob:none --quiet https://github.com/ant-research/EasyTemporalPointProcess.git /tmp/pip-req-build-ddqktd57\n", - " Resolved https://github.com/ant-research/EasyTemporalPointProcess.git to commit de2eef65c9ee66c1dff8dc12d8bef7de270db86f\n", - " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from easy_tpp==0.0.8) (6.0.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from easy_tpp==0.0.8) (1.26.4)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from easy_tpp==0.0.8) (2.2.2)\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from easy_tpp==0.0.8) (2.5.0+cu121)\n", - 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"# !pip install easy_tpp\n", - "\n", - "# or use the git main branch\n", - "!pip install git+https://github.com/ant-research/EasyTemporalPointProcess.git" + "text/plain": [ + "README.md: 0%| | 0.00/28.0 [00:00 Note: The pickle format from Google Drive will be deprecated in future releases, and we recommend using the JSON files from HuggingFace for better compatibility and performance.\n", - "\n", - "\n", - "### Option 1: Load Pickle Data Files (Deprecated Soon)\n", - "If you choose to use the pickle files, muanlly download the data files fromt he Google Drive mentioned above, place them under a data directory in your workspace, and specify the directory path in the configuration file.\n", - "\n", - "Here is an example configuration for loading a Taxi dataset in pickle format:" - ], - "metadata": { - "id": "I5YUvAc7bngQ" - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "```\n", - "data:\n", - " taxi:\n", - " data_format: pickle\n", - " train_dir: ./data/taxi/train.pkl\n", - " valid_dir: ./data/taxi/dev.pkl\n", - " test_dir: ./data/taxi/test.pkl\n", - "```\n", - "\n", - "Then we can launch the train/evaluation pipeline process. See [experiment_config](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/configs/experiment_config.yaml) for the full example.\n" - ], - "metadata": { - "id": "6zfSHKhDmFSS" - } + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5848c90c9f2144b588aa88290b529151", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "train.json: 0%| | 0.00/2.29M [00:00" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tpp_loader.plot_event_type_distribution()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F4Z-YvxkN66c" + }, + "source": [ + "#### Event Delta Time Distribution Plot\n", + "\n", + "\n", + "The `plot_event_delta_times_distribution` function visualizes the distribution of time intervals between consecutive events (delta times) in the dataset. This plot provides insights into the temporal patterns of events, such as whether they occur at regular intervals or vary widely. Understanding delta time distribution is crucial for TPP models since these patterns directly affect how the model learns to predict the timing of future events." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 }, + "id": "ZLB47wR1ODAH", + "outputId": "bf0340e0-d259-42ea-c718-209f2bd2ee53" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "#### Dataset Statistics\n", - "\n", - "\n", - "The `get_statistics` function retrieves statistical information about the dataset,\n", - "such as the distribution of event types, sequence lengths, and timing intervals. By specifying `split='train'`, this function targets only the training subset of the dataset. The resulting stats variable is printed to provide an overview of the dataset's\n", - "structure and characteristics, which can be helpful for understanding the data before model training." + "data": { + "image/png": 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", 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This visualization helps identify the frequency of different event types, which can be useful for analyzing class imbalance or the prevalence of certain types of events. Understanding event type distribution is essential for TPP models, as it informs the model about the likelihood and variety of event types." + "3d24235ee98a4daeaaaf85a7b5763f0b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3d4156a02f7a436f9d678531bdcad532": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_04ac17403fdd480d985d1f7885196c46", + "IPY_MODEL_0b0bc44093404ad8b396dbc7f86a6967", + "IPY_MODEL_f9d8cff1f4b24cd4b1aea8693746a81e" ], - "metadata": { - "id": "yA61VBnhLLEw" - } + "layout": "IPY_MODEL_9b8a0445912840c194aed0846228972f" + } }, - { - "cell_type": "code", - "source": [ - "tpp_loader.plot_event_type_distribution()" + "41522af32da04925bc328c4a9d7e3a82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_375ac02a2d6b43f4a70feaf921c5377d", + "IPY_MODEL_ae9d5f81adc343edb3f7de9dccb15f47", + "IPY_MODEL_03b8f2a927ff406b88d8ff844f620403" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 564 - }, - "id": "M3pheYYPNfYP", - "outputId": "3964b087-678f-4a69-ca34-50be977639cf" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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This plot provides insights into the temporal patterns of events, such as whether they occur at regular intervals or vary widely. 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