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semantic search inference - ml4hc.ipynb
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| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/pszemraj/f1168c3afc8f0a9166af27939546fd3e/semantic-search-inference-ml4hc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# <center> asymmetric semantic search inference on pre-embedded data\n", | |
| "\n", | |
| "> this is a notebook to demo/use generated embeddings on course data as text (slides, lecture audio, etc) via **asymmetric search** sort of like google.\n", | |
| "\n", | |
| "\n", | |
| "- this is largely based on SBERT docs found [here](https://www.sbert.net/examples/applications/semantic-search/README.html#python)\n", | |
| "\n", | |
| "by [Peter](https://peterszemraj.ch/)\n", | |
| "\n", | |
| "\n", | |
| "---\n" | |
| ], | |
| "metadata": { | |
| "id": "Ej0_4M8vbaFx" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# setup\n" | |
| ], | |
| "metadata": { | |
| "id": "sX1pA0zk0sqA" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "LrDWdEzv3LaX" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown add auto-Colab formatting with `IPython.display`\n", | |
| "from IPython.display import HTML, display\n", | |
| "# colab formatting\n", | |
| "def set_css():\n", | |
| " display(\n", | |
| " HTML(\n", | |
| " \"\"\"\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
| " }\n", | |
| " </style>\n", | |
| " \"\"\"\n", | |
| " )\n", | |
| " )\n", | |
| "\n", | |
| "get_ipython().events.register(\"pre_run_cell\", set_css)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "this notebook works perfectly fine without GPU, but if you want to use a GPU the below will tell you what it is\n" | |
| ], | |
| "metadata": { | |
| "id": "SzGHfgzizNoL" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!nvidia-smi" | |
| ], | |
| "metadata": { | |
| "id": "DJdHy1sSdbBj", | |
| "outputId": "dbf2ddf5-f9d3-49a9-be20-6560f3cb5fb7", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| } | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
| " }\n", | |
| " </style>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "id": "vH8Re5UelYAc", | |
| "cellView": "form", | |
| "outputId": "e3cdbac4-06b2-4d86-c8b6-2ae2a3c6b28b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
| " }\n", | |
| " </style>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[K |████████████████████████████████| 85 kB 2.7 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 35.3 MB 137 kB/s \n", | |
| "\u001b[K |████████████████████████████████| 4.7 MB 48.2 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 1.3 MB 32.9 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 120 kB 42.2 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 6.6 MB 30.2 MB/s \n", | |
| "\u001b[?25h Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@title installs\n", | |
| "\n", | |
| "!pip install -U sentence-transformers pyarrow -q\n", | |
| "\n", | |
| "import torch\n", | |
| "from pathlib import Path\n", | |
| "\n", | |
| "from sentence_transformers import SentenceTransformer, util\n", | |
| "import logging\n", | |
| "logging.basicConfig(\n", | |
| " level=logging.INFO, \n", | |
| " format=\"%(asctime)s - %(levelname)s - %(message)s\",\n", | |
| " filename=\"embed_coursedata.log\",\n", | |
| "\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "hf_tag = \"sentence-transformers/msmarco-distilbert-cos-v5\" #@param {type:\"string\"}\n", | |
| "\n", | |
| "use_gpu = torch.cuda.is_available()\n", | |
| "print(f\"using GPU = {use_gpu}\")\n", | |
| "model = SentenceTransformer(\n", | |
| " hf_tag,\n", | |
| " device='cuda' if use_gpu else 'cpu',\n", | |
| " )\n", | |
| "# model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L12-cos-v5')\n", | |
| "\n" | |
| ], | |
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| }, | |
| "execution_count": 4, | |
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| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## load data\n" | |
| ], | |
| "metadata": { | |
| "id": "t_17L9_H0vE1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@markdown Embeddings on documents are pre-computed :)\n", | |
| "\n", | |
| "url = \"https://www.dropbox.com/s/847egxwysc75g2p/embedded_course_data.pkl?dl=1\" #@param {type:\"string\"}\n" | |
| ], | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "WzlEqQ9PxLRe", | |
| "outputId": "44246f24-f2fd-4053-cf04-4eb4a7c8f30d", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| } | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
| " }\n", | |
| " </style>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data_path = \"embedded_course_data.pkl\"\n", | |
| "!wget -O $data_path $url" | |
| ], | |
| "metadata": { | |
| "id": "fMkGkeGbxWYw", | |
| "outputId": "cd672bf0-8680-43d2-ae67-5cf8b3efa6aa", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| } | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
| " }\n", | |
| " </style>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-09-02 14:17:49-- https://www.dropbox.com/s/847egxwysc75g2p/embedded_course_data.pkl?dl=1\n", | |
| "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.18, 2620:100:6017:18::a27d:212\n", | |
| "Connecting to www.dropbox.com (www.dropbox.com)|162.125.80.18|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 302 Found\n", | |
| "Location: /s/dl/847egxwysc75g2p/embedded_course_data.pkl [following]\n", | |
| "--2022-09-02 14:17:49-- https://www.dropbox.com/s/dl/847egxwysc75g2p/embedded_course_data.pkl\n", | |
| "Reusing existing connection to www.dropbox.com:443.\n", | |
| "HTTP request sent, awaiting response... 302 Found\n", | |
| "Location: https://uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com/cd/0/get/BsIRAYwDMKVONRsCzI0qk9lNvvuf-vYWyYk_mn9k6EqT6rxl0HtiKS4Skb59YlKY2TnVh8eLDmnV8TaPyYJFoGXBJiwLukj8b_614HCrhQ9ueSVssBGVkNCqfSFAJZetdg_r-lIVvocuePiA9uUXpMaGtMjyBcUMp1wRg3GYw0T6ag/file?dl=1# [following]\n", | |
| "--2022-09-02 14:17:50-- https://uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com/cd/0/get/BsIRAYwDMKVONRsCzI0qk9lNvvuf-vYWyYk_mn9k6EqT6rxl0HtiKS4Skb59YlKY2TnVh8eLDmnV8TaPyYJFoGXBJiwLukj8b_614HCrhQ9ueSVssBGVkNCqfSFAJZetdg_r-lIVvocuePiA9uUXpMaGtMjyBcUMp1wRg3GYw0T6ag/file?dl=1\n", | |
| "Resolving uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com (uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com)... 162.125.80.15, 2620:100:6030:15::a27d:500f\n", | |
| "Connecting to uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com (uc9beaf85f78916abdebe9585e3d.dl.dropboxusercontent.com)|162.125.80.15|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 5685949 (5.4M) [application/binary]\n", | |
| "Saving to: ‘embedded_course_data.pkl’\n", | |
| "\n", | |
| "embedded_course_dat 100%[===================>] 5.42M 11.8MB/s in 0.5s \n", | |
| "\n", | |
| "2022-09-02 14:17:55 (11.8 MB/s) - ‘embedded_course_data.pkl’ saved [5685949/5685949]\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import joblib\n", | |
| "data_path = Path(data_path).resolve()\n", | |
| "embedded_course = joblib.load(data_path)\n", | |
| "embedded_course.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| }, | |
| "id": "cBGRPiPKxv-k", | |
| "outputId": "25c06f15-0dae-4100-81ef-915c4beb8cb9" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <style>\n", | |
| " pre {\n", | |
| " white-space: pre-wrap;\n", | |
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| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " id_within_doc doc_text \\\n", | |
| "0 0 zurich natural language processing project 2 a... \n", | |
| "1 1 alizee pace 29. 03. 2021 pain exposure physica... \n", | |
| "2 2 ##359 doi : 10. 1007 / s00432 - 004 - 0552 mas... \n", | |
| "3 3 history of chronic hepatitis in urban shanghai... \n", | |
| "4 4 phase 3 trial in 3 ascending, orvolvement of a... \n", | |
| "\n", | |
| " doc_name doc_dir doc_relative_loc \\\n", | |
| "0 OCR_Project2ML4HNLP_ coursedocsJune22-ml4hc 0.0 \n", | |
| "1 OCR_Project2ML4HNLP_ coursedocsJune22-ml4hc 7.143 \n", | |
| "2 OCR_Project2ML4HNLP_ coursedocsJune22-ml4hc 14.286 \n", | |
| "3 OCR_Project2ML4HNLP_ coursedocsJune22-ml4hc 21.429 \n", | |
| "4 OCR_Project2ML4HNLP_ coursedocsJune22-ml4hc 28.571 \n", | |
| "\n", | |
| " doc_embeddings \n", | |
| "0 [-0.0071430462, -0.035852537, 0.06472769, -0.0... \n", | |
| "1 [-0.054871712, 0.0041954266, 0.035234813, 0.00... \n", | |
| "2 [0.012789174, 0.014841921, -0.01317768, -0.021... \n", | |
| "3 [-0.042795025, -0.038891725, 0.023976067, -0.0... \n", | |
| "4 [-0.05967743, -0.0730482, 0.05493018, -0.02719... " | |
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| "text/html": [ | |
| "\n", | |
| " <div id=\"df-73f5c187-1032-4cee-b227-fe2847e21236\">\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", | |
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| " <td>28.571</td>\n", | |
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| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
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| " }\n", | |
| "\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", | |
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| " [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", | |
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| " buttonEl.style.display =\n", | |
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| " const element = document.querySelector('#df-73f5c187-1032-4cee-b227-fe2847e21236');\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", | |
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| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
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| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@markdown `embedded_course` is a pandas df can be used for\n", | |
| "#@markdown whatever pandas things you need.\n", | |
| "\n", | |
| "print(f\"type is {type(embedded_course)}\")\n", | |
| "embedded_course.info()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| }, | |
| "cellView": "form", | |
| "id": "p7YL1KGPxZsz", | |
| "outputId": "ee9f5b8a-da58-4447-b459-0285026ec87c" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| "type is <class 'pandas.core.frame.DataFrame'>\n", | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 1471 entries, 0 to 1470\n", | |
| "Data columns (total 6 columns):\n", | |
| " # Column Non-Null Count Dtype \n", | |
| "--- ------ -------------- ----- \n", | |
| " 0 id_within_doc 1471 non-null Int64 \n", | |
| " 1 doc_text 1471 non-null string \n", | |
| " 2 doc_name 1471 non-null string \n", | |
| " 3 doc_dir 1471 non-null string \n", | |
| " 4 doc_relative_loc 1471 non-null Float64\n", | |
| " 5 doc_embeddings 1471 non-null object \n", | |
| "dtypes: Float64(1), Int64(1), object(1), string(3)\n", | |
| "memory usage: 72.0+ KB\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Query the embeddings\n" | |
| ], | |
| "metadata": { | |
| "id": "5kzc-FgIeiiZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "\n", | |
| "import pprint as pp\n", | |
| "import time\n", | |
| "from pathlib import Path\n", | |
| "\n", | |
| "import joblib\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import torch\n", | |
| "from sentence_transformers import SentenceTransformer, util\n", | |
| "#@markdown define `semantic_search`\n", | |
| "import pandas as pd\n", | |
| "def semantic_search(\n", | |
| " query: str,\n", | |
| " sbert_model,\n", | |
| " emdf: pd.DataFrame,\n", | |
| " embedding_col: str = \"doc_embeddings\",\n", | |
| " top_n=5,\n", | |
| " unique_col=\"doc_name\",\n", | |
| " return_unique=True,\n", | |
| "):\n", | |
| " \"\"\"\n", | |
| " semantic_search - core semantic search function using cosine similarity\n", | |
| " Parameters\n", | |
| " ----------\n", | |
| " query : str, text to search for\n", | |
| " sbert_model : sentence-transformers model to use for encoding\n", | |
| " emdf : dataframe with the encoded document embeddings\n", | |
| " embedding_col : str, column name of the embedding column in the emdf, by default \"doc_embeddings\"\n", | |
| " top_n : int, optional, by default 5, number of most-similar results to return\n", | |
| " Returns\n", | |
| " -------\n", | |
| " dict - a dictionary with the aggregated search result text as key, cosine similarity score as value\n", | |
| " \"\"\"\n", | |
| " top_n_loop = top_n + 30 if return_unique else top_n\n", | |
| " top_k = min(top_n_loop, len(emdf))\n", | |
| " _data = [np.array(a, dtype=np.float32) for a in emdf[embedding_col].values]\n", | |
| "\n", | |
| " use_gpu = torch.cuda.is_available()\n", | |
| "\n", | |
| " corpus_embeddings = torch.FloatTensor(_data)\n", | |
| " query_embedding = sbert_model.encode(query, convert_to_tensor=True)\n", | |
| "\n", | |
| " if use_gpu:\n", | |
| " corpus_embeddings = corpus_embeddings.to(\"cuda\")\n", | |
| " query_embedding = query_embedding.to(\"cuda\")\n", | |
| " else:\n", | |
| " corpus_embeddings = corpus_embeddings.cpu()\n", | |
| " query_embedding = query_embedding.cpu()\n", | |
| " cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]\n", | |
| " top_results = torch.topk(cos_scores, k=top_k)\n", | |
| "\n", | |
| " results = []\n", | |
| " docs_found = []\n", | |
| " result_rank = 0\n", | |
| " for score, idx in zip(top_results[0], top_results[1]):\n", | |
| "\n", | |
| " result_rank += 1\n", | |
| " idx = int(idx)\n", | |
| " _result = {}\n", | |
| " _result[\"Rank\"] = result_rank\n", | |
| " _result[\"Score (0-1)\"] = round(float(score.cpu().numpy()), 4)\n", | |
| "\n", | |
| " for c in emdf.columns:\n", | |
| " if c == embedding_col:\n", | |
| " continue\n", | |
| " _result[c] = emdf.loc[idx, c]\n", | |
| "\n", | |
| " if return_unique:\n", | |
| " # check if the result is already in the results list\n", | |
| " if _result[unique_col] in docs_found:\n", | |
| " continue\n", | |
| " else:\n", | |
| " results.append(_result)\n", | |
| " docs_found.append(_result[unique_col])\n", | |
| "\n", | |
| " else:\n", | |
| " results.append(_result)\n", | |
| "\n", | |
| " if len(results) >= top_n:\n", | |
| " break\n", | |
| " return results" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 17 | |
| }, | |
| "cellView": "form", | |
| "id": "bxMKbeijejch", | |
| "outputId": "bf701bf8-886d-4a3e-fd0f-c224f870e219" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
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| "data": { | |
| "text/plain": [ | |
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| " " | |
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| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "\n", | |
| "## <font color=\"DodgerBlue\"> _**Your Query Goes Here**_\n", | |
| "\n", | |
| "Enter the term/phrase you want to find results for (_think Google search_) into `query`, and adjust params. Then run the cell! Should only take a couple seconds max.\n", | |
| "\n", | |
| "- `return_unique`: whether or not you want the search results to be unique with respect to what text file (lecture1 audio, tecture 3 slides, etc) they are from\n", | |
| "- `number_results`: how many results to return\n", | |
| "\n", | |
| "If you want more details, you can use `embedded_course` as a standard dataframe to find things 👍" | |
| ], | |
| "metadata": { | |
| "id": "ZZlxdLf8zojH" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%%time\n", | |
| "query = \"jensen divergence metric downsides\" #@param {type:\"string\"}\n", | |
| "return_unique = True #@param {type:\"boolean\"}\n", | |
| "number_results = 5 #@param {type:\"integer\"}\n", | |
| "import pprint as pp\n", | |
| "result = semantic_search(\n", | |
| " query=query,\n", | |
| " sbert_model=model,\n", | |
| " emdf=embedded_course,\n", | |
| " return_unique=return_unique,\n", | |
| " top_n = number_results,\n", | |
| ")\n", | |
| "#@markdown note that `doc_relative_loc` is a _percentage_ of the way through\n", | |
| "#@markdown the parent text file that the snippet is found.\n", | |
| "pp.pprint(result)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "cellView": "form", | |
| "id": "qaAAlbqtel7e", | |
| "outputId": "bfc1be3e-ce95-4be1-d26b-a7d1bdf020b4" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| " " | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[{'Rank': 1,\n", | |
| " 'Score (0-1)': 0.3537,\n", | |
| " 'doc_dir': 'papers-referenced',\n", | |
| " 'doc_name': 'OCR Multilevel Medical Embedding of Electronic Health Records '\n", | |
| " 'for Predictive Healthcare',\n", | |
| " 'doc_relative_loc': 69.565,\n", | |
| " 'doc_text': 'the preferred nonlinear activation for hidden layers in many '\n", | |
| " 'studies this seems due t0 the regularizing effect of sigmoid '\n", | |
| " 'and tanh functions _ whereas relu can produce outputs as high '\n", | |
| " 'as infinity, sigmoid and tanh have bounded outputs considering '\n", | |
| " 'that sigmoid, tanh and relu all sum up the code embeddings in '\n", | |
| " 'visit v ( t ) before applying the nonlinear activation, '\n", | |
| " 'constraining the output of the nonlinear activation seems to '\n", | |
| " 'work favorably, especially in d3 where there are more 13 codes '\n", | |
| " 'per visit. this regularization benefit, however ; diminishes as '\n", | |
| " 'the dataset grows, which can be confirmed by table / 7lin '\n", | |
| " 'sectionf ] in addition, as can be seen by the performance of '\n", | |
| " 'sigmoid, lp, sigmoid suffers from the vanishing gradient '\n", | |
| " 'problem as opposed to tanh o relu that have larger gradient '\n", | |
| " 'values. e roc - auc of heart failure prediction on datasets d1, '\n", | |
| " 'dz, and d : table 6 : roc - auc of all models for hf prediction '\n", | |
| " 'on small datasets. values in the parentheses denote standard '\n", | |
| " 'deviations from 5 - fold random data splits two best values in '\n", | |
| " 'each column are marked in bold : d2 d ; visit complexity 0 - '\n", | |
| " '159, 5608',\n", | |
| " 'id_within_doc': 48},\n", | |
| " {'Rank': 2,\n", | |
| " 'Score (0-1)': 0.3488,\n", | |
| " 'doc_dir': 'coursedocsJune22-ml4hc',\n", | |
| " 'doc_name': 'OCR_Tutorial13-Examquestions_',\n", | |
| " 'doc_relative_loc': 90.476,\n", | |
| " 'doc_text': 'gan and the reasons to use wgan over gan. classical gans use '\n", | |
| " 'the jensen divergence metric ( not continuous ) to compare the '\n", | |
| " 'true and the synthetic data distribution wasserstein gans use '\n", | |
| " 'the earth - mover divergence ( continuous ) metric to compare '\n", | |
| " 'the true and the synthetic data distributions wgans are more '\n", | |
| " 'stable due to smoother gradients. they are preferred in '\n", | |
| " 'practice 34 machine learning for health care, spring 2022 '\n", | |
| " 'tutorial 13 alice bizeul 24. 05. 2022 ethics e question 1 1. '\n", | |
| " 'mark below the necessary criteria for consent : a voluntariness '\n", | |
| " 'b _ willfulness c capacity to understand d. trustworthiness e _ '\n", | |
| " 'adequate information 35 machine learning for health care, '\n", | |
| " 'spring 2022 tutorial 13 alice bizeul 24. 05. 2022 ethics e '\n", | |
| " 'question 1 1. mark below the necessary criteria for consent : a '\n", | |
| " 'voluntariness b willfulness c capacity to understand d _ '\n", | |
| " 'trustworthiness e adequate information 36 machine learning for '\n", | |
| " 'health care, spring 2022 tutorial 13 alice bizeul 24. 05. 2022 '\n", | |
| " 'questions? also, on moodle ( preferred : your classmates '\n", | |
| " 'probably have similar questions! ) or by email at alice bizeul '\n", | |
| " '@ aiethzch machine learning for health care, spring 2022',\n", | |
| " 'id_within_doc': 19},\n", | |
| " {'Rank': 3,\n", | |
| " 'Score (0-1)': 0.3356,\n", | |
| " 'doc_dir': 'lecture-audio-full',\n", | |
| " 'doc_name': 'SC_lecture_privac_transcription_15_GT',\n", | |
| " 'doc_relative_loc': 76.923,\n", | |
| " 'doc_text': ', and first, we tried to predict our patient readmission. it '\n", | |
| " \"worked okay, and then we said as it's that imbalances we tried \"\n", | |
| " 'to in, in addition, neurite the unbalanced the minority class '\n", | |
| " 'and see what hips and see if you can also with '\n", | |
| " 'disdegeneratedta, enrich thbbaseta to help the basil hospital '\n", | |
| " \"in the end and for this, we used a model as i said before, it's \"\n", | |
| " \"not viable gear. still, it's a little different, but we used a \"\n", | |
| " 'different loss term and a different way to quantify the '\n", | |
| " 'difference between the generated and training data instead of '\n", | |
| " 'the trans and china direction. the basasteine and the '\n", | |
| " 'passionate distance are different ways of measuring the '\n", | |
| " \"distance between distributions. so here's an example of the \"\n", | |
| " 'difference between the vassasteine distance and the sham '\n", | |
| " 'divergence used in a classic grain. so the classic grain uses '\n", | |
| " 'the genzenchanne divergence some compared distributions. still, '\n", | |
| " 'if these distributions are completely dishonest, as you see '\n", | |
| " 'here, this is a function where they are completely disjointed '\n", | |
| " 'except when this distribution here is zero, so they are the '\n", | |
| " \"same. and if you compute this distance here, there's a sudden\",\n", | |
| " 'id_within_doc': 20},\n", | |
| " {'Rank': 4,\n", | |
| " 'Score (0-1)': 0.3155,\n", | |
| " 'doc_dir': 'lecture-audio-full',\n", | |
| " 'doc_name': 'SC_lecture_genetics_p_2_v_2_c_transcription_12_GT',\n", | |
| " 'doc_relative_loc': 32.203,\n", | |
| " 'doc_text': 'so instead of using conditional probabilities, we have these '\n", | |
| " 'joint probabilities, which take just this systemic expression '\n", | |
| " 'here, and then, by a miracle, it worked quite nicely. so '\n", | |
| " 'instead of having this violation where everything is crowded, '\n", | |
| " 'you have the beautiful civilization where we see clusters in '\n", | |
| " 'our data ; the main reason for this was the properties of the '\n", | |
| " 'kashit distribution here that it has heavier tails so the '\n", | |
| " 'customers can kind of. they prefer to go away from one another. '\n", | |
| " 'so now i wanted to show you what kind of visualization the time '\n", | |
| " 'can create when we change this parameter of perplexity, which '\n", | |
| " 'affects how far each observation contains other observations so '\n", | |
| " 'hear the original later set was this one about two reasons to '\n", | |
| " 'closure is a sample with the ocean distribution. then we apply '\n", | |
| " 'themselves of different values of the perplexity parameters '\n", | |
| " 'from two to one hundred, and this is what the restrictions that '\n", | |
| " 'we can obtain so he would transform to do into two do, but this '\n", | |
| " 'is just for an illustration purpose. what you can see here is '\n", | |
| " 'only that with perplexity values about thirty and fifty. you '\n", | |
| " 'see this too. beautiful questions when perplexity is too low, '\n", | |
| " 'we see too many letters, so we cannot really capture the true '\n", | |
| " 'structure in the data, and then the',\n", | |
| " 'id_within_doc': 19},\n", | |
| " {'Rank': 6,\n", | |
| " 'Score (0-1)': 0.2879,\n", | |
| " 'doc_dir': 'papers-referenced',\n", | |
| " 'doc_name': 'OCR_3D Conditional GAN-based Super-resolution for CT Slice '\n", | |
| " 'Interval',\n", | |
| " 'doc_relative_loc': 28.571,\n", | |
| " 'doc_text': 'adversarial model d that tries to maximize it. g and d can be '\n", | |
| " 'implemented as convolutional neural networks ( cnn ). we also '\n", | |
| " 'use l loss to calculate pixel - wise appearance differences '\n", | |
| " 'between ground truth images and generated images, which has '\n", | |
| " 'been shown to give less blurring than the lz loss in diversity '\n", | |
| " 'of image - to - image translation tasks [ 7 ; therefore, the '\n", | |
| " 'out final objective is expressed as arg min max lcgan ( g, d ) '\n", | |
| " '+ all ( g ) ( 2 ) figure q ] illustrates the proposed '\n", | |
| " 'adversarial training procedure. note that the additional '\n", | |
| " 'conditions are not inputted into the generator. aj kudo et al. '\n", | |
| " 'positive examples 8 negative examples discriminator '\n", | |
| " 'discriminator ground truth thick image virtual thin thick image '\n", | |
| " 'body part 0 - level slice ( mm ) body part 0 - level slice ( mm '\n", | |
| " ') generator input ground truth condition thick image degrade 8 '\n", | |
| " 'mm m conditions body part : head slice : 8 mm 1. 6 gaussian '\n", | |
| " 'blurs ( 0 _ downsample spline interpolation fig. 2. adversarial '\n", | |
| " 'training framework of thick - thin slice translation on ct '\n", | |
| " 'images. in each training iteration, the thin slice input data '\n", | |
| " 'is randomly degraded to simulate thick slice data. for the '\n", | |
| " 'generator',\n", | |
| " 'id_within_doc': 6}]\n", | |
| "CPU times: user 167 ms, sys: 4.24 ms, total: 171 ms\n", | |
| "Wall time: 166 ms\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "d4KUgZo9eucn" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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