diff --git "a/03_faiss_semantic_search.ipynb" "b/03_faiss_semantic_search.ipynb" new file mode 100644--- /dev/null +++ "b/03_faiss_semantic_search.ipynb" @@ -0,0 +1,9013 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "HgskMpgi-QtB" + }, + "source": [ + "# Notebook 3: Semantic search over GitHub issues with FAISS\n", + "\n", + "**Dataset:** `BeNatural/github-scikit-learn-issues-dataset` (300 scikit-learn issues, 2010 to 2012)\n", + "\n", + "**What this notebook does**\n", + "1. Loads the dataset from the Hugging Face Hub and prepares the text.\n", + "2. Turns each issue into an *embedding*, a list of numbers that represents its meaning.\n", + "3. Builds a FAISS index so similar issues can be found quickly.\n", + "4. Runs example searches and explains the results.\n", + "5. Checks the results for bias and coverage gaps.\n", + "\n", + "**The idea of semantic search:** a normal search looks for matching words. Semantic search compares *meaning*, so a query can find an issue even if it uses different words." + ], + "id": "HgskMpgi-QtB" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NWIl7uNt-QtC" + }, + "source": [ + "## 1. Setup" + ], + "id": "NWIl7uNt-QtC" + }, + { + "cell_type": "code", + "metadata": { + "id": "YwoCLhkl-QtD" + }, + "execution_count": 1, + "outputs": [], + "source": [ + "!pip install -q datasets sentence-transformers faiss-cpu pandas numpy matplotlib" + ], + "id": "YwoCLhkl-QtD" + }, + { + "cell_type": "code", + "metadata": { + "id": "XcRe2nL7-QtD" + }, + "execution_count": 2, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import faiss\n", + "from datasets import load_dataset\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "os.makedirs(\"outputs\", exist_ok=True)" + ], + "id": "XcRe2nL7-QtD" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dsh1zo3H-QtD" + }, + "source": [ + "## 2. Load the dataset from Hugging Face" + ], + "id": "dsh1zo3H-QtD" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386, + "referenced_widgets": [ + "0867b63983f648d9839a490c38b28336", + "8e5018d1ed6347b494f19e7503753fb0", + "4b5d2e0aa2fc46c8a7300bfa269d5872", + "16c03d8eaa0d4b02a6522492c73c3fb8", + "a874ba2383594c97a277dfaa5c91e323", + "bdac92f906b44ed5bfad9b373aad9d05", + "7785b32abc7c4823bb8af1adc7d96e84", + "e2300f85f5854dcd928ede7dafc06b2e", + "d80dd279d9d54603bb08d98f0981e599", + "242fd2b4f9aa4d4d9db4b44564dbbbb1", + "e70f5e0b8df34a33b85dc04f8350b280", + "7e29b2e6f1504d1eb17d763eecfc7f3d", + "dd267d22d3c849898bfcdd99f03c5a66", + "4bbf6b2a99d84c378f1e621e39cbea2c", + "b317b676fac5488a8d486fbc4774c2f1", + "7e1cc660df174947a1fec07c18d971d0", + "17197aadac23459e8f6687f8eed70104", + "6d558d02b9414391b8ff308ca5d2d61b", + "53acb36924a44a16845a59e4459c1480", + "bdf4d78249574970a85c791278f68a5a", + "0b350c6c9212413d9f59044489a23875", + "b7d20ae7b5b3479f810c173b12ef0e14", + "9308bd15ab7e4fa1a56778ef799f617c", + "04b0bc820e89444d80827c86c253e6b6", + "ea0c20db20ad4e429430baf1017d57bf", + "928041db4706433b83ec22a34b47e1f6", + "f65535c747744f2f977a2715d62a41d9", + "41ea1bef960842fbae6ebafbee1fb2fc", + "0b066521dcf647469dd1e90e79f8f87c", + "3022815ddede4b359112aa70d9c6f883", + "469da11e671f4aff8f64f5d753f5859a", + "39f6689cc5cf4dd497184c2cb47a65a6", + "85c5917c1f5840cda4ec079c5809e004", + "af83d9bce6af481087843b159dc7ecfa", + "2fd73675e2a34ed0a3954491ab411d84", + "770e83ceb1b94cc890e07f1dc5f4d836", + "6b16be15fc92460a986d8682aa315e16", + "c09de72e41b04484b80ddef319224437", + "106fb4db0afe4e2e8c78014c1bf22fe7", + "3212c3632a7641a08ef0cf4000902617", + "285f18ace06d442788ec5f45e8349fe9", + "d1495b0be07e469dbafae09dd8e62502", + "6fe2a8127d9e45f9a1ce95133024572c", + "2419cb482d9e45d2ae4744aed0000602" + ] + }, + "id": "pq9wVdzM-QtD", + "outputId": "53fcd086-9441-457f-a767-20f457558ee9" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", + "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/8.64k [00:00\n", + "
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issue_numbertitlelabelscreated_at
01remove Y from fit in OneClassSVM2010-08-31 07:38:16+00:00
15Bayesian regression2010-09-11 11:40:28+00:00
27Return index of support vectors in sparse SVMEnhancement, Moderate2010-10-19 08:00:57+00:00
38LARS module broken2010-10-23 23:04:24+00:00
411implement warm restarts for logistic regressionNew Feature2010-10-26 07:28:50+00:00
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\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"df[[\\\"issue_number\\\", \\\"title\\\", \\\"labels\\\", \\\"created_at\\\"]]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"issue_number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 1,\n \"max\": 11,\n \"num_unique_values\": 5,\n \"samples\": [\n 5,\n 11,\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Bayesian regression\",\n \"implement warm restarts for logistic regression\",\n \"Return index of support vectors in sparse SVM\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"labels\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"\",\n \"Enhancement, Moderate\",\n \"New Feature\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"created_at\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2010-08-31 07:38:16+00:00\",\n \"max\": \"2010-10-26 07:28:50+00:00\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"2010-09-11 11:40:28+00:00\",\n \"2010-10-26 07:28:50+00:00\",\n \"2010-10-19 08:00:57+00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "ds = load_dataset(\n", + " \"BeNatural/github-scikit-learn-issues-dataset\",\n", + " data_files=\"github_issues.parquet\",\n", + " split=\"train\",\n", + ")\n", + "df = ds.to_pandas()\n", + "print(df.shape) # expected: (300, 14)\n", + "df[[\"issue_number\", \"title\", \"labels\", \"created_at\"]].head()" + ], + "id": "pq9wVdzM-QtD" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OOMkprC_-QtD" + }, + "source": [ + "## 3. Cleaning and preparing the text\n", + "The search needs one piece of text per issue. These steps prepare it:\n", + "1. Replace missing titles or bodies with empty text, so nothing breaks.\n", + "2. Join the title and body into one text field. The title is placed first because it is the most informative part.\n", + "3. Replace line breaks and repeated spaces with single spaces.\n", + "4. Cut each text to 1,000 characters. Very long texts add noise, and the embedding model has a length limit.\n", + "5. Check for duplicate texts.\n", + "\n", + "Nothing is deleted from the dataset. All 300 issues stay in the index." + ], + "id": "OOMkprC_-QtD" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZfV6q4Fd-QtD", + "outputId": "3ef4d335-7683-433c-9694-e56438e8a26b" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Empty bodies: 9\n", + "Duplicate texts: 0\n", + "\n", + "count 300.000000\n", + "mean 493.616667\n", + "std 331.669613\n", + "min 33.000000\n", + "25% 198.500000\n", + "50% 418.000000\n", + "75% 816.750000\n", + "max 1000.000000\n", + "Name: search_text, dtype: float64\n" + ] + } + ], + "source": [ + "df[\"title\"] = df[\"title\"].fillna(\"\")\n", + "df[\"body\"] = df[\"body\"].fillna(\"\")\n", + "\n", + "def clean_text(title, body):\n", + " text = f\"{title}. {body}\"\n", + " text = \" \".join(text.split()) # removes line breaks and extra spaces\n", + " return text[:1000]\n", + "\n", + "df[\"search_text\"] = [clean_text(t, b) for t, b in zip(df[\"title\"], df[\"body\"])]\n", + "\n", + "print(\"Empty bodies:\", (df[\"body\"].str.strip() == \"\").sum())\n", + "print(\"Duplicate texts:\", df[\"search_text\"].duplicated().sum())\n", + "print()\n", + "print(df[\"search_text\"].str.len().describe())" + ], + "id": "ZfV6q4Fd-QtD" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ux3UVMGs-QtE", + "outputId": "482f3c71-d80d-4d66-9d64-37be8567b49c" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "BEFORE: 'Add priors on the mean instead of assuming that the prior means are zero.\\nAdd more reference in the doc.\\nModify ARD in order to use a vector of hyperparameters for the precision instead of a single va'\n", + "\n", + "AFTER: Bayesian regression. Add priors on the mean instead of assuming that the prior means are zero. Add more reference in the doc. Modify ARD in order to use a vector of hyperparameters for the precision i\n" + ] + } + ], + "source": [ + "# One example before and after cleaning\n", + "i = 1\n", + "print(\"BEFORE:\", repr(df[\"body\"].iloc[i][:200]))\n", + "print()\n", + "print(\"AFTER: \", df[\"search_text\"].iloc[i][:200])" + ], + "id": "Ux3UVMGs-QtE" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "czJP7V68-QtE" + }, + "source": [ + "## 4. Create embeddings\n", + "An embedding model converts each text into a vector, a list of numbers. Texts with similar meaning end up with similar vectors.\n", + "\n", + "The model used is `multi-qa-mpnet-base-dot-v1`, the model used in the Hugging Face course tutorial. It was trained on question-and-answer pairs, so it suits short search queries matched against longer passages." + ], + "id": "czJP7V68-QtE" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 482, + "referenced_widgets": [ + "2b2365b6e89b4146b1a661489605e65c", + "983a170682a44277aa9b022aa382b6c4", + "f05022f63c2e4b8aaa8ae49822d667ec", + "ac884aa99f284578b21a5d507323f1ea", + "bd8ed4bc609e4f16afffd619f8416246", + "02cfde1881ae4c6aa299a344744493ad", + "402c3ed64dd746f39044f8e02d9216fa", + "9387ad7e96e847b99284af12862fe569", + "e665fd0b84434f5ea099f29037db2bc8", + "2c1d80efa9e447fbb670a911714a2c95", + "b7c2e851609c46f085d3f28bf952e749", + "987f62a5d23a4e42ada405cb378b1378", + "9fd4fc313ab5454088494c22be717eac", + "f4a5bf0dbed54a15af69baf875d1cddb", + "d4f5f67d5a114b2ab361636d6c8e4a0c", + "ba1a7854c7ab4c28bccafe93a2a9e5d9", + "1900370435cf42f7867cd8af7ec31bc2", + "f74fdd0946cc4475905977d8a54063a9", + 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+ }, + "metadata": {} + } + ], + "source": [ + "best = []\n", + "for q in queries:\n", + " r = search(q, k=5)\n", + " best.append({\"query\": q, \"top_score\": r[\"score\"].iloc[0], \"mean_top5\": r[\"score\"].mean().round(3)})\n", + "\n", + "best_df = pd.DataFrame(best)\n", + "display(best_df)\n", + "\n", + "best_df.set_index(\"query\")[\"top_score\"].plot(kind=\"barh\", color=\"steelblue\")\n", + "plt.xlabel(\"Top-1 similarity score\")\n", + "plt.title(\"How well each query matched the dataset\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"outputs/query_top_scores.png\")\n", + "plt.show()" + ], + "id": "SNRAn11i-QtF" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hfa5z593-QtF" + }, + "source": [ + "## 9. Bias and coverage checks\n", + "Search quality depends on what is in the dataset. These checks compare the results that come back with the dataset as a whole, using the top 5 results of every query." + ], + "id": "hfa5z593-QtF" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + }, + "id": "EvFfxtQ1-QtF", + "outputId": "6346d6db-cd1c-4a28-e165-4ffed1ecc679" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Issues with no label -> in results: 57%, in whole dataset: 78%\n", + "Top 3 authors ['amueller', 'fabianp', 'mblondel'] -> in results: 50%, in whole dataset: 44%\n", + "\n", + "Years of returned issues:\n", + "year\n", + "2010 4\n", + "2011 19\n", + "2012 7\n", + "Name: count, dtype: int64\n", + "\n", + "Issues returned by more than one query:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " times_returned\n", + "issue_number \n", + "735 2\n", + "388 2" + ], + "text/html": [ + "\n", + "
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Labels: how many returned issues have no label at all?\n", + "share_no_label_hits = (all_hits[\"labels\"].fillna(\"\") == \"\").mean()\n", + "share_no_label_all = (df[\"labels\"].fillna(\"\") == \"\").mean()\n", + "print(f\"Issues with no label -> in results: {share_no_label_hits:.0%}, in whole dataset: {share_no_label_all:.0%}\")\n", + "\n", + "# 2. Authors: do a few authors dominate the results?\n", + "top_authors = df[\"author\"].value_counts().head(3).index.tolist()\n", + "share_top_hits = all_hits[\"author\"].isin(top_authors).mean()\n", + "share_top_all = df[\"author\"].isin(top_authors).mean()\n", + "print(f\"Top 3 authors {top_authors} -> in results: {share_top_hits:.0%}, in whole dataset: {share_top_all:.0%}\")\n", + "\n", + "# 3. Years\n", + "all_hits[\"year\"] = pd.to_datetime(all_hits[\"created_at\"]).dt.year\n", + "print()\n", + "print(\"Years of returned issues:\")\n", + "print(all_hits[\"year\"].value_counts().sort_index())\n", + "\n", + "# 4. Repeats: are some issues returned for many different queries?\n", + "print()\n", + "print(\"Issues returned by more than one query:\")\n", + "display(all_hits[\"issue_number\"].value_counts()[lambda s: s > 1].to_frame(\"times_returned\"))" + ], + "id": "EvFfxtQ1-QtF" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s0b6KcjP-QtF", + "outputId": "3b02a6b7-230b-4359-cdc2-f9245c1e4107" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "count 300.000\n", + "mean 0.618\n", + "std 0.092\n", + "min 0.392\n", + "25% 0.558\n", + "50% 0.611\n", + "75% 0.672\n", + "max 0.998\n", + "dtype: float64\n", + "Issues with a near-duplicate (similarity above 0.9): 4\n", + "395 | Python 3 build fails on \"ValueError: 'scikits/learn' is not a directory\" <-> python3 build fails\n", + "433 | build issues <-> building issues\n", + "434 | building issues <-> build issues\n", + "764 | python3 build fails <-> Python 3 build fails on \"ValueError: 'scikits/learn' is not a directory\"\n" + ] + } + ], + "source": [ + "# Coverage: how similar is each issue to its nearest neighbour? (looks for near-duplicates)\n", + "scores, ids = index.search(embeddings, 2) # first neighbour is the issue itself\n", + "nearest = scores[:, 1]\n", + "print(pd.Series(nearest).describe().round(3))\n", + "\n", + "dupes = np.where(nearest > 0.9)[0]\n", + "print(\"Issues with a near-duplicate (similarity above 0.9):\", len(dupes))\n", + "for i in dupes[:5]:\n", + " print(df[\"issue_number\"].iloc[i], \"|\", df[\"title\"].iloc[i], \"<->\", df[\"title\"].iloc[ids[i, 1]])" + ], + "id": "s0b6KcjP-QtF" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CxLRiKNv-QtF" + }, + "source": [ + "### What the checks show about quality\n", + "- **Labels:** most issues have no label, so labels cannot be used to judge whether a result is relevant. Relevance has to be judged by reading the titles.\n", + "- **Authors:** a few authors wrote a large share of the dataset. Compare the two percentages above. Results that over-represent them reflect the dataset and not what users wanted.\n", + "- **Years:** every result is from 2010 to 2012, because that is all the dataset contains. The search cannot find anything about modern scikit-learn.\n", + "- **Out-of-scope query:** the search still returns an answer when nothing relevant exists. Low scores are the only warning, so a minimum score would be needed in a real system.\n", + "- **Near-duplicates:** the last check lists issues that are almost the same. These take up several of the top results and reduce variety." + ], + "id": "CxLRiKNv-QtF" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3cF_BWqN-QtF" + }, + "source": [ + "## 10. Limitations\n", + "- **Small, old dataset.** 300 issues from 2010 to 2012 mean many topics have no matching issue.\n", + "- **Text is truncated** to 1,000 characters, so information later in long issues is not searched.\n", + "- **Embedding model choice.** A different model could rank results differently. This one was trained on general question-and-answer text, not on software issues.\n", + "- **Exact search only.** `IndexFlatIP` is exact and suits 300 issues. A much larger dataset would need an approximate index.\n", + "- **No automatic scoring.** Relevance was judged by reading the titles, because there is no ground truth for which issue is the right answer to a query.\n", + "- **Comments are missing.** Only the issue title and body were searched, not the discussion that followed." + ], + "id": "3cF_BWqN-QtF" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AIoeDh-s-QtG" + }, + "source": [ + "## 11. Save the results" + ], + "id": "AIoeDh-s-QtG" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MsedCd4a-QtG", + "outputId": "2deabbe1-7611-4576-cd29-62fa8805761b" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['search_results.csv', 'github_issues.faiss', 'query_top_scores.png']\n" + ] + } + ], + "source": [ + "all_hits.to_csv(\"outputs/search_results.csv\", index=False)\n", + "print(os.listdir(\"outputs\"))" + ], + "id": "MsedCd4a-QtG" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of findings\n", + "\n", + "- **Relevance:** Queries about topics well covered in the data (logistic regression warm start, documentation) returned clearly relevant issues at ranks 1 to 3, with top scores of 0.78 and 0.62. Broader queries such as \"support vector machine problems\" returned a mix of relevant and loosely related issues.\n", + "- **Coverage gap:** The query about deep learning on GPUs, a topic not present in scikit-learn in 2010 to 2012, had the lowest scores (top score 0.446) and returned unrelated issues. The index still returns five results, so low scores are the only sign that nothing relevant exists.\n", + "- **Bias:** Labelled issues were over-represented in results (43% of results had a label against 22% of the dataset), and the three most frequent authors made up 50% of results against 44% of the dataset. Most results came from 2011.\n", + "- **Data quality:** Four issues were near-duplicates (build problems), which can use up several top-5 slots with the same content.\n", + "- **Implication:** Search quality is limited by a small, old dataset with sparse labels. Results should be judged by reading titles, since labels cannot be used to measure relevance for most issues." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "0867b63983f648d9839a490c38b28336": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "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_8e5018d1ed6347b494f19e7503753fb0", + 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