diff --git "a/01_data_collection_augmentation.ipynb" "b/01_data_collection_augmentation.ipynb" new file mode 100644--- /dev/null +++ "b/01_data_collection_augmentation.ipynb" @@ -0,0 +1,3927 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "MNrSAzxQGJuq" + }, + "source": [ + "# Notebook 1: Collect, augment and publish a GitHub issues dataset\n", + "\n", + "**Source:** public issues of the `scikit-learn/scikit-learn` repository, via the GitHub REST API\n", + "**Published dataset:** https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset\n", + "\n", + "**What this notebook does**\n", + "1. Explores one API response to see what GitHub returns.\n", + "2. Collects 300 issues with the `requests` library, removing pull requests.\n", + "3. Builds a structured table and adds two derived fields.\n", + "4. Cleans and validates the data.\n", + "5. Summarises the data (these figures are used in the dataset card).\n", + "6. Saves CSV and Parquet files.\n", + "7. Logs in to Hugging Face and pushes the files to the Hub.\n", + "8. Checks that the published dataset loads correctly." + ], + "id": "MNrSAzxQGJuq" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ViOkKMuiGJur" + }, + "source": [ + "## 1. Setup" + ], + "id": "ViOkKMuiGJur" + }, + { + "cell_type": "code", + "metadata": { + "id": "n9QXTUPcGJur" + }, + "execution_count": 1, + "outputs": [], + "source": [ + "!pip install -q requests pandas pyarrow huggingface_hub datasets" + ], + "id": "n9QXTUPcGJur" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XAyOTWUsGJus", + "outputId": "4db47633-1309-4c46-eeff-44c87d22b960" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collection date: 2026-11-05\n" + ] + } + ], + "source": [ + "import time\n", + "import datetime\n", + "import requests\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "\n", + "API_URL = \"https://api.github.com/repos/scikit-learn/scikit-learn/issues\"\n", + "TARGET = 300 # number of genuine issues to collect\n", + "HF_REPO = \"BeNatural/github-scikit-learn-issues-dataset\"\n", + "\n", + "collection_date = datetime.date.today()\n", + "print(\"Collection date:\", collection_date)" + ], + "id": "XAyOTWUsGJus" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3vShOl8kGJus" + }, + "source": [ + "## 2. Explore one API response\n", + "First, one small request to see what GitHub returns. GitHub's issues endpoint returns **both issues and pull requests**. A pull request can be recognised because it has a `pull_request` field." + ], + "id": "3vShOl8kGJus" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8xJ6GA-RGJus", + "outputId": "afc5e9db-6e4f-4b5a-f4df-f2911859c285" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Status code: 200\n", + "Records received: 5\n", + "35315 | pull request? True | MNT bump vendored array-api-compat to 1.16\n", + "35314 | pull request? True | WIP MNT Add CI job that tests for thread-safety bugs by enab\n", + "35313 | pull request? False | Add CI job with threaded parallelism on by default, to catch\n", + "35312 | pull request? True | DOC/TST document and test GaussianNB var_smoothing\n", + "35311 | pull request? True | PRF Compute KMeans' tolerance without np.var on dense data\n" + ] + } + ], + "source": [ + "response = requests.get(API_URL, params={\"state\": \"all\", \"per_page\": 5}, timeout=30)\n", + "print(\"Status code:\", response.status_code) # 200 means success\n", + "response.raise_for_status()\n", + "\n", + "sample = response.json()\n", + "print(\"Records received:\", len(sample))\n", + "for item in sample:\n", + " print(item[\"number\"], \"| pull request?\", \"pull_request\" in item, \"|\", item[\"title\"][:60])" + ], + "id": "8xJ6GA-RGJus" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hvlJozP-GJus" + }, + "source": [ + "## 3. Collect the issues\n", + "- `state=all` returns open and closed issues.\n", + "- `sort=created` and `direction=asc` return the **oldest issues first**.\n", + "- `per_page=100` is the maximum page size.\n", + "- Pull requests are skipped, so only genuine issues are kept.\n", + "- A short pause between pages avoids overloading the API.\n", + "- Collection stops once 300 issues have been gathered, and the list is trimmed to exactly 300.\n", + "\n", + "No access token is needed for this small number of requests, because the public API allows it within its rate limits." + ], + "id": "hvlJozP-GJus" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uwOzTrypGJus", + "outputId": "a336ad8f-607c-430c-eab4-57f76eeb906e" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Page 1: issues collected so far = 47\n", + "Page 2: issues collected so far = 79\n", + "Page 3: issues collected so far = 110\n", + "Page 4: issues collected so far = 155\n", + "Page 5: issues collected so far = 183\n", + "Page 6: issues collected so far = 224\n", + "Page 7: issues collected so far = 266\n", + "Page 8: issues collected so far = 318\n", + "\n", + "FINAL TOTAL: 300\n" + ] + } + ], + "source": [ + "all_issues = []\n", + "page = 1\n", + "\n", + "while len(all_issues) < TARGET:\n", + " response = requests.get(\n", + " API_URL,\n", + " params={\n", + " \"state\": \"all\",\n", + " \"per_page\": 100,\n", + " \"page\": page,\n", + " \"sort\": \"created\",\n", + " \"direction\": \"asc\",\n", + " },\n", + " timeout=30,\n", + " )\n", + " response.raise_for_status()\n", + " records = response.json()\n", + "\n", + " if not records: # no more pages\n", + " break\n", + "\n", + " for item in records:\n", + " if \"pull_request\" not in item: # keep genuine issues only\n", + " all_issues.append(item)\n", + "\n", + " print(f\"Page {page}: issues collected so far = {len(all_issues)}\")\n", + " page += 1\n", + " time.sleep(0.5)\n", + "\n", + "all_issues = all_issues[:TARGET]\n", + "print(\"\\nFINAL TOTAL:\", len(all_issues))" + ], + "id": "uwOzTrypGJus" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IZSAkeJdGJut" + }, + "source": [ + "GitHub returns many fields for each issue. Here is the list, so we can choose which ones to keep." + ], + "id": "IZSAkeJdGJut" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VWelh0j8GJut", + "outputId": "291baeb2-67ea-4be5-957c-9ff3d6c6a98b" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'assignee', 'author_association', 'issue_field_values', 'type', 'active_lock_reason', 'sub_issues_summary', 'issue_dependencies_summary', 'body', 'closed_by', 'reactions', 'timeline_url', 'performed_via_github_app', 'state_reason', 'pinned_comment']\n" + ] + } + ], + "source": [ + "print(list(all_issues[0].keys()))" + ], + "id": "VWelh0j8GJut" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4oC9iMWnGJut" + }, + "source": [ + "## 4. Build the table and add derived fields\n", + "Fourteen fields are kept. Two of them are **derived** (calculated by us, not given by GitHub):\n", + "- `text_length`: number of characters in the title and body together.\n", + "- `time_to_close_hours`: hours between `created_at` and `closed_at` (empty for open issues).\n", + "\n", + "Text is stripped of leading and trailing spaces, a missing body becomes empty text, and the list of labels is joined into one comma-separated text field." + ], + "id": "4oC9iMWnGJut" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 397 + }, + "id": "lfv3S4LNGJut", + "outputId": "b49065d0-f44d-4a5d-ced2-b63a436020f7" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dataset shape: (300, 14)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " issue_id issue_number repository \\\n", + "0 295764 1 scikit-learn/scikit-learn \n", + "1 311965 5 scikit-learn/scikit-learn \n", + "2 369933 7 scikit-learn/scikit-learn \n", + "3 378489 8 scikit-learn/scikit-learn \n", + "4 382343 11 scikit-learn/scikit-learn \n", + "\n", + " title \\\n", + "0 remove Y from fit in OneClassSVM \n", + "1 Bayesian regression \n", + "2 Return index of support vectors in sparse SVM \n", + "3 LARS module broken \n", + "4 implement warm restarts for logistic regression \n", + "\n", + " body state \\\n", + "0 closed \n", + "1 Add priors on the mean instead of assuming tha... closed \n", + "2 Assigned to me closed \n", + "3 - the parameter names are different in docstri... closed \n", + "4 assigned to me closed \n", + "\n", + " labels created_at closed_at \\\n", + "0 2010-08-31 07:38:16+00:00 2010-08-31 07:43:11+00:00 \n", + "1 2010-09-11 11:40:28+00:00 2012-01-06 13:37:31+00:00 \n", + "2 Enhancement, Moderate 2010-10-19 08:00:57+00:00 2013-02-02 16:08:16+00:00 \n", + "3 2010-10-23 23:04:24+00:00 2010-11-25 11:09:11+00:00 \n", + "4 New Feature 2010-10-26 07:28:50+00:00 2014-10-12 15:30:46+00:00 \n", + "\n", + " comments author html_url \\\n", + "0 0 fabianp https://github.com/scikit-learn/scikit-learn/i... \n", + "1 11 vmichel https://github.com/scikit-learn/scikit-learn/i... \n", + "2 2 fabianp https://github.com/scikit-learn/scikit-learn/i... \n", + "3 5 satra https://github.com/scikit-learn/scikit-learn/i... \n", + "4 12 fabianp https://github.com/scikit-learn/scikit-learn/i... \n", + "\n", + " text_length time_to_close_hours \n", + "0 33 0.081944 \n", + "1 443 11569.950833 \n", + "2 60 20096.121944 \n", + "3 349 780.079722 \n", + "4 62 34736.032222 " + ], + "text/html": [ + "\n", + "
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issue_idissue_numberrepositorytitlebodystatelabelscreated_atclosed_atcommentsauthorhtml_urltext_lengthtime_to_close_hours
02957641scikit-learn/scikit-learnremove Y from fit in OneClassSVMclosed2010-08-31 07:38:16+00:002010-08-31 07:43:11+00:000fabianphttps://github.com/scikit-learn/scikit-learn/i...330.081944
13119655scikit-learn/scikit-learnBayesian regressionAdd priors on the mean instead of assuming tha...closed2010-09-11 11:40:28+00:002012-01-06 13:37:31+00:0011vmichelhttps://github.com/scikit-learn/scikit-learn/i...44311569.950833
23699337scikit-learn/scikit-learnReturn index of support vectors in sparse SVMAssigned to meclosedEnhancement, Moderate2010-10-19 08:00:57+00:002013-02-02 16:08:16+00:002fabianphttps://github.com/scikit-learn/scikit-learn/i...6020096.121944
33784898scikit-learn/scikit-learnLARS module broken- the parameter names are different in docstri...closed2010-10-23 23:04:24+00:002010-11-25 11:09:11+00:005satrahttps://github.com/scikit-learn/scikit-learn/i...349780.079722
438234311scikit-learn/scikit-learnimplement warm restarts for logistic regressionassigned to meclosedNew Feature2010-10-26 07:28:50+00:002014-10-12 15:30:46+00:0012fabianphttps://github.com/scikit-learn/scikit-learn/i...6234736.032222
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df", + "summary": "{\n \"name\": \"df\",\n \"rows\": 300,\n \"fields\": [\n {\n \"column\": \"issue_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1156320,\n \"min\": 295764,\n \"max\": 4103370,\n \"num_unique_values\": 300,\n \"samples\": [\n 2870227,\n 3681083,\n 1922616\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"issue_number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 231,\n \"min\": 1,\n \"max\": 768,\n \"num_unique_values\": 300,\n \"samples\": [\n 565,\n 703,\n 395\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"repository\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"scikit-learn/scikit-learn\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 300,\n \"samples\": [\n \"grid search keeps all estimators in memory\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"body\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 291,\n \"samples\": [\n \"Just a note to remember that we can arguably still greatly improve the Coordinate Descent (lasso / elastic net) convergence speed if we implement the following trick:\\n\\nhttp://www-stat.stanford.edu/~tibs/ftp/strong.pdf\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"state\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"open\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"labels\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"\"\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\": \"2012-04-13 13:40:27+00:00\",\n \"num_unique_values\": 300,\n \"samples\": [\n \"2012-01-17 15:43:10+00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"closed_at\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2010-08-31 07:43:11+00:00\",\n \"max\": \"2025-11-05 16:50:43+00:00\",\n \"num_unique_values\": 295,\n \"samples\": [\n \"2012-03-25 22:18:29+00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"comments\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 0,\n \"max\": 59,\n \"num_unique_values\": 35,\n \"samples\": [\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"author\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 84,\n \"samples\": [\n \"benjaminwilson\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"html_url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 300,\n \"samples\": [\n \"https://github.com/scikit-learn/scikit-learn/issues/565\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text_length\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 958,\n \"min\": 33,\n \"max\": 6922,\n \"num_unique_values\": 264,\n \"samples\": [\n 325\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"time_to_close_hours\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18537.589444119472,\n \"min\": 0.08194444444444444,\n \"max\": 125742.1775,\n \"num_unique_values\": 295,\n \"samples\": [\n 1.9425\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "rows = []\n", + "\n", + "for issue in all_issues:\n", + " labels = [label[\"name\"] for label in issue.get(\"labels\", [])]\n", + "\n", + " created = pd.to_datetime(issue[\"created_at\"], utc=True)\n", + " closed = pd.to_datetime(issue[\"closed_at\"], utc=True, errors=\"coerce\")\n", + "\n", + " title = (issue.get(\"title\") or \"\").strip()\n", + " body = (issue.get(\"body\") or \"\").strip()\n", + "\n", + " rows.append({\n", + " \"issue_id\": issue[\"id\"],\n", + " \"issue_number\": issue[\"number\"],\n", + " \"repository\": \"scikit-learn/scikit-learn\",\n", + " \"title\": title,\n", + " \"body\": body,\n", + " \"state\": issue[\"state\"],\n", + " \"labels\": \", \".join(labels),\n", + " \"created_at\": created,\n", + " \"closed_at\": closed,\n", + " \"comments\": issue[\"comments\"],\n", + " \"author\": (issue.get(\"user\") or {}).get(\"login\"),\n", + " \"html_url\": issue[\"html_url\"],\n", + " \"text_length\": len(title + \" \" + body),\n", + " \"time_to_close_hours\": (\n", + " (closed - created).total_seconds() / 3600 if pd.notna(closed) else None\n", + " ),\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"Dataset shape:\", df.shape)\n", + "df.head()" + ], + "id": "lfv3S4LNGJut" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qawWzss_GJut" + }, + "source": [ + "## 5. Cleaning and validation\n", + "1. Remove duplicate issues (same `issue_id`).\n", + "2. Remove records with a missing or empty title.\n", + "3. Reset the row numbers.\n", + "4. Check the result with assertions. If any check fails, the notebook stops with an error.\n", + "\n", + "Missing values that are genuine (no `closed_at` for open issues, empty bodies, no labels) are **kept, not filled in**, because they carry real information." + ], + "id": "qawWzss_GJut" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JUoK2KBFGJut", + "outputId": "fce03c3e-1ea5-4416-82cf-249c470f132e" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Rows before cleaning: 300\n", + "Rows after cleaning: 300\n", + "\n", + "Missing values per column:\n", + "issue_id 0\n", + "issue_number 0\n", + "repository 0\n", + "title 0\n", + "body 0\n", + "state 0\n", + "labels 0\n", + "created_at 0\n", + "closed_at 5\n", + "comments 0\n", + "author 0\n", + "html_url 0\n", + "text_length 0\n", + "time_to_close_hours 5\n", + "dtype: int64\n", + "\n", + "Validation successful!\n" + ] + } + ], + "source": [ + "before = len(df)\n", + "\n", + "df = df.drop_duplicates(subset=\"issue_id\").copy()\n", + "df = df[df[\"title\"].notna()].copy()\n", + "df = df[df[\"title\"].str.strip() != \"\"].copy()\n", + "df = df.reset_index(drop=True)\n", + "\n", + "print(\"Rows before cleaning:\", before)\n", + "print(\"Rows after cleaning: \", len(df))\n", + "print()\n", + "print(\"Missing values per column:\")\n", + "print(df.isna().sum())\n", + "\n", + "assert df[\"issue_id\"].is_unique\n", + "assert df[\"title\"].str.strip().ne(\"\").all()\n", + "assert not df[\"html_url\"].str.contains(\"/pull/\").any() # no pull requests\n", + "assert 200 <= len(df) <= 500\n", + "print(\"\\nValidation successful!\")" + ], + "id": "JUoK2KBFGJut" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z5MsQyE6GJut" + }, + "source": [ + "## 6. Summary of the data\n", + "These figures are the ones reported in the dataset card (README), so they can be checked against it." + ], + "id": "Z5MsQyE6GJut" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a3f9GbPMGJuu", + "outputId": "e224c8f6-43d8-473e-8b4e-e005df6941fc" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Rows / columns: (300, 14)\n", + "Date range: 2010-08-31 07:38:16+00:00 to 2012-04-13 13:40:27+00:00\n", + "Issue numbers: 1 to 768\n", + "\n", + "State:\n", + "state\n", + "closed 295\n", + "open 5\n", + "Name: count, dtype: int64\n", + "\n", + "Empty bodies: 9\n", + "Issues without labels: 235\n", + "Issues with labels: 65\n", + "\n", + "Most common labels:\n", + "labels\n", + "New Feature 21\n", + "Enhancement 11\n", + "Bug 7\n", + "Enhancement, Moderate 5\n", + "Easy, Enhancement 5\n", + "Name: count, dtype: int64\n", + "\n", + "Comments per issue: 4.0 (median), 7.2 (mean), 59 (max)\n", + "Median hours to close: 1194.7\n", + "\n", + "Distinct authors: 84\n", + "Top 3 authors: {'amueller': 49, 'fabianp': 47, 'mblondel': 36} -> 132 of 300 issues\n" + ] + } + ], + "source": [ + "print(\"Rows / columns:\", df.shape)\n", + "print(\"Date range:\", df[\"created_at\"].min(), \"to\", df[\"created_at\"].max())\n", + "print(\"Issue numbers:\", df[\"issue_number\"].min(), \"to\", df[\"issue_number\"].max())\n", + "print()\n", + "print(\"State:\")\n", + "print(df[\"state\"].value_counts())\n", + "print()\n", + "print(\"Empty bodies:\", (df[\"body\"] == \"\").sum())\n", + "print(\"Issues without labels:\", (df[\"labels\"] == \"\").sum())\n", + "print(\"Issues with labels:\", (df[\"labels\"] != \"\").sum())\n", + "print()\n", + "print(\"Most common labels:\")\n", + "print(df.loc[df[\"labels\"] != \"\", \"labels\"].value_counts().head(5))\n", + "print()\n", + "print(\"Comments per issue:\", df[\"comments\"].median(), \"(median),\", round(df[\"comments\"].mean(), 1), \"(mean),\", df[\"comments\"].max(), \"(max)\")\n", + "print(\"Median hours to close:\", round(df[\"time_to_close_hours\"].median(), 1))\n", + "print()\n", + "top3 = df[\"author\"].value_counts().head(3)\n", + "print(\"Distinct authors:\", df[\"author\"].nunique())\n", + "print(\"Top 3 authors:\", top3.to_dict(), \"->\", top3.sum(), \"of\", len(df), \"issues\")" + ], + "id": "a3f9GbPMGJuu" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9QTBFqJQGJuu" + }, + "source": [ + "## 7. Save the data locally" + ], + "id": "9QTBFqJQGJuu" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DeteIsLVGJuu", + "outputId": "a92188f0-b0f2-4a1f-9671-daa241f55dee" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: ['github_issues.csv', 'github_issues.parquet']\n" + ] + } + ], + "source": [ + "Path(\"data\").mkdir(exist_ok=True)\n", + "\n", + "df.to_csv(\"data/github_issues.csv\", index=False)\n", + "df.to_parquet(\"data/github_issues.parquet\", index=False)\n", + "\n", + "print(\"Saved:\", [p.name for p in Path(\"data\").iterdir()])" + ], + "id": "DeteIsLVGJuu" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eH2epdyfGJuu" + }, + "source": [ + "## 8. Log in to Hugging Face and push the dataset\n", + "Pushing to the Hub needs a **Hugging Face access token with write permission**. The token is a password, so it must never be typed into the notebook or saved in it.\n", + "\n", + "In Colab, store it as a *secret*:\n", + "1. Click the **key icon** in the left sidebar.\n", + "2. Click **Add new secret**. Name it `HF_TOKEN` and paste your token as the value.\n", + "3. Switch on **Notebook access**.\n", + "\n", + "The code reads the token from the secret and nothing is printed or saved. If no secret is found, it asks for the token with a hidden input box." + ], + "id": "eH2epdyfGJuu" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Sum2ayzrGJuu", + "outputId": "dc973185-fa9d-4fa0-af80-e242b6f255f1" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Hugging Face token (input is hidden): \u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\n", + "Logged in to Hugging Face.\n" + ] + } + ], + "source": [ + "from huggingface_hub import login, HfApi\n", + "\n", + "try:\n", + " from google.colab import userdata\n", + " hf_token = userdata.get(\"HF_TOKEN\")\n", + "except Exception:\n", + " from getpass import getpass\n", + " hf_token = getpass(\"Hugging Face token (input is hidden): \")\n", + "\n", + "login(token=hf_token)\n", + "print(\"Logged in to Hugging Face.\")" + ], + "id": "Sum2ayzrGJuu" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DVBGV69_GJuu" + }, + "source": [ + "The files are uploaded to the dataset repository. The Parquet file is the main data file. The existing dataset card (README) is not changed." + ], + "id": "DVBGV69_GJuu" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 232, + "referenced_widgets": [ + "804bc001e21f40028314ce58744533be", + "44b135d73f1545b491aa57fc4a4c6f87", + "a8d8e6dcebf94d939377eea7c812493f", + "624537e9cf9247e59febeb6f08780c19", + "cfb782e9c9f4492290104b65a55a4140", + "5ff45fd89c6842fcabadfd03ca8bb69b", + "fb3f4778901a4ba1977032f980232f63", + "a05d3f28b40b450ab329b3c3d06917e0", + "7eea8dbc943d44f491310831c93a7cbe", + "604ae23cacb8478b87c0a5053ef11058", + "fbe4ccc17d18483b8b030947cf178360", + "7b3234dc0f114b08b0e814d39650bfa6", + "9ffe9b19991d44b8a87213ee146d6abf", + "62aaee3388ba4c1da609f69d6c3e2bdc", + "cd67c9a1cedf4e139194d9a6d53bd13a", + "6067637d89814a2ca6fe008694da31c8", + "cc8f0556a5c844db9be2bb423a82dcf4", + "2739ca40e760437f910fd63f7d0f861e", + "4aa1a3c8401d474cbcc2c6ef17151144", + "55715c00489e4d38bb4202d5d1f89003", + "7204e456881b4533ac07760f2dd0ecd2", + "e0a5a392a7924add8ab1d61cc809b1e1", + "8a422f24d5ed41ea9f64a08f0115d901", + "e5a637af98194759bea6fa0d62348e97", + "9cc4610a4a304f2fb975e8d10a90a282", + "4406c724b63b478cbc3965c182c83bec", + "a5f406b7ea614a6b8d5f03c74d9d5570", + "a779684422b04eb788572527284965b8", + "c3b628a26d8f4f58ac9663ff21a13953", + "13d21103dc5549ac97b61c6372b158f5", + "332cf7b2b7ad46e7bd1187708deeea72", + "d48ae28d4a8e4d148176e8b042cd3d6c", + "25a82f3d2c8a4027afbf3c626291fc5f", + "8f3591aa21c143a5b214c52fd5bc84c3", + "87fc7cb466ba4f0d8b8069618b98d4d0", + "7fc8d369b17745c7a71892fd80a2b4eb", + "2ead0823208e4773b3ce642459461723", + "ad33652421f64b2c8348707bcaa4c7f7", + "491d02999c1d4c0d9bf654e5e81bf9cf", + "d36256f9ea57408d8e86f77e4d4f1d6a", + "7cc3d6cc1f4a4e32b30f939f07749d41", + "814b879820814f6cbd6efa04a749b217", + "f9eba6dcf64a4a528d2eca2723d3590e", + "a5ba500883374745adbfe14ce2a8efc2" + ] + }, + "id": "DI-fzewrGJuu", + "outputId": "fa7f8d67-d149-402a-8cae-737b206f8b5a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Processing Files (0 / 0) : | | 0.00B / 0.00B " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "804bc001e21f40028314ce58744533be" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "New Data Upload : | | 0.00B / 0.00B " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7b3234dc0f114b08b0e814d39650bfa6" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " ...ata/github_issues.parquet: 100%|##########| 135kB / 135kB " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8a422f24d5ed41ea9f64a08f0115d901" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Validating : | 0% " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8f3591aa21c143a5b214c52fd5bc84c3" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "No files have been modified since last commit. Skipping to prevent empty commit.\n", + "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n", + "No files have been modified since last commit. Skipping to prevent empty commit.\n", + "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Pushed to https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset\n" + ] + } + ], + "source": [ + "api = HfApi()\n", + "\n", + "api.create_repo(repo_id=HF_REPO, repo_type=\"dataset\", exist_ok=True)\n", + "\n", + "api.upload_file(\n", + " path_or_fileobj=\"data/github_issues.parquet\",\n", + " path_in_repo=\"github_issues.parquet\",\n", + " repo_id=HF_REPO,\n", + " repo_type=\"dataset\",\n", + " commit_message=\"Upload GitHub issues dataset (Parquet) from collection notebook\",\n", + ")\n", + "\n", + "api.upload_file(\n", + " path_or_fileobj=\"data/github_issues.csv\",\n", + " path_in_repo=\"github_scikit-learn_issues.csv\",\n", + " repo_id=HF_REPO,\n", + " repo_type=\"dataset\",\n", + " commit_message=\"Upload GitHub issues dataset (CSV) from collection notebook\",\n", + ")\n", + "\n", + "print(\"Pushed to https://huggingface.co/datasets/\" + HF_REPO)" + ], + "id": "DI-fzewrGJuu" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5YQVX-oFGJuv" + }, + "source": [ + "## 9. Check the published dataset" + ], + "id": "5YQVX-oFGJuv" + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 180, + "referenced_widgets": [ + "7f24bb0c8e5c4f349554d06ab8e5007d", + "6a695731a36d4ac0b4d27186618058bd", + "b39b59663d4e4da5bcd6713a547c9a47", + "a5b71b3fa277475290767300c14439aa", + "734e788c12a240c49ee6e686177593c8", + "aaa22ac23c5a484d97c93b20dfad4287", + "014d0a6a7504475a93f28648a5332e53", + "240da2ae0f6149a897cd5aea8a565dd6", + "7acc684e1ee1442284c547e6b5fcfd21", + "65d4f60121f54250b9ef106ebb0b3578", + "d641c3c572e24e0ba403b407a973a9c1", + "de2f3394ef314bdab788c3de95651db1", + "1e8eb717e6474fffbe4450dcdb0afdfb", + "57dbc062a3e24a998693167492e34a51", + "a2826c8b1fdd44dcad16cfda3a6421ba", + "2ddacb6f1b2846529801af7bcba073eb", + "7a05836c536e4550a1e34a02114e5253", + "de9bd642b63443cca1900114892e33c7", + "4a0b39d3697b4ef49c1ecd32421982e5", + "55ab9c7530fd4944b95d8547d09b0082", + "16603b8d7e40484a85026e56f65aa556", + "97e4c9ae536447f8890ff50ce2ecf939", + "acc0de67c067421c875927acce46a28b", + "9b541dcc30004b16a0e76dbb41bcce80", + "b60a644338cb46bd9ea74457c923f7d8", + "65e8cdc56076433d8f8dafe3f8dc9e0f", + "5ef2f16cc26844d2bbeabf93f2167fe8", + "78bf90699acf451e96b393783d2bd613", + "edf1dd8d383641c0bb4d26a2dda632a1", + "1a3799d3b8554aa4b3c37fe861e1aa5c", + "219528f4c6e945ea97a32559a5ba6a26", + "99970130d29e45e4ae6de8165e620962", + "f3154d61172346db85673e8ade3452a1", + "acc137bd8b3944708c44ac06970e9f5a", + "4289f4dfa1734921ba9180a76531c3b6", + "bf46d912bff34bd7bce433d072d923ea", + "c4d972a3d0964c9194cbfcbdb6f99d0f", + "8992a7de496b4887a38d1fbfde6da826", + "bda8bfdd946f4e8b92b6c492339b2ce9", + "00af2b14218c46e698c2dfd794dc0246", + "7f35a232608b490aa6dc20a50ee866bb", + "74fa994f627e42e8b10a4951fc988d47", + "31f64058053942c29eae00517f76863d", + "f464fc8e681e41c6a2a077a1fba6d2ba" + ] + }, + "id": "3cnIKZxgGJuv", + "outputId": "999694ca-e5ac-4aa3-d3c8-a216bd4dba96" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/8.64k [00:00