diff --git "a/metrics_analysis.ipynb" "b/metrics_analysis.ipynb" new file mode 100644--- /dev/null +++ "b/metrics_analysis.ipynb" @@ -0,0 +1,71662 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Calculating metrics", + "id": "ec906728c28507f4" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Per-pair", + "id": "2f276f602431b4ed" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T18:45:28.703854Z", + "start_time": "2024-12-16T18:44:48.623127Z" + } + }, + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "\n", + "\n", + "df = load_dataset(\"JetBrains-Research/synthetic-commit-msg-edits\", \"all_pairs\", split=\"train\").to_pandas()\n", + "df.head()" + ], + "id": "ef9a042013203cd8", + "outputs": [ + { + "data": { + "text/plain": [ + "Downloading readme: 0%| | 0.00/3.07k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hashrepoG_textE_textG_typeE_typeis_related
02febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Enhance OptionOverrideProxy for multiple optio...synthetic_backwardexpert_labeledTrue
12febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Refactor OptionOverrideProxy and Backend class...synthetic_backwardsynthetic_forwardTrue
22febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Refactor OptionOverrideProxy and backend optio...synthetic_backwardsynthetic_forwardTrue
32febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Refactor: Enhance OptionOverrideProxy for mult...synthetic_backwardsynthetic_forwardTrue
42febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Refactor OptionOverrideProxy and add target-sp...synthetic_backwardsynthetic_forward_from_backwardFalse
\n", + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-15T18:12:16.716335Z", + "start_time": "2024-10-15T18:12:16.714130Z" + } + }, + "cell_type": "code", + "source": [ + "from generation_steps.metrics_analysis import REL_METRICS, AGGR_METRICS\n", + "\n", + "\n", + "def process_row(row):\n", + " if row[\"is_related\"]:\n", + " return {**{metric: REL_METRICS[metric](pred=row[\"G_text\"], ref=row[\"E_text\"]) for metric in REL_METRICS}, \n", + " **{metric: None for metric in AGGR_METRICS if metric not in REL_METRICS}}\n", + " else:\n", + " return {**{metric: None for metric in REL_METRICS if metric not in AGGR_METRICS}, \n", + " **{metric: AGGR_METRICS[metric](pred=row[\"G_text\"], ref=row[\"E_text\"]) for metric in AGGR_METRICS}}" + ], + "id": "f664b3fe8f5c9342", + "outputs": [], + "execution_count": 127 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-15T18:20:40.585492Z", + "start_time": "2024-10-15T18:12:18.614593Z" + } + }, + "cell_type": "code", + "source": [ + "from joblib import delayed, Parallel\n", + "from tqdm import tqdm\n", + "\n", + "\n", + "metrics = Parallel(n_jobs=-1)(\n", + " delayed(process_row)(row) for _, row in tqdm(df.iterrows())\n", + ")" + ], + "id": "350b687dcf294db1", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "0it [00:00, ?it/s]\u001B[A\n", + "16it [00:00, 118.39it/s]\u001B[A[nltk_data] Downloading package wordnet to\n", + "[nltk_data] /Users/Alexandra.Eliseeva/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "[nltk_data] Downloading package punkt to\n", + "[nltk_data] /Users/Alexandra.Eliseeva/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package omw-1.4 to\n", + "[nltk_data] /Users/Alexandra.Eliseeva/nltk_data...\n", + "[nltk_data] Package omw-1.4 is already 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\n", + "
" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 129 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-15T18:20:40.674189Z", + "start_time": "2024-10-15T18:20:40.642807Z" + } + }, + "cell_type": "code", + "source": "df.to_json(\"data_w_metrics.jsonl\", orient=\"records\", lines=True)", + "id": "14cf24c4b09a16bb", + "outputs": [], + "execution_count": 130 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T18:46:12.987470Z", + "start_time": "2024-12-16T18:45:39.465889Z" + } + }, + "cell_type": "code", + "source": [ + "from generation_steps.metrics_analysis import REL_METRICS, AGGR_METRICS\n", + "\n", + "\n", + "def process_row(row):\n", + " if row[\"is_related\"]:\n", + " return {**{metric: REL_METRICS[metric](pred=row[\"G_text\"], ref=row[\"E_text\"]) for metric in REL_METRICS}, \n", + " **{metric: AGGR_METRICS[metric](pred=row[\"G_text\"], ref=row[\"E_text\"]) for metric in AGGR_METRICS if metric not in REL_METRICS}}\n", + " else:\n", + " return {**{metric: None for metric in REL_METRICS if metric not in AGGR_METRICS}, \n", + " **{metric: AGGR_METRICS[metric](pred=row[\"G_text\"], ref=row[\"E_text\"]) for metric in AGGR_METRICS}}" + ], + "id": "bb5379f4b1a463c6", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" + ] + }, + { + "data": { + "text/plain": [ + "0it [00:00, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "ca3956512b2d4a63a4ecced3eab055e2" + } + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Downloading builder script: 0%| | 0.00/2.63k [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
hashrepoG_textE_textG_typeE_typeis_relatededitdisteditsimbleumeteorrouge1rouge2rougeLbertscorechrF
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42febb99eee8ed71c9122db88ca58dd33be0b9550mesonbuild/mesonEnhance OptionOverrideProxy and simplify optio...Refactor OptionOverrideProxy and add target-sp...synthetic_backwardsynthetic_forward_from_backwardFalse2700.4726560.3962430.4813030.7246380.4852940.5942030.89287565.518566
\n", + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:00:25.441423Z", + "start_time": "2024-12-16T19:00:25.402535Z" + } + }, + "cell_type": "code", + "source": "df.to_json(\"data_w_metrics_new.jsonl\", orient=\"records\", lines=True)", + "id": "6bb4e714c9538017", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Online", + "id": "b46895cfede47466" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:25:35.945895Z", + "start_time": "2024-12-16T19:25:35.904146Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_json(\"data_w_metrics_new.jsonl\", orient=\"records\", lines=True)" + ], + "id": "c41446553184be99", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:25:36.566295Z", + "start_time": "2024-12-16T19:25:36.512361Z" + } + }, + "cell_type": "code", + "source": [ + "from generation_steps.metrics_analysis import REL_METRICS, AGGR_METRICS\n", + "\n", + "\n", + "related_df = df.loc[df.is_related].groupby([\"G_text\", \"G_type\", \"hash\", \"repo\"]).apply(lambda g: g.to_dict(orient=\"records\"), include_groups=False).reset_index(name='related_pairs')\n", + "related_df[\"related_pairs\"] = related_df[\"related_pairs\"].apply(lambda pairs: [pair for pair in pairs if pair[\"E_type\"] == \"expert_labeled\"])\n", + "\n", + "for metric in AGGR_METRICS:\n", + " related_df[f\"online_{metric}\"] = related_df.related_pairs.apply(lambda pairs: sum(pair[metric] for pair in pairs) / len(pairs)) \n", + "online_dict = {f\"{row['repo']}%%%%{row['hash']}%%%%{row['G_text']}\": {metric: row[f\"online_{metric}\"] for metric in AGGR_METRICS} for _, row in related_df.iterrows()}" + ], + "id": "62b08afc86b2aab6", + "outputs": [], + "execution_count": 8 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:26:15.717450Z", + "start_time": "2024-12-16T19:26:15.713444Z" + } + }, + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "with open(\"online_new.json\", \"w\") as f:\n", + " json.dump(online_dict, f)" + ], + "id": "10e6904c110f87bf", + "outputs": [], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Correlations", + "id": "e2a06a1ab117109e" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Utils", + "id": "6a5449f991e2b175" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "304b00f1df3d77bd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:27:23.941487Z", + "start_time": "2024-12-16T19:27:23.724141Z" + } + }, + "cell_type": "code", + "source": [ + "import json\n", + "import pandas as pd\n", + "from generation_steps.metrics_analysis import REL_METRICS, AGGR_METRICS\n", + "\n", + "with open(\"online_new.json\", \"r\") as f:\n", + " d = {tuple(key.split(\"%%%%\")): value for key, value in json.load(f).items()}\n", + "\n", + "df = pd.read_json(\"data_w_metrics_new.jsonl\", orient=\"records\", lines=True)\n", + "df['tuple'] = list(zip(df[\"repo\"], df[\"hash\"], df[\"G_text\"]))\n", + "for metric in AGGR_METRICS:\n", + " df[f\"online_{metric}\"] = df['tuple'].map(d).apply(lambda x: x[metric])\n", + "df = df.drop(columns=[\"tuple\"])\n", + "\n", + "corr_df = df.loc[~df.is_related].groupby([\"G_text\", \"G_type\", \"hash\", \"repo\"] + [f\"online_{metric}\" for metric in AGGR_METRICS]).apply(lambda g: g.to_dict(orient=\"records\"), include_groups=False).reset_index(name='unrelated_pairs').copy()\n", + "corr_df.head()" + ], + "id": "701f03d172b636c7", + "outputs": [ + { + "data": { + "text/plain": [ + " G_text G_type \\\n", + "0 Add 4 block attack scenarios\\n\\nIntroduced a n... 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4Add Datastore index management capabilities\\n\\...synthetic_backwardc6d4ab680da88e14ceafe6f7947f0cf858158f2dappscale/gts227.00.2331080.1073360.5437350.2857140.2127660.2857140.88016551.151735[{'E_text': 'Add Datastore index updating func...
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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:27:46.467329Z", + "start_time": "2024-12-16T19:27:46.422331Z" + } + }, + "cell_type": "code", + "source": "df.to_json(\"all_pairs_with_metrics_new.jsonl\", orient=\"records\", lines=True)", + "id": "b98cee8685ef3bf7", + "outputs": [], + "execution_count": 13 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:27:49.779498Z", + "start_time": "2024-12-16T19:27:49.777477Z" + } + }, + "cell_type": "code", + "source": [ + "from generation_steps.metrics_analysis import AGGR_METRICS\n", + "\n", + "\n", + "METRICS = list(AGGR_METRICS.keys())" + ], + "id": "32a1aca18b1473bf", + "outputs": [], + "execution_count": 14 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:32:01.509278Z", + "start_time": "2024-12-16T19:32:01.505099Z" + } + }, + "cell_type": "code", + "source": [ + "import plotly.express as px\n", + "import plotly.subplots as sp\n", + "\n", + "\n", + "def plot_df(_df: pd.DataFrame, subset_name: str, online_metric: str):\n", + " # List of metrics to plot against 'online'\n", + " metrics = ['editdist', 'editsim', 'meteor', 'rouge1', 'rouge2', 'rougeL', 'bertscore', 'chrF', 'bleu']\n", + " \n", + " # Create a subplot grid, with the number of rows and columns based on the number of metrics\n", + " num_cols = 3\n", + " num_rows = -(-len(metrics) // num_cols) # Ceiling division to get the required number of rows\n", + " \n", + " # Initialize subplot figure\n", + " fig = sp.make_subplots(rows=num_rows, cols=num_cols, subplot_titles=metrics)\n", + " \n", + " # Add a scatter plot for each metric vs 'online'\n", + " for i, metric in enumerate(metrics):\n", + " row = i // num_cols + 1\n", + " col = i % num_cols + 1\n", + " scatter = px.scatter(_df, x=metric, y=f'online_{online_metric}', \n", + " hover_data={'hash': True, 'repo': True}, \n", + " labels={metric: metric, 'online': 'Online'})\n", + " \n", + " for trace in scatter['data']:\n", + " fig.add_trace(trace, row=row, col=col)\n", + " \n", + " # Update x-axis and y-axis labels for each subplot\n", + " fig.update_xaxes(title_text=metric, row=row, col=col)\n", + " fig.update_yaxes(title_text=f\"{online_metric} (online)\", row=row, col=col)\n", + " \n", + " # Update layout for better appearance\n", + " fig.update_layout(height=600, width=1400, title_text=f\"Metrics vs Online {online_metric} ({subset_name})\", showlegend=False)\n", + " \n", + " # Show the plot\n", + " fig.show()" + ], + "id": "4688720746370d8", + "outputs": [], + "execution_count": 32 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Full dataset", + "id": "85555645de20fd88" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:29:45.998254Z", + "start_time": "2024-12-16T19:29:45.940032Z" + } + }, + "cell_type": "code", + "source": [ + "_ = corr_df.copy()\n", + "for metric in METRICS:\n", + " if metric in [\"editdist\"]:\n", + " _[metric] = _.unrelated_pairs.apply(lambda pairs: min(pair[metric] for pair in pairs))\n", + " else:\n", + " _[metric] = _.unrelated_pairs.apply(lambda pairs: max(pair[metric] for pair in pairs))\n", + "_.head()" + ], + "id": "ed70dfe379a0f5a8", + "outputs": [ + { + "data": { + "text/plain": [ + " G_text G_type \\\n", + "0 Add 4 block attack scenarios\\n\\nIntroduced a n... synthetic_backward \n", + "1 Add 4 block attack scenarios test\\n\\nAdditiona... synthetic_backward \n", + "2 Add Datastore index functionality\\n\\n- Impleme... synthetic_backward \n", + "3 Add Datastore index handling and update relate... synthetic_backward \n", + "4 Add Datastore index management capabilities\\n\\... synthetic_backward \n", + "\n", + " hash \\\n", + "0 9a581830e4fa02eed501b4e1f546a2e2ea358e13 \n", + "1 9a581830e4fa02eed501b4e1f546a2e2ea358e13 \n", + "2 c6d4ab680da88e14ceafe6f7947f0cf858158f2d \n", + "3 c6d4ab680da88e14ceafe6f7947f0cf858158f2d \n", + "4 c6d4ab680da88e14ceafe6f7947f0cf858158f2d \n", + "\n", + " repo online_editdist online_editsim \\\n", + "0 bitcoinunlimited/bitcoinunlimited 128.0 0.412844 \n", + "1 bitcoinunlimited/bitcoinunlimited 10.0 0.937888 \n", + "2 appscale/gts 133.0 0.512821 \n", + "3 appscale/gts 507.0 0.073126 \n", + "4 appscale/gts 227.0 0.233108 \n", + "\n", + " online_bleu online_meteor online_rouge1 online_rouge2 ... \\\n", + "0 0.267250 0.714980 0.542373 0.245614 ... \n", + "1 0.909004 0.936284 0.960000 0.958333 ... \n", + "2 0.339667 0.511074 0.584615 0.253968 ... \n", + "3 0.024163 0.152672 0.075949 0.051948 ... \n", + "4 0.107336 0.543735 0.285714 0.212766 ... \n", + "\n", + " unrelated_pairs editdist editsim \\\n", + "0 [{'E_text': 'Add parallel block processing tes... 168 0.380952 \n", + "1 [{'E_text': 'Add new block attack scenarios an... 113 0.424908 \n", + "2 [{'E_text': 'add Datastore index updating func... 148 0.543210 \n", + "3 [{'E_text': 'Add Datastore index updating func... 336 0.385740 \n", + "4 [{'E_text': 'Add Datastore index updating func... 159 0.463343 \n", + "\n", + " bleu meteor rouge1 rouge2 rougeL bertscore chrF \n", + "0 0.243407 0.456752 0.523077 0.253968 0.400000 0.875274 46.029893 \n", + "1 0.195071 0.428988 0.529412 0.333333 0.459459 0.887696 42.802677 \n", + "2 0.301950 0.450317 0.600000 0.349206 0.523077 0.915362 63.105186 \n", + "3 0.187036 0.576769 0.538462 0.208696 0.311475 0.881502 60.682562 \n", + "4 0.193252 0.425522 0.556962 0.285714 0.419753 0.890090 56.247972 \n", + "\n", + "[5 rows x 23 columns]" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Stats", + "id": "aa624e91bd7f7dd1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:29:47.012429Z", + "start_time": "2024-12-16T19:29:47.009762Z" + } + }, + "cell_type": "code", + "source": "len(_)", + "id": "59ed2625132e89f7", + "outputs": [ + { + "data": { + "text/plain": [ + "116" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-16T19:29:47.470128Z", + "start_time": "2024-12-16T19:29:47.463197Z" + } + }, + "cell_type": "code", + "source": "_.unrelated_pairs.str.len().describe(percentiles=[.01, .05, .1, .25, .5, .75, .9, .95, .99]).to_frame().T", + "id": "ca0da7d390b0b30b", + "outputs": [ + { + "data": { + "text/plain": [ + " count mean std min 1% 5% 10% 25% \\\n", + "unrelated_pairs 116.0 44.310345 17.743787 4.0 10.45 16.75 22.0 30.0 \n", + "\n", + " 50% 75% 90% 95% 99% max \n", + "unrelated_pairs 42.0 57.25 74.0 74.0 74.0 74.0 " + ], + "text/html": [ + "
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