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"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Edit Distance Distribution",
"id": "bb1af2eaf6ddbce6"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Our dataset",
"id": "2827c98e00529ecb"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-15T18:48:57.008291Z",
"start_time": "2024-10-15T18:48:53.986563Z"
}
},
"cell_type": "code",
"source": [
"from generation_steps.metrics_analysis import edit_distance_fn\n",
"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_edit_distance = {\"Full\": [edit_distance_fn(pred=row[\"G_text\"], ref=row[\"E_text\"]) for _, row in df.loc[df.is_related].iterrows()],\n",
" \"Synthetic Backward\": [edit_distance_fn(pred=row[\"G_text\"], ref=row[\"E_text\"]) for _, row in df.loc[df.is_related & (df.G_type == \"synthetic_backward\") & (df.E_type == \"expert_labeled\")].iterrows()],\n",
" \"Synthetic Forward\": [edit_distance_fn(pred=row[\"G_text\"], ref=row[\"E_text\"]) for _, row in df.loc[df.is_related & (df.G_type == \"initial\") & (df.E_type == \"synthetic_forward\")].iterrows()] + [edit_distance_fn(pred=row[\"G_text\"], ref=row[\"E_text\"]) for _, row in df.loc[df.is_related & (df.G_type == \"synthetic_backward\") & (df.E_type == \"synthetic_forward_from_backward\")].iterrows()],\n",
" \"Expert-labeled\": [edit_distance_fn(pred=row[\"G_text\"], ref=row[\"E_text\"]) for _, row in df.loc[df.is_related & (df.G_type == \"initial\") & (df.E_type == \"expert_labeled\")].iterrows()]\n",
" }\n",
"\n",
"colors = {\"Expert-labeled\": \"#C19C0B\",\n",
" \"Synthetic Backward\": \"#913632\",\n",
" \"Synthetic Forward\": \"#58136a\",\n",
" \"Full\": \"#000000\"}"
],
"id": "8da5a9c5a27d176e",
"outputs": [],
"execution_count": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## FUS data",
"id": "3bcd6ecdc8ca813b"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-15T18:49:21.118726Z",
"start_time": "2024-10-15T18:49:21.115088Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"\n",
"PROD_LENGTH_RATIO = 1.772691712591923\n",
"\n",
"\n",
"df = pd.read_csv(\"data/fus_pycharm_fixed.csv\")\n",
"\n",
"bin_centers = []\n",
"bin_widths = []\n",
"for value in df[\"Metric Value\"]:\n",
" if len(value.split('-')) == 2:\n",
" lower, upper = map(float, value.split('-'))\n",
" center = (lower + upper) / 2\n",
" width = upper - lower\n",
" bin_centers.append(center * PROD_LENGTH_RATIO)\n",
" bin_widths.append(width * PROD_LENGTH_RATIO)\n",
" else:\n",
" center = float(value)\n",
" width = 1\n",
" bin_centers.append(center * PROD_LENGTH_RATIO)\n",
" bin_widths.append(width * PROD_LENGTH_RATIO)\n",
"df[\"center\"] = bin_centers\n",
"df[\"width\"] = bin_widths"
],
"id": "2d2db355e78bf5a6",
"outputs": [],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Chart",
"id": "dbc2e81dfe9cdf92"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-15T18:51:22.969205Z",
"start_time": "2024-10-15T18:51:22.910711Z"
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"from scipy.stats import gaussian_kde\n",
"import plotly.graph_objects as go\n",
"\n",
"df = df.iloc[1:]\n",
"traces = [go.Bar(\n",
" x=df[\"center\"],\n",
" y=df[\"Events\"] / (df[\"Events\"].sum() * df[\"width\"]),\n",
" width=df[\"width\"],\n",
" name='PyCharm logs (scaled)',\n",
" opacity=0.75,\n",
" marker=dict(color='#1bd88a'))]\n",
"\n",
"for key in df_edit_distance:\n",
" kde_x = np.linspace(0, 1200, 1000)\n",
" kde = gaussian_kde(df_edit_distance[key])\n",
" kde_line = go.Scatter(\n",
" x=kde_x, \n",
" y=kde(kde_x), \n",
" mode='lines', \n",
" name=key,\n",
" line=dict(color=colors[key], width=5)\n",
" ) \n",
" traces.append(kde_line)\n",
"\n",
"fig = go.Figure(data=traces)\n",
"\n",
"fig.update_layout(\n",
" bargap=0.1,\n",
" xaxis=dict(\n",
" title=dict(text=\"Edit Distance\", font=dict(size=30)),\n",
" range=[0, 1200],\n",
" showgrid=True,\n",
" gridcolor='lightgrey'\n",
" ),\n",
" yaxis=dict(\n",
" title=dict(text=\"Probability Density\", font=dict(size=30)),\n",
" range=[0, 0.004],\n",
" showgrid=True,\n",
" gridcolor='lightgrey',\n",
" tickvals=[0.0005, 0.001, 0.0015, 0.002, 0.0025, 0.003, 0.0035, 0.004],\n",
" tickformat=\".4f\"\n",
" ),\n",
" plot_bgcolor='rgba(0,0,0,0)',\n",
" paper_bgcolor='rgba(0,0,0,0)',\n",
" font=dict(size=24),\n",
" legend=dict(font=dict(size=30)),\n",
" width=1600,\n",
" height=600,\n",
")\n",
"\n",
"fig.show()"
],
"id": "885906640bd2c23f",
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
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"marker": {
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