{
 "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": [
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