{ "cells": [ { "cell_type": "markdown", "id": "925b048c", "metadata": {}, "source": [ "# Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "c3215835", "metadata": {}, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "import plotly.graph_objects as go\n", "import plotly.subplots as sp\n", "from pathlib import Path\n", "import numpy as np\n", "\n", "\n", "base_path = Path(\"/home/pseco/VsCodeProjects/assistance-engine/output\")" ] }, { "cell_type": "markdown", "id": "c8024618", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": null, "id": "7e4b843f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "d8b63d88", "metadata": {}, "source": [ "# Read and Prepare Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "d052f1bc", "metadata": {}, "outputs": [], "source": [ "candidates = {}\n", "\n", "with open(base_path / \"candidate_E_reward_10_coverage_stats.json\") as f:\n", " candidates[\"E\"] = json.load(f)\n", "\n", "with open(base_path / \"candidate_F_reward_10_coverage_stats.json\") as f:\n", " candidates[\"F\"] = json.load(f)\n", "\n", "with open(base_path / \"mbpp_avap_v2_reward_stats_A.json\") as f:\n", " candidates[\"A\"] = json.load(f)" ] }, { "cell_type": "code", "execution_count": 3, "id": "8d6a48f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Candidate E: 21 unique node types\n", "Top 5 node types:\n", " node_type frequency\n", "11 addResult 3\n", "4 ormAccessUpdate 2\n", "3 gather 2\n", "5 getListLen 2\n", "0 startLoop 2\n", "\n", "\n", "Candidate F: 18 unique node types\n", "Top 5 node types:\n", " node_type frequency\n", "8 ormAccessSelect 3\n", "0 startLoop 2\n", "1 randomString 2\n", "2 import 2\n", "4 RequestGet 2\n", "\n", "\n", "Candidate A: 34 unique node types\n", "Top 5 node types:\n", " node_type frequency\n", "0 AddVariableToJSON 10\n", "2 _status 10\n", "16 return 10\n", "3 addParam 10\n", "4 addResult 10\n", "\n" ] } ], "source": [ "data_for_viz = {}\n", "\n", "for candidate_name, stats in candidates.items():\n", " node_freq = stats.get(\"node_type_frequency\", {})\n", " \n", " if node_freq:\n", " df = pd.DataFrame({\n", " \"node_type\": list(node_freq.keys()),\n", " \"frequency\": list(node_freq.values())\n", " }).sort_values(\"frequency\", ascending=False)\n", " else:\n", " df = pd.DataFrame({\n", " \"node_type\": [],\n", " \"frequency\": []\n", " })\n", " \n", " data_for_viz[candidate_name] = {\n", " \"dataframe\": df,\n", " \"entropy\": stats.get(\"distribution_entropy\", 0),\n", " \"total_nodes\": len(node_freq)\n", " }\n", " \n", " print(f\"\\nCandidate {candidate_name}: {len(df)} unique node types\")\n", " if len(df) > 0:\n", " print(f\"Top 5 node types:\\n{df.head()}\\n\")" ] }, { "cell_type": "markdown", "id": "399a5931", "metadata": {}, "source": [ "# Analysis" ] }, { "cell_type": "code", "execution_count": 4, "id": "aa3a031e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "Candidate E\n", "==================================================\n", "Distribution Entropy: 4.2800\n", "Total Node Types: 21\n", "Total Cells: 9139\n", "Filled Cells: 10\n", "Fill Rate: 0.1100%\n", "\n", "==================================================\n", "Candidate F\n", "==================================================\n", "Distribution Entropy: 4.0600\n", "Total Node Types: 18\n", "Total Cells: 9139\n", "Filled Cells: 10\n", "Fill Rate: 0.1100%\n", "\n", "==================================================\n", "Candidate A\n", "==================================================\n", "Distribution Entropy: 4.9590\n", "Total Node Types: 34\n", "Total Cells: 0\n", "Filled Cells: 0\n", "Fill Rate: 0.0000%\n" ] } ], "source": [ "for candidate_name, data in candidates.items():\n", " entropy = data.get(\"distribution_entropy\", 0)\n", " node_count = 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(Distribution Entropy: 4.0600, Total Node Types: 18)" }, "xaxis": { "tickangle": 45, "title": { "text": "Node Types" } }, "yaxis": { "title": { "text": "Frequency" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for candidate_name in [\"A\", \"E\", \"F\"]:\n", " viz_data = data_for_viz[candidate_name]\n", " df = viz_data[\"dataframe\"]\n", " entropy = viz_data[\"entropy\"]\n", " \n", " if len(df) == 0:\n", " print(f\"\\nCandidate {candidate_name}: No node types found (Empty dataset)\")\n", " fig = go.Figure()\n", " fig.add_annotation(\n", " text=f\"No node types found
Distribution Entropy: {entropy:.4f}\",\n", " showarrow=False,\n", " font=dict(size=16)\n", " )\n", " fig.update_layout(\n", " title=f\"Candidate {candidate_name} - Node Type Distribution
(Distribution Entropy: {entropy:.4f})\",\n", " height=300\n", " )\n", " fig.show()\n", " else:\n", " fig = go.Figure()\n", " fig.add_trace(\n", " go.Bar(\n", " x=df[\"node_type\"],\n", " y=df[\"frequency\"],\n", " marker_color=colors[candidate_name],\n", " text=df[\"frequency\"],\n", " textposition=\"auto\"\n", " )\n", " )\n", " \n", " fig.update_layout(\n", " title=f\"Candidate {candidate_name} - Node Type Distribution
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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Comparative Analysis: Entropy and Node Type Distribution" }, "xaxis": { "anchor": "y", "domain": [ 0, 0.45 ], "title": { "text": "Candidate" } }, "xaxis2": { "anchor": "y2", "domain": [ 0.55, 1 ], "title": { "text": "Candidate" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Entropy Value" } }, "yaxis2": { "anchor": "x2", "domain": [ 0, 1 ], "title": { "text": "Number of Node Types" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comparison_data = {\n", " \"candidate\": [],\n", " \"entropy\": [],\n", " \"node_types\": [],\n", " \"total_frequency\": []\n", "}\n", "\n", "for candidate_name in [\"A\", \"E\", \"F\"]:\n", " viz_data = data_for_viz[candidate_name]\n", " df = viz_data[\"dataframe\"]\n", " \n", " comparison_data[\"candidate\"].append(candidate_name)\n", " comparison_data[\"entropy\"].append(viz_data[\"entropy\"])\n", " comparison_data[\"node_types\"].append(len(df))\n", " comparison_data[\"total_frequency\"].append(df[\"frequency\"].sum() if len(df) > 0 else 0)\n", "\n", "comp_df = pd.DataFrame(comparison_data)\n", "\n", "fig = sp.make_subplots(\n", " rows=1, cols=2,\n", " subplot_titles=(\"Distribution Entropy Comparison\", \"Node Type Count Comparison\")\n", ")\n", "\n", "fig.add_trace(\n", " go.Bar(\n", " x=comp_df[\"candidate\"],\n", " y=comp_df[\"entropy\"],\n", " name=\"Entropy\",\n", " marker_color=[\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\"]\n", " ),\n", " row=1, col=1\n", ")\n", "\n", "fig.add_trace(\n", " go.Bar(\n", " x=comp_df[\"candidate\"],\n", " y=comp_df[\"node_types\"],\n", " name=\"Node Types\",\n", " marker_color=[\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\"]\n", " ),\n", " row=1, col=2\n", ")\n", "\n", "fig.update_yaxes(title_text=\"Entropy Value\", row=1, col=1)\n", "fig.update_yaxes(title_text=\"Number of Node Types\", row=1, col=2)\n", "fig.update_xaxes(title_text=\"Candidate\", row=1, col=1)\n", "fig.update_xaxes(title_text=\"Candidate\", row=1, col=2)\n", "\n", "fig.update_layout(title=\"Comparative Analysis: Entropy and Node Type Distribution\", height=400)\n", "fig.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }