{
"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",
"import yaml\n",
"from scipy.stats import entropy as scipy_stats_entropy\n",
"import yaml\n",
"from scipy.stats import entropy as scipy_stats_entropy, entropy\n",
"from src.config import settings\n",
"\n",
"base_path = Path(\"/home/pseco/VsCodeProjects/assistance-engine/output\")"
]
},
{
"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": "725d9cd2",
"metadata": {},
"outputs": [],
"source": [
"colors = {\"A\": \"#1f77b4\", \"E\": \"#ff7f0e\", \"F\": \"#2ca02c\"}"
]
},
{
"cell_type": "markdown",
"id": "871a758b",
"metadata": {},
"source": [
"# Cell Fill Rate Heatmaps"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e674f19b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fill Rate Summary for E and F:\n",
"Candidate Fill Rate (%) Filled Cells Empty Cells Total Cells Node Types\n",
" E 0.11 10 9129 9139 21\n",
" F 0.11 10 9129 9139 18\n",
"\n",
"Both candidates have identical dimensions: 9139 total cells\n"
]
}
],
"source": [
"total_cells = 9139\n",
"candidates_E_F = [\"E\", \"F\"]\n",
"\n",
"fill_rate_data = []\n",
"for candidate in candidates_E_F:\n",
" stats = candidates[candidate]\n",
" fill_rate_data.append({\n",
" \"Candidate\": candidate,\n",
" \"Fill Rate (%)\": stats.get(\"fill_rate\", 0) * 100,\n",
" \"Filled Cells\": stats.get(\"filled_cells\", 0),\n",
" \"Empty Cells\": stats.get(\"empty_cells\", 0),\n",
" \"Total Cells\": stats.get(\"total_cells\", 0),\n",
" \"Node Types\": len(stats.get(\"node_type_frequency\", {}))\n",
" })\n",
"\n",
"fill_rate_df = pd.DataFrame(fill_rate_data)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c1fa5024",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"colorscale": [
[
0,
"#f0f0f0"
],
[
1,
"#ff7f0e"
]
],
"hovertemplate": "Row: %{y}
Col: %{x}
Status: %{z|Filled|Empty}",
"name": "E",
"showscale": false,
"type": "heatmap",
"xaxis": "x",
"yaxis": "y",
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" \n",
" stats = candidates[candidate]\n",
" filled_count = stats.get(\"filled_cells\", 0)\n",
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" grid_matrix[row, col] = 1\n",
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" )\n",
"\n",
"fig.update_xaxes(title_text=\"Cell Column Index\", row=1, col=1)\n",
"fig.update_yaxes(title_text=\"Cell Row Index\", row=1, col=1)\n",
"fig.update_xaxes(title_text=\"Cell Column Index\", row=1, col=2)\n",
"fig.update_yaxes(title_text=\"Cell Row Index\", row=1, col=2)\n",
"\n",
"fig.update_layout(\n",
" title_text=\"Cell Fill Rate Distribution: Candidates E vs F
\",\n",
" height=500,\n",
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},
"title": {
"text": "Fill Rate Metrics Heatmap: Candidates E vs F
Showing fill rate percentages and cell counts"
},
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"text": [
"\n",
"================================================================================\n",
"DETAILED FILL RATE STATISTICS: E vs F\n",
"================================================================================\n",
"\n",
"Candidate E:\n",
" Total Cells: 9,139\n",
" Filled Cells: 10\n",
" Empty Cells: 9,129\n",
" Low Quality: 0\n",
" Fill Rate (%): 0.1100%\n",
" Node Types: 21\n",
"\n",
"Candidate F:\n",
" Total Cells: 9,139\n",
" Filled Cells: 10\n",
" Empty Cells: 9,129\n",
" Low Quality: 0\n",
" Fill Rate (%): 0.1100%\n",
" Node Types: 18\n",
"================================================================================\n"
]
}
],
"source": [
"metrics = {\n",
" \"Fill Rate (%)\": [],\n",
" \"Filled Cells\": [],\n",
" \"Empty Cells\": [],\n",
" \"Low Quality\": [],\n",
" \"Coverage %\": []\n",
"}\n",
"\n",
"for candidate in [\"E\", \"F\"]:\n",
" stats = candidates[candidate]\n",
" filled = stats.get(\"filled_cells\", 0)\n",
" empty = stats.get(\"empty_cells\", 0)\n",
" total = stats.get(\"total_cells\", 9139)\n",
" low_quality = stats.get(\"low_quality_cells\", 0)\n",
" \n",
" metrics[\"Fill Rate (%)\"].append(stats.get(\"fill_rate\", 0) * 100)\n",
" metrics[\"Filled Cells\"].append(filled)\n",
" metrics[\"Empty Cells\"].append(empty / 100) # Scale down for visualization\n",
" metrics[\"Low Quality\"].append(low_quality)\n",
" metrics[\"Coverage %\"].append((filled / total) * 100)\n",
"\n",
"\n",
"metric_names = list(metrics.keys())\n",
"metric_matrix = [metrics[m] for m in metric_names]\n",
"\n",
"fig = go.Figure(\n",
" data=go.Heatmap(\n",
" z=metric_matrix,\n",
" x=[\"Candidate E\", \"Candidate F\"],\n",
" y=metric_names,\n",
" colorscale=\"Blues\",\n",
" hovertemplate=\"Metric: %{y}
Candidate: %{x}
Value: %{z:.2f}\",\n",
" text=[[f\"{v:.2f}\" for v in row] for row in metric_matrix],\n",
" texttemplate=\"%{text}\",\n",
" textfont={\"size\": 12}\n",
" )\n",
")\n",
"\n",
"fig.update_layout(\n",
" title=\"Fill Rate Metrics Heatmap: Candidates E vs F
Showing fill rate percentages and cell counts\",\n",
" xaxis_title=\"Candidate\",\n",
" yaxis_title=\"Metric\",\n",
" height=500,\n",
" width=700,\n",
" yaxis=dict(autorange=\"reversed\")\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "506dc5a8",
"metadata": {},
"source": [
"# Co-occurrence Analysis: Pairs and Trios of Node Types"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9b9c5dc5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded candidate examples:\n",
" Candidate E: 10 examples\n",
" Candidate F: 10 examples\n"
]
}
],
"source": [
"# Load the full candidate datasets to extract pairs and trios from examples\n",
"candidate_full_data = {}\n",
"\n",
"with open(base_path / \"candidate_E_reward_10.json\") as f:\n",
" candidate_full_data[\"E\"] = json.load(f)\n",
"\n",
"with open(base_path / \"candidate_F_reward_10.json\") as f:\n",
" candidate_full_data[\"F\"] = json.load(f)\n",
"\n",
"print(\"Loaded candidate examples:\")\n",
"print(f\" Candidate E: {len(candidate_full_data['E'])} examples\")\n",
"print(f\" Candidate F: {len(candidate_full_data['F'])} examples\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "aa86a24c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pairs of node types found:\n",
" Candidate E: 28 unique pairs\n",
" Candidate F: 24 unique pairs\n",
"\n",
"Trios of node types found:\n",
" Candidate E: 9 unique trios\n",
" Candidate F: 7 unique trios\n",
"\n",
"============================================================\n",
"TOP 10 PAIRS - Candidate E:\n",
"============================================================\n",
" avapConnector + startLoop: 1\n",
" gather + getQueryParamList: 1\n",
" gather + ormAccessUpdate: 1\n",
" getQueryParamList + ormAccessUpdate: 1\n",
" RequestPost + getListLen: 1\n",
" RequestPost + getTimeStamp: 1\n",
" getListLen + getTimeStamp: 1\n",
" addVar + end: 1\n",
" addVar + getDateTime: 1\n",
" end + getDateTime: 1\n",
"\n",
"============================================================\n",
"TOP 10 PAIRS - Candidate F:\n",
"============================================================\n",
" import + randomString: 1\n",
" import + startLoop: 1\n",
" randomString + startLoop: 1\n",
" RequestGet + _status: 1\n",
" addVar + itemFromList: 1\n",
" addVar + ormAccessUpdate: 1\n",
" itemFromList + ormAccessUpdate: 1\n",
" addParam + ormAccessSelect: 1\n",
" addParam + variableFromJSON: 1\n",
" ormAccessSelect + variableFromJSON: 1\n"
]
}
],
"source": [
"from itertools import combinations\n",
"\n",
"def extract_pairs_and_trios(examples):\n",
" \"\"\"Extract pairs and trios of node types from examples.\"\"\"\n",
" pairs_count = {}\n",
" trios_count = {}\n",
" \n",
" for example in examples:\n",
" if isinstance(example, dict):\n",
" # Get the node types used in this example from the _cell field\n",
" nodes_in_example = example.get(\"_cell\", [])\n",
" \n",
" if isinstance(nodes_in_example, list) and len(nodes_in_example) > 0:\n",
" nodes = list(set(nodes_in_example)) # Remove duplicates\n",
" \n",
" # Extract pairs\n",
" if len(nodes) >= 2:\n",
" for pair in combinations(sorted(nodes), 2):\n",
" pair_key = f\"{pair[0]} + {pair[1]}\"\n",
" pairs_count[pair_key] = pairs_count.get(pair_key, 0) + 1\n",
" \n",
" # Extract trios\n",
" if len(nodes) >= 3:\n",
" for trio in combinations(sorted(nodes), 3):\n",
" trio_key = f\"{trio[0]} + {trio[1]} + {trio[2]}\"\n",
" trios_count[trio_key] = trios_count.get(trio_key, 0) + 1\n",
" \n",
" return pairs_count, trios_count\n",
"\n",
"# Extract pairs and trios for both candidates\n",
"pairs_E, trios_E = extract_pairs_and_trios(candidate_full_data[\"E\"])\n",
"pairs_F, trios_F = extract_pairs_and_trios(candidate_full_data[\"F\"])\n",
"\n",
"print(\"Pairs of node types found:\")\n",
"print(f\" Candidate E: {len(pairs_E)} unique pairs\")\n",
"print(f\" Candidate F: {len(pairs_F)} unique pairs\")\n",
"print(f\"\\nTrios of node types found:\")\n",
"print(f\" Candidate E: {len(trios_E)} unique trios\")\n",
"print(f\" Candidate F: {len(trios_F)} unique trios\")\n",
"\n",
"# Show top pairs\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"TOP 10 PAIRS - Candidate E:\")\n",
"print(\"=\"*60)\n",
"for pair, count in sorted(pairs_E.items(), key=lambda x: x[1], reverse=True)[:10]:\n",
" print(f\" {pair}: {count}\")\n",
"\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"TOP 10 PAIRS - Candidate F:\")\n",
"print(\"=\"*60)\n",
"for pair, count in sorted(pairs_F.items(), key=lambda x: x[1], reverse=True)[:10]:\n",
" print(f\" {pair}: {count}\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fdade209",
"metadata": {},
"outputs": [
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"================================================================================\n",
"CO-OCCURRENCE ANALYSIS SUMMARY\n",
"================================================================================\n",
"\n",
"Candidate E:\n",
" Total unique pairs: 28\n",
" Total unique trios: 9\n",
" Most common pair: avapConnector + startLoop (appears 1 time(s))\n",
" Most common trio: gather + getQueryParamList + ormAccessUpdate (appears 1 time(s))\n",
"\n",
"Candidate F:\n",
" Total unique pairs: 24\n",
" Total unique trios: 7\n",
" Most common pair: import + randomString (appears 1 time(s))\n",
" Most common trio: import + randomString + startLoop (appears 1 time(s))\n",
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"# Create heatmap for top trios for E and F\n",
"fig = sp.make_subplots(\n",
" rows=1, cols=2,\n",
" subplot_titles=(\n",
" f\"Top Trios - Candidate E ({len(trios_E)} total trios)\",\n",
" f\"Top Trios - Candidate F ({len(trios_F)} total trios)\"\n",
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"# Get top 12 trios for each candidate\n",
"top_trios_E = sorted(trios_E.items(), key=lambda x: x[1], reverse=True)[:12]\n",
"top_trios_F = sorted(trios_F.items(), key=lambda x: x[1], reverse=True)[:12]\n",
"\n",
"# Create traces for E\n",
"fig.add_trace(\n",
" go.Bar(\n",
" y=[trio[0] for trio in top_trios_E],\n",
" x=[trio[1] for trio in top_trios_E],\n",
" orientation='h',\n",
" marker_color=colors[\"E\"],\n",
" text=[trio[1] for trio in top_trios_E],\n",
" textposition=\"auto\",\n",
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" ),\n",
" row=1, col=1\n",
")\n",
"\n",
"# Create traces for F\n",
"fig.add_trace(\n",
" go.Bar(\n",
" y=[trio[0] for trio in top_trios_F],\n",
" x=[trio[1] for trio in top_trios_F],\n",
" orientation='h',\n",
" marker_color=colors[\"F\"],\n",
" text=[trio[1] for trio in top_trios_F],\n",
" textposition=\"auto\",\n",
" name=\"Candidate F\"\n",
" ),\n",
" row=1, col=2\n",
")\n",
"\n",
"fig.update_xaxes(title_text=\"Frequency\", row=1, col=1)\n",
"fig.update_xaxes(title_text=\"Frequency\", row=1, col=2)\n",
"\n",
"fig.update_layout(\n",
" title_text=\"Co-occurrence Heatmap: Top Node Type Trios\",\n",
" height=700,\n",
" width=1400,\n",
" showlegend=False\n",
")\n",
"\n",
"fig.show()\n",
"\n",
"# Summary statistics\n",
"print(\"\\n\" + \"=\"*80)\n",
"print(\"CO-OCCURRENCE ANALYSIS SUMMARY\")\n",
"print(\"=\"*80)\n",
"print(f\"\\nCandidate E:\")\n",
"print(f\" Total unique pairs: {len(pairs_E)}\")\n",
"print(f\" Total unique trios: {len(trios_E)}\")\n",
"if top_pairs_E:\n",
" print(f\" Most common pair: {top_pairs_E[0][0]} (appears {top_pairs_E[0][1]} time(s))\")\n",
"if top_trios_E:\n",
" print(f\" Most common trio: {top_trios_E[0][0]} (appears {top_trios_E[0][1]} time(s))\")\n",
"\n",
"print(f\"\\nCandidate F:\")\n",
"print(f\" Total unique pairs: {len(pairs_F)}\")\n",
"print(f\" Total unique trios: {len(trios_F)}\")\n",
"if top_pairs_F:\n",
" print(f\" Most common pair: {top_pairs_F[0][0]} (appears {top_pairs_F[0][1]} time(s))\")\n",
"if top_trios_F:\n",
" print(f\" Most common trio: {top_trios_F[0][0]} (appears {top_trios_F[0][1]} time(s))\")\n",
"print(\"=\"*80)"
]
},
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"Node types included in co-occurrence matrices (top 12 by frequency):\n",
"\n",
"Candidate E: addResult, startLoop, ormAccessUpdate, gather, getListLen, getDateTime, end, avapConnector, getQueryParamList, RequestPost, getTimeStamp, addVar\n",
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"source": [
"# Create co-occurrence matrix heatmap showing node type relationships\n",
"def create_cooccurrence_matrix(examples, top_nodes=12):\n",
" \"\"\"Create a co-occurrence matrix for node types.\"\"\"\n",
" # Extract all nodes from examples\n",
" all_nodes_set = set()\n",
" examples_node_lists = []\n",
" \n",
" for example in examples:\n",
" if isinstance(example, dict):\n",
" nodes_in_example = example.get(\"_cell\", [])\n",
" if isinstance(nodes_in_example, list):\n",
" nodes = list(set(nodes_in_example))\n",
" examples_node_lists.append(nodes)\n",
" all_nodes_set.update(nodes)\n",
" \n",
" # Get top nodes by frequency\n",
" node_counts = {}\n",
" for nodes_list in examples_node_lists:\n",
" for node in nodes_list:\n",
" node_counts[node] = node_counts.get(node, 0) + 1\n",
" \n",
" top_nodes_list = sorted(node_counts.items(), key=lambda x: x[1], reverse=True)[:top_nodes]\n",
" top_node_names = [node[0] for node in top_nodes_list]\n",
" \n",
" # Create co-occurrence matrix\n",
" cooccurrence_matrix = np.zeros((len(top_node_names), len(top_node_names)))\n",
" \n",
" for nodes_list in examples_node_lists:\n",
" for i, node1 in enumerate(top_node_names):\n",
" for j, node2 in enumerate(top_node_names):\n",
" if node1 in nodes_list and node2 in nodes_list:\n",
" cooccurrence_matrix[i][j] += 1\n",
" \n",
" return cooccurrence_matrix, top_node_names\n",
"\n",
"# Create co-occurrence matrices for E and F\n",
"cooccur_matrix_E, nodes_E = create_cooccurrence_matrix(candidate_full_data[\"E\"], top_nodes=12)\n",
"cooccur_matrix_F, nodes_F = create_cooccurrence_matrix(candidate_full_data[\"F\"], top_nodes=12)\n",
"\n",
"# Create heatmap visualization\n",
"fig = sp.make_subplots(\n",
" rows=1, cols=2,\n",
" subplot_titles=(\n",
" \"Co-occurrence Matrix - Candidate E\",\n",
" \"Co-occurrence Matrix - Candidate F\"\n",
" ),\n",
" specs=[[{\"type\": \"heatmap\"}, {\"type\": \"heatmap\"}]]\n",
")\n",
"\n",
"fig.add_trace(\n",
" go.Heatmap(\n",
" z=cooccur_matrix_E,\n",
" x=nodes_E,\n",
" y=nodes_E,\n",
" colorscale=\"YlOrRd\",\n",
" hovertemplate=\"Node 1: %{y}
Node 2: %{x}
Co-occurrences: %{z}\",\n",
" text=cooccur_matrix_E.astype(int),\n",
" texttemplate=\"%{text}\",\n",
" textfont={\"size\": 9},\n",
" colorbar=dict(title=\"Count\", x=0.46)\n",
" ),\n",
" row=1, col=1\n",
")\n",
"\n",
"fig.add_trace(\n",
" go.Heatmap(\n",
" z=cooccur_matrix_F,\n",
" x=nodes_F,\n",
" y=nodes_F,\n",
" colorscale=\"YlGn\",\n",
" hovertemplate=\"Node 1: %{y}
Node 2: %{x}
Co-occurrences: %{z}\",\n",
" text=cooccur_matrix_F.astype(int),\n",
" texttemplate=\"%{text}\",\n",
" textfont={\"size\": 9},\n",
" colorbar=dict(title=\"Count\", x=1.02)\n",
" ),\n",
" row=1, col=2\n",
")\n",
"\n",
"fig.update_xaxes(tickangle=45, row=1, col=1)\n",
"fig.update_xaxes(tickangle=45, row=1, col=2)\n",
"\n",
"fig.update_layout(\n",
" title_text=\"Node Type Co-occurrence Matrix Heatmap
Shows how frequently node types appear together in examples\",\n",
" height=700,\n",
" width=1400\n",
")\n",
"\n",
"fig.show()\n",
"\n",
"print(\"\\nNode types included in co-occurrence matrices (top 12 by frequency):\")\n",
"print(f\"\\nCandidate E: {', '.join(nodes_E)}\")\n",
"print(f\"\\nCandidate F: {', '.join(nodes_F)}\")"
]
}
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