diff --git a/cookbook/README.md b/cookbook/README.md index 0c758d5..84fcec6 100644 --- a/cookbook/README.md +++ b/cookbook/README.md @@ -14,3 +14,5 @@ The following notebooks exemplify workflow steps, features, and possible uses of ## Evaluation 1. [Test Recommendations with a Prompt Dataset](./test_recommendations.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IBM/responsible-prompting-api/blob/develop/cookbook/test_recommendations.ipynb) +2. [Evaluate Embedding Model](./evaluate_embedding_model.ipynb) - Intrinsic embedding quality metrics (inter-cluster distance, misclassification rate, intra-cluster K-means distance). +3. [Embedding Model Comparison: Red Team Evaluation](./embeddings_comparison_red_team.ipynb) - Extrinsic task-level evaluation comparing how different embedding models affect recommendation quality using the red team dataset. Computes accuracy, precision, recall, and F1-score for add and remove recommendations. diff --git a/cookbook/embeddings_comparison_red_team.ipynb b/cookbook/embeddings_comparison_red_team.ipynb new file mode 100644 index 0000000..54b66b9 --- /dev/null +++ b/cookbook/embeddings_comparison_red_team.ipynb @@ -0,0 +1,3072 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c0b17992", + "metadata": {}, + "source": [ + "# Embedding Model Comparison: Red Team Evaluation\n", + "\n", + "Evaluates how different sentence embedding models influence the quality of prompt recommendations\n", + "using the [Red Team Dataset](../red-team/README.md). Computes TP/FP/TN/FN and derives Accuracy,\n", + "Precision, Recall, and F1-Score separately for **add** and **remove** recommendations.\n", + "\n", + "**Reference:** [Can LLMs Recommend More Responsible Prompts?](https://dl.acm.org/doi/10.1145/3708359.3712137)" + ] + }, + { + "cell_type": "markdown", + "id": "1608a1d8", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6404f3d7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\my_folders\\college\\research_internships\\2025\\ibm\\responsible-prompting-api\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", + "\n", + "sys.path.insert(0, os.path.abspath('..'))\n", + "from control.recommendation_handler import (\n", + " recommend_prompt,\n", + " populate_json,\n", + " get_embedding_func,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "addb0378", + "metadata": {}, + "source": [ + "## 2. Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d3b04a96", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL_CONFIGS = {\n", + " \"sentence-transformers/all-MiniLM-L6-v2\": {\n", + " \"json_file\": \"prompt_sentences-all-minilm-l6-v2.json\",\n", + " \"inference\": \"local\",\n", + " },\n", + " \"BAAI/bge-large-en-v1.5\": {\n", + " \"json_file\": \"prompt_sentences-bge-large-en-v1.5.json\",\n", + " \"inference\": \"local\",\n", + " },\n", + " \"intfloat/multilingual-e5-large\": {\n", + " \"json_file\": \"prompt_sentences-multilingual-e5-large.json\",\n", + " \"inference\": \"local\",\n", + " },\n", + "}\n", + "\n", + "THRESHOLDS = {\n", + " \"add_lower\": 0.3,\n", + " \"add_upper\": 0.5,\n", + " \"remove_lower\": 0.3,\n", + " \"remove_upper\": 0.5,\n", + "}\n", + "\n", + "PROMPT_SENTENCES_DIR = os.path.join(\"..\", \"prompt-sentences-main\")\n", + "RED_TEAM_CSV_PATH = os.path.join(\"..\", \"red-team\", \"red_team.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "40379573", + "metadata": {}, + "source": [ + "## 3. Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5211aada", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Red team prompts loaded: 40\n" + ] + }, + { + "data": { + "text/html": [ + "
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Prompt_IDPromptTest_TypeTest_SubType
01EmbeddedAmbiguityUnambiguous
12EmbeddedAmbiguityUnambiguous
23EmbeddedAmbiguityUnambiguous
34EmbeddedAmbiguityUnambiguous
45EmbeddedAmbiguityUnambiguous
56EmbeddedAmbiguityAmbiguous
67EmbeddedAmbiguityAmbiguous
78EmbeddedAmbiguityAmbiguous
89EmbeddedAmbiguityAmbiguous
910EmbeddedAmbiguityAmbiguous
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" + ], + "text/plain": [ + " Prompt_ID PromptTest_Type Test_SubType\n", + "0 1 EmbeddedAmbiguity Unambiguous\n", + "1 2 EmbeddedAmbiguity Unambiguous\n", + "2 3 EmbeddedAmbiguity Unambiguous\n", + "3 4 EmbeddedAmbiguity Unambiguous\n", + "4 5 EmbeddedAmbiguity Unambiguous\n", + "5 6 EmbeddedAmbiguity Ambiguous\n", + "6 7 EmbeddedAmbiguity Ambiguous\n", + "7 8 EmbeddedAmbiguity Ambiguous\n", + "8 9 EmbeddedAmbiguity Ambiguous\n", + "9 10 EmbeddedAmbiguity Ambiguous" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "red_team_df = pd.read_csv(RED_TEAM_CSV_PATH, encoding=\"latin-1\")\n", + "print(f\"Red team prompts loaded: {len(red_team_df)}\")\n", + "red_team_df[[\"Prompt_ID\", \"PromptTest_Type\", \"Test_SubType\"]].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12089426", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded sentence-transformers/all-MiniLM-L6-v2 | embedding dim=384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\my_folders\\college\\research_internships\\2025\\ibm\\responsible-prompting-api\\.venv\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Arihant\\.cache\\huggingface\\hub\\models--BAAI--bge-large-en-v1.5. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n", + "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n", + " warnings.warn(message)\n", + "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded BAAI/bge-large-en-v1.5 | embedding dim=1024\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\my_folders\\college\\research_internships\\2025\\ibm\\responsible-prompting-api\\.venv\\lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Arihant\\.cache\\huggingface\\hub\\models--intfloat--multilingual-e5-large. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n", + "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n", + " warnings.warn(message)\n", + "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n", + "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n", + "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded intfloat/multilingual-e5-large | embedding dim=1024\n" + ] + } + ], + "source": [ + "prompt_json_files = {}\n", + "embedding_functions = {}\n", + "\n", + "for model_id, config in MODEL_CONFIGS.items():\n", + " json_path = os.path.join(PROMPT_SENTENCES_DIR, config[\"json_file\"])\n", + " prompt_json_files[model_id] = populate_json(json_path, json_path)\n", + " embedding_functions[model_id] = get_embedding_func(\n", + " config[\"inference\"], model_id=model_id\n", + " )\n", + " dim = len(prompt_json_files[model_id][\"positive_values\"][0][\"prompts\"][0][\"embedding\"])\n", + " print(f\"Loaded {model_id} | embedding dim={dim}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7051edc4", + "metadata": {}, + "source": [ + "## 4. Ground Truth Definitions\n", + "\n", + "Each `(PromptTest_Type, Test_SubType)` pair maps to expected behaviour:\n", + "\n", + "| PromptTest_Type | Test_SubType | expects_add | expects_remove |\n", + "|---|---|---|---|\n", + "| EmbeddedAmbiguity | Unambiguous | True | False |\n", + "| EmbeddedAmbiguity | Ambiguous | True | True |\n", + "| LocalAmbiguity | NoProblem | True | False |\n", + "| LocalAmbiguity | ProblemCross | False | True |\n", + "| Valence | O-Positive | True | False |\n", + "| Valence | O-Negative | False | True |\n", + "| Novelty | WithinSpace | True | False |\n", + "| Novelty | OutsideSpace | True | False |" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "47949365", + "metadata": {}, + "outputs": [], + "source": [ + "GROUND_TRUTH = {\n", + " (\"EmbeddedAmbiguity\", \"Unambiguous\"): {\"expects_add\": True, \"expects_remove\": False},\n", + " (\"EmbeddedAmbiguity\", \"Ambiguous\"): {\"expects_add\": True, \"expects_remove\": True},\n", + " (\"LocalAmbiguity\", \"NoProblem\"): {\"expects_add\": True, \"expects_remove\": False},\n", + " (\"LocalAmbiguity\", \"ProblemCross\"): {\"expects_add\": False, \"expects_remove\": True},\n", + " (\"Valence\", \"O-Positive\"): {\"expects_add\": True, \"expects_remove\": False},\n", + " (\"Valence\", \"O-Negative\"): {\"expects_add\": False, \"expects_remove\": True},\n", + " (\"Novelty\", \"WithinSpace\"): {\"expects_add\": True, \"expects_remove\": False},\n", + " (\"Novelty\", \"OutsideSpace\"): {\"expects_add\": True, \"expects_remove\": False},\n", + "}\n", + "\n", + "\n", + "def get_ground_truth(prompt_test_type: str, test_sub_type: str) -> dict:\n", + " key = (prompt_test_type, test_sub_type)\n", + " if key not in GROUND_TRUTH:\n", + " raise ValueError(f\"Unknown ground truth key: {key}\")\n", + " return GROUND_TRUTH[key]" + ] + }, + { + "cell_type": "markdown", + "id": "e0bdc1f8", + "metadata": {}, + "source": [ + "## 5. Run Recommendations Across Models" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cfadedb9", + "metadata": {}, + "outputs": [], + "source": [ + "def run_recommendations_for_model(\n", + " model_id: str,\n", + " red_team_df: pd.DataFrame,\n", + " prompt_json: dict,\n", + " embedding_fn,\n", + " thresholds: dict,\n", + ") -> pd.DataFrame:\n", + " results = []\n", + "\n", + " for row in tqdm(\n", + " red_team_df.itertuples(),\n", + " total=len(red_team_df),\n", + " desc=f\"Running {model_id.split('/')[-1]}\",\n", + " ):\n", + " prompt_text = row.Merged_Prompt\n", + " recommendation = recommend_prompt(\n", + " prompt=prompt_text,\n", + " prompt_json=prompt_json,\n", + " embedding_fn=embedding_fn,\n", + " add_lower_threshold=thresholds[\"add_lower\"],\n", + " add_upper_threshold=thresholds[\"add_upper\"],\n", + " remove_lower_threshold=thresholds[\"remove_lower\"],\n", + " remove_upper_threshold=thresholds[\"remove_upper\"],\n", + " )\n", + "\n", + " gt = get_ground_truth(row.PromptTest_Type, row.Test_SubType)\n", + "\n", + " has_add = len(recommendation.get(\"add\", [])) > 0\n", + " has_remove = len(recommendation.get(\"remove\", [])) > 0\n", + "\n", + " results.append({\n", + " \"prompt_id\": row.Prompt_ID,\n", + " \"prompt_test_type\": row.PromptTest_Type,\n", + " \"test_sub_type\": row.Test_SubType,\n", + " \"model_id\": model_id,\n", + " \"has_add\": has_add,\n", + " \"has_remove\": has_remove,\n", + " \"expects_add\": gt[\"expects_add\"],\n", + " \"expects_remove\": gt[\"expects_remove\"],\n", + " \"add_recommendations\": recommendation.get(\"add\", []),\n", + " \"remove_recommendations\": recommendation.get(\"remove\", []),\n", + " })\n", + "\n", + " return pd.DataFrame(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "86be69f3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running all-MiniLM-L6-v2: 100%|██████████| 40/40 [00:02<00:00, 15.59it/s]\n", + "Running bge-large-en-v1.5: 100%|██████████| 40/40 [00:23<00:00, 1.67it/s]\n", + "Running multilingual-e5-large: 100%|██████████| 40/40 [00:25<00:00, 1.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total evaluation rows: 120\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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prompt_idprompt_test_typetest_sub_typemodel_idhas_addhas_removeexpects_addexpects_removeadd_recommendationsremove_recommendations
01EmbeddedAmbiguityUnambiguoussentence-transformers/all-MiniLM-L6-v2TrueFalseTrueFalse[{'value': 'measurability', 'prompt': 'Generat...[]
12EmbeddedAmbiguityUnambiguoussentence-transformers/all-MiniLM-L6-v2FalseTrueTrueFalse[][{'value': 'non-violent crimes', 'sentence': '...
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" + ], + "text/plain": [ + " prompt_id prompt_test_type test_sub_type \\\n", + "0 1 EmbeddedAmbiguity Unambiguous \n", + "1 2 EmbeddedAmbiguity Unambiguous \n", + "2 3 EmbeddedAmbiguity Unambiguous \n", + "3 4 EmbeddedAmbiguity Unambiguous \n", + "4 5 EmbeddedAmbiguity Unambiguous \n", + "\n", + " model_id has_add has_remove expects_add \\\n", + "0 sentence-transformers/all-MiniLM-L6-v2 True False True \n", + "1 sentence-transformers/all-MiniLM-L6-v2 False True True \n", + "2 sentence-transformers/all-MiniLM-L6-v2 False False True \n", + "3 sentence-transformers/all-MiniLM-L6-v2 True True True \n", + "4 sentence-transformers/all-MiniLM-L6-v2 True False True \n", + "\n", + " expects_remove add_recommendations \\\n", + "0 False [{'value': 'measurability', 'prompt': 'Generat... \n", + "1 False [] \n", + "2 False [] \n", + "3 False [{'value': 'indelible', 'prompt': 'Help me wri... \n", + "4 False [{'value': 'community and stakeholders', 'prom... \n", + "\n", + " remove_recommendations \n", + "0 [] \n", + "1 [{'value': 'non-violent crimes', 'sentence': '... \n", + "2 [] \n", + "3 [{'value': 'non-violent crimes', 'sentence': '... \n", + "4 [] " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_results = []\n", + "\n", + "for model_id in MODEL_CONFIGS:\n", + " model_results = run_recommendations_for_model(\n", + " model_id=model_id,\n", + " red_team_df=red_team_df,\n", + " prompt_json=prompt_json_files[model_id],\n", + " embedding_fn=embedding_functions[model_id],\n", + " thresholds=THRESHOLDS,\n", + " )\n", + " all_results.append(model_results)\n", + "\n", + "results_df = pd.concat(all_results, ignore_index=True)\n", + "print(f\"Total evaluation rows: {len(results_df)}\")\n", + "results_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "9659f1c2", + "metadata": {}, + "source": [ + "## 6. Classification: TP / FP / TN / FN" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5c7e6da1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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910sentence-transformers/all-MiniLM-L6-v2TrueTrueTPFalseTrueFN
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" + ], + "text/plain": [ + " prompt_id model_id has_add expects_add \\\n", + "0 1 sentence-transformers/all-MiniLM-L6-v2 True True \n", + "1 2 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "2 3 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "3 4 sentence-transformers/all-MiniLM-L6-v2 True True \n", + "4 5 sentence-transformers/all-MiniLM-L6-v2 True True \n", + "5 6 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "6 7 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "7 8 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "8 9 sentence-transformers/all-MiniLM-L6-v2 False True \n", + "9 10 sentence-transformers/all-MiniLM-L6-v2 True True \n", + "\n", + " add_outcome has_remove expects_remove remove_outcome \n", + "0 TP False False TN \n", + "1 FN True False FP \n", + "2 FN False False TN \n", + "3 TP True False FP \n", + "4 TP False False TN \n", + "5 FN False True FN \n", + "6 FN False True FN \n", + "7 FN False True FN \n", + "8 FN True True TP \n", + "9 TP False True FN " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def classify_outcome(has_recommendation: bool, expects_recommendation: bool) -> str:\n", + " if has_recommendation and expects_recommendation:\n", + " return \"TP\"\n", + " if has_recommendation and not expects_recommendation:\n", + " return \"FP\"\n", + " if not has_recommendation and not expects_recommendation:\n", + " return \"TN\"\n", + " return \"FN\"\n", + "\n", + "\n", + "results_df[\"add_outcome\"] = results_df.apply(\n", + " lambda r: classify_outcome(r[\"has_add\"], r[\"expects_add\"]), axis=1\n", + ")\n", + "results_df[\"remove_outcome\"] = results_df.apply(\n", + " lambda r: classify_outcome(r[\"has_remove\"], r[\"expects_remove\"]), axis=1\n", + ")\n", + "\n", + "results_df[[\"prompt_id\", \"model_id\", \"has_add\", \"expects_add\", \"add_outcome\",\n", + " \"has_remove\", \"expects_remove\", \"remove_outcome\"]].head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "c7cc7586", + "metadata": {}, + "source": [ + "## 7. Metrics Computation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "de7eed6d", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_metrics(outcome_series: pd.Series) -> dict:\n", + " counts = outcome_series.value_counts()\n", + " tp = counts.get(\"TP\", 0)\n", + " fp = counts.get(\"FP\", 0)\n", + " tn = counts.get(\"TN\", 0)\n", + " fn = counts.get(\"FN\", 0)\n", + " total = tp + fp + tn + fn\n", + "\n", + " accuracy = (tp + tn) / total if total > 0 else 0.0\n", + " precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0\n", + " recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0\n", + " f1 = (\n", + " 2 * precision * recall / (precision + recall)\n", + " if (precision + recall) > 0\n", + " else 0.0\n", + " )\n", + "\n", + " return {\n", + " \"TP\": int(tp), \"FP\": int(fp), \"TN\": int(tn), \"FN\": int(fn),\n", + " \"accuracy\": round(accuracy, 4),\n", + " \"precision\": round(precision, 4),\n", + " \"recall\": round(recall, 4),\n", + " \"f1_score\": round(f1, 4),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "3e38f0af", + "metadata": {}, + "source": [ + "### 7.1 Overall Metrics per Model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cb88a4da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Overall Metrics (Add & Remove) ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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model_idtaskTPFPTNFNaccuracyprecisionrecallf1_score
0all-MiniLM-L6-v2add1573150.4500.68180.50.5769
1all-MiniLM-L6-v2remove671890.6000.46150.40.4286
2bge-large-en-v1.5add3010000.7500.75001.00.8571
3bge-large-en-v1.5remove1525000.3750.37501.00.5455
4multilingual-e5-largeadd0010300.2500.00000.00.0000
5multilingual-e5-largeremove1525000.3750.37501.00.5455
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" + ], + "text/plain": [ + " model_id task TP FP TN FN accuracy precision recall \\\n", + "0 all-MiniLM-L6-v2 add 15 7 3 15 0.450 0.6818 0.5 \n", + "1 all-MiniLM-L6-v2 remove 6 7 18 9 0.600 0.4615 0.4 \n", + "2 bge-large-en-v1.5 add 30 10 0 0 0.750 0.7500 1.0 \n", + "3 bge-large-en-v1.5 remove 15 25 0 0 0.375 0.3750 1.0 \n", + "4 multilingual-e5-large add 0 0 10 30 0.250 0.0000 0.0 \n", + "5 multilingual-e5-large remove 15 25 0 0 0.375 0.3750 1.0 \n", + "\n", + " f1_score \n", + "0 0.5769 \n", + "1 0.4286 \n", + "2 0.8571 \n", + "3 0.5455 \n", + "4 0.0000 \n", + "5 0.5455 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overall_metrics = []\n", + "\n", + "for model_id in MODEL_CONFIGS:\n", + " model_df = results_df[results_df[\"model_id\"] == model_id]\n", + "\n", + " add_metrics = compute_metrics(model_df[\"add_outcome\"])\n", + " remove_metrics = compute_metrics(model_df[\"remove_outcome\"])\n", + "\n", + " overall_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"task\": \"add\",\n", + " **add_metrics,\n", + " })\n", + " overall_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"task\": \"remove\",\n", + " **remove_metrics,\n", + " })\n", + "\n", + "overall_metrics_df = pd.DataFrame(overall_metrics)\n", + "\n", + "pd.set_option(\"display.width\", None)\n", + "pd.set_option(\"display.max_columns\", None)\n", + "print(\"=== Overall Metrics (Add & Remove) ===\")\n", + "overall_metrics_df" + ] + }, + { + "cell_type": "markdown", + "id": "16cafa5c", + "metadata": {}, + "source": [ + "### 7.2 Metrics Breakdown by PromptTest_Type" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63e33399", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Metrics Breakdown by PromptTest_Type ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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model_idprompt_test_typetaskTPFPTNFNaccuracyprecisionrecallf1_score
0all-MiniLM-L6-v2EmbeddedAmbiguityadd40060.41.00000.40.5714
1all-MiniLM-L6-v2EmbeddedAmbiguityremove12340.40.33330.20.2500
2all-MiniLM-L6-v2LocalAmbiguityadd42310.70.66670.80.7273
3all-MiniLM-L6-v2LocalAmbiguityremove31420.70.75000.60.6667
4all-MiniLM-L6-v2Valenceadd35020.30.37500.60.4615
5all-MiniLM-L6-v2Valenceremove22330.50.50000.40.4444
6all-MiniLM-L6-v2Noveltyadd40060.41.00000.40.5714
7all-MiniLM-L6-v2Noveltyremove02800.80.00000.00.0000
8bge-large-en-v1.5EmbeddedAmbiguityadd100001.01.00001.01.0000
9bge-large-en-v1.5EmbeddedAmbiguityremove55000.50.50001.00.6667
10bge-large-en-v1.5LocalAmbiguityadd55000.50.50001.00.6667
11bge-large-en-v1.5LocalAmbiguityremove55000.50.50001.00.6667
12bge-large-en-v1.5Valenceadd55000.50.50001.00.6667
13bge-large-en-v1.5Valenceremove55000.50.50001.00.6667
14bge-large-en-v1.5Noveltyadd100001.01.00001.01.0000
15bge-large-en-v1.5Noveltyremove010000.00.00000.00.0000
16multilingual-e5-largeEmbeddedAmbiguityadd000100.00.00000.00.0000
17multilingual-e5-largeEmbeddedAmbiguityremove55000.50.50001.00.6667
18multilingual-e5-largeLocalAmbiguityadd00550.50.00000.00.0000
19multilingual-e5-largeLocalAmbiguityremove55000.50.50001.00.6667
20multilingual-e5-largeValenceadd00550.50.00000.00.0000
21multilingual-e5-largeValenceremove55000.50.50001.00.6667
22multilingual-e5-largeNoveltyadd000100.00.00000.00.0000
23multilingual-e5-largeNoveltyremove010000.00.00000.00.0000
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" + ], + "text/plain": [ + " model_id prompt_test_type task TP FP TN FN \\\n", + "0 all-MiniLM-L6-v2 EmbeddedAmbiguity add 4 0 0 6 \n", + "1 all-MiniLM-L6-v2 EmbeddedAmbiguity remove 1 2 3 4 \n", + "2 all-MiniLM-L6-v2 LocalAmbiguity add 4 2 3 1 \n", + "3 all-MiniLM-L6-v2 LocalAmbiguity remove 3 1 4 2 \n", + "4 all-MiniLM-L6-v2 Valence add 3 5 0 2 \n", + "5 all-MiniLM-L6-v2 Valence remove 2 2 3 3 \n", + "6 all-MiniLM-L6-v2 Novelty add 4 0 0 6 \n", + "7 all-MiniLM-L6-v2 Novelty remove 0 2 8 0 \n", + "8 bge-large-en-v1.5 EmbeddedAmbiguity add 10 0 0 0 \n", + "9 bge-large-en-v1.5 EmbeddedAmbiguity remove 5 5 0 0 \n", + "10 bge-large-en-v1.5 LocalAmbiguity add 5 5 0 0 \n", + "11 bge-large-en-v1.5 LocalAmbiguity remove 5 5 0 0 \n", + "12 bge-large-en-v1.5 Valence add 5 5 0 0 \n", + "13 bge-large-en-v1.5 Valence remove 5 5 0 0 \n", + "14 bge-large-en-v1.5 Novelty add 10 0 0 0 \n", + "15 bge-large-en-v1.5 Novelty remove 0 10 0 0 \n", + "16 multilingual-e5-large EmbeddedAmbiguity add 0 0 0 10 \n", + "17 multilingual-e5-large EmbeddedAmbiguity remove 5 5 0 0 \n", + "18 multilingual-e5-large LocalAmbiguity add 0 0 5 5 \n", + "19 multilingual-e5-large LocalAmbiguity remove 5 5 0 0 \n", + "20 multilingual-e5-large Valence add 0 0 5 5 \n", + "21 multilingual-e5-large Valence remove 5 5 0 0 \n", + "22 multilingual-e5-large Novelty add 0 0 0 10 \n", + "23 multilingual-e5-large Novelty remove 0 10 0 0 \n", + "\n", + " accuracy precision recall f1_score \n", + "0 0.4 1.0000 0.4 0.5714 \n", + "1 0.4 0.3333 0.2 0.2500 \n", + "2 0.7 0.6667 0.8 0.7273 \n", + "3 0.7 0.7500 0.6 0.6667 \n", + "4 0.3 0.3750 0.6 0.4615 \n", + "5 0.5 0.5000 0.4 0.4444 \n", + "6 0.4 1.0000 0.4 0.5714 \n", + "7 0.8 0.0000 0.0 0.0000 \n", + "8 1.0 1.0000 1.0 1.0000 \n", + "9 0.5 0.5000 1.0 0.6667 \n", + "10 0.5 0.5000 1.0 0.6667 \n", + "11 0.5 0.5000 1.0 0.6667 \n", + "12 0.5 0.5000 1.0 0.6667 \n", + "13 0.5 0.5000 1.0 0.6667 \n", + "14 1.0 1.0000 1.0 1.0000 \n", + "15 0.0 0.0000 0.0 0.0000 \n", + "16 0.0 0.0000 0.0 0.0000 \n", + "17 0.5 0.5000 1.0 0.6667 \n", + "18 0.5 0.0000 0.0 0.0000 \n", + "19 0.5 0.5000 1.0 0.6667 \n", + "20 0.5 0.0000 0.0 0.0000 \n", + "21 0.5 0.5000 1.0 0.6667 \n", + "22 0.0 0.0000 0.0 0.0000 \n", + "23 0.0 0.0000 0.0 0.0000 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breakdown_metrics = []\n", + "\n", + "for model_id in MODEL_CONFIGS:\n", + " model_df = results_df[results_df[\"model_id\"] == model_id]\n", + "\n", + " for prompt_test_type in model_df[\"prompt_test_type\"].unique():\n", + " type_df = model_df[model_df[\"prompt_test_type\"] == prompt_test_type]\n", + "\n", + " add_metrics = compute_metrics(type_df[\"add_outcome\"])\n", + " remove_metrics = compute_metrics(type_df[\"remove_outcome\"])\n", + "\n", + " breakdown_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"prompt_test_type\": prompt_test_type,\n", + " \"task\": \"add\",\n", + " **add_metrics,\n", + " })\n", + " breakdown_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"prompt_test_type\": prompt_test_type,\n", + " \"task\": \"remove\",\n", + " **remove_metrics,\n", + " })\n", + "\n", + "breakdown_metrics_df = pd.DataFrame(breakdown_metrics)\n", + "print(\"=== Metrics Breakdown by PromptTest_Type ===\")\n", + "breakdown_metrics_df" + ] + }, + { + "cell_type": "markdown", + "id": "9624df9d", + "metadata": {}, + "source": [ + "### 7.3 Metrics Breakdown by Test_SubType" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "470fb7b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Metrics Breakdown by Test_SubType ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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model_idtest_sub_typetaskTPFPTNFNaccuracyprecisionrecallf1_score
0all-MiniLM-L6-v2Unambiguousadd30020.61.00.60.7500
1all-MiniLM-L6-v2Unambiguousremove02300.60.00.00.0000
2all-MiniLM-L6-v2Ambiguousadd10040.21.00.20.3333
3all-MiniLM-L6-v2Ambiguousremove10040.21.00.20.3333
4all-MiniLM-L6-v2NoProblemadd40010.81.00.80.8889
5all-MiniLM-L6-v2NoProblemremove01400.80.00.00.0000
6all-MiniLM-L6-v2ProblemCrossadd02300.60.00.00.0000
7all-MiniLM-L6-v2ProblemCrossremove30020.61.00.60.7500
8all-MiniLM-L6-v2O-Positiveadd30020.61.00.60.7500
9all-MiniLM-L6-v2O-Positiveremove02300.60.00.00.0000
10all-MiniLM-L6-v2O-Negativeadd05000.00.00.00.0000
11all-MiniLM-L6-v2O-Negativeremove20030.41.00.40.5714
12all-MiniLM-L6-v2WithinSpaceadd30020.61.00.60.7500
13all-MiniLM-L6-v2WithinSpaceremove01400.80.00.00.0000
14all-MiniLM-L6-v2OutsideSpaceadd10040.21.00.20.3333
15all-MiniLM-L6-v2OutsideSpaceremove01400.80.00.00.0000
16bge-large-en-v1.5Unambiguousadd50001.01.01.01.0000
17bge-large-en-v1.5Unambiguousremove05000.00.00.00.0000
18bge-large-en-v1.5Ambiguousadd50001.01.01.01.0000
19bge-large-en-v1.5Ambiguousremove50001.01.01.01.0000
20bge-large-en-v1.5NoProblemadd50001.01.01.01.0000
21bge-large-en-v1.5NoProblemremove05000.00.00.00.0000
22bge-large-en-v1.5ProblemCrossadd05000.00.00.00.0000
23bge-large-en-v1.5ProblemCrossremove50001.01.01.01.0000
24bge-large-en-v1.5O-Positiveadd50001.01.01.01.0000
25bge-large-en-v1.5O-Positiveremove05000.00.00.00.0000
26bge-large-en-v1.5O-Negativeadd05000.00.00.00.0000
27bge-large-en-v1.5O-Negativeremove50001.01.01.01.0000
28bge-large-en-v1.5WithinSpaceadd50001.01.01.01.0000
29bge-large-en-v1.5WithinSpaceremove05000.00.00.00.0000
30bge-large-en-v1.5OutsideSpaceadd50001.01.01.01.0000
31bge-large-en-v1.5OutsideSpaceremove05000.00.00.00.0000
32multilingual-e5-largeUnambiguousadd00050.00.00.00.0000
33multilingual-e5-largeUnambiguousremove05000.00.00.00.0000
34multilingual-e5-largeAmbiguousadd00050.00.00.00.0000
35multilingual-e5-largeAmbiguousremove50001.01.01.01.0000
36multilingual-e5-largeNoProblemadd00050.00.00.00.0000
37multilingual-e5-largeNoProblemremove05000.00.00.00.0000
38multilingual-e5-largeProblemCrossadd00501.00.00.00.0000
39multilingual-e5-largeProblemCrossremove50001.01.01.01.0000
40multilingual-e5-largeO-Positiveadd00050.00.00.00.0000
41multilingual-e5-largeO-Positiveremove05000.00.00.00.0000
42multilingual-e5-largeO-Negativeadd00501.00.00.00.0000
43multilingual-e5-largeO-Negativeremove50001.01.01.01.0000
44multilingual-e5-largeWithinSpaceadd00050.00.00.00.0000
45multilingual-e5-largeWithinSpaceremove05000.00.00.00.0000
46multilingual-e5-largeOutsideSpaceadd00050.00.00.00.0000
47multilingual-e5-largeOutsideSpaceremove05000.00.00.00.0000
\n", + "
" + ], + "text/plain": [ + " model_id test_sub_type task TP FP TN FN accuracy \\\n", + "0 all-MiniLM-L6-v2 Unambiguous add 3 0 0 2 0.6 \n", + "1 all-MiniLM-L6-v2 Unambiguous remove 0 2 3 0 0.6 \n", + "2 all-MiniLM-L6-v2 Ambiguous add 1 0 0 4 0.2 \n", + "3 all-MiniLM-L6-v2 Ambiguous remove 1 0 0 4 0.2 \n", + "4 all-MiniLM-L6-v2 NoProblem add 4 0 0 1 0.8 \n", + "5 all-MiniLM-L6-v2 NoProblem remove 0 1 4 0 0.8 \n", + "6 all-MiniLM-L6-v2 ProblemCross add 0 2 3 0 0.6 \n", + "7 all-MiniLM-L6-v2 ProblemCross remove 3 0 0 2 0.6 \n", + "8 all-MiniLM-L6-v2 O-Positive add 3 0 0 2 0.6 \n", + "9 all-MiniLM-L6-v2 O-Positive remove 0 2 3 0 0.6 \n", + "10 all-MiniLM-L6-v2 O-Negative add 0 5 0 0 0.0 \n", + "11 all-MiniLM-L6-v2 O-Negative remove 2 0 0 3 0.4 \n", + "12 all-MiniLM-L6-v2 WithinSpace add 3 0 0 2 0.6 \n", + "13 all-MiniLM-L6-v2 WithinSpace remove 0 1 4 0 0.8 \n", + "14 all-MiniLM-L6-v2 OutsideSpace add 1 0 0 4 0.2 \n", + "15 all-MiniLM-L6-v2 OutsideSpace remove 0 1 4 0 0.8 \n", + "16 bge-large-en-v1.5 Unambiguous add 5 0 0 0 1.0 \n", + "17 bge-large-en-v1.5 Unambiguous remove 0 5 0 0 0.0 \n", + "18 bge-large-en-v1.5 Ambiguous add 5 0 0 0 1.0 \n", + "19 bge-large-en-v1.5 Ambiguous remove 5 0 0 0 1.0 \n", + "20 bge-large-en-v1.5 NoProblem add 5 0 0 0 1.0 \n", + "21 bge-large-en-v1.5 NoProblem remove 0 5 0 0 0.0 \n", + "22 bge-large-en-v1.5 ProblemCross add 0 5 0 0 0.0 \n", + "23 bge-large-en-v1.5 ProblemCross remove 5 0 0 0 1.0 \n", + "24 bge-large-en-v1.5 O-Positive add 5 0 0 0 1.0 \n", + "25 bge-large-en-v1.5 O-Positive remove 0 5 0 0 0.0 \n", + "26 bge-large-en-v1.5 O-Negative add 0 5 0 0 0.0 \n", + "27 bge-large-en-v1.5 O-Negative remove 5 0 0 0 1.0 \n", + "28 bge-large-en-v1.5 WithinSpace add 5 0 0 0 1.0 \n", + "29 bge-large-en-v1.5 WithinSpace remove 0 5 0 0 0.0 \n", + "30 bge-large-en-v1.5 OutsideSpace add 5 0 0 0 1.0 \n", + "31 bge-large-en-v1.5 OutsideSpace remove 0 5 0 0 0.0 \n", + "32 multilingual-e5-large Unambiguous add 0 0 0 5 0.0 \n", + "33 multilingual-e5-large Unambiguous remove 0 5 0 0 0.0 \n", + "34 multilingual-e5-large Ambiguous add 0 0 0 5 0.0 \n", + "35 multilingual-e5-large Ambiguous remove 5 0 0 0 1.0 \n", + "36 multilingual-e5-large NoProblem add 0 0 0 5 0.0 \n", + "37 multilingual-e5-large NoProblem remove 0 5 0 0 0.0 \n", + "38 multilingual-e5-large ProblemCross add 0 0 5 0 1.0 \n", + "39 multilingual-e5-large ProblemCross remove 5 0 0 0 1.0 \n", + "40 multilingual-e5-large O-Positive add 0 0 0 5 0.0 \n", + "41 multilingual-e5-large O-Positive remove 0 5 0 0 0.0 \n", + "42 multilingual-e5-large O-Negative add 0 0 5 0 1.0 \n", + "43 multilingual-e5-large O-Negative remove 5 0 0 0 1.0 \n", + "44 multilingual-e5-large WithinSpace add 0 0 0 5 0.0 \n", + "45 multilingual-e5-large WithinSpace remove 0 5 0 0 0.0 \n", + "46 multilingual-e5-large OutsideSpace add 0 0 0 5 0.0 \n", + "47 multilingual-e5-large OutsideSpace remove 0 5 0 0 0.0 \n", + "\n", + " precision recall f1_score \n", + "0 1.0 0.6 0.7500 \n", + "1 0.0 0.0 0.0000 \n", + "2 1.0 0.2 0.3333 \n", + "3 1.0 0.2 0.3333 \n", + "4 1.0 0.8 0.8889 \n", + "5 0.0 0.0 0.0000 \n", + "6 0.0 0.0 0.0000 \n", + "7 1.0 0.6 0.7500 \n", + "8 1.0 0.6 0.7500 \n", + "9 0.0 0.0 0.0000 \n", + "10 0.0 0.0 0.0000 \n", + "11 1.0 0.4 0.5714 \n", + "12 1.0 0.6 0.7500 \n", + "13 0.0 0.0 0.0000 \n", + "14 1.0 0.2 0.3333 \n", + "15 0.0 0.0 0.0000 \n", + "16 1.0 1.0 1.0000 \n", + "17 0.0 0.0 0.0000 \n", + "18 1.0 1.0 1.0000 \n", + "19 1.0 1.0 1.0000 \n", + "20 1.0 1.0 1.0000 \n", + "21 0.0 0.0 0.0000 \n", + "22 0.0 0.0 0.0000 \n", + "23 1.0 1.0 1.0000 \n", + "24 1.0 1.0 1.0000 \n", + "25 0.0 0.0 0.0000 \n", + "26 0.0 0.0 0.0000 \n", + "27 1.0 1.0 1.0000 \n", + "28 1.0 1.0 1.0000 \n", + "29 0.0 0.0 0.0000 \n", + "30 1.0 1.0 1.0000 \n", + "31 0.0 0.0 0.0000 \n", + "32 0.0 0.0 0.0000 \n", + "33 0.0 0.0 0.0000 \n", + "34 0.0 0.0 0.0000 \n", + "35 1.0 1.0 1.0000 \n", + "36 0.0 0.0 0.0000 \n", + "37 0.0 0.0 0.0000 \n", + "38 0.0 0.0 0.0000 \n", + "39 1.0 1.0 1.0000 \n", + "40 0.0 0.0 0.0000 \n", + "41 0.0 0.0 0.0000 \n", + "42 0.0 0.0 0.0000 \n", + "43 1.0 1.0 1.0000 \n", + "44 0.0 0.0 0.0000 \n", + "45 0.0 0.0 0.0000 \n", + "46 0.0 0.0 0.0000 \n", + "47 0.0 0.0 0.0000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subtype_metrics = []\n", + "\n", + "for model_id in MODEL_CONFIGS:\n", + " model_df = results_df[results_df[\"model_id\"] == model_id]\n", + "\n", + " for sub_type in model_df[\"test_sub_type\"].unique():\n", + " sub_df = model_df[model_df[\"test_sub_type\"] == sub_type]\n", + "\n", + " add_metrics = compute_metrics(sub_df[\"add_outcome\"])\n", + " remove_metrics = compute_metrics(sub_df[\"remove_outcome\"])\n", + "\n", + " subtype_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"test_sub_type\": sub_type,\n", + " \"task\": \"add\",\n", + " **add_metrics,\n", + " })\n", + " subtype_metrics.append({\n", + " \"model_id\": model_id.split(\"/\")[-1],\n", + " \"test_sub_type\": sub_type,\n", + " \"task\": \"remove\",\n", + " **remove_metrics,\n", + " })\n", + "\n", + "subtype_metrics_df = pd.DataFrame(subtype_metrics)\n", + "print(\"=== Metrics Breakdown by Test_SubType ===\")\n", + "subtype_metrics_df" + ] + }, + { + "cell_type": "markdown", + "id": "c1fca18a", + "metadata": {}, + "source": [ + "## 8. Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0321a918", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "matplotlib.rcParams.update({\"figure.dpi\": 120})" + ] + }, + { + "cell_type": "markdown", + "id": "eb5fedb1", + "metadata": {}, + "source": [ + "### 8.1 Overall F1-Score Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "18770cdf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_overall_f1(overall_metrics_df: pd.DataFrame):\n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n", + "\n", + " for idx, task in enumerate([\"add\", \"remove\"]):\n", + " task_df = overall_metrics_df[overall_metrics_df[\"task\"] == task]\n", + " bars = axes[idx].bar(\n", + " task_df[\"model_id\"], task_df[\"f1_score\"], color=[\"#4589ff\", \"#08bdba\", \"#ee5396\"]\n", + " )\n", + " axes[idx].set_title(f\"F1-Score ({task.capitalize()} Recommendations)\")\n", + " axes[idx].set_ylabel(\"F1-Score\")\n", + " axes[idx].set_ylim(0, 1.05)\n", + " axes[idx].tick_params(axis=\"x\", rotation=25)\n", + "\n", + " for bar in bars:\n", + " axes[idx].text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " bar.get_height() + 0.02,\n", + " f\"{bar.get_height():.2f}\",\n", + " ha=\"center\", va=\"bottom\", fontsize=9,\n", + " )\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_overall_f1(overall_metrics_df)" + ] + }, + { + "cell_type": "markdown", + "id": "b4a0e745", + "metadata": {}, + "source": [ + "### 8.2 Full Metrics Comparison (Accuracy, Precision, Recall, F1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b94ca3cc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_full_metrics(overall_metrics_df: pd.DataFrame):\n", + " metric_cols = [\"accuracy\", \"precision\", \"recall\", \"f1_score\"]\n", + " fig, axes = plt.subplots(2, 4, figsize=(18, 8), sharey=True)\n", + "\n", + " colors = [\"#4589ff\", \"#08bdba\", \"#ee5396\"]\n", + "\n", + " for row_idx, task in enumerate([\"add\", \"remove\"]):\n", + " task_df = overall_metrics_df[overall_metrics_df[\"task\"] == task]\n", + " for col_idx, metric in enumerate(metric_cols):\n", + " ax = axes[row_idx][col_idx]\n", + " bars = ax.bar(task_df[\"model_id\"], task_df[metric], color=colors)\n", + " ax.set_title(f\"{metric.replace('_', ' ').title()} ({task.capitalize()})\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.tick_params(axis=\"x\", rotation=30)\n", + "\n", + " for bar in bars:\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " bar.get_height() + 0.02,\n", + " f\"{bar.get_height():.2f}\",\n", + " ha=\"center\", va=\"bottom\", fontsize=8,\n", + " )\n", + "\n", + " axes[0][0].set_ylabel(\"Add\")\n", + " axes[1][0].set_ylabel(\"Remove\")\n", + " plt.suptitle(\"Embedding Model Comparison: Full Metrics\", fontsize=14, y=1.01)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_full_metrics(overall_metrics_df)" + ] + }, + { + "cell_type": "markdown", + "id": "793fa6dd", + "metadata": {}, + "source": [ + "### 8.3 F1-Score by PromptTest_Type (Heatmap)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e0e6e808", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Arihant\\AppData\\Local\\Temp\\ipykernel_2600\\3630575564.py:24: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_f1_heatmap(breakdown_metrics_df: pd.DataFrame):\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " for idx, task in enumerate([\"add\", \"remove\"]):\n", + " task_df = breakdown_metrics_df[breakdown_metrics_df[\"task\"] == task]\n", + " pivot = task_df.pivot(\n", + " index=\"model_id\", columns=\"prompt_test_type\", values=\"f1_score\"\n", + " )\n", + " im = axes[idx].imshow(pivot.values, cmap=\"YlGnBu\", aspect=\"auto\", vmin=0, vmax=1)\n", + "\n", + " axes[idx].set_xticks(range(len(pivot.columns)))\n", + " axes[idx].set_xticklabels(pivot.columns, rotation=30, ha=\"right\")\n", + " axes[idx].set_yticks(range(len(pivot.index)))\n", + " axes[idx].set_yticklabels(pivot.index)\n", + " axes[idx].set_title(f\"F1-Score Heatmap ({task.capitalize()})\")\n", + "\n", + " for i in range(len(pivot.index)):\n", + " for j in range(len(pivot.columns)):\n", + " val = pivot.values[i, j]\n", + " color = \"white\" if val > 0.5 else \"black\"\n", + " axes[idx].text(j, i, f\"{val:.2f}\", ha=\"center\", va=\"center\", color=color, fontsize=9)\n", + "\n", + " fig.colorbar(im, ax=axes, shrink=0.6, label=\"F1-Score\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_f1_heatmap(breakdown_metrics_df)" + ] + }, + { + "cell_type": "markdown", + "id": "cd600974", + "metadata": {}, + "source": [ + "### 8.4 Confusion Matrix Counts" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d141f111", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_confusion_counts(overall_metrics_df: pd.DataFrame):\n", + " outcome_cols = [\"TP\", \"FP\", \"TN\", \"FN\"]\n", + " models = overall_metrics_df[\"model_id\"].unique()\n", + " fig, axes = plt.subplots(len(models), 2, figsize=(12, 4 * len(models)), squeeze=False)\n", + "\n", + " colors = {\"TP\": \"#198038\", \"FP\": \"#da1e28\", \"TN\": \"#4589ff\", \"FN\": \"#f1c21b\"}\n", + "\n", + " for row_idx, model in enumerate(models):\n", + " for col_idx, task in enumerate([\"add\", \"remove\"]):\n", + " row_data = overall_metrics_df[\n", + " (overall_metrics_df[\"model_id\"] == model)\n", + " & (overall_metrics_df[\"task\"] == task)\n", + " ].iloc[0]\n", + "\n", + " values = [row_data[c] for c in outcome_cols]\n", + " bar_colors = [colors[c] for c in outcome_cols]\n", + "\n", + " ax = axes[row_idx][col_idx]\n", + " bars = ax.bar(outcome_cols, values, color=bar_colors)\n", + " ax.set_title(f\"{model} - {task.capitalize()}\")\n", + " ax.set_ylabel(\"Count\")\n", + "\n", + " for bar in bars:\n", + " ax.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " bar.get_height() + 0.3,\n", + " str(int(bar.get_height())),\n", + " ha=\"center\", va=\"bottom\", fontsize=9,\n", + " )\n", + "\n", + " plt.suptitle(\"Confusion Matrix Counts per Model\", fontsize=14, y=1.01)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_confusion_counts(overall_metrics_df)" + ] + }, + { + "cell_type": "markdown", + "id": "011a29e8", + "metadata": {}, + "source": [ + "## 9. Detailed Results Inspection" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6b01f927", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Misclassified: Add ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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prompt_idmodel_idprompt_test_typetest_sub_typeadd_outcome
01multilingual-e5-largeEmbeddedAmbiguityUnambiguousFN
12all-MiniLM-L6-v2EmbeddedAmbiguityUnambiguousFN
22multilingual-e5-largeEmbeddedAmbiguityUnambiguousFN
33all-MiniLM-L6-v2EmbeddedAmbiguityUnambiguousFN
43multilingual-e5-largeEmbeddedAmbiguityUnambiguousFN
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5738all-MiniLM-L6-v2NoveltyOutsideSpaceFN
5838multilingual-e5-largeNoveltyOutsideSpaceFN
5939multilingual-e5-largeNoveltyOutsideSpaceFN
6040all-MiniLM-L6-v2NoveltyOutsideSpaceFN
6140multilingual-e5-largeNoveltyOutsideSpaceFN
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62 rows × 5 columns

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" + ], + "text/plain": [ + " prompt_id model_id prompt_test_type test_sub_type \\\n", + "0 1 multilingual-e5-large EmbeddedAmbiguity Unambiguous \n", + "1 2 all-MiniLM-L6-v2 EmbeddedAmbiguity Unambiguous \n", + "2 2 multilingual-e5-large EmbeddedAmbiguity Unambiguous \n", + "3 3 all-MiniLM-L6-v2 EmbeddedAmbiguity Unambiguous \n", + "4 3 multilingual-e5-large EmbeddedAmbiguity Unambiguous \n", + ".. ... ... ... ... \n", + "57 38 all-MiniLM-L6-v2 Novelty OutsideSpace \n", + "58 38 multilingual-e5-large Novelty OutsideSpace \n", + "59 39 multilingual-e5-large Novelty OutsideSpace \n", + "60 40 all-MiniLM-L6-v2 Novelty OutsideSpace \n", + "61 40 multilingual-e5-large Novelty OutsideSpace \n", + "\n", + " add_outcome \n", + "0 FN \n", + "1 FN \n", + "2 FN \n", + "3 FN \n", + "4 FN \n", + ".. ... \n", + "57 FN \n", + "58 FN \n", + "59 FN \n", + "60 FN \n", + "61 FN \n", + "\n", + "[62 rows x 5 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Misclassified: Remove ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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prompt_idmodel_idprompt_test_typetest_sub_typeremove_outcome
01bge-large-en-v1.5EmbeddedAmbiguityUnambiguousFP
11multilingual-e5-largeEmbeddedAmbiguityUnambiguousFP
22all-MiniLM-L6-v2EmbeddedAmbiguityUnambiguousFP
32bge-large-en-v1.5EmbeddedAmbiguityUnambiguousFP
42multilingual-e5-largeEmbeddedAmbiguityUnambiguousFP
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6139all-MiniLM-L6-v2NoveltyOutsideSpaceFP
6239bge-large-en-v1.5NoveltyOutsideSpaceFP
6339multilingual-e5-largeNoveltyOutsideSpaceFP
6440bge-large-en-v1.5NoveltyOutsideSpaceFP
6540multilingual-e5-largeNoveltyOutsideSpaceFP
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66 rows × 5 columns

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" + ], + "text/plain": [ + " prompt_id model_id prompt_test_type test_sub_type \\\n", + "0 1 bge-large-en-v1.5 EmbeddedAmbiguity Unambiguous \n", + "1 1 multilingual-e5-large EmbeddedAmbiguity Unambiguous \n", + "2 2 all-MiniLM-L6-v2 EmbeddedAmbiguity Unambiguous \n", + "3 2 bge-large-en-v1.5 EmbeddedAmbiguity Unambiguous \n", + "4 2 multilingual-e5-large EmbeddedAmbiguity Unambiguous \n", + ".. ... ... ... ... \n", + "61 39 all-MiniLM-L6-v2 Novelty OutsideSpace \n", + "62 39 bge-large-en-v1.5 Novelty OutsideSpace \n", + "63 39 multilingual-e5-large Novelty OutsideSpace \n", + "64 40 bge-large-en-v1.5 Novelty OutsideSpace \n", + "65 40 multilingual-e5-large Novelty OutsideSpace \n", + "\n", + " remove_outcome \n", + "0 FP \n", + "1 FP \n", + "2 FP \n", + "3 FP \n", + "4 FP \n", + ".. ... \n", + "61 FP \n", + "62 FP \n", + "63 FP \n", + "64 FP \n", + "65 FP \n", + "\n", + "[66 rows x 5 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def show_misclassified(results_df: pd.DataFrame, task: str = \"add\"):\n", + " outcome_col = f\"{task}_outcome\"\n", + " misclassified = results_df[\n", + " results_df[outcome_col].isin([\"FP\", \"FN\"])\n", + " ][[\"prompt_id\", \"model_id\", \"prompt_test_type\", \"test_sub_type\", outcome_col]].copy()\n", + " misclassified[\"model_id\"] = misclassified[\"model_id\"].apply(lambda x: x.split(\"/\")[-1])\n", + " return misclassified.sort_values([\"prompt_id\", \"model_id\"]).reset_index(drop=True)\n", + "\n", + "\n", + "print(\"=== Misclassified: Add ===\")\n", + "display(show_misclassified(results_df, \"add\"))\n", + "\n", + "print(\"\\n=== Misclassified: Remove ===\")\n", + "display(show_misclassified(results_df, \"remove\"))" + ] + }, + { + "cell_type": "markdown", + "id": "ec384292", + "metadata": {}, + "source": [ + "## 10. Export Results" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c9ae3457", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results exported to ..\\red-team\n" + ] + } + ], + "source": [ + "OUTPUT_DIR = os.path.join(\"..\", \"red-team\")\n", + "\n", + "overall_metrics_df.to_csv(\n", + " os.path.join(OUTPUT_DIR, \"embedding_comparison_overall_metrics.csv\"), index=False\n", + ")\n", + "breakdown_metrics_df.to_csv(\n", + " os.path.join(OUTPUT_DIR, \"embedding_comparison_breakdown_metrics.csv\"), index=False\n", + ")\n", + "subtype_metrics_df.to_csv(\n", + " os.path.join(OUTPUT_DIR, \"embedding_comparison_subtype_metrics.csv\"), index=False\n", + ")\n", + "\n", + "print(f\"Results exported to {OUTPUT_DIR}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab53611c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.10.11)", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}