From 0006241bda7f35b954ff11218938d2b32f434fbb Mon Sep 17 00:00:00 2001 From: Yusuf Efe <52610406+efeyusuf@users.noreply.github.com> Date: Mon, 22 Jul 2024 14:39:26 -0400 Subject: [PATCH] Add files via upload --- ...ucination-Detection-Extended_Yusuf-Efe.pdf | Bin 0 -> 1423728 bytes Hallucination-Detector-Yusuf-Efe.ipynb | 3832 +++++++++++++++++ Hallucination_CSQA_Yusuf_Efe.ipynb | 1921 +++++++++ Hallucination_GSM8K_Yusuf_Efe.ipynb | 1712 ++++++++ 4 files changed, 7465 insertions(+) create mode 100644 Hallucination-Detection-Extended_Yusuf-Efe.pdf create mode 100644 Hallucination-Detector-Yusuf-Efe.ipynb create mode 100644 Hallucination_CSQA_Yusuf_Efe.ipynb create mode 100644 Hallucination_GSM8K_Yusuf_Efe.ipynb diff --git a/Hallucination-Detection-Extended_Yusuf-Efe.pdf b/Hallucination-Detection-Extended_Yusuf-Efe.pdf new file mode 100644 index 0000000000000000000000000000000000000000..bf21092204dcbd041d2c563e3ef6f85171e3da7b GIT binary patch literal 1423728 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Method\n", + "# 6) Answer Clustering and Semantic Entropy Calculation\n", + "# 7) Hallucination Detection Model - Trained to predict the label\n", + "# 8) Hallucination Detection Model - Trained to predict the semantic entropy score\n", + "# 9) Discussions\n" + ], + "metadata": { + "id": "5l_CnbYrzW42" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 0) Environment Set Up\n", + "Installing/importing required libraries and setting up Hugginface and Google Drive Connections" + ], + "metadata": { + "id": "QqvzX3Nbw3QK" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install transformers datasets torch tqdm scikit-learn matplotlib accelerate" + ], + "metadata": { + "collapsed": true, + "id": "xixufJkFznfS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import accelerate\n", + "from torch.nn import functional as F\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification\n", + "from datasets import load_from_disk, load_dataset\n", + "import random\n", + "import math\n", + "import os\n", + "from google.colab import drive\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split" + ], + "metadata": { + "id": "_CmzVHNozwZ4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "token = os.environ['HF_TOKEN']" + ], + "metadata": { + "id": "m0_mZ77U0y-H" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HYjJDHzv4SUC", + "outputId": "cb7c8488-f2ce-47f0-8765-31a5601bbf42" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Define the base path in your Google Drive\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "\n", + "# Create the directory if it doesn't exist\n", + "import os\n", + "os.makedirs(base_path, exist_ok=True)" + ], + "metadata": { + "id": "RWcLr5HE-0ca" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 1) Data Preprocessing\n", + "In the scope of the project, we decided to focus on questions that do not require context to be answered. Therefore, we selected TriviQA's data samples that lack context content (it was reported that these questions are designed to be answered without context).\n", + "\n", + "Additionally, the test_dataset part of TriviaQA does not publicly provide labels to maintain a private leaderboard. As a result, we created our test data using the validation split of the dataset.\n", + "\n", + "We generated the training and validation splits using the training split of the TriviaQA dataset." + ], + "metadata": { + "id": "6LCYay2KzWyY" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "3QeEdTo9zVmt" + }, + "outputs": [], + "source": [ + "#IF THE DATA SET IS ALREADY PROCESSED, SKIP FROM HERE 1:\n", + "train_dataset = load_dataset(\"mandarjoshi/trivia_qa\", \"rc\", split=\"train\", token=token)\n", + "valid_dataset = load_dataset(\"mandarjoshi/trivia_qa\", \"rc\", split=\"validation\", token=token)\n", + "test_dataset = load_dataset(\"mandarjoshi/trivia_qa\", \"rc\", split=\"test\", token=token)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Contexts are merged and the labels are reformatted\n", + "def format_dataset(example):\n", + " example[\"context\"] = \" \".join((\"\\n\".join(example[\"entity_pages\"][\"wiki_context\"])).split(\"\\n\"))\n", + " example[\"targets\"] = example[\"answer\"][\"aliases\"]\n", + " example[\"norm_target\"] = example[\"answer\"][\"normalized_value\"]\n", + " return example\n", + "\n", + "# Applying the above function to the data set\n", + "train_ds = train_dataset.map(format_dataset, remove_columns=[\"search_results\", \"question_source\", \"entity_pages\", \"answer\", \"question_id\"])\n", + "valid_ds = valid_dataset.map(format_dataset, remove_columns=[\"search_results\", \"question_source\", \"entity_pages\", \"answer\", \"question_id\"])\n", + "\n", + "# Selecting the samples with empty context\n", + "train_ds = train_ds.filter(lambda x: len(x['context']) == 0)\n", + "valid_ds = valid_ds.filter(lambda x: len(x['context']) == 0)" + ], + "metadata": { + "id": "CG9B0k-OnArY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Utilizing these, let's define our training/validation/test splits" + ], + "metadata": { + "id": "JTEdbi-40uPE" + } + }, + { + "cell_type": "code", + "source": [ + "# Shuffling the data sets\n", + "train_ds = train_ds.shuffle(seed=42)\n", + "valid_ds = valid_ds.shuffle(seed=42)\n", + "\n", + "# Select the first 6000 rows of train_ds as the training dataset\n", + "train_split = train_ds.select(range(6000))\n", + "\n", + "# Select the next 2000 rows of train_ds as the validation dataset\n", + "valid_split = train_ds.select(range(6000, 8000))\n", + "\n", + "# Use the shuffled valid_ds as the test dataset\n", + "test_split = valid_ds.select(range(1800))" + ], + "metadata": { + "id": "bPBEaKTU0tiB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(f\"training split size: {len(train_split)}\")\n", + "print(f\"validation split size: {len(valid_split)}\")\n", + "print(f\"test split size: {len(test_split)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gY6sMZTF2eMn", + "outputId": "c1fc074f-d274-4cfa-c81e-e7f9e3c2f67d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "training split size: 6000\n", + "validation split size: 2000\n", + "test split size: 1800\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here, we are saving our data sets to Drive to make the future environment setup easier" + ], + "metadata": { + "id": "7uv5jAwgz_nP" + } + }, + { + "cell_type": "code", + "source": [ + "# Define the base path for the nocontext_subset folder in your Google Drive\n", + "nocontext_subset_path = os.path.join(base_path, \"nocontext_subset\")\n", + "\n", + "# Create the directory if it doesn't exist\n", + "os.makedirs(nocontext_subset_path, exist_ok=True)\n", + "\n", + "# Define file paths\n", + "train_file_path = os.path.join(nocontext_subset_path, \"train_processed.jsonl\")\n", + "valid_file_path = os.path.join(nocontext_subset_path, \"valid_processed.jsonl\")\n", + "test_file_path = os.path.join(nocontext_subset_path, \"test_processed.jsonl\")\n", + "\n", + "# Save datasets to JSONL\n", + "train_split.to_json(train_file_path)\n", + "valid_split.to_json(valid_file_path)\n", + "test_split.to_json(test_file_path)\n", + "\n", + "\n", + "print(f\"Train dataset saved to {train_file_path}\")\n", + "print(f\"Validation dataset saved to {valid_file_path}\")\n", + "print(f\"Test dataset saved to {test_file_path}\")" + ], + "metadata": { + "id": "pofZb36kosQo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Loading the data from Google Drive" + ], + "metadata": { + "id": "gs95MtiE0Hm3" + } + }, + { + "cell_type": "code", + "source": [ + "#IF THE DATA SET IS ALREADY PROCESSED, SKIP TO HERE 1:\n", + "\n", + "# Define the base path for the nocontext_subset folder in your Google Drive\n", + "nocontext_subset_path = os.path.join(base_path, \"nocontext_subset\")\n", + "\n", + "# Define file paths\n", + "train_file_path = os.path.join(nocontext_subset_path, \"train_processed.jsonl\")\n", + "valid_file_path = os.path.join(nocontext_subset_path, \"valid_processed.jsonl\")\n", + "test_file_path = os.path.join(nocontext_subset_path, \"test_processed.jsonl\")\n", + "\n", + "# Load the datasets from JSONL files\n", + "train_dataset = load_dataset('json', data_files=train_file_path, split='train')\n", + "valid_dataset = load_dataset('json', data_files=valid_file_path, split='train')\n", + "test_dataset = load_dataset('json', data_files=test_file_path, split='train')" + ], + "metadata": { + "id": "xDbOQjFVxDEm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(f\"training split size: {len(train_dataset)}\")\n", + "print(f\"validation split size: {len(valid_dataset)}\")\n", + "print(f\"test split size: {len(test_dataset)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "opVgxPCu7Ezw", + "outputId": "ed086f02-648f-436f-ae53-4a9e8a189534" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "training split size: 6000\n", + "validation split size: 2000\n", + "test split size: 1800\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def check_dataset(dataset, name):\n", + " print(f\"\\nChecking {name} dataset:\")\n", + " print(f\"Number of samples: {len(dataset)}\")\n", + " print(f\"Features: {dataset.features}\")\n", + "\n", + " # Check for empty contexts\n", + " empty_contexts = sum(1 for sample in dataset if len(sample['context']) == 0)\n", + " print(f\"Number of samples with empty contexts: {empty_contexts}\")\n", + "\n", + " # Print a random sample\n", + " random_sample = random.choice(dataset)\n", + " print(\"\\nRandom sample:\")\n", + " print(f\"Question: {random_sample['question']}\")\n", + " print(f\"Context (first 100 characters): {random_sample['context'][:100]}...\")\n", + " print(f\"Targets: {random_sample['targets']}\")\n", + " print(f\"Normalized Target: {random_sample['norm_target']}\")\n", + "\n", + " # Check data types\n", + " print(\"\\nData types:\")\n", + " for key, value in random_sample.items():\n", + " print(f\"{key}: {type(value)}\")\n", + "\n", + "\n", + "# Check each dataset\n", + "for dataset, name in zip([train_dataset, valid_dataset, test_dataset], ['Training', 'Validation', 'Test']):\n", + " check_dataset(dataset, name)\n", + "\n", + "# Additional overall checks\n", + "print(\"\\nOverall checks:\")\n", + "print(f\"Total samples: {len(train_dataset) + len(valid_dataset) + len(test_dataset)}\")\n", + "print(f\"Expected total: 10000\")\n", + "\n", + "# Check for overlap between sets\n", + "train_questions = set(train_dataset['question'])\n", + "valid_questions = set(valid_dataset['question'])\n", + "test_questions = set(test_dataset['question'])\n", + "\n", + "overlap_train_valid = train_questions.intersection(valid_questions)\n", + "overlap_train_test = train_questions.intersection(test_questions)\n", + "overlap_valid_test = valid_questions.intersection(test_questions)\n", + "\n", + "print(f\"\\nOverlap between Train and Validation sets: {len(overlap_train_valid)}\")\n", + "print(f\"Overlap between Train and Test sets: {len(overlap_train_test)}\")\n", + "print(f\"Overlap between Validation and Test sets: {len(overlap_valid_test)}\")" + ], + "metadata": { + "id": "qqaoE2aR51Ms", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aa19203a-756f-4a54-82d8-5ded15a8a6f1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Checking Training dataset:\n", + "Number of samples: 6000\n", + "Features: {'question': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'targets': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'norm_target': Value(dtype='string', id=None)}\n", + "Number of samples with empty contexts: 6000\n", + "\n", + "Random sample:\n", + "Question: What Anglicized German word originally from 'harm-joy' refers to pleasure derived from misfortunes of others?\n", + "Context (first 100 characters): ...\n", + "Targets: ['Schadenfraude', 'Schaudenfreuda', 'Schaudenfreude', 'Schadenfroh', 'Morose delectation', 'Schauenfruede', 'Freudenschade', 'Delectatio morosa', 'Schadenfreud', 'Epichaerecacia', 'Epicaricacy', 'Schadenfreund', 'Chardenfreuder', 'Schaudenfraude', 'Schaeunfreude', 'Schadenhausenfreude', 'Schadenfruede', 'Schauenfreude', 'Schadenfreude experiment', 'Shadenfreude', 'Schadenfreuden', 'Epikairekakia', 'Schadenfreude']\n", + "Normalized Target: schadenfreude\n", + "\n", + "Data types:\n", + "question: \n", + "context: \n", + "targets: \n", + "norm_target: \n", + "\n", + "Checking Validation dataset:\n", + "Number of samples: 2000\n", + "Features: {'question': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'targets': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'norm_target': Value(dtype='string', id=None)}\n", + "Number of samples with empty contexts: 2000\n", + "\n", + "Random sample:\n", + "Question: What was the famous slogan used by Clinton's campaign team in the 1992 Presidential campaign to remind them of the Key issue of the election?\n", + "Context (first 100 characters): ...\n", + "Targets: ['Economy stupid', 'Economy, stupid', \"It's the economy, stupid!\", \"It's economy, stupid\", \"It's the constitution, stupid\", \"It's the economy, stupid\", 'It is economy stupid', 'It is the economy, stupid', 'The economy, stupid', \"It's economy stupid\", '\"\"\"IT\\'S THE ECONOMY, STUPID\"\"\"', 'It is economy, stupid', \"It's the economy stupid\", 'It is the economy stupid']\n", + "Normalized Target: it s economy stupid\n", + "\n", + "Data types:\n", + "question: \n", + "context: \n", + "targets: \n", + "norm_target: \n", + "\n", + "Checking Test dataset:\n", + "Number of samples: 1800\n", + "Features: {'question': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'targets': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'norm_target': Value(dtype='string', id=None)}\n", + "Number of samples with empty contexts: 1800\n", + "\n", + "Random sample:\n", + "Question: Who was the Roman goddess of war, regarded as the sister of Mars?\n", + "Context (first 100 characters): ...\n", + "Targets: ['Bellona', 'BELLONA', 'Bellona (disambiguation)']\n", + "Normalized Target: bellona\n", + "\n", + "Data types:\n", + "question: \n", + "context: \n", + "targets: \n", + "norm_target: \n", + "\n", + "Overall checks:\n", + "Total samples: 9800\n", + "Expected total: 10000\n", + "\n", + "Overlap between Train and Validation sets: 0\n", + "Overlap between Train and Test sets: 0\n", + "Overlap between Validation and Test sets: 0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The checks of the dataset splits have been completed, and there are no overlaps between the splits (ensured by verifying that there are no duplicates in the dataset).\n", + "\n", + "Thus, data preprocessing, splits, and duplicate checks are finalized. We can now proceed with the model and tokenizer loading phase." + ], + "metadata": { + "id": "25Yu2ufh-FXD" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 2) Loading the models and the tokenizers" + ], + "metadata": { + "id": "WoTa7n5u77fI" + } + }, + { + "cell_type": "markdown", + "source": [ + "Let us directly get the models and the tokenizers from huggingface hub:" + ], + "metadata": { + "id": "dV1M0E1WVHGl" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading Gemma 2B model and tokenizer...\")\n", + "gemma_tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\", token=token)\n", + "gemma_model = AutoModelForCausalLM.from_pretrained(\n", + " \"google/gemma-2b-it\",\n", + " device_map=\"auto\",\n", + " torch_dtype=torch.bfloat16,\n", + " output_hidden_states=True,\n", + " token=token\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 256, + "referenced_widgets": [ + "d8369814218a490eb984e2351ca1da81", + "08977d5eff334295b5cfc843adefaccc", + "579abe37962344caa0e69754f5c2c8c3", + "f4100cdb6e3d450b86117f39fad3ccea", + "343739c3d1104f048ce81770d32b0eee", + "9dbcdb71e2c54b419bb93e2dc3e33f0b", + "fa4a4e552be14bdaafeef84c54c77254", + "1c4c531e32e940bf84e95d6e3aeb61e5", + "85715cebf1e54cde803387bdcb9a5f4f", + "1c6fdf4f63494f42b0cf392265cce56f", + "791d52b5630340fb8307b8c4e29f8bc8" + ] + }, + "collapsed": true, + "id": "Jg6samrDVkXX", + "outputId": "c7693421-e6ec-43ac-c9aa-6529042f467f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading Gemma 2B model and tokenizer...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n", + "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n", + "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n", + "`config.hidden_activation` if you want to override this behaviour.\n", + "See https://github.com/huggingface/transformers/pull/29402 for more details.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00= threshold:\n", + " group.append(answer)\n", + " matched = True\n", + " break\n", + " if not matched:\n", + " grouped_answers.append([answer])\n", + " return grouped_answers\n", + "\n", + "def group_answers(question, answers, nli_model, nli_tokenizer, device, threshold=0.4):\n", + " return cluster_answers(question, answers, nli_model, nli_tokenizer, device, threshold)" + ], + "metadata": { + "id": "lmOeUIThBBzS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "And the code below is used to calculate the semantic entropy of the generated answers" + ], + "metadata": { + "id": "9IkQwAg7eE_X" + } + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "\n", + "def calculate_entropy(groups):\n", + " total_answers = sum(len(group) for group in groups)\n", + " probabilities = [len(group) / total_answers for group in groups]\n", + "\n", + " entropy = 0\n", + " for p in probabilities:\n", + " if p > 0: # Avoid log(0)\n", + " entropy -= p * math.log2(p) # Using log base 2 for entropy calculation\n", + "\n", + " return entropy" + ], + "metadata": { + "id": "EUUcV1XeBFpp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "In the following, we see an example of Gemma-2B model generating 10 answers for a given question. After that, these answers are clustered into semantic groups and the corresponding semantic entropy is calculated" + ], + "metadata": { + "id": "ph0P4vrzeNgt" + } + }, + { + "cell_type": "code", + "source": [ + "question = \"Who is the author of harry potter novels?\"\n", + "test_question = f\"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "\n", + "Question: What is the famous dance move of Michael Jackson?\n", + "Answer: moonwalk\n", + "\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "\n", + "Question: Who is the first president of United States?\n", + "Answer: george washington\n", + "\n", + "Question: {question}\n", + "Answer : \"\"\"\n", + "alt_answers = generate_alternative_answers(test_question, gemma_model, gemma_tokenizer, device)\n", + "print(alt_answers)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "L-obM6ihBQ7w", + "outputId": "06fc0f65-6856-4c7c-b7b9-2b807120ce71" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['7. J. K. Rowling', 'J.K. Rowling', 'J.K. Rowling', '7. J. K. Rowling', 'J.K. Rowling', 'J.K. Rowling', 'J.K. Rowling', '7. J. K. Rowling', '7. j. k. Rowling', 'J.K. Rowling']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Group the answers\n", + "grouped_answers = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + "print(\"\\nGrouped Answers:\")\n", + "for i, group in enumerate(grouped_answers, 1):\n", + " print(f\"Group {i}: {group}\")\n", + "\n", + "# Calculate entropy\n", + "entropy = calculate_entropy(grouped_answers)\n", + "print(f\"\\nEntropy of answer distribution: {entropy}\")" + ], + "metadata": { + "id": "Yx8RsCCJpju8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "adbfbe4a-b0d2-4757-e378-5117584e6828" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Grouped Answers:\n", + "Group 1: ['7. J. K. Rowling', 'J.K. Rowling', 'J.K. Rowling', '7. J. K. Rowling', 'J.K. Rowling', 'J.K. Rowling', 'J.K. Rowling', '7. J. K. Rowling', '7. j. k. Rowling', 'J.K. Rowling']\n", + "\n", + "Entropy of answer distribution: 0.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's create the batch processing code for these functions" + ], + "metadata": { + "id": "_WRn0U3wlivW" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 5) Batch Processing of the Method" + ], + "metadata": { + "id": "bbAmr8S_mE9b" + } + }, + { + "cell_type": "markdown", + "source": [ + "Adding few shot examples to be used in training and test time" + ], + "metadata": { + "id": "Yxa6jtQZp2sT" + } + }, + { + "cell_type": "code", + "source": [ + "FEW_SHOT_EXAMPLES = \"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "Question: What is the famous dance move of Michael Jackson?\n", + "Answer: moonwalk\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "Question: Who is the first president of United States?\n", + "Answer: george washington\n", + "\"\"\"" + ], + "metadata": { + "id": "gfdT0qDmp16q" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def format_question_with_examples(question):\n", + " return f\"{FEW_SHOT_EXAMPLES} Question: {question}\\nAnswer:\"" + ], + "metadata": { + "id": "RmzMl8sNp8RH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def save_item(item, file_path):\n", + " with open(file_path, 'a') as f:\n", + " json.dump(item, f)\n", + " f.write('\\n')\n", + "\n", + "def load_processed_items(file_path):\n", + " processed_items = []\n", + " if os.path.exists(file_path):\n", + " with open(file_path, 'r') as f:\n", + " for line in f:\n", + " processed_items.append(json.loads(line))\n", + " return processed_items" + ], + "metadata": { + "id": "mxgr4CzLuaZ-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# SKIP THIS IF THE ANSWERS AND EMBEDDINGS ARE ALREADY PRODUCED\n", + "import torch, json\n", + "from tqdm import tqdm\n", + "\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_save_path = os.path.join(base_path, \"processed_data/train_processed.jsonl\")\n", + "valid_save_path = os.path.join(base_path, \"processed_data/valid_processed.jsonl\")\n", + "test_save_path = os.path.join(base_path, \"processed_data/test_processed.jsonl\")\n", + "\n", + "os.makedirs(os.path.dirname(train_save_path), exist_ok=True)\n", + "\n", + "def monitor_memory():\n", + " print(f\"Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n", + " print(f\"Reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB\")\n", + "\n", + "def generate_answers_and_get_embeddings_batch(questions, model, tokenizer, device, temperature=0, additional_tokens=25):\n", + " formatted_questions = [format_question_with_examples(q) for q in questions]\n", + "\n", + " inputs = tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n", + " question_lengths = inputs.attention_mask.sum(dim=1)\n", + " max_length = inputs.input_ids.size(1) + additional_tokens\n", + "\n", + " with torch.no_grad():\n", + " generation_outputs = model.generate(\n", + " **inputs,\n", + " max_length=max_length,\n", + " num_return_sequences=1,\n", + " temperature=temperature,\n", + " return_dict_in_generate=True,\n", + " output_hidden_states=True\n", + " )\n", + "\n", + " generated_sequences = generation_outputs.sequences\n", + " full_responses = tokenizer.batch_decode(generated_sequences, skip_special_tokens=True)\n", + " answers = [full_response[len(formatted_q):].strip() for full_response, formatted_q in zip(full_responses, formatted_questions)]\n", + "\n", + " # Extract the last token's embedding for each question\n", + " last_token_embeddings = [generation_outputs.hidden_states[0][-1][i][question_length-1].cpu()\n", + " for i, question_length in enumerate(question_lengths)]\n", + "\n", + " # Clear intermediate variables\n", + " del inputs, generation_outputs, generated_sequences\n", + " torch.cuda.empty_cache()\n", + "\n", + " return answers, last_token_embeddings\n", + "\n", + "def generate_alternative_answers_batch(questions, model, tokenizer, device, n=10, temperature=0.8, top_p=0.9, additional_tokens=25):\n", + " formatted_questions = [format_question_with_examples(q) for q in questions]\n", + "\n", + " inputs = tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n", + " max_length = inputs.input_ids.size(1) + additional_tokens\n", + "\n", + " with torch.no_grad():\n", + " outputs = model.generate(\n", + " **inputs,\n", + " max_length=max_length,\n", + " num_return_sequences=n,\n", + " temperature=temperature,\n", + " top_p=top_p,\n", + " do_sample=True\n", + " )\n", + "\n", + " # Reshape outputs to group alternative answers for each question\n", + " outputs = outputs.view(len(questions), n, -1)\n", + "\n", + " all_alternative_answers = []\n", + " for i, formatted_q in enumerate(formatted_questions):\n", + " alternative_answers = []\n", + " for output in outputs[i]:\n", + " full_response = tokenizer.decode(output, skip_special_tokens=True)\n", + " answer = full_response[len(formatted_q):].strip()\n", + " alternative_answers.append(answer)\n", + " all_alternative_answers.append(alternative_answers)\n", + "\n", + " # Clear intermediate variables\n", + " del inputs, outputs\n", + " torch.cuda.empty_cache()\n", + "\n", + " return all_alternative_answers\n", + "\n", + "def process_dataset_batch(dataset, gemma_model, gemma_tokenizer, device, save_path, is_test=False, save_interval=100, batch_size=8):\n", + " processed_data = load_processed_items(save_path)\n", + " start_index = len(processed_data)\n", + "\n", + " for i in tqdm(range(start_index, len(dataset), batch_size), initial=start_index//batch_size, total=len(dataset)//batch_size):\n", + " batch = dataset[i:i+batch_size]\n", + "\n", + " if isinstance(batch, str):\n", + " questions = [batch]\n", + " elif isinstance(batch, dict):\n", + " questions = batch.get('question', [])\n", + " if isinstance(questions, str):\n", + " questions = [questions]\n", + " elif isinstance(batch, list):\n", + " questions = [item['question'] if isinstance(item, dict) else item for item in batch]\n", + " else:\n", + " raise ValueError(f\"Unexpected batch type: {type(batch)}\")\n", + "\n", + " monitor_memory() # Check memory before generation\n", + "\n", + " # Generate best guess with temperature 0\n", + " answers_0_7, last_token_embeddings = generate_answers_and_get_embeddings_batch(\n", + " questions, gemma_model, gemma_tokenizer, device, temperature=0, additional_tokens=25\n", + " )\n", + "\n", + " if not is_test:\n", + " # Generate 10 alternative answers for each question\n", + " all_alt_answers = generate_alternative_answers_batch(\n", + " questions, gemma_model, gemma_tokenizer, device, n=10, temperature=0.8, additional_tokens=25\n", + " )\n", + "\n", + " # Process and save each item in the batch\n", + " for j, question in enumerate(questions):\n", + " norm_target = dataset[i+j].get('norm_target', '') # Get the normalized target\n", + " targets = dataset[i+j].get('targets', []) # Get the list of alternative targets\n", + "\n", + " if is_test:\n", + " processed_item = {\n", + " 'question': question,\n", + " 'answer_0_7': answers_0_7[j],\n", + " 'last_token_embedding': last_token_embeddings[j].tolist(),\n", + " 'norm_target': norm_target,\n", + " 'targets': targets\n", + " }\n", + " else:\n", + " processed_item = {\n", + " 'question': question,\n", + " 'answer_0_7': answers_0_7[j],\n", + " 'last_token_embedding': last_token_embeddings[j].tolist(),\n", + " 'alt_answers': all_alt_answers[j],\n", + " 'norm_target': norm_target,\n", + " 'targets': targets\n", + " }\n", + "\n", + " save_item(processed_item, save_path)\n", + " processed_data.append(processed_item)\n", + "\n", + " if (i + batch_size) % save_interval == 0:\n", + " print(f\"Processed and saved {i + batch_size} items\")\n", + "\n", + " # Clear GPU memory after each batch\n", + " torch.cuda.empty_cache()\n", + " #monitor_memory() # Check memory after clearing\n", + "\n", + " return processed_data\n", + "\n", + "# Process datasets with batch processing\n", + "batch_size = 8\n", + "#print(\"Debug: First item in dataset:\")\n", + "#print(train_dataset[0])\n", + "\n", + "train_processed = process_dataset_batch(train_dataset, gemma_model, gemma_tokenizer, device, train_save_path, batch_size=batch_size)\n", + "valid_processed = process_dataset_batch(valid_dataset, gemma_model, gemma_tokenizer, device, valid_save_path, batch_size=batch_size)\n", + "test_processed = process_dataset_batch(test_dataset, gemma_model, gemma_tokenizer, device, test_save_path, is_test=True, batch_size=batch_size)" + ], + "metadata": { + "id": "s_lZbNVPqFVy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's read the saved files" + ], + "metadata": { + "id": "OOdE80s965D9" + } + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "def read_jsonl(file_path):\n", + " data = []\n", + " with open(file_path, 'r', encoding='utf-8') as file:\n", + " for line in file:\n", + " data.append(json.loads(line.strip()))\n", + " return data" + ], + "metadata": { + "id": "zu2X2kNp3kOd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Define the file paths\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_file_path = os.path.join(base_path, \"processed_data/train_processed.jsonl\")\n", + "valid_file_path = os.path.join(base_path, \"processed_data/valid_processed.jsonl\")\n", + "test_file_path = os.path.join(base_path, \"processed_data/test_processed.jsonl\")\n", + "\n", + "# Read the files\n", + "train_data_read = read_jsonl(train_file_path)\n", + "valid_data_read = read_jsonl(valid_file_path)\n", + "test_data_read = read_jsonl(test_file_path)\n", + "\n", + "# Print some information about the loaded data\n", + "print(f\"Loaded {len(train_data_read)} training samples\")\n", + "print(f\"Loaded {len(valid_data_read)} validation samples\")\n", + "print(f\"Loaded {len(test_data_read)} test samples\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X4ffTht43kQv", + "outputId": "6115bccf-2511-47de-ad81-5e72c8e90f93" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded 6000 training samples\n", + "Loaded 2000 validation samples\n", + "Loaded 1800 test samples\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 6) Answer Clustering and Semantic Entropy Calculation" + ], + "metadata": { + "id": "VI85UOXy_GSG" + } + }, + { + "cell_type": "markdown", + "source": [ + "So far, we calculated the best guess for each question (temperature=0 guess) as well as 10 alternative answers (generated with top_p=0.9 and temperature=0.8)\n", + "\n", + "Now, we will cluster the answers into semantic groups and calculate the corresponding semantic entropy" + ], + "metadata": { + "id": "tux3OOsJ_J9t" + } + }, + { + "cell_type": "code", + "source": [ + "#IF DATA ALREADY SAVED, YOU CAN SKIP FROM HERE 2:\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import json\n", + "from tqdm import tqdm\n", + "import math\n", + "import os\n", + "\n", + "# Assuming you have already defined these functions:\n", + "# check_entailment, bidirectional_entailment, cluster_answers, group_answers, calculate_entropy\n", + "\n", + "def process_item(item, nli_model, nli_tokenizer, device):\n", + " question = item['question']\n", + " alt_answers = item['alt_answers']\n", + "\n", + " # Generate clustered groups\n", + " clustered_groups = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + " item['clustered_groups'] = clustered_groups\n", + "\n", + " # Calculate semantic entropy\n", + " semantic_entropy = calculate_entropy(clustered_groups)\n", + " item['semantic_entropy'] = semantic_entropy\n", + "\n", + " return item\n", + "\n", + "def process_and_save_data(data, nli_model, nli_tokenizer, device, output_path):\n", + " with open(output_path, 'w', encoding='utf-8') as f:\n", + " for item in tqdm(data, desc=\"Processing items\"):\n", + " processed_item = process_item(item, nli_model, nli_tokenizer, device)\n", + " json.dump(processed_item, f)\n", + " f.write('\\n')\n", + "\n", + "# Assuming nli_model and nli_tokenizer are already loaded and available\n", + "\n", + "# Define input and output paths\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_input_path = os.path.join(base_path, \"processed_data/train_processed.jsonl\")\n", + "valid_input_path = os.path.join(base_path, \"processed_data/valid_processed.jsonl\")\n", + "train_output_path = os.path.join(base_path, \"processed_data/train_processed_clustered.jsonl\")\n", + "valid_output_path = os.path.join(base_path, \"processed_data/valid_processed_clustered.jsonl\")\n", + "\n", + "# Read the input files\n", + "def read_jsonl(file_path):\n", + " data = []\n", + " with open(file_path, 'r', encoding='utf-8') as file:\n", + " for line in file:\n", + " data.append(json.loads(line.strip()))\n", + " return data\n", + "\n", + "train_data = read_jsonl(train_input_path)\n", + "valid_data = read_jsonl(valid_input_path)\n", + "\n", + "# Process and save train data\n", + "print(\"Processing train data...\")\n", + "process_and_save_data(train_data, nli_model, nli_tokenizer, device, train_output_path)\n", + "\n", + "# Process and save validation data\n", + "print(\"Processing validation data...\")\n", + "process_and_save_data(valid_data, nli_model, nli_tokenizer, device, valid_output_path)\n", + "\n", + "print(\"Processing complete. New files saved with 'clustered' extension.\")" + ], + "metadata": { + "id": "mR5NbHjBCbs0", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eea3dd3b-82d2-4484-f1ef-0474e02349de" + }, + "execution_count": null, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing train data...\n" + ] + }, + { + "metadata": { + "tags": null + }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing items: 100%|██████████| 6000/6000 [1:08:12<00:00, 1.47it/s]\n" + ] + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing validation data...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Processing items: 100%|██████████| 2000/2000 [22:22<00:00, 1.49it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Processing complete. New files saved with 'clustered' extension.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import json\n", + "import re\n", + "from tqdm import tqdm\n", + "\n", + "# Define a set of stop words to remove so that they won't lead to false positives in prediction <-> label comparison\n", + "STOP_WORDS = set(['and', 'in', 'on', 'or', 'at', 'the', 'a', 'an', 'of', 'to', 'for', 'with', 'by', 'from', 'up', 'about', 'into', 'over', 'after'])\n", + "\n", + "def preprocess_target(text):\n", + " # Convert to lowercase\n", + " text = text.lower()\n", + " # Remove punctuation and apostrophe s's\n", + " text = re.sub(r\"[^\\w\\s]|'s\", \"\", text)\n", + " # Split into words, remove trailing 's', filter out stop words, and keep words of 4 or more characters\n", + " words = [word[:-1] if word.endswith('s') else word for word in text.split()\n", + " if word not in STOP_WORDS and len(word) >= 4]\n", + " return words\n", + "\n", + "def compare_answer_target(answer, target):\n", + " target_words = preprocess_target(target)\n", + " answer = answer.lower() # Convert answer to lowercase for case-insensitive comparison\n", + "\n", + " # Check if any target word is in the answer\n", + " return any(word in answer for word in target_words)\n", + "\n", + "def process_file(file_path):\n", + " processed_data = []\n", + " with open(file_path, 'r', encoding='utf-8') as file:\n", + " for line in tqdm(file, desc=f\"Processing {os.path.basename(file_path)}\"):\n", + " item = json.loads(line)\n", + " answer = item['answer_0_7']\n", + " target = item['norm_target']\n", + "\n", + " item['Label'] = compare_answer_target(answer, target)\n", + " processed_data.append(item)\n", + "\n", + " return processed_data\n", + "\n", + "# Define file paths\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_path = os.path.join(base_path, \"processed_data/train_processed_clustered.jsonl\")\n", + "valid_path = os.path.join(base_path, \"processed_data/valid_processed_clustered.jsonl\")\n", + "test_path = os.path.join(base_path, \"processed_data/test_processed.jsonl\")\n", + "\n", + "# Process each file\n", + "train_data = process_file(train_path)\n", + "valid_data = process_file(valid_path)\n", + "test_data = process_file(test_path)\n", + "\n", + "# Save processed data\n", + "def save_processed_data(data, file_path):\n", + " with open(file_path, 'w', encoding='utf-8') as file:\n", + " for item in data:\n", + " json.dump(item, file)\n", + " file.write('\\n')\n", + "\n", + "save_processed_data(train_data, os.path.join(base_path, \"processed_data/train_processed_labeled.jsonl\"))\n", + "save_processed_data(valid_data, os.path.join(base_path, \"processed_data/valid_processed_labeled.jsonl\"))\n", + "save_processed_data(test_data, os.path.join(base_path, \"processed_data/test_processed_labeled.jsonl\"))\n", + "\n", + "print(\"Processing complete. New files saved with 'labeled' extension.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KSAASuhgLs2c", + "outputId": "3871bc65-fa1a-4d36-c623-fb590f8fa898" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Processing train_processed_clustered.jsonl: 6000it [00:02, 2274.01it/s]\n", + "Processing valid_processed_clustered.jsonl: 2000it [00:00, 2209.67it/s]\n", + "Processing test_processed.jsonl: 1800it [00:00, 2298.85it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Processing complete. New files saved with 'labeled' extension.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#IF DATA ALREADY SAVED, YOU CAN SKIP TO HERE 2:\n", + "\n", + "import json\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "def load_jsonl(file_path):\n", + " data = []\n", + " with open(file_path, 'r', encoding='utf-8') as file:\n", + " for line in tqdm(file, desc=f\"Loading {os.path.basename(file_path)}\"):\n", + " data.append(json.loads(line.strip()))\n", + " return data\n", + "\n", + "# Define the base path and file paths\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_file_path = os.path.join(base_path, \"processed_data/train_processed_labeled.jsonl\")\n", + "valid_file_path = os.path.join(base_path, \"processed_data/valid_processed_labeled.jsonl\")\n", + "test_file_path = os.path.join(base_path, \"processed_data/test_processed_labeled.jsonl\")\n", + "\n", + "# Load the data\n", + "train_data_labeled = load_jsonl(train_file_path)\n", + "valid_data_labeled = load_jsonl(valid_file_path)\n", + "test_data_labeled = load_jsonl(test_file_path)\n", + "\n", + "# Print some information to verify the loading\n", + "print(f\"Loaded {len(train_data_labeled)} training samples\")\n", + "print(f\"Loaded {len(valid_data_labeled)} validation samples\")\n", + "print(f\"Loaded {len(test_data_labeled)} test samples\")\n", + "\n", + "# Optionally, you can print the keys of the first item in each dataset to verify the structure\n", + "print(\"\\nKeys in a training sample:\")\n", + "print(list(train_data_labeled[0].keys()))\n", + "print(\"\\nKeys in a validation sample:\")\n", + "print(list(valid_data_labeled[0].keys()))\n", + "print(\"\\nKeys in a test sample:\")\n", + "print(list(test_data_labeled[0].keys()))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_mdpk59-o0O2", + "outputId": "7a3b7954-69ad-4c79-eea7-7a516498176e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Loading train_processed_labeled.jsonl: 6000it [00:03, 1948.56it/s]\n", + "Loading valid_processed_labeled.jsonl: 2000it [00:01, 1311.37it/s]\n", + "Loading test_processed_labeled.jsonl: 1800it [00:01, 1438.97it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded 6000 training samples\n", + "Loaded 2000 validation samples\n", + "Loaded 1800 test samples\n", + "\n", + "Keys in a training sample:\n", + "['question', 'answer_0_7', 'last_token_embedding', 'alt_answers', 'norm_target', 'targets', 'clustered_groups', 'semantic_entropy', 'Label']\n", + "\n", + "Keys in a validation sample:\n", + "['question', 'answer_0_7', 'last_token_embedding', 'alt_answers', 'norm_target', 'targets', 'clustered_groups', 'semantic_entropy', 'Label']\n", + "\n", + "Keys in a test sample:\n", + "['question', 'answer_0_7', 'last_token_embedding', 'norm_target', 'targets', 'Label']\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Print some statistics\n", + "for dataset, name in zip([train_data_labeled, valid_data_labeled, test_data_labeled], ['Train', 'Validation', 'Test']):\n", + " total = len(dataset)\n", + " true_count = sum(1 for item in dataset if item['Label'])\n", + " false_count = total - true_count\n", + " print(f\"{name} dataset:\")\n", + " print(f\" Total entries: {total}\")\n", + " print(f\" True labels: {true_count} ({true_count/total:.2%})\")\n", + " print(f\" False labels: {false_count} ({false_count/total:.2%})\")\n", + " print()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NovQKNtBpg5_", + "outputId": "3d1eddf4-52f8-4f9c-f683-026e3612de10" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train dataset:\n", + " Total entries: 6000\n", + " True labels: 1220 (20.33%)\n", + " False labels: 4780 (79.67%)\n", + "\n", + "Validation dataset:\n", + " Total entries: 2000\n", + " True labels: 392 (19.60%)\n", + " False labels: 1608 (80.40%)\n", + "\n", + "Test dataset:\n", + " Total entries: 1800\n", + " True labels: 368 (20.44%)\n", + " False labels: 1432 (79.56%)\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def calculate_average_entropy(data, label):\n", + " entropies = [item['semantic_entropy'] for item in data if item['Label'] == label]\n", + " return sum(entropies) / len(entropies) if entropies else 0\n", + "\n", + "# Calculate and print average entropies for train and validation sets\n", + "for dataset, name in zip([train_data_labeled, valid_data_labeled], ['Train', 'Validation']):\n", + " true_avg_entropy = calculate_average_entropy(dataset, True)\n", + " false_avg_entropy = calculate_average_entropy(dataset, False)\n", + "\n", + " print(f\"{name} dataset:\")\n", + " print(f\" Average entropy for True labels: {true_avg_entropy:.4f}\")\n", + " print(f\" Average entropy for False labels: {false_avg_entropy:.4f}\")\n", + " print(f\" Difference (True - False): {true_avg_entropy - false_avg_entropy:.4f}\")\n", + " print()\n", + "\n", + "# Calculate overall statistics for train and validation combined\n", + "all_data = train_data_labeled + valid_data_labeled\n", + "overall_true_avg_entropy = calculate_average_entropy(all_data, True)\n", + "overall_false_avg_entropy = calculate_average_entropy(all_data, False)\n", + "\n", + "print(\"Overall statistics (Train + Validation):\")\n", + "print(f\" Average entropy for True labels: {overall_true_avg_entropy:.4f}\")\n", + "print(f\" Average entropy for False labels: {overall_false_avg_entropy:.4f}\")\n", + "print(f\" Difference (True - False): {overall_true_avg_entropy - overall_false_avg_entropy:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9CYtH5VUi80C", + "outputId": "fabfe33b-4b1e-4772-a54e-8ede9834a75c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train dataset:\n", + " Average entropy for True labels: 0.7165\n", + " Average entropy for False labels: 1.5288\n", + " Difference (True - False): -0.8123\n", + "\n", + "Validation dataset:\n", + " Average entropy for True labels: 0.7605\n", + " Average entropy for False labels: 1.4707\n", + " Difference (True - False): -0.7102\n", + "\n", + "Overall statistics (Train + Validation):\n", + " Average entropy for True labels: 0.7272\n", + " Average entropy for False labels: 1.5142\n", + " Difference (True - False): -0.7870\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We have great results! Our hypothesis seems to be correct about the negative correlation between semantic entropy and accuracy in model outputs." + ], + "metadata": { + "id": "WaSHZ_0wmuwh" + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's train a logistic regression model that takes the question's last token's final layer hidden state as input and predicts the entropy value as output. If we can derive a good logistic regression model, we can use this to predict whether a given question will lead the Gemma-2B model to hallucinate or not!" + ], + "metadata": { + "id": "UuUl5xVKnBq3" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 7) Hallucination Detection Model - Trained to predict the label\n" + ], + "metadata": { + "id": "DdJX8CZZEpMg" + } + }, + { + "cell_type": "markdown", + "source": [ + "In the first version of our logistic regressor, we will use the \"best guess's accuracy\" as our label, which serves as a supervised task. In the next section, we will replace this label with the semantic entropy as the target (y) of the logistic regression, where the predicted semantic entropy will function as a proxy for the accuracy of the model's generations.\n", + "\n", + "Now, let's focus on the scenario where we are using the best guess accuracy as the label for our training:" + ], + "metadata": { + "id": "CxXQG6qUmck5" + } + }, + { + "cell_type": "code", + "source": [ + "# IF DATA FRAMES ARE ALREADY SAVED, SKIP FROM HERE 3:\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score, precision_score, recall_score, f1_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "# Define the base path\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "\n", + "# 1. Data Preparation\n", + "def prepare_data(data):\n", + " df = pd.DataFrame(data)\n", + " columns = ['last_token_embedding', 'Label']\n", + " if 'semantic_entropy' in df.columns:\n", + " columns.append('semantic_entropy')\n", + " return df[columns]\n", + "\n", + "# Prepare DataFrames\n", + "train_df_labeled = prepare_data(train_data_labeled)\n", + "valid_df_labeled = prepare_data(valid_data_labeled)\n", + "test_df_labeled = prepare_data(test_data_labeled)\n", + "\n", + "# Save DataFrames as CSV\n", + "train_csv_path = os.path.join(base_path, \"processed_data/train_df_labeled.csv\")\n", + "valid_csv_path = os.path.join(base_path, \"processed_data/valid_df_labeled.csv\")\n", + "test_csv_path = os.path.join(base_path, \"processed_data/test_df_labeled.csv\")\n", + "\n", + "train_df_labeled.to_csv(train_csv_path, index=False)\n", + "valid_df_labeled.to_csv(valid_csv_path, index=False)\n", + "test_df_labeled.to_csv(test_csv_path, index=False)\n", + "\n", + "print(\"DataFrames saved as CSV files.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K3oPh-_EwbEi", + "outputId": "8d6b3103-bfd4-422c-857d-33ae8442a077" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "DataFrames saved as CSV files.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# IF DATA FRAMES ARE ALREADY SAVED, SKIP TO HERE 3:\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.metrics import roc_curve\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Save DataFrames as CSV\n", + "train_csv_path = os.path.join(base_path, \"processed_data/train_df_labeled.csv\")\n", + "valid_csv_path = os.path.join(base_path, \"processed_data/valid_df_labeled.csv\")\n", + "test_csv_path = os.path.join(base_path, \"processed_data/test_df_labeled.csv\")\n", + "\n", + "# Read CSV files back into DataFrames\n", + "train_df_labeled = pd.read_csv(train_csv_path)\n", + "valid_df_labeled = pd.read_csv(valid_csv_path)\n", + "test_df_labeled = pd.read_csv(test_csv_path)\n", + "print(\"DataFrames loaded from CSV files.\")\n", + "\n", + "# Convert string representation of list back to numpy array\n", + "train_df_labeled['embedding'] = train_df_labeled['last_token_embedding'].apply(lambda x: np.array(eval(x)))\n", + "valid_df_labeled['embedding'] = valid_df_labeled['last_token_embedding'].apply(lambda x: np.array(eval(x)))\n", + "test_df_labeled['embedding'] = test_df_labeled['last_token_embedding'].apply(lambda x: np.array(eval(x)))\n", + "\n", + "# Combine train and valid for threshold selection\n", + "combined_df = pd.concat([train_df_labeled, valid_df_labeled])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-DmK05QFxtFm", + "outputId": "757ccd0a-0751-49cc-dc49-9899711bc9ad" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "DataFrames loaded from CSV files.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Assuming 'combined_df' is your DataFrame containing both training and validation data\n", + "\n", + "# Separate the data for True and False labels\n", + "true_entropy = combined_df[combined_df['Label'] == True]['semantic_entropy']\n", + "false_entropy = combined_df[combined_df['Label'] == False]['semantic_entropy']\n", + "\n", + "# Define the number of bins and range\n", + "bins = 50\n", + "range_min = min(combined_df['semantic_entropy'].min(), 0) # In case there are negative values\n", + "range_max = combined_df['semantic_entropy'].max()\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot histogram for False labels (red)\n", + "plt.hist(false_entropy, bins=bins, range=(range_min, range_max), color='red', alpha=0.5, label='False')\n", + "\n", + "# Plot histogram for True labels (green)\n", + "plt.hist(true_entropy, bins=bins, range=(range_min, range_max), color='green', alpha=0.5, label='True')\n", + "\n", + "# Customize the plot\n", + "plt.title('Distribution of Semantic Entropy for True and False Labels', fontsize=16)\n", + "plt.xlabel('Semantic Entropy', fontsize=14)\n", + "plt.ylabel('Frequency', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add text with data statistics\n", + "plt.text(0.05, 0.95, f\"True samples: {len(true_entropy)}\\nFalse samples: {len(false_entropy)}\",\n", + " transform=plt.gca().transAxes, verticalalignment='top', fontsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print some statistics\n", + "print(f\"True labels - Mean: {true_entropy.mean():.4f}, Std: {true_entropy.std():.4f}\")\n", + "print(f\"False labels - Mean: {false_entropy.mean():.4f}, Std: {false_entropy.std():.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 641 + }, + "id": "RM-hdTfd5ZeR", + "outputId": "02340c20-7baf-41d1-828e-6bd818a862d3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "True labels - Mean: 0.7272, Std: 0.8510\n", + "False labels - Mean: 1.5142, Std: 0.9598\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As can be seen from the graph, True samples tend to result from lower semantic entropy, while false samples tend to result from higher semantic entropy." + ], + "metadata": { + "id": "0uGvkzzDnGtQ" + } + }, + { + "cell_type": "code", + "source": [ + "# 1. Data Analysis and Visualization\n", + "\n", + "# Class distribution\n", + "plt.figure(figsize=(10, 6))\n", + "sns.countplot(x='Label', data=combined_df)\n", + "plt.title('Class Distribution')\n", + "plt.show()\n", + "\n", + "print(\"Class distribution:\")\n", + "print(combined_df['Label'].value_counts(normalize=True))\n", + "\n", + "# Entropy distribution (if available)\n", + "if 'semantic_entropy' in combined_df.columns:\n", + " plt.figure(figsize=(12, 6))\n", + " sns.histplot(data=combined_df, x='semantic_entropy', hue='Label', kde=True, bins=30)\n", + " plt.title('Distribution of Semantic Entropy by Label')\n", + " plt.show()\n", + "\n", + " print(\"\\nEntropy statistics:\")\n", + " print(combined_df.groupby('Label')['semantic_entropy'].describe())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "s3gdnIY3TPQ_", + "outputId": "c43aeab0-1347-4b1d-b81c-ff7a0ec16880" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Class distribution:\n", + "Label\n", + "False 0.7985\n", + "True 0.2015\n", + "Name: proportion, dtype: float64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Entropy statistics:\n", + " count mean std min 25% 50% 75% max\n", + "Label \n", + "False 6388.0 1.514209 0.959819 0.0 0.721928 1.570951 2.321928 3.321928\n", + "True 1612.0 0.727224 0.850998 0.0 0.000000 0.468996 1.295462 3.321928\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 2. Model Training and Evaluation\n", + "\n", + "# Prepare the data\n", + "X_train = np.stack(combined_df['embedding'].values)\n", + "y_train = combined_df['Label']\n", + "\n", + "X_test = np.stack(test_df_labeled['embedding'].values)\n", + "y_test = test_df_labeled['Label']\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Train the model\n", + "model = LogisticRegression(max_iter=5000, solver='lbfgs', C=0.001)\n", + "model.fit(X_train_scaled, y_train)\n", + "\n", + "# Cross-validation\n", + "cv_scores = cross_val_score(model, X_train_scaled, y_train, cv=5)\n", + "print(\"\\nCross-validation scores:\", cv_scores)\n", + "print(f\"Mean CV score: {cv_scores.mean():.4f} (+/- {cv_scores.std() * 2:.4f})\")\n", + "\n", + "# Predictions on test set\n", + "y_pred = model.predict(X_test_scaled)\n", + "y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MFFi5HRjTQi6", + "outputId": "35288ce9-37d3-4651-b41c-0ecce5b3e3f7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Cross-validation scores: [0.795 0.8 0.793125 0.79375 0.79375 ]\n", + "Mean CV score: 0.7951 (+/- 0.0050)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n", + "from sklearn.model_selection import cross_val_score\n", + "import os\n", + "\n", + "# 3. Model Evaluation\n", + "\n", + "# Classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "# Confusion Matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "plt.title('Confusion Matrix')\n", + "plt.ylabel('True Label')\n", + "plt.xlabel('Predicted Label')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", + "auc = roc_auc_score(y_test, y_pred_proba)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {auc:.2f})')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', label='Random Classifier')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "VKUzFCVFTQgc", + "outputId": "0c177875-3cd9-48d1-de26-a40f3af21fee" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " False 0.80 1.00 0.89 1432\n", + " True 0.60 0.02 0.05 368\n", + "\n", + " accuracy 0.80 1800\n", + " macro avg 0.70 0.51 0.47 1800\n", + "weighted avg 0.76 0.80 0.71 1800\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The threshold used in the confusion matrix above was not the optimal one. The defualt threshold of 0.5 was used here, which explains the poor performance.\n", + " So, let's optimize the threshold for F1 score and plot the confusion matrix again" + ], + "metadata": { + "id": "bcRHPxVdZUEr" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "# ... [previous code remains the same] ...\n", + "\n", + "# After plotting the ROC curve\n", + "\n", + "# Find optimal threshold\n", + "thresholds = np.linspace(0, 1, 100)\n", + "f1_scores = [f1_score(y_test, y_pred_proba >= threshold) for threshold in thresholds]\n", + "optimal_threshold = thresholds[np.argmax(f1_scores)]\n", + "\n", + "print(f\"\\nOptimal threshold (maximizing F1 score): {optimal_threshold:.4f}\")\n", + "\n", + "# New predictions with optimal threshold\n", + "y_pred_optimal = (y_pred_proba >= optimal_threshold).astype(int)\n", + "\n", + "# New confusion matrix\n", + "cm_optimal = confusion_matrix(y_test, y_pred_optimal)\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm_optimal, annot=True, fmt='d', cmap='Blues')\n", + "plt.title(f'Confusion Matrix (Threshold: {optimal_threshold:.4f})')\n", + "plt.ylabel('True Label')\n", + "plt.xlabel('Predicted Label')\n", + "plt.show()\n", + "\n", + "# New classification report\n", + "print(\"\\nClassification Report (with optimal threshold):\")\n", + "print(classification_report(y_test, y_pred_optimal))\n", + "\n", + "# Visualize threshold vs F1 score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(thresholds, f1_scores)\n", + "plt.axvline(optimal_threshold, color='r', linestyle='--', label=f'Optimal Threshold: {optimal_threshold:.4f}')\n", + "plt.title('F1 Score vs. Threshold')\n", + "plt.xlabel('Threshold')\n", + "plt.ylabel('F1 Score')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "evtYkXWGZJr6", + "outputId": "6a0ce37d-ad3a-4c98-fc7d-0615a9056d7d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Optimal threshold (maximizing F1 score): 0.2121\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (with optimal threshold):\n", + " precision recall f1-score support\n", + "\n", + " False 0.85 0.56 0.68 1432\n", + " True 0.26 0.61 0.37 368\n", + "\n", + " accuracy 0.57 1800\n", + " macro avg 0.56 0.59 0.52 1800\n", + "weighted avg 0.73 0.57 0.61 1800\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's see two examples of our model predicting the hallucination probability:" + ], + "metadata": { + "id": "GFzgpKShZmi0" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import numpy as np\n", + "\n", + "def get_prediction(embedding, model, scaler):\n", + "\n", + " if isinstance(embedding, torch.Tensor):\n", + " embedding = embedding.float().cpu().numpy()\n", + "\n", + " embedding = embedding.astype(np.float32\n", + " embedding = embedding.reshape(1, -1\n", + " embedding_scaled = scaler.transform(embedding)\n", + " prob_true = model.predict_proba(embedding_scaled)[0, 1]\n", + "\n", + " return prob_true\n" + ], + "metadata": { + "id": "rnXMsnVueoS0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "First, let's ask a simple question" + ], + "metadata": { + "id": "c39eWtCcaIyG" + } + }, + { + "cell_type": "code", + "source": [ + "# Checking the best guess of the model\n", + "question = \"Which team is Kobe Bryant a legend for?\"\n", + "test_question = f\"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "\n", + "Question: In golf what is the old-fashioned name for a No 3 wood?\n", + "Answer: spoon\n", + "\n", + "Question: When was Turkish Republic founded?\n", + "Answer: 1923\n", + "\n", + "Question: {question}\n", + "Answer : \"\"\"\n", + "generated_answer, last_token_embedding = generate_answer_and_get_embedding(test_question, gemma_model, gemma_tokenizer, device)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Generated answer (temp=0): {generated_answer}\")\n", + "print(f\"Last token embedding: {last_token_embedding}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7vkg8TWWZyul", + "outputId": "0761c4fc-44d0-4d9c-bc80-13e7a1bbdaa8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: Which team is Kobe Bryant a legend for?\n", + "Generated answer (temp=0): Los Angeles Lakers\n", + "Last token embedding: tensor([-1.4531, -1.5547, -3.0312, ..., 0.5977, -0.0386, 0.2559],\n", + " dtype=torch.bfloat16)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "prob_true = get_prediction(last_token_embedding, model, scaler)\n", + "\n", + "print(f\"Probability of True label: {prob_true:.4f}\")\n", + "\n", + "predicted_label = prob_true >= optimal_threshold\n", + "print(f\"Predicted label: {predicted_label}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HLB_nzmwZysC", + "outputId": "98a757d7-fe9c-4f7f-8062-2c306679c3aa" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Probability of True label: 0.4347\n", + "Predicted label: True\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's see how the model would generate 10 alternative outputs for this question:" + ], + "metadata": { + "id": "3KA6ijBUbzLF" + } + }, + { + "cell_type": "code", + "source": [ + "alt_answers = generate_alternative_answers(test_question, gemma_model, gemma_tokenizer, device)\n", + "# Group the answers\n", + "grouped_answers = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + "print(\"\\nGrouped Answers:\")\n", + "for i, group in enumerate(grouped_answers, 1):\n", + " print(f\"Group {i}: {group}\")\n", + "\n", + "# Calculate entropy\n", + "entropy = calculate_entropy(grouped_answers)\n", + "print(f\"\\nEntropy of answer distribution: {entropy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BVGn2L9aZypf", + "outputId": "d8d60636-18d7-4e22-cd4c-f946481bbd73" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Grouped Answers:\n", + "Group 1: ['Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers']\n", + "\n", + "Entropy of answer distribution: 0.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "It is great! As we can see, just from the final layer embedding of the question, we predicted with high confidence that the model would be confident about this question and answer it correctly. When we checked the best guess (temperature=0) and the 10 alternatives (temperature=0.8), they all resulted in the right answer, with even 0 entropy!" + ], + "metadata": { + "id": "em3O5O6_cesK" + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's ask a difficult question and see if we are going to predict hallucination beforehand" + ], + "metadata": { + "id": "CDwM_lJ-c4JO" + } + }, + { + "cell_type": "code", + "source": [ + "# Checking the best guess of the model\n", + "question = \"Who is the only person to have received both a Nobel Prize and an Academy Award?\"\n", + "test_question = f\"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "\n", + "Question: In golf what is the old-fashioned name for a No 3 wood?\n", + "Answer: spoon\n", + "\n", + "Question: When was Turkish Republic founded?\n", + "Answer: 1923\n", + "\n", + "Question: {question}\n", + "Answer : \"\"\"\n", + "generated_answer, last_token_embedding = generate_answer_and_get_embedding(test_question, gemma_model, gemma_tokenizer, device)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Generated answer (temp=0): {generated_answer}\")\n", + "print(f\"Last token embedding: {last_token_embedding}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0j11NG2rc3fX", + "outputId": "0acf7c6f-bd43-49d6-cb56-1992b19407ad" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: Who is the only person to have received both a Nobel Prize and an Academy Award?\n", + "Generated answer (temp=0): 0ne person: Leonardo DiCaprio\n", + "Last token embedding: tensor([ 0.8242, 0.4473, -3.7344, ..., -0.2471, 1.3281, 2.0312],\n", + " dtype=torch.bfloat16)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "prob_true = get_prediction(last_token_embedding, model, scaler)\n", + "\n", + "print(f\"Probability of True label: {prob_true:.4f}\")\n", + "\n", + "predicted_label = prob_true >= optimal_threshold\n", + "print(f\"Predicted label: {predicted_label}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "umIgSFtseM4t", + "outputId": "7a22dc96-c26e-4bcf-a87f-a94d928792b4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Probability of True label: 0.0476\n", + "Predicted label: False\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "alt_answers = generate_alternative_answers(test_question, gemma_model, gemma_tokenizer, device)\n", + "# Group the answers\n", + "grouped_answers = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + "print(\"\\nGrouped Answers:\")\n", + "for i, group in enumerate(grouped_answers, 1):\n", + " print(f\"Group {i}: {group}\")\n", + "\n", + "# Calculate entropy\n", + "entropy = calculate_entropy(grouped_answers)\n", + "print(f\"\\nEntropy of answer distribution: {entropy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-ErnrK-nePJj", + "outputId": "8b4c4eda-fe4c-4856-cf57-46331d27eb98" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Grouped Answers:\n", + "Group 1: ['0scar', '0utstandingly both Michael Douglas and Leonardo DiCaprio', '0ne person - Leonardo DiCaprio', '0ne person: Martin Scorsese', '0scar\\n\\nQuestion: What is the largest city in the world by land area?\\nAnswer: Tokyo\\n\\nQuestion: What', '0scar Wilde', '0ne person - Nelson Mandela']\n", + "Group 2: ['0ne person: Leonardo DiCaprio']\n", + "Group 3: ['0ne person: Nelson Mandela', '0ne person: Nelson Mandela']\n", + "\n", + "Entropy of answer distribution: 1.1567796494470395\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Good news! The correct answer was George Bernard Shaw and Bob Dylan, but our model hallucinated and guessed it as Leonardo DiCaprio and several other actors/directors. We predicted this hallucination just from the question's last token embeddings! We also checked the behavior of the 10 alternative answers, and they indeed included many irrelevant answers with high entropy!" + ], + "metadata": { + "id": "FK-9trHKfI4H" + } + }, + { + "cell_type": "code", + "source": [ + "# 9. Save the model and scaler\n", + "import joblib\n", + "joblib.dump(model, os.path.join(base_path, 'labeled_logistic_regression_model.joblib'))\n", + "joblib.dump(scaler, os.path.join(base_path, 'labeled_scaler.joblib'))\n", + "print(\"Model and scaler saved.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aNS4GjhpjpaZ", + "outputId": "9f3064c9-0c22-44be-f4e2-32fe7b3e2586" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model and scaler saved.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "In the code above, we trained our logistic regression model using the ground truth labels of the best guesses' accuracy. This approach is supervised learning based on the model's performance on best guess accuracy. Now, let's train another logistic regression model, this time with the objective of predicting semantic entropy. Since our hypothesis was that high semantic entropy indicates a higher probability of hallucination, we will use this model as an estimator for hallucination detection. We will then compare the performance of this new model to the previous one!" + ], + "metadata": { + "id": "eeUWRik7fafX" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 8) Hallucination Detection Model - Trained to predict the semantic entropy score\n" + ], + "metadata": { + "id": "E2u0Md07fac5" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\n", + "import os\n", + "import joblib\n", + "\n", + "# Load the data\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "train_csv_path = os.path.join(base_path, \"processed_data/train_df_labeled.csv\")\n", + "valid_csv_path = os.path.join(base_path, \"processed_data/valid_df_labeled.csv\")\n", + "test_csv_path = os.path.join(base_path, \"processed_data/test_df_labeled.csv\")\n", + "\n", + "train_df = pd.read_csv(train_csv_path)\n", + "valid_df = pd.read_csv(valid_csv_path)\n", + "test_df = pd.read_csv(test_csv_path)\n", + "\n", + "# Combine train and validation sets\n", + "combined_df = pd.concat([train_df, valid_df])\n", + "\n", + "# Convert string representation of list back to numpy array\n", + "combined_df['embedding'] = combined_df['last_token_embedding'].apply(lambda x: np.array(eval(x)))\n", + "test_df['embedding'] = test_df['last_token_embedding'].apply(lambda x: np.array(eval(x)))" + ], + "metadata": { + "id": "wVfiRzBQodug" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Analyze semantic entropy distribution\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(data=combined_df, x='semantic_entropy', kde=True)\n", + "plt.title('Distribution of Semantic Entropy')\n", + "plt.show()\n", + "\n", + "# Calculate potential thresholds\n", + "mean_entropy = combined_df['semantic_entropy'].mean()\n", + "median_entropy = combined_df['semantic_entropy'].median()\n", + "entropy_std = combined_df['semantic_entropy'].std()\n", + "\n", + "print(f\"Mean Entropy: {mean_entropy:.4f}\")\n", + "print(f\"Median Entropy: {median_entropy:.4f}\")\n", + "print(f\"Entropy Standard Deviation: {entropy_std:.4f}\")\n", + "\n", + "# Determine the optimal threshold\n", + "thresholds = np.linspace(combined_df['semantic_entropy'].min(), combined_df['semantic_entropy'].max(), 100)\n", + "f1_scores = []\n", + "\n", + "for threshold in thresholds:\n", + " low_entropy = (combined_df['semantic_entropy'] <= threshold).astype(int)\n", + " f1 = f1_score(combined_df['Label'], low_entropy)\n", + " f1_scores.append(f1)\n", + "\n", + "optimal_threshold = thresholds[np.argmax(f1_scores)]\n", + "print(f\"Optimal threshold: {optimal_threshold:.4f}\")\n", + "\n", + "# Plot F1 scores vs thresholds\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(thresholds, f1_scores)\n", + "plt.axvline(optimal_threshold, color='r', linestyle='--', label=f'Optimal Threshold: {optimal_threshold:.4f}')\n", + "plt.title('F1 Score vs Entropy Threshold')\n", + "plt.xlabel('Entropy Threshold')\n", + "plt.ylabel('F1 Score')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "f5pFdY5rouxP", + "outputId": "f3c39e81-c372-4250-86c4-6b56d4a3fa8f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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0tbVs2VIrV65UWVmZrW3WrFknTBF/NrUNGjRIlZWVevfdd+3a33jjDZlMJrvt/x2DBg1SWlqavvzyS1tbRUWF3nnnHfn4+Kh3795nvc4dO3YoNTX1hPacnBytWLFCgYGBatq0qVxcXDRkyBB9++232rRp0wn9Dx8+fNbbPpljoz3Hj+7k5uZqypQpNV7n2RzPIUOGKDMz84Tj+dea6oq3t/dZfUYGDRokSXrzzTft2idNmiRJGjx4sF37wYMH7WYozMvL06effqouXbooLCzM1u7q6qqbb77ZNtrZsWPHE0YzAeAYRrYA4DR+/fVXbd26VRUVFUpPT9fChQs1b948RUdH68cff5SHh8dJXzthwgQtXbpUgwcPVnR0tDIyMvTee++pefPmtnsBtWzZUgEBAfrggw/k6+srb29v9ezZ84RrUM5UUFCQLrroIo0cOVLp6el688031apVK7vp6e+880598803uvzyy3XjjTcqJSVF//3vf0+YTvtsarvyyivVt29fPfXUU9qzZ486d+6suXPn6ocfftBDDz102qnCz9Tdd9+tf//737r99tuVlJSkmJgYffPNN1q2bJnefPPNU15DdzJ//vmnbrnlFg0cOFAXX3yxgoKCdODAAU2bNk0HDx7Um2++aQs///znP7Vo0SL17NlTd911l9q3b6+srCytW7dO8+fPrzZg10T//v1lsVh05ZVX6h//+IcKCgr00UcfKSQkRIcOHarROrt06SIXFxe98sorys3Nlbu7u+0+Xn81fPhwffrppxo7dqxWr16tiy++WIWFhZo/f77uu+++005znpuba3cPuuPVZDKJbt26af78+Zo0aZIiIiIUGxurnj17nrR/586dNWLECH344YfKyclR7969tXr1ak2bNk3XXHON+vbta9e/TZs2GjVqlNasWaPQ0FB98sknSk9PrzbcDh8+XG+//bYWLVpkm54eAKrlnEkQAaD+Ozb1+7GHxWIxwsLCjMsuu8x466237KYYP+avU78vWLDAuPrqq42IiAjDYrEYERERxs0332xs377d7nU//PCD0b59e8PV1dVuqvXevXsb8fHx1dZ3sqnfP//8c2PcuHFGSEiI4enpaQwePLjaKcz/9a9/Gc2aNTPc3d2NXr16GWvXrj1hnaeq7a9TvxuGYeTn5xsPP/ywERERYbi5uRmtW7c2XnvttROmCpdkjB49+oSaTjYl/V+lp6cbI0eONIKDgw2LxWJ07Nix2unpz3Tq9/T0dOOf//yn0bt3byM8PNxwdXU1AgMDjUsvvdT45ptvqu0/evRoIzIy0nBzczPCwsKMfv36GR9++KGtz7Hj8fXXX9u99mS3FDj2vXP8bQJ+/PFHo1OnToaHh4cRExNjvPLKK7Yp2Xfv3n3a/azueH700UdGixYtDBcXF7tp4KvrW1RUZDz11FNGbGysbT+vv/56IyUl5VRv5ymnfj/+81HdPh//Hh2/j1u3bjUuueQSw9PT05Bk+z452ToMwzDKy8uN559/3lZ/ZGSkMW7cOKOkpMSu37H3b86cOUanTp0Md3d3o127diccu+PFx8cbZrPZ2L9//ynfCwCNm8kwGsCVswAAAA4SExOjDh06aNasWWf8mq5duyooKEgLFixwYGUAGjqu2QIAADgLa9eu1fr16zV8+HBnlwKgnuOaLQAAgDOwadMmJSUl6V//+pfCw8N10003ObskAPUcI1sAAABn4JtvvtHIkSNVXl6uzz///JST4wCAJHHNFgAAAAA4ACNbAAAAAOAAhC0AAAAAcAAmyDhDVqtVBw8elK+vr0wmk7PLAQAAAOAkhmEoPz9fERERMptPPn5F2DpDBw8eVGRkpLPLAAAAAFBP7Nu3T82bNz/pcsLWGfL19ZVU9Yb6+fk5uRoAAAAAzpKXl6fIyEhbRjgZwtYZOnbqoJ+fH2ELAAAAwGkvL2KCDAAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADiAq7MLQM2kpqYqMzPT2WVIkoKDgxUVFeXsMgAAAIB6hbDVAKWmpqpdXJyKi4qcXYokydPLS1uTkwlcAAAAwHEIWw1QZmamiouKNOzx1xQa1dKptaSnpmj6K48qMzOTsAUAAAAch7DVgIVGtVTz1vHOLgMAAABANZggAwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAB3Bq2Fq6dKmuvPJKRUREyGQyaebMmSf0SU5O1lVXXSV/f395e3urR48eSk1NtS0vKSnR6NGj1aRJE/n4+GjIkCFKT0+3W0dqaqoGDx4sLy8vhYSE6NFHH1VFRYWjdw8AAABAI+bUsFVYWKjOnTtr8uTJ1S5PSUnRRRddpHbt2mnx4sXasGGDnnnmGXl4eNj6PPzww/rpp5/09ddfa8mSJTp48KCuu+462/LKykoNHjxYZWVlWr58uaZNm6apU6fq2Wefdfj+AQAAAGi8XJ258YEDB2rgwIEnXf7UU09p0KBBevXVV21tLVu2tH2dm5ur//znP5oxY4YuvfRSSdKUKVMUFxenlStX6oILLtDcuXO1ZcsWzZ8/X6GhoerSpYteeOEFPf744xo/frwsFovjdhAAAABAo1Vvr9myWq36+eef1aZNGw0YMEAhISHq2bOn3amGSUlJKi8vV2Jioq2tXbt2ioqK0ooVKyRJK1asUMeOHRUaGmrrM2DAAOXl5Wnz5s0n3X5paany8vLsHgAAAABwpupt2MrIyFBBQYH++c9/6vLLL9fcuXN17bXX6rrrrtOSJUskSWlpabJYLAoICLB7bWhoqNLS0mx9jg9ax5YfW3YyEydOlL+/v+0RGRlZi3sHAAAA4FxXb8OW1WqVJF199dV6+OGH1aVLFz3xxBO64oor9MEHHzh8++PGjVNubq7tsW/fPodvEwAAAMC5o96GreDgYLm6uqp9+/Z27XFxcbbZCMPCwlRWVqacnBy7Punp6QoLC7P1+evshMeeH+tTHXd3d/n5+dk9AAAAAOBM1duwZbFY1KNHD23bts2uffv27YqOjpYkdevWTW5ublqwYIFt+bZt25SamqqEhARJUkJCgjZu3KiMjAxbn3nz5snPz++EIAcAAAAAtcWpsxEWFBRo586dtue7d+/W+vXrFRQUpKioKD366KO66aabdMkll6hv376aPXu2fvrpJy1evFiS5O/vr1GjRmns2LEKCgqSn5+f7r//fiUkJOiCCy6QJPXv31/t27fXbbfdpldffVVpaWl6+umnNXr0aLm7uztjtwEAAAA0Ak4NW2vXrlXfvn1tz8eOHStJGjFihKZOnaprr71WH3zwgSZOnKgHHnhAbdu21bfffquLLrrI9po33nhDZrNZQ4YMUWlpqQYMGKD33nvPttzFxUWzZs3Svffeq4SEBHl7e2vEiBGaMGFC3e0oAAAAgEbHqWGrT58+MgzjlH3uuOMO3XHHHSdd7uHhocmTJ5/0xsiSFB0drV9++aXGdQIAAADA2aq312wBAAAAQENG2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA7g1LC1dOlSXXnllYqIiJDJZNLMmTNP2veee+6RyWTSm2++adeelZWlYcOGyc/PTwEBARo1apQKCgrs+mzYsEEXX3yxPDw8FBkZqVdffdUBewMAAAAA/+PUsFVYWKjOnTtr8uTJp+z3/fffa+XKlYqIiDhh2bBhw7R582bNmzdPs2bN0tKlS3X33Xfblufl5al///6Kjo5WUlKSXnvtNY0fP14ffvhhre8PAAAAABzj6syNDxw4UAMHDjxlnwMHDuj+++/XnDlzNHjwYLtlycnJmj17ttasWaPu3btLkt555x0NGjRIr7/+uiIiIjR9+nSVlZXpk08+kcViUXx8vNavX69JkybZhTIAAAAAqE31+potq9Wq2267TY8++qji4+NPWL5ixQoFBATYgpYkJSYmymw2a9WqVbY+l1xyiSwWi63PgAEDtG3bNmVnZ59026WlpcrLy7N7AAAAAMCZqtdh65VXXpGrq6seeOCBapenpaUpJCTErs3V1VVBQUFKS0uz9QkNDbXrc+z5sT7VmThxovz9/W2PyMjIv7MrAAAAABqZehu2kpKS9NZbb2nq1KkymUx1vv1x48YpNzfX9ti3b1+d1wAAAACg4aq3Yeu3335TRkaGoqKi5OrqKldXV+3du1ePPPKIYmJiJElhYWHKyMiwe11FRYWysrIUFhZm65Oenm7X59jzY32q4+7uLj8/P7sHAAAAAJypehu2brvtNm3YsEHr16+3PSIiIvToo49qzpw5kqSEhATl5OQoKSnJ9rqFCxfKarWqZ8+etj5Lly5VeXm5rc+8efPUtm1bBQYG1u1OAQAAAGg0nDobYUFBgXbu3Gl7vnv3bq1fv15BQUGKiopSkyZN7Pq7ubkpLCxMbdu2lSTFxcXp8ssv11133aUPPvhA5eXlGjNmjIYOHWqbJv6WW27R888/r1GjRunxxx/Xpk2b9NZbb+mNN96oux0FAAAA0Og4NWytXbtWffv2tT0fO3asJGnEiBGaOnXqGa1j+vTpGjNmjPr16yez2awhQ4bo7bffti339/fX3LlzNXr0aHXr1k3BwcF69tlnmfYdAAAAgEM5NWz16dNHhmGccf89e/ac0BYUFKQZM2ac8nWdOnXSb7/9drblAQAAAECN1dtrtgAAAACgISNsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAB3Bq2Fq6dKmuvPJKRUREyGQyaebMmbZl5eXlevzxx9WxY0d5e3srIiJCw4cP18GDB+3WkZWVpWHDhsnPz08BAQEaNWqUCgoK7Pps2LBBF198sTw8PBQZGalXX321LnYPAAAAQCPm1LBVWFiozp07a/LkyScsKyoq0rp16/TMM89o3bp1+u6777Rt2zZdddVVdv2GDRumzZs3a968eZo1a5aWLl2qu+++27Y8Ly9P/fv3V3R0tJKSkvTaa69p/Pjx+vDDDx2+fwAAAAAaL1dnbnzgwIEaOHBgtcv8/f01b948u7Z3331X559/vlJTUxUVFaXk5GTNnj1ba9asUffu3SVJ77zzjgYNGqTXX39dERERmj59usrKyvTJJ5/IYrEoPj5e69ev16RJk+xC2V+VlpaqtLTU9jwvL68W9hgAAABAY9GgrtnKzc2VyWRSQECAJGnFihUKCAiwBS1JSkxMlNls1qpVq2x9LrnkElksFlufAQMGaNu2bcrOzj7ptiZOnCh/f3/bIzIy0jE7BQAAAOCc1GDCVklJiR5//HHdfPPN8vPzkySlpaUpJCTErp+rq6uCgoKUlpZm6xMaGmrX59jzY32qM27cOOXm5toe+/btq83dAQAAAHCOc+pphGeqvLxcN954owzD0Pvvv18n23R3d5e7u3udbAsAAADAuafeh61jQWvv3r1auHChbVRLksLCwpSRkWHXv6KiQllZWQoLC7P1SU9Pt+tz7PmxPgAAAABQ2+r1aYTHgtaOHTs0f/58NWnSxG55QkKCcnJylJSUZGtbuHChrFarevbsaeuzdOlSlZeX2/rMmzdPbdu2VWBgYN3sCAAAAIBGx6lhq6CgQOvXr9f69eslSbt379b69euVmpqq8vJyXX/99Vq7dq2mT5+uyspKpaWlKS0tTWVlZZKkuLg4XX755brrrru0evVqLVu2TGPGjNHQoUMVEREhSbrllltksVg0atQobd68WV9++aXeeustjR071lm7DQAAAKARcOpphGvXrlXfvn1tz48FoBEjRmj8+PH68ccfJUldunSxe92iRYvUp08fSdL06dM1ZswY9evXT2azWUOGDNHbb79t6+vv76+5c+dq9OjR6tatm4KDg/Xss8+ectp3AAAAAPi7nBq2+vTpI8MwTrr8VMuOCQoK0owZM07Zp1OnTvrtt9/Ouj4AAAAAqKl6fc0WAAAAADRUhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAZwatpYuXaorr7xSERERMplMmjlzpt1ywzD07LPPKjw8XJ6enkpMTNSOHTvs+mRlZWnYsGHy8/NTQECARo0apYKCArs+GzZs0MUXXywPDw9FRkbq1VdfdfSuAQAAAGjknBq2CgsL1blzZ02ePLna5a+++qrefvttffDBB1q1apW8vb01YMAAlZSU2PoMGzZMmzdv1rx58zRr1iwtXbpUd999t215Xl6e+vfvr+joaCUlJem1117T+PHj9eGHHzp8/wAAAAA0Xq7O3PjAgQM1cODAapcZhqE333xTTz/9tK6++mpJ0qeffqrQ0FDNnDlTQ4cOVXJysmbPnq01a9aoe/fukqR33nlHgwYN0uuvv66IiAhNnz5dZWVl+uSTT2SxWBQfH6/169dr0qRJdqEMAAAAAGpTvb1ma/fu3UpLS1NiYqKtzd/fXz179tSKFSskSStWrFBAQIAtaElSYmKizGazVq1aZetzySWXyGKx2PoMGDBA27ZtU3Z29km3X1paqry8PLsHAAAAAJypehu20tLSJEmhoaF27aGhobZlaWlpCgkJsVvu6uqqoKAguz7VreP4bVRn4sSJ8vf3tz0iIyP/3g4BAAAAaFTqbdhytnHjxik3N9f22Ldvn7NLAgAAANCA1NuwFRYWJklKT0+3a09PT7ctCwsLU0ZGht3yiooKZWVl2fWpbh3Hb6M67u7u8vPzs3sAAAAAwJmqt2ErNjZWYWFhWrBgga0tLy9Pq1atUkJCgiQpISFBOTk5SkpKsvVZuHChrFarevbsaeuzdOlSlZeX2/rMmzdPbdu2VWBgYB3tDQAAAIDGxqlhq6CgQOvXr9f69eslVU2KsX79eqWmpspkMumhhx7Siy++qB9//FEbN27U8OHDFRERoWuuuUaSFBcXp8svv1x33XWXVq9erWXLlmnMmDEaOnSoIiIiJEm33HKLLBaLRo0apc2bN+vLL7/UW2+9pbFjxzpprwEAAAA0Bk6d+n3t2rXq27ev7fmxADRixAhNnTpVjz32mAoLC3X33XcrJydHF110kWbPni0PDw/ba6ZPn64xY8aoX79+MpvNGjJkiN5++23bcn9/f82dO1ejR49Wt27dFBwcrGeffZZp3wEAAAA4lFPDVp8+fWQYxkmXm0wmTZgwQRMmTDhpn6CgIM2YMeOU2+nUqZN+++23GtcJAAAAAGer3l6zBQAAAAANGWELAAAAAByAsAUAAAAADlCjsNWiRQsdOXLkhPacnBy1aNHibxcFAAAAAA1djcLWnj17VFlZeUJ7aWmpDhw48LeLAgAAAICG7qxmI/zxxx9tX8+ZM0f+/v6255WVlVqwYIFiYmJqrTgAAAAAaKjOKmwdu5mwyWTSiBEj7Ja5ubkpJiZG//rXv2qtOAAAAABoqM4qbFmtVklSbGys1qxZo+DgYIcUBQAAAAANXY1uarx79+7argMAAAAAzik1CluStGDBAi1YsEAZGRm2Ea9jPvnkk79dGAAAAAA0ZDUKW88//7wmTJig7t27Kzw8XCaTqbbrAgAAAIAGrUZh64MPPtDUqVN122231XY9AAAAAHBOqNF9tsrKynThhRfWdi0AAAAAcM6oUdi68847NWPGjNquBQAAAADOGTU6jbCkpEQffvih5s+fr06dOsnNzc1u+aRJk2qlOAAAAABoqGoUtjZs2KAuXbpIkjZt2mS3jMkyAAAAAKCGYWvRokW1XQcAAAAAnFNqdM0WAAAAAODUajSy1bdv31OeLrhw4cIaFwQAAAAA54Iaha1j12sdU15ervXr12vTpk0aMWJEbdQFAAAAAA1ajcLWG2+8UW37+PHjVVBQ8LcKAgAAAIBzQa1es3Xrrbfqk08+qc1VAgAAAECDVKtha8WKFfLw8KjNVQIAAABAg1Sj0wivu+46u+eGYejQoUNau3atnnnmmVopDAAAAAAashqFLX9/f7vnZrNZbdu21YQJE9S/f/9aKQwAAAAAGrIaha0pU6bUdh0AAAAAcE6pUdg6JikpScnJyZKk+Ph4de3atVaKAgAAAICGrkZhKyMjQ0OHDtXixYsVEBAgScrJyVHfvn31xRdfqGnTprVZIwAAAAA0ODWajfD+++9Xfn6+Nm/erKysLGVlZWnTpk3Ky8vTAw88UNs1AgAAAECDU6ORrdmzZ2v+/PmKi4uztbVv316TJ09mggwAAAAAUA1HtqxWq9zc3E5od3Nzk9Vq/dtFAQAAAEBDV6Owdemll+rBBx/UwYMHbW0HDhzQww8/rH79+tVacQAAAADQUNUobL377rvKy8tTTEyMWrZsqZYtWyo2NlZ5eXl65513artGAAAAAGhwanTNVmRkpNatW6f58+dr69atkqS4uDglJibWanEAAAAA0FCd1cjWwoUL1b59e+Xl5clkMumyyy7T/fffr/vvv189evRQfHy8fvvtN0fVCgAAAAANxlmFrTfffFN33XWX/Pz8Tljm7++vf/zjH5o0aVKtFQcAAAAADdVZha0///xTl19++UmX9+/fX0lJSX+7KAAAAABo6M4qbKWnp1c75fsxrq6uOnz48N8uCgAAAAAaurMKW82aNdOmTZtOunzDhg0KDw//20UBAAAAQEN3VmFr0KBBeuaZZ1RSUnLCsuLiYj333HO64ooraq04AAAAAGiozmrq96efflrfffed2rRpozFjxqht27aSpK1bt2ry5MmqrKzUU0895ZBCAQAAAKAhOauRrdDQUC1fvlwdOnTQuHHjdO211+raa6/Vk08+qQ4dOuj3339XaGhorRVXWVmpZ555RrGxsfL09FTLli31wgsvyDAMWx/DMPTss88qPDxcnp6eSkxM1I4dO+zWk5WVpWHDhsnPz08BAQEaNWqUCgoKaq1OAAAAAPirs76pcXR0tH755RdlZ2dr586dMgxDrVu3VmBgYK0X98orr+j999/XtGnTFB8fr7Vr12rkyJHy9/fXAw88IEl69dVX9fbbb2vatGmKjY3VM888owEDBmjLli3y8PCQJA0bNkyHDh3SvHnzVF5erpEjR+ruu+/WjBkzar1mAAAAAJBqELaOCQwMVI8ePWqzlhMsX75cV199tQYPHixJiomJ0eeff67Vq1dLqhrVevPNN/X000/r6quvliR9+umnCg0N1cyZMzV06FAlJydr9uzZWrNmjbp37y5JeueddzRo0CC9/vrrioiIcOg+AAAAAGiczuo0wrp24YUXasGCBdq+fbukqvt8/f777xo4cKAkaffu3UpLS1NiYqLtNf7+/urZs6dWrFghSVqxYoUCAgJsQUuSEhMTZTabtWrVqpNuu7S0VHl5eXYPAAAAADhTNR7ZqgtPPPGE8vLy1K5dO7m4uKiyslIvvfSShg0bJklKS0uTpBOuEwsNDbUtS0tLU0hIiN1yV1dXBQUF2fpUZ+LEiXr++edrc3cAAAAANCL1emTrq6++0vTp0zVjxgytW7dO06ZN0+uvv65p06Y5fNvjxo1Tbm6u7bFv3z6HbxMAAADAuaNej2w9+uijeuKJJzR06FBJUseOHbV3715NnDhRI0aMUFhYmCQpPT3d7mbK6enp6tKliyQpLCxMGRkZduutqKhQVlaW7fXVcXd3l7u7ey3vEQAAAIDGol6PbBUVFclsti/RxcVFVqtVkhQbG6uwsDAtWLDAtjwvL0+rVq1SQkKCJCkhIUE5OTlKSkqy9Vm4cKGsVqt69uxZB3sBAAAAoDGq1yNbV155pV566SVFRUUpPj5ef/zxhyZNmqQ77rhDkmQymfTQQw/pxRdfVOvWrW1Tv0dEROiaa66RJMXFxenyyy/XXXfdpQ8++EDl5eUaM2aMhg4dykyEAAAAABymXoetd955R88884zuu+8+ZWRkKCIiQv/4xz/07LPP2vo89thjKiws1N13362cnBxddNFFmj17tu0eW5I0ffp0jRkzRv369ZPZbNaQIUP09ttvO2OXAAAAADQSJsMwDGcX0RDk5eXJ399fubm58vPzc2ot69atU7du3TR28ndq3jreqbXs37FZk0Zfp6SkJJ133nlOrQUAAACoC2eaDer1NVsAAAAA0FARtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAArs4uAGenpLxSaQUVkomcjIYrNTVVmZmZzi5DkhQcHKyoqChnlwEAAM5BhK0GZvPBXN33y2FFPvy15h9yVVhZmkJ83dUhwl8WVwIY6r/U1FS1i4tTcVGRs0uRJHl6eWlrcjKBCwAA1DrCVgOTkVcqi4tUJnfllku5afnalpavP/fl6NJ2IYpu4u3sEoFTyszMVHFRkYY9/ppCo1o6tZb01BRNf+VRZWZmErYAAECtI2w1MAM7hiu4NEwXXnalrn/qPZn8w7TxQK7ySio0c/1BtQ/308Wtg+Xh5uLsUoFTCo1qqeat451dBgAAgMNw3lkD5GI2qSLnkCK8DPWICdKtPaPVubm/JGnLoTx9uXafisoqnFwlAAAA0LgRts4BFlez+rQN0Q3dmsvH3VU5ReX6Yf1BlVVYnV0aAAAA0GgRts4hEQGeuq5rM3m4mZWRX6qfNx5SpdVwdlkAAABAo0TYOscEelt0dedmcjWblJpVpHlb0mUYBC4AAACgrhG2zkFh/h4a3ClcZpO0LT1fSanZzi4JAAAAaHQIW+eomCbe6tM2RJK0cleWsgrLnFwRAAAA0LgQts5hHSL8FB3kpUqrofnJ6bJyOiEAAABQZwhb5zCTyaR+cSGyuJh1KLdE6/flOLskAAAAoNEgbJ3jfD3cdFHrYEnSipQjyinidEIAAACgLhC2GoEOEX6KDPRUhdXQ/OQMZicEAAAA6gBhqxEwmUxKjAuVq9mkAznF2p1Z6OySAAAAgHMeYauR8PN0U5fIAEnS8pQjTJYBAAAAOBhhqxHpHh0od1ezjhSWaeuhfGeXAwAAAJzTCFuNiLubi3rEBEmSVuw6oopKq5MrAgAAAM5dhK1GpnNzf/m4u6qgtEIb9uc6uxwAAADgnEXYamRcXczq2aJqdGvNniyVVlQ6uSIAAADg3ETYaoTah/kp0MtNJRVW/ZGa4+xyAAAAgHMSYasRMptNuqBFE0nSn/tyVM61WwAAAECtq/dh68CBA7r11lvVpEkTeXp6qmPHjlq7dq1tuWEYevbZZxUeHi5PT08lJiZqx44dduvIysrSsGHD5Ofnp4CAAI0aNUoFBQV1vSv1SqsQH/l7Vo1ubT6Y5+xyAAAAgHNOvQ5b2dnZ6tWrl9zc3PTrr79qy5Yt+te//qXAwEBbn1dffVVvv/22PvjgA61atUre3t4aMGCASkpKbH2GDRumzZs3a968eZo1a5aWLl2qu+++2xm7VG+YTSadFxUgSVqXmq1KK/fdAgAAAGqTq7MLOJVXXnlFkZGRmjJliq0tNjbW9rVhGHrzzTf19NNP6+qrr5YkffrppwoNDdXMmTM1dOhQJScna/bs2VqzZo26d+8uSXrnnXc0aNAgvf7664qIiKh226WlpSotLbU9z8s790Z/2of7aeWuLOWXVGhHRr7ahfk5uyQAAADgnFGvR7Z+/PFHde/eXTfccINCQkLUtWtXffTRR7blu3fvVlpamhITE21t/v7+6tmzp1asWCFJWrFihQICAmxBS5ISExNlNpu1atWqk2574sSJ8vf3tz0iIyMdsIfO5epiVpfIAElS0t5sGQajWwAAAEBtqddha9euXXr//ffVunVrzZkzR/fee68eeOABTZs2TZKUlpYmSQoNDbV7XWhoqG1ZWlqaQkJC7Ja7uroqKCjI1qc648aNU25uru2xb9++2ty1eqNTc3+5uZiUWVCmvVlFzi4HAAAAOGfU69MIrVarunfvrpdfflmS1LVrV23atEkffPCBRowY4dBtu7u7y93d3aHbqA883FzUoZm//kjNUdKebMU08XZ2SQAAAMA5oV6PbIWHh6t9+/Z2bXFxcUpNTZUkhYWFSZLS09Pt+qSnp9uWhYWFKSMjw255RUWFsrKybH0au66RATKbpP05xTqcX3r6FwAAAAA4rXodtnr16qVt27bZtW3fvl3R0dGSqibLCAsL04IFC2zL8/LytGrVKiUkJEiSEhISlJOTo6SkJFufhQsXymq1qmfPnnWwF/Wfr4ebWoX4SJL+3J/j3GIAAACAc0S9DlsPP/ywVq5cqZdfflk7d+7UjBkz9OGHH2r06NGSJJPJpIceekgvvviifvzxR23cuFHDhw9XRESErrnmGklVI2GXX3657rrrLq1evVrLli3TmDFjNHTo0JPORNgYdW4eIEnampavkvJK5xYDAAAAnAPqddjq0aOHvv/+e33++efq0KGDXnjhBb355psaNmyYrc9jjz2m+++/X3fffbd69OihgoICzZ49Wx4eHrY+06dPV7t27dSvXz8NGjRIF110kT788ENn7FK9Fe7voaY+7qq0GtzkGAAAAKgF9XqCDEm64oordMUVV5x0uclk0oQJEzRhwoST9gkKCtKMGTMcUd45w2QyqVOkvxYkZ2jD/hx1jQqQ2WRydlkAAABAg1XvwxbqTttQX/2+I1N5JRXac6RQLYJ9nF3SWUtNTVVmZqazy5AkBQcHKyoqytllAAAAwEkIW7BxczErPsJP61JztGFfboMLW6mpqWoXF6fiovpxvzBPLy9tTU4mcAEAADRShC3Y6dQ8QOtSc7Q3q0jZRWUK9LI4u6QzlpmZqeKiIg17/DWFRrV0ai3pqSma/sqjyszMJGwBAAA0UoQt2PH3dFNssLd2ZxZqw/5c9W7T1NklnbXQqJZq3jre2WUAAACgkavXsxHCOTo285ckbT2Up4pKq5OrAQAAABomwhZOEN3ESz7uriqpsGrn4QJnlwMAAAA0SIQtnMBsMik+wk+StPkA99wCAAAAaoKwhWrFR/jJJGl/TrGyi8qcXQ4AAADQ4BC2UC1fDzdFN/GSxOgWAAAAUBOELZxUh6MTZWw5lKdKq+HkagAAAICGhbCFk4pp4i0vi4uKyyu1i4kyAAAAgLNC2MJJuZhNah9eNVHGpoOcSggAAACcDW5qjFOKj/DT2r3ZSs0qUl5xufw83ZxdEgAAp5WamqrMzExnlyFJCg4OVlRUlLPLAOAEhC2cUoCXRZGBntqXXazNB/OU0LKJs0sCUEf4ZRUNVWpqqtrFxam4qMjZpUiSPL28tDU5me9hoBEibOG0OjTzrwpbh3LVMzZIZrPJ2SUBcDB+WUVDlpmZqeKiIg17/DWFRrV0ai3pqSma/sqjyszM5PsXaIQIWzitFk295enmosLSSu05UqgWTX2cXRIAB+OXVZwLQqNaqnnreGeXAaARI2zhtFzNZsWF+2pdao42HcwjbAGNCL+sAgBQc4QtnJH4CH+tS83RnsxC5ZeUy9eDiTIANE5cywYAOFOELZyRIG+LIgI8dDCnRFsO5alnLBNlAGh8uJYNAHA2CFs4Yx0i/HUwp0SbD+apR0yQzCYmygDQuHAtGwDgbBC2cMZah/hoyfbDyi+pUGpWkWKaeDu7JABwCq5lAwCcCbOzC0DD4epiVrswX0nSpgO5Tq4GAAAAqN8IWzgr8RH+kqTdmYUqLK1wcjUAAABA/UXYwllp6uuuUD93WQ0pOS3P2eUAAAAA9RZhC2etw9HRrc0H8mQYTi4GAAAAqKcIWzhrbUJ95eZiUk5xuTJLmZEQAAAAqA5hC2fN4mpW29CqiTJ2F/AtBAAAAFSH35RRI8cmyjhQZJbZw8fJ1QAAAAD1D2ELNRLq565gH4usMsm7fR9nlwMAAADUO9zUGDViMpnUIcJfi7cflk/nATKYKQMAnCI5OdnZJUiSgoODFRUV5ewyAKBeIWyhxtqG+WrpjgxZQmK1Patc3ZxdEAA0InlZhyVJt956q5MrqeLp5aWtyckELgA4DmELNebh5qJIL6v2FrpobkqRbr7M2RUBQONRXFB1r8PB/3hKbTs5989d6akpmv7Ko8rMzCRsAcBxCFv4W2J9qsLWsn3FyikqU4CXxdklAUCj0iQiWs1bxzu7DABANZggA39LkMVQWXqKyiqlb5L2O7scAAAAoN4gbOFvMZmk/D9+lSTNWJXKRBkAAADAUZxGiL+tMHmJml1xv3ZlFmpFyhFd2CrY2SUBAIBqpKamKjMz09llSGIGSzQOhC38bUZZsXpHe2pOSpH+u2ovYQsAgHooNTVV7eLiVFxU5OxSJDGDJRoHwhZqxYCWXpqTUqS5m9OVkVeiED8PZ5cEAACOk5mZqeKiIg17/DWFRrV0ai3MYInGgrCFWhET4KZu0YFK2putL9fs0/39Wju7JJxjDMNQTnG58orLVVhaqYKyChWXVcpqGFLVf3J1McnL4iIvN1d5ubso0MsiPw9XmUwmZ5ePc1BFpVUlFVaVlFceffzv67JKq8orDZVXWlVeaVWF7WtDlYYhwzBkGJLVMGRIMgzZrnk1m01yMZlkNplkNktmU9VzFxeT3FzMshx9HFFT+fUcogPlXnI/lCeLq9m23N3tf/+6mrk8G/ZCo1oygyVQRwhbqDW3XhClpL3Zmr4qVff0aSk3F37Ao+YqKq3al12s/dlFSs8r1eH8UpVVWs96PW4uJgV5W9TUx13NAjzVLNBTvh5uDqgYDZFhGMovrVBuUblyj4b53OMeOcXlyikqV05RmXKKynXwSK6a3TtFM/e5qTI1xcnVByuwz0jtLJd2bkk/aS8Xs0nuruajDxfb1xY3++e2r//S7mI28QcLAKihBhW2/vnPf2rcuHF68MEH9eabb0qSSkpK9Mgjj+iLL75QaWmpBgwYoPfee0+hoaG216Wmpuree+/VokWL5OPjoxEjRmjixIlydW1Qu1/vDeoYrpd+3qq0vBL9uilNV3WOcHZJaGBKyyu1PaNAuw4XaH92sSqs9rNbuphNCvB0k4+7q7zdXeVpcZHLsV8CTVUBraisUsVllSoorVBOUbnKKw2l55UqPa9Umw5W3QTWz8NVUU285FdmklwIXrXJMAyVVxq2EZ7i8kqVVlhVaTVUaTVkNY79K1UahqzW089gmpfjIv9et+iLTflamrX9jOqoOFpD8XEjTsVHayoorbCFqbzicp1BCXZc/Zqq8uhrTCbJw9VFHm5mebi5HH1UjSq5HX24uphkOfqvm8vR8CLJZDLJbJJMMslkqhrBMmTIaq0a8bIaxtH3qOp5hdVQeYVVZZVWlVVYdWBvinZsXKeYLr3k6R+k8grDtqzsaD9JqrQaKiqrVFFZpaTys9tZSS4mkyzHAlk1Ac3ialZxvlne8X21+kCJygKOyNfDVb7ubvL1cJWPhyt/fAPQaDWYtLFmzRr9+9//VqdOnezaH374Yf3888/6+uuv5e/vrzFjxui6667TsmXLJEmVlZUaPHiwwsLCtHz5ch06dEjDhw+Xm5ubXn75ZWfsyjnL3dVFw3pG6a0FOzRl2W7CFs6IYRhKzSrSlkN5SjlcqMrjfvP1cXdVTBMvhfl7KMTXQ0HeFrmYz/wv7FZr1amHRwpKlZZXogM5xcrIL1VeSYU2HciT5KbIBz7XK8uydJvrQfVrFypPi4sD9vLcUVFpVXZRubKLyqrCSkm58ksqVFBSYQszZxteTs9FARfdoq+2FEhbdtT2yiVJFlez/D3dqn0EelkU4OWmAC83ZR5M1X2jhmvkuFcV07qdLC5mp436JO1drpW/vKnBPdurS9dOJyy3GlXhrNT2qPzf1+WVdu1lx/c7uqyswipDVaG4+GhYVfHJqnFV8BWP6J/LsqVlK09Y6uFmlq9HVfjy9XCTr7vr0a+rnvscfR7oZVETH4uaeLuriY9FQd4WebjxmQTQcDWIsFVQUKBhw4bpo48+0osvvmhrz83N1X/+8x/NmDFDl156qSRpypQpiouL08qVK3XBBRdo7ty52rJli+bPn6/Q0FB16dJFL7zwgh5//HGNHz9eFovFWbt1Thp2QZTeX5yiP1Jz9EdqtrpGBTq7JNRTlVZp4/5crUvNVk7x//7a3sTborZhvopp4q1gH8vf+kXWbK46hTDI26LWob6SVDUikFOs3ZmF2pmWo2KLh1YdKNWqGX/I2+KiAR3CdHWXZurVsolcG/lf483u3kovMenQnixl5JUqI79EeSUVZ/RaF7OparTHtWq0x9Vs+t+1SGYd/bfquqTTHeGC3Cz9ufRXXX/DjWra9MxmO3UxmeRhcZHn0dEmz6MPdzezvC2u8veyD1Rn+gv9Omu6ytJ2ytu16g9M9ZnZZJK7m4vcaxhWjo1S2kJaeTXB7OiynOwc7dyyXp269ZTVxaL8kgrlHw3gko6OLladDny2fNxdFeR9LIT9L4iF+3sozN9T4f4eCvev+mMMpzsCqG8aRNgaPXq0Bg8erMTERLuwlZSUpPLyciUmJtra2rVrp6ioKK1YsUIXXHCBVqxYoY4dO9qdVjhgwADde++92rx5s7p27VrtNktLS1Va+r8fCnl5eQ7Ys3NPiK+Hrugcru/WHdCUZXsIWzhBYZlV/hcO1a8H3VRqzZBUNarQLtRX7SP8FOLr7tBfmCyuZsUGeys22FutzYf17oT/0/2v/ker0qzan12s79Yd0HfrDqiJt0VXdArXVV2a6byogHP+l7iKSqs2HczTmt1Z+nN/jtbuylDkQ1/q9wxJGUfs+rq7mhXkbVGAp1vVKIWnq3zdXeVlcbWdTudai9f57N9xWIvmfaB//PMunXdex1pZJ07PZDLJ4lp1CqHvafru35Gp5V89q1ceT9J5551nay+vtKqwtCp4HT8Kml9abgtkVY9y5ZVUKKeoTEcKynSksFRZhWUqrzRUUFqhgtIKpWaderpyi6vZFrzcK4sVcMlwpeSbVZ5ZKH9PN/l5uDb6P6AAqHv1Pmx98cUXWrdundasWXPCsrS0NFksFgUEBNi1h4aGKi0tzdbn+KB1bPmxZSczceJEPf/883+z+sbpjl6x+m7dAf2y8ZCeHBSnMH+mgYdUVFahqcv36L2FGQq4+FaVWiVfD1d1jQxQfIS/LK51/0uQySSVZ+zSsI5+en14V61LzdGP6w9o1oZDOlJYpmkr9mrair2KCvLSNV0idHXXZmrZ1KfO63QEq9XQ1rR8rdh1RCtSMrVqV5byS08ctfJ2NdQsyFchfh4K8a0aUfB0cznnwydqh5uLWQFeFgV4nf1ZJIZhKK+kQlmFZTpSUKojhWW2rw/nV50afCi3RAdzSpRZUKqyCqv2HinS3iNVocw/4Uatz5bWZx+0rdPb3UX+Hm5VI5seVaOafkdHN70sfF8DqH31Omzt27dPDz74oObNmycPj7r9hX3cuHEaO3as7XleXp4iIyPrtIaGqkMzf50fE6TVe7L035V79X8D2jq7JDhRRaVVn69O1VsLdiizoEySVJaZqovaRqhnp1Yyn8U1WI5kMpnULTpQ3aID9cwV7bUs5Yh++OOA5mxOU2pWkd5euFNvL9ypTs39dU2XZrqyc4Sa+ro7u+wzZhiGUg4X2sLVipQjyi6ynyzBz8NV58c2UdeoAHkWH9ZdQ/rroX99quatw51UNRozk8lkO80zNtj7lH3LKqxKPxq+DuUWa83mFL03dYbaXjRY5S4eyi2umiynsLRShaWVOphbcsI6XM0mu/Dl7+mmAM+qa/V8PdzO6npRADimXoetpKQkZWRk2J2SUFlZqaVLl+rdd9/VnDlzVFZWppycHLvRrfT0dIWFhUmSwsLCtHr1arv1pqen25adjLu7u9zdG84vUvXNyF4xWr0nSzNWp2rMpa24wLmRWrXriJ77cbO2puVLUtUIUSs3PXL9GEW++029CVp/5epiVu82TdW7TVMVl1VqXnK6Zv5xQEu2H9aG/bnasD9XL/2SrF6tgjWoQ5guax+qJj717/8X+7KKtCLliJanZGp5yhFl/OV6GS+Li3rEBOnClk10YctgtY/ws/1CuW5dnqwlBc4oGzhrFlezIoO8FBnkJUmKtKbrpfn/1oXXDlDz1tEyDEMl5db/TetfUq7coqNT/R89vbHCauhIYZmOFJadsH6TSfLz+F/48vd0Ozpi5yY/ghiAU6jXYatfv37auHGjXdvIkSPVrl07Pf7444qMjJSbm5sWLFigIUOGSJK2bdum1NRUJSQkSJISEhL00ksvKSMjQyEhIZKkefPmyc/PT+3bt6/bHWpELmsfqmYBnjqQU6xvkvbr1guinV0S6lBabole/iVZP/5ZdfqOv6ebHunfRjefH6WNf66XjLO/X5azeFpcdFXnCF3VOUJHCko1a8MhzVx/QH+k5mjp9sNauv2wnvx+o3rEBOmy9qG6uHVTtQn1qfPTkSqthnZk5Ctpb7aS9mZr9e4s7c+2nzrO4mpWt6jAqnDVqok6NQ9gSm40CiaTSZ4WF3laXKo9tb3SaiivpJr7rBVV3Wut0mrY2vZm/WXdqjolOsCr6jpGf69jocwiP896/WsWgDpQr/8v4Ovrqw4dOti1eXt7q0mTJrb2UaNGaezYsQoKCpKfn5/uv/9+JSQk6IILLpAk9e/fX+3bt9dtt92mV199VWlpaXr66ac1evRoRq4cyNXFrDsvjtXzP23Rv5emaGiPSC5MbgRKKyr1n993692FO1VUVimTSbr5/Cj9X/+2CvJu+DN/NvFx14gLYzTiwhjtySzUrA0HNWdzujYeyNWq3VlatTtLUrJCfN11UetgdY8OUteoALUJ9a31v3znFJVp04E8Je3N1tq9WVqfmnPCNVeuZpM6RwbowpZNlNCyic6LCmSUGaiGi9mkQC+LAqu5tswwDNu92Y6Fr5yjtz7IKSpXhbXq2rK8kgqlVrNuLxc3hdz0oj5Ym6seBbsU3cRLMcHeigry4vMINAL1OmydiTfeeENms1lDhgyxu6nxMS4uLpo1a5buvfdeJSQkyNvbWyNGjNCECROcWHXjMLRHlN5duFP7soo1a8MhXdO1mbNLggMt2pqhCbO2aHdmoSSpW3Sgnr8qXh2a+Tu5MseICfbWmEtba8ylrbU/u0hzN6dryfbDWrW76nS9Y7MaSlWn68WF+6lFsLdaNPVRbLC3mvq6V01j7WORj7ur3UjY8TO4ZRWW6WBOsQ7kFGt/drFSDhdoW1r+CacEHttOl8gAdY8O1HnRgeoeEyQf9wb/v3nAqUwm09F7hLmp+V8m2DWMqhtGV4WwMuUUHT8iVjWbYlGlSZ4xXTR3V5Hm7ko+br1SuJ+Hopt4KybYSzFNvG1fRwd5c88/4BzR4H4KL1682O65h4eHJk+erMmTJ5/0NdHR0frll18cXBn+ytPiojsuitVrc7bpvcU7dVXniHp7jQ5qbu+RQk34aYsWbK2axj3Yx11PDmqna7s2azQzezUP9NIdF8XqjotiVVJeqaS92Vqekqn1+3L0575cFZRW2E7vOxmzqeqv6yaZVFZ5ZqdZNg/0tE3qcV5UoNqF+TKCDNQhk8kkb3dXebu7qlmgp92yY0Fsx/bt+nbKu7rn/55RiZuv9hwp1J7MIhWUVuhgbokO5pZoxa4jJ6w7zM+jahSsibdigr0V08RL0U28Fd3ES978EQVoMPi0wqFuS4jWB4tTtD29QPOT09U//uSTkqBhKSqr0ORFO/XR0t0qq7TK1WzSyF4xeqBfa/l6uDm7PKfxcHNRr1bB6tWq6ua7lVZDOzMKtD09X7sOF2pXZoH2HCmqmsq6oMx201erIVkrDUnGcesyK9DLooiAqhu3NgvwVIum3mod6qvWIT6N+n0G6rtjQSzYw1DhpgW6peOrtgm/DMNQVmGZ9hwp0p7MQu09Ulj19ZFC7c4sVH5JhdLySpSWV3L09GR7oX7uahXio9YhvmoZ4qNWTX3UOtRHTbix89+SmpqqzMxMZ5chSQoODlZUVJSzy0AtIGzBofw83DT8wmhNXpSiyYt26rL2ofwgaOAMw9DPGw/ppZ+Tdejo9MkXtw7Wc1e2V6uQ0936tPFxMZvUNsxXbcOqf2+KyypVWFYhq9VQpWHIakjeFhd5u7syeQVwjjKZTGri464mPu7qFm1/bqJhGMopKq8aATs6CnZ8GMspKld6XqnS80q1bKf9iFiAl5tah/ioVYiPWoX4qlWIj9qF+Tr8ZvHngtTUVLWLi1Nx0alvnl1XPL28tDU5mcB1DiBsweFG9orVf37frT/352rZziO6qHWws0tCDW1Ly9f4HzfbTnlpHuippwe314B4QnRNHZshDQCkqiAW6G1RoLdFXaMCT1ieU1SmXZmF2pleoJ2HC7Qzo0A7MvK1P7tYOUXlWrMnW2v22J+yHOjlprhwPwWZS+TdoZ+yy0wKq7Ry2vFxMjMzVVxUpGGPv6bQqJZOrSU9NUXTX3lUmZmZhK1zAGELDhfs466hPaI0dfkevb1wh3q1asIv5g3M4fxSTZq3XV+uSZXVkNxdzbq3T0vd07sls2kBQB0K8LLovCiLzvtLECsuq1TK4QKlHAtg6VUhbHdmobKLyrU8peqPZMGDH9bCNGlReooCvSwK9rEo2MddTX3cFeLnLi9L4/7VMDSqpZq3jnd2GTiHNO5PFOrM3Ze00IzVqVq9O0uLtx9W37Yhzi4JZ6CkvGoq9/cW7VRhWdW1RQM7hOnJQXG2m4cCAJzP0+KiDs38T5gBtqS8UjvSC5R8KE9LNuzUt/NXyje2o8qtJmUVlimrsEzb0/93A3NfD1eF+nooxM9doX4eCvV1lzt/VANqjLCFOhER4KnbL4zRh0t36ZVft6p366bMTFiPWa2GfvjzgF6bvU0Hj16X1bm5v56+or16xAQ5uToAwJnycHNRx+b+6tjcX61cDuu9O5/ULe9+p8CotjpcUKrMo4/D+aXKLipXfkmF8kuqTlE8JsDT7bjwVRXEuKYUODOELdSZ+/q01OerU7U1LV8//HlA13Zt7uySUI2Vu47o5V+StWF/riSpWYCnHru8ra7sxNT9AHAuMJkkHw9X+Xi4KjbY29ZeWlGpjLxSZeSXKj2vROl5Jcorqai6kXNxuW0EzGSSQnzdFeHvaZstlenogerxyUCdCfCy6N4+LfXq7G16fc52DeoYLndXTk2oL1buOqK35u+wTX7h4+6qe/u01KiLYs/567KSk5NP36kOMNUvAGdyd3VRZJCX3WnixeWVysgrOToDYonS80tUWFppmxHxj305kqpGvyICPBUR4KGIAE8FeLpxfTYgwhbq2MgLYzVt+R4dyCnWf1ematRFsc4uqVEzDENLth/W+4tTbPdycXMx6aYekXqwXxs19XV3coWOlZd1WJJ06623OrmSKkz1C6C+8XRzOXoz5f+NgOWVlOtgTrEO5pToYG6xjhSU2Ua/thzKs70uIsBDzQO9FBnoqSDuAYZGirCFOuVpcdHDiW30xHcb9e7CHbqhe3P5cWPWOldSXqnv1h3QJ8t2a2dG1WkhFhezbuzRXPf2aaVmAZ5OrrBuFBdU/VIw+B9PqW2nbk6thal+ATQUfh5u8gtzU7swP0lSaXmlDuVWBa+DOVU3Yy4ur1TK4UKlHC6UJHlZXBQZ6KXIIE8mWEKjQthCnbu+W3N99NsupRwu1KS52zX+KqZYrStbDubpq7X7NHP9AeUUlUuqOl3wxu6RuuuSWIX7N46Q9VdNIqKZ6hcAasjdzUUxwd6KOXr9V4XVqsP5pTqQXax92cU6mFOsorJKbUvP17b0fEmSt6ubAhPv0bpDJWpfXnnOn66OxouwhTrn6mLW+Kviddt/Vmvaij26tmszdY4McHZZ56wDOcX6deMh/bD+oDYeyLW1Nwvw1MheMbqpR6R8GV0EANQSV7NZ4f6eCvf3VPeYqvCVllui1Kwi7csqVnpeiQorTPLrdoVe/C1br62YqwtbNlHfdiHq2zaEkS+cUwhbcIqLWzfVNV0iNHP9QY37bqN+HNOLO9nXEqvV0Na0fC3ZflizN6fpz6MXL0uSq1k6P8JDl8Z6qnOou1zMOdqxJeek63KE+jIZBRqe+vC9Ux9qABoaV7NZzQO91DzQS2pZNevh+s3bNG/+fMUkDNaRYqsWbTusRdsOS9qslk29lRgXqv7xYeoaGcBMuGjQCFtwmqevaK/F2w9ry6E8fbJst+6+pKWzS2qQrFZDuzILlLQ3Wyt3Zem3HZnKLCi1LTeZpM7h3vrt87eV8+cCpRTn6XMn1ntMQUHB6TsBqn8TmUh8/55MfQmj9aUOVM/d1UXNvAxlzZmsuS+NlHez1lq09bAWbctQ0t7so9d67dK/l+5SiK+7+seHakB8mC5o0YT7e6HBIWzBaYJ93PXkwDg99u0GTZq3XQM7hHPqwGmUlFdqZ0aBtqXla3t6vram5Wv9vhzlFpfb9fOyuCihRdUpGf3jQ7V/xxZ1e/B7DXv8NYVGOTfUJq9eol+nvaWSkhKn1oGGoz5NZML3b/XqYyCWCMUNgclkUrswP7UL89O9fVoqt7hcv+04rLmb07Vwa4Yy8kv135Wp+u/KVPl5uKpfXKgGdghT77ZNuX0MGgTCFpzqhu7N9e26/Vq1O0tPfr9R00aez+kCksorrdqTWaht6fnanlZ1QfH29ALtPVIoq3Fifw83szo3D1CPmCBd1DpY50UFyuL6v7/+7T/6b2hUS6dPBJGemuLU7aPhqg8TmfD9W736FIil+hmK68NoW32o4XT8Pd10RacIXdEpQqUVlVqeckRzN6dp3pZ0ZRaU6fs/Duj7Pw7I18NVA+LDdFXnCF3YsgmXIqDeImzBqUwmk16+rqMGvfWbftuRqfeXpGh031bOLqvOGIahtLwSbTqQp+3p+bYRq5TDBSqvrCZVSQrwclPbUF+1DfNV61BfdWrmr/YRfpxaAcDp6kMglupXKK6Po34NZcTP3dVFfdtWTZrx4jWG1qVma/amNP284ZDS8kr0TdJ+fZO0X028LRrYMUxXdW6m7tGB/NEW9QphC07XsqmPJlwdr8e/3ah/zd2mrlEBurBlsLPLqnWGYehQbok2HsjVpgO5tn8zC8qq7e9tcVGbMF+1Da0KVW1DfdUmzEdNfdy5MSQANBD1adSvPo74nSkXs0k9YoLUIyZITw2K05o9Wfppw0H9sjFNRwrLbKcaRvh76LrzmmtIt+aKDfY+/YoBByNsoV64sXuk1uzJ1jdJ+/XA53/o5wcuVqifh7PL+ttSc8u1cfkerdx1RGv2ZFUbrFzMJrUO8VFcuJ/ahPqqbZiP2oT6qlmAJ6EKAM4R9WHUrz6N+P0dZrNJPVs0Uc8WTTT+yngtSzmin/48qDmb0nQwt0TvLtqpdxftVLfoQA05r7kGdwqXvye3OIFzELZQL5hMJr1wdQdtOpCrrWn5un/GH5pxV88Gdw52WYVV+7KLtOmIi5rdN00PzcmUlGlb7mo2qXWorzo281PHZv7q0MxfceF+3MwRAIAacHUxq3ebpurdpqlevKaD5ien65uk/Vq6/bCS9mYraW+2nv9ps/rHh+mGbs11UatgTjNEnSJsod7wtLjovWHn6ap3l2n1niw9/u1GvXZ9p3r/P8XSikrtPlyo7RkFSj1SpErDkOQiV98msrhI58cGK6FlE13QIkjxEf4EKwAAHMDDzcU2uUZ6Xolm/nFA367br+3pBfrpz4P66c+Dah7oqZvPj9IN3Zor5Bw4gwb1H2EL9UqLpj5646Yuuue/Sfp23X65uZj08rUd613gqrQa2nukUFsO5WnPkSJVHjdFoL+nm4JdSrT8P89p8Vcfq2cP58/MBQBAYxLq56F/9G6puy9poY0HcvVN0n59/8cB7c8u1mtztmnSvO1KjAvRLT2jdXGrc+86cdQfhC3UO5e1D9WkGzvr4S/X64s1++TqUnWKYX24fulIQam2HMrT1rR8FZVV2toDvdzUJtRXrUN8FORt0YGdW7Rwz3q5uTi/ZgAAGiuTyaROzQPUqXmAxg2M0y8bD2nG6lQl7c3WnM3pmrM5Xc0DPXVJM1eZvfydXS7OQYQt1EtXd2mmikpD//fNn/rvylSZZNKzV7Z3yvTmpRWV2p5eoC0H85SW978ZnDzdXBQX7qt2YX4K9rHUizAIAACq52lx0ZBuVTMVbkvL1+erU/Xduv3an12sGdlS83unak2mWa4hJQrz5xRD1A7CFuqtId2aq8Jq1ePfbtRnK/cq+VCe3r65qyICPB2+bcMwtC+7WFsO5Sklo0AVR08TNJmk2Cbeah/hp5gm3nKpZ6c3AgCA02sb5qvxV8XriYHt9POGQ/pgwRbtyJJSi6TUtfsU6ueuzs0D1DrUR67mhjVZF+oXwhbqtZt6RMnXw02PfbNBa/dma/Dbv2nSjV3Ut12IQ7aXW1yuLYfylHwoT/klFbb2IG+L4sP91DbMV97ufGwAADgXeLhVjXbFmjKUcMXN6nnvazpQ5Kr0vFLN3ZKu33ZkqkMzP3VuHsDPf9QI3zWo9wZ1DFf7cD+N+XydNh3I08ipa3Rl5wjdf2krtQn1/dvrLymv1O7Mqsku9mcX29otrma1CfVRfLi/Qv24kTAAAOeyskPb1aNJpQac10qbDuZp4/5cFZRWaM2ebK3bm6O2Yb7qGhWgYB93Z5eKBoSwhQYhJthb3957oV7+OVnTVuy1TeE6qGOY7ry4hTo3DzjjdRmGodzicu09UqSUzAIdyC7WcZMJKjLIU+3D/dSqqU+Du88XAAD4e7wsrjo/JkjdowKVklmgP1JzdCi3RFsO5WnLoTxFB3npvOhARQZ68odYnBZhCw2Gu6uLnr+6g27sEal3F+7Ur5vS9MvGqoe/p5viglzk03WwDhaZZMoulrtbVVAqLK1QYVmlCkoqlJZXorTcEhWXV9qtO8jbojYhPooL95Mfd5kHAKDRM5tNah3iq9YhvjqUW6x1qTlKySjQ3qwi7c0qUrCPRedFBapNqC/XcOOkCFtocOIj/PX+rd20LS1f7y3eqQXJGcotLtfKA+Vq0v9erciUlLn/lOtwMZkU6ueuFk191KKptwK9LHVTPAAAaHDC/T01uKOncovL9UdqtrYcylNmQZnmbknXspRMdW4eoI7N/OXh5uLsUlHPELbQYLUN89VbQ7uqotKqDQdy9c3SDfr4h0VqFtdNhou7SiusMmTI2+Iqb3dXeVtcFOzrrnB/DzX1dWd2IQAAcFb8Pd3Up22ILmjRRBsP5OrPfTkqLK3U8pQjWrMnS/ER/jovKkC+HpwlgyqELTR4ri5mnRcVKLX31cTbntdtk79T89ZtnV0WAAA4R3m4uahHTJDOiwrUtvR8rUvN1pGCMq3fl6MN+3MUF+6nbtGBnDkDwhYAAABQEy5mk9qH+ykuzFepWUVauydb+3OKtflgnjYfzFPrEB91jwlUiC83SW6sCFsAAADA32AymRTdxFvRTbx1KLdYa/Zka3dmoXZkFGhHRoGim3ipR3SQmgV6OrtU1DHCFgAAAFBLwv09dVVnT2UWlGrtnmxtT8/X3iNF2nukSOH+HuoRE6SYJl5MG99IELYAAACAWhbs467LO4TpghZBSkrNVvLBfB3KLdGPfx5UsI9F58cEqVWID6HrHEfYAgAAABwkwMuifu1C1TO2if5IzdbGA7nKLCjTL5vSFORdFbpah/rITOg6JxG2AAAAAAfzcXfVxa2bqkdMkNbvy9Ef+3KUVVim2ZvTtGq3m86PCVKbUF9nl4laRtgCAAAA6oiHm4suaNFEXaMC9Oe+XK1LzVZ2UbnmbEnXyt1ZauVplszcHPlcwV1dAQAAgDrm7uqi82ODNLJXjC5s2UQebmblFpcrKctVEXf9W/N2FamswursMvE3MbIFAAAAOIm7a9UNkjs3D9DGA7las+uwFBCm99fm6ustc3VdO2/1i/WSm4tzrukKDg5WVFSUU7Z9Lqj3YWvixIn67rvvtHXrVnl6eurCCy/UK6+8orZt29r6lJSU6JFHHtEXX3yh0tJSDRgwQO+9955CQ0NtfVJTU3Xvvfdq0aJF8vHx0YgRIzRx4kS5utb7twAAAADnOIurWd2iA2U5tEHf/DhLfj2vV6aC9OG6PL23ZJfyVn6jgg1zZVSU1Wldnl5e2pqcTOCqoXqfNJYsWaLRo0erR48eqqio0JNPPqn+/ftry5Yt8vb2liQ9/PDD+vnnn/X111/L399fY8aM0XXXXadly5ZJkiorKzV48GCFhYVp+fLlOnTokIYPHy43Nze9/PLLztw9AAAAwKasMFf5a3/URd06yy2wm7bnuajYN1hBl92jiAH/UBu/SsX6WOVaBxcDpaemaPorjyozM5OwVUP1PmzNnj3b7vnUqVMVEhKipKQkXXLJJcrNzdV//vMfzZgxQ5deeqkkacqUKYqLi9PKlSt1wQUXaO7cudqyZYvmz5+v0NBQdenSRS+88IIef/xxjR8/XhaLxRm7BgAAAFSraUSkupwXp4usVm05mKe1e7OVX1KhDTmu2lHoom7RgerYzF+WukhdqLEGd3Ryc3MlSUFBQZKkpKQklZeXKzEx0danXbt2ioqK0ooVKyRJK1asUMeOHe1OKxwwYIDy8vK0efPmardTWlqqvLw8uwcAAABQl1zNZnVqHqARCTHq1y5Efh6uKi6v1O87MzVl+W6t3ZPFRBr1WIMKW1arVQ899JB69eqlDh06SJLS0tJksVgUEBBg1zc0NFRpaWm2PscHrWPLjy2rzsSJE+Xv7297REZG1vLeAAAAAGfGxWxSh2b+Gp4Qo8viQuXv6aaScquWpRzR1OV7lLQ3W+WVhK76pkGFrdGjR2vTpk364osvHL6tcePGKTc31/bYt2+fw7cJAAAAnIqL2aT2EX4afkG0LmtfFbpsI13L9mgdoateqffXbB0zZswYzZo1S0uXLlXz5s1t7WFhYSorK1NOTo7d6FZ6errCwsJsfVavXm23vvT0dNuy6ri7u8vd3b2W9wIAAAD4+8xmk9qH+6ldqK+S0/K0eneW8koq9NvOTCWlZqtbdKA6NfOXq0uDGls559T7d98wDI0ZM0bff/+9Fi5cqNjYWLvl3bp1k5ubmxYsWGBr27Ztm1JTU5WQkCBJSkhI0MaNG5WRkWHrM2/ePPn5+al9+/Z1syMAAABALTObTYqPqDq9sF9c1TVdRWWV+m1HpqYs36M/UrNVwUiX09T7ka3Ro0drxowZ+uGHH+Tr62u7xsrf31+enp7y9/fXqFGjNHbsWAUFBcnPz0/333+/EhISdMEFF0iS+vfvr/bt2+u2227Tq6++qrS0ND399NMaPXo0o1cAAABo8FzMJnWI8FdcmJ+SD+Vp9Z4s5ZdUaOmOTCXtzVb3mCB1iPBjpKuO1fuw9f7770uS+vTpY9c+ZcoU3X777ZKkN954Q2azWUOGDLG7qfExLi4umjVrlu69914lJCTI29tbI0aM0IQJE+pqNwDgjCQnJzu7BEn1pw4AwNk5NpFGXLifthzK05qjoWvJ9sNauzdLPaKDFE/oqjP1PmwZhnHaPh4eHpo8ebImT5580j7R0dH65ZdfarM0AKg1eVmHJUm33nqrkyuxV1BQ4OwSAAA14GI2qWMzf8WF+2rLwTyt2ZOtgtIKLd5+WGv3Zqt7TGBV6DITuhyp3octAGgMiguq7uU3+B9PqW2nbk6uRkpevUS/TntLJSUlzi4FAPA3HLtPV/sIP20+mKe1x0LXtsNauydbPWICFR/hLxezydmlnpMIWwBQjzSJiFbz1vHOLkPpqSnOLgEAUItczWZ1bh6g+PCq0LVmb5YKSiu0aFvVSFePmCC1D/cjdNUywhYAAADQSLi6mNU5MkDxEX7adDBPa49e07Vwa4bW7MnS+TFBiiN01RrCFgAAANDIuLqY1SUyQB0i/LTxQK7W7s1WfkmFFhwLXbFB8j391Ak4DcIWAAAA0Ei5upjVNSpQHZr5a+OBXCXtzVZeSYXmJ2fI29VN3h36qdJK6qopph8BAAAAGjk3F7POiwrU7RfG6OJWwfJ0c1FhhUnBgx/WA7MP6/s/9hO6aoCwBQAAAEDS0dAVHaiRvWLUIaBClUW5OlRQqYe//FP931iiH/88KCuh64xxGiEAAACcor7cQL2+1FGfuLmY1dbPql+eHaUnp8zRrJQSpRwu1AOf/6F3F+7Qw4ltNCA+TGYm0jglwhYAAADqFDdybziM8hJdF+ejx4ckaMqyPfrot13anl6ge6evU1y4nx5ObK3L2ofKZCJ0VYewBQAAgDrFjdwbHl8PNz3Qr7VGXBij//y+W5/8vlvJh/J092dJ6tDMT2Mva6O+bUMIXX9B2AIAAIBTcCP3hsff001jL2ujO3rF6KPfdmnKsj3adCBPd0xdq86RARp7WRtd0jqY0HUUE2QAAAAAOCsBXhY9OqCdfnusr/7Ru4U83Vz0574cjfhkta7/YIWW7cyUYTCRBmELAAAAQI008XHXuIFxWvpYX426KFburmYl7c3WsI9X6aYPV2rlriPOLtGpCFsAAAAA/pamvu565or2WvpYX91+YYwsLmat3p2loR+u1LCPV2rtnixnl+gUhC0AAAAAtSLUz0Pjr4rXksf66NYLouTmYtKynUd0/QcrNPyT1fojNdvZJdYpwhYAAACAWhXu76kXr+moRf/XRzefHylXs0lLtx/Wte8t1x1T12jj/lxnl1gnCFsAAAAAHKJ5oJcmXtdJCx/poxu6NZeL2aSFWzN05bu/665P12rzwXM7dBG2AAAAADhUVBMvvXZDZ80f21vXdm0ms0matyVdg9/+Xff+N0nb0vKdXaJDELYAAAAA1InYYG+9cVMXzX34El3ZOUImk/TrpjRd/tZSjZmxTjszzq3QRdgCAAAAUKdahfjqnZu7avaDl2hQxzAZhjRrwyFd9sZSPfTFH9p1uMDZJdYKwhYAAAAAp2gb5qv3hnXTLw9crP7tQ2UY0sz1B5U4aYn+7+s/tfdIobNL/FtcnV0AAAAAgPorOTm5TrZzTwezLosI1peb87X2UKm+Sdqv79bt18VRnrqunbe6tgxXVFRUndRSWwhbAAAAAE6Ql3VYknTrrbfW+bYt4W0U0OsWebbsriV7i7V4T6FKU2bp8ydvVd/z2tZ5PTVF2AIAAABwguKCPEnS4H88pbadujmlhuyycm3NddHBYrM8Wl2glSkZhC0AAAAA54YmEdFq3jreKdtuLqmjpM2bt+j7X+fp0iF3OKWOmmKCDAAAAAD1mr/FUNbc9+TmYnJ2KWeFsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4QKMKW5MnT1ZMTIw8PDzUs2dPrV692tklAQAAADhHNZqw9eWXX2rs2LF67rnntG7dOnXu3FkDBgxQRkaGs0sDAAAAcA5qNGFr0qRJuuuuuzRy5Ei1b99eH3zwgby8vPTJJ584uzQAAAAA5yBXZxdQF8rKypSUlKRx48bZ2sxmsxITE7VixYpqX1NaWqrS0lLb89zcXElSXl6eY4s9AwUFBZKk/Ts2q7S4yKm1HN6/W5KUlJRkq8tZtm3bJon3pTr16b1JT02RJKXt2a4Uby9qqYe1SPWrHmqhlrNVn+qhlvpfi1S/6qGW6h373aqgoKBe/D5+rAbDME7Zz2Scrsc54ODBg2rWrJmWL1+uhIQEW/tjjz2mJUuWaNWqVSe8Zvz48Xr++efrskwAAAAADci+ffvUvHnzky5vFCNbNTFu3DiNHTvW9txqtSorK0tNmjSRyWRyYmVVSToyMlL79u2Tn5+fU2vByXGcGgaOU8PAcWoYOE4NA8epYeA41W+GYSg/P18RERGn7NcowlZwcLBcXFyUnp5u156enq6wsLBqX+Pu7i53d3e7toCAAEeVWCN+fn58+BoAjlPDwHFqGDhODQPHqWHgODUMHKf6y9/f/7R9GsUEGRaLRd26ddOCBQtsbVarVQsWLLA7rRAAAAAAakujGNmSpLFjx2rEiBHq3r27zj//fL355psqLCzUyJEjnV0aAAAAgHNQowlbN910kw4fPqxnn31WaWlp6tKli2bPnq3Q0FBnl3bW3N3d9dxzz51wmiPqF45Tw8Bxahg4Tg0Dx6lh4Dg1DBync0OjmI0QAAAAAOpao7hmCwAAAADqGmELAAAAAByAsAUAAAAADkDYAgAAAAAHIGzVU5MnT1ZMTIw8PDzUs2dPrV69+pT9v/76a7Vr104eHh7q2LGjfvnllzqqtHE7m+M0depUmUwmu4eHh0cdVtv4LF26VFdeeaUiIiJkMpk0c+bM075m8eLFOu+88+Tu7q5WrVpp6tSpDq+zsTvb47R48eITPksmk0lpaWl1U3AjNXHiRPXo0UO+vr4KCQnRNddco23btp32dfx8qls1OU78fKp777//vjp16mS7YXFCQoJ+/fXXU76Gz1LDRNiqh7788kuNHTtWzz33nNatW6fOnTtrwIABysjIqLb/8uXLdfPNN2vUqFH6448/dM011+iaa67Rpk2b6rjyxuVsj5NUdRf4Q4cO2R579+6tw4obn8LCQnXu3FmTJ08+o/67d+/W4MGD1bdvX61fv14PPfSQ7rzzTs2ZM8fBlTZuZ3ucjtm2bZvd5ykkJMRBFUKSlixZotGjR2vlypWaN2+eysvL1b9/fxUWFp70Nfx8qns1OU4SP5/qWvPmzfXPf/5TSUlJWrt2rS699FJdffXV2rx5c7X9+Sw1YAbqnfPPP98YPXq07XllZaURERFhTJw4sdr+N954ozF48GC7tp49exr/+Mc/HFpnY3e2x2nKlCmGv79/HVWHv5JkfP/996fs89hjjxnx8fF2bTfddJMxYMAAB1aG453JcVq0aJEhycjOzq6TmlC9jIwMQ5KxZMmSk/bh55Pznclx4udT/RAYGGh8/PHH1S7js9RwMbJVz5SVlSkpKUmJiYm2NrPZrMTERK1YsaLa16xYscKuvyQNGDDgpP3x99XkOElSQUGBoqOjFRkZecq/YME5+Cw1LF26dFF4eLguu+wyLVu2zNnlNDq5ubmSpKCgoJP24TPlfGdynCR+PjlTZWWlvvjiCxUWFiohIaHaPnyWGi7CVj2TmZmpyspKhYaG2rWHhoae9HqEtLS0s+qPv68mx6lt27b65JNP9MMPP+i///2vrFarLrzwQu3fv78uSsYZONlnKS8vT8XFxU6qCn8VHh6uDz74QN9++62+/fZbRUZGqk+fPlq3bp2zS2s0rFarHnroIfXq1UsdOnQ4aT9+PjnXmR4nfj45x8aNG+Xj4yN3d3fdc889+v7779W+fftq+/JZarhcnV0A0FgkJCTY/cXqwgsvVFxcnP7973/rhRdecGJlQMPStm1btW3b1vb8wgsvVEpKit544w199tlnTqys8Rg9erQ2bdqk33//3dml4BTO9Djx88k52rZtq/Xr1ys3N1fffPONRowYoSVLlpw0cKFhYmSrngkODpaLi4vS09Pt2tPT0xUWFlbta8LCws6qP/6+mhynv3Jzc1PXrl21c+dOR5SIGjjZZ8nPz0+enp5Oqgpn4vzzz+ezVEfGjBmjWbNmadGiRWrevPkp+/LzyXnO5jj9FT+f6obFYlGrVq3UrVs3TZw4UZ07d9Zbb71VbV8+Sw0XYauesVgs6tatmxYsWGBrs1qtWrBgwUnP401ISLDrL0nz5s07aX/8fTU5Tn9VWVmpjRs3Kjw83FFl4izxWWq41q9fz2fJwQzD0JgxY/T9999r4cKFio2NPe1r+EzVvZocp7/i55NzWK1WlZaWVruMz1ID5uwZOnCiL774wnB3dzemTp1qbNmyxbj77ruNgIAAIy0tzTAMw7jtttuMJ554wtZ/2bJlhqurq/H6668bycnJxnPPPWe4ubkZGzdudNYuNApne5yef/55Y86cOUZKSoqRlJRkDB061PDw8DA2b97srF045+Xn5xt//PGH8ccffxiSjEmTJhl//PGHsXfvXsMwDOOJJ54wbrvtNlv/Xbt2GV5eXsajjz5qJCcnG5MnTzZcXFyM2bNnO2sXGoWzPU5vvPGGMXPmTGPHjh3Gxo0bjQcffNAwm83G/PnznbULjcK9995r+Pv7G4sXLzYOHTpkexQVFdn68PPJ+WpynPj5VPeeeOIJY8mSJcbu3buNDRs2GE888YRhMpmMuXPnGobBZ+lcQtiqp9555x0jKirKsFgsxvnnn2+sXLnStqx3797GiBEj7Pp/9dVXRps2bQyLxWLEx8cbP//8cx1X3DidzXF66KGHbH1DQ0ONQYMGGevWrXNC1Y3HsSnC//o4dlxGjBhh9O7d+4TXdOnSxbBYLEaLFi2MKVOm1Hndjc3ZHqdXXnnFaNmypeHh4WEEBQUZffr0MRYuXOic4huR6o6RJLvPCD+fnK8mx4mfT3XvjjvuMKKjow2LxWI0bdrU6Nevny1oGQafpXOJyTAMo+7G0QAAAACgceCaLQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAjurTp48eeughZ5cBADhHmAzDMJxdBAAAdWnx4sXq27evsrOzFRAQYGvPysqSm5ubfH19nVfccUwmk77//ntdc801zi4FAFADrs4uAACA+iIoKMjZJZy1srIyWSwWZ5cBAKgGpxECAP6Wb775Rh07dpSnp6eaNGmixMREFRYWSpI+/vhjxcXFycPDQ+3atdN7771ne92ePXtkMpn01Vdf6eKLL5anp6d69Oih7du3a82aNerevbt8fHw0cOBAHT582Pa6NWvW6LLLLlNwcLD8/f3Vu3dvrVu3zq4mk8mkjz/+WNdee628vLzUunVr/fjjj7bt9u3bV5IUGBgok8mk22+/XdKJpxGWlpbq8ccfV2RkpNzd3dWqVSv95z//OaP3ZdOmTRo4cKB8fHwUGhqq2267TZmZmbblffr00QMPPKDHHntMQUFBCgsL0/jx423LY2JiJEnXXnutTCaT7fn48ePVpUsXffzxx4qNjZWHh4ckKTU1VVdffbV8fHzk5+enG2+8Uenp6bb1HXvdv//9b0VGRsrLy0s33nijcnNzJUlLly6Vm5ub0tLS7PbjoYce0sUXX3xG+wwAsEfYAgDU2KFDh3TzzTfrjjvuUHJyshYvXqzrrrtOhmFo+vTpevbZZ/XSSy8pOTlZL7/8sp555hlNmzbNbh3PPfecnn76aa1bt06urq665ZZb9Nhjj+mtt97Sb7/9pp07d+rZZ5+19c/Pz9eIESP0+++/a+XKlWrdurUGDRqk/Px8u/U+//zzuvHGG7VhwwYNGjRIw4YNU1ZWliIjI/Xtt99KkrZt26ZDhw7prbfeqnb/hg8frs8//1xvv/22kpOT9e9//1s+Pj6nfV9ycnJ06aWXqmvXrlq7dq1mz56t9PR03XjjjXb9pk2bJm9vb61atUqvvvqqJkyYoHnz5kmqCpWSNGXKFB06dMj2XJJ27typb7/9Vt99953Wr18vq9Wqq6++WllZWVqyZInmzZunXbt26aabbrLb3s6dO/XVV1/pp59+0uzZs/XHH3/ovvvukyRdcsklatGihT777DNb//Lyck2fPl133HHHafcZAFANAwCAGkpKSjIkGXv27DlhWcuWLY0ZM2bYtb3wwgtGQkKCYRiGsXv3bkOS8fHHH9uWf/7554YkY8GCBba2iRMnGm3btj1pDZWVlYavr6/x008/2dokGU8//bTteUFBgSHJ+PXXXw3DMIxFixYZkozs7Gy7dfXu3dt48MEHDcMwjG3bthmSjHnz5p3mXTjRCy+8YPTv39+ubd++fYYkY9u2bbZtXXTRRXZ9evToYTz++ON2+/H999/b9XnuuecMNzc3IyMjw9Y2d+5cw8XFxUhNTbW1bd682ZBkrF692vY6FxcXY//+/bY+v/76q2E2m41Dhw4ZhmEYr7zyihEXF2db/u233xo+Pj5GQUHBWb8HAADDYGQLAFBjnTt3Vr9+/dSxY0fdcMMN+uijj5Sdna3CwkKlpKRo1KhR8vHxsT1efPFFpaSk2K2jU6dOtq9DQ0MlSR07drRry8jIsD1PT0/XXXfdpdatW8vf319+fn4qKChQamrqSdfr7e0tPz8/u/Wczvr16+Xi4qLevXuf8WuO+fPPP7Vo0SK7fW/Xrp0k2e3/8TVKUnh4+BnVGB0draZNm9qeJycnKzIyUpGRkba29u3bKyAgQMnJyba2qKgoNWvWzPY8ISFBVqtV27ZtkyTdfvvt2rlzp1auXClJmjp1qm688UZ5e3ufze4DAI5iggwAQI25uLho3rx5Wr58uebOnat33nlHTz31lH766SdJ0kcffaSePXue8Jrjubm52b42mUzVtlmtVtvzESNG6MiRI3rrrbcUHR0td3d3JSQkqKys7KTrrW49p+Pp6XnGff+qoKBAV155pV555ZUTloWHh//tGh0VfkJCQnTllVdqypQpio2N1a+//qrFixc7ZFsA0BgQtgAAf4vJZFKvXr3Uq1cvPfvss4qOjtayZcsUERGhXbt2adiwYbW6vWXLlum9997ToEGDJEn79u2zm3jiTBybva+ysvKkfTp27Cir1aolS5YoMTHxrNZ/3nnn6dtvv1VMTIxcXWv+o9bNze2UNR4TFxenffv2ad++fbbRrS1btignJ0ft27e39UtNTdXBgwcVEREhSVq5cqXMZrPatm1r63PnnXfq5ptvVvPmzdWyZUv16tWrxvUDQGPHaYQAgBpbtWqVXn75Za1du1apqan67rvvdPjwYcXFxen555/XxIkT9fbbb2v79u3auHGjpkyZokmTJv2tbbZu3VqfffaZkpOTtWrVKg0bNuysR6Gio6NlMpk0a9YsHT58WAUFBSf0iYmJ0YgRI3THHXdo5syZ2r17txYvXqyvvvrqtOsfPXq0srKydPPNN2vNmjVKSUnRnDlzNHLkyDMKT8fXsGDBAqWlpSk7O/uk/RITE9WxY0cNGzZM69at0+rVqzV8+HD17t1b3bt3t/Xz8PDQiBEj9Oeff+q3337TAw88oBtvvFFhYWG2PgMGDJCfn59efPFFjRw58oxrBQCciLAFAKgxPz8/LV26VIMGDVKbNm309NNP61//+pcGDhyoO++8Ux9//LGmTJmijh07qnfv3po6dapiY2P/1jb/85//KDs7W+edd55uu+02PfDAAwoJCTmrdTRr1kzPP/+8nnjiCYWGhmrMmDHV9nv//fd1/fXX67777lO7du1011132aa1P5WIiAgtW7ZMlZWV6t+/vzp27KiHHnpIAQEBMpvP/Efvv/71L82bN0+RkZHq2rXrSfuZTCb98MMPCgwM1CWXXKLExES1aNFCX375pV2/Vq1a6brrrtOgQYPUv39/derUyW46fkkym826/fbbVVlZqeHDh59xrQCAE5kMwzCcXQQAAHCs8ePHa+bMmVq/fv1p+44aNUqHDx+23ZsMAFAzXLMFAAAkSbm5udq4caNmzJhB0AKAWsBphAAAnKV77rnHblr34x/33HOPs8ursauvvlr9+/fXPffco8suu8zZ5QBAg8dphAAAnKWMjAzl5eVVu8zPz++sryEDAJybCFsAAAAA4ACcRggAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4wP8DxO1AetyqWPkAAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean Entropy: 1.3556\n", + "Median Entropy: 1.3610\n", + "Entropy Standard Deviation: 0.9905\n", + "Optimal threshold: 0.9060\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create binary entropy labels (1 for low entropy, 0 for high entropy)\n", + "combined_df['low_entropy'] = (combined_df['semantic_entropy'] <= optimal_threshold).astype(int)\n", + "\n", + "# Prepare data for logistic regression\n", + "X = np.stack(combined_df['embedding'].values)\n", + "y = combined_df['low_entropy']\n", + "\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_val_scaled = scaler.transform(X_val)" + ], + "metadata": { + "id": "WoldSRM7qeua" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create binary entropy labels (1 for low entropy, 0 for high entropy)\n", + "combined_df['low_entropy'] = (combined_df['semantic_entropy'] <= optimal_threshold).astype(int)\n", + "\n", + "# Prepare data for logistic regression\n", + "X = np.stack(combined_df['embedding'].values)\n", + "y = combined_df['low_entropy']\n", + "\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_val_scaled = scaler.transform(X_val)" + ], + "metadata": { + "id": "8jlxnMrsrOxi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train logistic regression model\n", + "model = LogisticRegression(max_iter=5000, solver='lbfgs', C=0.001)\n", + "model.fit(X_train_scaled, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "id": "sPDIzeSVrZFy", + "outputId": "938f32da-c9c1-4092-8758-6a94f79396cc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LogisticRegression(C=0.001, max_iter=5000)" + ], + "text/html": [ + "
LogisticRegression(C=0.001, max_iter=5000)
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" + ] + }, + "metadata": {}, + "execution_count": 108 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Cross-validation\n", + "cv_scores = cross_val_score(model, X_train_scaled, y_train, cv=5)\n", + "print(\"\\nCross-validation scores:\", cv_scores)\n", + "print(f\"Mean CV score: {cv_scores.mean():.4f} (+/- {cv_scores.std() * 2:.4f})\")\n", + "\n", + "# Predictions on validation set\n", + "y_val_pred_proba = model.predict_proba(X_val_scaled)[:, 1] # Probability of low entropy (True label)\n", + "\n", + "# Evaluate performance on 'Label' column\n", + "y_val_true_label = y_val # This is the correct way to get validation labels\n", + "\n", + "# Find the optimal threshold for Label prediction\n", + "fpr, tpr, thresholds = roc_curve(y_val_true_label, y_val_pred_proba)\n", + "optimal_idx = np.argmax(tpr - fpr)\n", + "optimal_threshold_label = thresholds[optimal_idx]\n", + "print(f\"Optimal threshold for Label prediction: {optimal_threshold_label:.4f}\")\n", + "\n", + "# Make predictions using the optimal threshold\n", + "y_val_pred_label = (y_val_pred_proba >= optimal_threshold_label).astype(int)\n", + "\n", + "# Classification report\n", + "print(\"\\nValidation Set Classification Report (using optimal threshold):\")\n", + "print(classification_report(y_val_true_label, y_val_pred_label))\n", + "\n", + "# Confusion Matrix\n", + "cm = confusion_matrix(y_val_true_label, y_val_pred_label)\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "plt.title(f'Validation Set Confusion Matrix (Threshold: {optimal_threshold_label:.4f})')\n", + "plt.ylabel('True Label')\n", + "plt.xlabel('Predicted Label')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "auc = roc_auc_score(y_val_true_label, y_val_pred_proba)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {auc:.2f})')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', label='Random Classifier')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "BO1g-snEra0k", + "outputId": "0842b321-3927-4452-f524-8c954d06ff7b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Cross-validation scores: [0.64921875 0.65 0.640625 0.64140625 0.6328125 ]\n", + "Mean CV score: 0.6428 (+/- 0.0126)\n", + "Optimal threshold for Label prediction: 0.3534\n", + "\n", + "Validation Set Classification Report (using optimal threshold):\n", + " precision recall f1-score support\n", + "\n", + " 0 0.68 0.60 0.63 995\n", + " 1 0.44 0.53 0.48 605\n", + "\n", + " accuracy 0.57 1600\n", + " macro avg 0.56 0.56 0.56 1600\n", + "weighted avg 0.59 0.57 0.58 1600\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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P8QdzYGAgISEh/PTTT/z000+peowiRYq8sOannv6iCg8PN/nI83kpBd4SJUokObZkyZKsXr0aMO95flHd0dHRTJkyhcWLF3P37l2TqcL+G9rM9d/g8jSQPHnyBMA4Z2jx4sVNjrOyskrx4+/nubi4AM+ew7So6+l9Hjx4kHHjxnH48GGioqJMjg8NDcXV1dW4ndLrITX34efnB0D58uXN+h6eMvf9kdrX7nfffUevXr3w9vamWrVqtGrVip49e1K0aFGza3z6x8urfo9AilPY+fj4sGDBAvR6PX5+fkyaNInAwMAkfxg4Ozu/NGD+973o4uJirN3cWl8W0l/1tZtayf0/W1lZ0bFjR5YvX05sbCy2trasX7+e+Ph4kzD7Oj9zRfYlYVZkKzVr1qR69eqAofewXr16dO/enStXruDk5GSc33Ho0KHJ9lZB0vDyInq9ngoVKvD9998nu9/b2/uF53fp0oVJkyYRFBSEs7MzmzZtolu3bsaewKf1vvvuu0nG1j5VsWJFk+3U9MqCYUzp77//ztmzZ2nQoEGyx5w9exYgVb1lz3uV5zm5ugcNGsTixYsZPHgwtWvXxtXVFZ1OR9euXVOcqzO1UpqWKaVgYq7SpUsDcO7cOSpXrpzq815Wl5+fH02bNqV06dJ8//33eHt7Y2Njw59//smMGTOSPC/JPa/m3serMvf9kdrX7jvvvEP9+vXZsGEDf/31F9OmTePbb79l/fr1tGzZ8rXrTq3cuXMDz/4A+i9HR0eTseZ169alatWqfPnll8yaNcvYXqZMGU6fPk1AQECSP2ae+u97sXTp0pw6dYrbt2+/9OfM8548eZLsH6PPM/e1m1I4TkxMTLY9pf/nrl27Mn/+fLZu3Uq7du1YvXo1pUuXplKlSsZjXvdnrsieJMyKbMvS0pIpU6bQuHFjZs+ezciRI409N9bW1ia/ZJJTrFgxzp8//9Jjzpw5Q9OmTVP1set/denShQkTJrBu3Try5s1LWFiY8UIHAA8PD5ydnUlMTHxpveZ6++23mTJlCkuXLk02zCYmJrJ8+XLc3d2pW7euyb5r164lOf7q1avGHktznucXWbt2Lb169WL69OnGtpiYGEJCQkyOe5Xn/mWeToB//fp1GjdubGxPSEjA398/yR8R/9WyZUssLS357bff0vRCmj/++IPY2Fg2bdpkEnzM+Xg1tfdRrFgxAM6fP//CP/JSev5f9/3xIvnz5+eTTz7hk08+4dGjR1StWpVJkyYZw2xqH+/pa/Vl7/XkPA19N2/eTNXxFStW5N1332X+/PkMHTrU+Ny//fbbrFixgqVLl/LVV18lOS8sLIyNGzdSunRp4/9D69atWbFiBb/99hujRo1K1eMnJCRw+/Zt2rRp88LjzH3turu7J3lPAmaviNagQQPy58/PqlWrqFevHrt27WL06NEmx6Tna0pkXTJmVmRrjRo1ombNmsycOZOYmBg8PT1p1KgR8+fP5/79+0mODwwMNN7u2LEjZ86cYcOGDUmOe9pL9s4773D37l0WLFiQ5Jjo6GjjVfkpKVOmDBUqVGDVqlWsWrWK/PnzmwRLS0tLOnbsyLp165L9Zft8veaqU6cOvr6+LF68ONkVhkaPHs3Vq1cZPnx4kp6U33//3WTM67Fjxzh69KgxSJjzPL+IpaVlkp7SH3/8MUmPj6OjI0Cyv1BfVfXq1cmdOzcLFiwgISHB2L5s2bIUe+Ke5+3tTb9+/fjrr7/48ccfk+zX6/VMnz6dO3fumFXX057b/w65WLx4cZrfR/PmzXF2dmbKlCnExMSY7Hv+XEdHx2SHfbzu+yM5iYmJSR7L09OTAgUKEBsb+9Ka/svDw4MGDRqwaNEiAgICTPa9rJfey8sLb29vs1bDGj58OPHx8SY9i506daJs2bJMnTo1yX3p9Xo+/vhjnjx5wrhx40zOqVChApMmTeLw4cNJHic8PDxJELx48SIxMTHUqVPnhTWa+9otVqwYoaGhxt5jgPv37yf7s/NFLCws6NSpE3/88Qe//vorCQkJJkMMIH1eUyLrk55Zke0NGzaMzp07s2TJEvr378+cOXOoV68eFSpUoF+/fhQtWpSHDx9y+PBh7ty5Y1zucdiwYcaVpfr27Uu1atUIDg5m06ZNzJs3j0qVKvHee++xevVq+vfvz+7du6lbty6JiYlcvnyZ1atXs337duOwh5R06dKFsWPHYmdnx/vvv4+FhenfmFOnTmX37t3UqlWLfv36UbZsWYKDgzl58iQ7duwgODj4lZ+bpUuX0rRpU9q2bUv37t2pX78+sbGxrF+/nj179tClSxeGDRuW5LzixYtTr149Pv74Y2JjY5k5cya5c+dm+PDhxmNS+zy/yNtvv82vv/6Kq6srZcuW5fDhw+zYscP48e5TlStXxtLSkm+//ZbQ0FBsbW1p0qQJnp6er/zc2NjYMH78eAYNGkSTJk1455138Pf3Z8mSJRQrVixVvULTp0/Hz8+PTz/9lPXr1/P222/j7u5OQEAAa9as4fLlyyY98anRvHlzbGxsaN26NR999BEREREsWLAAT0/PZP9weJ37cHFxYcaMGXzwwQfUqFGD7t274+7uzpkzZ4iKiuKXX34BoFq1aqxatYohQ4ZQo0YNnJycaN26dZq8P/4rPDycggUL0qlTJ+MSrjt27OD48eMmPfgp1ZScWbNmUa9ePapWrcqHH35IkSJF8Pf3Z8uWLZw+ffqF9bRt25YNGzakaiwqGIYJtGrVip9//pkxY8aQO3dubGxsWLt2LU2bNqVevXomK4AtX76ckydP8sUXX5i8VqytrVm/fj2+vr40aNCAd955h7p162Jtbc2FCxeMn6o8P7XY33//jYODA82aNXtpnea8drt27cqIESNo3749n376KVFRUcydO5eSJUuafaFmly5d+PHHHxk3bhwVKlRIMsVeerymRDaQ8RMoCJH2Ulo0QSnDCjPFihVTxYoVM0795Ofnp3r27Kny5cunrK2tlZeXl3r77bfV2rVrTc59/PixGjhwoPLy8jJOzt2rVy+TabLi4uLUt99+q8qVK6dsbW2Vu7u7qlatmpowYYIKDQ01HvffqbmeunbtmnFi9wMHDiT7/T18+FANGDBAeXt7K2tra5UvXz7VtGlT9dNPPxmPeTrl1Jo1a8x67sLDw9X48eNVuXLllL29vXJ2dlZ169ZVS5YsSTI10fOLJkyfPl15e3srW1tbVb9+fXXmzJkk952a5/lF/3dPnjxRffr0UXny5FFOTk6qRYsW6vLly8k+lwsWLFBFixZVlpaWqVo04b/PU0qT6c+aNUsVLlxY2draqpo1a6qDBw+qatWqqTfffDMVz65htaSff/5Z1a9fX7m6uipra2tVuHBh1adPH5Opj1JaAezp8/P8QhGbNm1SFStWVHZ2dsrHx0d9++23atGiRUmOe7poQnJSex9Pj61Tp46yt7dXLi4uqmbNmmrFihXG/REREap79+7Kzc0tyaIJqX1/8O9k+snhuam5YmNj1bBhw1SlSpWUs7OzcnR0VJUqVUqy4ENKNaX0/3z+/HnVvn175ebmpuzs7FSpUqXUmDFjkq3neSdPnlRAkqmiUlo0QSml9uzZk2S6MaWUevTokRoyZIgqXry4srW1VW5ubsrX19c4HVdynjx5osaOHasqVKigHBwclJ2dnSpfvrwaNWqUun//vsmxtWrVUu++++5Lv6enUvvaVUqpv/76S5UvX17Z2NioUqVKqd9+++2FiyakRK/XK29vbwWob775JtljUvuaEjmHTqk0utpBCJHt+fv7U6RIEaZNm8bQoUO1LkcTer0eDw8POnTokOxHnSLnadq0KQUKFODXX3/VupQUnT59mqpVq3Ly5EmzLkgUIiuQMbNCCJGCmJiYJOMmly5dSnBwMI0aNdKmKJHpTJ48mVWrVpl9wVNGmjp1Kp06dZIgK7IlGTMrhBApOHLkCJ9//jmdO3cmd+7cnDx5koULF1K+fHk6d+6sdXkik6hVqxZxcXFal/FCK1eu1LoEIdKNhFkhhEiBj48P3t7ezJo1i+DgYHLlykXPnj2ZOnWqyephQgghtCNjZoUQQgghRJYlY2aFEEIIIUSWJWFWCCGEEEJkWTluzKxer+fevXs4OzvLUnhCCCGEEJmQUorw8HAKFCiQZDGh/8pxYfbevXt4e3trXYYQQgghhHiJ27dvU7BgwRcek+PCrLOzM2B4clxcXDSuRgghhBBC/FdYWBje3t7G3PYiOS7MPh1a4OLiImFWCCGEECITS82QULkATAghhBBCZFkSZoUQQgghRJYlYVYIIYQQQmRZOW7MbGoopUhISCAxMVHrUoTI1qytrbG0tNS6DCGEEFmYhNn/iIuL4/79+0RFRWldihDZnk6no2DBgjg5OWldihBCiCxKwuxz9Ho9N2/exNLSkgIFCmBjYyMLKwiRTpRSBAYGcufOHUqUKCE9tEIIIV6JhNnnxMXFodfr8fb2xsHBQetyhMj2PDw88Pf3Jz4+XsKsEEKIVyIXgCXjZcumCSHShnzyIYQQ4nVJahNCCCGEEFmWhFkhhBBCCJFlSZgV4l+PHz/G09MTf39/rUvJNt544w3WrVundRlCCCGyMQmz2UTv3r3R6XTodDqsra0pUqQIw4cPJyYmJsmxmzdvpmHDhjg7O+Pg4ECNGjVYsmRJsve7bt06GjVqhKurK05OTlSsWJGJEycSHBz8wnp2795Nq1atyJ07Nw4ODpQtW5YvvviCu3fvpsW3my4mTZpE27Zt8fHxSbKvRYsWWFpacvz48ST7GjVqxODBg5O0L1myBDc3N5O2sLAwRo8eTenSpbGzsyNfvnz4+vqyfv16lFJp9J0ktWfPHqpWrYqtrS3FixdP8f/7KX9/f+Pr6fmvI0eOGI9p1KhRsse89dZbxmO++uorRo4ciV6vT69vTQghRA4nYTYbefPNN7l//z43btxgxowZzJ8/n3Hjxpkc8+OPP9K2bVvq1q3L0aNHOXv2LF27dqV///4MHTrU5NjRo0fTpUsXatSowdatWzl//jzTp0/nzJkz/PrrrynWMX/+fHx9fcmXLx/r1q3j4sWLzJs3j9DQUKZPn/7K319cXNwrn/syUVFRLFy4kPfffz/JvoCAAA4dOsTAgQNZtGjRKz9GSEgIderUYenSpYwaNYqTJ0+yb98+unTpwvDhwwkNDX2dbyFFN2/e5K233qJx48acPn2awYMH88EHH7B9+/aXnrtjxw7u379v/KpWrZpx3/r16032nT9/HktLSzp37mw8pmXLloSHh7N169Z0+d6EEEIIVA4TGhqqABUaGppkX3R0tLp48aKKjo42tun1ehUZG6/Jl16vT/X31atXL9W2bVuTtg4dOqgqVaoYtwMCApS1tbUaMmRIkvNnzZqlAHXkyBGllFJHjx5VgJo5c2ayj/fkyZNk22/fvq1sbGzU4MGDX3jeuHHjVKVKlUz2zZgxQxUuXDjJ9/TNN9+o/PnzKx8fHzVq1ChVs2bNJPdbsWJFNWHCBOP2ggULVOnSpZWtra0qVaqUmjNnTrL1PLVmzRrl4eGR7L7x48errl27qkuXLilXV1cVFRVlsr9hw4bqs88+S3Le4sWLlaurq3H7448/Vo6Ojuru3btJjg0PD1fx8fEvrPFVDR8+XJUrV86krUuXLqpFixYpnnPz5k0FqFOnTqX6cWbMmKGcnZ1VRESESXufPn3Uu+++m+w5yb3nhBBCiBfltf/SdJ7Zffv2MW3aNE6cOMH9+/fZsGED7dq1e+E5e/bsYciQIVy4cAFvb2+++uorevfunW41RscnUnbsy3uw0sPFiS1wsHm1/6Lz589z6NAhChcubGxbu3Yt8fHxSXpgAT766CO+/PJLVqxYQa1atVi2bBlOTk588sknyd7/fz8+f2rNmjXExcUxfPhws85Lyc6dO3FxceHvv/82tk2ZMgU/Pz+KFSsGwIULFzh79qxxbOayZcsYO3Yss2fPpkqVKpw6dYp+/frh6OhIr169kn2c/fv3m/Q6PqWUYvHixcyZM4fSpUtTvHhx1q5dy3vvvWfW96HX61m5ciU9evSgQIECSfa/aAWs/fv307Jlyxfe//z58+nRo0ey+w4fPoyvr69JW4sWLZIdGvFfbdq0ISYmhpIlSzJ8+HDatGmT4rELFy6ka9euODo6mrTXrFmTqVOnvvSxhBBCiFehaZiNjIykUqVK9O3blw4dOrz0+Kcfl/bv359ly5axc+dOPvjgA/Lnz0+LFi0yoOLMbfPmzTg5OZGQkEBsbCwWFhbMnj3buP/q1au4urqSP3/+JOfa2NhQtGhRrl69CsC1a9coWrQo1tbWZtVw7do1XFxckn2MV+Ho6MjPP/+MjY2Nsa1SpUosX76cMWPGAIbwWqtWLYoXLw7AuHHjmD59uvE1VaRIES5evMj8+fNTDLO3bt1KNmTu2LGDqKgo4+vr3XffZeHChWaH2aCgIJ48eULp0qXNOg+gevXqnD59+oXH5M2bN8V9Dx48SLI/b968hIWFER0djb29fZJznJycmD59OnXr1sXCwoJ169bRrl07fv/992QD7bFjxzh//jwLFy5Msq9AgQLcvn0bvV4vczgLIYRIc5qG2ZYtW760x+l58+bNo0iRIsZxl2XKlOHAgQPMmDEj3cKsvbUlFydqE5Ttrc1bEalx48bMnTuXyMhIZsyYgZWVFR07dnylx1aveDGSUipNJ8KvUKGCSZAF6NGjB4sWLWLMmDEopVixYgVDhgwBDH8g+fn58f7779OvXz/jOQkJCbi6uqb4ONHR0djZ2SVpX7RoEV26dMHKyvBW6datG8OGDTPpGU6NV30+Aezt7Y1BPaPkyZPH+JwC1KhRg3v37jFt2rRkw+zChQupUKECNWvWTLLP3t4evV5PbGxsssFZCCFE5pKoVxy98ZiwmHhjm06fgLKwon4JDxxtM9cCspmrmpd4lY9LY2NjiY2NNW6HhYWZ9Zg6ne6VP+rPaI6OjsbQs2jRIipVqmRyUVPJkiUJDQ3l3r17SXoh4+Li8PPzo3HjxsZjDxw4QHx8vFm9s08f4/79+y/snbWwsEgS8OLj45Mc99+PrMEQKEeMGMHJkyeJjo7m9u3bdOnSBYCIiAgAFixYQK1atUzOe9FyqXny5OHJkycmbcHBwWzYsIH4+Hjmzp1rbE9MTGTRokVMmjQJABcXl2Qv3goJCTEGaA8PD9zc3Lh8+XKKNaTkdYcZ5MuXj4cPH5q0PXz4EBcXF7PCZa1atUyGezwVGRnJypUrmThxYrLnBQcH4+joKEFWCCGyiPcWHuWQ3+N/txRdLPfwvuWfdIobzx/D3sp0YTZLfeb3so9LkzNlyhRcXV2NX97e3hlRquYsLCz48ssv+eqrr4zPTceOHbG2tk52RoF58+YRGRlJt27dAOjevTsRERH873//S/b+Q0JCkm3v1KkTNjY2fPfddy88z8PDgwcPHpgE2pd9lP5UwYIFadiwIcuWLWPZsmU0a9YMT09PwPB6KFCgADdu3KB48eImX0WKFEnxPqtUqcLFixdN2pYtW0bBggU5c+YMp0+fNn5Nnz6dJUuWkJiYCECpUqU4efJkkvs8efIkJUuWBAz/H127dmXZsmXcu3cvybEREREkJCQkW9vTYQYv+nrRWNbatWuzc+dOk7a///6b2rVrp3hOck6fPp3sHyhr1qwhNjaWd999N9nzzp8/T5UqVcx6LCGEENq4HRxlDLKORPOr2wK+tV5ASYu7jMhzEFsr8z41zhDpeSWaOQC1YcOGFx5TokQJNXnyZJO2LVu2KCDJFeZPxcTEqNDQUOPX7du3zZrNIKtIbjaD+Ph45eXlpaZNm2ZsmzFjhrKwsFBffvmlunTpkrp+/bqaPn26srW1VV988YXJ+cOHD1eWlpZq2LBh6tChQ8rf31/t2LFDderUKcVZDpRSas6cOUqn06m+ffuqPXv2KH9/f3XgwAH14YcfGmdSuHjxotLpdGrq1Knq+vXravbs2crd3T3Z2QySs2DBAlWgQAGVJ08e9euvvybZZ29vr3744Qd15coVdfbsWbVo0SI1ffr0FGs+e/assrKyUsHBwca2SpUqqREjRiQ5NiQkRNnY2KjNmzcrpZTy8/NTdnZ2atCgQerMmTPq8uXLavr06crKykpt3brVeN7jx49V6dKlVcGCBdUvv/yiLly4oK5evaoWLlyoihcvnuIMEa/rxo0bysHBQQ0bNkxdunRJzZkzR1laWqpt27YZj/nxxx9VkyZNjNtLlixRy5cvV5cuXVKXLl1SkyZNUhYWFmrRokVJ7r9evXqqS5cuKT5+w4YN1cSJE5Pdl5Xfc0IIkd0cv/lYFR6xWRUesVm1HDlbxc+orNQ4F6XGuyu1/3ulEhMzrBZzZjPIUmG2fv36SaZAWrRokXJxcUn145g7NVdWkVLwmzJlivLw8DCZLmnjxo2qfv36ytHRUdnZ2alq1aolG1KUUmrVqlWqQYMGytnZWTk6OqqKFSuqiRMnvjR4/f3336pFixbK3d1d2dnZqdKlS6uhQ4eqe/fuGY+ZO3eu8vb2Vo6Ojqpnz55q0qRJqQ6zT548Uba2tsrBwUGFh4cn2b9s2TJVuXJlZWNjo9zd3VWDBg3U+vXrX1hzzZo11bx585RSSv3zzz8KUMeOHUv22JYtW6r27dsbt48dO6aaNWumPDw8lKurq6pVq1ayr+eQkBA1cuRIVaJECWVjY6Py5s2rfH191YYNG8yais1cu3fvNj4fRYsWVYsXLzbZP27cOJPnfsmSJapMmTLKwcFBubi4qJo1a6o1a9Ykud/Lly8rQP3111/JPu6dO3eUtbW1un37drL7s/J7TgghsoOImHi189ID1fC7Xf8G2T/U6C8/U/Hj8xiC7PSySt06kuF1mRNmdUql47JDZtDpdC+dmmvEiBH8+eefnDt3ztjWvXt3goOD2bZtW6oeJywsDFdXV0JDQ3FxcTHZFxMTw82bNylSpEiyFwOJ7G3Lli0MGzaM8+fPy1X3aWTEiBE8efKEn376Kdn98p4TQoiMFR4Tz++n7xEZm0BUXCKzdl4z2e+ju89OuxFYqgQo2RLa/Q8ccmV4nS/Ka/+l6QjeiIgIrl+/bty+efMmp0+fJleuXBQqVIhRo0Zx9+5dli5dCkD//v2ZPXs2w4cPp2/fvuzatYvVq1ezZcsWrb4FkY289dZbXLt2jbt37+aYsdXpzdPT02RWBCGEENqJS9Dz467r/LTvRpJ9tlYWvPdGYd6r3QjL63rQx8Mbn0AazlCUXjTtmd2zZ4/x6vnn9erViyVLltC7d2/8/f3Zs2ePyTmff/45Fy9epGDBgowZM8asRROkZ1aIzEPec0IIkf4CHkex9fx9pmw1nVGnY9WCoBQf2P5NmVotIH9FjSpMypye2UwzzCCjSJgVIvOQ95wQQqSPQ35B/Hr4Fol6xV8XTadntLe25H/vVqVxIWvYOBAub4ZcxaD/frBJOiWmFrLMMAMhhBBCCJF29HpFmzkHOH836bz6VQq58WH9orQolw+LeydgXh8IDQBLG6jVH6wdNKj49UmYFUIIIYTIwuIT9fxvtx8PwqLZcOouMfF6476PGhalcC5HSuVzolrhXKAUHJ4NO8aDPgHci0DnxVAg684HLmFWCCGEECILCI2K5+bjSAA2nr7LuhN3sLGyJCgiNtnjL05sYbqKaWwErHsfrv47A1S59tB6Fti9+GP8zE7CrBBCCCFEJhcTn0ij/9vNk6j/Lv1uunrkF81KYmdtSYeqXqZBFgzDCBJiwdIWWk6Fan2yxGwFLyNhVgghhBAik3oYFkOfxce5FxpNyL9BtqC7PWAYHzupQwXyu9phqdNRzMMJC4v/hFP9v9NsWdmChQV0+AkiHkK+Chn9raQbCbNCCCGEEJnUG1N28vy8Uz65HdgzLOm0psmKCIQNH4FrQWgzy9Dm5Gn4ykZkmSORJnQ6Hb///rvWZaQoo+rbs2cPOp2OkJAQY9vvv/9O8eLFsbS0ZPDgwSxZsgQ3N7d0r0UIIUTWEhOfSGRsAuEx8Xyx+gzdfjpiDLIVC7qy/INabBxQL3V35n8A5tUDv51wdjU88U+3urUmPbPZRO/evfnll18AsLKyomDBgnTu3JmJEydm+/k7Hzx4wKRJk9iyZQt3797F09OTypUrM3jwYJo2bZqhtdSpU4f79+/j6upqbPvoo4/o06cPn376Kc7OzlhZWdGqVasMrUsIIUTmER4Tz/5rQcQnPpt1YOPpe+y6/CjZ460tdfzSpybujjYvv3N9IuyfDnumgNJDnlLQeQm4+6RN8ZmQhNls5M0332Tx4sXEx8dz4sQJevXqhU6n49tvv9W6tHTj7+9P3bp1cXNzY9q0aVSoUIH4+Hi2b9/OgAEDuHz58svvJA3Z2NiQL18+43ZERASPHj2iRYsWFChQwNhub2//Wo8THx+PtbX1a92HEEKI9BUZm8C07VcI/M9sA1vO3k/V+a721nzdrjwlPJ1SF2TDH8L6fnBzr2G7cg9oNS3TLISQXmSYQWrFRab8FR9jxrHRqTv2Fdja2pIvXz68vb1p164dvr6+/P3338b9jx8/plu3bnh5eeHg4ECFChVYsWKFyX00atSITz/9lOHDh5MrVy7y5cvH+PHjTY65du0aDRo0wM7OjrJly5o8xlPnzp2jSZMm2Nvbkzt3bj788EMiIiKM+3v37k27du2YPHkyefPmxc3NjYkTJ5KQkMCwYcPIlSsXBQsWZPHixS/8nj/55BN0Oh3Hjh2jY8eOlCxZknLlyjFkyBCOHDmS4nkjRoygZMmSODg4ULRoUcaMGUN8/LMrRM+cOUPjxo1xdnbGxcWFatWq8c8//wBw69YtWrdujbu7O46OjpQrV44///wTMB1msGfPHpydnQFo0qQJOp2OPXv2JDvMYOPGjVStWhU7OzuKFi3KhAkTSEh4doWqTqdj7ty5tGnTBkdHRyZNmvTC50UIIYS2lFL8sPMaSw75s+XsfZOv59Urnsf41axsXnYMacjlr9/k8tdvcmZcc9pUKkCZ/KmYOkuvh6VtDUHW2gHazYN2/8v2QRakZzb1JhdIeV+J5tBjzbPtacUhPir5YwvXgz5bnm3PrABRj5MeNz701er81/nz5zl06BCFCxc2tsXExFCtWjVGjBiBi4sLW7Zs4b333qNYsWLUrFnTeNwvv/zCkCFDOHr0KIcPH6Z3797UrVuXZs2aodfr6dChA3nz5uXo0aOEhoYyePBgk8eOjIykRYsW1K5dm+PHj/Po0SM++OADBg4cyJIlS4zH7dq1i4IFC7Jv3z4OHjzI+++/z6FDh2jQoAFHjx5l1apVfPTRRzRr1oyCBQsm+R6Dg4PZtm0bkyZNwtEx6Zv1ReNSnZ2dWbJkCQUKFODcuXP069cPZ2dnhg8fDkCPHj2oUqUKc+fOxdLSktOnTxt7QgcMGEBcXBz79u3D0dGRixcv4uTklOQx6tSpw5UrVyhVqhTr1q2jTp065MqVC39/f5Pj9u/fT8+ePZk1axb169fHz8+PDz/8EIBx48YZjxs/fjxTp05l5syZWFnJW1cIIbSWkKjn5wM3uR8SnWTfupN3iYh91ikxvnVZk/25nWxpWT4fVpZp1K9oYQHNJsDOidBpMXiUTJv7zQLkN2I2snnzZpycnEhISCA2NhYLCwtmz55t3O/l5cXQoUON24MGDWL79u2sXr3aJMxWrFjRGKJKlCjB7Nmz2blzJ82aNWPHjh1cvnyZ7du3Gz82nzx5Mi1btjSev3z5cmJiYli6dKkxZM6ePZvWrVvz7bffkjdvXgBy5crFrFmzsLCwoFSpUnz33XdERUXx5ZdfAjBq1CimTp3KgQMH6Nq1a5Lv9/r16yilKF26tNnP1VdffWW87ePjw9ChQ1m5cqUxzAYEBDBs2DDjfZcoUcJ4fEBAAB07dqRCBcO0JkWLFk32MWxsbPD09DR+r88PP3jehAkTGDlyJL169TLe39dff83w4cNNwmz37t3p06eP2d+rEEKI9HHc/wlTt758ONvPPavjWzZv2hcQdh+Cb4BPXcN2yRZQrClY5qx4l7O+29fx5b2U9+ksTbeHXX/Bsf/5C2zwuVev6T8aN27M3LlziYyMZMaMGVhZWdGxY0fj/sTERCZPnszq1au5e/cucXFxxMbG4uBguhZzxYoVTbbz58/Po0eGQemXLl3C29vbZPxn7dq1TY6/dOkSlSpVMuktrVu3Lnq9nitXrhjDbLly5bCwePZ85M2bl/Llyxu3LS0tyZ07t/Gx/0s9P1eJmVatWsWsWbPw8/MjIiKChIQEXFyefYwzZMgQPvjgA3799Vd8fX3p3LkzxYoVA+DTTz/l448/5q+//sLX15eOHTsmec7McebMGQ4ePGgydCAxMZGYmBiioqKM/z/Vq1d/5ccQQgiRtmLiE/lpnx8Ans62dK3hneQYOxtL3qnuTR4n27Qv4PoOWP+RYQ7Z/gfArZChPYcFWZAwm3rmjDlJr2NfwtHRkeLFiwOwaNEiKlWqxMKFC3n//fcBmDZtGj/88AMzZ86kQoUKODo6MnjwYOLi4kzu578XFul0OvR6PWktuccx57FLlCiBTqcz+yKvw4cP06NHDyZMmECLFi1wdXVl5cqVTJ8+3XjM+PHj6d69O1u2bGHr1q2MGzeOlStX0r59ez744ANatGjBli1b+Ouvv5gyZQrTp09n0KBBZtXxVEREBBMmTKBDhw5J9j0/E0VyQymEEEJkHKUUgeGx6BWsO3mH3VcCASiSx5EhzUtlTBGJCbD7Gzgww7CdrwLoE158TjYnYTabsrCw4Msvv2TIkCF0794de3t7Dh48SNu2bXn33XcB0Ov1XL16lbJly77k3p4pU6YMt2/f5v79++TPnx8gyYVWZcqUYcmSJURGRhoD2MGDB43DCdJKrly5aNGiBXPmzOHTTz9NEvZCQkKSHTf7dCzx6NGjjW23bt1KclzJkiUpWbIkn3/+Od26dWPx4sW0b98eAG9vb/r370///v0ZNWoUCxYseOUwW7VqVa5cuWL8Q0QIIUTmc/1ROL7f70t23+i3ymRMEaF3YO37cPvf37s1PoDmk8A6e0/B+TIym0E21rlzZywtLZkzZw5g6Mn8+++/OXToEJcuXeKjjz7i4cOHZt2nr68vJUuWpFevXpw5c4b9+/ebhEIwXDxlZ2dHr169OH/+PLt372bQoEG89957xiEGaWXOnDkkJiZSs2ZN1q1bx7Vr17h06RKzZs1KMvzhqRIlShAQEMDKlSvx8/Nj1qxZbNiwwbg/OjqagQMHsmfPHm7dusXBgwc5fvw4ZcoYflgNHjyY7du3c/PmTU6ePMnu3buN+17F2LFjWbp0KRMmTODChQtcunSJlStXmozrFUIIoZ0fd15LEmStLHQ42lgyo0slKhZ0S/8irm43LIJw+wjYuhjmjn1reo4PsiBhNluzsrJi4MCBfPfdd0RGRvLVV19RtWpVWrRoQaNGjciXLx/t2rUz6z4tLCzYsGED0dHR1KxZkw8++CDJNFEODg5s376d4OBgatSoQadOnWjatKnJxWhppWjRopw8eZLGjRvzxRdfUL58eZo1a8bOnTuZO3dusue0adOGzz//nIEDB1K5cmUOHTrEmDFjjPstLS15/PgxPXv2pGTJkrzzzju0bNmSCRMmAIbxrAMGDKBMmTK8+eablCxZkv/973+v/D20aNGCzZs389dff1GjRg3eeOMNZsyYYTIThRBCCG08iYxj+t9Xjdtda3hzfVJLrk9uxYWJb9K+StLZdtLF1e0Q/QQKVIGP9kG59hnzuFmATr3OVTRZUFhYGK6uroSGhppc8AOGqatu3rxJkSJFsv2qWUJkBvKeE0Jkdof8gui+4CgAmwfVo7yX60vOSCfxMXB0HrzxMVilwwVlmcyL8tp/Sc+sEEIIIcRzlFJcvBfG7suPjEEWyNgge2kzrHrPsDwtGIYT1BucI4KsueQCMCGEEEKIf90Liead+Ye588R0IYQvW5k/p/krSYiFv8caemEBTi6F6jLH+ItImBVCCCFEjvQkMo6giFjD7ah4hq89g/9j0xU8i+RxpE2lAnzYoFj6FxR8A9b0gfunDdt1BkGVd9P/cbM4CbNCCCGEyHECHkfh+/1e4hKTn8u8bH4XlvStgadzBo3nv7ABNn0KsWFg7w7t5xtW9BIvJWE2GTnsmjghNCPvNSFERolNSGT42rPcDjb0vJ4MCDHuc3cwLNgTn6hoXjYvnzQuRnFP54wrbv902DnRcNv7Dei0EFwzaJaEbEDC7HOerj4VFRWFvb29xtUIkf09XX3O0tLyJUcKIcSrS9Qrvv/7KhtPJ12avlnZvCzoqfFy4SXfhH3/B7X6Q+PROXJJ2tchz9ZzLC0tcXNz49GjR4BhvlSdTqdxVUJkT3q9nsDAQBwcHLCykh9FQoj0MX+vH99uu4z+uQ+CfnqvGgCWFjpqFsmlTWFB1yHPvys/5i0Hn54C53za1JLFyW+Q/8iXz/BCehpohRDpx8LCgkKFCskfjUKINHMvJJoO/zvEg7CYZPcv7FWdpmXSdjVKs8RHw9YRcHoZ9NkG3jUM7RJkX5mE2f/Q6XTkz58fT09P4uPjtS5HiGzNxsYGCwuZ7loIkTbiE/W8NWs/T6KS/v6e3rkSTct44uZgo0Fl/wq8Amt6w6OLgA7unngWZsUrkzCbAktLSxnHJ4QQQmQhsQl6Y5BtVMqD7zpVxEKnI5eDDRYWGn8CdHo5bPkC4qPA0RM6LoCijbStKZuQMCuEEEKILC0hUc+yowEEBD+bI3beu9Wws84EnVJxkbBlKJxZbtgu0hA6LABnDYc6ZDMSZoUQQgiR6UTGJhgXNFj9z222nn+AZQrj6689ijDZtrGywCKzjMU/v84QZHUW0OhLqD8ELDJByM5GJMwKIYQQIlOIiU9k0+l7+D+O5H97/F7pPvrWLcIbRXNhY5VJxuNXec8wNrZCZ/Cpp3U12ZKEWSGEEEJkCr8ducU3Wy6ZtDnZGqJKdHwi/+tRFRc762TPtbTQUdnbTfsQGxsOe7+DhsPB1hl0Omj9g7Y1ZXMSZoUQQgihuXUn7pgE2fZVvGhbuQCNSnlqWJWZHpwzzFbw+DpEBkL7eVpXlCNImBVCCCGEZmLiE/lu2xUWHbxpbJvVrQptKhXQsCozKQX/LIJtoyAxFly8oFpvravKMSTMCiGEECLDJeoVvx25xbhNF0zav25XPmsF2ZhQ+OMzuLDBsF3yTWg3Fxw0WlksB5IwK4QQQoh09yg8hkUH/ImMTQDg1yO3TPbbWlnwXaeKvFk+C62E9egSrOgGT26ChRX4ToDaAwzjZEWGkTArhBBCiHSllKLmpJ0p7v+xWxVaZ6Xe2KccchvmkXUtBJ0XQ8HqWleUI0mYFUIIIUS6uf4onMUH/Y3bBVzt6FzdGwBnOyveqeGd4gwFmVJ8NFjbG247eUKPNeBeGOzdta0rB5MwK4QQQog0ExGbQFi0YUnZoIhY2sw+aLL/0KimWpSVNu78A2v6gO84qNDJ0FagsqYlCQmzQgghhEgD5+6EsubEbZYevpXs/tpFc/NB/SIZXFUaUQoOz4Ed40CfAAdnQrkOYJFJFmbI4STMCiGEEOKVxcQn8vXmiyw7GmDS/nTxgrgEPe/XK8KYt8tqUd7riwqG3z+Gq9sM22XbQZtZEmQzEQmzQgghhHglX/1+jt+OmIbYtyrkp289H6oVzgZTUwUchbV9IewOWNrCm1Ogel+ZrSCTkTArhBBCCLM9iYwzCbKWFjq2D25AcU8nDatKQ0/8YUkrw7CCXMWg8xLIX1HrqkQyJMwKIYQQItXiE/X8uOs6s3ZeM7ZtHlSP8l6uGlaVDtx9oFZ/iHgIb88AW2etKxIpkDArhBBCiBfaePouZ26HApgsOwvwRtFc2SfI+h8At8LgZpg6jGYTQWchwwoyOQmzQgghhEgiLkHP5Qdh9Fl8nMeRcckes6RPDRqV8szgytKBPhH2T4c9U8CrOvT5EyytwcJS68pEKkiYFUIIIYRRUEQsv5+6yzdbLiXZ90mjYgDkcbKle61C2Flng7AX8QjWfQA39xq2cxeHxHhDmBVZgoRZIYQQQhjN3nWdJYf8Tdqalc3LzC6VcbTNZrHhxl5DkI18BNYO8NZ0qNxd66qEmbLZq1IIIYQQryowPNYYZCt5u9Gxqhc9a/toWlO60CfC3m9h73eAAs+y0GkxeJbWujLxCiTMCiGEEAKA0RvOGW+3r1wgewZZMAwjuLwFUFC1J7z5Ldg4aF2VeEUSZoUQQgjB7eAo/rr4EABnOyveqlhA44rSkbWdYd7Ye6ehYmetqxGvScKsEEIIIfhx17N5Y7cMqo+Hs62G1aSxxATY/Q1YO0LDYYa2PCUMXyLLkzArhBBC5HBHbjxm9T93ACjv5UKh3NnoI/fQO7D2fbh9xDBnbPkOkLuY1lWJNCRhVgghhMjB9HpF15+OGLendaqkYTVp7Op22PARRD8BWxdo/YME2WxIwqwQQgiRg+249NB4e0aXSpTJ76JhNWkkMR52ToBDPxq281eGzoshV1FNyxLpQ8KsEEIIkUNFxCbw4a8njNuts8NFX0rBr+3Bf79hu1Z/w7K0VtloDLAwIWFWCCGEyKHuh0Qbb//StyZWlhYaVpNGdDrDuNgHZ6HtHCjTWuuKRDqTMCuEEELkQEopei8+DoC7gzUNS3poXNFrSIiFsLvPhhFU6wOl3wYnT23rEhkiG/wJJoQQQghzrDoeQN2pu7j7b8+sdVbukQ2+CQubwy9tDRd6gaF3VoJsjpGFX71CCCGEMNft4ChGrDvHvdAYY9vOLxpqWNFruPA7zG8A909DXDg89tO6IqEBGWYghBBC5BDhMfE0m7HXuD2sRSk6VSuIs521hlW9gvgY+Gs0HP/ZsO39BnRaCK4Fta1LaELCrBBCCJFDXH0YTky8HoA+dX0Y0Li4xhW9gsd+sKYXPDhn2K73OTQeDZZZLJCLNCNhVgghhMgBbgdH0XHuYQAcbSwZ17qcxhW9ot2TDEHWITe0/wlK+GpdkdCYhFkhhBAiBxi36YLxds86PtoV8rpa/R+gg+Zfg0s2mBdXvDa5AEwIIYTIxqLiEvjq93PsuvwIgMK5HRjxZmmNqzJD4BXYPdmwGAKAQy7D+FgJsuJf0jMrhBBCZGFxCXoS9Yq9VwP57cgtFMpk/8Hrj02213xUOyPLez2nV8CWIRAfBe5FoHI3rSsSmZCEWSGEECILCo+JZ+S6c2w5dz/V52weVA9PF7t0rCqNxEXCn8Pg9DLDdpEGUKyJtjWJTEvCrBBCCJGFhETFMXPHNZYc8k92/9DmJfHO5WDS5mBjRYOSebC1ssyACl/Tw4uwpjcEXQGdBTQaBfW/AIssULvQhIRZIYQQIovQ6xVT/rzMqn9uG9usLHRs+bQ+3rnssbG0wCorr+Z1bi1sHAgJ0eCUDzr+DEXqa12VyOQkzAohhBBZxFs/HuDS/TDj9o/dqtCqQn4sLXQaVpWGHPNAQoxhSEH7n8DJQ+uKRBYgYVYIIYTIAk4GPDEJsr+9X4t6JfJoWFEaiYsEG0fD7aKNoM+fhhW9LLJwD7PIUPJKEUIIIbKANf/cAcDV3pozY5tn/SCrFBxfCDMrGlb1eqpwHQmywizyahFCCCEyueDIOFYcCwBgxJulcXXI4ku3xoTB2j6GabeiguDEYq0rElmY5mF2zpw5+Pj4YGdnR61atTh27NgLj585cyalSpXC3t4eb29vPv/8c2JiYjKoWiGEECJjnbsTStWv/zZuF3DLAlNrvci9UzC/AVzYABZW0Pwb8J2odVUiC9N0zOyqVasYMmQI8+bNo1atWsycOZMWLVpw5coVPD09kxy/fPlyRo4cyaJFi6hTpw5Xr16ld+/e6HQ6vv/+ew2+AyGEECL9BDyOovXsA8bthiU9aFgyi14UpRQc+wn++goS48C1EHRaBN41tK5MZHGa9sx+//339OvXjz59+lC2bFnmzZuHg4MDixYtSvb4Q4cOUbduXbp3746Pjw/NmzenW7duL+3NFUIIIbKamPhEBq44adzuWsObX/rWRKfLojMXnF4GW4cbgmzpt6H/PgmyIk1oFmbj4uI4ceIEvr6+z4qxsMDX15fDhw8ne06dOnU4ceKEMbzeuHGDP//8k1atWqX4OLGxsYSFhZl8CSGEEJmZUorSY7Zx9k4oAJ7OtoxqVUbjql5ThXegUG1481vo8hvYu2tdkcgmNBtmEBQURGJiInnz5jVpz5s3L5cvX072nO7duxMUFES9evVQSpGQkED//v358ssvU3ycKVOmMGHChDStXQghhEhPF++bdrwsfb8mrvZZ7KIvpeDcGijXHiytwcoGev8pMxWINJelXlF79uxh8uTJ/O9//+PkyZOsX7+eLVu28PXXX6d4zqhRowgNDTV+3b59O8VjhRBCCK3FJiQye9d14/aVb96kdD4XDSt6BVHBsKIbrO8Huyc9a5cgK9KBZj2zefLkwdLSkocPH5q0P3z4kHz58iV7zpgxY3jvvff44IMPAKhQoQKRkZF8+OGHjB49Gotk3iS2trbY2tqm/TcghBBCpIO1J+6w9fwDAMrkd8HWylLjiswUcBTW9oWwO2BpA64Fta5IZHOahVkbGxuqVavGzp07adeuHQB6vZ6dO3cycODAZM+JiopKElgtLQ1vcqVUutYrhBBCpAe9XrHpzD0ehMXw14UHnAwIMe6b2qGCdoWZS6+HQz/Azq9BJUKuYtB5CeSvqHVlIpvTdGquIUOG0KtXL6pXr07NmjWZOXMmkZGR9OnTB4CePXvi5eXFlClTAGjdujXff/89VapUoVatWly/fp0xY8bQunVrY6gVQgghMquouATiEvTsuxbE0kP+APxz60myx37XsSKVvN0yrrjXERkEG/rD9X/nwy3fCVrPBFtnTcsSOYOmYbZLly4EBgYyduxYHjx4QOXKldm2bZvxorCAgACTntivvvoKnU7HV199xd27d/Hw8KB169ZMmjQppYcQQgghMoWt5+4zaMUpEvQpf5LYsWpB7G0s+LB+MQrldsjA6l5T9BO4dQis7KDld1C1J2TVKcRElqNTOezz+bCwMFxdXQkNDcXFJYsNqBdCCJFlfbP5Ij8fuGnSNrJlaXxyO2JloeONYrlxstW0j+n1XN4C7j6Qt5zWlYhswJy8loXfNUIIIUTW8PXmiyz8N8h+1KAow98sjQ6wsMiivZcRjwzDCup/AT51DW2l39K2JpFjSZgVQggh0tG6E3eMQRagSiE3LLNqiAW4sQfW9YPIR/DEHwYeBwu5bkVoR8KsEEIIkQ72Xg1k4LKThMcmGNt2ftGQYh5OGlb1GvSJsPdb2PsdoMCjjGG2AgmyQmMSZoUQQog0tuJYAKPWnzNpW9KnRtYNsmH3DQsg+O83bFd5z3Chl00WukhNZFsSZoUQQog0MHjlKS7dD+d6YASJz81Y8E278rSqkJ9cjjYaVvcaQu/A/IYQFQTWjoYptyq+o3VVQhhJmBVCCCFe09iN5/n99L0k7Qt7VadpmbwaVJSGXLygSH0Ium4YVpCnuNYVCWFCwqwQQgjxGlYeC2Dp4VsA5HK0YXa3KqCDyt5uONhk0V+zoXfBxhHs3Qzzxbb5ESyswNpe68qESCKLvsuEEEKIzGHF8dvG2yfHNNOwkjRydbth2i2fevDOUkOYlZW8RCYmYVYIIYR4BauP32bvtUAu3Q8DYFHv6hpX9JoS42HnBDj0o2E75BbEhBp6Z4XIxCTMCiGEEGb69bA/YzZeMG5X8HKlcSlPDSt6TSEBsLYv3Dlu2K75ETT/Gqxsta1LiFSQMCuEEEKYISwm3iTIjm9dlmbl8qHTZdGFEC5tho2fGHphbV2h7Wwo20brqoRINQmzQgghRCrdDo6i87zDxu0/P61P2QIvXjc+U4uPhq0jDEHWqxp0WgTuPlpXJYRZJMwKIYQQqRAVl0C/pf/wICwGgEreblk7yIJhdoJOC+HSH9B0HFhl0blwRY4mYVYIIYRIQWxCIkNWnSEgOIpzd0MBsLe2ZFa3KtQvkUfj6l7Rhd8hMe7ZwgeF3jB8CZFFSZgVQgghUrD9wkO2nLtv0talhjfNymbBhRDiY+Cv0XD8Z7CyhwJVZQEEkS1ImBVCCCGeo9crEv5djvbbrZeN7Yv71MDW0oLqPrm0Ku3VPfaDNb3hwVnDdq2PwL2wpiUJkVYkzAohhBD/uvYwnGYz9iVp71u3SNadeuvcWvjjM4iLAIfc0H4+lMgGizsI8S8Js0IIIXK81f/c5o8z99h/LSjJPncHa/o3LKpBVa9JKdgyBP5ZZNguVMdwsZdLAW3rEiKNSZgVQgiRo8XEJzJq/TkS/x1aUCa/C+9UL0iHqgUBcLCxxNrSQssSX41OZ+iJRQcNhkLDkWApv/ZF9iOvaiGEEDnO7iuPOPhvL2xodLwxyG4eVI/yXq5alvb6YiPA1slwu+FIKNEcvGtqW5MQ6UjCrBBCiBzhwLUgZu64yq3gKALDY5Psf7ti/qwdZOMi4c/h8PA8vP+XYSlaSysJsiLbkzArhBAi21txLIBR68+ZtFUr7E6Nf2cmqOHjTpPSWfQCL4BHlwyzFQReBp0F+O+H4r5aVyVEhpAwK4QQItubtv0KAO2reNGjViHcHKwp7umscVVpQCk49Rv8OQwSosEpH3T8GYrU17oyITKMhFkhhBDZ2vVHEQRHxgEwvk05XO2tNa4ojcSGw+YhcG61YbtYE2j/Ezh5aFuXEBlMwqwQQohsbfnRAAB8y+TNPkEW4I/BcH4t6CyhyWio+zlYZMFZF4R4TRJmhRBCZFvn74ay6OBNAHq8UUjjatJYk6/g4QV4ewYUrq11NUJoRsKsEEKIbGXNP7f5x/8JAcFRHL7xGABrSx0NS2Txj99jwuD6DijfwbCdqwh8fEh6Y0WOJ2FWCCFElvb5qtPsuxoIQGRcAjHx+iTHfNG8FBYWuowuLe3cO22YreDJTbBzeTZTgQRZISTMCiGEyFqi4hL45dAtwmLiiYlPZMOpu8keN7R5SSwsdDQvm4/ink4ZXGUaUQqOLYC/RkNiHLh6g20WngtXiHQgYVYIIUSWsuLYbb7ddjlJ++ZB9bCxMvRUFsrlgJ21ZUaXlraiQ2DTQLj0h2G7VCtoOwcccmlalhCZjYRZIYQQWcrJgCcA1C6amzL5XQCoWSRX1l6967/unoA1fSDkFlhYQ/OvoVZ/0GXhoRJCpBMJs0IIIbKM83dD2XL2PgADGhenXok8GleUTgKvGoKsW2HovBi8qmldkRCZloRZIYQQWcbkPy8Zb1comI16YsEwPvZpz2vlbhAfCeU7gb2bpmUJkdnJZZBCCCGyhB0XH3LIzzDV1i99a2avBRACjsLC5hD5+FlbjQ8kyAqRCtIzK4QQItO6FxLNtUcRBEfG8vmqMwD0ruNDw5JZfM7Yp/R6ODQLdk4ElQi7vobWM7WuSogsRcKsEEKITOnivTBazdpv0pbHyYbRb5XRqKI0FhkEG/rD9b8N2+U7QrOJ2tYkRBYkYVYIIUSmc/p2CN1+OmLcLlfABQudjj51fbC2zAYj5PwPwrr3Ifw+WNlBy2+hai+ZrUCIVyBhVgghRKZyMuAJHf53CIDcjjbM7l6V2sVya1xVGrq0GVa/B0oPuUtA5yWQr7zWVQmRZUmYFUIIoZlR689y+nYoz/dHXrwfZry94ZO6FMrtkPGFpaci9cGtEHi/AW9NB9ssujqZEJmEhFkhhBCaGLDsJFvO3U92n7WljqV9a2WfIPvgPOQtZxhGYOcK/XaDvbsMKxAiDUiYFUIIkWFuB0fx/d9XuXQ/jMsPwo3tS/rUQPdcsCvh6UQBN3stSkxb+kTY+x3s/RZaTYOa/QztsiStEGlGwqwQQogMER4TT58lx7n+KMKk/eiXTcnrYqdRVeko/AGs+wD8/52R4dGlFx8vhHglEmaFEEKku5CoOPr+G2TzutgyvEVpLCygVpHc2TPI+u2C9R9CZCBYO8LbM6BSF62rEiJbkjArhBAiXdwLicb/cSQJiYpvtlzk6sMIXOys+Llnjey3FO1TiQmwZwrsnw4oyFseOi0Gj5JaVyZEtiVhVgghRJoLioil+Yx9RMQmGNvyutiytG8tSuVz1rCydPbwPByYASio1gfenALW2WDsrxCZmIRZIYQQaW71P7eJiE3Axc6KfK52eLnZM7FtebxzZZPZCVJSoDI0/xqc8xlW9BJCpDsJs0IIIdJUol6x/GgAAGPeLkvn6t4aV5SOEuNh92So1BU8Shnaag/QtiYhchgJs0IIIVItKi6BoWvOcC8kJsVjLtwLJT5R4WhjSetKBTKwugwWchvW9oU7x+DqNvhoH1haa12VEDmOhFkhhBCpkpCoZ9zGC/x57kGqjq9Y0A07a8t0rkojl/+E3z+GmBCwdYVGIyXICqERCbNCCCFS5Yed11hz4o5x++ee1VM8VqeD6j7ZcGGAhDjYMQ6O/M+wXaAqdF4M7j6aliVETiZhVgghxEtdfxTBvL1+AJTO58zot8pQv4SHxlVlsMggWNYZ7p00bL8xAHzHg5WNpmUJkdNJmBVCCPFCer1i7MbzxCcqGpfyYFFv06Vncww7N7CyM/zbbi6UbqV1RUIIJMwKIYR4iV+P3OKQ32NsrSyY0KZ8zgqyCbGAztD7amkFnRaCPgHcCmldmRDiXxZaFyCEECJzO3A9CIDedXwolDubzxP7vMd+8LOvYYzsUy4FJMgKkclIz6wQQggTSin2XQtixdEArj0K536oYRounzyOGleWgc6vg02fQVw4hN2F+kPBMbfWVQkhkiFhVgghhIkZO64xa+e1JO2FsvvqXQDx0bBtJJxYYtguVAc6/ixBVohMTMKsEEIIo3/8g1l84CYA3WoW4u2K+bGy0JHbyZbink4aV5fOAq/Cmt7w6AKgg/pfQKNRhrGyQohMS96hQgiRw527E8rF+6FsPH2PQ36PASiV15lJ7cpjYZFDLvZKiIWlbSH8Hjh6QIefoFgTrasSQqTCa4XZmJgY7Ozs0qoWIYQQGSgmPpFPV5zir4sPjW3Wljo6VSvIZ01L5pwgC2BlC29OhuMLDcMKnPNpXZEQIpXMns1Ar9fz9ddf4+XlhZOTEzdu3ABgzJgxLFy4MM0LFEIIkT7+PHffGGS93OzpW7cIe4Y1ZkqHiuRzzQEdFY8ugf/BZ9vl2kOvPyTICpHFmB1mv/nmG5YsWcJ3332Hjc2zVU/Kly/Pzz//nKbFCSGESD9nbocAhhW99g9vzNjWZfFys9e2qIygFJz6DX5qDKt7QviDZ/ty0hy6QmQTZofZpUuX8tNPP9GjRw8sLS2N7ZUqVeLy5ctpWpwQQoj0c/ZuKAAfNyqWc4YUxEbAhv6wcQAkREO+CqCzfPl5QohMy+wxs3fv3qV48eJJ2vV6PfHx8WlSlBBCiPQVFhPPqYAQAHxy55D5Yx+cN8xW8Pga6Cyg8WioNwQsZP0gIbIys8Ns2bJl2b9/P4ULFzZpX7t2LVWqVEmzwoQQQqSPJ5FxvLfoqHHb0TabT2yjlGHe2G0jISEGnAsYlqUtXEfryoQQacDsn2Bjx46lV69e3L17F71ez/r167ly5QpLly5l8+bN6VGjEEKINPI4IpZ3Fx7j0v0wHG0s+bxZSYp5ZPOeWZ0Obh81BNnizaD9fFkEQYhsRKeUUuaetH//fiZOnMiZM2eIiIigatWqjB07lubNm6dHjWkqLCwMV1dXQkNDcXFx0bocIYTIEAv23WD3lUfcehzF3ZBo8jjZsrxfLUrmdda6tPSj1LMLumIj4NxqqNpbhhUIkQWYk9deKcxmZRJmhRA5zV8XHvDhryeM257Otizv90b2XdFLKTj+M9zcC52XSngVIgsyJ6+Z/Q4vWrQojx8/TtIeEhJC0aJFzb07IYQQ6eyTZSeNt2d2qcy2wQ2yb5CNDoE1veDPoXDpD7j8h9YVCSHSmdljZv39/UlMTEzSHhsby927d9OkKCGEEGlj/l4/EvSGD+BGvFmadlW8NK4oHd09AWv6QMgtsLCGZhOhTButqxJCpLNUh9lNmzYZb2/fvh1XV1fjdmJiIjt37sTHxydNixNCCPHqztwOYcrWZ/N/96xd+AVHZ2FKwZG58PdY0MeDWyHovAS8qmldmRAiA6Q6zLZr1w4AnU5Hr169TPZZW1vj4+PD9OnT07Q4IYQQ5ouMTSAgOIpuC44Y22Z2qZx9p+DaOhyO/WS4XaY1tJkN9m6aliSEyDip/smm1+sBKFKkCMePHydPnjzpVpQQQohXcy8kmjpTd5m0+ZbJy5vl82lUUQao1BVOLwff8VDjA1mSVogcxuw/02/evJkedQghhHgN4THxhMckmATZPE62FHS3Z0aXSthZZ6MlW/V6eHge8lc0bHtVg8HnwCGXtnUJITTxSp85RUZGsnfvXgICAoiLizPZ9+mnn5p1X3PmzGHatGk8ePCASpUq8eOPP1KzZs0Ujw8JCWH06NGsX7+e4OBgChcuzMyZM2nVqtWrfCtCCJHlxCXo+fviQ0KiDT9/910NZPuFhybHtKtcgJlds+GqjJGP4ff+cGMvfLDjWaCVICtEjmV2mD116hStWrUiKiqKyMhIcuXKRVBQEA4ODnh6epoVZletWsWQIUOYN28etWrVYubMmbRo0YIrV67g6emZ5Pi4uDiaNWuGp6cna9euxcvLi1u3buHm5mbutyGEEFnOcf9gftp3g78vPnzhcTV9cjGjS+WMKSoj3ToEa9+H8HtgaQuPrz0Ls0KIHMvsRRMaNWpEyZIlmTdvHq6urpw5cwZra2veffddPvvsMzp06JDq+6pVqxY1atRg9uzZgGFcrre3N4MGDWLkyJFJjp83bx7Tpk3j8uXLWFtbm1O2kSyaIITISq49DMcvMAKA/r+dTLK/Rbm8ANhbWzKwSXGKe2bDFb30ejjwPeyeDCoRcheHzr9AvvJaVyaESCfpugKYm5sbR48epVSpUri5uXH48GHKlCnD0aNH6dWrF5cvX375nWDoZXVwcGDt2rXGmRIAevXqRUhICBs3bkxyTqtWrciVKxcODg5s3LgRDw8PunfvzogRI7C0TH48WGxsLLGxscbtsLAwvL29JcwKITK9O0+iaDJ9L3EJepP2esXz8E4Nb5qU9sQpu85Q8FREIGz4EPz+HQtcsQu89T3YZtNFH4QQgHlh1uyfgtbW1lj8uzSgp6cnAQEBlClTBldXV27fvp3q+wkKCiIxMZG8efOatOfNmzfFQHzjxg127dpFjx49+PPPP7l+/TqffPIJ8fHxjBs3LtlzpkyZwoQJE1JdlxBCZAYRsQnU+3Y3YLiQyye3AwAezrZMbl8Bd0cbLcvLOGdXGYKslT289X9QuYfMViCEMGF2mK1SpQrHjx+nRIkSNGzYkLFjxxIUFMSvv/5K+fLp+5GPXq/H09OTn376CUtLS6pVq8bdu3eZNm1aimF21KhRDBkyxLj9tGdWCCEyswt3Q423p3WuSONSSa8jyBHe+ASe3DRMueVZRutqhBCZkIW5J0yePJn8+fMDMGnSJNzd3fn4448JDAxk/vz5qb6fPHnyYGlpycOHphcyPHz4kHz5kp8PMX/+/JQsWdJkSEGZMmV48OBBklkVnrK1tcXFxcXkSwghMru//r3IK5+LXc4KsuEPYPPnEB9t2LawgLemS5AVQqTI7J7Z6tWrG297enqybdu2V3pgGxsbqlWrxs6dO41jZvV6PTt37mTgwIHJnlO3bl2WL1+OXq83DnW4evUq+fPnx8Ymh3zkJoTI9taduMPCA4Y5vce1LqtxNRnIbxes/xAiA8HCClpN07oiIUQWYHbPbEpOnjzJ22+/bdY5Q4YMYcGCBfzyyy9cunSJjz/+mMjISPr06QNAz549GTVqlPH4jz/+mODgYD777DOuXr3Kli1bmDx5MgMGDEirb0MIITR1/VE4X6w5A8CHDYrSskJ+jSvKAIkJsPNr+LWDIch6loMa/bSuSgiRRZjVM7t9+3b+/vtvbGxs+OCDDyhatCiXL19m5MiR/PHHH7Ro0cKsB+/SpQuBgYGMHTuWBw8eULlyZbZt22a8KCwgIMDYAwvg7e3N9u3b+fzzz6lYsSJeXl589tlnjBgxwqzHFUKIzGrBvmerLH7SqJiGlWSQsHuGuWMDDhm2q/WGN6eCtb2mZQkhso5UT821cOFC+vXrR65cuXjy5Am5c+fm+++/Z9CgQXTp0oXPPvuMMmUy/5gmmWdWCJFZKaWoO3UX90JjGPt2WfrWK6J1Sekr4Ais7A5Rj8HGCVr/ABU6aV2VECITMCevpXqYwQ8//MC3335LUFAQq1evJigoiP/973+cO3eOefPmZYkgK4QQmZlfYCT3QmOwsbSgW81CWpeT/lwLgtJDvorw0T4JskKIV5LqYQZ+fn507twZgA4dOmBlZcW0adMoWLBguhUnhBA5RUx8Ih/++g8ANYq4Y2+T/EIwWV5MKNi5Gm67FoRef0DuEmBtp21dQogsK9U9s9HR0Tg4GCbt1ul02NraGqfoEkII8erO3gnhnfmHuREYCUCjktl0Kq4rW+GHSnD5z2dt+SpIkBVCvBazLgD7+eefcXIyLCGYkJDAkiVLyJMnj8kxn376adpVJ4QQ2dysndf4/u+rALjYWfFhg6K8V7uwxlWlsYQ42DkBDs82bB//GUq30rYmIUS2keoLwHx8fNC9ZAlBnU7HjRs30qSw9CIXgAkhMoOAx1GM2XievVcDASjv5cL/ulej0L/L1mYbT/xhbV+4e8Kw/cYn4DsBrGRucCFEyszJa6numfX393/duoQQQgB/nLnHl+vPER6bAEAeJxs2DqiHpcWLOwyynIubYONAiP13nGy7uVD6La2rEkJkM2avACaEEOLVjfn9PL8euQVA9cLuDGpagvIFXLJfkL1/Bla/Z7hdsAZ0WgRuOWCGBiFEhpMwK4QQGUApxby9N4xBtk9dH0a3KoOVZZotxJi55K8E1d8HG0doOhYsrbWuSAiRTUmYFUKIdKSUYtflR8zadZ0zt0MAKJnXiTFvlcUiu/XGXvgdCtUGZ8Mqjrw1HV5yrYUQQrwuCbNCCPGa4hL0/HMrmIRExe0nUZy7E8rZO6HcDIokUa+IS9QDYGdtQf+GxfisaYmXXlCbpcRHw7ZRcGIxFGkA7/0OFpYSZIUQGULCrBBCvKKQqDjm7b3BquMBPImKT/E4BxtL3qtdmH71i5LHyTYDK8wAQddgTW94eB7QGcbHpm6SHCGESBOvFGb9/PxYvHgxfn5+/PDDD3h6erJ161YKFSpEuXLl0rpGIYTIlIavPctfFx8at0vnc8bD2ZYKXq5ULOhKqXwuWFvqyO1omz1X9Dq7Gv4YDPGR4JAHOvwExZtqXZUQIocxO8zu3buXli1bUrduXfbt28ekSZPw9PTkzJkzLFy4kLVr16ZHnUIIkSlcuh/G3xcfsvvKI04FhABQr3geJrevkP3miE1JXBRsHQ6nfjVs+9SHDgvARVaFFEJkPLPD7MiRI/nmm28YMmQIzs7OxvYmTZowe/bsNC1OCCEyk5CoON79+SiPI+OMbb5l8vLTe9Wy38VcL6L0cPsooIOGI6DhcMMYWSGE0IDZYfbcuXMsX748SbunpydBQUFpUpQQQmQ2v5+6y+erTxuHg5b3cmHEm6WpX8JD28IyklKGi7psnaDzEogMhKKNtK5KCJHDmR1m3dzcuH//PkWKFDFpP3XqFF5eXmlWmBBCaC0hUU9sgp6Jf1xk1T+3je0lPJ1Y/VFtHGxyyDW0sRHw51DIWx7qDDS05ZXrI4QQmYPZP4m7du3KiBEjWLNmDTqdDr1ez8GDBxk6dCg9e/ZMjxqFECLD7bnyiP6/nSAmXm/SPqFNOXrV8dGmKC08vGCYrSDoKljZQcV3wMlT66qEEMLI7KVnJk+eTOnSpfH29iYiIoKyZcvSoEED6tSpw1dffZUeNQohRIY77PfYJMjmcrRh/nvV6FYzhyzJqhScWAILmhiCrHN+eHe9BFkhRKajU+rVJgQMCAjg/PnzREREUKVKFUqUKJHWtaWLsLAwXF1dCQ0NxcXFRetyhBAai0vQc/ZOCAn6Zz8KA4KjmLDpApFxifRvWIwBjYvhbJeDlmONCYPNg+H8OsN2cV9oPx8c82halhAi5zAnr5k9zODAgQPUq1ePQoUKUahQDumhEEJkKw9CY1h29BbRcYn8fOBmisfVLpqbT3JakE2Mh4XNIPAy6Cyh6Vio8ylYmP1BnhBCZAizw2yTJk3w8vKiW7duvPvuu5QtWzY96hJCiHQRFBHLG1N2JruvmIcjADqdjmZl8zKkWUmsLXNYiLO0hirvwZG50GkRFKqldUVCCPFCZg8zCAoKYuXKlaxYsYLDhw9TsWJFevToQbdu3ShYsGB61ZlmZJiBEDlTTHwiM3ZcZf7eG8Y2Lzd7WlcqQD4XW959ozBWOS24PhUTCpFBkLuYYVspiA0DO1dt6xJC5Fjm5LVXHjMLcPPmTZYvX86KFSu4fPkyDRo0YNeuXa96dxlCwqwQOU9Cop46U3fxKDzW2OZbxpOfe9XQsKpM4u5Jw2wFltbw4R6wdX7ZGUIIke7Sdczs84oUKcLIkSOpVKkSY8aMYe/eva9zd0IIkeZiExJp9v0+kyC7/pM6VC3krmFVmYBScHQe/DUG9PHgVgjC7oOHhFkhRNbyymH24MGDLFu2jLVr1xITE0Pbtm2ZMmVKWtYmhBCvbfuFhwQERwFgoYOLE9/EzjqHL70a/QQ2DoTLmw3bpd+GtnPA3k3TsoQQ4lWYHWZHjRrFypUruXfvHs2aNeOHH36gbdu2ODg4pEd9QgjxysJi4vl0xSnjtt/kVuh0Og0rygRuH4e1fSE0ACxtoPkkqNnPsEytEEJkQWaH2X379jFs2DDeeecd8uSROQeFEJnXwOXPguwnjYpJkAXY+60hyLoXgc6LoUAVrSsSQojXYnaYPXjwYHrUIYQQaSYkKo49VwLZdzUQAAcbS/rULaJxVZlE2zmwdyr4TgA7uQhWCJH1pSrMbtq0iZYtW2Jtbc2mTZteeGybNm3SpDAhhHgVN4Miafx/e0za/h7SEA9nW20K0tqtw+C3C5qMNmw754W3Z2hbkxBCpKFUhdl27drx4MEDPD09adeuXYrH6XQ6EhMT06o2IYQwS2xCokmQLeBqx2Dfkni52WtXlFb0ejg4A3ZNApUI+StBmbe1rkoIIdJcqsKsXq9P9rYQQmQmPRYcNd7uWbswE9uW17AaDUUEwoYPDT2yABW7QNFGmpYkhBDpxezlbpYuXUpsbGyS9ri4OJYuXZomRQkhxKuIiE0AwMnWKucG2Zv7YV49Q5C1soc2s6H9fLB10royIYRIF2aH2T59+hAaGpqkPTw8nD59+qRJUUIIYa59VwO5/CAcgFndKmtbjFYOz4GlbSDiAeQpBR/uhqrvybRbQohszezZDJRSyU5vc+fOHVxdZR1vIUTG++WQP+M2XTBuly+QQ38W5SoKSg+Ve0CraWDjqHVFQgiR7lIdZqtUqYJOp0On09G0aVOsrJ6dmpiYyM2bN3nzzTfTpUghhEhJVFwC4/94FmRndauCp4udhhVlsOiQZyt3lWoJ/XaDV1UtKxJCiAyV6jD7dBaD06dP06JFC5ycno2/srGxwcfHh44dO6Z5gUII8SKX7oejlOH2b+/Xol6JHLKYS2KCYb7YfxbBh3vBzdvQLkFWCJHDpDrMjhs3DgAfHx+6dOmCnV0O6vkQQmRKcQl6Os49BECdYrlzTpANuwfrPoBb/y5ic3Ej1BmobU1CCKERs8fM9urVKz3qEEIIs208fdd4O8es8HVth2HarajHYOMErX+ACp20rkoIITSTqjCbK1curl69Sp48eXB3d3/h+ubBwcFpVpwQQiQnIVHPmI0XWHEsAID6JfLQrGxejatKZ4nxsHsSHPh39a58FaDzL5C7mLZ1CSGExlIVZmfMmIGzs7Px9ovCrBBCpKeD14Po8fOzxRHqFMvNT+9V17CiDHJk7rMgW6MfNP8GrGW4lxBC6JR6eulEzhAWFoarqyuhoaG4uLhoXY4Qwgy3HkfScNoe47ZPbge2DW6AnbWldkVllPho+LUD1PoIyrXTuhohhEhX5uQ1sxdNOHnyJOfOnTNub9y4kXbt2vHll18SFxdnfrVCCJFKh/0eG29PbFuOPcMaZ98gmxAHxxeCPtGwbW0Pff6UICuEEP9hdpj96KOPuHr1KgA3btygS5cuODg4sGbNGoYPH57mBQohxFPH/Z8AMLBxcXrW9tG2mPT05BYsfhO2DIH905+1yxAvIYRIwuwwe/XqVSpXrgzAmjVraNiwIcuXL2fJkiWsW7curesTQgijE7cMF5hW93HXuJJ0dOkPmF8f7p4AO1fIW07rioQQIlN7peVs9Xo9ADt27ODtt98GwNvbm6CgoLStTggh/rXx9F38H0eh00HVwtkwzCbEwl9j4Nh8w3bBGtBpEbgV0rYuIYTI5MwOs9WrV+ebb77B19eXvXv3MnfuXABu3rxJ3rzZfGocIUSG0+sVP+66zowdhuFN5Qq44GJnrXFVaSz4BqzpA/dPG7brDIKm48Aym32fQgiRDswOszNnzqRHjx78/vvvjB49muLFiwOwdu1a6tSpk+YFCiFytoUHbhqDbPOyeRnSvKTGFaWDuEh4dAns3aH9fCjZQuuKhBAiy0izqbliYmKwtLTE2jpz9yTI1FxCZB2JekW9b3dxPzSGkS1L079hNlogQCnTC7oub4H8lcC1oHY1CSFEJmFOXjO7Z/apEydOcOnSJQDKli1L1apVX/WuhBAiWQ/CYrgfGoOVhY4+dX20LiftBF2H9f2g1f9BwWqGttJvaVuTEEJkUWaH2UePHtGlSxf27t2Lm5sbACEhITRu3JiVK1fi4eGR1jUKIXKQ5z8sik8wXGxqZanD1iqbzCd7dg1sHgxxEbB1GHywU6bcEkKI12B2mB00aBARERFcuHCBMmXKAHDx4kV69erFp59+yooVK9K8SCFEzqDXK9rMOcD5u2Em7e4ONhpVlIbiomDrcDj1q2Hbpz50/FmCrBBCvCazw+y2bdvYsWOHMciCYZjBnDlzaN68eZoWJ4TIOf7xD2bSn5eSBFknWyvGtc7ic60GXoE1veHRRUAHDUdAw+FgkU16m4UQQkNmh1m9Xp/sRV7W1tbG+WeFECK1bgZFMn7TBfZeDQTA0kJHp6oFGdGyNAAONpZZe8naR5dgQROIjwJHT0NvbNGGWlclhBDZhtlhtkmTJnz22WesWLGCAgUKAHD37l0+//xzmjZtmuYFCiGyp5j4RObu8WPuXj/iEvRYW+roVK0gHzcsTqHcDlqXl3Y8SkORBhAfDR0WgLPMxy2EEGnJ7DA7e/Zs2rRpg4+PD97e3gDcvn2b8uXL89tvv6V5gUKI7CUuQc+uyw+ZuvUy/o+jAGhQ0oOJbcrhk8dR4+rSyKNL4OoNtk6GMbEdF4K1vQwrEEKIdGB2mPX29ubkyZPs3LnTODVXmTJl8PX1TfPihBDZi1KK1j8e4MrDcADyutgy9u1ytKqQD112uBBKKTi51HChV9m2hgUQdDpDqBVCCJEuzAqzq1atYtOmTcTFxdG0aVMGDRqUXnUJIbIZpRRD15w1BtmOVQsyvk1ZnLPL0rSx4bD5czi3xrAd9RgSYsHaTtu6hBAim0t1mJ07dy4DBgygRIkS2Nvbs379evz8/Jg2bVp61ieEyCbGbDzPupN3AHC2s2Jyh/LZZ+7Y+2cNsxUE+4HOEpqOhTqfgoWF1pUJIUS2l+qftLNnz2bcuHFcuXKF06dP88svv/C///0vPWsTQmQDTyLjGLX+HL8dCTC2bR5UL3sEWaXg+M/ws68hyLoUhD5bod5gCbJCCJFBUv3T9saNG/Tq1cu43b17dxISErh//366FCaEyPriE/UMXHGSFceeBdnFfWpQOHc2udAr+gnsmQqJsVCyJfTfD4VqaV2VEELkKKkeZhAbG4uj47NfQBYWFtjY2BAdHZ0uhQkhsjbDGNkzHLz+GABPZ1smti1PgxLZaMlrh1yG6bYeXYQ3PpHVvIQQQgNmXQA2ZswYHByezf8YFxfHpEmTcHV1NbZ9//33aVedECLLUEoxcMUpjt8MBkCvFEERcVhZ6JjasSIdq3pl/RkLlIKj88E5H5RrZ2gr1tjwJYQQQhOpDrMNGjTgypUrJm116tThxo0bxu0s/4tKCPFKlFLM2HGNLWdNhx1Z6GBC23J0qlZQo8rSUPQT2DgQLm8GG2fwrgkuBbSuSgghcrxUh9k9e/akYxlCiKxs89n7zNp5DYACrnb83KsGALmdbMjrkg2mprrzD6zpA6EBYGljmK3AOb/WVQkhhOAVFk0QQojnxSXombbd8KlNpYKuLO1bC1eHbDJ3rF4PR+bAjvGgTwD3ItB5MRSoonVlQggh/iVhVgjxWhYeuElAcBTuDtas+PANHGyyyY+VxARY9S5c3WrYLtceWs8COxdt6xJCCGEim/zWEUJoYelhf77ddhmA2sVyZ58gC2BpBbmKgqUttJwK1frIbAVCCJEJZaPfPEKIjBSfqGfsxgsAuDlYM7FteY0rSgN6PcSGgb2bYdt3PFTtCZ6ltaxKCCHEC8gSNUIIs0XEJlBi9Fbj9sJe1cnjZKthRWkgMgiWd4blXSAx3tBmZSNBVgghMrlXCrP79+/n3XffpXbt2ty9exeAX3/9lQMHDqRpcUKIzKnfL/8Yb5fM60QVb3cNq0kD/gdgXj24vgPun4H7Z7WuSAghRCqZHWbXrVtHixYtsLe359SpU8TGxgIQGhrK5MmT07xAIUTmExpt6Ln0crNny6f1sbDIomNJ9Ymw9zv4pTWE34c8paDfLihYTevKhBBCpJLZYfabb75h3rx5LFiwAGvrZ9Pv1K1bl5MnT6ZpcUKIzCc4Mo5LD8IA2PBJHawts+hopfCH8Gt72D0JlB4q94APd0PeslpXJoQQwgxmXwB25coVGjRokKTd1dWVkJCQtKhJCJFJJeoVf198gFJQ3NMJz6y8IMKGj+DmXrB2gLe+h8rdtK5ICCHEKzC7SyVfvnxcv349SfuBAwcoWrToKxUxZ84cfHx8sLOzo1atWhw7dixV561cuRKdTke7du1e6XGFEKl3/m4ojf5vNyPWnQPA1T6LL4zQ8jsoWBM+3CtBVgghsjCzw2y/fv347LPPOHr0KDqdjnv37rFs2TKGDh3Kxx9/bHYBq1atYsiQIYwbN46TJ09SqVIlWrRowaNHj154nr+/P0OHDqV+/fpmP6YQIvX0esWBa0F0nHuI28HRAFhb6mhWNq/GlZkp7D6cXfNs26MkvP+X4V8hhBBZlk4ppcw5QSnF5MmTmTJlClFRUQDY2toydOhQvv76a7MLqFWrFjVq1GD27NkA6PV6vL29GTRoECNHjkz2nMTERBo0aEDfvn3Zv38/ISEh/P7776l6vLCwMFxdXQkNDcXFRVbyEeJFdl9+RJ8lx43bpfM5s+qj2lmvV/b6Dlj/IUQ/gV6bwaeu1hUJIYR4AXPymtk9szqdjtGjRxMcHMz58+c5cuQIgYGBrxRk4+LiOHHiBL6+vs8KsrDA19eXw4cPp3jexIkT8fT05P3333/pY8TGxhIWFmbyJYRIneeDrKONJd+/UzlrBdnEBNgxHn7rCFGPIW85cMpiPcpCCCFe6JVXALOxsaFs2de76jcoKIjExETy5jX95ZI3b14uX76c7DkHDhxg4cKFnD59OlWPMWXKFCZMmPBadQqREz3/oc3EtuV4p7o3dtaWGlZkptA7sPZ9uH3EsF3jA2g+Cayz8EVrQgghkjA7zDZu3BjdC9Yn37Vr12sV9CLh4eG89957LFiwgDx58qTqnFGjRjFkyBDjdlhYGN7e3ulVohDZQsDjKAavOmXcfrNcvqwVZK9uN8xWEP0EbF2gzSwo117rqoQQQqQDs8Ns5cqVTbbj4+M5ffo058+fp1evXmbdV548ebC0tOThw4cm7Q8fPiRfvnxJjvfz88Pf35/WrVsb2/R6PQBWVlZcuXKFYsWKmZxja2uLrW0WX2ZTiAz29ZaLnAwIAeCtCvnxcM5i76HQ24Ygm78ydF4MuV5tphUhhBCZn9lhdsaMGcm2jx8/noiICLPuy8bGhmrVqrFz507j9Fp6vZ6dO3cycODAJMeXLl2ac+fOmbR99dVXhIeH88MPP0iPqxBp4NjNYP6+aPgD862K+ZnTvarGFaWSUvD0U6Pq74OVPVToBFZZLIgLIYQwyyuPmf2vd999l5o1a/J///d/Zp03ZMgQevXqRfXq1alZsyYzZ84kMjKSPn36ANCzZ0+8vLyYMmUKdnZ2lC9f3uR8Nzc3gCTtQohXM2LdWePtL5plkWmrLm2Gfd9Bz01g72YItVV6aF2VEEKIDJBmYfbw4cPY2Zl/YUWXLl0IDAxk7NixPHjwgMqVK7Nt2zbjRWEBAQFYWGTR5TKFyGL8gyK5GRQJwCeNilHUw0njil4iIRb+HgdH5xq2D8+GJl9pW5MQQogMZfY8sx06dDDZVkpx//59/vnnH8aMGcO4cePStMC0JvPMCpGyulN3cTfEsDDCsS+bZu7laoNvwJo+cP+0YbvOIGg6Diyz0NRhQgghkmVOXjO7Z9bV1dVk28LCglKlSjFx4kSaN29u7t0JITKBmPhEZuy4agyy/+tRNXMH2QsbYNOnEBsG9rmg/Two2ULrqoQQQmjArDCbmJhInz59qFChAu7u7ulVkxAig41cd5bfT98DYFCT4rSqkF/jil7gn8WwebDhtvcb0GkRuHppWpIQQgjtmDUY1dLSkubNmxMSEpJO5QghMpJ/UCR/nrtvDLI9axfmc99MftFXmTbgUhDqDYHeWyTICiFEDmf2MIPy5ctz48YNihQpkh71CCEyyKyd1/j+76vG7Q5VvJjYNpPOCnL7GHjXNNx2zA0DjoCts7Y1CSGEyBTMnibgm2++YejQoWzevJn79+8TFhZm8iWEyPziEvTGIOtka0UlbzeGvVlK46qSER8NmwbBwmZwatmzdgmyQggh/pXqntmJEyfyxRdf0KpVKwDatGljsqytUgqdTkdiYmLaVymESFOTtlw03j42uikONmk2S1/aCbwCa3rDo4uADiIeaF2REEKITCjVv8EmTJhA//792b17d3rWI4RIB7EJiczaeY2Tt0KIT9Tzz60nADQt7Ym9taXG1SXj9ArYMgTio8DREzougKKNtK5KCCFEJpTqMPt0OtqGDRumWzFCiLQXGB5L/99OcOLfAPvUF81KMqhpCY2qSkFcJPw5DE7/O6SgaCPosACcPDUtSwghROZl1meLzw8rEEJkftcehjNg+UmuPozA2c6K4S1K4epgg5ebHVULZcLp9e6dgtPLQWcBjb6E+kPAIhP2HAshhMg0zAqzJUuWfGmgDQ4Ofq2ChBCvJzoukSM3HrPmxG22nn+AUpDHyYZVH9WmWGZfntanHjT/BgpUNtwWQgghXsKsMDthwoQkK4AJITKPIzce88XqM8aVvADeLJePYW+WypxBNjYc/voK6n4GuYoa2uoM1LYmIYQQWYpZYbZr1654esrYNSEym0PXg5i4+SKXH4Qb29pX8eKjhkUpne/Fa1pr5sE5w2wFj6/Dwwvw/t8gQ5mEEEKYKdVhVsbLCpH5BDyOYtnRWyw6eJP4RGVs//PT+pQtkElDrFLwzyLYNgoSY8HFyzC0QH7GCCGEeAVmz2YghMgc7jyJot3/DhIcGQdAtcLujH27LPlc7cjrYqdxdSmICYU/PoMLGwzbJd+EdnPBIZe2dQkhhMiyUh1m9Xp9etYhhDBDbEIi9b41zPnsYmdFrzo+9K1bBHdHG40re4En/rC0HTy5CRZW4DsBag+QHlkhhBCvJRMu+yOEeJlWP+w33l7YuwY1fLJAz6ZzAbB3A30h6LwYClbXuiIhhBDZgIRZIbKYdSfu4BcYadyuXjgTzhf7VHQI2DiBpRVY2UCX38DGEewzcc1CCCGyFAutCxBCpN7p2yF8seaMcfvChBaZ9+LMOydgfn3YM/lZm2tBCbJCCCHSlIRZIbKAyNgEftrnR9efDhvbJrYth6NtJvxwRSk4NBsWNYeQAMPFXnGRLz9PCCGEeAWZ8DehEOIppRQLD9xkzu7rPImKB6BoHkc+bFCUtpW9NK4uGVHB8PsncHWrYbtsO2gzyzC0QAghhEgHEmaFyIRuB0cRE5/IimO3WXTwJgCFczswoHFx2lfxwtoyE36oEnAU1vaFsDtgaQtvToHqfWW2AiGEEOlKwqwQmcyyo7cYveG8Sds71QsyuX0FrDJjiAXD/LHLOkNsKOQqBp2XQP6KWlclhBAiB5AwK0Qms+r4bQCcbK2wttSR28mWQU1KZN4gC2DnCi2ngt8ueHsG2DprXZEQQogcQsKsEJnInSdRnL0TioUOdg9thIezrdYlpcz/oGHxg0K1DNuVu0OlbjKsQAghRIaSMCuExm49juTHXdcJiYrnYVgMADV8cmXeIKtPhP3fG6bccsoH/Q+AY27DPgmyQgghMpiEWSE0FJ+op+G0PUna21QukPHFpEbEI1jfD27sMWwXbQTWdlpWJIQQIoeTMCtEBlFK8fP+m1x/FGFs23r+vvH2l61K42JnjbOdNW+Wz6dFiS92Yy+s+wAiH4G1A7w13TC0QAghhNCQhFkhMsi+a0FM+vNSsvsGNSnOhw2KZXBFqaTXw96psPc7QIFnWcNsBR6ltK5MCCGEkDArREZZf/IOAPVL5OGNormN7c3L5qVE3kx89b9OB4GXAQVVe8Kb34KNg9ZVCSGEEICEWSHS3e3gKL5YfYZj/sEAdKjqRfsqBTWuKhX0erCwMITZNj9CuQ5Qrp3WVQkhhBAmMvHElUJkD7uvPDIGWYBCuTJ5r2ZiAuwYD2v7gFKGNjtXCbJCCCEyJemZFSKdHLnxmDm7rxMQHAVA7aK5+bpdeYp7Omlc2QuE3oG178PtI4btWwfBp562NQkhhBAvIGFWiDQUl6Bnz5VHRMQmMHHzRUKi4o37yhZwydxB9up22PARRD8BWxdoM0uCrBBCiExPwqwQaeTivTBazdqfpP3jRsWoWsid+iXyaFBVKiTGw84JcOhHw3b+ytB5MeQqqmlZQgghRGpImBUijfy8/4bJdsOSHrxT3Zu3KubXqKJUWtsXLm0y3K7VH5pNBKtMuvqYEEII8R8SZoVIA2fvhLD+1F0AKni5svLDN3C0zSJvrzc+NoyNbT0LyrytdTVCCCGEWbLIb1shMrdBK04Zb/eu45O5g2xCLDw4BwWrG7YL14HB58DGUdu6hBBCiFcgU3MJ8ZrO3A7h1mPDjAWNSnlk7mEFwTdhYXP4pTUEXnnWLkFWCCFEFpWJu4+EyHyCI+OIiU8kUa8Yse4sIVHxXLwfZtz/Q9cq2FlbaljhC1z4HTYNgtgwsHeH8AeyJK0QQogsT8KsEC+hlGLnpUfM3HmV83fDUjyuaw1vXO2tM7CyVIqPgb9Gw/GfDdvetaDTInDNAquQCSGEEC8hYVaIlzhzJ5QPlv5j0mZjZYFSiqJ5nPjyrTJYW+io5uOuUYUv8NgP1vQyjJEFqPc5NB4NlpkwdAshhBCvQMKsEC/xJDIOABc7K96qmJ/BviXJ62KncVWpdHaVIcg65Ib2P0EJX60rEkIIIdKUhFkhUqlwbkemdKiodRnmaTAcYiOgzkBwKaB1NUIIIUSak9kMhMhOAq/Chv6G6bcALK3gzckSZIUQQmRb0jMrRHZxegVsGQLxUeDiBU3HaF2REEIIke4kzArxEv/bc13rEl4sLhL+HAanlxm2izSEmh9qW5MQQgiRQSTMCpGC3Vcecf5OKMf9nwDgncte44qS8egSrOkNgZdBZwGNRkH9L8Aik851K4QQQqQxCbNCJONJZBzvLzmOXhm2PZxt+aFrFW2L+q/LW2Dt+5AQDU75oNNC8KmndVVCCCFEhpIwK0Qy7oZEo1dgoYPutQrRs7YP1paZ7HpJzzKG+WIL14H288HJQ+uKhBBCiAwnYVaIZMzeZRgnq1fwTbsKGlfznIjAZ6E1V1H4YAfkLgEWmSxoCyGEEBlEfgMKkYzw2HgA6hTLrXEl/1IKji+EmRXAb9ezdo9SEmSFEELkaPJbUIhk+D2KBGBoi1IaVwLEhMLaPoZptxKi4dxarSsSQgghMg0ZZiDEf4TFxPMgLAaA4p5O2hZz7xSs6QNPboKFFfiOhzcGaFuTEEIIkYlImBXiP347cguAvC62uNhZa1OEUnDsJ/jrK0iMA9dC0GkReNfQph4hhBAik5IwK3KsmPhE7ofG8DAshrN3Qjh7J5Rzd0O59TgKgAJuGs4re3MvbB1uuF36bWg7G+zdtatHCCGEyKQkzIocKTYhkabT93I3JDrJPgsdtK5UgA/qFdWgsn8VbQRVe4FnWaj1Eeh02tUihBBCZGISZkWOdML/iTHIernZU8HLlQoFXSlbwIUSnk4UdHfI2IKUguM/Q7kO4PjvDAptZmVsDUIIIUQWJGFW5Ejz9t0AoEMVL77vUlnbYqKC4feP4eo2uPY3dFsp020JIYQQqSRhVuQ4G0/fZd/VQADKFnDRtpiAo7C2L4TdAUtbKNlchhQIIYQQZpAwK3KU4WvPsPqfOwCUK+DC2xULaFOIXg+HfoCdX4NKhFzFoPMSyF9Rm3qEEEKILErCrMgx/AIjjEH2rQr5mdWtCpYWGvSCRgXD+n5wfYdhu3wnaD0TbJ0zvhYhhBAii5MwK3KEJ5FxjN14HoCaRXIxp0dV7YrRWUDQNbCyg5bfQdWeMrRACCGEeEUSZkW2FxGbQP3vdhMRmwDAx42KZXwRer0hsOp0YO8G7ywFS2vIWy7jaxFCCCGyEblkWmR7K44GGIPsWxXy06CER8YWEPEIfmsP/yx81lagsgRZIYQQIg1Iz6zIlqLjEtly7j7hMfFM+vOSsT3Dhxfc2AvrPoDIR3D/DFTsImNjhRBCiDQkYVZkK9svPOC3I7c4cesJUXGJJvs2fFIn4wrRJ8Leb2Hvd4ACjzKG2QokyAohhBBpSsKsyFZm77rOubuhxm3fMnkpnNuBjxoUxdPFLmOKCLtvmK3Af79hu8p7hgu9bDJ4VTEhhBAiB5AwK7KF28FRzN3rZwyyXWt485lvCfK72mdsIbER8FMjiHgA1o6GKbcqvpOxNQghhBA5iIRZkeWFRsfTa9ExbgRFAuCT24Fxrcthb2OZ8cXYOkHND+DCRsOwgjzFM74GIYQQIgeRMCuytES94tMVp7gRFImznRWfNilBx2oFMzbIht6FhBjI/e+UX/WGQO1BYJ1BwxqEEEKIHEzCrMiyvt12mZ/33yA+UWFnbcGKfm9Q3ss1Y4u4uh029Afn/NBvJ1jbg4Wl4UsIIYQQ6U7mmRVZUmB4LPP2+hGfqACY1qlSxgbZxHj46ytY/g5EB4OlFUQ/ybjHF0IIIQQgPbMii/rr4gOUAidbK3YMaUg+1wz8SD8kANb2hTvHDds1P4LmX4OVbcbVIIQQQgggk/TMzpkzBx8fH+zs7KhVqxbHjh1L8dgFCxZQv3593N3dcXd3x9fX94XHi+xDKcWuyw9ZdvQWK44FADCgcfGMDbKXt8C8eoYga+sK7/wKrb6TICuEEEJoRPMwu2rVKoYMGcK4ceM4efIklSpVokWLFjx69CjZ4/fs2UO3bt3YvXs3hw8fxtvbm+bNm3P37t0MrlxktBO3ntB3yT+M3nCe83fDAGhZPl/GFaDXw6EfISYUClSF/vugbJuMe3whhBBCJKFTSiktC6hVqxY1atRg9uzZAOj1ery9vRk0aBAjR4586fmJiYm4u7sze/Zsevbs+dLjw8LCcHV1JTQ0FBcXl9euX2Scbefv0/+3kwC0KJeXGj65+KB+0YwtIvQO/LMIGo4EK5uMfWwhhBAihzAnr2k6ZjYuLo4TJ078f3t3Hmdj/f9//DFnmAUzY8ssDLL7WhvrkPrQFJIImeKLJCrrz7QpfQz5WvKJSmkvoz5TIz6RolGUMJQsQ9mXQWLGVmaM2c/798f5OHUygxkz5zjjeb/dzq3zfp/3dV2v423ydHlf18Wzzz5r77NYLERERLBx48ar2seFCxfIycmhcuXK+X6elZVFVlaWvZ2amnptRYvLrNt/GoBeLUN49YFbnHPQnUshZSd0mWhrB9SAOyY559giIiJyRS5dZnD69Gny8vIIDAx06A8MDCQ5Ofmq9vHMM88QEhJCREREvp/PmDGDgIAA+ys0NPSa6xbXuBhme99SveQPlpMJy5+ARUNg7SxIWlvyxxQREZFCc+u7GcycOZO4uDjWrFmDj0/+FwE9++yzREVF2dupqakKtG5mybZj7D6Rxtn0bAAqlyvhf94/cxAWPQTJO2ztjv8PaoaX7DFFRESkSFwaZqtWrYqnpycpKSkO/SkpKQQFXf7CnpdeeomZM2eyatUqmjdvXuA4b29vvL11pbk7OnrmAj1fX8+5jByH/vLeJfjb9ufF8MU4yD4P5arAfe9A/fzP+ouIiIjruXSZgZeXF61atWL16tX2PqvVyurVqwkPL/hM2KxZs5g6dSrx8fG0bt3aGaWKE6Vl5jD2k23c9q/vHILso7fV4cW+zahXrULJHHjlRPjPMFuQrdURHluvICsiInKdc/kyg6ioKIYMGULr1q1p27Ytr7zyCunp6QwdOhSAwYMHU716dWbMmAHAiy++yKRJk/j444+pXbu2fW1thQoVqFChhEKOOFWLKV9j/cs9Nro2CWRO/5Yle0YWoHorwANue9J2twJPl/94iIiIyBW4/E/ryMhITp06xaRJk0hOTqZly5bEx8fbLwo7evQoFsufJ5DffPNNsrOz6devn8N+oqOjmTx5sjNLlxJwMjXTIch+Pf42GgT6ldwBz5+ECtVs75v2gcCmcFODkjueiIiIFCuX32fW2XSf2evbip9PMDLWdi/ZA9O6U8azhFbCZKfDiqdg/ze25QR+gVfeRkRERJyiMHnN5U8AE7nolVX77EE2OMCn5ILsyd3wbhdIjIULpyHp+5I5joiIiJQ4ly8zEAG4kJ3LK6v229sLHm5b/AcxBrb923ZGNjcDKgRB3/fg5k7FfywRERFxCoVZcSljDKM/2cbyHSfsfUtHdSz+dbJZ5+HL8fDzp7Z23S62225VuKl4jyMiIiJOpTArLvXHhRyHIBsc4EOLGgHFf6C1/7IFWQ9P26NpO44Hi1bZiIiIuDuFWXGZnDwrj8dusbdXRd1G3Zsq4OHhUfwHu+0pOJFou+VWLT3NS0REpLTQqSlxme/2nOSHQ2cBqF7Rt3iDbGYqbHjNtk4WwLsCDP5cQVZERKSU0ZlZcZlXV/95wdeXY24tviB7PBEWD4Wzh2ztDmOKZ78iIiJy3VGYFZcwxrDzeCoAD7atSaXyXsWxU9j0Lnw9EfKyISAUQttf+35FRETkuqUwK0738Y9HeXnVPnv7rv8phgcWZPwBy0bD7i9s7YY9oNfrUK7yte9bRERErlsKs+JUGdl5PLfkZ4e+9nWqXNtOf9sKi4bAH0fBUhbumgrtHoOSuJBMRERErisKs+JUb6w5YH//fI/G3NsiBF8vz2vbqTGQehwq1oL750P1VtdYpYiIiLgLhVlxmtw8K699awuzFg94pFOdou/MmgeW/4bgGq0gMhZqtgffitdeqIiIiLgN3ZpLnObQ6XT7+3kDwoq+o6M/wry2kPyX5QoNuynIioiI3IB0ZlZKVGZOHi/G72Hz4d/JzMmz93dvFlz4nVmtsGEurH4BTJ7tvwMXFWO1IiIi4m4UZqXEZObkET5jNb9fyHHob16Ux9Wmn4Ylj8GBb2ztpn3hnleuvUgRERFxawqzUiKyc60s3nLMHmRHda5L69q222SFhVYq3M6ObIDFD0PaCSjjA91fhLAhuluBiIiIKMxK8dl9IpXdJ1JZsOEw24+ds/eP6lyXp7o2KtpOj2yEmB5grFClPtwfA0FNi6dgERERcXsKs1Islmw7xviF2y/pv7dFCFF3Niz6jkPbQu1O4BcMPWaDd4VrqFJERERKG4VZuSZnzmexL+U805bvASDQ35tm1QMYdmsdmlb3x8+nbOF3evQHCG4BZX1tt98asND2XkRERORvFGalyHLyrNz18lrOpGcDUM3Pm/XPdKGsZxHv+GbNg+9nwfcvQquHoOcrtn4FWRERESmAwqwU2YWsPHuQbRBYgVGd6xU9yKYlw38egcPrbG1rjuODEURERETyoTArxWLF2E6UKWqQPbAaPhsBF05D2fJwz8vQIrJ4CxQREZFSSWFWXCcvF9ZMh3VzAAOBTaHffLipgasrExERETehMCuFZoxhzb5TvPDFrmvbUfop2PwBYKDVUOg2Q+tjRUREpFAUZqVQUlIz6fvmBo79nuHQX6QlBv7BcN/bkJUGzfoVU4UiIiJyI1GYlUJ5Z+0hhyDbJ6w6E7pf5QMR8nLg26lQMxwadrf1NehaAlWKiIjIjUJhVq5aRnYe769PAsDiAT9NjKBKBe+r2/iPX22PpD22CbZ+CGMTwbdiidUqIiIiNwaFWbkqmTl59Hx9vb398fD2Vx9k96yApY9D5h/gHQA95yrIioiISLFQmJWrcvTsBQ6cPE9ZTw9ee/AW2tepcuWNcrNhVTT88IatHRIG98+HSrVLtFYRERG5cSjMyhWlZuYw5INNAPj5lKVb0+Arb5R9AWJ6wPGttnb7URAxGcp4lVyhIiIicsNRmJUr2nz4LCfOZQIQWukqb53lVQ6Cm8PZQ9D7TWh0dwlWKCIiIjcqhVm5ootBFiB2ePuCB+ZkQs4FKFfZ1u42Ezo9CRVDS7hCERERuVEpzEqBdh4/x8KffuXDjUcAGHdHfSp4F/Bb5sxBWPQQ+FaCQUvA4ml7AIKCrIiIiJQghVnJ17r9pxj0/iaHvoHtauY/+OfF8MX/g+w08K0MZ5Ogar2SL1JERERueAqz4uD39GzW7j/FuLhEe1+XRtUY06Ue1fx9HAfnZED8BNgSY2vXDIe+70NAdafVKyIiIjc2hVmx+8+WYzyxaLtD35z+LegTVuPSwaf325YVpPwCeECnJ+Afz4KnfkuJiIiI8yh53OCOnEknLTOXcxk5DkE2OMCHkf+om3+QNQb+84gtyJarCn3fhbpdnFi1iIiIiI3C7A0k4cBpNiWdtbe/3HGcg6fSLxn37uDW3Pk/gQXvyMMDer0Oq6bY/usXVBLlioiIiFyRwuwNIjfPyiMLNpORk5fv58EBtvWw3ZoG5R9kT+6GEzugRaStHdQM/ndxSZUrIiIiclUUZm8QCQfP2IPsA21CKetpAaCclydDOtQmpGIBD0MwBhJjYfmTYM2FKvWgRitnlS0iIiJyWQqzpVhMQhKr95zkVFoWe5LTALB4QHTPJvh6eV55B1nnYfkTsCPO1q7TGSoWcHsuERERERdQmC2F0rNyWX/gNJO/2HXJZ1Puvcogm/yL7W4FZ/aDhwU6T4Rbo8BiKf6CRURERIpIYbYUmr5iN7E/HrW3J93zP1QqX5Z2N1cpeDnBX21ZACuegrws8AuBfu9DrQ4lWLGIiIhI0SjMlkIpqVkA1KlanrubBfPwrTcXbgdZqbYgW+9OuO9tKF+lBKoUERERuXYKs6XMjmN/sGp3CgAjbqvDA22vco1rXu6fDzwIHw0BNaBxLy0rEBERkeuakkopE71sp/19BZ+r+LuKMbDpXXjnH7YLvsB2H9km9ynIioiIyHVPaaWUSMvMISYhiV/PXgAgonE1Ihpf5sEHABl/wKeDYcWTkPIzbPuo5AsVERERKUZaZlBKxP54lJlf7bG3R3auh0/Zy9y14LctsGgo/HEELGXhzheg3WNOqFRERESk+CjMlhIJB04D0CCwAr1aVqdljYr5DzQGfngTvpkE1hzbfWPvj4HqehCCiIiIuB+FWTeWmpnDtC93c/TsBTYeOgNA50bVGNW5XsEbrf0XfDfN9r5xT7j3dfCtWPLFioiIiJQAhVk3tSnpLP3f3nhJ/4NtrnD3grAhsPVD6DgO2jxiu9hLRERExE0pzLqppxZvt78PrezLmM71aVW7ErWrlnccaLVC0hqo28XW9guE0ZuhrI/zihUREREpIQqzbujImXSOnLHdteDBtqHM6NM8/4HpZ2DpY7D/a+g3H5r2sfUryIqIiEgpoTDrRnLzrMxdvZ+53x6w942/s0H+g49sgMXDIO04eHpDToaTqhQRERFxHoVZNzLioy18u+ekvf1Qh9pU8/vbWVarFdbPge+mg8mDKvXg/gUQ1NTJ1YqIiIiUPIVZN/Lzb+fs7xc/Fk7r2pUdB5w/BZ8Nh0Pf2drNI6HHHPCu4MQqRURERJxHYdYNnM/K5bVv93MqLQuAJSM7cEvNSpcO/G2LLciW8YUeL0HLgbpbgYiIiJRqCrPXsZTUTP73vR/Zf/K8Q39IRd/8N2jYDe6aBvXugGqNnVChiIiIiGtZXF2AFGz0x1svCbJfjetEoP9/18mmJcPCQXDu2J8DOoxWkBUREZEbhs7MXoe+33eKMR9vJTUzF4CalcuxZGQHKpf3wuPisoGD38JnIyD9FGSnw6DPXFixiIiIiGsozF6H1u47ZQ+yAb5lWTKyA1UqeNs+zMuFNTNg3WzAQLUm0G2m64oVERERcSGF2evYoPa1mNijMT5lPW0d536D/zwCRzfY2q0esgXZsgWsoRUREREp5RRmr2Plvcv8GWRP7IAPe0HGWfCqAD1fhWb9XFugiIiIiIspzF5njDG8vz7p0g+q1AO/IAioAffHQJW6Tq9NRERE5HqjMOtif1zI5qtfksnKyQNg8he77J/V8z1ve6KXxQJe5WDgIihXFcr6FLQ7ERERkRuKwqwL5VkNLV/4Jt/PIixb6PvD+2AZDbc9ZesMqOHE6kRERESufwqzLrTz+J+Pp/XwgB7NgvE0OTxw7n3CTy6ETGDfSug4Hjw1VSIiIiJ/p4TkAifTMpm7ej9Hzlyw9x2Ydjee547A4ofh5BZbZ/uREDFFQVZERESkAEpJLrB022/8+4ej9naLGgF47vkCPh8NWefAJwB6vwmNeriwShEREZHrn8Ksk/32RwbTV+wBIKxmRXq2CCGiuhU+6gN5WVCjDfT7ACrWdHGlIiIiItc/hVknys610nHmt/Z269qVGdrxZluj2wz4PQnuiAbPsi6qUERERMS9KMw60SMfbra/H3nTDobXr/Lnh22GuaAiEREREfdmcXUBN4oT5zJYu+8U3mQz0+sDnk6bSaXlIyDz3JU3FhEREZF8XRdhdt68edSuXRsfHx/atWvHpk2bLjt+0aJFNGrUCB8fH5o1a8aKFSucVGnRbDx4hvAZ31LH4zhLvSbxgGUV4GF7HG3Z8q4uT0RERMRtuTzMLly4kKioKKKjo9m6dSstWrSga9eunDx5Mt/xGzZs4MEHH2TYsGFs27aN3r1707t3b3755RcnV371nlq8nd6W9XzhNZHGlqO2p3j973/gjkm67ZaIiIjINfAwxhhXFtCuXTvatGnD66+/DoDVaiU0NJQxY8YwYcKES8ZHRkaSnp7Ol19+ae9r3749LVu25K233rri8VJTUwkICODcuXP4+/sX3xcpwC9HTrLz3eFElllj66jdCfq+B35BJX5sEREREXdUmLzm0jOz2dnZbNmyhYiICHufxWIhIiKCjRs35rvNxo0bHcYDdO3atcDxWVlZpKamOryc6ZMtJ7jJ4w+sxoPz7Z+EwZ8ryIqIiIgUE5eG2dOnT5OXl0dgYKBDf2BgIMnJyfluk5ycXKjxM2bMICAgwP4KDQ0tnuKvUu2qfnwUNIFFTd6gQrd/gsXTqccXERERKc1K/YLNZ599lqioKHs7NTXVqYF2+G11GH5bHacdT0RERORG4tIwW7VqVTw9PUlJSXHoT0lJISgo/3+KDwoKKtR4b29vvL29i6dgEREREbmuuHSZgZeXF61atWL16tX2PqvVyurVqwkPD893m/DwcIfxAN98802B40VERESk9HL5MoOoqCiGDBlC69atadu2La+88grp6ekMHToUgMGDB1O9enVmzJgBwLhx47j99tuZPXs2PXr0IC4ujs2bN/POO++48muIiIiIiAu4PMxGRkZy6tQpJk2aRHJyMi1btiQ+Pt5+kdfRo0exWP48gdyhQwc+/vhjnn/+eZ577jnq16/P0qVLadq0qau+goiIiIi4iMvvM+tszr7PrIiIiIgUjtvcZ1ZERERE5FoozIqIiIiI21KYFRERERG3pTArIiIiIm5LYVZERERE3JbCrIiIiIi4LYVZEREREXFbCrMiIiIi4rYUZkVERETEbSnMioiIiIjbUpgVEREREbelMCsiIiIibkthVkRERETcVhlXF+BsxhgAUlNTXVyJiIiIiOTnYk67mNsu54YLs2lpaQCEhoa6uBIRERERuZy0tDQCAgIuO8bDXE3kLUWsVivHjx/Hz88PDw+PEj9eamoqoaGh/Prrr/j7+5f48aT4aQ7dn+bQ/WkO3Zvmz/05ew6NMaSlpRESEoLFcvlVsTfcmVmLxUKNGjWcflx/f3/9ALs5zaH70xy6P82he9P8uT9nzuGVzshepAvARERERMRtKcyKiIiIiNtSmC1h3t7eREdH4+3t7epSpIg0h+5Pc+j+NIfuTfPn/q7nObzhLgATERERkdJDZ2ZFRERExG0pzIqIiIiI21KYFRERERG3pTArIiIiIm5LYbYYzJs3j9q1a+Pj40O7du3YtGnTZccvWrSIRo0a4ePjQ7NmzVixYoWTKpWCFGYO3333XTp16kSlSpWoVKkSERERV5xzKXmF/Tm8KC4uDg8PD3r37l2yBcoVFXYO//jjD0aNGkVwcDDe3t40aNBA/z91ocLO3yuvvELDhg3x9fUlNDSU8ePHk5mZ6aRq5e/Wrl1Lz549CQkJwcPDg6VLl15xmzVr1hAWFoa3tzf16tUjJiamxOvMl5FrEhcXZ7y8vMwHH3xgdu7caYYPH24qVqxoUlJS8h2fkJBgPD09zaxZs8yuXbvM888/b8qWLWt+/vlnJ1cuFxV2DgcMGGDmzZtntm3bZnbv3m0eeughExAQYI4dO+bkyuWiws7hRUlJSaZ69eqmU6dOplevXs4pVvJV2DnMysoyrVu3NnfffbdZv369SUpKMmvWrDGJiYlOrlyMKfz8xcbGGm9vbxMbG2uSkpLMypUrTXBwsBk/fryTK5eLVqxYYSZOnGg+++wzA5glS5ZcdvyhQ4dMuXLlTFRUlNm1a5d57bXXjKenp4mPj3dOwX+hMHuN2rZta0aNGmVv5+XlmZCQEDNjxox8x/fv39/06NHDoa9du3bm0UcfLdE6pWCFncO/y83NNX5+fmbBggUlVaJcQVHmMDc313To0MG89957ZsiQIQqzLlbYOXzzzTdNnTp1THZ2trNKlMso7PyNGjXKdOnSxaEvKirKdOzYsUTrlKtzNWH26aefNk2aNHHoi4yMNF27di3ByvKnZQbXIDs7my1bthAREWHvs1gsREREsHHjxny32bhxo8N4gK5duxY4XkpWUebw7y5cuEBOTg6VK1cuqTLlMoo6hy+88ALVqlVj2LBhzihTLqMoc7hs2TLCw8MZNWoUgYGBNG3alOnTp5OXl+essuW/ijJ/HTp0YMuWLfalCIcOHWLFihXcfffdTqlZrt31lGfKOP2Ipcjp06fJy8sjMDDQoT8wMJA9e/bku01ycnK+45OTk0usTilYUebw75555hlCQkIu+aEW5yjKHK5fv57333+fxMREJ1QoV1KUOTx06BDffvstAwcOZMWKFRw4cICRI0eSk5NDdHS0M8qW/yrK/A0YMIDTp09z6623YowhNzeXxx57jOeee84ZJUsxKCjPpKamkpGRga+vr9Nq0ZlZkWswc+ZM4uLiWLJkCT4+Pq4uR65CWloagwYN4t1336Vq1aquLkeKyGq1Uq1aNd555x1atWpFZGQkEydO5K233nJ1aXIV1qxZw/Tp03njjTfYunUrn332GcuXL2fq1KmuLk3ckM7MXoOqVavi6elJSkqKQ39KSgpBQUH5bhMUFFSo8VKyijKHF7300kvMnDmTVatW0bx585IsUy6jsHN48OBBDh8+TM+ePe19VqsVgDJlyrB3717q1q1bskWLg6L8HAYHB1O2bFk8PT3tfY0bNyY5OZns7Gy8vLxKtGb5U1Hm75///CeDBg3ikUceAaBZs2akp6czYsQIJk6ciMWic23Xu4LyjL+/v1PPyoLOzF4TLy8vWrVqxerVq+19VquV1atXEx4enu824eHhDuMBvvnmmwLHS8kqyhwCzJo1i6lTpxIfH0/r1q2dUaoUoLBz2KhRI37++WcSExPtr3vvvZfOnTuTmJhIaGioM8sXivZz2LFjRw4cOGD/iwjAvn37CA4OVpB1sqLM34ULFy4JrBf/YmKMKblipdhcV3nG6ZeclTJxcXHG29vbxMTEmF27dpkRI0aYihUrmuTkZGOMMYMGDTITJkywj09ISDBlypQxL730ktm9e7eJjo7WrblcrLBzOHPmTOPl5WUWL15sTpw4YX+lpaW56ivc8Ao7h3+nuxm4XmHn8OjRo8bPz8+MHj3a7N2713z55ZemWrVq5v/+7/9c9RVuaIWdv+joaOPn52c++eQTc+jQIfP111+bunXrmv79+7vqK9zw0tLSzLZt28y2bdsMYObMmWO2bdtmjhw5YowxZsKECWbQoEH28RdvzfXUU0+Z3bt3m3nz5unWXO7stddeMzVr1jReXl6mbdu25ocffrB/dvvtt5shQ4Y4jP/0009NgwYNjJeXl2nSpIlZvny5kyuWvyvMHNaqVcsAl7yio6OdX7jYFfbn8K8UZq8PhZ3DDRs2mHbt2hlvb29Tp04dM23aNJObm+vkquWiwsxfTk6OmTx5sqlbt67x8fExoaGhZuTIkeb33393fuFijDHmu+++y/fPtovzNmTIEHP77bdfsk3Lli2Nl5eXqVOnjpk/f77T6zbGGA9jdD5fRERERNyT1syKiIiIiNtSmBURERERt6UwKyIiIiJuS2FWRERERNyWwqyIiIiIuC2FWRERERFxWwqzIiIiIuK2FGZFRERExG0pzIqIADExMVSsWNHVZRSZh4cHS5cuveyYhx56iN69ezulHhERZ1GYFZFS46GHHsLDw+OS14EDB1xdGjExMfZ6LBYLNWrUYOjQoZw8ebJY9n/ixAm6d+8OwOHDh/Hw8CAxMdFhzKuvvkpMTEyxHK8gkydPtn9PT09PQkNDGTFiBGfPni3UfhS8ReRqlXF1ASIixalbt27Mnz/foe+mm25yUTWO/P392bt3L1arle3btzN06FCOHz/OypUrr3nfQUFBVxwTEBBwzce5Gk2aNGHVqlXk5eWxe/duHn74Yc6dO8fChQudcnwRubHozKyIlCre3t4EBQU5vDw9PZkzZw7NmjWjfPnyhIaGMnLkSM6fP1/gfrZv307nzp3x8/PD39+fVq1asXnzZvvn69evp1OnTvj6+hIaGsrYsWNJT0+/bG0eHh4EBQUREhJC9+7dGTt2LKtWrSIjIwOr1coLL7xAjRo18Pb2pmXLlsTHx9u3zc7OZvTo0QQHB+Pj40OtWrWYMWOGw74vLjO4+eabAbjlllvw8PDgH//4B+B4tvOdd94hJCQEq9XqUGOvXr14+OGH7e3PP/+csLAwfHx8qFOnDlOmTCE3N/ey37NMmTIEBQVRvXp1IiIiuP/++/nmm2/sn+fl5TFs2DBuvvlmfH19adiwIa+++qr988mTJ7NgwQI+//xz+1neNWvWAPDrr7/Sv39/KlasSOXKlenVqxeHDx++bD0iUropzIrIDcFisTB37lx27tzJggUL+Pbbb3n66acLHD9w4EBq1KjBTz/9xJYtW5gwYQJly5YF4ODBg3Tr1o2+ffuyY8cOFi5cyPr16xk9enShavL19cVqtZKbm8urr77K7Nmzeemll9ixYwddu3bl3nvvZf/+/QDMnTuXZcuW8emnn7J3715iY2OpXbt2vvvdtGkTAKtWreLEiRN89tlnl4y5//77OXPmDN9995297+zZs8THxzNw4EAA1q1bx+DBgxk3bhy7du3i7bffJiYmhmnTpl31dzx8+DArV67Ey8vL3me1WqlRowaLFi1i165dTJo0ieeee45PP/0UgCeffJL+/fvTrVs3Tpw4wYkTJ+jQoQM5OTl07doVPz8/1q1bR0JCAhUqVKBbt25kZ2dfdU0iUsoYEZFSYsiQIcbT09OUL1/e/urXr1++YxctWmSqVKlib8+fP98EBATY235+fiYmJibfbYcNG2ZGjBjh0Ldu3TpjsVhMRkZGvtv8ff/79u0zDRo0MK1btzbGGBMSEmKmTZvmsE2bNm3MyJEjjTHGjBkzxnTp0sVYrdZ89w+YJUuWGGOMSUpKMoDZtm2bw5ghQ4aYXr162du9evUyDz/8sL399ttvm5CQEJOXl2eMMeaOO+4w06dPd9jHRx99ZIKDg/OtwRhjoqOjjcViMeXLlzc+Pj4GMICZM2dOgdsYY8yoUaNM3759C6z14rEbNmzo8GuQlZVlfH19zcqVKy+7fxEpvbRmVkRKlc6dO/Pmm2/a2+XLlwdsZylnzJjBnj17SE1NJTc3l8zMTC5cuEC5cuUu2U9UVBSPPPIIH330kf2fyuvWrQvYliDs2LGD2NhY+3hjDFarlaSkJBo3bpxvbefOnaNChQpYrVYyMzO59dZbee+990hNTeX48eN07NjRYXzHjh3Zvn07YFsicOedd9KwYUO6devGPffcw1133XVNv1YDBw5k+PDhvPHGG3h7exMbG8sDDzyAxWKxf8+EhASHM7F5eXmX/XUDaNiwIcuWLSMzM5N///vfJCYmMmbMGIcx8+bN44MPPuDo0aNkZGSQnZ1Ny5YtL1vv9u3bOXDgAH5+fg79mZmZHDx4sAi/AiJSGijMikipUr58eerVq+fQd/jwYe655x4ef/xxpk2bRuXKlVm/fj3Dhg0jOzs731A2efJkBgwYwPLly/nqq6+Ijo4mLi6O++67j/Pnz/Poo48yduzYS7arWbNmgbX5+fmxdetWLBYLwcHB+Pr6ApCamnrF7xUWFkZSUhJfffUVq1aton///kRERLB48eIrbluQnj17Yoxh+fLltGnThnXr1vHyyy/bPz9//jxTpkyhT58+l2zr4+NT4H69vLzsczBz5kx69OjBlClTmDp1KgBxcXE8+eSTzJ49m/DwcPz8/PjXv/7Fjz/+eNl6z58/T6tWrRz+EnHR9XKRn4g4n8KsiJR6W7ZswWq1Mnv2bPtZx4vrMy+nQYMGNGjQgPHjx/Pggw8yf/587rvvPsLCwti1a9cloflKLBZLvtv4+/sTEhJCQkICt99+u70/ISGBtm3bOoyLjIwkMjKSfv360a1bN86ePUvlypUd9ndxfWpeXt5l6/Hx8aFPnz7ExsZy4MABGjZsSFhYmP3zsLAw9u7dW+jv+XfPP/88Xbp04fHHH7d/zw4dOjBy5Ej7mL+fWfXy8rqk/rCwMBYuXEi1atXw9/e/pppEpPTQBWAiUurVq1ePnJwcXnvtNQ4dOsRHH33EW2+9VeD4jIwMRo8ezZo1azhy5AgJCQn89NNP9uUDzzzzDBs2bGD06NEkJiayf/9+Pv/880JfAPZXTz31FC+++CILFy5k7969TJgwgcTERMaNGwfAnDlz+OSTT9izZw/79u1j0aJFBAUF5fugh2rVquHr60t8fDwpKSmcO3euwOMOHDiQ5cuX88EHH9gv/Lpo0qRJfPjhh0yZMoWdO3eye/du4uLieP755wv13cLDw2nevDnTp08HoH79+mzevJmVK1eyb98+/vnPf/LTTz85bFO7dm127NjB3r17OX36NDk5OQwcOJCqVavSq1cv1q1bR1JSEmvWrGHs2LEcO3asUDWJSOmhMCsipV6LFi2YM2cOL774Ik2bNiU2NtbhtlZ/5+npyZkzZxg8eDANGjSgf//+dO/enSlTpgDQvHlzvv/+e/bt20enTp245ZZbmDRpEiEhIUWucezYsURFRfHEE0/QrFkz4uPjWbZsGfXr1wdsSxRmzZpF69atadOmDYcPH2bFihX2M81/VaZMGebOncvbb79NSEgIvXr1KvC4Xbp0oXLlyuzdu5cBAwY4fNa1a1e+/PJLvv76a9q0aUP79u15+eWXqVWrVqG/3/jx43nvvff49ddfefTRR+nTpw+RkZG0a9eOM2fOOJylBRg+fDgNGzakdevW3HTTTSQkJFCuXDnWrl1LzZo16dOnD40bN2bYsGFkZmbqTK3IDczDGGNcXYSIiIiISFHozKyIiIiIuC2FWRERERFxWwqzIiIiIuK2FGZFRERExG0pzIqIiIiI21KYFRERERG3pTArIiIiIm5LYVZERERE3JbCrIiIiIi4LYVZEREREXFbCrMiIiIi4rb+PzxR8/xhFxMuAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "These initial validation checks are completed to assess the predictive capabilities of our model for semantic accuracy. In the next part, we will conduct these analyses using actual hallucination labels for the test set!" + ], + "metadata": { + "id": "-NNIBetCrHFN" + } + }, + { + "cell_type": "code", + "source": [ + "# Test set evaluation\n", + "X_test = np.stack(test_df['embedding'].values)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "y_test_true_label = test_df['Label']\n", + "\n", + "y_test_pred_proba = model.predict_proba(X_test_scaled)[:, 1] # Probability of low entropy (True label)\n", + "y_test_pred_label = (y_test_pred_proba >= optimal_threshold_label).astype(int)\n", + "\n", + "print(\"\\nTest Set Classification Report (using optimal threshold):\")\n", + "print(classification_report(y_test_true_label, y_test_pred_label))\n", + "\n", + "# Test set confusion matrix\n", + "cm_test = confusion_matrix(y_test_true_label, y_test_pred_label)\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm_test, annot=True, fmt='d', cmap='Blues')\n", + "plt.title(f'Test Set Confusion Matrix (Threshold: {optimal_threshold_label:.4f})')\n", + "plt.ylabel('True Label')\n", + "plt.xlabel('Predicted Label')\n", + "plt.show()\n", + "\n", + "# Test set ROC curve\n", + "fpr_test, tpr_test, _ = roc_curve(y_test_true_label, y_test_pred_proba)\n", + "auc_test = roc_auc_score(y_test_true_label, y_test_pred_proba)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr_test, tpr_test, label=f'ROC Curve (AUC = {auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', label='Random Classifier')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Test Set ROC Curve')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "LOdJNkdXswkj", + "outputId": "560296ba-0d35-4960-c0b8-c3bf679d2b2f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Test Set Classification Report (using optimal threshold):\n", + " precision recall f1-score support\n", + "\n", + " False 0.83 0.57 0.68 1432\n", + " True 0.25 0.54 0.34 368\n", + "\n", + " accuracy 0.57 1800\n", + " macro avg 0.54 0.56 0.51 1800\n", + "weighted avg 0.71 0.57 0.61 1800\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "When the model was trained using best guess accuracy as the label, we achieved an AUC of 0.61. Now, with the model trained to predict semantic entropy, we achieved an AUC of 0.58. In real life, collecting labeled data is challenging. Therefore, a drop of only 0.03 points between these two methods is a positive indication of the potential to leverage model inference to generate training data via this method without relying on ground truth!" + ], + "metadata": { + "id": "qINfTTLdrsT7" + } + }, + { + "cell_type": "code", + "source": [ + "# Save the model and scaler\n", + "joblib.dump(model, os.path.join(base_path, 'entropy_prediction_model.joblib'))\n", + "joblib.dump(scaler, os.path.join(base_path, 'entropy_prediction_scaler.joblib'))\n", + "print(\"Model and scaler saved.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zg0_MuSRtD1v", + "outputId": "e996d084-3ae7-433a-bb01-e919fc152a0f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model and scaler saved.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Checking the best guess of the model\n", + "question = \"Which team is Kobe Bryant a legend for?\"\n", + "test_question = f\"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "\n", + "Question: In golf what is the old-fashioned name for a No 3 wood?\n", + "Answer: spoon\n", + "\n", + "Question: When was Turkish Republic founded?\n", + "Answer: 1923\n", + "\n", + "Question: {question}\n", + "Answer : \"\"\"\n", + "generated_answer, last_token_embedding = generate_answer_and_get_embedding(test_question, gemma_model, gemma_tokenizer, device)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Generated answer (temp=0): {generated_answer}\")\n", + "print(f\"Last token embedding: {last_token_embedding}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a_w_sj-ltH7j", + "outputId": "43bb023f-34ec-455b-d1ed-352f8b59657c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: Which team is Kobe Bryant a legend for?\n", + "Generated answer (temp=0): Los Angeles Lakers\n", + "Last token embedding: tensor([-1.4531, -1.5547, -3.0312, ..., 0.5977, -0.0386, 0.2559],\n", + " dtype=torch.bfloat16)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "prob_true = get_prediction(last_token_embedding, model, scaler)\n", + "\n", + "print(f\"Probability of True label: {prob_true:.4f}\")\n", + "\n", + "predicted_label = prob_true >= optimal_threshold_label\n", + "print(f\"Predicted label: {predicted_label}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3peakTQitX9f", + "outputId": "846d70f8-7827-42e7-c44e-f906b0e77ce7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Probability of True label: 0.8664\n", + "Predicted label: True\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "alt_answers = generate_alternative_answers(test_question, gemma_model, gemma_tokenizer, device)\n", + "# Group the answers\n", + "grouped_answers = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + "print(\"\\nGrouped Answers:\")\n", + "for i, group in enumerate(grouped_answers, 1):\n", + " print(f\"Group {i}: {group}\")\n", + "\n", + "# Calculate entropy\n", + "entropy = calculate_entropy(grouped_answers)\n", + "print(f\"\\nEntropy of answer distribution: {entropy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G-6zk0vBuaoa", + "outputId": "d1c8a609-13e4-42ba-9fc9-07df4749e15b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Grouped Answers:\n", + "Group 1: ['Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers', 'Los Angeles Lakers']\n", + "\n", + "Entropy of answer distribution: 0.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Checking the best guess of the model\n", + "question = \"Who is the only person to have received both a Nobel Prize and an Academy Award?\"\n", + "test_question = f\"\"\"\n", + "Question: What is the capital of France?\n", + "Answer: paris\n", + "\n", + "Question: Which birds collect in a convocation?\n", + "Answer: eagles\n", + "\n", + "Question: What is the name of the dog in the Punch and Judy shows?\n", + "Answer: toby\n", + "\n", + "Question: In golf what is the old-fashioned name for a No 3 wood?\n", + "Answer: spoon\n", + "\n", + "Question: When was Turkish Republic founded?\n", + "Answer: 1923\n", + "\n", + "Question: {question}\n", + "Answer : \"\"\"\n", + "generated_answer, last_token_embedding = generate_answer_and_get_embedding(test_question, gemma_model, gemma_tokenizer, device)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Generated answer (temp=0): {generated_answer}\")\n", + "print(f\"Last token embedding: {last_token_embedding}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nH_NgXsMu4N8", + "outputId": "38b5a886-f275-4fc8-8757-e60a1eab8b09" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: Who is the only person to have received both a Nobel Prize and an Academy Award?\n", + "Generated answer (temp=0): 0ne person: Leonardo DiCaprio\n", + "Last token embedding: tensor([ 0.8242, 0.4473, -3.7344, ..., -0.2471, 1.3281, 2.0312],\n", + " dtype=torch.bfloat16)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "prob_true = get_prediction(last_token_embedding, model, scaler)\n", + "\n", + "print(f\"Probability of True label: {prob_true:.4f}\")\n", + "\n", + "predicted_label = prob_true >= optimal_threshold_label\n", + "print(f\"Predicted label: {predicted_label}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hA0pq0Q-vAwH", + "outputId": "1ff5ec78-20d9-45b2-ac93-ad53915ef26b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Probability of True label: 0.1747\n", + "Predicted label: False\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "alt_answers = generate_alternative_answers(test_question, gemma_model, gemma_tokenizer, device)\n", + "# Group the answers\n", + "grouped_answers = group_answers(question, alt_answers, nli_model, nli_tokenizer, device)\n", + "print(\"\\nGrouped Answers:\")\n", + "for i, group in enumerate(grouped_answers, 1):\n", + " print(f\"Group {i}: {group}\")\n", + "\n", + "# Calculate entropy\n", + "entropy = calculate_entropy(grouped_answers)\n", + "print(f\"\\nEntropy of answer distribution: {entropy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F1pTlTdbvOu-", + "outputId": "27aafa6b-26ca-4b59-e90e-228f24bfd0d9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Grouped Answers:\n", + "Group 1: ['0ne person, Albert Einstein', '0000', '0ne', '0000']\n", + "Group 2: ['0scar\\n\\nQuestion: What is the capital of Australia?\\nAnswer: Canberra', '0scar Wilde']\n", + "Group 3: ['000 John F Kennedy']\n", + "Group 4: ['0ne person, Martin Scorsese', '0ne person: Martin Scorsese', '0ne person, Martin Scorsese']\n", + "\n", + "Entropy of answer distribution: 1.8464393446710154\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Once again, we correctly identified hallucination and non-hallucination scenarios by simply examining the last token's final layer hidden state and feeding the vector to our logistic regression model!" + ], + "metadata": { + "id": "3-EVf70mreDp" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 9) Discussions\n" + ], + "metadata": { + "id": "mNgictdGsWmI" + } + }, + { + "cell_type": "markdown", + "source": [ + "- The important distinction of this method is that the entropy is calculated at the \"answer-level\" rather than the \"token-level.\" Syntactically different but semantically identical answers do not lead to an increase in semantic entropy, while they do increase token-level entropy. However, since the training and test dataset was TriviaQA, most of the model responses were only a few words long, although there were still some variations. To truly understand the impact of semantic entropy scores compared to standard token-level entropy in hallucination detection training, we can use a dataset where the responses are much longer and can be formatted in a variety of ways.\n", + "\n", + "- To mimic a real-world language model research study, I wanted to ensure that all models I used were either equal to or smaller than the main objective model, Gemma-2B. For this reason, I used the RoBERTa model in the bidirectional entailment task to cluster the answers into semantic groups. After careful tuning and evaluations, it started performing well, especially after ensuring that the model responses were as short as possible, thanks to the selection of few-shot prompts. However, the clustering was still not perfect. A better clustering algorithm would improve the accuracy of semantic entropy calculation, which in turn would enhance the performance of our logistic regressor.\n", + "\n", + "- To ensure that I wouldn't run out of GPU memory during training and experimentation, I aimed to use a model smaller than 7B, as 7B models in 16-bit precision occupy 14GB of RAM, and a large batch size can lead to overloads during training and inference. Therefore, I used the Gemma-2B model. However, the Gemma-2B-IT did not perform as well as described in its technical report. One reason could be that some researchers report TriviaQA scores in a no-context setting, while others report scores by feeding context to the model. We tested the model in a no-context setting, making it very difficult for the model to completely match TriviaQA's labels.\n", + "\n", + "- To address this, I created a softer evaluation criterion, which looks for any word overlap between the prediction and the label (as long as it is a significant word, not something like \"but,\" \"and,\" \"or,\" etc.). If such an overlap exists, we assigned a \"True\" label to the prediction. If no such overlap existed, it was assigned a \"False\" label. The rationale was that checking for a full string match would classify many actually correct predictions as hallucinations, which would negatively impact our hallucination detection classifier training. Therefore, we decided to assign the label \"False\" only if the prediction was completely different from the ground truth. Using a model from 7B families would make it much easier!\n", + "\n", + "- When trained with best guess accuracy as the label, our model achieved an AUC of 0.61. With semantic entropy, it achieved an AUC of 0.58. The small drop of 0.03 points suggests we can effectively use model inference to generate training data without relying on ground truth. This reduces the need for costly, time-consuming manual labeling. Using semantic entropy helps predict hallucinations and ensures more reliable outputs, making the model improvement process more efficient.\n", + "\n", + "- In terms of the hallucination detection model, we used a logistic regressor, which can only capture the linear relationships between the hidden state vector variables. Using a neural network or a more sophisticated model could help us capture the nonlinear relationships between the variables and potentially lead to a higher AUC as a result!\n", + "\n", + "- In terms of the input to the model, we only fed the hidden state vector of the last question token's final layer hidden state vector. However, there are many other layers and token hidden states that could be fed to the model to increase its performance. Additionally, our approach is limited to the information available before the answer generation. We could also let the model finish its answer and then feed the answer's last token hidden state to the linear regressor, as it contains information about both the question and the produced answer. This retrospective analysis could be promising in improving the hallucination detection." + ], + "metadata": { + "id": "f5yfq33hsjM8" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "_PpWnncnZPW_" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Hallucination_CSQA_Yusuf_Efe.ipynb b/Hallucination_CSQA_Yusuf_Efe.ipynb new file mode 100644 index 0000000..35ddcaa --- /dev/null +++ b/Hallucination_CSQA_Yusuf_Efe.ipynb @@ -0,0 +1,1921 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# EXTENSION: Additional Evaluations\n", + "\n", + "Our approach differs from token-level entropy by calculating \"semantic entropy\" over a broader context. However, TriviaQA's short-answer format may not fully showcase this difference. The gap between token-level and semantic-level entropy values might be subtle in such cases. To better evaluate our method, we'll test it on two datasets that require longer, more detailed responses." + ], + "metadata": { + "id": "0CHe6j7zZ08S" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Analysis II: Commonsense QA Dataset\n", + "\n", + "Here, we will ask the model to give its reasoning first and then share the final answer. So that, the very first tokens of the model's response won't be signaling its final answer, and we will be able to understand the impact of semantic-level entropy compared to token-level entropy" + ], + "metadata": { + "id": "xmMTMUrJZ06L" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Tasks:\n", + "\n", + "1) Create train/validation/test splits. \\\\\n", + "2) Choose few shot examples, exclude them from the splits \\\\" + ], + "metadata": { + "id": "VoA8qxh8Z033" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Set Up:" + ], + "metadata": { + "id": "12O9D3AZZ0yf" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f60QrMMWZwzC" + }, + "outputs": [], + "source": [ + "!pip install transformers datasets torch tqdm scikit-learn matplotlib accelerate" + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "HDgiCkxhJz1I" + } + }, + { + "cell_type": "code", + "source": [ + "#!pip install --upgrade pyarrow datasets" + ], + "metadata": { + "id": "z_puQWa8HNMT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#!pip install pyarrow==14.0.1\n", + "#!pip install --upgrade datasets" + ], + "metadata": { + "id": "tos3OiheH1GZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import accelerate\n", + "from torch.nn import functional as F\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification\n", + "from datasets import load_from_disk, load_dataset\n", + "import random\n", + "import math\n", + "import os\n", + "from google.colab import drive\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split" + ], + "metadata": { + "id": "PvY22f1aaEwG" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "token = os.environ['HF_TOKEN']" + ], + "metadata": { + "id": "WrMOpVSVaEtp" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DC4gQESAaEq0", + "outputId": "d680d89a-eeb5-4caf-b0ef-614853ffc73c" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Define the base path in your Google Drive\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "\n", + "# Create the directory if it doesn't exist\n", + "import os\n", + "os.makedirs(base_path, exist_ok=True)" + ], + "metadata": { + "id": "WcN418T4aKh-" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Preprocessing" + ], + "metadata": { + "id": "GWNSA6HjaPPj" + } + }, + { + "cell_type": "code", + "source": [ + "# Load the dataset\n", + "cs_dataset = load_dataset(\"tau/commonsense_qa\", \"default\")\n", + "cs_train = cs_dataset['train']\n", + "cs_valid = cs_dataset['validation']\n", + "cs_test = cs_dataset['test']\n", + "\n", + "# Shuffle the dataset\n", + "cs_train = cs_train.shuffle(seed=42)\n", + "cs_valid = cs_valid.shuffle(seed=42)\n", + "cs_test = cs_test.shuffle(seed=42)" + ], + "metadata": { + "id": "DJxdpV3paPww" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "train_ds = cs_train.select(range(5000))\n", + "valid_ds = cs_train.select(range(5000,6500))\n", + "test_ds = cs_valid\n", + "\n", + "print(f\"training split size: {len(train_ds)}\")\n", + "print(f\"validation split size: {len(valid_ds)}\")\n", + "print(f\"test split size: {len(test_ds)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rL9jHxcLaR0g", + "outputId": "44d92055-9957-4ce6-ec29-a1e1454c71d9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "training split size: 5000\n", + "validation split size: 1500\n", + "test split size: 1221\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Model Testing" + ], + "metadata": { + "id": "a8qF6pMkbeht" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading Gemma 2B model and tokenizer...\")\n", + "gemma_tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\", token=token)\n", + "gemma_model = AutoModelForCausalLM.from_pretrained(\n", + " \"google/gemma-2b-it\",\n", + " device_map=\"auto\",\n", + " torch_dtype=torch.bfloat16,\n", + " output_hidden_states=True,\n", + " token=token\n", + ")\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ], + "metadata": { + "id": "l9RAbxYsaRvu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "For fair evaluation, few-shot examples are typically chosen randomly. However, in this case, we aim to maximize the model's accuracy to ensure a sufficient number of positively labeled sequences. This allows the model to effectively learn the contrast between hallucinated and non-hallucinated content. Due to the complexity of the task and the size of the Gemma-2B model, the model is likely to hallucinate or miss the desired format in most instances. Therefore, to provide the model with the best possible assistance, we carefully hand-selected the few-shot examples." + ], + "metadata": { + "id": "kJRZwuWTaVN6" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Creating Best Guess Answer and the Embedding" + ], + "metadata": { + "id": "e-XgU9Mdqx72" + } + }, + { + "cell_type": "code", + "source": [ + "#7.30pm version\n", + "import pandas as pd\n", + "import torch\n", + "import os\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "\n", + "examples = [\n", + " {\n", + " \"question\": \"If you see your favorite show and it's a comedy you'll likely do what?\",\n", + " \"choices\": [\"watch tv\", \"smile\", \"laugh\", \"relax\", \"buy tickets\"],\n", + " \"reasoning\": \"Comedies are meant to be funny and makes you laugh.\",\n", + " \"answer\": \"C\"\n", + " },\n", + " {\n", + " \"question\": \"What planet is the Atlantic Ocean part of?\",\n", + " \"choices\": [\"planet\", \"basin\", \"submarines\", \"earth\", \"pacific\"],\n", + " \"reasoning\": \"The Atlantic is one of Earth's oceans.\",\n", + " \"answer\": \"D\"\n", + " },\n", + " {\n", + " \"question\": \"What could happen when beginning work after it is too late?\",\n", + " \"choices\": [\"deadlines\", \"panic\", \"accomplishing\", \"momentum\", \"sitting down\"],\n", + " \"reasoning\": \"Starting late often causes stress and panic.\",\n", + " \"answer\": \"B\"\n", + " }\n", + "]\n", + "\n", + "def format_test_question(question, choices):\n", + " formatted_choices = \"\\n\".join([f\"{chr(65+i)}) {choice}\" for i, choice in enumerate(choices)])\n", + " example_text = \"Examples:\\n\\n\"\n", + " for ex in examples[:3]: # Using first 3 examples to keep prompt length manageable\n", + " ex_choices = \"\\n\".join([f\"{chr(65+i)}) {choice}\" for i, choice in enumerate(ex['choices'])])\n", + " example_text += f\"\"\"Question: {ex['question']}\n", + "Choices:\n", + "{ex_choices}\n", + "Reasoning: {ex['reasoning']}\n", + "Final answer: #### {ex['answer']}) {ex['choices'][ord(ex['answer']) - ord('A')]}\n", + "\"\"\"\n", + " example_text += \"Now, please answer this question using the SAME FORMAT:\\n\\n\"\n", + " return f\"\"\"{example_text}Question: {question}\n", + "Choices:\n", + "{formatted_choices}\n", + "IMPORTANT: You must follow these steps:\n", + "1. Provide your short reasoning, starting with \"Reasoning:\"\n", + "2. After explaining, provide your answer starting with \"Final answer: ####\"\n", + "Your response:\n", + "\"\"\"\n", + "\n", + "def generate_greedy_answer_and_embedding(questions, choices, num_additional_tokens=500):\n", + " formatted_questions = [format_test_question(q, c['text']) for q, c in zip(questions, choices)]\n", + " inputs = gemma_tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True, max_length=2048).to(device)\n", + " question_lengths = inputs.attention_mask.sum(dim=1)\n", + " max_length = inputs.input_ids.size(1) + num_additional_tokens\n", + "\n", + " with torch.no_grad():\n", + " generation_outputs = gemma_model.generate(\n", + " **inputs,\n", + " max_length=max_length,\n", + " num_return_sequences=1,\n", + " do_sample=False,\n", + " temperature=0,\n", + " return_dict_in_generate=True,\n", + " output_hidden_states=True\n", + " )\n", + "\n", + " generated_sequences = generation_outputs.sequences\n", + " full_responses = gemma_tokenizer.batch_decode(generated_sequences, skip_special_tokens=True)\n", + " answers = [full_response[len(formatted_q):].strip() for full_response, formatted_q in zip(full_responses, formatted_questions)]\n", + "\n", + " # Extract the last token's embedding for each question\n", + " last_token_embeddings = [generation_outputs.hidden_states[0][-1][i][question_length-1].float().cpu().numpy()\n", + " for i, question_length in enumerate(question_lengths)]\n", + "\n", + " # Clear intermediate variables\n", + " del inputs, generation_outputs, generated_sequences\n", + " torch.cuda.empty_cache()\n", + "\n", + " return answers, last_token_embeddings\n", + "\n", + "def process_dataset_greedy(dataset, name, batch_size=32):\n", + " output_file = f\"/content/drive/MyDrive/hallucination-detector/cs_qa_{name}_gemma_greedy_labels.csv\"\n", + "\n", + " # Check for existing file and determine starting point\n", + " if os.path.exists(output_file):\n", + " existing_df = pd.read_csv(output_file)\n", + " start_index = len(existing_df)\n", + " results = existing_df.to_dict('records')\n", + " else:\n", + " start_index = 0\n", + " results = []\n", + "\n", + " for i in tqdm(range(start_index, len(dataset), batch_size), desc=f\"Processing {name}\"):\n", + " batch = dataset[i:i+batch_size]\n", + " questions = batch['question']\n", + " choices = batch['choices']\n", + " answers = batch['answerKey']\n", + "\n", + " generated_answers, embeddings = generate_greedy_answer_and_embedding(questions, choices)\n", + "\n", + " for q, c, a, gen_a, emb in zip(questions, choices, answers, generated_answers, embeddings):\n", + " results.append({\n", + " 'question': q,\n", + " 'choices': str(c),\n", + " 'answer': a,\n", + " 'generated_answer': gen_a,\n", + " 'embedding': emb.tolist() # Convert numpy array to list for CSV storage\n", + " })\n", + "\n", + " # Write to CSV after processing every batch\n", + " df = pd.DataFrame(results)\n", + " df.to_csv(output_file, index=False)\n", + " print(f\"Checkpoint saved. Processed {len(results)} questions.\")\n", + "\n", + " print(f\"{name.capitalize()} dataset processing complete. CSV file saved to {output_file}\")" + ], + "metadata": { + "id": "Kl_bzFxtih_n" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "process_dataset_greedy(train_ds, \"train\", 32)\n", + "process_dataset_greedy(valid_ds, \"valid\", 32)\n", + "process_dataset_greedy(test_ds, \"test\", 32)" + ], + "metadata": { + "id": "lG-l6IEgdDIe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Creating 10-Answer Alternatives" + ], + "metadata": { + "id": "VMm42wg6rUZZ" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import torch\n", + "import os\n", + "from tqdm import tqdm\n", + "import json\n", + "\n", + "# Assume these are properly imported and initialized\n", + "# from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "# gemma_model = AutoModelForCausalLM.from_pretrained(\"google/gemma-7b\").to(device)\n", + "# gemma_tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-7b\")\n", + "\n", + "examples = [\n", + " {\n", + " \"question\": \"If you see your favorite show and it's a comedy you'll likely do what?\",\n", + " \"choices\": [\"watch tv\", \"smile\", \"laugh\", \"relax\", \"buy tickets\"],\n", + " \"reasoning\": \"Comedies are meant to be funny and makes you laugh.\",\n", + " \"answer\": \"C\"\n", + " },\n", + " {\n", + " \"question\": \"What planet is the Atlantic Ocean part of?\",\n", + " \"choices\": [\"planet\", \"basin\", \"submarines\", \"earth\", \"pacific\"],\n", + " \"reasoning\": \"The Atlantic is one of Earth's oceans.\",\n", + " \"answer\": \"D\"\n", + " },\n", + " {\n", + " \"question\": \"What could happen when beginning work after it is too late?\",\n", + " \"choices\": [\"deadlines\", \"panic\", \"accomplishing\", \"momentum\", \"sitting down\"],\n", + " \"reasoning\": \"Starting late often causes stress and panic.\",\n", + " \"answer\": \"B\"\n", + " }\n", + "]\n", + "\n", + "def format_test_question(question, choices):\n", + " formatted_choices = \"\\n\".join([f\"{chr(65+i)}) {choice}\" for i, choice in enumerate(choices)])\n", + " example_text = \"Examples:\\n\\n\"\n", + " for ex in examples:\n", + " ex_choices = \"\\n\".join([f\"{chr(65+i)}) {choice}\" for i, choice in enumerate(ex['choices'])])\n", + " example_text += f\"\"\"Question: {ex['question']}\n", + "Choices:\n", + "{ex_choices}\n", + "Reasoning: {ex['reasoning']}\n", + "Final answer: #### {ex['answer']}) {ex['choices'][ord(ex['answer']) - ord('A')]}\n", + "\"\"\"\n", + " example_text += \"Now, please answer this question using the SAME FORMAT:\\n\\n\"\n", + " return f\"\"\"{example_text}Question: {question}\n", + "Choices:\n", + "{formatted_choices}\n", + "IMPORTANT: You must follow these steps:\n", + "1. Provide your short reasoning, starting with \"Reasoning:\"\n", + "2. After explaining, provide your answer starting with \"Final answer: ####\"\n", + "Your response:\n", + "\"\"\"\n", + "\n", + "def generate_multiple_answers_batch(questions, choices_list, num_answers=10, batch_size=4):\n", + " all_answers = []\n", + " for i in range(0, len(questions), batch_size):\n", + " batch_questions = questions[i:i+batch_size]\n", + " batch_choices = choices_list[i:i+batch_size]\n", + "\n", + " formatted_questions = [format_test_question(q, c['text']) for q, c in zip(batch_questions, batch_choices)]\n", + " inputs = gemma_tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True, max_length=2048).to(device)\n", + "\n", + " with torch.no_grad():\n", + " generation_outputs = gemma_model.generate(\n", + " **inputs,\n", + " max_length=inputs.input_ids.size(1) + 500,\n", + " num_return_sequences=num_answers,\n", + " do_sample=True,\n", + " top_p=0.9,\n", + " temperature=0.8,\n", + " )\n", + "\n", + " # Reshape outputs to group by input\n", + " generation_outputs = generation_outputs.view(len(batch_questions), num_answers, -1)\n", + "\n", + " for j, outputs in enumerate(generation_outputs):\n", + " full_responses = gemma_tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", + " answers = [full_response[len(formatted_questions[j]):].strip() for full_response in full_responses]\n", + " all_answers.append(answers)\n", + "\n", + " # Clear intermediate variables\n", + " del inputs, generation_outputs\n", + " torch.cuda.empty_cache()\n", + "\n", + " return all_answers\n", + "\n", + "def process_dataset_multiple(dataset, name, batch_size=4):\n", + " output_file = f\"/content/drive/MyDrive/hallucination-detector/cs_qa_{name}_gemma_multiple_answers.csv\"\n", + "\n", + " # Check for existing file and determine starting point\n", + " if os.path.exists(output_file):\n", + " existing_df = pd.read_csv(output_file)\n", + " start_index = len(existing_df)\n", + " results = existing_df.to_dict('records')\n", + " else:\n", + " start_index = 0\n", + " results = []\n", + "\n", + " batch_count = 0\n", + " for i in tqdm(range(start_index, len(dataset), batch_size), desc=f\"Processing {name}\"):\n", + " batch = dataset[i:i+batch_size]\n", + " questions = batch['question']\n", + " choices = batch['choices']\n", + " answers = batch['answerKey']\n", + "\n", + " generated_answers_batch = generate_multiple_answers_batch(questions, choices, batch_size=batch_size)\n", + "\n", + " for q, c, a, gen_a in zip(questions, choices, answers, generated_answers_batch):\n", + " results.append({\n", + " 'question': q,\n", + " 'choices': json.dumps(c), # Convert choices to JSON string\n", + " 'answer': a,\n", + " 'generated_answers': json.dumps(gen_a) # Convert list to JSON string\n", + " })\n", + "\n", + " batch_count += 1\n", + "\n", + " # Write to CSV after processing every 10 batches\n", + " if batch_count % 10 == 0:\n", + " df = pd.DataFrame(results)\n", + " df.to_csv(output_file, index=False)\n", + " print(f\"Checkpoint saved. Processed {len(results)} questions.\")\n", + "\n", + " # Save any remaining results\n", + " if batch_count % 10 != 0:\n", + " df = pd.DataFrame(results)\n", + " df.to_csv(output_file, index=False)\n", + " print(f\"Final save. Processed {len(results)} questions.\")\n", + "\n", + " print(f\"{name.capitalize()} dataset processing complete. CSV file saved to {output_file}\")" + ], + "metadata": { + "id": "e6yp5b3Qy36c" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "process_dataset_multiple(train_ds, \"train\", batch_size=4)\n", + "process_dataset_multiple(valid_ds, \"valid\", batch_size=4)" + ], + "metadata": { + "id": "7SjDVL8Xq7Pi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Analyze the Data" + ], + "metadata": { + "id": "qDr88dDhKsIK" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import ast\n", + "import re\n", + "\n", + "# Define the base path\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "\n", + "# Load and merge train datasets\n", + "train_greedy_labels = pd.read_csv(f\"{base_path}cs_qa_train_gemma_greedy_labels.csv\")\n", + "train_multiple_answers = pd.read_csv(f\"{base_path}cs_qa_train_gemma_multiple_answers.csv\")\n", + "train_data = pd.concat([train_greedy_labels, train_multiple_answers.drop(['question', 'answer', 'choices'], axis=1)], axis=1)\n", + "\n", + "# Load and merge valid datasets\n", + "valid_greedy_labels = pd.read_csv(f\"{base_path}cs_qa_valid_gemma_greedy_labels.csv\")\n", + "valid_multiple_answers = pd.read_csv(f\"{base_path}cs_qa_valid_gemma_multiple_answers.csv\")\n", + "valid_data = pd.concat([valid_greedy_labels, valid_multiple_answers.drop(['question', 'answer', 'choices'], axis=1)], axis=1)\n", + "\n", + "# Load test dataset (no merging needed)\n", + "test_data = pd.read_csv(f\"{base_path}cs_qa_test_gemma_greedy_labels.csv\")\n", + "\n", + "def extract_options(choices_string):\n", + " try:\n", + " choices_dict = ast.literal_eval(choices_string)\n", + " options = choices_dict['text']\n", + " if len(options) != 5:\n", + " raise ValueError(f\"Expected 5 options, but found {len(options)}\")\n", + " return options\n", + " except (ValueError, KeyError, SyntaxError) as e:\n", + " print(f\"Error parsing choices: {e}\")\n", + " return []\n", + "\n", + "def extract_answer(text, options):\n", + " if not isinstance(text, str):\n", + " return 'Z'\n", + "\n", + " text_lower = text.lower()\n", + " options_lower = [opt.lower() for opt in options]\n", + "\n", + " # Check after ####\n", + " if '####' in text_lower:\n", + " after_hash = text_lower.split('####')[1].strip()\n", + "\n", + " # Look for options after ####\n", + " for idx, option in enumerate(options_lower):\n", + " if option in after_hash:\n", + " return chr(65 + idx)\n", + "\n", + " # Look for A), B), C), D), E) after ####\n", + " match = re.search(r'\\b([a-e])\\)', after_hash)\n", + " if match:\n", + " return match.group(1).upper()\n", + "\n", + " # Look for options in the whole text\n", + " for idx, option in enumerate(options_lower):\n", + " if option in text_lower:\n", + " return chr(65 + idx)\n", + "\n", + " return 'Z'\n", + "\n", + "# Process datasets\n", + "for dataset in [train_data, valid_data, test_data]:\n", + " dataset['options'] = dataset['choices'].apply(extract_options)\n", + " dataset['answer_pred'] = dataset.apply(lambda row: extract_answer(row['generated_answer'], row['options']), axis=1)\n", + " dataset['is_correct'] = dataset['answer'] == dataset['answer_pred']\n", + "\n", + "def print_distribution_accuracy_and_breakdown(dataset, title):\n", + " print(f\"\\nDistribution for {title}:\")\n", + "\n", + " # Predicted answer distribution\n", + " pred_counts = dataset['answer_pred'].value_counts().reindex(['A', 'B', 'C', 'D', 'E', 'Z'], fill_value=0)\n", + " pred_total = pred_counts.sum()\n", + " pred_percentages = (pred_counts / pred_total * 100).round(2)\n", + "\n", + " print(\"Predicted Answer Distribution:\")\n", + " for answer, count in pred_counts.items():\n", + " print(f\"{answer}: {count} ({pred_percentages[answer]}%)\")\n", + "\n", + " # Actual answer distribution\n", + " actual_counts = dataset['answer'].value_counts().reindex(['A', 'B', 'C', 'D', 'E'], fill_value=0)\n", + " actual_total = actual_counts.sum()\n", + " actual_percentages = (actual_counts / actual_total * 100).round(2)\n", + "\n", + " print(\"\\nActual Answer Distribution:\")\n", + " for answer, count in actual_counts.items():\n", + " print(f\"{answer}: {count} ({actual_percentages[answer]}%)\")\n", + "\n", + " # Calculate accuracy\n", + " accuracy = dataset['is_correct'].mean() * 100\n", + " print(f\"\\nOverall Accuracy: {accuracy:.2f}%\")\n", + "\n", + " # Breakdown of correct predictions\n", + " print(\"\\nBreakdown of Correct Predictions:\")\n", + " for answer in ['A', 'B', 'C', 'D', 'E', 'Z']:\n", + " predictions = dataset[dataset['answer_pred'] == answer]\n", + " correct_predictions = predictions[predictions['is_correct']]\n", + " total_predictions = len(predictions)\n", + " correct_count = len(correct_predictions)\n", + " if total_predictions > 0:\n", + " percentage = round((correct_count / total_predictions) * 100, 2)\n", + " else:\n", + " percentage = 0.0\n", + " print(f\"{answer}: {correct_count} out of {total_predictions} ({percentage}%)\")\n", + "\n", + "# Print distributions, accuracy, and breakdown for each dataset\n", + "for name, data in [('Train', train_data), ('Valid', valid_data), ('Test', test_data)]:\n", + " print_distribution_accuracy_and_breakdown(data, f'{name} Data')" + ], + "metadata": { + "id": "25MAlOe8q6_5", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb0c376f-844d-4636-d304-a68af99e96ba" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Distribution for Train Data:\n", + "Predicted Answer Distribution:\n", + "A: 1380 (27.6%)\n", + "B: 272 (5.44%)\n", + "C: 367 (7.34%)\n", + "D: 235 (4.7%)\n", + "E: 74 (1.48%)\n", + "Z: 2672 (53.44%)\n", + "\n", + "Actual Answer Distribution:\n", + "A: 968 (19.36%)\n", + "B: 1029 (20.58%)\n", + "C: 991 (19.82%)\n", + "D: 1031 (20.62%)\n", + "E: 981 (19.62%)\n", + "\n", + "Overall Accuracy: 16.82%\n", + "\n", + "Breakdown of Correct Predictions:\n", + "A: 364 out of 1380 (26.38%)\n", + "B: 134 out of 272 (49.26%)\n", + "C: 160 out of 367 (43.6%)\n", + "D: 129 out of 235 (54.89%)\n", + "E: 54 out of 74 (72.97%)\n", + "Z: 0 out of 2672 (0.0%)\n", + "\n", + "Distribution for Valid Data:\n", + "Predicted Answer Distribution:\n", + "A: 378 (25.2%)\n", + "B: 94 (6.27%)\n", + "C: 118 (7.87%)\n", + "D: 61 (4.07%)\n", + "E: 22 (1.47%)\n", + "Z: 827 (55.13%)\n", + "\n", + "Actual Answer Distribution:\n", + "A: 282 (18.8%)\n", + "B: 297 (19.8%)\n", + "C: 321 (21.4%)\n", + "D: 287 (19.13%)\n", + "E: 313 (20.87%)\n", + "\n", + "Overall Accuracy: 15.53%\n", + "\n", + "Breakdown of Correct Predictions:\n", + "A: 99 out of 378 (26.19%)\n", + "B: 42 out of 94 (44.68%)\n", + "C: 47 out of 118 (39.83%)\n", + "D: 32 out of 61 (52.46%)\n", + "E: 13 out of 22 (59.09%)\n", + "Z: 0 out of 827 (0.0%)\n", + "\n", + "Distribution for Test Data:\n", + "Predicted Answer Distribution:\n", + "A: 330 (27.03%)\n", + "B: 73 (5.98%)\n", + "C: 85 (6.96%)\n", + "D: 65 (5.32%)\n", + "E: 16 (1.31%)\n", + "Z: 652 (53.4%)\n", + "\n", + "Actual Answer Distribution:\n", + "A: 239 (19.57%)\n", + "B: 255 (20.88%)\n", + "C: 241 (19.74%)\n", + "D: 251 (20.56%)\n", + "E: 235 (19.25%)\n", + "\n", + "Overall Accuracy: 17.61%\n", + "\n", + "Breakdown of Correct Predictions:\n", + "A: 92 out of 330 (27.88%)\n", + "B: 38 out of 73 (52.05%)\n", + "C: 36 out of 85 (42.35%)\n", + "D: 35 out of 65 (53.85%)\n", + "E: 14 out of 16 (87.5%)\n", + "Z: 0 out of 652 (0.0%)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import ast\n", + "import math\n", + "import re\n", + "from collections import Counter\n", + "\n", + "def convert_string_to_list(string_representation):\n", + " try:\n", + " evaluated_list = ast.literal_eval(string_representation)\n", + " if isinstance(evaluated_list, list) and len(evaluated_list) == 10:\n", + " return [str(item) for item in evaluated_list]\n", + " else:\n", + " raise ValueError(\"The string does not represent a list of 10 elements\")\n", + " except (ValueError, SyntaxError) as e:\n", + " print(f\"Error parsing the string: {e}\")\n", + " return None\n", + "\n", + "def extract_answer(text, options):\n", + " if not isinstance(text, str):\n", + " return 'Z'\n", + "\n", + " text_lower = text.lower()\n", + " options_lower = [opt.lower() for opt in options]\n", + "\n", + " # Check after ####\n", + " if '####' in text_lower:\n", + " after_hash = text_lower.split('####')[1].strip()\n", + "\n", + " # Look for options after ####\n", + " for idx, option in enumerate(options_lower):\n", + " if option in after_hash:\n", + " return chr(65 + idx)\n", + "\n", + " # Look for A), B), C), D), E) after ####\n", + " match = re.search(r'\\b([a-e])\\)', after_hash)\n", + " if match:\n", + " return match.group(1).upper()\n", + "\n", + " # Look for options in the whole text\n", + " for idx, option in enumerate(options_lower):\n", + " if option in text_lower:\n", + " return chr(65 + idx)\n", + "\n", + " return 'Z'\n", + "\n", + "def calculate_entropy(frequencies):\n", + " total = sum(frequencies.values())\n", + " entropy = 0\n", + " for answer, count in frequencies.items():\n", + " if answer != 'Z':\n", + " p = count / total\n", + " if p > 0:\n", + " entropy -= p * math.log2(p)\n", + " else:\n", + " for _ in range(count):\n", + " p = 1 / total\n", + " entropy -= p * math.log2(p)\n", + " return round(entropy, 2) # Round to 2 decimal places\n", + "\n", + "def process_generated_answers(df):\n", + " def label_and_calculate_entropy(row):\n", + " generated_answers = convert_string_to_list(row['generated_answers'])\n", + " if generated_answers is None:\n", + " return {'frequencies': {}, 'entropy': None}\n", + "\n", + " labeled_answers = [extract_answer(answer, row['options']) for answer in generated_answers]\n", + " frequencies = Counter(labeled_answers)\n", + " for answer in 'ABCDEZ':\n", + " if answer not in frequencies:\n", + " frequencies[answer] = 0\n", + "\n", + " entropy = calculate_entropy(frequencies)\n", + "\n", + " return {'frequencies': dict(frequencies), 'entropy': entropy}\n", + "\n", + " results = df.apply(label_and_calculate_entropy, axis=1)\n", + " df['answer_frequencies'] = results.apply(lambda x: x['frequencies'])\n", + " df['entropy'] = results.apply(lambda x: x['entropy'])\n", + "\n", + " return df\n", + "\n", + "# Process train_data and valid_data\n", + "train_data = process_generated_answers(train_data)\n", + "valid_data = process_generated_answers(valid_data)" + ], + "metadata": { + "id": "orQv6qyZeM0-" + }, + "execution_count": 72, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Anlysis and Hallucination Detection Model Training" + ], + "metadata": { + "id": "oEg_durBgPXU" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "# Combine train and valid data if they're separate\n", + "combined_df = pd.concat([train_data, valid_data], ignore_index=True)\n", + "\n", + "# Separate the data for correct and incorrect labels\n", + "correct_entropy = combined_df[combined_df['is_correct'] == True]['entropy']\n", + "incorrect_entropy = combined_df[combined_df['is_correct'] == False]['entropy']\n", + "\n", + "# Define the number of bins and range\n", + "bins = 50\n", + "range_min = min(combined_df['entropy'].min(), 0) # In case there are negative values\n", + "range_max = combined_df['entropy'].max()\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot histogram for incorrect labels (red)\n", + "plt.hist(incorrect_entropy, bins=bins, range=(range_min, range_max), color='red', alpha=0.5, label='Incorrect')\n", + "\n", + "# Plot histogram for correct labels (green)\n", + "plt.hist(correct_entropy, bins=bins, range=(range_min, range_max), color='green', alpha=0.5, label='Correct')\n", + "\n", + "# Customize the plot\n", + "plt.title('Distribution of Entropy for Correct and Incorrect Labels', fontsize=16)\n", + "plt.xlabel('Entropy', fontsize=14)\n", + "plt.ylabel('Frequency', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add text with data statistics\n", + "plt.text(0.05, 0.95, f\"Correct samples: {len(correct_entropy)}\\nIncorrect samples: {len(incorrect_entropy)}\",\n", + " transform=plt.gca().transAxes, verticalalignment='top', fontsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print some statistics\n", + "print(f\"Correct labels - Mean: {correct_entropy.mean():.4f}, Std: {correct_entropy.std():.4f}\")\n", + "print(f\"Incorrect labels - Mean: {incorrect_entropy.mean():.4f}, Std: {incorrect_entropy.std():.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 641 + }, + "id": "je4YVliAeMy4", + "outputId": "c284415f-44bf-483f-fefa-4678dd7cc4ae" + }, + "execution_count": 73, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correct labels - Mean: 1.5815, Std: 0.8158\n", + "Incorrect labels - Mean: 2.3951, Std: 0.8087\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Assuming combined_df is your DataFrame with 'entropy' and 'is_correct' columns\n", + "# If you're only using train_data, replace combined_df with train_data\n", + "\n", + "# Prepare the data\n", + "y_true = combined_df['is_correct']\n", + "y_scores = -combined_df['entropy'] # Negative because lower entropy means higher confidence\n", + "\n", + "# Calculate ROC curve and AUC\n", + "fpr, tpr, thresholds = roc_curve(y_true, y_scores)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "# Find the best threshold using Youden's J statistic\n", + "j_scores = tpr - fpr\n", + "best_idx = np.argmax(j_scores)\n", + "best_threshold = thresholds[best_idx]\n", + "\n", + "# Create predictions using the best threshold\n", + "y_pred = (y_scores >= best_threshold).astype(int)\n", + "\n", + "# Calculate confusion matrix\n", + "cm = confusion_matrix(y_true, y_pred)\n", + "\n", + "# Create the ROC curve plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate', fontsize=14)\n", + "plt.ylabel('True Positive Rate', fontsize=14)\n", + "plt.title('ROC Curve (semantic-entropy-as-predictor)', fontsize=16)\n", + "plt.legend(loc=\"lower right\", fontsize=12)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add text with data statistics\n", + "plt.text(0.05, 0.95, f\"Total samples: {len(y_true)}\\nCorrect samples: {sum(y_true)}\\nIncorrect samples: {len(y_true) - sum(y_true)}\",\n", + " transform=plt.gca().transAxes, verticalalignment='top', fontsize=12)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print AUC score and best threshold\n", + "print(f\"AUC-ROC Score: {roc_auc:.4f}\")\n", + "print(f\"Best Threshold: {-best_threshold:.4f}\") # Note: we negate it back to get the original entropy threshold\n", + "\n", + "# Plot confusion matrix\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "plt.title('Confusion Matrix for Best Threshold', fontsize=16)\n", + "plt.xlabel('Predicted Label', fontsize=14)\n", + "plt.ylabel('True Label', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_true, y_pred, target_names=['Incorrect', 'Correct']))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "WHV3LvdxeMw-", + "outputId": "ff47bbc1-4747-4c01-f289-abcf935e3372" + }, + "execution_count": 74, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "AUC-ROC Score: 0.7648\n", + "Best Threshold: 2.1700\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.94 0.62 0.75 5426\n", + " Correct 0.29 0.78 0.42 1074\n", + "\n", + " accuracy 0.65 6500\n", + " macro avg 0.61 0.70 0.59 6500\n", + "weighted avg 0.83 0.65 0.69 6500\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Plain logistic regression with is_correct as the label" + ], + "metadata": { + "id": "WJNQquVqhA5R" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "import ast\n", + "\n", + "# Function to convert string representation of list to numpy array\n", + "def string_to_numpy(s):\n", + " return np.array(ast.literal_eval(s))\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].apply(string_to_numpy).values)\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_correct, y_test_correct = train_test_split(X, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# The rest of the code remains the same\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# The rest of the code remains unchanged\n", + "...\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Create a logistic regression model\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga')\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_correct)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best threshold using only training data\n", + "best_threshold = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for correctness (determined from training data): {best_threshold:.4f}\")\n", + "\n", + "# Create predictions using the best threshold for both train and test\n", + "y_train_pred_optimized = (y_train_pred_proba >= best_threshold).astype(int)\n", + "y_test_pred_optimized = (y_test_pred_proba >= best_threshold).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets\n", + "fpr_train, tpr_train, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test, tpr_test, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curve\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized threshold\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold:\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold:\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized threshold\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train) - Threshold: {best_threshold:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax1.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test) - Threshold: {best_threshold:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax2.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "nZS27PkWeMu6", + "outputId": "bae32509-e047-44da-9256-c9a03d48d48c" + }, + "execution_count": 76, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5200, 2048)\n", + "Shape of test data: (1300, 2048)\n", + "Proportion of correct answers in training set: 0.16538461538461538\n", + "Proportion of correct answers in test set: 0.1646153846153846\n", + "Best parameters: {'C': 0.01, 'penalty': 'l2'}\n", + "Number of features selected: 500\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 993, 2674, 1205, 236, 46, 21, 10, 9, 3, 3]), array([0.00252227, 0.09827914, 0.19403602, 0.28979289, 0.38554977,\n", + " 0.48130664, 0.57706351, 0.67282039, 0.76857726, 0.86433414,\n", + " 0.96009101]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([139, 498, 400, 193, 40, 13, 11, 2, 0, 4]), array([0.00285132, 0.07582566, 0.14879999, 0.22177432, 0.29474865,\n", + " 0.36772298, 0.44069731, 0.51367165, 0.58664598, 0.65962031,\n", + " 0.73259464]))\n", + "\n", + "y_train_correct distribution: [4340 860]\n", + "y_test_correct distribution: [1086 214]\n", + "\n", + "Best threshold for correctness (determined from training data): 0.2121\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.89 0.82 0.85 4340\n", + " Correct 0.34 0.48 0.40 860\n", + "\n", + " accuracy 0.76 5200\n", + " macro avg 0.62 0.65 0.63 5200\n", + "weighted avg 0.80 0.76 0.78 5200\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.86 0.79 0.82 1086\n", + " Correct 0.23 0.32 0.27 214\n", + "\n", + " accuracy 0.71 1300\n", + " macro avg 0.54 0.56 0.55 1300\n", + "weighted avg 0.75 0.71 0.73 1300\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Class-weighted Logistic Regression with Is_Correct as the Label" + ], + "metadata": { + "id": "bjFs1aGrj7u3" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "import ast\n", + "\n", + "# Function to convert string representation of list to numpy array\n", + "def string_to_numpy(s):\n", + " return np.array(ast.literal_eval(s))\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].apply(string_to_numpy).values)\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_correct, y_test_correct = train_test_split(X, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# Compute class weights\n", + "class_weights = compute_class_weight('balanced', classes=np.unique(y_train_correct), y=y_train_correct)\n", + "class_weight_dict = dict(zip(np.unique(y_train_correct), class_weights))\n", + "\n", + "print(\"Class weights:\", class_weight_dict)\n", + "\n", + "# Create a logistic regression model with class weights\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga', class_weight=class_weight_dict)\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_correct)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best threshold using only training data\n", + "best_threshold = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for correctness (determined from training data): {best_threshold:.4f}\")\n", + "\n", + "# Create predictions using the best threshold for both train and test\n", + "y_train_pred_optimized = (y_train_pred_proba >= best_threshold).astype(int)\n", + "y_test_pred_optimized = (y_test_pred_proba >= best_threshold).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets\n", + "fpr_train, tpr_train, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test, tpr_test, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curve\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized threshold\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold:\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold:\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized threshold\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train) - Threshold: {best_threshold:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax1.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test) - Threshold: {best_threshold:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax2.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "G4t_rEDGbJHI", + "outputId": "1aca7cd7-fde5-4716-c95f-362b413651c0" + }, + "execution_count": 77, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5200, 2048)\n", + "Shape of test data: (1300, 2048)\n", + "Proportion of correct answers in training set: 0.16538461538461538\n", + "Proportion of correct answers in test set: 0.1646153846153846\n", + "Class weights: {False: 0.5990783410138248, True: 3.0232558139534884}\n", + "Best parameters: {'C': 0.01, 'penalty': 'l2'}\n", + "Number of features selected: 500\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 142, 217, 515, 954, 1240, 1178, 688, 189, 56, 21]), array([0.00145534, 0.10067581, 0.19989627, 0.29911674, 0.39833721,\n", + " 0.49755767, 0.59677814, 0.6959986 , 0.79521907, 0.89443953,\n", + " 0.99366 ]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([ 42, 54, 133, 203, 288, 295, 200, 59, 20, 6]), array([0.00512793, 0.10053828, 0.19594864, 0.291359 , 0.38676935,\n", + " 0.48217971, 0.57759006, 0.67300042, 0.76841078, 0.86382113,\n", + " 0.95923149]))\n", + "\n", + "y_train_correct distribution: [4340 860]\n", + "y_test_correct distribution: [1086 214]\n", + "\n", + "Best threshold for correctness (determined from training data): 0.5455\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.90 0.76 0.83 4340\n", + " Correct 0.32 0.57 0.41 860\n", + "\n", + " accuracy 0.73 5200\n", + " macro avg 0.61 0.67 0.62 5200\n", + "weighted avg 0.80 0.73 0.76 5200\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.86 0.74 0.80 1086\n", + " Correct 0.23 0.40 0.29 214\n", + "\n", + " accuracy 0.68 1300\n", + " macro avg 0.55 0.57 0.54 1300\n", + "weighted avg 0.76 0.68 0.71 1300\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Now, let's train using entropy score as the label. and then use it as a proxy for the hallucination" + ], + "metadata": { + "id": "Z1WS06OXm0ts" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "import ast\n", + "\n", + "# Function to convert string representation of list to numpy array\n", + "def string_to_numpy(s):\n", + " return np.array(ast.literal_eval(s))\n", + "\n", + "# Create new column based on entropy (inverted)\n", + "combined_df['entropy_label'] = (combined_df['entropy'] < 2.17).astype(int)\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].apply(string_to_numpy).values)\n", + "y_entropy = combined_df['entropy_label'].values\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_entropy, y_test_entropy, y_train_correct, y_test_correct = train_test_split(X, y_entropy, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of low entropy in training set:\", y_train_entropy.mean())\n", + "print(\"Proportion of low entropy in test set:\", y_test_entropy.mean())\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Create a logistic regression model\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga')\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_entropy)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_entropy distribution:\", np.bincount(y_train_entropy))\n", + "print(\"y_test_entropy distribution:\", np.bincount(y_test_entropy))\n", + "print(\"y_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best thresholds using only training data\n", + "best_threshold_entropy = find_best_threshold(y_train_entropy, y_train_pred_proba)\n", + "best_threshold_correct = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for entropy (determined from training data): {best_threshold_entropy:.4f}\")\n", + "print(f\"Best threshold for correctness (determined from training data): {best_threshold_correct:.4f}\")\n", + "\n", + "# Create predictions using the best thresholds for both train and test\n", + "y_train_pred_optimized_entropy = (y_train_pred_proba >= best_threshold_entropy).astype(int)\n", + "y_test_pred_optimized_entropy = (y_test_pred_proba >= best_threshold_entropy).astype(int)\n", + "y_train_pred_optimized_correct = (y_train_pred_proba >= best_threshold_correct).astype(int)\n", + "y_test_pred_optimized_correct = (y_test_pred_proba >= best_threshold_correct).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets (entropy labels)\n", + "fpr_train_entropy, tpr_train_entropy, _ = roc_curve(y_train_entropy, y_train_pred_proba)\n", + "roc_auc_train_entropy = roc_auc_score(y_train_entropy, y_train_pred_proba)\n", + "fpr_test_entropy, tpr_test_entropy, _ = roc_curve(y_test_entropy, y_test_pred_proba)\n", + "roc_auc_test_entropy = roc_auc_score(y_test_entropy, y_test_pred_proba)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets (is_correct labels)\n", + "fpr_train_correct, tpr_train_correct, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train_correct = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test_correct, tpr_test_correct, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test_correct = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curves\n", + "plt.figure(figsize=(12, 10))\n", + "plt.plot(fpr_train_entropy, tpr_train_entropy, color='blue', lw=2, label=f'Train ROC curve (Entropy, AUC = {roc_auc_train_entropy:.2f})')\n", + "plt.plot(fpr_test_entropy, tpr_test_entropy, color='red', lw=2, label=f'Test ROC curve (Entropy, AUC = {roc_auc_test_entropy:.2f})')\n", + "plt.plot(fpr_train_correct, tpr_train_correct, color='green', lw=2, label=f'Train ROC curve (Is Correct, AUC = {roc_auc_train_correct:.2f})')\n", + "plt.plot(fpr_test_correct, tpr_test_correct, color='orange', lw=2, label=f'Test ROC curve (Is Correct, AUC = {roc_auc_test_correct:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized thresholds\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold (Entropy):\")\n", + "print(classification_report(y_train_entropy, y_train_pred_optimized_entropy, target_names=['High Entropy', 'Low Entropy']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold (Entropy):\")\n", + "print(classification_report(y_test_entropy, y_test_pred_optimized_entropy, target_names=['High Entropy', 'Low Entropy']))\n", + "\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold (Is Correct):\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized_correct, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold (Is Correct):\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized_correct, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized thresholds\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 20))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_entropy, y_train_pred_optimized_entropy), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train - Entropy) - Threshold: {best_threshold_entropy:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['High Entropy', 'Low Entropy'])\n", + "ax1.set_yticklabels(['High Entropy', 'Low Entropy'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_entropy, y_test_pred_optimized_entropy), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test - Entropy) - Threshold: {best_threshold_entropy:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['High Entropy', 'Low Entropy'])\n", + "ax2.set_yticklabels(['High Entropy', 'Low Entropy'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized_correct), annot=True, fmt='d', cmap='Blues', ax=ax3)\n", + "ax3.set_title(f'Confusion Matrix (Train - Is Correct) - Threshold: {best_threshold_correct:.4f}')\n", + "ax3.set_ylabel('True Label')\n", + "ax3.set_xlabel('Predicted Label')\n", + "ax3.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax3.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized_correct), annot=True, fmt='d', cmap='Blues', ax=ax4)\n", + "ax4.set_title(f'Confusion Matrix (Test - Is Correct) - Threshold: {best_threshold_correct:.4f}')\n", + "ax4.set_ylabel('True Label')\n", + "ax4.set_xlabel('Predicted Label')\n", + "ax4.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax4.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "M1o6lzEibJEr", + "outputId": "3e58a562-f9e9-4e62-e8cf-f1eff4686680" + }, + "execution_count": 78, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5200, 2048)\n", + "Shape of test data: (1300, 2048)\n", + "Proportion of low entropy in training set: 0.44346153846153846\n", + "Proportion of low entropy in test set: 0.44461538461538463\n", + "Proportion of correct answers in training set: 0.16538461538461538\n", + "Proportion of correct answers in test set: 0.1646153846153846\n", + "Best parameters: {'C': 0.01, 'penalty': 'l2'}\n", + "Number of features selected: 500\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 113, 245, 710, 1103, 1136, 974, 511, 227, 116, 65]), array([8.43719378e-04, 1.00201712e-01, 1.99559704e-01, 2.98917697e-01,\n", + " 3.98275689e-01, 4.97633682e-01, 5.96991674e-01, 6.96349667e-01,\n", + " 7.95707659e-01, 8.95065652e-01, 9.94423644e-01]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([ 36, 64, 185, 262, 286, 249, 121, 50, 30, 17]), array([0.00480904, 0.10356555, 0.20232206, 0.30107856, 0.39983507,\n", + " 0.49859158, 0.59734809, 0.69610459, 0.7948611 , 0.89361761,\n", + " 0.99237411]))\n", + "\n", + "y_train_entropy distribution: [2894 2306]\n", + "y_test_entropy distribution: [722 578]\n", + "y_train_correct distribution: [4340 860]\n", + "y_test_correct distribution: [1086 214]\n", + "\n", + "Best threshold for entropy (determined from training data): 0.3737\n", + "Best threshold for correctness (determined from training data): 0.4848\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold (Entropy):\n", + " precision recall f1-score support\n", + "\n", + "High Entropy 0.79 0.51 0.62 2894\n", + " Low Entropy 0.58 0.83 0.68 2306\n", + "\n", + " accuracy 0.65 5200\n", + " macro avg 0.68 0.67 0.65 5200\n", + "weighted avg 0.69 0.65 0.65 5200\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold (Entropy):\n", + " precision recall f1-score support\n", + "\n", + "High Entropy 0.75 0.50 0.60 722\n", + " Low Entropy 0.56 0.80 0.66 578\n", + "\n", + " accuracy 0.63 1300\n", + " macro avg 0.66 0.65 0.63 1300\n", + "weighted avg 0.67 0.63 0.62 1300\n", + "\n", + "\n", + "Classification Report (Train) - Optimized Threshold (Is Correct):\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.88 0.64 0.74 4340\n", + " Correct 0.24 0.58 0.34 860\n", + "\n", + " accuracy 0.63 5200\n", + " macro avg 0.56 0.61 0.54 5200\n", + "weighted avg 0.78 0.63 0.68 5200\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold (Is Correct):\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.87 0.64 0.74 1086\n", + " Correct 0.23 0.53 0.32 214\n", + "\n", + " accuracy 0.62 1300\n", + " macro avg 0.55 0.59 0.53 1300\n", + "weighted avg 0.77 0.62 0.67 1300\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import ast\n", + "\n", + "def extract_options(choices_string):\n", + " try:\n", + " # Convert the string to a dictionary\n", + " choices_dict = ast.literal_eval(choices_string)\n", + "\n", + " # Extract the 'text' values\n", + " options = choices_dict['text']\n", + "\n", + " # Ensure we have exactly 5 options\n", + " if len(options) != 5:\n", + " raise ValueError(\"Expected 5 options, but found {len(options)}\")\n", + "\n", + " return options\n", + " except (ValueError, KeyError, SyntaxError) as e:\n", + " print(f\"Error parsing choices: {e}\")\n", + " return []\n", + "\n", + "# Example usage:\n", + "# sample_choices = \"{'label': ['A', 'B', 'C', 'D', 'E'], 'text': ['pizza parlor', 'store', 'house', 'pie shop', 'stove']}\"\n", + "# options = extract_options(sample_choices)\n", + "# print(options)" + ], + "metadata": { + "id": "kA8wLdbLq69r" + }, + "execution_count": 47, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Usage example:\n", + "# Assuming you have gemma_model, gemma_tokenizer, and device already set up\n", + "input_data = test_ds_40[2] # Or any other index\n", + "result = process_single_input(input_data, gemma_model, gemma_tokenizer, device)\n", + "\n", + "print(result['generated_answer'])" + ], + "metadata": { + "id": "4Csxax8riUzO" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Hallucination_GSM8K_Yusuf_Efe.ipynb b/Hallucination_GSM8K_Yusuf_Efe.ipynb new file mode 100644 index 0000000..9107338 --- /dev/null +++ b/Hallucination_GSM8K_Yusuf_Efe.ipynb @@ -0,0 +1,1712 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# EXTENSION: Additional Evaluations\n", + "\n", + "Our approach differs from token-level entropy by calculating \"semantic entropy\" over a broader context. However, TriviaQA's short-answer format may not fully showcase this difference. The gap between token-level and semantic-level entropy values might be subtle in such cases. To better evaluate our method, we'll test it on two datasets that require longer, more detailed responses." + ], + "metadata": { + "id": "0CHe6j7zZ08S" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Analysis I: GSM8K Dataset\n", + "\n", + "The GSM8K dataset aligns well with our goals, as it requires generating long-form solutions. Additionally, the evaluation can be conducted via string matching after the #### delimiter in the output, simplifying the evalaution process." + ], + "metadata": { + "id": "xmMTMUrJZ06L" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Tasks:\n", + "\n", + "1) Create train/validation/test splits. \\\\\n", + "2) Choose few shot examples, exclude them from the splits \\\\" + ], + "metadata": { + "id": "VoA8qxh8Z033" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Set Up:" + ], + "metadata": { + "id": "12O9D3AZZ0yf" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f60QrMMWZwzC" + }, + "outputs": [], + "source": [ + "!pip install transformers datasets torch tqdm scikit-learn matplotlib accelerate" + ] + }, + { + "cell_type": "code", + "source": [ + "#!pip install --upgrade pyarrow datasets" + ], + "metadata": { + "id": "z_puQWa8HNMT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#!pip install pyarrow==14.0.1\n", + "#!pip install --upgrade datasets" + ], + "metadata": { + "id": "tos3OiheH1GZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import accelerate\n", + "from torch.nn import functional as F\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification\n", + "from datasets import load_from_disk, load_dataset\n", + "import random\n", + "import math\n", + "import os\n", + "from google.colab import drive\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd" + ], + "metadata": { + "id": "PvY22f1aaEwG" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "token = os.environ['HF_TOKEN']" + ], + "metadata": { + "id": "WrMOpVSVaEtp" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DC4gQESAaEq0", + "outputId": "5e29097f-262e-43f5-86fb-2268d2883ffb" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Define the base path in your Google Drive\n", + "base_path = \"/content/drive/MyDrive/hallucination-detector/\"\n", + "\n", + "# Create the directory if it doesn't exist\n", + "import os\n", + "os.makedirs(base_path, exist_ok=True)" + ], + "metadata": { + "id": "WcN418T4aKh-" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Preprocessing" + ], + "metadata": { + "id": "GWNSA6HjaPPj" + } + }, + { + "cell_type": "code", + "source": [ + "# Load the dataset\n", + "gsm8k_dataset = load_dataset(\"openai/gsm8k\", \"main\")\n", + "gsm8k_train = gsm8k_dataset['train']\n", + "gsm8k_test = gsm8k_dataset['test']\n", + "\n", + "# Shuffle the dataset\n", + "gsm8k_train = gsm8k_train.shuffle(seed=42)\n", + "gsm8k_test = gsm8k_test.shuffle(seed=42)" + ], + "metadata": { + "id": "DJxdpV3paPww" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "train_ds = gsm8k_train.select(range(5000))\n", + "valid_ds = gsm8k_train.select(range(5000,7000))\n", + "test_ds = gsm8k_test\n", + "\n", + "print(f\"training split size: {len(train_ds)}\")\n", + "print(f\"validation split size: {len(valid_ds)}\")\n", + "print(f\"test split size: {len(test_ds)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rL9jHxcLaR0g", + "outputId": "c7fa4ba1-ac64-4a68-eeb6-379381d92da8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "training split size: 5000\n", + "validation split size: 2000\n", + "test split size: 1319\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Model Testing" + ], + "metadata": { + "id": "a8qF6pMkbeht" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading Gemma 2B model and tokenizer...\")\n", + "gemma_tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\", token=token)\n", + "gemma_model = AutoModelForCausalLM.from_pretrained(\n", + " \"google/gemma-2b-it\",\n", + " device_map=\"auto\",\n", + " torch_dtype=torch.bfloat16,\n", + " output_hidden_states=True,\n", + " token=token\n", + ")\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ], + "metadata": { + "id": "l9RAbxYsaRvu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "For fair evaluation, few-shot examples are typically chosen randomly. However, in this case, we aim to maximize the model's accuracy to ensure a sufficient number of positively labeled sequences. This allows the model to effectively learn the contrast between hallucinated and non-hallucinated content. Due to the complexity of the task and the size of the Gemma-2B model, the model is likely to hallucinate in most instances. The official Gemma technical report indicates an accuracy of only 14% for this task. Therefore, to provide the model with the best possible assistance, we carefully hand-selected the few-shot examples." + ], + "metadata": { + "id": "kJRZwuWTaVN6" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Best Guess and 10 Alternative Generations" + ], + "metadata": { + "id": "o0k1EKPQYsRR" + } + }, + { + "cell_type": "code", + "source": [ + "few_shot_gsm8k = f\"\"\"\n", + "Question: {gsm8k_train[7001]['question']}\n", + "Answer: {gsm8k_train[7001]['answer']}\\n\n", + "Question: {gsm8k_train[7006]['question']}\n", + "Answer: {gsm8k_train[7006]['answer']}\\n\n", + "Question: {gsm8k_train[7008]['question']}\n", + "Answer: {gsm8k_train[7008]['answer']}\\n\n", + "Question: {gsm8k_train[7010]['question']}\n", + "Answer: {gsm8k_train[7010]['answer']}\\n\n", + "Question: {gsm8k_train[7012]['question']}\n", + "Answer: {gsm8k_train[7012]['answer']}\\n\n", + "\"\"\"\n", + "\n", + "def format_question_with_examples(question):\n", + " return f\"{few_shot_gsm8k}Question: {question}\\nAnswer:\"" + ], + "metadata": { + "id": "B3-O12U2aRyS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import torch\n", + "import os\n", + "from tqdm import tqdm\n", + "import ast\n", + "\n", + "def generate_predictions_batch(questions, num_alternatives=10, temperature=0.8, top_p=0.9):\n", + " formatted_questions = [format_question_with_examples(q) for q in questions]\n", + " inputs = gemma_tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n", + " max_length = inputs.input_ids.size(1) + 200\n", + "\n", + " with torch.no_grad():\n", + " outputs = gemma_model.generate(**inputs, max_length=max_length,\n", + " num_return_sequences=num_alternatives,\n", + " do_sample=True,\n", + " top_p=top_p,\n", + " temperature=temperature)\n", + "\n", + " # Reshape outputs to group by question\n", + " outputs = outputs.reshape(len(questions), num_alternatives, -1)\n", + "\n", + " all_alternatives = []\n", + " for i, formatted_q in enumerate(formatted_questions):\n", + " responses = gemma_tokenizer.batch_decode(outputs[i], skip_special_tokens=True)\n", + " predictions = [response[len(formatted_q):].strip() for response in responses]\n", + " all_alternatives.append(predictions)\n", + "\n", + " return all_alternatives\n", + "\n", + "def process_dataset(dataset, name, batch_size=1, checkpoint_interval=10, num_alternatives=10, temperature=0.8, top_p=0.9):\n", + " output_file = f\"/content/drive/MyDrive/hallucination-detector/gsm8k_{name}_gemma_alternatives.csv\"\n", + "\n", + " # Check for existing file and determine starting point\n", + " if os.path.exists(output_file):\n", + " existing_df = pd.read_csv(output_file)\n", + " start_index = len(existing_df)\n", + " results = existing_df.to_dict('records')\n", + " else:\n", + " start_index = 0\n", + " results = []\n", + "\n", + " for i in tqdm(range(start_index, len(dataset), batch_size), desc=f\"Processing {name}\"):\n", + " batch = dataset[i:i+batch_size]\n", + "\n", + " questions = batch['question']\n", + " answers = batch['answer']\n", + "\n", + " alternatives = generate_predictions_batch(questions, num_alternatives=num_alternatives,\n", + " temperature=temperature, top_p=top_p)\n", + "\n", + " for q, a, alts in zip(questions, answers, alternatives):\n", + " results.append({\n", + " 'question': q,\n", + " 'answer': a,\n", + " 'alternatives': str(alts) # Convert list to string for CSV storage\n", + " })\n", + "\n", + " # Write to CSV after processing every 5 questions\n", + " if (i + batch_size) % (checkpoint_interval * batch_size) == 0 or (i + batch_size) >= len(dataset):\n", + " df = pd.DataFrame(results)\n", + " df.to_csv(output_file, index=False)\n", + " print(f\"Checkpoint saved. Processed {len(results)} questions.\")\n", + "\n", + " print(f\"{name.capitalize()} dataset processing complete. CSV file saved to {output_file}\")" + ], + "metadata": { + "id": "msDnTeIS-1qf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Process each dataset\n", + "datasets = [('train', train_ds), ('valid', valid_ds)]\n", + "\n", + "for dataset_name, dataset in datasets:\n", + " process_dataset(dataset, dataset_name, num_alternatives=10, temperature=0.8, top_p=0.9)" + ], + "metadata": { + "id": "iMPN7WCK-1s3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# After processing, when you want to load and use the data:\n", + "def load_processed_data(file_path):\n", + " df = pd.read_csv(file_path)\n", + " df['alternatives'] = df['alternatives'].apply(ast.literal_eval)\n", + " return df\n", + "\n", + "# Example usage:\n", + "# train_data = load_processed_data(\"/content/drive/MyDrive/hallucination-detector/gsm8k_train_gemma_alternatives.csv\")\n", + "# valid_data = load_processed_data(\"/content/drive/MyDrive/hallucination-detector/gsm8k_valid_gemma_alternatives.csv\")" + ], + "metadata": { + "id": "JZDuBsq6-1vF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's generate the temperature=0 best guess for each question in all train/valid/test splits" + ], + "metadata": { + "id": "44XumK0sZXXd" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Best Guess Answer and Associated Hidden Vector" + ], + "metadata": { + "id": "xsnQmU5yZgYj" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import torch\n", + "import os\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "\n", + "def generate_greedy_answer_and_embedding(questions, num_additional_tokens=200):\n", + " formatted_questions = [format_question_with_examples(q) for q in questions]\n", + " inputs = gemma_tokenizer(formatted_questions, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n", + " question_lengths = inputs.attention_mask.sum(dim=1)\n", + " max_length = inputs.input_ids.size(1) + num_additional_tokens\n", + "\n", + " with torch.no_grad():\n", + " generation_outputs = gemma_model.generate(\n", + " **inputs,\n", + " max_length=max_length,\n", + " num_return_sequences=1,\n", + " temperature=0,\n", + " do_sample=False,\n", + " return_dict_in_generate=True,\n", + " output_hidden_states=True\n", + " )\n", + "\n", + " generated_sequences = generation_outputs.sequences\n", + " full_responses = gemma_tokenizer.batch_decode(generated_sequences, skip_special_tokens=True)\n", + " answers = [full_response[len(formatted_q):].strip() for full_response, formatted_q in zip(full_responses, formatted_questions)]\n", + "\n", + " # Extract the last token's embedding for each question\n", + " last_token_embeddings = [generation_outputs.hidden_states[0][-1][i][question_length-1].float().cpu().numpy()\n", + " for i, question_length in enumerate(question_lengths)]\n", + "\n", + " # Clear intermediate variables\n", + " del inputs, generation_outputs, generated_sequences\n", + " torch.cuda.empty_cache()\n", + "\n", + " return answers, last_token_embeddings\n", + "\n", + "def process_dataset_greedy(dataset, name, batch_size=8):\n", + " output_file = f\"/content/drive/MyDrive/hallucination-detector/gsm8k_{name}_gemma_greedy_labels.csv\"\n", + "\n", + " # Check for existing file and determine starting point\n", + " if os.path.exists(output_file):\n", + " existing_df = pd.read_csv(output_file)\n", + " start_index = len(existing_df)\n", + " results = existing_df.to_dict('records')\n", + " else:\n", + " start_index = 0\n", + " results = []\n", + "\n", + " for i in tqdm(range(start_index, len(dataset), batch_size), desc=f\"Processing {name}\"):\n", + " batch = dataset[i:i+batch_size]\n", + " questions = batch['question']\n", + " answers = batch['answer']\n", + "\n", + " generated_answers, embeddings = generate_greedy_answer_and_embedding(questions)\n", + "\n", + " for q, a, gen_a, emb in zip(questions, answers, generated_answers, embeddings):\n", + " results.append({\n", + " 'question': q,\n", + " 'answer': a,\n", + " 'generated_answer': gen_a,\n", + " 'embedding': emb.tolist() # Convert numpy array to list for CSV storage\n", + " })\n", + "\n", + " # Write to CSV after processing every batch\n", + " df = pd.DataFrame(results)\n", + " df.to_csv(output_file, index=False)\n", + " print(f\"Checkpoint saved. Processed {len(results)} questions.\")\n", + "\n", + " print(f\"{name.capitalize()} dataset processing complete. CSV file saved to {output_file}\")\n", + "\n", + "# Usage\n", + "process_dataset_greedy(train_ds, \"train\")\n", + "process_dataset_greedy(valid_ds, \"valid\")\n", + "process_dataset_greedy(test_ds, \"test\")" + ], + "metadata": { + "id": "cRb-EC2i-1xL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Helper Functions" + ], + "metadata": { + "id": "-PgAlrTXuIhA" + } + }, + { + "cell_type": "code", + "source": [ + "import ast\n", + "import pandas as pd\n", + "import re\n", + "from collections import Counter\n", + "import math\n", + "\n", + "def extract_number(string):\n", + " pattern = r'-?\\d+/\\d+|-?\\d*\\.?\\d+'\n", + " numbers = re.findall(pattern, string)\n", + "\n", + " if numbers:\n", + " last_number = numbers[-1]\n", + "\n", + " if '/' in last_number:\n", + " numerator, denominator = map(int, last_number.split('/'))\n", + " result = numerator / denominator\n", + " else:\n", + " result = float(last_number)\n", + "\n", + " return f\"{result:.2f}\"\n", + " else:\n", + " return \"None\"\n", + "\n", + "def calculate_entropy(frequencies):\n", + " total = sum(frequencies)\n", + " probabilities = [f / total for f in frequencies]\n", + " entropy = -sum(p * math.log2(p) for p in probabilities if p > 0)\n", + " return f\"{entropy:.2f}\"\n", + "\n", + "def process_dataframe(df):\n", + " # Apply extract_number to 'answer' and 'generated_answer' columns\n", + " df['answer_y'] = df['answer'].apply(extract_number)\n", + " df['generated_answer_y'] = df['generated_answer'].apply(extract_number)\n", + "\n", + " def process_alternatives(alt_string):\n", + " alt_list = ast.literal_eval(alt_string)\n", + " return [extract_number(alt) for alt in alt_list]\n", + "\n", + " df['alternatives_y'] = df['alternatives'].apply(process_alternatives)\n", + "\n", + " def get_frequencies(alt_y):\n", + " counter = Counter(alt_y)\n", + " none_count = counter.pop(\"None\", 0)\n", + " frequencies = list(counter.values())\n", + " if none_count > 0:\n", + " frequencies.append(none_count)\n", + " return [str(f) for f in frequencies]\n", + "\n", + " df['frequencies'] = df['alternatives_y'].apply(get_frequencies)\n", + "\n", + " df['entropy'] = df['frequencies'].apply(lambda x: calculate_entropy([int(f) for f in x]))\n", + "\n", + " return df\n", + "\n", + "def process_dataframe_without_alternatives(df):\n", + " # Apply extract_number to 'answer' and 'generated_answer' columns\n", + " df['answer_y'] = df['answer'].apply(extract_number)\n", + " df['generated_answer_y'] = df['generated_answer'].apply(extract_number)\n", + "\n", + " return df\n", + "\n", + "# You can now use either function depending on whether you have the 'alternatives' column or not\n", + "\n", + "# For dataframes with 'alternatives' column:\n", + "# merged_df_train = process_dataframe(merged_df_train)\n", + "\n", + "# For dataframes without 'alternatives' column:\n", + "# merged_df_train = process_dataframe_without_alternatives(merged_df_train)" + ], + "metadata": { + "id": "s0k6H4BIapiS" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Analyzing the Resulting Outputs" + ], + "metadata": { + "id": "sp5qoCrUjon5" + } + }, + { + "cell_type": "code", + "source": [ + "best_guess_df_train = pd.read_csv(\"/content/drive/MyDrive/hallucination-detector/gsm8k_train_gemma_greedy_labels.csv\")\n", + "best_guess_df_valid = pd.read_csv(\"/content/drive/MyDrive/hallucination-detector/gsm8k_valid_gemma_greedy_labels.csv\")\n", + "best_guess_df_test = pd.read_csv(\"/content/drive/MyDrive/hallucination-detector/gsm8k_test_gemma_greedy_labels.csv\")\n", + "\n", + "alternatives_df_train = pd.read_csv(\"/content/drive/MyDrive/hallucination-detector/gsm8k_train_gemma_alternatives.csv\")\n", + "alternatives_df_valid = pd.read_csv(\"/content/drive/MyDrive/hallucination-detector/gsm8k_valid_gemma_alternatives.csv\")" + ], + "metadata": { + "id": "edyE18EXkZ8T" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "common_columns = ['question', 'answer']\n", + "\n", + "# Identify unique columns in each dataframe\n", + "best_guess_unique = [col for col in best_guess_df_train.columns if col not in common_columns]\n", + "alternatives_unique = [col for col in alternatives_df_train.columns if col not in common_columns]\n", + "\n", + "# Merge the dataframes\n", + "merged_df_train = best_guess_df_train[common_columns + best_guess_unique].merge(\n", + " alternatives_df_train[alternatives_unique],\n", + " left_index=True,\n", + " right_index=True\n", + ")" + ], + "metadata": { + "id": "F0gRToG3nx9X" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "common_columns = ['question', 'answer']\n", + "\n", + "# Identify unique columns in each dataframe\n", + "best_guess_unique = [col for col in best_guess_df_valid.columns if col not in common_columns]\n", + "alternatives_unique = [col for col in alternatives_df_valid.columns if col not in common_columns]\n", + "\n", + "# Merge the dataframes\n", + "merged_df_valid = best_guess_df_valid[common_columns + best_guess_unique].merge(\n", + " alternatives_df_valid[alternatives_unique],\n", + " left_index=True,\n", + " right_index=True\n", + ")" + ], + "metadata": { + "id": "wivrd1TZpfk5" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "merged_df_test = best_guess_df_test" + ], + "metadata": { + "id": "wJBF1L72pLtl" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### Extracting the numbers" + ], + "metadata": { + "id": "vN-YGnP2t8RW" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\n", + "import os\n", + "import joblib" + ], + "metadata": { + "id": "qE_MyKM--sGZ" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Apply the function to your dataframe\n", + "df_train = process_dataframe(merged_df_train)\n", + "df_valid = process_dataframe(merged_df_valid)\n", + "df_test = process_dataframe_without_alternatives(merged_df_test)" + ], + "metadata": { + "id": "hZMzwbztzP3B" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Combine train and validation sets\n", + "combined_df = pd.concat([df_train, df_valid])\n", + "\n", + "# Convert 'entropy' to numeric, replacing any non-convertible values with NaN\n", + "combined_df['entropy'] = pd.to_numeric(combined_df['entropy'], errors='coerce')\n", + "\n", + "# Convert 'embedding' from string to list\n", + "combined_df['embedding'] = combined_df['embedding'].apply(ast.literal_eval)\n", + "\n", + "# Remove rows with NaN values in the 'entropy' column\n", + "combined_df = combined_df.dropna(subset=['entropy'])\n", + "\n", + "# Create 'is_correct' column for combined_df and df_test\n", + "combined_df['is_correct'] = (combined_df['answer_y'] == combined_df['generated_answer_y']).astype(int)\n", + "df_test['is_correct'] = (df_test['answer_y'] == df_test['generated_answer_y']).astype(int)\n", + "\n", + "print(combined_df.columns)\n", + "print(df_test.columns)\n", + "print(combined_df['is_correct'].value_counts(normalize=True))\n", + "print(f\"Number of samples in combined set: {len(combined_df)}\")\n", + "print(f\"Number of samples in test set: {len(df_test)}\")" + ], + "metadata": { + "id": "XzsNg_ED2Yn5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Separate the data for correct and incorrect labels\n", + "correct_entropy = combined_df[combined_df['is_correct'] == 1]['entropy']\n", + "incorrect_entropy = combined_df[combined_df['is_correct'] == 0]['entropy']\n", + "\n", + "# Define the number of bins and range\n", + "bins = 50\n", + "range_min = min(combined_df['entropy'].min(), 0) # In case there are negative values\n", + "range_max = combined_df['entropy'].max()\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot histogram for incorrect labels (red)\n", + "plt.hist(incorrect_entropy, bins=bins, range=(range_min, range_max), color='red', alpha=0.5, label='Incorrect')\n", + "\n", + "# Plot histogram for correct labels (green)\n", + "plt.hist(correct_entropy, bins=bins, range=(range_min, range_max), color='green', alpha=0.5, label='Correct')\n", + "\n", + "# Customize the plot\n", + "plt.title('Distribution of Entropy for Correct and Incorrect Labels', fontsize=16)\n", + "plt.xlabel('Entropy', fontsize=14)\n", + "plt.ylabel('Frequency', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add text with data statistics\n", + "plt.text(0.05, 0.95, f\"Correct samples: {len(correct_entropy)}\\nIncorrect samples: {len(incorrect_entropy)}\",\n", + " transform=plt.gca().transAxes, verticalalignment='top', fontsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print some statistics\n", + "print(f\"Correct labels - Mean: {correct_entropy.mean():.4f}, Std: {correct_entropy.std():.4f}\")\n", + "print(f\"Incorrect labels - Mean: {incorrect_entropy.mean():.4f}, Std: {incorrect_entropy.std():.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 641 + }, + "id": "BbuSZDLV2Ylr", + "outputId": "23fd0c2e-2ca4-4bbe-928d-9f2bf1fb342b" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correct labels - Mean: 1.2674, Std: 0.8477\n", + "Incorrect labels - Mean: 1.9558, Std: 0.8009\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Creating the ROC graph, when semantic entropy is used as the predictor of hallucination" + ], + "metadata": { + "id": "wQrvJULQVobS" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from sklearn.metrics import roc_curve, auc, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Assuming combined_df is your DataFrame with 'entropy' and 'is_correct' columns\n", + "\n", + "# Prepare the data\n", + "y_true = combined_df['is_correct']\n", + "y_scores = -combined_df['entropy'] # Negative because lower entropy means higher confidence\n", + "\n", + "# Calculate ROC curve and AUC\n", + "fpr, tpr, thresholds = roc_curve(y_true, y_scores)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "# Find the best threshold using Youden's J statistic\n", + "j_scores = tpr - fpr\n", + "best_idx = np.argmax(j_scores)\n", + "best_threshold = thresholds[best_idx]\n", + "\n", + "# Create predictions using the best threshold\n", + "y_pred = (y_scores >= best_threshold).astype(int)\n", + "\n", + "# Calculate confusion matrix\n", + "cm = confusion_matrix(y_true, y_pred)\n", + "\n", + "# Create the ROC curve plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate', fontsize=14)\n", + "plt.ylabel('True Positive Rate', fontsize=14)\n", + "plt.title('ROC Curve (semantic-entropy-as-predictor)', fontsize=16)\n", + "plt.legend(loc=\"lower right\", fontsize=12)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add text with data statistics\n", + "plt.text(0.05, 0.95, f\"Total samples: {len(y_true)}\\nCorrect samples: {sum(y_true)}\\nIncorrect samples: {len(y_true) - sum(y_true)}\",\n", + " transform=plt.gca().transAxes, verticalalignment='top', fontsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print AUC score and best threshold\n", + "print(f\"AUC-ROC Score: {roc_auc:.4f}\")\n", + "print(f\"Best Threshold: {-best_threshold:.4f}\") # Note: we negate it back to get the original entropy threshold\n", + "\n", + "# Plot confusion matrix\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "plt.title('Confusion Matrix for Best Threshold', fontsize=16)\n", + "plt.xlabel('Predicted Label', fontsize=14)\n", + "plt.ylabel('True Label', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print classification report\n", + "from sklearn.metrics import classification_report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_true, y_pred, target_names=['Incorrect', 'Correct']))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "Lv-DLl53Vnxz", + "outputId": "66166673-d8eb-42d2-9917-1a9c78651593" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "AUC-ROC Score: 0.7230\n", + "Best Threshold: 1.6900\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.95 0.65 0.77 6252\n", + " Correct 0.19 0.68 0.30 748\n", + "\n", + " accuracy 0.66 7000\n", + " macro avg 0.57 0.67 0.54 7000\n", + "weighted avg 0.86 0.66 0.72 7000\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Plain logistic regression with is_correct as the label\n", + "\n" + ], + "metadata": { + "id": "WHJ7qw7Q5sC_" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].values)\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_correct, y_test_correct = train_test_split(X, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Create a logistic regression model\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga')\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_correct)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best threshold using only training data\n", + "best_threshold = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for correctness (determined from training data): {best_threshold:.4f}\")\n", + "\n", + "# Create predictions using the best threshold for both train and test\n", + "y_train_pred_optimized = (y_train_pred_proba >= best_threshold).astype(int)\n", + "y_test_pred_optimized = (y_test_pred_proba >= best_threshold).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets\n", + "fpr_train, tpr_train, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test, tpr_test, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curve\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized threshold\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold:\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold:\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized threshold\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train) - Threshold: {best_threshold:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax1.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test) - Threshold: {best_threshold:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax2.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "m0liKa0HMGaM", + "outputId": "296d26bb-52ff-4cd5-82c9-9c7a6b619617" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5600, 2048)\n", + "Shape of test data: (1400, 2048)\n", + "Proportion of correct answers in training set: 0.10946428571428571\n", + "Proportion of correct answers in test set: 0.09642857142857143\n", + "Best parameters: {'C': 0.01, 'penalty': 'l1'}\n", + "Number of features selected: 23\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 528, 200, 461, 1295, 1163, 778, 547, 354, 135, 139]), array([0.0463903 , 0.06084856, 0.07530681, 0.08976507, 0.10422332,\n", + " 0.11868157, 0.13313983, 0.14759808, 0.16205634, 0.17651459,\n", + " 0.19097284]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([152, 28, 176, 302, 246, 211, 129, 74, 46, 36]), array([0.04789528, 0.06220303, 0.07651079, 0.09081854, 0.10512629,\n", + " 0.11943405, 0.1337418 , 0.14804955, 0.16235731, 0.17666506,\n", + " 0.19097281]))\n", + "\n", + "y_train_correct distribution: [4987 613]\n", + "y_test_correct distribution: [1265 135]\n", + "\n", + "Best threshold for correctness (determined from training data): 0.1111\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.92 0.56 0.70 4987\n", + " Correct 0.14 0.60 0.23 613\n", + "\n", + " accuracy 0.57 5600\n", + " macro avg 0.53 0.58 0.46 5600\n", + "weighted avg 0.83 0.57 0.65 5600\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.93 0.56 0.70 1265\n", + " Correct 0.12 0.59 0.21 135\n", + "\n", + " accuracy 0.56 1400\n", + " macro avg 0.53 0.57 0.45 1400\n", + "weighted avg 0.85 0.56 0.65 1400\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Class-weighted Logistic Regression with Is_Correct as the Label" + ], + "metadata": { + "id": "HNnIxmRc6iiI" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].values)\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_correct, y_test_correct = train_test_split(X, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# Compute class weights\n", + "class_weights = compute_class_weight('balanced', classes=np.unique(y_train_correct), y=y_train_correct)\n", + "class_weight_dict = dict(zip(np.unique(y_train_correct), class_weights))\n", + "\n", + "print(\"Class weights:\", class_weight_dict)\n", + "\n", + "# Create a logistic regression model with class weights\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga', class_weight=class_weight_dict)\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_correct)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best threshold using only training data\n", + "best_threshold = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for correctness (determined from training data): {best_threshold:.4f}\")\n", + "\n", + "# Create predictions using the best threshold for both train and test\n", + "y_train_pred_optimized = (y_train_pred_proba >= best_threshold).astype(int)\n", + "y_test_pred_optimized = (y_test_pred_proba >= best_threshold).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets\n", + "fpr_train, tpr_train, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test, tpr_test, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curve\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Train ROC curve (AUC = {roc_auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC curve (AUC = {roc_auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized threshold\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold:\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold:\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized threshold\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train) - Threshold: {best_threshold:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax1.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test) - Threshold: {best_threshold:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax2.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "eQpNCKyN6i4v", + "outputId": "554d48ce-cca8-45bb-b889-57d04edde571" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5600, 2048)\n", + "Shape of test data: (1400, 2048)\n", + "Proportion of correct answers in training set: 0.10946428571428571\n", + "Proportion of correct answers in test set: 0.09642857142857143\n", + "Class weights: {0: 0.5614597954682173, 1: 4.567699836867863}\n", + "Best parameters: {'C': 0.01, 'penalty': 'l1'}\n", + "Number of features selected: 104\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 43, 265, 516, 694, 848, 1067, 1114, 721, 275, 57]), array([0.14697758, 0.20868312, 0.27038867, 0.33209421, 0.39379975,\n", + " 0.4555053 , 0.51721084, 0.57891638, 0.64062193, 0.70232747,\n", + " 0.76403301]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([ 26, 90, 147, 176, 202, 263, 259, 157, 60, 20]), array([0.17562352, 0.2334203 , 0.29121708, 0.34901386, 0.40681065,\n", + " 0.46460743, 0.52240421, 0.58020099, 0.63799777, 0.69579455,\n", + " 0.75359133]))\n", + "\n", + "y_train_correct distribution: [4987 613]\n", + "y_test_correct distribution: [1265 135]\n", + "\n", + "Best threshold for correctness (determined from training data): 0.5354\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.93 0.72 0.81 4987\n", + " Correct 0.19 0.55 0.29 613\n", + "\n", + " accuracy 0.70 5600\n", + " macro avg 0.56 0.63 0.55 5600\n", + "weighted avg 0.85 0.70 0.75 5600\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold:\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.92 0.71 0.80 1265\n", + " Correct 0.13 0.43 0.20 135\n", + "\n", + " accuracy 0.68 1400\n", + " macro avg 0.53 0.57 0.50 1400\n", + "weighted avg 0.84 0.68 0.74 1400\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Now, let's train using entropy score as the label. and then use it as a proxy for the hallucination" + ], + "metadata": { + "id": "cRm6kggvoyGf" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, make_scorer, classification_report, confusion_matrix, f1_score\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# Create new column based on entropy (inverted)\n", + "combined_df['entropy_label'] = (combined_df['entropy'] < 1.69).astype(int)\n", + "\n", + "# Prepare data for classification\n", + "X = np.stack(combined_df['embedding'].values)\n", + "y_entropy = combined_df['entropy_label'].values\n", + "y_correct = combined_df['is_correct'].values\n", + "X_train, X_test, y_train_entropy, y_test_entropy, y_train_correct, y_test_correct = train_test_split(X, y_entropy, y_correct, test_size=0.2, random_state=42)\n", + "\n", + "# Scale the features\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "print(\"Shape of training data:\", X_train.shape)\n", + "print(\"Shape of test data:\", X_test.shape)\n", + "print(\"Proportion of low entropy in training set:\", y_train_entropy.mean())\n", + "print(\"Proportion of low entropy in test set:\", y_test_entropy.mean())\n", + "print(\"Proportion of correct answers in training set:\", y_train_correct.mean())\n", + "print(\"Proportion of correct answers in test set:\", y_test_correct.mean())\n", + "\n", + "# PCA for dimensionality reduction\n", + "pca = PCA(n_components=500)\n", + "X_train_selected = pca.fit_transform(X_train_scaled)\n", + "X_test_selected = pca.transform(X_test_scaled)\n", + "\n", + "# Define the parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'C': [0.01, 1, 100],\n", + " 'penalty': ['l1', 'l2'],\n", + "}\n", + "\n", + "# Create a logistic regression model\n", + "model = LogisticRegression(max_iter=1000, random_state=42, solver='saga')\n", + "\n", + "# Perform grid search with cross-validation\n", + "grid_search = GridSearchCV(\n", + " model, param_grid, cv=3, scoring='roc_auc', n_jobs=-1,\n", + " return_train_score=True, error_score='raise'\n", + ")\n", + "\n", + "grid_search.fit(X_train_selected, y_train_entropy)\n", + "\n", + "# Get the best model\n", + "best_model = grid_search.best_estimator_\n", + "print(f\"Best parameters: {grid_search.best_params_}\")\n", + "\n", + "# Get the number of non-zero coefficients (selected features)\n", + "n_selected_features = np.sum(best_model.coef_ != 0)\n", + "print(f\"Number of features selected: {n_selected_features}\")\n", + "\n", + "# Make predictions on train and test sets\n", + "y_train_pred_proba = best_model.predict_proba(X_train_selected)[:, 1]\n", + "y_test_pred_proba = best_model.predict_proba(X_test_selected)[:, 1]\n", + "\n", + "# Diagnostic prints\n", + "print(\"\\ny_train_pred_proba distribution:\")\n", + "print(np.histogram(y_train_pred_proba, bins=10))\n", + "print(\"\\ny_test_pred_proba distribution:\")\n", + "print(np.histogram(y_test_pred_proba, bins=10))\n", + "\n", + "print(\"\\ny_train_entropy distribution:\", np.bincount(y_train_entropy))\n", + "print(\"y_test_entropy distribution:\", np.bincount(y_test_entropy))\n", + "print(\"y_train_correct distribution:\", np.bincount(y_train_correct))\n", + "print(\"y_test_correct distribution:\", np.bincount(y_test_correct))\n", + "\n", + "# Function to find the best threshold\n", + "def find_best_threshold(y_true, y_pred_proba):\n", + " thresholds = np.linspace(0, 1, 100)\n", + " f1_scores = [f1_score(y_true, (y_pred_proba >= threshold).astype(int)) for threshold in thresholds]\n", + " best_threshold = thresholds[np.argmax(f1_scores)]\n", + " return best_threshold\n", + "\n", + "# Find best thresholds using only training data\n", + "best_threshold_entropy = find_best_threshold(y_train_entropy, y_train_pred_proba)\n", + "best_threshold_correct = find_best_threshold(y_train_correct, y_train_pred_proba)\n", + "\n", + "print(f\"\\nBest threshold for entropy (determined from training data): {best_threshold_entropy:.4f}\")\n", + "print(f\"Best threshold for correctness (determined from training data): {best_threshold_correct:.4f}\")\n", + "\n", + "# Create predictions using the best thresholds for both train and test\n", + "y_train_pred_optimized_entropy = (y_train_pred_proba >= best_threshold_entropy).astype(int)\n", + "y_test_pred_optimized_entropy = (y_test_pred_proba >= best_threshold_entropy).astype(int)\n", + "y_train_pred_optimized_correct = (y_train_pred_proba >= best_threshold_correct).astype(int)\n", + "y_test_pred_optimized_correct = (y_test_pred_proba >= best_threshold_correct).astype(int)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets (entropy labels)\n", + "fpr_train_entropy, tpr_train_entropy, _ = roc_curve(y_train_entropy, y_train_pred_proba)\n", + "roc_auc_train_entropy = roc_auc_score(y_train_entropy, y_train_pred_proba)\n", + "fpr_test_entropy, tpr_test_entropy, _ = roc_curve(y_test_entropy, y_test_pred_proba)\n", + "roc_auc_test_entropy = roc_auc_score(y_test_entropy, y_test_pred_proba)\n", + "\n", + "# Calculate ROC curve and AUC for train and test sets (is_correct labels)\n", + "fpr_train_correct, tpr_train_correct, _ = roc_curve(y_train_correct, y_train_pred_proba)\n", + "roc_auc_train_correct = roc_auc_score(y_train_correct, y_train_pred_proba)\n", + "fpr_test_correct, tpr_test_correct, _ = roc_curve(y_test_correct, y_test_pred_proba)\n", + "roc_auc_test_correct = roc_auc_score(y_test_correct, y_test_pred_proba)\n", + "\n", + "# Plot ROC curves\n", + "plt.figure(figsize=(12, 10))\n", + "plt.plot(fpr_train_entropy, tpr_train_entropy, color='blue', lw=2, label=f'Train ROC curve (Entropy, AUC = {roc_auc_train_entropy:.2f})')\n", + "plt.plot(fpr_test_entropy, tpr_test_entropy, color='red', lw=2, label=f'Test ROC curve (Entropy, AUC = {roc_auc_test_entropy:.2f})')\n", + "plt.plot(fpr_train_correct, tpr_train_correct, color='green', lw=2, label=f'Train ROC curve (Is Correct, AUC = {roc_auc_train_correct:.2f})')\n", + "plt.plot(fpr_test_correct, tpr_test_correct, color='orange', lw=2, label=f'Test ROC curve (Is Correct, AUC = {roc_auc_test_correct:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "# Print classification reports with optimized thresholds\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold (Entropy):\")\n", + "print(classification_report(y_train_entropy, y_train_pred_optimized_entropy, target_names=['High Entropy', 'Low Entropy']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold (Entropy):\")\n", + "print(classification_report(y_test_entropy, y_test_pred_optimized_entropy, target_names=['High Entropy', 'Low Entropy']))\n", + "\n", + "print(\"\\nClassification Report (Train) - Optimized Threshold (Is Correct):\")\n", + "print(classification_report(y_train_correct, y_train_pred_optimized_correct, target_names=['Incorrect', 'Correct']))\n", + "print(\"\\nClassification Report (Test) - Optimized Threshold (Is Correct):\")\n", + "print(classification_report(y_test_correct, y_test_pred_optimized_correct, target_names=['Incorrect', 'Correct']))\n", + "\n", + "# Plot confusion matrices with optimized thresholds\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 20))\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_entropy, y_train_pred_optimized_entropy), annot=True, fmt='d', cmap='Blues', ax=ax1)\n", + "ax1.set_title(f'Confusion Matrix (Train - Entropy) - Threshold: {best_threshold_entropy:.4f}')\n", + "ax1.set_ylabel('True Label')\n", + "ax1.set_xlabel('Predicted Label')\n", + "ax1.set_xticklabels(['High Entropy', 'Low Entropy'])\n", + "ax1.set_yticklabels(['High Entropy', 'Low Entropy'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_entropy, y_test_pred_optimized_entropy), annot=True, fmt='d', cmap='Blues', ax=ax2)\n", + "ax2.set_title(f'Confusion Matrix (Test - Entropy) - Threshold: {best_threshold_entropy:.4f}')\n", + "ax2.set_ylabel('True Label')\n", + "ax2.set_xlabel('Predicted Label')\n", + "ax2.set_xticklabels(['High Entropy', 'Low Entropy'])\n", + "ax2.set_yticklabels(['High Entropy', 'Low Entropy'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_train_correct, y_train_pred_optimized_correct), annot=True, fmt='d', cmap='Blues', ax=ax3)\n", + "ax3.set_title(f'Confusion Matrix (Train - Is Correct) - Threshold: {best_threshold_correct:.4f}')\n", + "ax3.set_ylabel('True Label')\n", + "ax3.set_xlabel('Predicted Label')\n", + "ax3.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax3.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "sns.heatmap(confusion_matrix(y_test_correct, y_test_pred_optimized_correct), annot=True, fmt='d', cmap='Blues', ax=ax4)\n", + "ax4.set_title(f'Confusion Matrix (Test - Is Correct) - Threshold: {best_threshold_correct:.4f}')\n", + "ax4.set_ylabel('True Label')\n", + "ax4.set_xlabel('Predicted Label')\n", + "ax4.set_xticklabels(['Incorrect', 'Correct'])\n", + "ax4.set_yticklabels(['Incorrect', 'Correct'])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "mqsKndX0owzg", + "outputId": "42e79c1a-3e6e-4fcd-9fbf-aaa75ae97d46" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of training data: (5600, 2048)\n", + "Shape of test data: (1400, 2048)\n", + "Proportion of low entropy in training set: 0.3575\n", + "Proportion of low entropy in test set: 0.33214285714285713\n", + "Proportion of correct answers in training set: 0.10946428571428571\n", + "Proportion of correct answers in test set: 0.09642857142857143\n", + "Best parameters: {'C': 0.01, 'penalty': 'l1'}\n", + "Number of features selected: 54\n", + "\n", + "y_train_pred_proba distribution:\n", + "(array([ 68, 384, 665, 1198, 1286, 768, 490, 500, 184, 57]), array([0.15325659, 0.19813273, 0.24300886, 0.287885 , 0.33276114,\n", + " 0.37763727, 0.42251341, 0.46738955, 0.51226568, 0.55714182,\n", + " 0.60201796]))\n", + "\n", + "y_test_pred_proba distribution:\n", + "(array([ 55, 130, 186, 326, 264, 143, 135, 105, 46, 10]), array([0.17666617, 0.21918522, 0.26170426, 0.30422331, 0.34674235,\n", + " 0.38926139, 0.43178044, 0.47429948, 0.51681853, 0.55933757,\n", + " 0.60185662]))\n", + "\n", + "y_train_entropy distribution: [3598 2002]\n", + "y_test_entropy distribution: [935 465]\n", + "y_train_correct distribution: [4987 613]\n", + "y_test_correct distribution: [1265 135]\n", + "\n", + "Best threshold for entropy (determined from training data): 0.3131\n", + "Best threshold for correctness (determined from training data): 0.3636\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification Report (Train) - Optimized Threshold (Entropy):\n", + " precision recall f1-score support\n", + "\n", + "High Entropy 0.77 0.36 0.49 3598\n", + " Low Entropy 0.41 0.81 0.55 2002\n", + "\n", + " accuracy 0.52 5600\n", + " macro avg 0.59 0.58 0.52 5600\n", + "weighted avg 0.64 0.52 0.51 5600\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold (Entropy):\n", + " precision recall f1-score support\n", + "\n", + "High Entropy 0.79 0.35 0.49 935\n", + " Low Entropy 0.39 0.82 0.52 465\n", + "\n", + " accuracy 0.51 1400\n", + " macro avg 0.59 0.58 0.51 1400\n", + "weighted avg 0.66 0.51 0.50 1400\n", + "\n", + "\n", + "Classification Report (Train) - Optimized Threshold (Is Correct):\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.91 0.61 0.73 4987\n", + " Correct 0.14 0.54 0.23 613\n", + "\n", + " accuracy 0.60 5600\n", + " macro avg 0.53 0.57 0.48 5600\n", + "weighted avg 0.83 0.60 0.67 5600\n", + "\n", + "\n", + "Classification Report (Test) - Optimized Threshold (Is Correct):\n", + " precision recall f1-score support\n", + "\n", + " Incorrect 0.92 0.60 0.73 1265\n", + " Correct 0.12 0.52 0.20 135\n", + "\n", + " accuracy 0.59 1400\n", + " macro avg 0.52 0.56 0.46 1400\n", + "weighted avg 0.84 0.59 0.68 1400\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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