{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal: end to end inference and evaluation\n", "\n", "given a csv, make predictions and evaluate predictions, then return results in a csv" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The test_dataset contains 12938 items.\n", "Making inference on test set\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "12938it [02:37, 82.28it/s] " ] }, { "name": "stdout", "output_type": "stream", "text": [ "Inference done.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import json\n", "\n", "with open(\"mode.json\", \"r\") as json_file:\n", " mode_dict = json.load(json_file)\n", "\n", "mode = mode_dict.get(\"mode\", \"none\")\n", "model_name = mode_dict.get(\"model\", \"none\")\n", "train_epochs = mode_dict.get(\"train_epochs\", \"none\")\n", "fold_group = mode_dict.get(\"fold_group\", \"none\")\n", "\n", "base_dir = f\"train_{fold_group}_{model_name}_{mode}_{train_epochs}\"\n", "checkpoints = [d for d in os.listdir(base_dir) if d.startswith(\"checkpoint-\")]\n", "\n", "model_checkpoint = os.path.join(base_dir, checkpoints[0]) if checkpoints else None\n", "\n", "data_path = f\"../../data_preprocess/dataset/{fold_group}/test.csv\"\n", "\n", "try:\n", " df = pd.read_csv(data_path)\n", "except UnicodeDecodeError:\n", " df = pd.read_csv(data_path, encoding='ISO-8859-1')\n", "\n", "df = df.dropna(subset=['tag_description']).reset_index(drop=True)\n", "\n", "df_org = df.copy()\n", "df[['thing', 'property', 'tag_description', 'min', 'max', 'MDM', 'pattern']] = df[['thing', 'property', 'tag_description', 'min', 'max', 'MDM', 'pattern']].astype(\"string\")\n", "\n", "from datasets import Dataset\n", "\n", "def process_df(df, mode='only_td'):\n", " output_list = []\n", " for _, row in df.iterrows():\n", " try:\n", " if mode == 'only_td':\n", " input_str = f\"{str(row['tag_description'])}\"\n", " elif mode == 'tn_td':\n", " input_str = f\"{str(row['tag_name'])}{str(row['tag_description'])}\"\n", " elif mode == 'tn_td_min_max':\n", " input_str = f\"{str(row['tag_name'])}{str(row['tag_description'])}{row['min']}{row['max']}\"\n", " elif mode == 'td_min_max':\n", " input_str = f\"{str(row['tag_description'])}{row['min']}{row['max']}\"\n", " elif mode == 'td_unit':\n", " input_str = f\"{str(row['tag_description'])}{str(row['unit'])}\"\n", " elif mode == 'tn_td_unit':\n", " input_str = f\"{str(row['tag_name'])}{str(row['tag_description'])}{str(row['unit'])}\"\n", " elif mode == 'td_min_max_unit':\n", " input_str = f\"{str(row['tag_description'])}{row['min']}{row['max']}{str(row['unit'])}\"\n", " else:\n", " raise ValueError(\"Invalid mode specified\")\n", "\n", " output_list.append({\n", " 'translation': {\n", " 'ships_idx': row['ships_idx'],\n", " 'input': input_str,\n", " 'thing_property': f\"{row['thing']}{row['property']}\",\n", " 'answer_thing': row['thing'],\n", " 'answer_property': row['property'],\n", " 'MDM': row['MDM'],\n", " }\n", " })\n", " except Exception as e:\n", " print(f\"Error processing row: {e}\")\n", " return output_list\n", "\n", "processed_data = process_df(df, mode=mode)\n", "test_dataset = Dataset.from_list(processed_data)\n", "print(f\"The test_dataset contains {len(test_dataset)} items.\")\n", "\n", "from transformers.pipelines.pt_utils import KeyDataset\n", "from transformers import pipeline, BartTokenizer\n", "from tqdm import tqdm\n", "\n", "# Use BartTokenizer for BART inference\n", "tokenizer = BartTokenizer.from_pretrained(model_name, return_tensors=\"pt\")\n", "additional_special_tokens = [\n", " \"\", \"\", \"\", \"\", \n", " \"\", \"\", \"\", \"\", \n", " \"\", \"\", \"\", \"\", \n", " \"\", \"\"\n", "]\n", "tokenizer.add_special_tokens({\"additional_special_tokens\": additional_special_tokens})\n", "\n", "# Use BART model for inference\n", "pipe = pipeline(\"text2text-generation\", model=model_checkpoint, tokenizer=tokenizer, return_tensors=True, max_length=128, device=0)\n", "\n", "# Check what token-ids the special tokens are\n", "thing_start_id = tokenizer.convert_tokens_to_ids(\"\")\n", "thing_end_id = tokenizer.convert_tokens_to_ids(\"\")\n", "property_start_id = tokenizer.convert_tokens_to_ids(\"\")\n", "property_end_id = tokenizer.convert_tokens_to_ids(\"\")\n", "\n", "def extract_seq(tokens, start_value, end_value):\n", " if start_value in tokens and end_value in tokens:\n", " return tokens[tokens.index(start_value)+1:tokens.index(end_value)]\n", " return None\n", "\n", "def extract_seq_from_output(output):\n", " tokens = output[0][\"generated_token_ids\"].tolist()\n", " p_thing = tokenizer.decode(extract_seq(tokens, thing_start_id, thing_end_id)) if thing_start_id in tokens and thing_end_id in tokens else None\n", " p_property = tokenizer.decode(extract_seq(tokens, property_start_id, property_end_id)) if property_start_id in tokens and property_end_id in tokens else None\n", " return p_thing, p_property\n", "\n", "# Inference and storing predictions\n", "p_thing_list = []\n", "p_property_list = []\n", "print(\"Making inference on test set\")\n", "\n", "# Process the test set through the pipeline and generate predictions\n", "for out in tqdm(pipe(KeyDataset(test_dataset[\"translation\"], \"input\"), batch_size=256)):\n", " p_thing, p_property = extract_seq_from_output(out)\n", " p_thing_list.append(p_thing)\n", " p_property_list.append(p_property)\n", "\n", "print(\"Inference done.\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thing prediction accuracy: 0.9793861658268438\n", "Correct thing predictions: 2138, Incorrect thing predictions: 45\n", "Property prediction accuracy: 0.9752633989922126\n", "Correct property predictions: 2129, Incorrect property predictions: 10809\n", "total accuracy: 0.9601465872652314\n", "Correct total predictions: 2096, Incorrect total predictions: 87\n" ] } ], "source": [ "answer_thing = [item['answer_thing'] for item in test_dataset[\"translation\"]]\n", "answer_property = [item['answer_property'] for item in test_dataset[\"translation\"]]\n", "mdm_list = [item['MDM'] for item in test_dataset[\"translation\"]]\n", "\n", "mdm_count = 0\n", "for i in range(len(mdm_list)):\n", " if(mdm_list[i] == \"True\"):mdm_count = mdm_count + 1 \n", "\n", "def correctness_test(input, reference, mdm_list):\n", " assert(len(input) == len(reference))\n", " correctness_list = []\n", " for i in range(len(input)):\n", " if(mdm_list[i] == \"True\"):\n", " correctness_list.append(input[i] == reference[i])\n", " else:correctness_list.append(False)\n", " return correctness_list\n", "\n", "# Compare with answer to evaluate correctness\n", "thing_correctness = correctness_test(p_thing_list, answer_thing, mdm_list)\n", "property_correctness = correctness_test(p_property_list, answer_property, mdm_list)\n", "\n", "correctness_mdm = []\n", "for i in range(len(mdm_list)):\n", " if(thing_correctness[i] & property_correctness[i]):\n", " correctness_mdm.append(True)\n", " else: \n", " correctness_mdm.append(False)\n", " \n", " \n", "# Calculate accuracy\n", "thing_accuracy = sum(thing_correctness) / mdm_count\n", "property_accuracy = sum(property_correctness) / mdm_count\n", "total_accuracy = sum(correctness_mdm) / mdm_count\n", "\n", "# Count True/False values\n", "thing_true_count = thing_correctness.count(True)\n", "thing_false_count = 0\n", "for i in range(len(thing_correctness)):\n", " if mdm_list[i] == \"True\" and thing_correctness[i] == False:\n", " thing_false_count += 1\n", "\n", "property_true_count = property_correctness.count(True)\n", "property_false_count = property_correctness.count(False)\n", "total_true_count = correctness_mdm.count(True)\n", "total_false_count = mdm_count - correctness_mdm.count(True)\n", "\n", "# Print results\n", "print(\"Thing prediction accuracy:\", thing_accuracy)\n", "print(f\"Correct thing predictions: {thing_true_count}, Incorrect thing predictions: {thing_false_count}\")\n", "print(\"Property prediction accuracy:\", property_accuracy)\n", "print(f\"Correct property predictions: {property_true_count}, Incorrect property predictions: {property_false_count}\")\n", "print(\"total accuracy:\", total_accuracy)\n", "print(f\"Correct total predictions: {total_true_count}, Incorrect total predictions: {total_false_count}\")\n", "\n", "# Create a DataFrame with the results\n", "dict = {\n", " 'p_thing': p_thing_list,\n", " 'p_property': p_property_list,\n", " 'p_thing_correct': thing_correctness,\n", " 'p_property_correct': property_correctness\n", "}\n", "\n", "df_pred = pd.DataFrame(dict)\n", "\n", "# Read the mode from the JSON file\n", "with open(\"mode.json\", \"r\") as json_file:\n", " mode_dict = json.load(json_file)\n", "\n", "# Add the model key to the dictionary\n", "mode_dict[\"model\"] = model_name\n", "mode_dict[\"train_epochs\"] = train_epochs\n", "\n", "# Save the updated dictionary back to the JSON file\n", "with open(\"mode.json\", \"w\") as json_file:\n", " json.dump(mode_dict, json_file)\n", "\n", "\n", "# Check if the file exists and is not empty\n", "if os.path.exists(\"results.json\") and os.path.getsize(\"results.json\") > 0:\n", " # Read the existing results.json file\n", " with open(\"results.json\", \"r\") as json_file:\n", " try:\n", " results_dict = json.load(json_file)\n", " except json.JSONDecodeError:\n", " results_dict = {}\n", "else:\n", " results_dict = {}\n", "\n", "# Add the new model_checkpoint key with the accuracy values as an object\n", "\n", "model_key = model_checkpoint \n", "\n", "results_dict[model_key] = {\n", " \"thing_accuracy\": thing_accuracy,\n", " \"thing_true\": thing_true_count,\n", " \"thing_false\": thing_false_count,\n", " \"property_accuracy\": property_accuracy,\n", " \"property_true\": property_true_count,\n", " \"property_false\": property_false_count,\n", " \"total_accuracy\": total_accuracy,\n", " \"total_true\": total_true_count,\n", " \"total_false\": total_false_count \n", "}\n", "\n", "# Save the updated dictionary back to the results.json file\n", "with open(\"results.json\", \"w\") as json_file:\n", " json.dump(results_dict, json_file, indent=4)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 2 }