300 lines
13 KiB
Plaintext
300 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Goal: end to end inference and evaluation\n",
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"\n",
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"given a csv, make predictions and evaluate predictions, then return results in a csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"12938it [00:07, 1674.63it/s] \n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import os\n",
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"import json\n",
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"from transformers.pipelines.pt_utils import KeyDataset\n",
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"from transformers import pipeline\n",
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"from tqdm import tqdm\n",
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"from transformers import AutoTokenizer\n",
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"from datasets import Dataset\n",
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"\n",
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"with open(\"mode.json\", \"r\") as json_file:\n",
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" mode_dict = json.load(json_file)\n",
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"\n",
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"mode = mode_dict.get(\"mode\", \"none\")\n",
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"model_name = mode_dict.get(\"model\", \"none\")\n",
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"train_epochs = mode_dict.get(\"train_epochs\", \"none\")\n",
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"fold_group = mode_dict.get(\"fold_group\", \"none\")\n",
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"\n",
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"base_dir = f\"train_{fold_group}_{model_name}_{mode}_{train_epochs}\"\n",
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"checkpoints = [d for d in os.listdir(base_dir) if d.startswith(\"checkpoint-\")]\n",
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"\n",
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"model_checkpoint = os.path.join(base_dir, checkpoints[0]) if checkpoints else None\n",
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"\n",
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"data_path = f\"../../data_preprocess/dataset/{fold_group}/test.csv\"\n",
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"\n",
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"try:\n",
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" df = pd.read_csv(data_path)\n",
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"except UnicodeDecodeError:\n",
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" df = pd.read_csv(data_path, encoding='ISO-8859-1')\n",
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"\n",
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"df = df.dropna(subset=['tag_description']).reset_index(drop=True)\n",
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"df_org = df.copy()\n",
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"df[['thing', 'property', 'tag_description', 'min', 'max', 'MDM', 'pattern']] = df[['thing', 'property', 'tag_description', 'min', 'max', 'MDM', 'pattern']].astype(\"string\")\n",
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"\n",
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"def process_df(df, mode='only_td'):\n",
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" output_list = []\n",
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" for _, row in df.iterrows():\n",
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" try:\n",
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" if mode == 'only_td':\n",
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" input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END>\"\n",
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" elif mode == 'tn_td':\n",
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" input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END>\"\n",
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" elif mode == 'tn_td_min_max':\n",
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" input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"\n",
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" elif mode == 'td_min_max':\n",
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" input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"\n",
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" elif mode == 'td_unit':\n",
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" input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END><UNIT_START>{str(row['unit'])}<UNIT_END>\"\n",
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" elif mode == 'tn_td_unit':\n",
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" input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END><UNIT_START>{str(row['unit'])}<UNIT_END>\"\n",
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" elif mode == 'td_min_max_unit':\n",
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" input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END><UNIT_START>{str(row['unit'])}<UNIT_END>\"\n",
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" else:\n",
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" raise ValueError(\"Invalid mode specified\")\n",
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"\n",
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" output_list.append({\n",
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" 'translation': {\n",
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" 'ships_idx': row['ships_idx'],\n",
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" 'input': input_str,\n",
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" 'thing_property': f\"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>\",\n",
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" 'answer_thing': row['thing'],\n",
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" 'answer_property': row['property'],\n",
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" 'MDM': row['MDM'],\n",
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" }\n",
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" })\n",
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" except Exception as e:\n",
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" print(f\"Error processing row: {e}\")\n",
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" return output_list\n",
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"\n",
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"processed_data = process_df(df, mode=mode)\n",
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"test_dataset = Dataset.from_list(processed_data)\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name, return_tensors=\"pt\")\n",
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"additional_special_tokens = [\"<THING_START>\", \"<THING_END>\", \"<PROPERTY_START>\", \"<PROPERTY_END>\", \"<TN_START>\", \"<TN_END>\", \"<TD_START>\", \"<TD_END>\", \"<MIN_START>\", \"<MIN_END>\", \"<MAX_START>\", \"<MAX_END>\", \"<UNIT_START>\", \"<UNIT_END>\"]\n",
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"tokenizer.add_special_tokens({\"additional_special_tokens\": additional_special_tokens})\n",
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"\n",
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"pipe = pipeline(\"translation_XX_to_YY\", model=model_checkpoint, tokenizer=tokenizer, return_tensors=True, max_length=128, device=0)\n",
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"\n",
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"thing_start_id = tokenizer.convert_tokens_to_ids(\"<THING_START>\")\n",
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"thing_end_id = tokenizer.convert_tokens_to_ids(\"<THING_END>\")\n",
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"property_start_id = tokenizer.convert_tokens_to_ids(\"<PROPERTY_START>\")\n",
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"property_end_id = tokenizer.convert_tokens_to_ids(\"<PROPERTY_END>\")\n",
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"\n",
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"def extract_seq(tokens, start_value, end_value):\n",
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" if start_value in tokens and end_value in tokens:\n",
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" return tokens[tokens.index(start_value)+1:tokens.index(end_value)]\n",
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" return None\n",
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"\n",
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"def extract_seq_from_output(output):\n",
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" tokens = output[0][\"translation_token_ids\"].tolist()\n",
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" 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",
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" 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",
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" return p_thing, p_property\n",
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"\n",
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"p_thing_list = []\n",
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"p_property_list = []\n",
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"\n",
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"for out in tqdm(pipe(KeyDataset(test_dataset[\"translation\"], \"input\"), batch_size=256)):\n",
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" p_thing, p_property = extract_seq_from_output(out)\n",
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" p_thing_list.append(p_thing)\n",
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" p_property_list.append(p_property)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Thing prediction accuracy: 0.9793861658268438\n",
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"Correct thing predictions: 2138, Incorrect thing predictions: 10800\n",
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"Property prediction accuracy: 0.9651855245075585\n",
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"Correct property predictions: 2107, Incorrect property predictions: 10831\n",
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"Total accuracy: 0.9496106275767293\n",
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"Correct total predictions: 2073, Incorrect total predictions: 110\n"
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]
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}
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],
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"source": [
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"answer_thing = [item['answer_thing'] for item in test_dataset[\"translation\"]]\n",
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"answer_property = [item['answer_property'] for item in test_dataset[\"translation\"]]\n",
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"mdm_list = [item['MDM'] for item in test_dataset[\"translation\"]]\n",
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"\n",
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"mdm_count = sum([1 for mdm in mdm_list if mdm == \"True\"])\n",
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"\n",
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"def correctness_test(input, reference, mdm_list):\n",
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" assert len(input) == len(reference)\n",
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" return [input[i] == reference[i] if mdm_list[i] == \"True\" else False for i in range(len(input))]\n",
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"\n",
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"thing_correctness = correctness_test(p_thing_list, answer_thing, mdm_list)\n",
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"property_correctness = correctness_test(p_property_list, answer_property, mdm_list)\n",
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"\n",
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"correctness_mdm = [thing_correctness[i] & property_correctness[i] for i in range(len(mdm_list))]\n",
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"\n",
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"thing_accuracy = sum(thing_correctness) / mdm_count\n",
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"property_accuracy = sum(property_correctness) / mdm_count\n",
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"total_accuracy = sum(correctness_mdm) / mdm_count\n",
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"\n",
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"thing_true_count = thing_correctness.count(True)\n",
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"thing_false_count = thing_correctness.count(False)\n",
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"\n",
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"property_true_count = property_correctness.count(True)\n",
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"property_false_count = property_correctness.count(False)\n",
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"\n",
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"total_true_count = correctness_mdm.count(True)\n",
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"total_false_count = mdm_count - total_true_count\n",
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"\n",
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"print(\"Thing prediction accuracy:\", thing_accuracy)\n",
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"print(f\"Correct thing predictions: {thing_true_count}, Incorrect thing predictions: {thing_false_count}\")\n",
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"print(\"Property prediction accuracy:\", property_accuracy)\n",
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"print(f\"Correct property predictions: {property_true_count}, Incorrect property predictions: {property_false_count}\")\n",
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"print(\"Total accuracy:\", total_accuracy)\n",
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"print(f\"Correct total predictions: {total_true_count}, Incorrect total predictions: {total_false_count}\")\n",
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"\n",
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"df_pred = pd.DataFrame({\n",
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" 'p_thing': p_thing_list,\n",
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" 'p_property': p_property_list,\n",
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" 'p_thing_correct': thing_correctness,\n",
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" 'p_property_correct': property_correctness\n",
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"})\n",
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"\n",
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"with open(\"mode.json\", \"r\") as json_file:\n",
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" mode_dict = json.load(json_file)\n",
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"\n",
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"mode_dict[\"model\"] = model_name\n",
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"mode_dict[\"train_epochs\"] = train_epochs\n",
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"\n",
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"with open(\"mode.json\", \"w\") as json_file:\n",
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" json.dump(mode_dict, json_file)\n",
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"\n",
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"if os.path.exists(\"results.json\") and os.path.getsize(\"results.json\") > 0:\n",
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" with open(\"results.json\", \"r\") as json_file:\n",
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" try:\n",
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" results_dict = json.load(json_file)\n",
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" except json.JSONDecodeError:\n",
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" results_dict = {}\n",
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"else:\n",
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" results_dict = {}\n",
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"\n",
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"model_key = model_checkpoint\n",
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"\n",
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"results_dict[model_key] = {\n",
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" \"thing_accuracy\": thing_accuracy,\n",
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" \"thing_true\": thing_true_count,\n",
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" \"thing_false\": thing_false_count,\n",
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" \"property_accuracy\": property_accuracy,\n",
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" \"property_true\": property_true_count,\n",
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" \"property_false\": property_false_count,\n",
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" \"total_accuracy\": total_accuracy,\n",
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" \"total_true\": total_true_count,\n",
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" \"total_false\": total_false_count\n",
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"}\n",
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"\n",
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"with open(\"results.json\", \"w\") as json_file:\n",
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" json.dump(results_dict, json_file, indent=4)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Updated data saved to ../0.result/5/test_p.csv\n",
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"Updated data saved to 0.dresult/td_unit/google/t5-efficient-tiny/5/test_p.csv\n"
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]
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}
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],
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"source": [
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"import os\n",
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"\n",
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"df_pred = pd.DataFrame({\n",
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" 'p_thing': p_thing_list,\n",
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" 'p_property': p_property_list,\n",
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" 'p_thing_correct': thing_correctness,\n",
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" 'p_property_correct': property_correctness,\n",
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"})\n",
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"\n",
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"df_org['p_thing'] = df_pred['p_thing']\n",
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"df_org['p_property'] = df_pred['p_property']\n",
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"df_org['p_thing_correct'] = df_pred['p_thing_correct']\n",
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"df_org['p_property_correct'] = df_pred['p_property_correct']\n",
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"df_org['p_correct'] = df_pred['p_thing_correct'] & df_org['p_property_correct']\n",
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"\n",
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"df_master = pd.read_csv('../../data_import/data_model_master_export.csv')\n",
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"\n",
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"df_org['pattern'] = df_org['thing'].str.replace(r'\\d', '#', regex=True) + \" \" + df_org['property'].str.replace(r'\\d', '#', regex=True)\n",
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"df_org['p_pattern'] = df_org['p_thing'].str.replace(r'\\d', '#', regex=True) + \" \" + df_org['p_property'].str.replace(r'\\d', '#', regex=True)\n",
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"df_master['master_pattern'] = df_master['thing'] + \" \" + df_master['property']\n",
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"\n",
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"master_patterns = set(df_master['master_pattern'])\n",
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"df_org['p_MDM'] = df_org['p_pattern'].apply(lambda x: x in master_patterns)\n",
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"\n",
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"output_path = f\"../0.result/{fold_group}/test_p.csv\"\n",
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"debug_output_path = f\"0.dresult/{mode}/{model_name}/{fold_group}/test_p.csv\"\n",
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"\n",
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"os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
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"df_org.to_csv(output_path, index=False, encoding='utf-8-sig')\n",
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"\n",
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"os.makedirs(os.path.dirname(debug_output_path), exist_ok=True)\n",
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"df_org.to_csv(debug_output_path, index=False, encoding='utf-8-sig')\n",
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"\n",
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"print(f\"Updated data saved to {output_path}\")\n",
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"print(f\"Updated data saved to {debug_output_path}\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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