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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "8b3ee6e2-ca9c-40fa-b4c6-a9596f075f79",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"env: OPENAI_API_KEY=\"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\"\n"
]
}
],
"source": [
"import argparse\n",
"import concurrent\n",
"from dotenv import load_dotenv\n",
"from tqdm import tqdm\n",
"import textgrad as tg\n",
"from textgrad.tasks import load_task\n",
"import numpy as np\n",
"import random\n",
"load_dotenv(override=True)\n",
"import os\n",
"import json\n",
"\n",
"%env OPENAI_API_KEY=\"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\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4ec9a29b-9162-4fe3-b32d-4de4397c6483",
"metadata": {
"execution": {
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{
"name": "stderr",
"output_type": "stream",
"text": [
"0it [00:00, ?it/s]\n"
]
}
],
"source": [
"data_path = \"/root/notebooks/MT_TQ/TQ/DataPrep_Prompting_Experiments/labeled_data/parsed/\"\n",
"json_files = [os.path.join(root, file) for root, _, files in os.walk(data_path) for file in files if file.endswith('.json') and 'PLDL' in file]\n",
"\n",
"training_samples = []\n",
"for json_file in tqdm(json_files):\n",
" with open(json_file, 'r') as file:\n",
" data = json.load(file)\n",
" sampled_items = random.sample(data[\"data\"], 20)\n",
" training_samples.extend(sampled_items)\n",
"\n",
"datapoints = []\n",
"\n",
"for sample in training_samples:\n",
" datapoint = {\"input\":{}}\n",
" datapoint[\"input\"][\"src_text\"] = sample[\"main_src_text\"]\n",
" datapoint[\"input\"][\"tgt_text\"] = sample[\"tgt_text\"]\n",
" datapoint[\"input\"][\"src_prev\"] = sample[\"tt_src_prev\"]\n",
" datapoint[\"input\"][\"src_next\"] = sample[\"tt_src_next\"]\n",
" datapoint[\"input\"][\"tgt_prev\"] = sample[\"tt_tgt_prev\"]\n",
" datapoint[\"input\"][\"tgt_next\"] = sample[\"tt_tgt_next\"]\n",
" datapoint[\"input\"][\"src_lang\"] = sample[\"src_lang\"]\n",
" datapoint[\"input\"][\"tgt_lang\"] = sample[\"tgt_lang\"]\n",
" datapoint[\"evaluation\"] = sample[\"labelers\"][0][\"annotation\"]\n",
" datapoints.append(datapoint)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a894ce72-d451-44fa-aaa5-85bf8e6dc9da",
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"def set_seed(seed):\n",
" np.random.seed(seed)\n",
" random.seed(seed)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4eeaa266-3ca2-4360-b80b-b38aa3bbdb70",
"metadata": {
"execution": {
"iopub.execute_input": "2025-05-09T17:33:55.982807Z",
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"outputs": [],
"source": [
"def eval_sample(item, eval_fn, model):\n",
" \"\"\"\n",
" This function allows us to evaluate if an answer to a question in the prompt is a good answer.\n",
"\n",
" \"\"\"\n",
" x, y = item\n",
" x = tg.Variable(x, requires_grad=False, role_description=\"query to the language model\")\n",
" y = tg.Variable(y, requires_grad=False, role_description=\"correct answer for the query\")\n",
" response = model(x)\n",
" try:\n",
" eval_output_variable = eval_fn(inputs=dict(prediction=response, ground_truth_answer=y))\n",
" return int(eval_output_variable.value)\n",
" except:\n",
" eval_output_variable = eval_fn([x, y, response])\n",
" eval_output_parsed = eval_fn.parse_output(eval_output_variable)\n",
" return int(eval_output_parsed)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c7e57f9d-c0ff-4139-9e61-b93510599353",
"metadata": {
"execution": {
"iopub.execute_input": "2025-05-09T17:34:08.606301Z",
"iopub.status.busy": "2025-05-09T17:34:08.605538Z",
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"shell.execute_reply": "2025-05-09T17:34:08.611911Z",
"shell.execute_reply.started": "2025-05-09T17:34:08.606262Z"
}
},
"outputs": [],
"source": [
"def eval_dataset(test_set, eval_fn, model, max_samples: int=None):\n",
" if max_samples is None:\n",
" max_samples = len(test_set)\n",
" accuracy_list = []\n",
" with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:\n",
" futures = []\n",
" for _, sample in enumerate(test_set):\n",
" \n",
" future = executor.submit(eval_sample, sample, eval_fn, model)\n",
" futures.append(future)\n",
" if len(futures) >= max_samples:\n",
" break\n",
" tqdm_loader = tqdm(concurrent.futures.as_completed(futures), total=len(futures), position=0)\n",
" for future in tqdm_loader:\n",
" acc_item = future.result()\n",
" accuracy_list.append(acc_item)\n",
" tqdm_loader.set_description(f\"Accuracy: {np.mean(accuracy_list)}\")\n",
" return accuracy_list "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "039af9f3-a124-4a50-98a7-e728a913c069",
"metadata": {
"execution": {
"iopub.execute_input": "2025-05-09T17:34:22.703336Z",
"iopub.status.busy": "2025-05-09T17:34:22.702980Z",
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},
"outputs": [],
"source": [
"def run_validation_revert(system_prompt: tg.Variable, results, model, eval_fn, val_set):\n",
" val_performance = np.mean(eval_dataset(val_set, eval_fn, model))\n",
" previous_performance = np.mean(results[\"validation_acc\"][-1])\n",
" print(\"val_performance: \", val_performance)\n",
" print(\"previous_performance: \", previous_performance)\n",
" previous_prompt = results[\"prompt\"][-1]\n",
" \n",
" if val_performance < previous_performance:\n",
" print(f\"rejected prompt: {system_prompt.value}\")\n",
" system_prompt.set_value(previous_prompt)\n",
" val_performance = previous_performance\n",
"\n",
" results[\"validation_acc\"].append(val_performance)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "031ebb6e-f5ff-45b0-a810-d1bd81ef6d2a",
"metadata": {
"execution": {
"iopub.execute_input": "2025-05-09T17:40:38.476352Z",
"iopub.status.busy": "2025-05-09T17:40:38.475979Z",
"iopub.status.idle": "2025-05-09T17:40:38.701947Z",
"shell.execute_reply": "2025-05-09T17:40:38.701394Z",
"shell.execute_reply.started": "2025-05-09T17:40:38.476327Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train/Val/Test Set Lengths: 50 100 100\n"
]
}
],
"source": [
"set_seed(12)\n",
"llm_api_eval = tg.get_engine(engine_name=\"gpt-4o\")\n",
"llm_api_test = tg.get_engine(engine_name=\"gpt-3.5-turbo-0125\")\n",
"tg.set_backward_engine(llm_api_eval, override=True)\n",
"\n",
"# Load the data and the evaluation function\n",
"train_set, val_set, test_set, eval_fn = load_task(\"BBH_object_counting\", evaluation_api=llm_api_eval)\n",
"print(\"Train/Val/Test Set Lengths: \", len(train_set), len(val_set), len(test_set))\n",
"STARTING_SYSTEM_PROMPT = train_set.get_task_description()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bde34303-2f52-415f-b117-264e266b84f0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-05-09T17:40:39.330651Z",
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"shell.execute_reply": "2025-05-09T17:40:39.398116Z",
"shell.execute_reply.started": "2025-05-09T17:40:39.330626Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/100 [00:00<?, ?it/s]\n"
]
},
{
"ename": "AssertionError",
"evalue": "Value must be a string, int, or image (bytes). Got: <class 'numpy.int64'>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[15], line 18\u001b[0m\n\u001b[1;32m 15\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m tg\u001b[38;5;241m.\u001b[39mTextualGradientDescent(engine\u001b[38;5;241m=\u001b[39mllm_api_eval, parameters\u001b[38;5;241m=\u001b[39m[system_prompt])\n\u001b[1;32m 17\u001b[0m results \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_acc\u001b[39m\u001b[38;5;124m\"\u001b[39m: [], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m: [], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation_acc\u001b[39m\u001b[38;5;124m\"\u001b[39m: []}\n\u001b[0;32m---> 18\u001b[0m results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_acc\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mappend(\u001b[43meval_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 19\u001b[0m results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation_acc\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mappend(eval_dataset(val_set, eval_fn, model))\n\u001b[1;32m 20\u001b[0m results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mappend(system_prompt\u001b[38;5;241m.\u001b[39mget_value())\n",
"Cell \u001b[0;32mIn[7], line 15\u001b[0m, in \u001b[0;36meval_dataset\u001b[0;34m(test_set, eval_fn, model, max_samples)\u001b[0m\n\u001b[1;32m 13\u001b[0m tqdm_loader \u001b[38;5;241m=\u001b[39m tqdm(concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mas_completed(futures), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(futures), position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m tqdm_loader:\n\u001b[0;32m---> 15\u001b[0m acc_item \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m accuracy_list\u001b[38;5;241m.\u001b[39mappend(acc_item)\n\u001b[1;32m 17\u001b[0m tqdm_loader\u001b[38;5;241m.\u001b[39mset_description(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mmean(accuracy_list)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/apps/python3.10/lib/python3.10/concurrent/futures/_base.py:451\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
"File \u001b[0;32m/apps/python3.10/lib/python3.10/concurrent/futures/_base.py:403\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 405\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/apps/python3.10/lib/python3.10/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
"Cell \u001b[0;32mIn[6], line 8\u001b[0m, in \u001b[0;36meval_sample\u001b[0;34m(item, eval_fn, model)\u001b[0m\n\u001b[1;32m 6\u001b[0m x, y \u001b[38;5;241m=\u001b[39m item\n\u001b[1;32m 7\u001b[0m x \u001b[38;5;241m=\u001b[39m tg\u001b[38;5;241m.\u001b[39mVariable(x, requires_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, role_description\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery to the language model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mtg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mVariable\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequires_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrole_description\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcorrect answer for the query\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m response \u001b[38;5;241m=\u001b[39m model(x)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
"File \u001b[0;32m~/notebooks/MT_TQ/Libraries/timedlibs/lib/python3.10/site-packages/textgrad/variable.py:43\u001b[0m, in \u001b[0;36mVariable.__init__\u001b[0;34m(self, value, image_path, predecessors, requires_grad, role_description)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m requires_grad) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mlen\u001b[39m(_predecessor_requires_grad) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf the variable does not require grad, none of its predecessors should require grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn this case, following predecessors require grad: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_predecessor_requires_grad\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(value) \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mint\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue must be a string, int, or image (bytes). Got: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(value))\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 45\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(value)\n",
"\u001b[0;31mAssertionError\u001b[0m: Value must be a string, int, or image (bytes). Got: <class 'numpy.int64'>"
]
}
],
"source": [
"train_loader = tg.tasks.DataLoader(train_set, batch_size=3, shuffle=True)\n",
"\n",
"\n",
"# Testing the 0-shot performance of the evaluation engine\n",
"system_prompt = tg.Variable(STARTING_SYSTEM_PROMPT, \n",
" requires_grad=True, \n",
" role_description=\"system prompt to the language model\")\n",
"model_evaluation = tg.BlackboxLLM(llm_api_eval, system_prompt)\n",
"\n",
"system_prompt = tg.Variable(STARTING_SYSTEM_PROMPT, \n",
" requires_grad=True,\n",
" role_description=\"structured system prompt to a somewhat capable language model that specifies the behavior and strategies for the QA task\")\n",
"model = tg.BlackboxLLM(llm_api_test, system_prompt)\n",
"\n",
"optimizer = tg.TextualGradientDescent(engine=llm_api_eval, parameters=[system_prompt])\n",
"\n",
"results = {\"test_acc\": [], \"prompt\": [], \"validation_acc\": []}\n",
"results[\"test_acc\"].append(eval_dataset(test_set, eval_fn, model))\n",
"results[\"validation_acc\"].append(eval_dataset(val_set, eval_fn, model))\n",
"results[\"prompt\"].append(system_prompt.get_value())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47c15231-22ff-459b-b5cc-ca32aaa62332",
"metadata": {},
"outputs": [],
"source": [
"for epoch in range(3):\n",
" for steps, (batch_x, batch_y) in enumerate((pbar := tqdm(train_loader, position=0))):\n",
" pbar.set_description(f\"Training step {steps}. Epoch {epoch}\")\n",
" optimizer.zero_grad()\n",
" losses = []\n",
" for (x, y) in zip(batch_x, batch_y):\n",
" x = tg.Variable(x, requires_grad=False, role_description=\"query to the language model\")\n",
" y = tg.Variable(y, requires_grad=False, role_description=\"correct answer for the query\")\n",
" response = model(x)\n",
" try:\n",
" eval_output_variable = eval_fn(inputs=dict(prediction=response, ground_truth_answer=y))\n",
" except:\n",
" eval_output_variable = eval_fn([x, y, response])\n",
" losses.append(eval_output_variable)\n",
" total_loss = tg.sum(losses)\n",
" total_loss.backward()\n",
" optimizer.step()\n",
" \n",
" run_validation_revert(system_prompt, results, model, eval_fn, val_set)\n",
" \n",
" print(\"sys prompt: \", system_prompt)\n",
" test_acc = eval_dataset(test_set, eval_fn, model)\n",
" results[\"test_acc\"].append(test_acc)\n",
" results[\"prompt\"].append(system_prompt.get_value())\n",
" if steps == 3:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c5e93f5-8d1c-4b87-a6d1-811714982d47",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "67a4583f-162c-4e2d-b061-798f6c676a28",
"metadata": {},
"outputs": [],
"source": [
"class TranslationQualityAssessor(dspy.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.assess = dspy.ChainOfThought(TranslationQualitySignature)\n",
"\n",
" def forward(self, src_lang, tgt_lang, src_text, translation, src_prev=\"\", tgt_prev=\"\", src_next=\"\", tgt_next=\"\"):\n",
" context = f\"\"\"Previous Context:\n",
" Source: {src_prev}\n",
" Translation: {tgt_prev}\n",
" \n",
" Next Context:\n",
" Source: {src_next}\n",
" Translation: {tgt_next}\"\"\"\n",
"\n",
" result = self.assess(\n",
" context=context,\n",
" source=f\"Source ({src_lang}): {src_text}\",\n",
" translation=f\"Translation ({tgt_lang}): {translation}\"\n",
" )\n",
" \n",
" return result.evaluation\n",
"\n",
"class TranslationMetrics:\n",
" @staticmethod\n",
" def exact_match_score(pred, gold):\n",
" try:\n",
" pred_json = json.loads(pred)\n",
" gold_json = gold\n",
" \n",
" accuracy_match = (str(pred_json.get('Accuracy Score')) == str(gold_json.get('Accuracy Score')))\n",
" readability_match = (str(pred_json.get('Readability Score')) == str(gold_json.get('Readability Score')))\n",
" \n",
" return (accuracy_match and readability_match)\n",
" except:\n",
" return False\n",
" \n",
" @staticmethod\n",
" def partial_match_score(pred, gold):\n",
" try:\n",
" pred_json = json.loads(pred)\n",
" gold_json = gold\n",
" \n",
" # Score comparison\n",
" accuracy_diff = abs(float(pred_json.get('Accuracy Score', 0)) - float(gold_json.get('Accuracy Score', 0)))\n",
" readability_diff = abs(float(pred_json.get('Readability Score', 0)) - float(gold_json.get('Readability Score', 0)))\n",
" \n",
" # Issues comparison\n",
" pred_accuracy_issues = set(str(issue) for issue in pred_json.get('Accuracy Issues', []))\n",
" gold_accuracy_issues = set(str(issue) for issue in gold_json.get('Accuracy Issues', []))\n",
" pred_readability_issues = set(str(issue) for issue in pred_json.get('Readability Issues', []))\n",
" gold_readability_issues = set(str(issue) for issue in gold_json.get('Readability Issues', []))\n",
" \n",
" # Calculate Jaccard similarity for issues\n",
" accuracy_issues_sim = len(pred_accuracy_issues & gold_accuracy_issues) / max(1, len(pred_accuracy_issues | gold_accuracy_issues))\n",
" readability_issues_sim = len(pred_readability_issues & gold_readability_issues) / max(1, len(pred_readability_issues | gold_readability_issues))\n",
" \n",
" # Combine scores (0.6 weight to scores, 0.4 to issues similarity)\n",
" score_component = 1 - ((accuracy_diff + readability_diff) / 8)\n",
" issues_component = (accuracy_issues_sim + readability_issues_sim) / 2\n",
" \n",
" final_score = 0.6 * score_component + 0.4 * issues_component\n",
" return max(0, final_score)\n",
" except:\n",
" return 0\n",
"\n",
"def prepare_dataset(file_path):\n",
" with open(file_path, 'r') as f:\n",
" data = json.load(f)\n",
" \n",
" prepared_data = []\n",
" \n",
" for item in data:\n",
" example = dspy.Example(\n",
" context=f\"\"\"Previous Context:\n",
" Source: {item['src_prev']}\n",
" Translation: {item['tgt_prev']}\n",
" \n",
" Next Context:\n",
" Source: {item['src_next']}\n",
" Translation: {item['tgt_next']}\"\"\",\n",
" source=f\"Source ({item['src_lang']}): {item['src_text']}\",\n",
" translation=f\"Translation ({item['tgt_lang']}): {item['main_text']}\",\n",
" evaluation=json.dumps(item['evaluation'], ensure_ascii=False)\n",
" ).with_inputs(\"context\", \"source\", \"translation\")\n",
" \n",
" prepared_data.append(example)\n",
" \n",
" # Split data: 70% train, 15% dev, 15% test\n",
" train_size = int(0.7 * len(prepared_data))\n",
" dev_size = int(0.15 * len(prepared_data))\n",
" \n",
" train_data = prepared_data[:train_size]\n",
" dev_data = prepared_data[train_size:train_size + dev_size]\n",
" test_data = prepared_data[train_size + dev_size:]\n",
" \n",
" return train_data, dev_data, test_data\n",
"\n",
"def optimize_translation_quality_assessment():\n",
" # Initialize DSPy\n",
" lm = TranslationQualityLM()\n",
" dspy.settings.configure(lm=lm)\n",
" \n",
" # Load and prepare dataset\n",
" train_data, dev_data, test_data = prepare_dataset('translation_quality_dataset.json')\n",
" \n",
" # Create evaluator\n",
" evaluator = Evaluate(\n",
" metrics={\n",
" 'exact_match': TranslationMetrics.exact_match_score,\n",
" 'partial_match': TranslationMetrics.partial_match_score\n",
" }\n",
" )\n",
" \n",
" # Initialize module\n",
" assessor = TranslationQualityAssessor()\n",
" \n",
" # Initialize MIPROv2 optimizer\n",
" optimizer = dspy.MIPROv2(\n",
" metric=lambda x: x['partial_match'],\n",
" max_rounds=5, # Number of optimization rounds\n",
" max_traces=10, # Number of traces per round\n",
" max_depth=3, # Maximum depth of reasoning chains\n",
" num_candidate_prompts=5, # Number of candidate prompts to generate\n",
" num_rounds_per_prompt=3, # Number of rounds per candidate prompt\n",
" temperature=0.7,\n",
" verbose=True\n",
" )\n",
" \n",
" # Compile the module with optimization\n",
" compiled_assessor = optimizer.compile(\n",
" assessor,\n",
" trainset=train_data,\n",
" devset=dev_data,\n",
" eval_kwargs={\n",
" 'metric': 'partial_match',\n",
" 'num_threads': 4,\n",
" 'batch_size': 8\n",
" }\n",
" )\n",
" \n",
" # Evaluate on test set\n",
" results = []\n",
" for example in test_data:\n",
" pred = compiled_assessor(\n",
" context=example.context,\n",
" source=example.source,\n",
" translation=example.translation\n",
" )\n",
" \n",
" result = evaluator.evaluate(\n",
" predictions=[pred],\n",
" ground_truth=[example.evaluation]\n",
" )\n",
" results.append(result)\n",
" \n",
" # Calculate and print final metrics\n",
" avg_exact_match = np.mean([r['exact_match'] for r in results])\n",
" avg_partial_match = np.mean([r['partial_match'] for r in results])\n",
" \n",
" print(f\"Average Exact Match Score: {avg_exact_match:.3f}\")\n",
" print(f\"Average Partial Match Score: {avg_partial_match:.3f}\")\n",
" \n",
" return compiled_assessor\n",
"\n",
"if __name__ == \"__main__\":\n",
" optimized_assessor = optimize_translation_quality_assessment()"
]
}
],
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|