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run_h_test.py
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run_h_test.py
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import random
from collections import defaultdict
import json
from tqdm import tqdm
from pprint import pprint
from data import generate_spelled_math, generate_end_punctuation, generate_hyphenated_word, generate_palindrome, generate_repeated_word, generate_rhyme, generate_spelled_number, generate_start_vowel, generate_uppercase, generate_end_ly
import utils
import os
"""Dataset Collection
"""
def gen_data(random_seed):
return {
"end_punctuation_instances" : generate_end_punctuation.generate(random_seed),
"hyphenated_word_instances" : generate_hyphenated_word.generate(random_seed),
"spelled_math_instances" : generate_spelled_math.generate(random_seed),
"palindrome_instances" : generate_palindrome.generate(random_seed),
"end_ly_instances" : generate_end_ly.generate(random_seed),
"repeated_word_instances" : generate_repeated_word.generate(random_seed),
"rhyme_instances" : generate_rhyme.generate(random_seed),
"spelled_number_instances" : generate_spelled_number.generate(random_seed),
"start_vowel_instances" : generate_start_vowel.generate(random_seed),
"uppercase_instances" : generate_uppercase.generate(random_seed)
}
def process_data(h_data_ab, random_seed, k_shot):
random.seed(random_seed)
full_processed_data = {}
for category, instances in h_data_ab.items():
processed_data = defaultdict(list)
true_test_idx = random.sample(range(0, len(instances[0])), 100)
false_test_idx = random.sample(range(0, len(instances[1])), 100)
# Remaining indices for examples, excluding test indices
true_example_idx = [item for item in list(range(0, len(instances[0]))) if item not in true_test_idx][:int(k_shot/2)]
false_example_idx = [item for item in list(range(0, len(instances[1]))) if item not in false_test_idx][:int(k_shot/2)]
for idx in true_example_idx:
processed_data[f'true_example'].append(instances[0][idx])
for idx in true_test_idx:
processed_data[f'true_test'].append(instances[0][idx])
for idx in false_example_idx:
processed_data[f'false_example'].append(instances[1][idx])
for idx in false_test_idx:
processed_data[f'false_test'].append(instances[1][idx])
full_processed_data[category] = processed_data
return full_processed_data
"""Run Test
"""
def run_test(model = "gpt-3.5-turbo-0613", model_temperature = 0.7, random_seed = 12062023, api_key = None, output_file = None, k_shot = 50, chain_of_thought = False):
# to store results
result_dict = defaultdict(list)
# keys
for key_cat, key in api_key.items():
os.environ[key_cat] = key
h_data_ab = gen_data(random_seed)
full_processed_data = process_data(h_data_ab, random_seed, k_shot)
for category, category_dict in full_processed_data.items():
true_example = category_dict[f'true_example']
true_test = category_dict[f'true_test']
false_example = category_dict[f'false_example']
false_test = category_dict[f'false_test']
for test in tqdm(true_test):
response = utils.get_response(
model = model,
messages = [{"content": utils.prompter(true_example, false_example, test, chain_of_thought), "role": "user"}],
chain_of_thought = chain_of_thought,
temperature = model_temperature
)
print(utils.prompter(true_example, false_example, test, chain_of_thought))
result_dict[category].append((True, utils.determine_binary_answers(response, chain_of_thought)))
if output_file != None:
with open(output_file, 'w') as json_file:
json.dump(result_dict, json_file)
for test in tqdm(false_test):
response = utils.get_response(
model = model,
messages = [{"content": utils.prompter(true_example, false_example, test, chain_of_thought), "role": "user"}],
chain_of_thought = chain_of_thought,
temperature = model_temperature
)
print(utils.prompter(true_example, false_example, test, chain_of_thought))
result_dict[category].append((False, utils.determine_binary_answers(response, chain_of_thought)))
if output_file != None:
with open(output_file, 'w') as json_file:
json.dump(result_dict, json_file)
return result_dict
def interpret(result_dict, output_file = None):
interpreted_result_dict = {}
for category, result in result_dict.items():
n_correct = 0
n_total = 0
n_correct_if_no_response_is_ignored = 0
n_total_if_no_response_is_ignored = 0
for one_test in result:
true_label = one_test[0]
pred_label = one_test[1]
if true_label == pred_label:
n_correct += 1
n_total += 1
if pred_label != None:
if true_label == pred_label:
n_correct_if_no_response_is_ignored += 1
n_total_if_no_response_is_ignored += 1
try:
acc_2 = round(n_correct_if_no_response_is_ignored/n_total_if_no_response_is_ignored, 3)
except:
acc_2 = 0
interpreted_result_dict[category] = {
'acc': round(n_correct/n_total, 3),
'acc (ignoring out of context response)': acc_2,
'ratio, out of context response': round((n_total - n_total_if_no_response_is_ignored)/n_total, 3)
}
if output_file != None:
with open(output_file, 'w') as json_file:
json.dump(interpreted_result_dict, json_file)
return interpreted_result_dict
api_key = {
"OPENAI_API_KEY": "",
"COHERE_API_KEY": "",
"HUGGINGFACE_API_KEY": "" ,
'AI21_API_KEY': "",
"REPLICATE_API_KEY": "",
"ALEPHALPHA_API_KEY": "",
"AWS_ACCESS_KEY_ID": "",
"AWS_SECRET_ACCESS_KEY": "",
"AWS_REGION_NAME": "",
"ANTHROPIC_API_KEY": ""
}
model_list = ["anthropic/claude-3-opus-20240229"] # ["command", "gpt-3.5-turbo-0613", "gpt-4-0613"]
k_list = [14] # [4, 50] # create directories results-{k} first
for model in model_list:
for k in k_list:
model_dir = model.split("/")[-1]
result_dict = run_test(model= model, api_key=api_key, output_file=f'results-{k}/result_dict_{model_dir}.json', chain_of_thought=False)
pprint(result_dict)
interpreted_result_dict = interpret(result_dict, output_file=f'results-{k}/interpreted_result_dict_{model_dir}.json')
pprint(interpreted_result_dict)