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metric.py
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metric.py
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import tiktoken
import numpy as np
from time import time
from numpy import cov
from numpy import trace
from numpy import iscomplexobj
from scipy.linalg import sqrtm
import os
# calculate inception score with Keras
import torch
import argparse
import csv
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
from datasets import load_dataset
from dpsda.logging import *
from utility_eval.compute_mauve import *
from utility_eval.precision_recall import *
from apis.utils import set_seed
import time
os.environ["TOKENIZERS_PARALLELISM"] = "false"
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
def num_tokens_from_string(string, encoding):
"""Returns the number of tokens in a text string."""
try:
num_tokens = len(encoding.encode(string))
except:
num_tokens = 0
return num_tokens
# calculate frechet inception distance
def calculate_fid(act1, act2):
# calculate mean and covariance statistics
mu1, sigma1 = act1.mean(axis=0), cov(act1, rowvar=False)
mu2, sigma2 = act2.mean(axis=0), cov(act2, rowvar=False)
# calculate sum squared difference between means
ssdiff = np.sum((mu1 - mu2) ** 2.0)
# calculate sqrt of product between cov
covmean = sqrtm(sigma1.dot(sigma2))
# check and correct imaginary numbers from sqrt
if iscomplexobj(covmean):
covmean = covmean.real
# calculate score
fid = ssdiff + trace(sigma1 + sigma2 - 2.0 * covmean)
return fid
def calculate_all_metrics(synthetic_embeddings, original_embeddings, k=3):
method_name = ""
p_feats = synthetic_embeddings # feature dimension = 1024
q_feats = original_embeddings
result = compute_mauve(p_feats, q_feats)
print("MAUVE: ", result.mauve)
p_hist, q_hist = result.p_hist, result.q_hist
kl, tv, wass = calculate_other_metrics(p_hist, q_hist)
state = knn_precision_recall_features(
original_embeddings, synthetic_embeddings, nhood_sizes=[k])
print(state)
from geomloss import SamplesLoss # See also ImagesLoss, VolumesLoss
# feature dimension = 1024
p_feats = torch.from_numpy(synthetic_embeddings)
q_feats = torch.from_numpy(original_embeddings)
# Define a Sinkhorn (~Wasserstein) loss between sampled measures
loss = SamplesLoss(loss="sinkhorn", p=2, blur=.05)
# By default, use constant weights = 1/number of samples
sinkhorn_loss = loss(p_feats, q_feats).item()
print("Sinkhorn loss: %.3f" % sinkhorn_loss)
return state['precision'], state['recall'], state['f1'], result.mauve, kl, tv, wass, sinkhorn_loss
def eval_one_file(syn_fname, all_original_embeddings, model, csv_fname, batch_size, private_data_size, num_run, k, dataset="yelp", min_token_threshold=100):
syn_data = load_dataset("csv", data_files=syn_fname)
synthetic_data = []
if dataset == "yelp":
for index, d in enumerate(syn_data['train']['text']):
try:
if not d.startswith("Business Category: "):
synthetic_data.append(d)
except:
continue
elif dataset == "openreview" or dataset == "pubmed":
for index, d in enumerate(syn_data['train']['text']):
len_d = num_tokens_from_string(d, encoding)
if len_d > min_token_threshold:
synthetic_data.append(d)
else:
synthetic_data = [d for d in syn_data['train']['text']]
print("--- syn data len %d ---" % (len(synthetic_data)))
start_time = time.time()
with torch.no_grad():
synthetic_embeddings = []
for i in tqdm(range(len(synthetic_data) // batch_size+1)):
embeddings = model.encode(
synthetic_data[i * batch_size:(i + 1) * batch_size])
synthetic_embeddings.append(embeddings)
all_synthetic_embeddings = np.concatenate(synthetic_embeddings)
print("--- %s seconds for computing emb ---" % (time.time() - start_time))
fid = calculate_fid(all_original_embeddings, all_synthetic_embeddings)
print('FID : %.3f' % fid, len(all_original_embeddings),
len(all_synthetic_embeddings))
all_run_results = []
for run in range(num_run):
if (private_data_size != -1) and (len(all_original_embeddings) > private_data_size):
rand_index = np.random.choice(
list(range(len(all_original_embeddings))), size=private_data_size, replace=False)
original_embeddings = all_original_embeddings[rand_index]
else:
original_embeddings = all_original_embeddings
print("pri emb len", len(original_embeddings))
if (private_data_size != -1) and (len(all_synthetic_embeddings) > private_data_size):
rand_index = np.random.choice(list(
range(len(all_synthetic_embeddings))), size=private_data_size, replace=False)
synthetic_embeddings = all_synthetic_embeddings[rand_index]
else:
synthetic_embeddings = all_synthetic_embeddings
print("syn emb len", len(synthetic_embeddings))
start_time = time.time()
precision, recall, f1, mauve, kl, tv, wass, sinkhorn_loss = calculate_all_metrics(
synthetic_embeddings, original_embeddings, k)
print("--- %s seconds for computing metric ---" %
(time.time() - start_time))
with open(csv_fname, 'a', newline='') as file:
writer = csv.writer(file)
if run == 0:
writer.writerow(["run", "fid", "precision", "recall",
"f1", "mauve", "kl", "tv", "wass", "sinkhorn_loss"])
row_list = [
round(fid, 4),
round(precision, 4),
round(recall, 4),
round(f1, 4),
round(mauve, 4),
round(kl, 4),
round(tv, 4),
round(wass, 4),
round(sinkhorn_loss, 4),
]
writer.writerow([run]+row_list)
all_run_results.append(row_list)
mean_run_results = np.mean(np.array(all_run_results), axis=0).tolist()
mean_run_results = [round(x, 4) for x in mean_run_results]
with open(csv_fname, 'a', newline='') as file:
writer = csv.writer(file)
writer.writerow(["avg"] + mean_run_results)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--original_file", type=str,
default="", required=False)
parser.add_argument(
'--train_data_embeddings_file',
type=str,
default="")
parser.add_argument("--synthetic_file", type=str,
default="",
required=False)
parser.add_argument("--synthetic_folder", type=str,
default="",
required=False)
parser.add_argument("--synthetic_iteration", type=int,
default=20,
required=False)
parser.add_argument("--synthetic_start_iter", type=int,
default=0,
required=False)
parser.add_argument("--min_token_threshold", type=int,
default=100,
required=False)
parser.add_argument("--model_name_or_path", type=str,
default="stsb-roberta-base-v2", required=False)
parser.add_argument("--metric", type=str, default="fid")
parser.add_argument("--batch_size", type=int, required=False, default=1024)
parser.add_argument("--private_data_size", type=int,
required=False, default=5000)
parser.add_argument("--k", type=int, required=False, default=3)
parser.add_argument("--run", type=int, required=False, default=1)
parser.add_argument("--dataset", type=str, default="yelp",
choices=["yelp", "pubmed", "openreview"],
required=False)
args = parser.parse_args()
set_seed(seed=0, n_gpu=1)
model = SentenceTransformer(args.model_name_or_path)
model.eval()
dataset2embedding_file = {
"yelp": f"result/embeddings/{args.model_name_or_path}/yelp_train_all.embeddings.npz",
"pubmed": f"result/embeddings/{args.model_name_or_path}/pubmed_train_all.embeddings.npz",
"openreview": f"result/embeddings/{args.model_name_or_path}/openreview_train_all.embeddings.npz",
}
args.train_data_embeddings_file = dataset2embedding_file[args.dataset]
all_original_embeddings, original_labels = load_embeddings(
args.train_data_embeddings_file)
if args.private_data_size == -1:
args.run = 1
if args.synthetic_folder == '':
if args.synthetic_file != '':
csv_fname = os.path.join(os.path.dirname(
args.synthetic_file), 'eval_metric.csv')
eval_one_file(syn_fname=args.synthetic_file, all_original_embeddings=all_original_embeddings, model=model,
csv_fname=csv_fname, batch_size=args.batch_size,
private_data_size=args.private_data_size,
num_run=args.run, k=args.k, dataset=args.dataset, min_token_threshold=args.min_token_threshold)
else:
for _iter in range(args.synthetic_start_iter, args.synthetic_iteration + 1):
syn_data_file = os.path.join(
args.synthetic_folder, str(_iter), 'samples.csv')
if os.path.isfile(syn_data_file):
csv_fname = os.path.join(
args.synthetic_folder, str(_iter), 'eval_metric.csv')
if os.path.exists(csv_fname):
continue
print(f'Processing {csv_fname}')
eval_one_file(syn_fname=syn_data_file, all_original_embeddings=all_original_embeddings, model=model,
csv_fname=csv_fname, batch_size=args.batch_size,
private_data_size=args.private_data_size,
num_run=args.run, k=args.k, dataset=args.dataset, min_token_threshold=args.min_token_threshold)
else:
print(f"{syn_data_file} does not exist")
if __name__ == "__main__":
main()