From 453bf34d6ea4617c8b4648625a68827cdf93bd6b Mon Sep 17 00:00:00 2001 From: louishsu Date: Sun, 5 Sep 2021 15:23:39 +0800 Subject: [PATCH] baseline 5fold --- .gitignore | 2 + ...ine.json => bert_span-baseline-fold0.json} | 10 +- ...4\344\272\214\351\230\266\346\256\265.zip" | Bin 0 -> 548314 bytes .../xxcq_mid.json" | 5247 +++++++++++++++++ eda.ipynb | 38 +- evaluate.py | 143 +- main.py | 86 +- prepare_data.py | 6 + run_span.py | 94 +- scripts/clear_before_submit.sh | 4 + scripts/run_span.sh | 33 +- utils.py | 9 + 12 files changed, 5491 insertions(+), 181 deletions(-) rename args/{bert_span-baseline.json => bert_span-baseline-fold0.json} (88%) create mode 100644 "data/\344\277\241\346\201\257\346\212\275\345\217\226_\347\254\254\344\272\214\351\230\266\346\256\265.zip" create mode 100644 "data/\344\277\241\346\201\257\346\212\275\345\217\226_\347\254\254\344\272\214\351\230\266\346\256\265/xxcq_mid.json" create mode 100644 scripts/clear_before_submit.sh diff --git a/.gitignore b/.gitignore index d2b3160..c779ac0 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ *.pyc +output.json +args/pred* output/* cache/* tmp/* diff --git a/args/bert_span-baseline.json b/args/bert_span-baseline-fold0.json similarity index 88% rename from args/bert_span-baseline.json rename to args/bert_span-baseline-fold0.json index 5e6f261..4bc4940 100644 --- a/args/bert_span-baseline.json +++ b/args/bert_span-baseline-fold0.json @@ -1,13 +1,13 @@ { - "version": "baseline", + "version": "baseline-fold0", "device": "cuda:0", "n_gpu": 1, "task_name": "ner", "dataset_name": "cail_ner", - "data_dir": "./data/ner-ctx0-train0.8-seed42/", - "train_file": "train.json", - "dev_file": "dev.json", - "test_file": "dev.json", + "data_dir": "./data/ner-ctx0-5fold-seed42/", + "train_file": "train.0.json", + "dev_file": "dev.0.json", + "test_file": "dev.0.json", "model_type": "bert_span", "model_name_or_path": "/home/louishsu/NewDisk/Garage/weights/transformers/chinese-roberta-wwm", "output_dir": 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zTisRlB}N<{7({}KAZiu{6by(lh{@NCnf!&f`p$I5&Lq{7$B==n5-OePfv$VFUH3df zv|I(_ytLqT;!N|{IN=?L+6ra{R=#8GBGawg+8^+UYVBjPn%T_fl_@0al>}SY+6ApW zEUtZ;J}q1Ory7bcopsB+H8$$R3XQ|)aD1?rWoa9)u+lN$9Y0|f&O1VRH7+dsmFn) z-Vq!+A!=FFHxNEnNI|r?H$hqOAXGHE0fx>Wy&VvzNgpu*)2z!WL13>^neM83_ro#ssR+rJE zjnNptcuH10cng$u(6;FW#4WcO88Xj%>lnb#a~Q`6uh^geGa#1G=>;MXr7+_Si5i0( zi{&=pP?*UYcU<}Z#NxV#GO-*J3? zR`v@g;X4=`nolaW-(fh*RKNXu3oj-PdFKq!*S<L2*s{bNur2@!Lm}!)#uTAynP2L~m&Ne|%3O+ACf>IoQt$fZH2Kk3SqC%+2Re zZv3RIT@>ixWf%yE-U#^NlTXhu3Lo1tKv<ySJZkT71ijak`TI*#hPfFxv7jyS0H&A*4XsS#uO8bEM=awuIDT4utdm= zYNuKB(HX-$7hFpS+<~&8W@j-7kodJY>|5P5rhu)WY>Fi-y$(HA;M$5NYKRS&DtM_H zt3=eos%V&M&W>C#vov{r_3(o7YU;fRUKZlXWbgT8^AFwth5iUBQw*UtiM%@>+<(aX zD8lW" + prediction_positions_label_map[k] + label_error = [k + "-" + ground_truth_positions_content_map[k].split("-")[0] + "-" + \ + ground_truth_positions_label_map[k] + "-" + prediction_positions_label_map[k] for k in itersection_positions if ground_truth_positions_label_map[k] != prediction_positions_label_map[k]] + # 混淆矩阵 + y_true = [ground_truth_positions_label_map[k] for k in itersection_positions] + y_pred = [prediction_positions_label_map[k] for k in itersection_positions] + labels = list(LABEL_MEANING_MAP.values()) + cm = confusion_matrix(y_true, y_pred, labels=labels) + disp = ConfusionMatrixDisplay(cm, display_labels=labels).plot() label_error_type_count_map = Counter(["-".join(e.split("-")[-2:]) for e in label_error]) # 保存 - for list_name in ["location_missed", "location_found", "label_error"]: + for list_name in ["ground_truth_entities", "prediction_entities", + "location_missed", "location_found", "label_error"]: with open(os.path.join("tmp", list_name + ".txt"), "w") as f: for line in locals()[list_name]: f.write(line + "\n") print(label_error_type_count_map) + print(labels) + plt.savefig(os.path.join("tmp", "cm.jpg")) if __name__ == '__main__': - # print(get_score(sys.argv[1], sys.argv[2])) - # print(get_score( + ground_truth_path, output_path = sys.argv[1], sys.argv[2] + for label, score in get_scores(ground_truth_path, output_path).items(): + print(LABEL_MEANING_MAP.get(label, label)) + print(score) + # analyze_error(ground_truth_path, output_path) + + # for label, score in get_scores( + # "./data/ner-ctx0-5fold-seed42/dev.gt.0.json", + # "output/ner-cail_ner-bert_span-baseline-42/test_prediction.json" + # ).items(): + # print(LABEL_MEANING_MAP.get(label, label)) + # print(score) + # analyze_error( # "./data/ner-ctx0-5fold-seed42/dev.gt.0.json", - # "output/ner-cail_ner-bert_span-baseline-fold0-42/test_prediction.json" - # )) - analyze_error( - "./data/ner-ctx0-5fold-seed42/dev.gt.0.json", - "output/ner-cail_ner-bert_span-baseline-fold0-42/test_prediction.json" - ) + # "output/ner-cail_ner-bert_span-baseline-42/test_prediction.json" + # ) diff --git a/main.py b/main.py index 0565eaa..11e23d7 100644 --- a/main.py +++ b/main.py @@ -2,45 +2,67 @@ import os import json import utils -from run_span import NerArgumentParser +import torch +import numpy as np +from run_span import NerArgumentParser, CailNerProcessor, predict_decode_batch infile = "/input/input.json" outfile = "/output/output.json" -# def main(): +def main(): + local_debug = True + version = "baseline" + model_type = "bert_span" + dataset_name = "cail_ner" + n_splits = 5 + seed=42 -# model_path = "./output/ner-cail_ner-bert_span-baseline-42/" -# # json_file = "./args/bert_span-baseline.json" -# json_file = os.path.join(model_path, "training_args.json") -# parser = NerArgumentParser() -# args = parser.parse_args_from_json(json_file=json_file) -# args.test_file = "test.json" -# args.do_train = False -# args.do_eval = False -# args.do_test = True -# args.per_gpu_eval_batch_size = 1 -# args.fp16 = False -# parser.save_args_to_json("./args/pred.json", args) -# os.system(f"rm -rf {os.path.join(model_path, 'test_prediction.json')}") - -# version = args.version -# data_dir = args.data_dir -# model_type = args.model_type -# dataset_name = args.dataset_name -# seed = args.seed + test_examples = None + test_batches = None + for k in range(n_splits): + model_path = f"./output/ner-{dataset_name}-{model_type}-{version}-fold{k}-{seed}/" + # 生成测试运行参数 + json_file = os.path.join(model_path, "training_args.json") + parser = NerArgumentParser() + args = parser.parse_args_from_json(json_file) + args.do_train, args.do_eval, args.do_predict = False, False, True + args.per_gpu_eval_batch_size = 1 + args.fp16 = False + # 生成测试数据集 + if not local_debug: + raw_samples = utils.load_raw(infile) + utils.save_samples(os.path.join(args.data_dir, "test.json"), raw_samples) + args.test_file = "test.json" + else: + args.test_file = "dev.0.json" + parser.save_args_to_json(f"./args/pred.{k}.json", args) + # 确保目录下预测输出文件被清除 + os.system(f"rm -rf {os.path.join(model_path, 'test_*')}") -# # upload -# raw_samples = utils.load_raw(infile) -# utils.save_samples(os.path.join(data_dir, "test.json"), raw_samples) -# os.system(f"sudo /home/user/miniconda/bin/python3 run_span.py ./args/pred.json") -# os.system(f"sudo cp ./output/ner-{dataset_name}-{model_type}-{version}-{seed}/test_prediction.json {outfile}") -# exit(0) + if local_debug: + # 线下预测测试 + os.system(f"python run_span.py ./args/pred.{k}.json") + else: + # 线上预测阶段 + os.system(f"sudo /home/user/miniconda/bin/python3 run_span.py ./args/pred.{k}.json") + + test_examples_ = torch.load(os.path.join(model_path, 'test_examples.pkl')) + test_batches_ = torch.load(os.path.join(model_path, 'test_batches.pkl')) + if test_examples is None: + test_examples, test_batches = test_examples_, test_batches_ + else: + for i, (batch, batch_) in enumerate(zip(test_batches, test_batches_)): + test_batches[i]["logits"] = batch["logits"] + batch_["logits"] -# # local test -# os.system(f"python run_span.py ./args/pred.json") -# os.system(f"mv ./output/ner-{dataset_name}-{model_type}-{version}-{seed}/test_prediction.json ./output.json") - -# TODO: kfold + # 模型集成 + results = [] + for i, (example, batch) in enumerate(zip(test_examples, test_batches)): + results.append(predict_decode_batch(example[1], batch, CailNerProcessor().id2label)) + # 保存结果 + output_predict_file = "output.json" if local_debug else outfile + with open(output_predict_file, "w") as writer: + for record in results: + writer.write(json.dumps(record) + '\n') if __name__ == '__main__': main() diff --git a/prepare_data.py b/prepare_data.py index 44f6725..ba35217 100644 --- a/prepare_data.py +++ b/prepare_data.py @@ -98,10 +98,16 @@ for fold_no, (train_index, dev_index) in enumerate(kf.split(raw_samples)): train_samples = [raw_samples[index] for index in train_index] dev_samples = [raw_samples[index] for index in dev_index] + dev_samples = sorted(dev_samples, key=lambda x: x["id"]) utils.save_samples(os.path.join(args.output_dir, f"train.{fold_no}.json"), train_samples) utils.save_samples(os.path.join(args.output_dir, f"dev.{fold_no}.json"), dev_samples) logging.info(f"Fold[{fold_no}/{args.n_splits}] Number of training data: {len(train_samples)}, number of dev data: {len(dev_samples)}") + train_label_counter = utils.count_entity_labels(train_samples) + dev_label_counter = utils.count_entity_labels(dev_samples) + logging.info(f"{train_label_counter}") + logging.info(f"{dev_label_counter}") + # dev groundtruth dev_groundtruths = [] for sample in dev_samples: diff --git a/run_span.py b/run_span.py index c367a2c..3535773 100644 --- a/run_span.py +++ b/run_span.py @@ -44,6 +44,7 @@ f1_score, precision_score, recall_score, get_entities ) +from evaluate import score from utils import LABEL_MEANING_MAP, get_ner_tags class BertConfigSpanV2(BertConfig): @@ -95,8 +96,9 @@ def forward(self, hidden_states, spans): logits = self.classifier(spans_embedding) return logits - - def decode_batch(self, + + @staticmethod + def decode_batch( batch, # (batch_size, num_spans, num_labels) spans, # (batch_size, num_spans, 3) span_mask, # (batch_size, num_spans) @@ -766,8 +768,8 @@ def train(args, model, processor, tokenizer): torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) - scheduler.step() # Update learning rate schedule optimizer.step() + scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.evaluate_during_training and \ @@ -783,8 +785,8 @@ def train(args, model, processor, tokenizer): logger.info("\t".join(f"{metric:s}={value:f}" for metric, value in metrics.items())) if args.save_best_checkpoints: - if eval_results["micro avg"]["f1-score"] > best_f1: - best_f1 = eval_results["micro avg"]["f1-score"] + if eval_results["avg"]["f"] > best_f1: + best_f1 = eval_results["avg"]["f"] # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(999999)) if not os.path.exists(output_dir): @@ -861,23 +863,54 @@ def evaluate(args, model, processor, tokenizer, prefix=""): nb_eval_steps += 1 # calculate metrics - preds = model.span.decode_batch(logits, batch["spans"], batch["span_mask"]) + preds = SpanV2.decode_batch(logits, batch["spans"], batch["span_mask"]) for pred_no, (pred, input_len, start, end) in enumerate(zip( preds, batch["input_len"], batch["sent_start"], batch["sent_end"])): pred = [(LABEL_MEANING_MAP[id2label[t]], b, e) for t, b, e in pred if id2label[t] != "O"] - pred = get_ner_tags(pred, input_len - 2) - y_pred.append(pred[start: end]) - - labels = model.span.decode_batch(batch["label"], batch["spans"], batch["span_mask"], is_logits=False) + pred = [(t, b - start, e - start) for t, b, e in pred] + sample = eval_dataset.examples[args.eval_batch_size * step + pred_no][1] + label_entities_map = {v: [] for v in LABEL_MEANING_MAP.values()} + for t, b, e in pred: + label_entities_map[t].append(f"{b};{e+1}") + entities = [{"label": k, "span": v} for k, v in label_entities_map.items()] + y_pred.append({"id": sample["id"].split("-")[-1], "entities": entities}) + + labels = SpanV2.decode_batch(batch["label"], batch["spans"], batch["span_mask"], is_logits=False) for label_no, (label, input_len, start, end) in enumerate(zip( labels, batch["input_len"], batch["sent_start"], batch["sent_end"])): label = [(LABEL_MEANING_MAP[id2label[t]], b, e) for t, b, e in label if id2label[t] != "O"] - label = get_ner_tags(label, input_len - 2) - y_true.append(label[start: end]) - - results = classification_report(y_true, y_pred, digits=6, output_dict=True, scheme=args.scheme) + label = [(t, b - start, e - start) for t, b, e in label] + sample = eval_dataset.examples[args.eval_batch_size * step + label_no][1] + label_entities_map = {v: [] for v in LABEL_MEANING_MAP.values()} + for t, b, e in label: + label_entities_map[t].append(f"{b};{e+1}") + entities = [{"label": k, "span": v} for k, v in label_entities_map.items()] + y_true.append({"id": sample["id"].split("-")[-1], "entities": entities}) + + y_true = {y["id"]: {"entities": y["entities"]} for y in y_true} + y_pred = {y["id"]: {"entities": y["entities"]} for y in y_pred} + results = dict() + results["avg"] = score(y_true, y_pred) + for label in LABEL_MEANING_MAP.values(): + results[label] = score(y_true, y_pred, [label]) results['loss'] = eval_loss / nb_eval_steps return results + +def predict_decode_batch(example, batch, id2label): + logits = batch["logits"] + preds = SpanV2.decode_batch(logits, batch["spans"], batch["span_mask"]) + pred, input_len = preds[0], batch["input_len"][0] + start, end = batch["sent_start"].item(), batch["sent_end"].item() + pred = [(id2label[t], b, e) for t, b, e in pred if id2label[t] != "O"] + pred = [(t, b - start, e - start) for t, b, e in pred] + label_entities_map = {label: [] for label in LABEL_MEANING_MAP.keys()} + for t, b, e in pred: + label_entities_map[t].append(f"{b};{e+1}") + entities = [{"label": label, "span": label_entities_map[label]} \ + for label in LABEL_MEANING_MAP.keys()] + # 预测结果文件为一个json格式的文件,包含两个字段,分别为``id``和``entities`` + id_ = example["id"].split("-")[1] + return {"id": id_, "entities": entities} def predict(args, model, processor, tokenizer, prefix=""): pred_output_dir = args.output_dir @@ -898,6 +931,7 @@ def predict(args, model, processor, tokenizer, prefix=""): if isinstance(model, nn.DataParallel): model = model.module pbar = tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc="Predicting...") + batch_all = [] for step, batch in pbar: model.eval() with torch.no_grad(): @@ -907,27 +941,23 @@ def predict(args, model, processor, tokenizer, prefix=""): if args.model_type.split('_')[0] in ["roberta", "xlnet"]: batch["token_type_ids"] = None outputs = model(**batch) - logits = outputs['logits'] - - preds = model.span.decode_batch(logits, batch["spans"], batch["span_mask"]) - pred, input_len = preds[0], batch["input_len"][0] - pred = [(id2label[t], b, e) for t, b, e in pred if id2label[t] != "O"] - pred = get_ner_tags(pred, input_len - 2) - pred = pred[batch["sent_start"][0]: batch["sent_end"][0]] - label_entities_map = {label: [] for label in LABEL_MEANING_MAP.keys()} - for t, b, e in get_entities(pred): - label_entities_map[t].append(f"{b};{e+1}") - entities = [{"label": label, "span": label_entities_map[label]} for label in LABEL_MEANING_MAP.keys()] - # 预测结果文件为一个json格式的文件,包含两个字段,分别为``id``和``entities`` - results.append({ - "id": test_dataset.examples[step][1]["id"].split("-")[1], - "entities": entities, - }) - + logits = outputs['logits'] # (batch_size=1, num_spans, num_labels) + batch["logits"] = logits.detach() + batch.pop("input_ids") + batch.pop("attention_mask") + batch.pop("token_type_ids") + # 解码输出 + example = test_dataset.examples[step][1] + results.append(predict_decode_batch(example, batch, id2label)) + # for k-fold + batch_all.append({k: v.detach().cpu() for k, v in batch.items()}) logger.info("\n") with open(output_predict_file, "w") as writer: for record in results: writer.write(json.dumps(record) + '\n') + # for k-fold + torch.save(batch_all, os.path.join(args.output_dir, "test_batches.pkl")) + torch.save(test_dataset.examples, os.path.join(args.output_dir, "test_examples.pkl")) PROCESSER_CLASS = { "cail_ner": CailNerProcessor, @@ -966,7 +996,7 @@ def load_dataset(args, processor, tokenizer, data_type='train'): args = parser.parse_args_from_json(json_file=os.path.abspath(sys.argv[1])) else: args = parser.build_arguments().parse_args() - # args = parser.parse_args_from_json(json_file="args/bert_span-baseline.json") + # args = parser.parse_args_from_json(json_file="args/bert_span-baseline-fold0.json") # Set seed before initializing model. seed_everything(args.seed) diff --git a/scripts/clear_before_submit.sh b/scripts/clear_before_submit.sh new file mode 100644 index 0000000..553b6f9 --- /dev/null +++ b/scripts/clear_before_submit.sh @@ -0,0 +1,4 @@ +rm -rf args/pred* +rm -rf tmp/* +rm -rf output/*/test* +rm -rf output/*/checkpoint-999999/ \ No newline at end of file diff --git a/scripts/run_span.sh b/scripts/run_span.sh index b8a7114..6b0e822 100644 --- a/scripts/run_span.sh +++ b/scripts/run_span.sh @@ -1,32 +1,3 @@ -# python run_span.py \ -# --version=baseline \ -# --data_dir=./data/ner-ctx0-5fold-seed42/ \ -# --train_file=train.json \ -# --dev_file=dev.json \ -# --test_file=test.json \ -# --model_type=bert_span \ -# --model_name_or_path=/home/louishsu/NewDisk/Garage/weights/transformers/chinese-roberta-wwm/ \ -# --do_train \ -# --do_eval \ -# --do_predict \ -# --overwrite_output_dir \ -# --evaluate_during_training \ -# --evaluate_each_epoch \ -# --save_best_checkpoints \ -# --max_span_length=50 \ -# --width_embedding_dim=128 \ -# --train_max_seq_length=512 \ -# --eval_max_seq_length=512 \ -# --do_lower_case \ -# --per_gpu_train_batch_size=12 \ -# --per_gpu_eval_batch_size=24 \ -# --gradient_accumulation_steps=2 \ -# --learning_rate=2e-5 \ -# --other_learning_rate=1e-4 \ -# --num_train_epochs=8.0 \ -# --warmup_proportion=0.1 \ -# --seed=42 - for k in 0 1 2 3 4 do python run_span.py \ @@ -44,7 +15,7 @@ python run_span.py \ --evaluate_during_training \ --evaluate_each_epoch \ --save_best_checkpoints \ - --max_span_length=50 \ + --max_span_length=35 \ --width_embedding_dim=128 \ --train_max_seq_length=512 \ --eval_max_seq_length=512 \ @@ -54,7 +25,7 @@ python run_span.py \ --gradient_accumulation_steps=2 \ --learning_rate=2e-5 \ --other_learning_rate=1e-4 \ - --num_train_epochs=5.0 \ + --num_train_epochs=8.0 \ --warmup_proportion=0.1 \ --seed=42 python evaluate.py \ diff --git a/utils.py b/utils.py index b615891..3402677 100644 --- a/utils.py +++ b/utils.py @@ -3,6 +3,7 @@ import torch import random import numpy as np +from collections import Counter def seed_everything(seed=None, reproducibility=True): ''' @@ -64,6 +65,14 @@ def load_raw(filepath): raw.append(r) return raw +def count_entity_labels(samples): + labels = [] + for sample in samples: + for label, *other in sample["entities"]: + labels.append(LABEL_MEANING_MAP[label]) + counter = Counter(labels) + return counter + def save_samples(filename, samples): with open(filename, "w") as f: for sample in samples: