|
from collections import Counter |
|
from typing import List |
|
|
|
import datasets |
|
import matplotlib.pyplot as plt |
|
import pandas as pd |
|
from constants import (event_centered_2_descriptions, |
|
physical_entity_2_descriptions, relations_map, |
|
social_intercation_2_descriptions) |
|
|
|
|
|
def show_bar(relation: List): |
|
c = dict(Counter(relation)) |
|
|
|
keys = list(c.keys()) |
|
values = list(c.values()) |
|
plt.bar(keys, values) |
|
plt.xticks(rotation=25, fontsize=8) |
|
plt.yticks(fontsize=8) |
|
|
|
plt.xlabel('relations') |
|
plt.ylabel('numbers') |
|
plt.title('relations analysis') |
|
|
|
for i in range(len(keys)): |
|
plt.text(i, values[i] + 10, str(values[i]), ha='center', fontsize=10) |
|
|
|
plt.show() |
|
|
|
def read_file(data_path: str): |
|
df = pd.read_csv(data_path, sep='\t', header=None) |
|
|
|
df.columns = ['event', 'relation', 'tail'] |
|
print(df.head()) |
|
|
|
event = df['event'].tolist() |
|
relation = df['relation'].tolist() |
|
tail = df['tail'].tolist() |
|
|
|
return event, relation, tail |
|
|
|
def make_base_dataset(event: List[str], relation: List[str], tail: List[str]): |
|
new_event, new_relation, new_tail = [], [], [] |
|
knowledge_type = [] |
|
relation_description = [] |
|
|
|
prev_event, prev_relation, prev_tail = None, None, None |
|
|
|
for i in range(len(event)): |
|
if i > 0 and event[i] == prev_event and relation[i] == prev_relation: |
|
new_tail[-1].extend( [tail[i]] ) |
|
else: |
|
new_event.append(event[i]) |
|
new_relation.append(relation[i]) |
|
|
|
relation_list = [] |
|
for r in list(relations_map.values()): |
|
relation_list.extend(r) |
|
if relation[i] not in relation_list: |
|
raise ValueError(f'dont find match knowledge type named {relation[i]}, please check it!') |
|
|
|
for k, v in relations_map.items(): |
|
if relation[i] in v: |
|
knowledge_type.append(k) |
|
if k == 'social_intercation': |
|
relation_description.append(social_intercation_2_descriptions[relation[i]]) |
|
elif k == 'physical_entity': |
|
relation_description.append(physical_entity_2_descriptions[relation[i]]) |
|
elif k == 'event_centered': |
|
relation_description.append(event_centered_2_descriptions[relation[i]]) |
|
else: |
|
raise KeyError(f"dont find match relation type named {relation[i]} in dict, please check it!") |
|
|
|
new_tail.append( [tail[i]] ) |
|
prev_event, prev_relation, prev_tail = event[i], relation[i], tail[i] |
|
|
|
df = pd.DataFrame({ |
|
'knowledge_type': knowledge_type, |
|
'event': new_event, |
|
'relation': new_relation, |
|
'relation_description': relation_description, |
|
'tail': new_tail, |
|
}) |
|
|
|
print(df.head()) |
|
return df |
|
|
|
def get_dataset(data_path: str): |
|
event, relation, tail = read_file(data_path=data_path) |
|
df = make_base_dataset(event=event, relation=relation, tail=tail) |
|
dataset = datasets.Dataset.from_pandas(df, split='train') |
|
|
|
print(dataset) |
|
return dataset |
|
|
|
def upload_dataset(dataset, repo_id :str, access_token: str, private: bool): |
|
dataset.push_to_hub( |
|
repo_id = repo_id, |
|
private = private, |
|
token = access_token, |
|
) |
|
|
|
if __name__ == '__main__': |
|
train_dataset = get_dataset('./dataset/train.tsv') |
|
valid_dataset = get_dataset('./dataset/dev.tsv') |
|
test_dataset = get_dataset('./dataset/test.tsv') |
|
|
|
dataset = datasets.DatasetDict({ |
|
'train': train_dataset, |
|
'validation': valid_dataset, |
|
'test': test_dataset |
|
}) |
|
|
|
print(dataset) |
|
|
|
upload_dataset(dataset, repo_id='Estwld/atomic2020-origin', private=False, access_token='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') |
|
|
