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import glob
import json
import os
import zipfile
from typing import List
import datasets
import pandas as pd
from datasets import ClassLabel, Value
_URLS = {
"go_emotions": {
"urls": [
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv",
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv",
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv",
],
"license": "apache license 2.0"
},
"daily_dialog": {
"urls": ["http://yanran.li/files/ijcnlp_dailydialog.zip"],
"license": "CC BY-NC-SA 4.0"
},
"emotion": {
"data": ["data/data.jsonl.gz"],
"license": "educational/research"
}
}
_CLASS_NAMES = [
"no emotion",
"happiness",
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"optimism",
"pride",
"realization",
"relief",
"remorse",
"sadness",
"surprise",
"neutral",
]
class EmotionsDatasetConfig(datasets.BuilderConfig):
def __init__(self, features, label_classes, **kwargs):
super().__init__(**kwargs)
self.features = features
self.label_classes = label_classes
class EmotionsDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
EmotionsDatasetConfig(
name="all",
label_classes=_CLASS_NAMES,
features=["text", "label", "dataset", "license"]
),
EmotionsDatasetConfig(
name="multilingual",
label_classes=_CLASS_NAMES,
features=["text", "label", "dataset", "license", "es", "it", "fr", "de"]
)
]
DEFAULT_CONFIG_NAME = "all"
def _info(self):
if self.config.name == "all":
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
'text': Value(dtype='string', id=None),
'label': ClassLabel(names=_CLASS_NAMES, id=None),
'dataset': Value(dtype='string', id=None),
'license': Value(dtype='string', id=None)
}
)
)
else:
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
'text': Value(dtype='string', id=None),
'label': ClassLabel(names=_CLASS_NAMES, id=None),
'dataset': Value(dtype='string', id=None),
'license': Value(dtype='string', id=None),
'es': Value(dtype='string', id=None),
'it': Value(dtype='string', id=None),
'fr': Value(dtype='string', id=None),
'de': Value(dtype='string', id=None),
}
)
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
splits = []
if self.config.name == "all":
for k, v in _URLS.items():
downloaded_files = dl_manager.download_and_extract(v.get("urls", v.get("data")))
splits.append(datasets.SplitGenerator(name=k,
gen_kwargs={"filepaths": downloaded_files,
"dataset": k,
"license": v.get("license")}))
else:
k = self.config.name
v = _URLS.get(k)
downloaded_files = dl_manager.download_and_extract(["data/many_emotions.tar.xz"])
splits.append(datasets.SplitGenerator(name=k,
gen_kwargs={"filepaths": downloaded_files,
"dataset": "many_emotions"}))
return splits
def process_daily_dialog(self, filepaths, dataset):
# TODO move outside
emo_mapping = {0: "no emotion", 1: "anger", 2: "disgust",
3: "fear", 4: "happiness", 5: "sadness", 6: "surprise"}
for i, filepath in enumerate(filepaths):
if os.path.isdir(filepath):
emotions = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_emotion.txt"), "r").read()
text = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_text.txt"), "r").read()
else:
# TODO check if this can be removed
archive = zipfile.ZipFile(filepath, 'r')
emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode()
text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode()
emotions = emotions.split("\n")
text = text.split("\n")
for idx_out, (e, t) in enumerate(zip(emotions, text)):
if len(t.strip()) > 0:
cast_emotions = [int(j) for j in e.strip().split(" ")]
cast_dialog = [d.strip() for d in t.split("__eou__") if len(d)]
for idx_in, (ce, ct) in enumerate(zip(cast_emotions, cast_dialog)):
uid = f"daily_dialog_{i}_{idx_out}_{idx_in}"
yield uid, {"text": ct,
"id": uid,
"dataset": dataset,
"license": license,
"label": emo_mapping[ce]}
def _generate_examples(self, filepaths, dataset, license=None):
if dataset == "go_emotions":
for i, filepath in enumerate(filepaths):
df = pd.read_csv(filepath)
current_classes = list(set(df.columns).intersection(set(_CLASS_NAMES)))
df = df[["text"] + current_classes]
df = df[df[current_classes].sum(axis=1) == 1].reset_index(drop=True)
for row_idx, row in df.iterrows():
uid = f"go_emotions_{i}_{row_idx}"
yield uid, {"text": row["text"],
"id": uid,
"dataset": dataset,
"license": license,
"label": row[current_classes][row == 1].index.item()}
elif dataset == "daily_dialog":
for d in self.process_daily_dialog(filepaths, dataset):
yield d
elif dataset == "emotion":
emo_mapping = {0: "sadness", 1: "joy", 2: "love",
3: "anger", 4: "fear", 5: "surprise"}
for i, filepath in enumerate(filepaths):
with open(filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
uid = f"{dataset}_{idx}"
example = json.loads(line)
example.update({
"id": uid,
"dataset": dataset,
"license": license,
"label": emo_mapping[example["label"]]
})
yield uid, example
elif dataset == "many_emotions":
for _, folder in enumerate(filepaths):
for i, filepath in glob.glob(f"{folder}/*"):
with open(filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
example = json.loads(line)
example.update({
"label": _CLASS_NAMES[example["label"]]
})
yield example["id"], example
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