Upload emotions_dataset.py

emotions_dataset.py

@@ -1,3 +1,4 @@
+import zipfile
 from typing import List
 
 import datasets
@@ -11,7 +12,12 @@ DATASETS_URLS = [{
         "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"}
+    "license": "apache license 2.0"},
+    {
+        "name": "daily_dialog",
+        "urls": ["http://yanran.li/files/ijcnlp_dailydialog.zip"],
+        "license": "CC BY-NC-SA 4.0"
+    }
 ]
 
 _CLASS_NAMES = [
@@ -84,21 +90,44 @@ class EmotionsDataset(datasets.GeneratorBasedBuilder):
         splits = []
         for d in DATASETS_URLS:
             downloaded_files = dl_manager.download_and_extract(d.get("urls"))
-            splits.append(datasets.SplitGenerator(name=d.get("name"), gen_kwargs={"filepaths": downloaded_files,
-                                                                                  "dataset": d.get("name"),
-                                                                                  "license": d.get("license")}))
+            splits.append(datasets.SplitGenerator(name=d.get("name"),
+                                                  gen_kwargs={"filepaths": downloaded_files,
+                                                              "dataset": d.get("name"),
+                                                              "license": d.get("license")}))
         return splits
 
     def _generate_examples(self, filepaths, dataset, license):
-        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"{i}_{row_idx}"
-                yield uid, {"text": row["text"],
-                            "id": uid,
-                            "dataset": dataset,
-                            "license": license,
-                            "label": row[current_classes][row == 1].index.item()}
+        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":
+            emo_mapping = {0: "no emotion", 1: "anger", 2: "disgust",
+                           3: "fear", 4: "happiness", 5: "sadness", 6: "surprise"}
+            for i, filepath in enumerate(filepaths):
+                with zipfile.ZipFile(filepath, 'r') as archive:
+                    emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode().split("\n")
+                    text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode().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]}
+
+
+print()