ReDIAL dataset with multiple configuration.

.gitattributes

@@ -52,3 +52,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+movies_merged.csv filter=lfs diff=lfs merge=lfs -text
+movies_with_mentions.csv filter=lfs diff=lfs merge=lfs -text
+valid_data filter=lfs diff=lfs merge=lfs -text
+test_data filter=lfs diff=lfs merge=lfs -text
+train_data filter=lfs diff=lfs merge=lfs -text

data/train-00000-of-00001-e743925b24aaa562.parquet → movies_merged.csv

@@ -1,3 +1,3 @@
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data/test-00000-of-00001-f21aec36c6804b6f.parquet → movies_with_mentions.csv

@@ -1,3 +1,3 @@
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+oid sha256:0d408daa474ac95b959a739a261dddd997dd800f5ea585da15a434a26187dbb0
+size 226703

redial.py

@@ -0,0 +1,195 @@
+import json
+import re
+from typing import List
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+class RedialConfig(datasets.BuilderConfig):
+    """BuilderConfig for ReDIAL."""
+
+    def __init__(self, features, **kwargs):
+        """BuilderConfig for ReDIAL.
+
+        Args:
+        features: *list[string]*, list of the features that will appear in the
+            feature dict. Should not include "label".
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.features = features
+
+_URL = "./"
+_URLS = {
+    "train": _URL + "train_data",
+    "valid": _URL + "valid_data",
+    "test": _URL + "test_data",
+}
+
+
+
+
+class ReDIAL(datasets.GeneratorBasedBuilder):
+    DEFAULT_CONFIG_NAME = "rec"
+    BUILDER_CONFIGS = [
+        RedialConfig(
+            name="SA",
+            description="For using the ReDIAL dataset to train sentiment analysis on movies in sentences",
+            features={
+                "id": datasets.Value("int32"),
+                "movieName": datasets.Value("string"),
+                "messages": datasets.features.Sequence(datasets.Value("string")),
+                "senders": datasets.features.Sequence(datasets.Value("int32")),
+                "form": datasets.features.Sequence(
+                    datasets.Value("int32"), length=6
+                )
+            },
+            # certain information(e.g. movie_occurrences) is model-specific, and we leave it for Dataset.map
+        ),
+        RedialConfig(
+            name="SA_debug",
+            description="For using the ReDIAL dataset to train sentiment analysis on movies in sentences",
+            features={
+                "id": datasets.Value("int32"),
+                "movieName": datasets.Value("string"),
+                "messages": datasets.features.Sequence(datasets.Value("string")),
+                "senders": datasets.features.Sequence(datasets.Value("int32")),
+                "form": datasets.features.Sequence(
+                    datasets.Value("int32"), length=6
+                )
+            },
+        ),
+        RedialConfig(
+            name="autorec",
+            description="For training autorec model on ReDIAL data",
+            features=datasets.Features({
+                "movieIds": datasets.Sequence(datasets.Value("int32")),
+                "ratings": datasets.Sequence(datasets.Value("float"))
+            }),
+        ),
+        RedialConfig(
+            name="rec",
+            description="For using the ReDIAL dataset to train recommender",
+            features={
+                "movieIds": datasets.Sequence(datasets.Value("int32")),
+                "messages": datasets.features.Sequence(datasets.Value("string")),
+                "senders": datasets.features.Sequence(datasets.Value("int32")),
+            },
+        ),
+
+    ]
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.last_sender = None
+
+
+    def _processMessage(self, msg, initialId):
+        """
+        msg example:     {
+          "timeOffset": 0,
+          "text": "Hi I am looking for a movie like @111776",
+          "senderWorkerId": 956,
+          "messageId": 204171
+        },
+        """
+        res =  {
+            "text": msg["text"],
+            "sender": 1 if msg["senderWorkerId"] == initialId else -1
+        }
+        return res
+
+    def _flattenMessages(self, conversation):
+        messages = []
+        senders = []
+        for message in conversation["messages"]:
+            role = 1 if message["senderWorkerId"] == conversation["initiatorWorkerId"] else -1
+            text = message["text"]
+            if len(senders) > 0 and senders[-1] == role:
+                messages[-1] += "\n" + text
+            else:
+                senders.append(role)
+                messages.append(text)
+        return messages, senders
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description= self.config.description,
+            features=datasets.Features(self.config.features),
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+
+        if self.config.name == "autorec":
+            with open(filepath, encoding="utf-8") as f:
+                idx = 0
+                for line in f:
+                    conversation = json.loads(line)
+                    movieIds = []
+                    ratings = []
+                    if len(conversation["initiatorQuestions"]) == 0:
+                        continue
+                    for id, form in conversation["initiatorQuestions"].items():
+                        rating = int(form["liked"])
+                        if rating < 2:
+                            movieIds.append(id)
+                            ratings.append(rating)
+                    if len(movieIds) > 0:
+                        yield idx, {
+                            "movieIds": movieIds,
+                            "ratings": ratings
+                        }
+                        idx += 1
+
+        elif "SA" in self.config.name:
+            Idx = 0
+            date_pattern = re.compile(r'\(\d{4}\)') # To match e.g. "(2009)"
+            with open(filepath, encoding="utf-8") as f:
+                for line in f:
+                    conversation = json.loads(line)
+                    init_q = conversation["initiatorQuestions"]
+                    resp_q = conversation["respondentQuestions"]
+                    msgs, senders = self._flattenMessages(conversation)
+                    # get movies that are in both forms.
+                    gen = [key for key in init_q if key in resp_q]
+                    for id in gen:
+                        # remove date from movie name
+                        movieName = date_pattern.sub('', conversation["movieMentions"][id]).strip(" ")
+                        if len(movieName) == 0:
+                            continue
+                        yield Idx, {
+                            "id": int(id),
+                            "movieName": movieName,
+                            "messages": msgs,
+                            "senders": senders,
+                            "form": [init_q[id]["suggested"], init_q[id]["seen"], init_q[id]["liked"],
+                                     resp_q[id]["suggested"], resp_q[id]["seen"], resp_q[id]["liked"],]
+                        }
+                        Idx += 1
+                    if Idx > 100 and "debug" in self.config.name:
+                        break
+        elif "rec" in self.config.name:
+            Idx = 0
+            with open(filepath, encoding="utf-8") as f:
+                for line in f:
+                    conversation = json.loads(line)
+                    msgs, senders = self._flattenMessages(conversation)
+
+                    yield Idx, {
+                        "messages": msgs,
+                        "senders": senders,
+                        "movieIds": [int(movieId) for movieId in conversation["movieMentions"]]
+                    }
+                    Idx += 1

data/validation-00000-of-00001-8390a3aedba185de.parquet → test_data

@@ -1,3 +1,3 @@
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train_data

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valid_data

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