Create script-generation.py

script-generation.py

@@ -0,0 +1,304 @@
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+_VERSION = "1.2.1"
+
+_CITATION = f"""
+@dataset{{unsplash-lite-dataset,
+  title  = {{Unsplash Lite Dataset {_VERSION}}},
+  url    = {{\\url{{https://github.com/unsplash/datasets}}}},
+  author = {{Unsplash}},
+  year   = {{2023}},
+  month  = {{May}},
+  day    = {{02}},
+}}
+"""
+
+_DESCRIPTION = """
+This dataset, available for commercial and noncommercial usage,
+contains 25k nature-themed Unsplash photos, 25k keywords, and 1M searches.
+"""
+
+_HOMEPAGE = f"https://github.com/unsplash/datasets/tree/{_VERSION}"
+
+_URL = f"https://unsplash.com/data/lite/{_VERSION}"
+
+_LICENSE = "Unsplash Dataset License"
+
+_TSV = (
+    "collections",
+    "colors",
+    "conversions",
+    "keywords",
+    "photos",
+)
+
+_FEATURES = datasets.Features(
+    {
+        "photo": {
+            "id": datasets.Value("string"),
+            "url": datasets.Value("string"),
+            "image_url": datasets.Value("string"),
+            "submitted_at": datasets.Value("string"),
+            "featured": datasets.Value("bool"),
+            "width": datasets.Value("uint16"),
+            "height": datasets.Value("uint16"),
+            "aspect_ratio": datasets.Value("float32"),
+            "description": datasets.Value("string"),
+            "blur_hash": datasets.Value("string"),
+        },
+        "photographer": {
+            "username": datasets.Value("string"),
+            "first_name": datasets.Value("string"),
+            "last_name": datasets.Value("string"),
+        },
+        "exif": {
+            "camera_make": datasets.Value("string"),
+            "camera_model": datasets.Value("string"),
+            "iso": datasets.Value("string"),
+            "aperture_value": datasets.Value("string"),
+            "focal_length": datasets.Value("string"),
+            "exposure_time": datasets.Value("string"),
+        },
+        "location": {
+            "name": datasets.Value("string"),
+            "latitude": datasets.Value("float32"),
+            "longitude": datasets.Value("float32"),
+            "country": datasets.Value("string"),
+            "city": datasets.Value("string"),
+        },
+        "stats": {
+            "views": datasets.Value("uint32"),
+            "downloads": datasets.Value("uint32"),
+        },
+        "ai": {
+            "description": datasets.Value("string"),
+            "primary_landmark_name": datasets.Value("string"),
+            "primary_landmark_latitude": datasets.Value("string"),
+            "primary_landmark_longitude": datasets.Value("string"),
+            "primary_landmark_confidence": datasets.Value("string"),
+        },
+        "keywords": [
+            {
+                "keyword": datasets.Value("string"),
+                "ai_service_1_confidence": datasets.Value("string"),
+                "ai_service_2_confidence": datasets.Value("string"),
+                "suggested_by_user": datasets.Value("bool"),
+            },
+        ],
+        "collections": [
+            {
+                "collection_id": datasets.Value("string"),
+                "collection_title": datasets.Value("string"),
+                "photo_collected_at": datasets.Value("string"),
+            },
+        ],
+        "conversions": [
+            {
+                "converted_at": datasets.Value("string"),
+                "conversion_type": datasets.Value("string"),
+                "keyword": datasets.Value("string"),
+                "anonymous_user_id": datasets.Value("string"),
+                "conversion_country": datasets.Value("string"),
+            },
+        ],
+        "colors": [
+            {
+                "hex": datasets.Value("string"),
+                "red": datasets.Value("uint8"),
+                "green": datasets.Value("uint8"),
+                "blue": datasets.Value("uint8"),
+                "keyword": datasets.Value("string"),
+                "ai_coverage": datasets.Value("float32"),
+                "ai_score": datasets.Value("float32"),
+            },
+        ],
+    },
+)
+
+def df_withprefix(df, prefix, exclude=None):
+    columns = [col for col in df.columns if col.startswith(prefix)]
+    if exclude is not None:
+        columns = [col for col in columns if exclude not in col]
+    if "photo_id" not in columns:
+        columns.append("photo_id")
+
+    return df[columns].rename(columns=lambda col: col.removeprefix(prefix))
+
+class Unsplash(datasets.GeneratorBasedBuilder):
+    """The Unsplash Lite dataset."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 100
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=_FEATURES,
+            supervised_keys=None,
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            version=_VERSION,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # raise NotImplementedError()
+
+        archive_path = Path(dl_manager.download_and_extract(_URL))
+
+        # read all tsv files
+        dataframes = {}
+        for doc in _TSV:
+            # read all tsv files for this document type
+            frames = []
+            for filename in archive_path.glob(f"{doc}.tsv*"):
+                frame = pd.read_csv(filename, sep="\t", header=0)
+                frames.append(frame)
+
+            # concatenate all subframes into one
+            concat_frames = pd.concat(frames, axis=0, ignore_index=True)
+
+            if doc != "photos":
+                dataframes[doc] = concat_frames
+            else:
+                # split "photos" into "photo", "photographer", "exif", "location", "stats", "ai"
+                dataframes["photo"] = df_withprefix(concat_frames, "photo_", "location")
+                dataframes["photo"]["blur_hash"] = concat_frames["blur_hash"]
+                dataframes["photographer"] = df_withprefix(concat_frames, "photographer_")
+                dataframes["exif"] = df_withprefix(concat_frames, "exif_")
+                dataframes["location"] = df_withprefix(concat_frames, "photo_location_")
+                dataframes["stats"] = df_withprefix(concat_frames, "stats_")
+                dataframes["ai"] = df_withprefix(concat_frames, "ai_")
+
+        # preprocess some columns
+        dataframes["photo"]["featured"] = dataframes["photo"]["featured"].map({"t": True, "f": False})
+        dataframes["keywords"]["suggested_by_user"] = dataframes["keywords"]["suggested_by_user"].map({"t": True, "f": False})
+
+        # cast columns to appropriate dtypes
+        for doc in dataframes.keys():
+            if doc in _TSV:
+                features = _FEATURES[doc][0]
+            else:
+                features = _FEATURES[doc]
+
+            dataframes[doc].astype({
+                key: features[key].dtype
+                for key in features.keys()
+            })
+
+        # groupby "photo_id" if not "photo" dataframe
+        for key in _TSV[:-1]:
+            dataframes[key] = dataframes[key].groupby("photo_id")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"dataframes": dataframes},
+            ),
+        ]
+
+    def _generate_examples(self, dataframes):
+        # iterate over rows of "photos" dataframe
+        photo_id_frames = {}
+        for index, row in dataframes["photo"].iterrows():
+            photo_id = row["id"]
+            photographer = dataframes["photographer"].iloc[index]
+            exif = dataframes["exif"].iloc[index]
+            location = dataframes["location"].iloc[index]
+            stats = dataframes["stats"].iloc[index]
+            ai = dataframes["ai"].iloc[index]
+
+            for key in _TSV[:-1]:
+                try:
+                    photo_id_frames[key] = dataframes[key].get_group(photo_id)
+                except:
+                    photo_id_frames[key] = pd.DataFrame()
+
+            data = {
+                "photo": {
+                    "id": photo_id,
+                    "url": row["url"],
+                    "image_url": row["image_url"],
+                    "submitted_at": row["submitted_at"],
+                    "featured": row["featured"],
+                    "width": row["width"],
+                    "height": row["height"],
+                    "aspect_ratio": row["aspect_ratio"],
+                    "description": row["description"],
+                    "blur_hash": row["blur_hash"],
+                },
+                "photographer": {
+                    "username": photographer["username"],
+                    "first_name": photographer["first_name"],
+                    "last_name": photographer["last_name"],
+                },
+                "exif": {
+                    "camera_make": exif["camera_make"],
+                    "camera_model": exif["camera_model"],
+                    "iso": exif["iso"],
+                    "aperture_value": exif["aperture_value"],
+                    "focal_length": exif["focal_length"],
+                    "exposure_time": exif["exposure_time"],
+                },
+                "location": {
+                    "name": location["name"],
+                    "latitude": location["latitude"],
+                    "longitude": location["longitude"],
+                    "country": location["country"],
+                    "city": location["city"],
+                },
+                "stats": {
+                    "views": stats["views"],
+                    "downloads": stats["downloads"],
+                },
+                "ai": {
+                    "description": ai["description"],
+                    "primary_landmark_name": ai["primary_landmark_name"],
+                    "primary_landmark_latitude": ai["primary_landmark_latitude"],
+                    "primary_landmark_longitude": ai["primary_landmark_longitude"],
+                    "primary_landmark_confidence": ai["primary_landmark_confidence"],
+                },
+                "keywords": [
+                    {
+                        "keyword": keyword["keyword"],
+                        "ai_service_1_confidence": keyword["ai_service_1_confidence"],
+                        "ai_service_2_confidence": keyword["ai_service_2_confidence"],
+                        "suggested_by_user": keyword["suggested_by_user"],
+                    }
+                    for _, keyword in photo_id_frames["keywords"].iterrows()
+                ],
+                "collections": [
+                    {
+                        "collection_id": collection["collection_id"],
+                        "collection_title": str(collection["collection_title"]),
+                        "photo_collected_at": collection["photo_collected_at"],
+                    }
+                    for _, collection in photo_id_frames["collections"].iterrows()
+                ],
+                "conversions": [
+                    {
+                        "converted_at": conversion["converted_at"],
+                        "conversion_type": conversion["conversion_type"],
+                        "keyword": conversion["keyword"],
+                        "anonymous_user_id": conversion["anonymous_user_id"],
+                        "conversion_country": str(conversion["conversion_country"]),
+                    }
+                    for _, conversion in photo_id_frames["conversions"].iterrows()
+                ],
+                "colors": [
+                    {
+                        "hex": color["hex"],
+                        "red": color["red"],
+                        "green": color["green"],
+                        "blue": color["blue"],
+                        "keyword": color["keyword"],
+                        "ai_coverage": color["ai_coverage"],
+                        "ai_score": color["ai_score"],
+                    }
+                    for _, color in photo_id_frames["colors"].iterrows()
+                ],
+            }
+
+            yield index, data