backend/time-series-forecasting/services/dataset_service.py

# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
from typing import List, Optional

from models import dataset

DATASETS = [
    dataset.CSVDataset(
        "sample_data/sales_forecasting.csv",
        display_name="Retail Sales",
        time_column="date",
        description="This is sales data from a fictional sporting goods company with several stores across the city. It includes sales data for several products, grouped in several categories.",
        icon="storefront",
        recommended_model_parameters={
            "bqml_arimaplus": {
                "targetColumn": "sales",
                "timeColumn": "date",
                "timeSeriesIdentifierColumn": "product_at_store",
                "dataGranularityUnit": "day",
                "dataGranularityCount": 1,
            },
            "automl-forecasting": {
                "targetColumn": "sales",
                "timeColumn": "date",
                "timeSeriesIdentifierColumn": "product_at_store",
                "dataGranularityUnit": "day",
                "dataGranularityCount": 1,
                "timeSeriesAttributeColumns": [
                    "product_type",
                    "product_category",
                    "store",
                    "product",
                ],
                "columnSpecs": {
                    "date": "timestamp",
                    "sales": "numeric",
                    "product_type": "categorical",
                    "product_category": "categorical",
                    "product": "categorical",
                    "store": "categorical",
                },
            },
        },
        recommended_prediction_parameters={
            "bqml_arimaplus": {
                "forecastHorizon": 120,
            },
            "automl-forecasting": {"forecastHorizon": 30, "contextWindow": 30},
        },
    ),
    dataset.CSVDataset(
        "sample_data/iowa_liquor_sales.csv",
        display_name="Iowa Liquor Sales",
        time_column="date",
        description="This dataset contains the spirits purchase information of Iowa Class “E” liquor licensees by product and date of purchase. This dataset was simplified for demonstration purposes.",
        icon="liquor",
        recommended_model_parameters={
            "bqml_arimaplus": {
                "targetColumn": "sale_dollars",
                "timeColumn": "date",
                "timeSeriesIdentifierColumn": "county_and_city",
                "dataFrequency": "daily",
            }
        },
        recommended_prediction_parameters={
            "bqml_arimaplus": {
                "forecastHorizon": 120,
            }
        },
    ),
]


def get_datasets() -> List[dataset.Dataset]:
    return DATASETS


def get_dataset(dataset_id: str) -> Optional[dataset.Dataset]:
    """Get the dataset given the dataset_id.

    Args:
        dataset_id (str): Dataset id.

    Returns:
        Optional[dataset.Dataset]: The dataset.
    """

    target_dataset = None

    for dataset in get_datasets():
        if str(dataset.id) == dataset_id:
            target_dataset = dataset
            break

    if target_dataset is not None:
        return target_dataset
    else:
        logging.error(f"Dataset id {dataset_id} does not exist!")
        return None