PyPI 官网下载 | synthetic_sample-1.0.1.tar.gz

# synthetic_sample synthetic_sample is a data generation application for producing synthetic sales transactions over a time series, including associated shipment and product data ## Usage Sample data is generated by running `synthetic_sample_generator.py` and using ``` python3 synthetic_sample_generator.py --json_filepath JSON_FILEPATH --output_directory OUTPUT_DIRECTORY --create_records ``` where - `json_filepath` is the filepath to the input JSON (see Request Requirements below) - `output_directory` is the directory to save output data to, in CSV format - `create_records` is a flag that indicates that raw record data should also be saved to the output directory. Running without this flag results in only aggregate output data ## Request Requirements The required input format is a JSON with the following fields: - Required: - `start_date`: date in the first period to include, e.g. if 2020/02/15 is provided, the full week of that date will be included - `end_date`: date in the last period to include, e.g. if 2020/02/15 is provided, the full week of that date will be included - `annual_growth_factor`: year over year growth factor, 10% growth corresponds to a value of 1.1 - `period_type`: indicates what type of curve to generate, supports "month" or "week" - at least one of - `total_sales`: total number of sales for the period - `total_packages`: total number of packages shipped for the period - `total_quantity`: total number of items sold for the period - `annual_sales`: annualized number of sales for the period - `annual_packages`: annualized number of packages shipped for the period - `annual_quantity`: annualized number of items sold for the period - `curve_definition`: Definition of the curve to create, either as a list of dictionaries with each feature or as a string indicating the name of the default curve to use. - If a list of dictionaries is provided, they must adhere to the following structure - Required Keys: - `anchor_type`: Type of annual anchor used to define the feature - Possible Values: "holiday", "week_of_year", "month_of_year", "day_of_year" - `anchor_point`: Annual point to define the feature - Possible values: (string) - holiday name, (int) - week or day of year - `anchor_value`: Cumulative percent of total sales (0.0-1.0) completed by the end of the period of the anchor_point - Optional Keys: - `relative_start`: Number of periods before the anchor_point to define a relative cumulative percent value - `start_value`: Cumulative percent of total sales (0.0-1.0) completed by the end of the period indicated by relative_start - `relative_end`: Number of periods before the anchor_point to define a relative cumulative percent value - `end_value`: Cumulative percent of total sales (0.0-1.0) completed by the end of the period indicated by relative_end - If a string is provided, it must correspond to a default in `synthetic_sample/defaults/curves/{period_type}/{curve_definition}.json` - Initial set of available curves are - `modern_brand` - `modern_distributor` - `traditional_brand` - `traditional_distributor` - Optional: - `default_type`: string indicating the type of defaults to use, these can be found as JSON in `synthetic_sample/defaults/lib/` - `product_distribution`: dictionary of product labels (i.e. SKUs) and their relative weights - `week_distribution`: dictionary of weeks of the month (where 1 is the first week and -1 is the last) and their relative weights - `weekday_distribution`: dictionary of weekdays (where 0 is Monday and 6 is Sunday) and their relative weights - `seasonal_distribution`: dictionary of seasons ("Q1"..."Q4") and their relative weights - `modifiers`: list of any modifiers to apply. - "covid": Applies a 33% boost to all periods between 2020/3/26 and 2021/9/1 ### Example: The below request will generate data for each month starting 2018-06 and ending 2020-12. ```JSON { "start_date": "2018-06-01", "end_date": "2020-12-31", "total_sales": 1000000, "total_packages": 1500000, "total_quantity": 6000000, "annual_growth_factor": 1.15, "product_distribution": { "AAA-01" : 1, "AAA-02" : 2.5, "AAA-11" : 5.6, "BBB-10" : 0.5, "BBB-20" : 1 }, "week_distribution": { "1": 0.1, "-1": 0.5 }, "weekday_distribution": { "0": 0.0, "1": 0.0, "2": 0.0, "3": 0.0, "4": 0.0, "5": 2.0, "6": 1.0 }, "seasonal_distribution": { "Q1": 1, "Q2": 1, "Q3": 1, "Q4": 1 }, "period_type": "month", "curve_definition": [ { "anchor_type": "month_of_year", "anchor_point": 1, "anchor_value": 0.0424 }, { "anchor_type": "month_of_year", "anchor_point": 2, "anchor_value": 0.103 }, { "anchor_type": "month_of_year", "anchor_point": 3, "anchor_value": 0.203 }, { "anchor_type": "month_of_year", "anchor_point": 4, "anchor_value": 0.3152 }, { "anchor_type": "month_of_year", "anchor_point": 5, "anchor_value": 0.4139 }, { "anchor_type": "month_of_year", "anchor_point": 6, "anchor_value": 0.4776 }, { "anchor_type": "month_of_year", "anchor_point": 7, "anchor_value": 0.5321 }, { "anchor_type": "month_of_year", "anchor_point": 8, "anchor_value": 0.5897 }, { "anchor_type": "month_of_year", "anchor_point": 9, "anchor_value": 0.6715 }, { "anchor_type": "month_of_year", "anchor_point": 10, "anchor_value": 0.7836 }, { "anchor_type": "month_of_year", "anchor_point": 11, "anchor_value": 0.9018 }, { "anchor_type": "month_of_year", "anchor_point": 12, "anchor_value": 1.0 } ] } ```