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Taipy's Core entities

This documentation focuses on providing necessary information to use the Taipy Core entities, and in particular the capabilities related to scenario management. It is assumed that the reader already knows the Taipy Core concepts described in a previous chapter.

Let’s assume also that the configuration represented in the picture below is implemented in the module my_config.py.

scenarios

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from datetime import datetime
from taipy import Config, Frequency, Scope
import pandas as pd


def write_orders_plan(data: pd.DataFrame):
    insert_data = list(data[["date", "product_id", "number_of_products"]].itertuples(index=False, name=None))
    return [
        "DELETE FROM orders",
        ("INSERT INTO orders VALUES (?, ?, ?)", insert_data)
    ]


def train(sales_history: pd.DataFrame):
    print("Running training")
    return "TRAINED_MODEL"


def predict(model, current_month):
    print("Running predicting")
    return "SALES_PREDICTIONS"


def plan(sales_predictions, capacity):
    print("Running planning")
    return "PRODUCTION_ORDERS"


def compare(previous_month_prediction, current_month_prediction):
    print("Comparing previous month and current month sale predictions")
    return "COMPARISON_RESULT"


# Configure all six data nodes
sales_history_cfg = Config.configure_csv_data_node(
    id="sales_history", scope=Scope.GLOBAL, default_path="path/sales.csv"
)
trained_model_cfg = Config.configure_data_node(id="trained_model", scope=Scope.CYCLE)
current_month_cfg = Config.configure_data_node(id="current_month", scope=Scope.CYCLE, default_data=datetime(2020, 1, 1))
sales_predictions_cfg = Config.configure_data_node(id="sales_predictions", scope=Scope.CYCLE)
capacity_cfg = Config.configure_data_node(id="capacity")
orders_cfg = Config.configure_sql_data_node(
    id="orders",
    db_username="admin",
    db_password="ENV[PWD]",
    db_name="production_planning",
    db_engine="mssql",
    read_query="SELECT orders.ID, orders.date, products.price, orders.number_of_products FROM orders INNER JOIN products ON orders.product_id=products.ID",
    write_query_builder=write_orders_plan,
)

# Configure the three tasks
training_cfg = Config.configure_task("training", train, sales_history_cfg, [trained_model_cfg])
predicting_cfg = Config.configure_task(
    id="predicting", function=predict, input=[trained_model_cfg, current_month_cfg], output=sales_predictions_cfg
)
planning_cfg = Config.configure_task(
    id="planning", function=plan, input=[sales_predictions_cfg, capacity_cfg], output=[orders_cfg]
)

# Configure the two pipelines
sales_pipeline_cfg = Config.configure_pipeline(id="sales", task_configs=[training_cfg, predicting_cfg])
production_pipeline_cfg = Config.configure_pipeline(id="production", task_configs=[planning_cfg])

# Configure the scenario
monthly_scenario_cfg = Config.configure_scenario(
    id="scenario_configuration",
    pipeline_configs=[sales_pipeline_cfg, production_pipeline_cfg],
    frequency=Frequency.MONTHLY,
    comparators={sales_predictions_cfg.id: compare},
)

Please refer to the configuration documentation to have information on how to configure a Taipy application.

The next section presents the scenario creation.