CsvExporter

CsvExporter

Exports detailed prediction results to a wide-format CSV file. Each row represents a single forecast point, with per-model prediction and error columns.

Parameters

Parameter	Type	Default	Description
path	str	Required	Output CSV file path
extra_columns	List[str]	[]	Columns from the source dataset to include

Basic Usage

from epftoolbox2.exporters import CsvExporter

exporter = CsvExporter("results.csv")

Output Format

The CSV contains the following columns:

Base columns:

• run_date — date the forecast was made
• target_date — date being forecasted
• hour — hour of day (0-23)
• horizon — forecast horizon (1 to max)
• day_in_test — day index in the test period
• actual — actual observed value

Per-model columns:

• {model}_prediction — model’s prediction
• {model}_error — residual (prediction - actual)

Extra columns (optional):

• Any columns from the source dataset, joined by target_date + hour

Example Output

For a pipeline with models OLS and LassoCV, and extra_columns=["is_holiday"]:

run_date	target_date	hour	horizon	actual	OLS_prediction	OLS_error	LassoCV_prediction	LassoCV_error	is_holiday
2024-02-01	2024-02-02	0	1	48.50	45.23	-3.27	46.10	-2.40	0

Extra Columns

Use extra_columns to include columns from the source dataset (e.g., calendar features, weather data). Columns are joined by matching target_date and hour from the results to the source dataset’s DatetimeIndex.

exporter = CsvExporter(
    "results.csv",
    extra_columns=["is_holiday", "load_forecast", "warsaw_temperature_2m"],
)

If a requested column does not exist in the source dataset, it is silently skipped.

In Pipeline

from epftoolbox2.pipelines import ModelPipeline
from epftoolbox2.models import OLSModel, LassoCVModel
from epftoolbox2.evaluators import MAEEvaluator, RMSEEvaluator
from epftoolbox2.exporters import CsvExporter

pipeline = (
    ModelPipeline()
    .add_model(OLSModel(predictors=predictors, name="OLS"))
    .add_model(LassoCVModel(predictors=predictors, cv=7, name="LassoCV"))
    .add_evaluator(MAEEvaluator())
    .add_evaluator(RMSEEvaluator())
    .add_exporter(CsvExporter(
        "results.csv",
        extra_columns=["is_holiday", "load_forecast"],
    ))
)

report = pipeline.run(data=df, test_start="2024-02-01", test_end="2024-03-01", target="price", horizon=7)
# Results saved to results.csv