processing.agg_predict.agg_predict

processing.agg_predict.agg_predict(predictions, weights=None)

Aggregates multiple prediction columns into a single combined prediction series.

The combination is a weighted sum of the prediction columns. If no weights are provided, a default weighting scheme based on specific predefined columns is used.

Parameters

Name	Type	Description	Default
predictions	pd.DataFrame	DataFrame containing the prediction columns.	required
weights	Optional[Union[Dict[str, float], List[float], np.ndarray]]	Dictionary mapping column names to their weights, or a list/array of weights corresponding to the order of columns in `predictions`. If None, defaults to summing all columns (weight=1.0 for each column).	`None`

Returns

Name	Type	Description
	pd.Series	pd.Series: A Series containing the aggregated values.

Raises

Name	Type	Description
	ValueError	If a column specified in weights (or default weights) is missing from predictions.
	ValueError	If weights is a list/array and its length does not match the number of columns in predictions.

Examples

import pandas as pd
from spotforecast2_safe.processing import agg_predict

df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})

result_dict = agg_predict(df, weights={"A": 1.0, "B": -1.0})
print(result_dict.tolist())
assert result_dict.tolist() == [-2.0, -2.0]

result_list = agg_predict(df, weights=[0.5, 2.0])
print(result_list.tolist())
assert result_list.tolist() == [6.5, 9.0]

[-2.0, -2.0]
[6.5, 9.0]

import pandas as pd
from spotforecast2_safe.processing import agg_predict

# Default behaviour: equal weight 1.0 for every column (sum)
df = pd.DataFrame({"X": [10.0, 20.0], "Y": [5.0, 3.0]})
result_default = agg_predict(df)
print(result_default.tolist())
assert result_default.tolist() == [15.0, 23.0]

[15.0, 23.0]