Source code for sensai.ensemble.models
from typing import Sequence, List
import pandas as pd
import numpy as np
from .ensemble_base import EnsembleRegressionVectorModel
from ..vector_model import VectorRegressionModel
[docs]class AveragingVectorRegressionModel(EnsembleRegressionVectorModel):
def __init__(self, models: Sequence[VectorRegressionModel], weights: Sequence[float] = None, num_processes=1):
if weights is not None:
if len(weights) != len(models):
raise Exception(f"Number of weights does not match number of vectorRegressionModels: {len(weights)} != {len(models)}")
else:
weights = 1 / len(models) * np.ones(len(models))
self.weights = weights
super().__init__(models, num_processes=num_processes)
[docs] def aggregate_predictions(self, predictions_data_frames: List[pd.DataFrame]) -> pd.DataFrame:
combined_prediction = pd.DataFrame()
for cur_prediction_df, weight in zip(predictions_data_frames, self.weights):
if combined_prediction.empty:
combined_prediction = weight * cur_prediction_df
continue
if not set(combined_prediction.columns) == set(cur_prediction_df.columns):
raise Exception(f"Cannot combine different sets of columns for prediction: {combined_prediction.columns}, "
f"{cur_prediction_df.columns}")
for column in cur_prediction_df.columns:
combined_prediction[column] += weight * cur_prediction_df[column]
return combined_prediction