def from_dfs(cls, Xdf, ydf, ML=None, bounds=None, features=None): X = Xdf.values y = ydf.values preprocessor = Rescaler.from_data(X) postprocessor = Normalizer.from_data(y) if not ML: from sklearn.svm import SVR as ML if ydf.ndim == 1: predictor = ML() predictor.metric = ydf.name else: def create_predictor(metric): ml = ML() ml.metric = metric return ml predictors = [create_predictor(col) for col in ydf] predictor = MultiPredictor(predictors) if not features: features = tuple(Xdf) self = cls(predictor, preprocessor, postprocessor, bounds, features) self.fit(X, y) return self
def create_predictor(metric): ml = ML() ml.metric = metric return ml