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Source code for lumin.nn.metrics.eval_metric

import numpy as np
import pandas as pd
from abc import ABC, abstractmethod
from typing import Optional

from ..data.fold_yielder import FoldYielder

__all__ = ['EvalMetric']


[docs]class EvalMetric(ABC): r''' Abstract class for evaluating performance of a model using some metric Arguments: targ_name: name of group in fold file containing regression targets wgt_name: name of group in fold file containing datapoint weights ''' def __init__(self, targ_name:str, wgt_name:Optional[str]=None): self.targ_name,self.wgt_name,self.lower_metric_better = targ_name,wgt_name,True
[docs] def get_df(self, fy:FoldYielder, idx:int, y_pred:np.ndarray) -> pd.DataFrame: r''' Returns a DataFrame for the given fold containing targets, weights, and predictions Arguments: fy: :class:`~lumin.nn.data.fold_yielder.FoldYielder` interfacing to data idx: fold index corresponding to fold for which y_pred was computed y_pred: predictions for fold Returns: DataFrame for the given fold containing targets, weights, and predictions ''' df = pd.DataFrame() if self.wgt_name is not None: df['gen_weight'] = fy.get_column(column=self.wgt_name, n_folds=1, fold_idx=idx) targets = fy.get_column(column=self.targ_name, n_folds=1, fold_idx=idx) if len(targets.shape) > 1: for t in range(targets.shape[-1]): df[f'gen_target_{t}'] = targets[:,t] else: df['gen_target'] = targets if len(y_pred.shape) > 1 and y_pred.shape[-1] > 1: for p in range(y_pred.shape[-1]): df[f'pred_{p}'] = y_pred[:,p] else: df['pred'] = y_pred.squeeze() return df
[docs] @abstractmethod def evaluate(self, fy:FoldYielder, idx:int, y_pred:np.ndarray) -> float: r''' Evaluate the required metric for a given fold and set of predictions Arguments: fy: :class:`~lumin.nn.data.fold_yielder.FoldYielder` interfacing to data idx: fold index corresponding to fold for which y_pred was computed y_pred: predictions for fold Returns: metric value ''' pass
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