LAP/torchlap/model_evaluation/binary_fortelling.py

from typing import List, TYPE_CHECKING, Dict, Callable
from functools import partial

import numpy as np
from torch import Tensor

from ..data.data_loader import DataLoader
from ..models.model import Model
from ..model_evaluation.evaluator import Evaluator
if TYPE_CHECKING:
    from ..configs.base_config import BaseConfig


class BinForetellerEvaluator(Evaluator):

    def __init__(self, kw_prefix: str, model: Model, data_loader: DataLoader, conf: 'BaseConfig'):
        super(BinForetellerEvaluator, self).__init__(model, data_loader, conf)

        self._tp_by_kw: Dict[str, int] = dict()
        self._fp_by_kw: Dict[str, int] = dict()
        self._tn_by_kw: Dict[str, int] = dict()
        self._fn_by_kw: Dict[str, int] = dict()

        self._kw_prefix = kw_prefix

    def reset(self):
        self._tp_by_kw: Dict[str, int] = dict()
        self._fp_by_kw: Dict[str, int] = dict()
        self._tn_by_kw: Dict[str, int] = dict()
        self._fn_by_kw: Dict[str, int] = dict()

    def update_summaries_based_on_model_output(self, model_output: Dict[str, Tensor]) -> None:

        gt = self.data_loader.get_current_batch_samples_labels()

        # looking for prefixes
        for k in model_output.keys():
            if k.startswith(self._kw_prefix):

                if k not in self._tp_by_kw:
                    self._tp_by_kw[k] = 0
                    self._tn_by_kw[k] = 0
                    self._fp_by_kw[k] = 0
                    self._fn_by_kw[k] = 0

                pred = (model_output[k].cpu().numpy() >= 0.5).astype(int)
                self._tp_by_kw[k] += np.sum(np.logical_and(gt == 1, pred == 1))
                self._fp_by_kw[k] += np.sum(np.logical_and(gt == 0, pred == 1))
                self._tn_by_kw[k] += np.sum(np.logical_and(gt == 0, pred == 0))
                self._fn_by_kw[k] += np.sum(np.logical_and(gt == 1, pred == 0))

    def get_titles_of_evaluation_metrics(self) -> List[str]:
        return [f'{k}_{metric}' for k in self._tp_by_kw.keys() for metric in ['Acc', 'Sens', 'Spec', 'AvgSS']]

    def _get_values_of_evaluation_metrics(self, kw) -> List[str]:

        tp = self._tp_by_kw[kw]
        tn = self._tn_by_kw[kw]
        fp = self._fp_by_kw[kw]
        fn = self._fn_by_kw[kw]

        p = tp + fn
        n = tn + fp

        if p + n > 0:
            accuracy = (tp + tn) * 100.0 / (n + p)
        else:
            accuracy = -1

        if p > 0:
            sensitivity = 100.0 * tp / p
        else:
            sensitivity = -1

        if n > 0:
            specificity = 100.0 * tn / max(n, 1)
        else:
            specificity = -1

        if sensitivity > -1 and specificity > -1:
            avg_ss = 0.5 * (sensitivity + specificity)
        elif sensitivity > -1:
            avg_ss = sensitivity
        else:
            avg_ss = specificity

        return ['%.2f' % accuracy, '%.2f' % sensitivity,
                '%.2f' % specificity, '%.2f' % avg_ss]

    def get_values_of_evaluation_metrics(self) -> List[str]:
        return [
            v
            for k in self._tp_by_kw.keys()
            for v in self._get_values_of_evaluation_metrics(k)]

    @classmethod
    def standard_creator(cls, prefix_kw: str) -> Callable[[Model, DataLoader, 'BaseConfig'], 'BinForetellerEvaluator']:
        return partial(BinForetellerEvaluator, prefix_kw)

    def print_evaluation_metrics(self, title: str) -> None:
        """ For more readable printing! """

        print(f'{title}:')
        for k in self._tp_by_kw.keys():
            print(
                f'\t{k}:: ' +
                ', '.join([f'{m_name}: {m_val}'
                 for m_name, m_val in zip(['Acc', 'Sens', 'Spec', 'AvgSS'], self._get_values_of_evaluation_metrics(k))]))