meter.py

import numpy as np


class RunningMeter:
    def __init__(self, args):
        # Tracking at a per epoch level
        self.loss = {"train": [], "val": [], "test": []}
        self.accuracy = {"train": [], "val": [], "test": []}
        self.f1_score = {"train": [], "val": [], "test": []}
        self.f1_score_weighted = {"train": [], "val": [], "test": []}
        self.confusion_matrix = {"train": [], "val": [], "test": []}
        self.accuracy_steps = {"train": [], "val": [], "test": []}

        self.epochs = np.arange(0, args.num_epochs)

        self.best_meter = BestMeter()

        self.args = args

    def update(
        self,
        phase,
        loss,
        accuracy,
        f1_score,
        f1_score_weighted,
        confusion_matrix,
        accuracy_steps,
    ):
        # Update the metrics for every phase
        self.loss[phase].append(loss)
        self.accuracy[phase].append(accuracy)
        self.f1_score[phase].append(f1_score)
        self.f1_score_weighted[phase].append(f1_score_weighted)
        self.confusion_matrix[phase].append(confusion_matrix)
        self.accuracy_steps[phase].append(accuracy_steps)

    def get(self):
        return (
            self.loss,
            self.accuracy,
            self.f1_score,
            self.f1_score_weighted,
            self.confusion_matrix,
            self.accuracy_steps,
            self.epochs,
        )

    def update_best_meter(self, best_meter):
        self.best_meter = best_meter


class BestMeter:
    def __init__(self):
        # Storing the best values
        self.loss = {"train": np.inf, "val": np.inf, "test": np.inf}
        self.accuracy = {"train": 0.0, "val": 0.0, "test": 0.0}
        self.f1_score = {"train": 0.0, "val": 0.0, "test": 0.0}
        self.f1_score_weighted = {"train": 0.0, "val": 0.0, "test": 0.0}
        self.confusion_matrix = {"train": [], "val": [], "test": []}
        self.accuracy_steps = {"train": [], "val": [], "test": []}
        self.epoch = 0

    def update(
        self,
        phase,
        loss,
        accuracy,
        f1_score,
        f1_score_weighted,
        confusion_matrix,
        accuracy_steps,
        epoch,
    ):
        self.loss[phase] = loss
        self.accuracy[phase] = accuracy
        self.f1_score[phase] = f1_score
        self.f1_score_weighted[phase] = f1_score_weighted
        self.confusion_matrix[phase] = confusion_matrix
        self.accuracy_steps[phase] = accuracy_steps
        self.epoch = epoch

    def get(self):
        return (
            self.loss,
            self.accuracy,
            self.f1_score,
            self.f1_score_weighted,
            self.confusion_matrix,
            self.epoch,
            self.accuracy_steps,
        )

    def display(self):
        print("The best epoch is {}".format(self.epoch))
        for phase in ["train", "val", "test"]:
            print(
                "Phase: {}, loss: {}, accuracy: {}, f1_score: {}, f1_score "
                "weighted: {}".format(
                    phase,
                    self.loss[phase],
                    self.accuracy[phase],
                    self.f1_score[phase],
                    self.f1_score_weighted[phase],
                ),
                self.accuracy_steps,
            )