def evaluate(self, n_folds=10):
matrices = []
f1 = 0
precision = 0
recall = 0
for train, test in self.__dp.split(n_folds,
ActivityDataHeaders.START_TIME):
self.fit(train)
processed_dataset = self.process_dataset(test)
predictions = self.__predict(processed_dataset)
metric = Metrics(processed_dataset[ActivityDataHeaders.LABEL],
pd.DataFrame(predictions))
f1 += metric.f1()
precision += metric.precision()
recall += metric.recall()
matrices += [metric.confusion_matrix()]
f1 /= n_folds
precision /= n_folds
recall /= n_folds
matrices = np.array(matrices)
return f1, precision, recall, matrices
def evaluate(self, n_folds=10):
matrices = []
f1 = 0
precision = 0
recall = 0
for train, test in self.__dp.split(n_folds, ActivityDataHeaders.START_TIME):
self.fit(train, to_save=False)
batches = self.__create_batches(test)
_, truth = self.__flat(batches)
truth = self.__hot_encoder.inverse_transform(truth)
truth = pd.DataFrame(truth)
prediction = self.predict(test)
metric = Metrics(truth, pd.DataFrame(prediction))
f1 += metric.f1()
precision += metric.precision()
recall += metric.recall()
matrices += [metric.confusion_matrix()]
f1 /= n_folds
precision /= n_folds
recall /= n_folds
matrices = np.array(matrices)
return f1, precision, recall, matrices
