def macro_mae(y_test, y_pred, classes):
cat_to_class_mapping = {v: int(k) for k, v in
get_labels_to_categories_map(classes).items()}
_y_test = [cat_to_class_mapping[y] for y in y_test]
_y_pred = [cat_to_class_mapping[y] for y in y_pred]
c = Counter(_y_pred)
print(c)
classes = set(_y_test)
micro_m = {}
for c in classes:
class_sentences = [(t, p) for t, p in zip(_y_test, _y_pred) if t == c]
yt = [y[0] for y in class_sentences]
yp = [y[1] for y in class_sentences]
micro_m[c] = mean_absolute_error(yt, yp)
# pprint.pprint(sorted(micro_m.items(), key=lambda x: x[1], reverse=True))
return numpy.mean(list(micro_m.values()))
rnn_cells=64,
attention="context",
clipnorm=.1,
classes=len(classes))
print(nn_model.summary())
############################################################################
# CALLBACKS
############################################################################
# define metrics and class weights
if TASK == "BD":
cat_to_class_mapping = {
v: k
for k, v in get_labels_to_categories_map(classes).items()
}
metrics = {
"accuracy": (lambda y_test, y_pred: accuracy_score(y_test, y_pred)),
"recall":
(lambda y_test, y_pred: recall_score(y_test, y_pred, average='macro')),
"precision": (lambda y_test, y_pred: precision_score(
y_test, y_pred, average='macro'))
}
else:
cat_to_class_mapping = {
v: int(k)
for k, v in get_labels_to_categories_map(classes).items()
}
def macro_mae(y_test, y_pred):
"f1_weighted": (lambda y_test, y_pred:
f1_score(y_test, y_pred, average='weighted',
labels=[class_to_cat_mapping['positive'],
class_to_cat_mapping['neutral'],
class_to_cat_mapping['negative']])),
"M_recall": (
lambda y_test, y_pred: recall_score(y_test, y_pred, average='macro')),
"M_precision": (
lambda y_test, y_pred: precision_score(y_test, y_pred,
average='macro')),
"accuracy": (
lambda y_test, y_pred: accuracy_score(y_test, y_pred))
}
classes = ['positive', 'negative', 'neutral']
class_to_cat_mapping = get_labels_to_categories_map(
classes) # {'negative': 0, 'neutral': 1, 'positive': 2}
cat_to_class_mapping = {v: k for k, v in
get_labels_to_categories_map(classes).items()} # {0: 'negative', 1: 'neutral', 2: 'positive'}
_datasets = {}
_datasets["1-train"] = training,
_datasets["2-val"] = validation if not FINAL else testing
if not FINAL:
_datasets["3-test"] = testing
metrics_callback = MetricsCallback(datasets=_datasets, metrics=metrics)
plotting = PlottingCallback(grid_ranges=(0.7, 1), height=5,
plot_name="model_{}_{}_{}".format(COPRPUSNAME, PREPROCESS_TYP, MODEL_FILE_NUMBER)) # benchmarks={"SE17": 0.681},
tensorboard = TensorBoard(log_dir='./logs/{}'.format(COPRPUSNAME))
_callbacks = []
# CALLBACKS
############################################################################
metrics = {
"f1_pn": (lambda y_test, y_pred:
f1_score(y_test, y_pred, average='macro',
labels=[class_to_cat_mapping['positive'],
class_to_cat_mapping['negative']])),
"M_recall": (
lambda y_test, y_pred: recall_score(y_test, y_pred, average='macro')),
"M_precision": (
lambda y_test, y_pred: precision_score(y_test, y_pred,
average='macro'))
}
classes = ['positive', 'negative', 'neutral']
class_to_cat_mapping = get_labels_to_categories_map(classes)
cat_to_class_mapping = {v: k for k, v in
get_labels_to_categories_map(classes).items()}
_datasets = {}
_datasets["1-train"] = training,
_datasets["2-val"] = validation if not FINAL else testing
if not FINAL:
_datasets["3-test"] = testing
metrics_callback = MetricsCallback(datasets=_datasets, metrics=metrics)
weights = WeightsCallback(parameters=["W"], stats=["raster", "mean", "std"])
# plotting = PlottingCallback(grid_ranges=(0.5, 0.75), height=5, benchmarks={"SE17": 0.67, "goal": 0.71})
plotting = PlottingCallback(grid_ranges=(0.5, 0.75), height=5,
benchmarks={"SE17": 0.681})