def _validate_k_fold_top(self, model, x_train, y_train, x_test, y_test):
validation_quantities = []
for k_min in self.k_mins:
for k_max in self.k_maxs:
for dist_percentage in self.dist_percentages:
print(
f"k_min, k_max, dist_percentage: {k_min}, {k_max}, {dist_percentage}"
)
pipeline_list = [
('extract_subspaces',
SubSpaceExtraction(dist_percentage=dist_percentage,
k_min=k_min,
k_max=k_max,
metric="euclidean",
n_jobs=-1)),
('compute_diagrams',
VietorisRipsPersistence(n_jobs=-1))
]
top_pipeline = Pipeline(pipeline_list)
diagrams_train, _ = top_pipeline.fit_transform_resample(
x_train, y_train)
top_features_train = extract_topological_features(
diagrams_train)
x_train_model = np.concatenate(
[x_train, top_features_train], axis=1)
model.fit(x_train_model, y_train)
x_test_model = extract_features_for_prediction(
x_train, y_train, x_test, y_test, top_pipeline)
score = model.score(x_test_model, y_test)
output_dictionary = {
"k_min": k_min,
"k_max": k_max,
"dist_percentage": dist_percentage,
"score": score
}
validation_quantities.append(output_dictionary)
return validation_quantities
def cross_validate(self, full_x, full_y, splitting_dates):
train_split_date = splitting_dates[0]
val_split_date = splitting_dates[1]
end_date = splitting_dates[2]
train_x = full_x[(full_x.date < train_split_date) |
(full_x.date >= end_date)]
train_y = full_y[(full_x.date < train_split_date) |
(full_x.date >= end_date)]
val_x = full_x[(full_x.date >= train_split_date)
& (full_x.date < val_split_date)]
val_y = full_y[(full_x.date >= train_split_date)
& (full_x.date < val_split_date)]
test_x = full_x[(full_x.date >= val_split_date)
& (full_x.date < end_date)]
test_y = full_y[(full_x.date >= val_split_date)
& (full_x.date < end_date)]
train_x.pop("date")
val_x.pop("date")
test_x.pop("date")
train_x = train_x.values
train_y = train_y.values
val_x = val_x.values
val_y = val_y.values
test_x = test_x.values
test_y = test_y.values
print("START VALIDATING MODEL")
models_cv = self._validate_k_fold_model(train_x, train_y, val_x, val_y)
best_model_params = best_combination(models_cv)
best_model_params.pop("score")
best_model = RandomForestClassifier(**best_model_params)
best_model.fit(train_x, train_y)
score = best_model.score(test_x, test_y)
print(f'score no_top {score}')
print(f'best model parameters no_top {best_model_params}')
print("START VALIDATING PARAMS")
topo_cv = self._validate_k_fold_top(best_model, train_x, train_y,
val_x, val_y)
best_topo = best_combination(topo_cv)
best_topo.pop("score")
best_topo_pipeline_list = [
('extract_subspaces', SubSpaceExtraction(**best_topo)),
('compute_diagrams', VietorisRipsPersistence(n_jobs=-1))
]
best_topo_pipeline = Pipeline(best_topo_pipeline_list)
train_x_for_test = np.concatenate([train_x, val_x], axis=0)
train_y_for_test = np.concatenate([train_y, val_y], axis=0)
diagrams_train, _ = best_topo_pipeline.fit_transform_resample(
train_x_for_test, train_y_for_test)
print("EXTRACTING TOPOLOGICAL FEATURES TRAIN")
top_features_train = extract_topological_features(diagrams_train)
x_train_model = np.concatenate([train_x_for_test, top_features_train],
axis=1)
best_model.fit(x_train_model, train_y_for_test)
print("EXTRACTING TOPOLOGICAL FEATURES TEST")
x_test_model = extract_features_for_prediction(x_train_model,
train_y_for_test,
test_x, test_y,
best_topo_pipeline)
score_top = best_model.score(x_test_model, test_y)
val_x_with_topo = extract_features_for_prediction(
train_x, train_y, val_x, val_y, best_topo_pipeline)
print('START VALIDATING MODEL WITH OPTIMAL TOPOLOGY')
model_config_with_topo = self._validate_k_fold_model(
x_train_model, train_y, val_x_with_topo, val_y)
best_model_config_with_topo = best_combination(model_config_with_topo)
best_model_config_with_topo.pop('score')
best_model_with_topo = RandomForestClassifier(
**best_model_config_with_topo)
best_model_with_topo.fit(x_train_model, train_y_for_test)
score_best_topo_and_model = best_model_with_topo.score(
x_test_model, test_y)
print(f'score best model and topo_feat {score_best_topo_and_model}')
return best_model_params, best_topo, best_model_config_with_topo, score, score_top, score_best_topo_and_model