def __init__(self, *args):
super().__init__(*args)
self.k = k
self.model = GaussianProcess(encoder=self.encode,
dim=dim,
shape=self.shape)
self.beta = beta
def __init__(self, *args):
super().__init__(*args)
self.k = k
self.model = GaussianProcess(encoder=self.encode,
dim=dim,
shape=self.shape)
self.beta = beta
if len(self.prior):
self.model.embed.fit(*zip(*self.prior.items()),
epochs=initial_epochs)
class Agent(agents.random.RandomAgent(epochs)):
def __init__(self, *args):
super().__init__(*args)
self.k = k
self.model = GaussianProcess(encoder=self.encode,
dim=dim,
shape=self.shape)
self.beta = beta
def act(self, seqs):
t = 1 + len(self.seen) // self.batch
D = len(seqs) + len(self.seen)
beta = lambda t: beta * 2 * np.log(D * t**2 * np.pi**2 / 3)
mu = self.model.interpolate(seqs)
sigma = self.model.uncertainty(seqs)
seqs = np.array(seqs)
ucb = mu + 2 * np.sqrt(beta(t + 1)) * sigma
selected = np.argsort(ucb)[-int(k * self.batch):]
if k != 1.:
idx = utils.mcmc.mcmc(self.batch,
self.model.embed(seqs[selected]),
iters=1000)
selected = selected[idx]
return seqs[selected]
def observe(self, data):
super().observe(data)
self.model.fit(*zip(*self.seen.items()), epochs=epochs)
self.model.mll()
def walk_forward_cv(self):
"""
Runs walk-forward cross-validation, and saves cross-validation
metrics.
"""
for output_name in self.output_names:
print('\t\t\t|--Prediction type: {}'.format(output_name))
optimal_params_by_model = {}
cv_metadata_by_model = {}
cv_predictions_by_model = {}
print('\t\t\t\t|--KNN Model')
knn = KNN()
knn.cv_params = self.cv_params
knn.test_name = self.test_name
knn.full_df = self.full_df
knn.feature_names = self.feature_names
knn.output_name = output_name
knn.run_knn_cv()
optimal_params_by_model['KNN'] = knn.knn_optimal_params
cv_predictions_by_model['KNN'] = knn.knn_cv_predictions
print('\t\t\t\t|--Elastic Net Model')
elastic_net = ElasticNet()
elastic_net.cv_params = self.cv_params
elastic_net.test_name = self.test_name
elastic_net.full_df = self.full_df
elastic_net.feature_names = self.feature_names
elastic_net.feature_dict = self.feature_dict
elastic_net.output_name = output_name
elastic_net.run_elastic_net_cv()
optimal_params_by_model[
'Elastic_Net'] = elastic_net.elastic_net_optimal_params
cv_metadata_by_model['Elastic_Net'] = elastic_net.metadata
cv_predictions_by_model[
'Elastic_Net'] = elastic_net.elastic_net_cv_predictions
print('\t\t\t\t|--Naive Bayes Model')
naive_bayes = NaiveBayes()
naive_bayes.cv_params = self.cv_params
naive_bayes.test_name = self.test_name
naive_bayes.full_df = self.full_df
naive_bayes.feature_names = self.feature_names
naive_bayes.feature_dict = self.feature_dict
naive_bayes.output_name = output_name
naive_bayes.run_bayes_cv()
cv_predictions_by_model[
'Naive_Bayes'] = naive_bayes.bayes_cv_predictions
optimal_params_by_model[
'Naive_Bayes'] = naive_bayes.bayes_optimal_params
print('\t\t\t\t|--SVM Model')
svm = SupportVectorMachine()
svm.cv_params = self.cv_params
svm.test_name = self.test_name
svm.full_df = self.full_df
svm.feature_names = self.feature_names
svm.output_name = output_name
svm.run_svm_cv()
optimal_params_by_model['SVM'] = svm.svm_optimal_params
cv_metadata_by_model['SVM'] = svm.metadata
cv_predictions_by_model['SVM'] = svm.svm_cv_predictions
print('\t\t\t\t|--Gaussian Process Model')
gauss = GaussianProcess()
gauss.cv_params = self.cv_params
gauss.test_name = self.test_name
gauss.full_df = self.full_df
gauss.feature_names = self.feature_names
gauss.feature_dict = self.feature_dict
gauss.output_name = output_name
gauss.run_gauss_cv()
cv_predictions_by_model[
'Gaussian_Process'] = gauss.gauss_cv_predictions
cv_metadata_by_model['Gaussian_Process'] = gauss.metadata
optimal_params_by_model[
'Gaussian_Process'] = gauss.gauss_optimal_params
print('\t\t\t\t|--XGBoost Model')
xgboost = XGBoost()
xgboost.cv_params = self.cv_params
xgboost.test_name = self.test_name
xgboost.full_df = self.full_df
xgboost.feature_names = self.feature_names
xgboost.feature_dict = self.feature_dict
xgboost.output_name = output_name
xgboost.run_xgboost_cv()
optimal_params_by_model['XGBoost'] = xgboost.xgboost_optimal_params
cv_metadata_by_model['XGBoost'] = xgboost.metadata
cv_predictions_by_model['XGBoost'] = xgboost.xgboost_cv_predictions
self.optimal_params_by_output[
output_name] = optimal_params_by_model
self.cv_metadata_by_output[output_name] = cv_metadata_by_model
self.cv_predictions_by_output[
output_name] = cv_predictions_by_model
def walk_forward_prediction(self):
"""
Runs walk-forward prediction, and saves prediction metrics.
"""
for output_name in self.output_names:
print('\t\t\t|--Prediction type: {}'.format(output_name))
prediction_errors_by_model = {}
predictions_by_model = {}
pred_metadata_by_model = {}
print('\t\t\t\t|--KNN Model')
knn = KNN()
knn.pred_indices = self.pred_indices
knn.full_df = self.full_df
knn.feature_names = self.feature_names
knn.output_name = output_name
knn.knn_optimal_params = self.optimal_params_by_output[
output_name]['KNN']
knn.run_knn_prediction()
prediction_errors_by_model['KNN'] = knn.knn_pred_error
predictions_by_model['KNN'] = knn.knn_predictions
print('\t\t\t\t|--Elastic Net Model')
elastic_net = ElasticNet()
elastic_net.pred_indices = self.pred_indices
elastic_net.full_df = self.full_df
elastic_net.feature_names = self.feature_names
elastic_net.feature_dict = self.feature_dict
elastic_net.output_name = output_name
elastic_net.elastic_net_optimal_params = self.optimal_params_by_output[
output_name]['Elastic_Net']
elastic_net.run_elastic_net_prediction()
prediction_errors_by_model[
'Elastic_Net'] = elastic_net.elastic_net_pred_error
predictions_by_model[
'Elastic_Net'] = elastic_net.elastic_net_predictions
pred_metadata_by_model['Elastic_Net'] = elastic_net.metadata
print('\t\t\t\t|--Naive Bayes Model')
naive_bayes = NaiveBayes()
naive_bayes.pred_indices = self.pred_indices
naive_bayes.full_df = self.full_df
naive_bayes.feature_names = self.feature_names
naive_bayes.output_name = output_name
naive_bayes.run_bayes_prediction()
prediction_errors_by_model[
'Naive_Bayes'] = naive_bayes.bayes_pred_error
predictions_by_model['Naive_Bayes'] = naive_bayes.bayes_predictions
print('\t\t\t\t|--SVM Model')
svm = SupportVectorMachine()
svm.pred_indices = self.pred_indices
svm.full_df = self.full_df
svm.feature_names = self.feature_names
svm.output_name = output_name
svm.svm_optimal_params = self.optimal_params_by_output[
output_name]['SVM']
svm.run_svm_prediction()
prediction_errors_by_model['SVM'] = svm.svm_pred_error
predictions_by_model['SVM'] = svm.svm_predictions
pred_metadata_by_model['SVM'] = svm.metadata
print('\t\t\t\t|--Gaussian Process Model')
gauss = GaussianProcess()
gauss.pred_indices = self.pred_indices
gauss.full_df = self.full_df
gauss.feature_names = self.feature_names
gauss.output_name = output_name
gauss.run_gauss_prediction()
prediction_errors_by_model[
'Gaussian_Process'] = gauss.gauss_pred_error
predictions_by_model['Gaussian_Process'] = gauss.gauss_predictions
pred_metadata_by_model['Gaussian_Process'] = gauss.metadata
print('\t\t\t\t|--XGBoost Model')
xgboost = XGBoost()
xgboost.pred_indices = self.pred_indices
xgboost.full_df = self.full_df
xgboost.feature_names = self.feature_names
xgboost.feature_dict = self.feature_dict
xgboost.output_name = output_name
xgboost.xgboost_optimal_params = self.optimal_params_by_output[
output_name]['XGBoost']
xgboost.run_xgboost_prediction()
prediction_errors_by_model['XGBoost'] = xgboost.xgboost_pred_error
predictions_by_model['XGBoost'] = xgboost.xgboost_predictions
pred_metadata_by_model['XGBoost'] = xgboost.metadata
print('\t\t\t\t|--Weighted Average Model')
weighted_average = WeightedAverage()
weighted_average.model_names = self.model_names
weighted_average.cv_results = self.optimal_params_by_output[
output_name]
weighted_average.predictions_by_model = predictions_by_model
weighted_average.run_weighted_average_prediction()
predictions_by_model[
'Weighted_Average'] = weighted_average.weighted_average_predictions
pred_metadata_by_model[
'Weighted_Average'] = weighted_average.metadata
self.prediction_errors_by_output[
output_name] = prediction_errors_by_model
self.predictions_by_output[output_name] = predictions_by_model
self.pred_metadata_by_output[output_name] = pred_metadata_by_model