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‎cpp/pythonbinding.cpp‎

Lines changed: 59 additions & 59 deletions
Original file line numberDiff line numberDiff line change
@@ -97,70 +97,70 @@ PYBIND11_MODULE(aplr_cpp, m)
9797
.def_readwrite("boosting_steps_before_pruning_is_done", &APLRRegressor::boosting_steps_before_pruning_is_done)
9898
.def(py::pickle(
9999
[](const APLRRegressor &a) { // __getstate__
100-
/* Return a tuple that fully encodes the state of the object */
101-
return py::make_tuple(a.m, a.v, a.random_state, a.loss_function, a.link_function, a.n_jobs, a.validation_ratio, a.intercept, a.bins,
102-
a.verbosity, a.max_interaction_level, a.max_interactions, a.validation_error_steps, a.term_names, a.term_coefficients, a.terms,
103-
a.interactions_eligible, a.min_observations_in_split, a.ineligible_boosting_steps_added, a.max_eligible_terms,
104-
a.number_of_base_terms, a.feature_importance, a.dispersion_parameter, a.min_training_prediction_or_response,
105-
a.max_training_prediction_or_response, a.validation_tuning_metric, a.validation_indexes, a.quantile, a.m_optimal,
106-
a.intercept_steps, a.boosting_steps_before_pruning_is_done);
100+
/* Return a tuple that fully encodes the state of the object */
101+
return py::make_tuple(a.m, a.v, a.random_state, a.loss_function, a.link_function, a.n_jobs, a.validation_ratio, a.intercept, a.bins,
102+
a.verbosity, a.max_interaction_level, a.max_interactions, a.validation_error_steps, a.term_names, a.term_coefficients, a.terms,
103+
a.interactions_eligible, a.min_observations_in_split, a.ineligible_boosting_steps_added, a.max_eligible_terms,
104+
a.number_of_base_terms, a.feature_importance, a.dispersion_parameter, a.min_training_prediction_or_response,
105+
a.max_training_prediction_or_response, a.validation_tuning_metric, a.validation_indexes, a.quantile, a.m_optimal,
106+
a.intercept_steps, a.boosting_steps_before_pruning_is_done);
107107
},
108108
[](py::tuple t) { // __setstate__
109-
if (t.size() != 31)
110-
throw std::runtime_error("Invalid state!");
109+
if (t.size() != 31)
110+
throw std::runtime_error("Invalid state!");
111111

112-
/* Create a new C++ instance */
113-
size_t m = t[0].cast<size_t>();
114-
double v = t[1].cast<double>();
115-
uint_fast32_t random_state = t[2].cast<uint_fast32_t>();
116-
std::string loss_function = t[3].cast<std::string>();
117-
std::string link_function = t[4].cast<std::string>();
118-
size_t n_jobs = t[5].cast<size_t>();
119-
double validation_ratio = t[6].cast<double>();
120-
double intercept = t[7].cast<double>();
121-
size_t bins = t[8].cast<size_t>();
122-
size_t verbosity = t[9].cast<size_t>();
123-
size_t max_interaction_level = t[10].cast<size_t>();
124-
size_t max_interactions = t[11].cast<size_t>();
125-
VectorXd validation_error_steps = t[12].cast<VectorXd>();
126-
std::vector<std::string> term_names = t[13].cast<std::vector<std::string>>();
127-
VectorXd term_coefficients = t[14].cast<VectorXd>();
128-
std::vector<Term> terms = t[15].cast<std::vector<Term>>();
129-
size_t interactions_eligible = t[16].cast<size_t>();
130-
size_t min_observations_in_split = t[17].cast<size_t>();
131-
size_t ineligible_boosting_steps_added = t[18].cast<size_t>();
132-
size_t max_eligible_terms = t[19].cast<size_t>();
133-
size_t number_of_base_terms = t[20].cast<size_t>();
134-
VectorXd feature_importance = t[21].cast<VectorXd>();
135-
double dispersion_parameter = t[22].cast<double>();
136-
double min_training_prediction_or_response = t[23].cast<double>();
137-
double max_training_prediction_or_response = t[24].cast<double>();
138-
std::string validation_tuning_metric = t[25].cast<std::string>();
139-
std::vector<size_t> validation_indexes = t[26].cast<std::vector<size_t>>();
140-
double quantile = t[27].cast<double>();
141-
size_t m_optimal = t[28].cast<size_t>();
142-
VectorXd intercept_steps = t[29].cast<VectorXd>();
143-
size_t boosting_steps_before_pruning_is_done = t[30].cast<size_t>();
112+
/* Create a new C++ instance */
113+
size_t m = t[0].cast<size_t>();
114+
double v = t[1].cast<double>();
115+
uint_fast32_t random_state = t[2].cast<uint_fast32_t>();
116+
std::string loss_function = t[3].cast<std::string>();
117+
std::string link_function = t[4].cast<std::string>();
118+
size_t n_jobs = t[5].cast<size_t>();
119+
double validation_ratio = t[6].cast<double>();
120+
double intercept = t[7].cast<double>();
121+
size_t bins = t[8].cast<size_t>();
122+
size_t verbosity = t[9].cast<size_t>();
123+
size_t max_interaction_level = t[10].cast<size_t>();
124+
size_t max_interactions = t[11].cast<size_t>();
125+
VectorXd validation_error_steps = t[12].cast<VectorXd>();
126+
std::vector<std::string> term_names = t[13].cast<std::vector<std::string>>();
127+
VectorXd term_coefficients = t[14].cast<VectorXd>();
128+
std::vector<Term> terms = t[15].cast<std::vector<Term>>();
129+
size_t interactions_eligible = t[16].cast<size_t>();
130+
size_t min_observations_in_split = t[17].cast<size_t>();
131+
size_t ineligible_boosting_steps_added = t[18].cast<size_t>();
132+
size_t max_eligible_terms = t[19].cast<size_t>();
133+
size_t number_of_base_terms = t[20].cast<size_t>();
134+
VectorXd feature_importance = t[21].cast<VectorXd>();
135+
double dispersion_parameter = t[22].cast<double>();
136+
double min_training_prediction_or_response = t[23].cast<double>();
137+
double max_training_prediction_or_response = t[24].cast<double>();
138+
std::string validation_tuning_metric = t[25].cast<std::string>();
139+
std::vector<size_t> validation_indexes = t[26].cast<std::vector<size_t>>();
140+
double quantile = t[27].cast<double>();
141+
size_t m_optimal = t[28].cast<size_t>();
142+
VectorXd intercept_steps = t[29].cast<VectorXd>();
143+
size_t boosting_steps_before_pruning_is_done = t[30].cast<size_t>();
144144

145-
APLRRegressor a(m, v, random_state, loss_function, link_function, n_jobs, validation_ratio, 100, bins, verbosity, max_interaction_level,
146-
max_interactions, min_observations_in_split, ineligible_boosting_steps_added, max_eligible_terms, dispersion_parameter,
147-
validation_tuning_metric, quantile);
148-
a.intercept = intercept;
149-
a.validation_error_steps = validation_error_steps;
150-
a.term_names = term_names;
151-
a.term_coefficients = term_coefficients;
152-
a.terms = terms;
153-
a.interactions_eligible = interactions_eligible;
154-
a.number_of_base_terms = number_of_base_terms;
155-
a.feature_importance = feature_importance;
156-
a.min_training_prediction_or_response = min_training_prediction_or_response;
157-
a.max_training_prediction_or_response = max_training_prediction_or_response;
158-
a.validation_indexes = validation_indexes;
159-
a.m_optimal = m_optimal;
160-
a.intercept_steps = intercept_steps;
161-
a.boosting_steps_before_pruning_is_done = boosting_steps_before_pruning_is_done;
145+
APLRRegressor a(m, v, random_state, loss_function, link_function, n_jobs, validation_ratio, 100, bins, verbosity, max_interaction_level,
146+
max_interactions, min_observations_in_split, ineligible_boosting_steps_added, max_eligible_terms, dispersion_parameter,
147+
validation_tuning_metric, quantile);
148+
a.intercept = intercept;
149+
a.validation_error_steps = validation_error_steps;
150+
a.term_names = term_names;
151+
a.term_coefficients = term_coefficients;
152+
a.terms = terms;
153+
a.interactions_eligible = interactions_eligible;
154+
a.number_of_base_terms = number_of_base_terms;
155+
a.feature_importance = feature_importance;
156+
a.min_training_prediction_or_response = min_training_prediction_or_response;
157+
a.max_training_prediction_or_response = max_training_prediction_or_response;
158+
a.validation_indexes = validation_indexes;
159+
a.m_optimal = m_optimal;
160+
a.intercept_steps = intercept_steps;
161+
a.boosting_steps_before_pruning_is_done = boosting_steps_before_pruning_is_done;
162162

163-
return a;
163+
return a;
164164
}));
165165

166166
py::class_<Term>(m, "Term", py::module_local())

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