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Diffstat (limited to 'gensvm/cython_wrapper/wrapper.pyx')
| -rw-r--r-- | gensvm/cython_wrapper/wrapper.pyx | 223 |
1 files changed, 223 insertions, 0 deletions
diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx new file mode 100644 index 0000000..4ded637 --- /dev/null +++ b/gensvm/cython_wrapper/wrapper.pyx @@ -0,0 +1,223 @@ +""" +Wrapper for GenSVM + +Not implemented yet: + - vector of instance weights + - class weights + - seed model + - max_iter = -1 for unlimited + +""" + +from __future__ import print_function + +from libc.stdlib cimport malloc, free + +import numpy as np +cimport numpy as np + +cimport wrapper + +np.import_array() + +GENSVM_KERNEL_TYPES = ["linear", "poly", "rbf", "sigmoid"] + +def train_wrap( + np.ndarray[np.float64_t, ndim=2, mode='c'] X, + np.ndarray[np.int_t, ndim=1, mode='c'] y, + long n_class, + double p=1.0, + double lmd=pow(2, -8), + double kappa=0.0, + double epsilon=1e-6, + int weight_idx=1, + str kernel='linear', + double gamma=1.0, + double coef=0.0, + double degree=2.0, + double kernel_eigen_cutoff=1e-8, + int max_iter=100000000, + int random_seed=-1, + np.ndarray[np.float64_t, ndim=2, mode='c'] seed_V=None + ): + """ + """ + + # Initialize model and data + cdef GenModel *model = gensvm_init_model() + cdef GenData *data = gensvm_init_data() + cdef GenModel *seed_model = gensvm_init_model() + cdef long n_obs + cdef long n_var + + # get the kernel index + kernel_index = GENSVM_KERNEL_TYPES.index(kernel) + + # get the number of classes + n_obs = X.shape[0] + n_var = X.shape[1] + + # Set the data + set_data(data, X.data, y.data, X.shape, n_class) + + # Set the model + set_model(model, p, lmd, kappa, epsilon, weight_idx, kernel_index, degree, + gamma, coef, kernel_eigen_cutoff, max_iter, random_seed) + + if not seed_V is None: + set_seed_model(seed_model, p, lmd, kappa, epsilon, weight_idx, + kernel_index, degree, gamma, coef, kernel_eigen_cutoff, + max_iter, random_seed, seed_V.data, n_var, n_class) + else: + gensvm_free_model(seed_model) + seed_model = NULL + + # Check the parameters + error_msg = check_model(model) + if error_msg: + gensvm_free_model(model) + free_data(data) + error_repl = error_msg.decode('utf-8') + raise ValueError(error_repl) + + # Do the actual training + with nogil: + gensvm_train(model, data, seed_model) + + # update the number of variables (this may have changed due to kernel) + n_var = get_m(model) + + # copy the results + cdef np.ndarray[np.float64_t, ndim=2, mode='c'] V + V = np.empty((n_var+1, n_class-1)) + copy_V(V.data, model) + + # get other results from model + iter_count = get_iter_count(model) + training_error = get_training_error(model) + fit_status = get_status(model) + n_SV = gensvm_num_sv(model) + + # free model and data + gensvm_free_model(model); + free_data(data); + + return (V, n_SV, iter_count, training_error, fit_status) + + +def predict_wrap( + np.ndarray[np.float64_t, ndim=2, mode='c'] X, + np.ndarray[np.float64_t, ndim=2, mode='c'] V + ): + """ + """ + + cdef long n_test_obs + cdef long n_var + cdef long n_class + + n_test_obs = X.shape[0] + n_var = X.shape[1] + n_class = V.shape[1] + 1 + + # output vector + cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions + predictions = np.empty((n_test_obs, ), dtype=np.int) + + # do the prediction + with nogil: + gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, + predictions.data) + + return predictions + +def grid_wrap( + np.ndarray[np.float64_t, ndim=2, mode='c'] X, + np.ndarray[np.int_t, ndim=1, mode='c'] y, + candidate_params, + int store_predictions, + np.ndarray[np.int_t, ndim=1, mode='c'] cv_idx, + int n_folds, + ): + """ + """ + + cdef GenQueue *queue = gensvm_init_queue() + cdef GenData *data = gensvm_init_data() + cdef GenTask *task + cdef long n_obs + cdef long n_var + cdef long n_class + cdef long n_tasks = len(candidate_params) + + # get the number of classes + classes = np.unique(y) + n_obs = X.shape[0] + n_var = X.shape[1] + n_class = classes.shape[0] + + set_data(data, X.data, y.data, X.shape, n_class) + + cdef GenTask **tasks = <GenTask **>malloc(n_tasks * sizeof(GenTask *)) + + ID = 0 + for candidate in candidate_params: + params = { + 'p': 1.0, + 'lmd': 1e-5, + 'kappa': 0.0, + 'epsilon': 1e-6, + 'weight_idx': 1, + 'kernel': GENSVM_KERNEL_TYPES.index('linear'), + 'gamma': 1.0, + 'coef': 0.0, + 'degree': 2.0, + 'max_iter': 1e8 + } + params.update(candidate) + if 'kernel' in candidate: + params['kernel'] = GENSVM_KERNEL_TYPES.index(candidate['kernel']) + if 'weights' in candidate: + params['weight_idx'] = 1 if candidate['weights'] == 'unit' else 2 + + task = gensvm_init_task() + set_task(task, ID, data, n_folds, params['p'], params['lmd'], + params['kappa'], params['epsilon'], params['weight_idx'], + params['kernel'], params['degree'], params['gamma'], + params['coef'], params['max_iter']) + + tasks[ID] = task + ID += 1 + + set_queue(queue, n_tasks, tasks) + + with nogil: + gensvm_train_q_helper(queue, cv_idx.data, store_predictions) + + cdef np.ndarray[np.int_t, ndim=1, mode='c'] pred + + results = dict() + results['params'] = [] + results['duration'] = [] + results['scores'] = [] + results['predictions'] = [] + for ID in range(n_tasks): + results['params'].append(candidate_params[ID]) + results['duration'].append(get_task_duration(tasks[ID])) + results['scores'].append(get_task_performance(tasks[ID])) + if store_predictions: + pred = np.zeros((n_obs, ), dtype=np.int) + copy_task_predictions(tasks[ID], pred.data, n_obs) + results['predictions'].append(pred.copy()) + + gensvm_free_queue(queue) + free_data(data) + + return results + + +def set_verbosity_wrap(int verbosity): + """ + Control verbosity of gensvm library + """ + set_verbosity(verbosity) |