def main(readcsv=read_csv, method='defaultDense'): # Input data set parameters train_file = os.path.join('data', 'batch', 'k_nearest_neighbors_train.csv') predict_file = os.path.join('data', 'batch', 'k_nearest_neighbors_test.csv') # Read data. Let's use 5 features per observation nFeatures = 5 nClasses = 5 train_data = readcsv(train_file, range(nFeatures)) train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1)) # Create an algorithm object and call compute train_algo = d4p.bf_knn_classification_training(nClasses=nClasses) # 'weights' is optional argument, let's use equal weights # in this case results must be the same as without weights weights = np.ones((train_data.shape[0], 1)) train_result = train_algo.compute(train_data, train_labels, weights) # Now let's do some prediction predict_data = readcsv(predict_file, range(nFeatures)) predict_labels = readcsv(predict_file, range(nFeatures, nFeatures + 1)) # Create an algorithm object and call compute predict_algo = d4p.bf_knn_classification_prediction(nClasses=nClasses) predict_result = predict_algo.compute(predict_data, train_result.model) # We expect less than 170 mispredicted values assert np.count_nonzero(predict_labels != predict_result.prediction) < 170 return (train_result, predict_result, predict_labels)
def compute(train_data, train_labels, predict_data, nClasses): # Create an algorithm object and call compute train_algo = d4p.bf_knn_classification_training(nClasses=nClasses) train_result = train_algo.compute(train_data, train_labels) # Create an algorithm object and call compute predict_algo = d4p.bf_knn_classification_prediction(nClasses=nClasses) predict_result = predict_algo.compute(predict_data, train_result.model) return predict_result
def main(readcsv=read_csv, method='defaultDense'): # Input data set parameters train_file = os.path.join('..', 'data', 'batch', 'k_nearest_neighbors_train.csv') predict_file = os.path.join('..', 'data', 'batch', 'k_nearest_neighbors_test.csv') # Read data. Let's use 5 features per observation nFeatures = 5 nClasses = 5 train_data = readcsv(train_file, range(nFeatures)) train_labels = readcsv(train_file, range(nFeatures, nFeatures + 1)) predict_data = readcsv(predict_file, range(nFeatures)) predict_labels = readcsv(predict_file, range(nFeatures, nFeatures + 1)) train_data = to_numpy(train_data) train_labels = to_numpy(train_labels) predict_data = to_numpy(predict_data) try: from dppl import device_context, device_type gpu_context = lambda: device_context(device_type.gpu, 0) except: from daal4py.oneapi import sycl_context gpu_context = lambda: sycl_context('gpu') # It is possible to specify to make the computations on GPU with gpu_context(): sycl_train_data = sycl_buffer(train_data) sycl_train_labels = sycl_buffer(train_labels) sycl_predict_data = sycl_buffer(predict_data) # Create an algorithm object and call compute train_algo = d4p.bf_knn_classification_training(nClasses=nClasses) train_result = train_algo.compute(sycl_train_data, sycl_train_labels) # Create an algorithm object and call compute predict_algo = d4p.bf_knn_classification_prediction() predict_result = predict_algo.compute(sycl_predict_data, train_result.model) # We expect less than 170 mispredicted values assert np.count_nonzero(predict_labels != predict_result.prediction) < 170 return (predict_result, predict_labels)