def test_all_hyperparameters_regressor(sagemaker_session): knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type='sign', dimension_reduction_target='2', index_type='faiss.Flat', index_metric='COSINE', faiss_index_ivf_nlists='auto', faiss_index_pq_m=1, **ALL_REQ_ARGS) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS['k']), sample_size=str(ALL_REQ_ARGS['sample_size']), predictor_type=str(ALL_REQ_ARGS['predictor_type']), dimension_reduction_type='sign', dimension_reduction_target='2', index_type='faiss.Flat', index_metric='COSINE', faiss_index_ivf_nlists='auto', faiss_index_pq_m='1' )
def test_all_hyperparameters_classifier(sagemaker_session): test_params = ALL_REQ_ARGS.copy() test_params['predictor_type'] = PREDICTOR_TYPE_CLASSIFIER knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type='fjlt', dimension_reduction_target='2', index_type='faiss.IVFFlat', index_metric='L2', faiss_index_ivf_nlists='20', **test_params) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS['k']), sample_size=str(ALL_REQ_ARGS['sample_size']), predictor_type=str(PREDICTOR_TYPE_CLASSIFIER), dimension_reduction_type='fjlt', dimension_reduction_target='2', index_type='faiss.IVFFlat', index_metric='L2', faiss_index_ivf_nlists='20' )
def test_all_hyperparameters_regressor(sagemaker_session): knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type='sign', dimension_reduction_target='2', index_type='faiss.Flat', index_metric='COSINE', faiss_index_ivf_nlists='auto', faiss_index_pq_m=1, **ALL_REQ_ARGS) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS['k']), sample_size=str(ALL_REQ_ARGS['sample_size']), predictor_type=str(ALL_REQ_ARGS['predictor_type']), dimension_reduction_type='sign', dimension_reduction_target='2', index_type='faiss.Flat', index_metric='COSINE', faiss_index_ivf_nlists='auto', faiss_index_pq_m='1')
def test_all_hyperparameters_regressor(sagemaker_session): knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type="sign", dimension_reduction_target="2", index_type="faiss.Flat", index_metric="COSINE", faiss_index_ivf_nlists="auto", faiss_index_pq_m=1, **ALL_REQ_ARGS) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS["k"]), sample_size=str(ALL_REQ_ARGS["sample_size"]), predictor_type=str(ALL_REQ_ARGS["predictor_type"]), dimension_reduction_type="sign", dimension_reduction_target="2", index_type="faiss.Flat", index_metric="COSINE", faiss_index_ivf_nlists="auto", faiss_index_pq_m="1", )
def test_all_hyperparameters_classifier(sagemaker_session): test_params = ALL_REQ_ARGS.copy() test_params['predictor_type'] = PREDICTOR_TYPE_CLASSIFIER knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type='fjlt', dimension_reduction_target='2', index_type='faiss.IVFFlat', index_metric='L2', faiss_index_ivf_nlists='20', **test_params) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS['k']), sample_size=str(ALL_REQ_ARGS['sample_size']), predictor_type=str(PREDICTOR_TYPE_CLASSIFIER), dimension_reduction_type='fjlt', dimension_reduction_target='2', index_type='faiss.IVFFlat', index_metric='L2', faiss_index_ivf_nlists='20')
def test_all_hyperparameters_classifier(sagemaker_session): test_params = ALL_REQ_ARGS.copy() test_params["predictor_type"] = PREDICTOR_TYPE_CLASSIFIER knn = KNN(sagemaker_session=sagemaker_session, dimension_reduction_type="fjlt", dimension_reduction_target="2", index_type="faiss.IVFFlat", index_metric="L2", faiss_index_ivf_nlists="20", **test_params) assert knn.hyperparameters() == dict( k=str(ALL_REQ_ARGS["k"]), sample_size=str(ALL_REQ_ARGS["sample_size"]), predictor_type=str(PREDICTOR_TYPE_CLASSIFIER), dimension_reduction_type="fjlt", dimension_reduction_target="2", index_type="faiss.IVFFlat", index_metric="L2", faiss_index_ivf_nlists="20", )