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",
)
