Python load_data_redis示例

示例#1

def test_load_data_redis(mocker, key, data):
    mocker.patch('src.middleware.redis_client.redis_client.get',
                 return_value=data)
    mocker.patch('json.loads', return_value=data)
    result = store_data_job.load_data_redis(key)
    assert result['data'] == data['data']
    assert result['prediction'] == data['prediction']

示例#2

def test_load_data_redis(mocker, key, data):
    mocker.patch("src.middleware.redis_client.redis_client.get",
                 return_value=data)
    mocker.patch("json.loads", return_value=data)
    result = store_data_job.load_data_redis(key)
    assert result["data"] == data["data"]
    assert result["prediction"] == data["prediction"]

示例#3

文件： _predict.py 项目： ksomemo-kaggle/ml-system-in-action

def _predict_from_redis_cache(job_id: str,
                              data_class: callable = Data) -> Data:
    data_dict = store_data_job.load_data_redis(job_id)
    if data_dict is None:
        return None
    data = data_class(**data_dict)
    __predict(data)
    return data

示例#4

def _predict_async_get(job_id: str) -> Dict[str, List[float]]:
    result = {job_id: {'prediction': []}}
    if PlatformConfigurations.platform == PLATFORM_ENUM.DOCKER_COMPOSE.value:
        data_dict = store_data_job.load_data_redis(job_id)
        result[job_id]['prediction'] = data_dict['prediction']
        return result

    elif PlatformConfigurations.platform == PLATFORM_ENUM.KUBERNETES.value:
        data_dict = store_data_job.load_data_redis(job_id)
        result[job_id]['prediction'] = data_dict['prediction']
        return result

    elif PlatformConfigurations.platform == PLATFORM_ENUM.TEST.value:
        data_dict = store_data_job.load_data_redis(job_id)
        result[job_id]['prediction'] = data_dict['prediction']
        return result

    else:
        return result

示例#5

def _predict_async_get_label(
        job_id: str) -> Dict[str, Dict[str, Dict[str, float]]]:
    result = {job_id: {'prediction': []}}
    if PlatformConfigurations.platform == PLATFORM_ENUM.DOCKER_COMPOSE.value:
        data_dict = store_data_job.load_data_redis(job_id)
        if result[job_id]['prediction'] is None:
            result[job_id]['prediction'] = data_dict['prediction']
            return result
        argmax = int(np.argmax(np.array(data_dict['prediction'])[0]))
        result[job_id]['prediction'] = {
            data_dict['labels'][argmax]: data_dict['prediction'][0][argmax]
        }
        return result

    elif PlatformConfigurations.platform == PLATFORM_ENUM.KUBERNETES.value:
        data_dict = store_data_job.load_data_redis(job_id)
        if result[job_id]['prediction'] is None:
            result[job_id]['prediction'] = data_dict['prediction']
            return result
        argmax = int(np.argmax(np.array(data_dict['prediction'])[0]))
        result[job_id]['prediction'] = {
            data_dict['labels'][argmax]: data_dict['prediction'][0][argmax]
        }
        return result

    elif PlatformConfigurations.platform == PLATFORM_ENUM.TEST.value:
        data_dict = store_data_job.load_data_redis(job_id)
        if result[job_id]['prediction'] is None:
            result[job_id]['prediction'] = data_dict['prediction']
            return result
        argmax = int(np.argmax(np.array(data_dict['prediction'])[0]))
        result[job_id]['prediction'] = {
            data_dict['labels'][argmax]: data_dict['prediction'][0][argmax]
        }
        return result

    else:
        return result

示例#6

def _predict_from_redis_cache(job_id: str,
                              data_class: callable = Data) -> Data:
    data_dict = store_data_job.load_data_redis(job_id)
    if data_dict is None:
        return None
    if 'image_data' in data_dict.keys():
        image_key = store_data_job.make_image_key(job_id)
        if isinstance(data_dict['image_data'],
                      str) and data_dict['image_data'] == image_key:
            image_data = store_data_job.get_image_redis(image_key)
            data_dict['image_data'] = image_data
    data = data_class(**data_dict)
    __predict(data)
    return data