Beispiel #1
0
def save_model_file(results, save_to_s3):
    """
    Saves a machine learning model to file or uploads to S3 as needed
    results - Dictionary of results from ML
    save_to_s3 - Boolean indicating whether or not to upload results
    """
    success = False
    if save_to_s3:
        pickled_model = ml_grading_util.get_pickle_data(
            results['prompt'], results['feature_ext'], results['classifier'],
            results['text'], results['score'])
        success, s3_public_url = ml_grading_util.upload_to_s3(
            pickled_model, results['relative_model_path'],
            str(settings.S3_BUCKETNAME))

    try:
        ml_grading_util.dump_model_to_file(results['prompt'],
                                           results['feature_ext'],
                                           results['classifier'],
                                           results['text'], results['score'],
                                           results['model_path'])
        if success:
            return True, s3_public_url
        else:
            return True, "Saved model to file."
    except:
        return False, "Could not save model."
Beispiel #2
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def store_model_locally(created_model,results):
    relative_model_path= created_model.model_relative_path
    full_model_path = os.path.join(settings.ML_MODEL_PATH,relative_model_path)
    try:
        ml_grading_util.dump_model_to_file(results['prompt'], results['extractor'],
            results['model'], results['text'],results['score'],full_model_path)
    except Exception:
        error_message="Could not save model to file."
        log.exception(error_message)
        return False, error_message

    return True, "Saved file."
Beispiel #3
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def store_model_locally(created_model, results):
    relative_model_path = created_model.model_relative_path
    full_model_path = os.path.join(settings.ML_MODEL_PATH, relative_model_path)
    try:
        ml_grading_util.dump_model_to_file(results['prompt'],
                                           results['extractor'],
                                           results['model'], results['text'],
                                           results['score'], full_model_path)
    except Exception:
        error_message = "Could not save model to file."
        log.exception(error_message)
        return False, error_message

    return True, "Saved file."
Beispiel #4
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def save_model_file(results, save_to_s3):
    success=False
    if save_to_s3:
        pickled_model=ml_grading_util.get_pickle_data(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],
            results['score'])
        success, s3_public_url=ml_grading_util.upload_to_s3(pickled_model, results['relative_model_path'], str(settings.S3_BUCKETNAME))

    try:
        ml_grading_util.dump_model_to_file(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],results['score'],results['model_path'])
        if success:
            return True, s3_public_url
        else:
            return True, "Saved model to file."
    except:
        return False, "Could not save model."
Beispiel #5
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def store_model_locally(created_model,results):
    """
    Saves a model to a local file.
    created_model - instance of CreatedModel (django model)
    results - result dictionary to save
    """
    relative_model_path= created_model.model_relative_path
    full_model_path = os.path.join(settings.ML_MODEL_PATH,relative_model_path)
    try:
        ml_grading_util.dump_model_to_file(results['prompt'], results['extractor'],
            results['model'], results['text'],results['score'],full_model_path)
    except:
        error_message="Could not save model to file."
        log.exception(error_message)
        return False, error_message

    return True, "Saved file."
Beispiel #6
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def save_model_file(results, save_to_s3):
    success=False
    if save_to_s3:
        pickled_model=ml_grading_util.get_pickle_data(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],
            results['score'])
        success, s3_public_url=ml_grading_util.upload_to_s3(pickled_model, results['relative_model_path'], str(settings.S3_BUCKETNAME))

    try:
        ml_grading_util.dump_model_to_file(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],results['score'],results['model_path'])
        if success:
            return True, s3_public_url
        else:
            return True, "Saved model to file."
    except Exception:
        return False, "Could not save model."
Beispiel #7
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def store_model_locally(created_model, results):
    """
    Saves a model to a local file.
    created_model - instance of CreatedModel (django model)
    results - result dictionary to save
    """
    relative_model_path = created_model.model_relative_path
    full_model_path = os.path.join(settings.ML_MODEL_PATH, relative_model_path)
    try:
        ml_grading_util.dump_model_to_file(results['prompt'],
                                           results['extractor'],
                                           results['model'], results['text'],
                                           results['score'], full_model_path)
    except:
        error_message = "Could not save model to file."
        log.exception(error_message)
        return False, error_message

    return True, "Saved file."
def save_model_file(results, save_to_s3):
    """
    Saves a machine learning model to file or uploads to S3 as needed
    results - Dictionary of results from ML
    save_to_s3 - Boolean indicating whether or not to upload results
    """
    success=False
    if save_to_s3:
        pickled_model=ml_grading_util.get_pickle_data(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],
            results['score'])
        success, s3_public_url=ml_grading_util.upload_to_s3(pickled_model, results['relative_model_path'], str(settings.S3_BUCKETNAME))

    try:
        ml_grading_util.dump_model_to_file(results['prompt'], results['feature_ext'],
            results['classifier'], results['text'],results['score'],results['model_path'])
        if success:
            return True, s3_public_url
        else:
            return True, "Saved model to file."
    except:
        return False, "Could not save model."