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How to properly save/load model #2294
How to properly save/load model #2294
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Hi
I am really new to catboost. I would like some clarification about saving/loading model.
The way I save/load model, I did it as in here : https://catboost.ai/en/docs/concepts/python-usages-examples#load-from-file
I saved trained model using .cbm format. The problem is when I a load model back and I want to call model.get_best_iteration() it return None . model.feature_importance_ also returned None . Here is my code
params = < 'iterations' : 10000, 'early_stopping_rounds' : 1000, 'loss_function' : 'Logloss', 'eval_metric' : 'F1', 'task_type' : 'GPU', 'gpu_ram_part' : 0.3, 'train_dir' : 'model/catboost_info', >model = CatBoostClassifier(**params) print('fit the model. ') start = time.time() model.fit( X=train_data, use_best_model=True, eval_set=valid_data, ) train_time = time.time() - start print('done training. ') print('save the model and train time..') model.save_model('model/catboost.cbm') print('load model and train time. ') cat = CatBoostClassifier() cat.load_model('model/catboost.cbm') with open('dump/catboost_train_time.pickle', 'rb') as f: train_time = pickle.load(f) print('finish..') print(f'train time : ') print(f'last iteration: ') The text was updated successfully, but these errors were encountered:
mlflow.catboost
The mlflow.catboost module provides an API for logging and loading CatBoost models. This module exports CatBoost models with the following flavors:
This is the main flavor that can be loaded back into CatBoost.
Produced for use by generic pyfunc-based deployment tools and batch inference.
mlflow.catboost. get_default_conda_env ( ) [source] Returns
The default Conda environment for MLflow Models produced by calls to save_model() and log_model() .
mlflow.catboost. get_default_pip_requirements ( ) [source] Returns
A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.
mlflow.catboost. load_model ( model_uri , dst_path = None ) [source]
Load a CatBoost model from a local file or a run.
- model_uri – The location, in URI format, of the MLflow model. For example:
- /Users/me/path/to/local/model
- relative/path/to/local/model
- s3://my_bucket/path/to/model
- runs://run-relative/path/to/model
For more information about supported URI schemes, see Referencing Artifacts.
mlflow.catboost. log_model ( cb_model , artifact_path , conda_env = None , code_paths = None , registered_model_name = None , signature : mlflow.models.signature.ModelSignature = None , input_example : Union [ pandas.core.frame.DataFrame , numpy.ndarray , dict , list , csr_matrix , csc_matrix , str , bytes ] = None , await_registration_for = 300 , pip_requirements = None , extra_pip_requirements = None , metadata = None , ** kwargs ) [source]
Log a CatBoost model as an MLflow artifact for the current run.
- cb_model – CatBoost model (an instance of CatBoost, CatBoostClassifier, CatBoostRanker, or CatBoostRegressor) to be saved.
- artifact_path – Run-relative artifact path.
- conda_env – Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env() . If None , a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements() . pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml . The following is an example dictionary representation of a conda environment:
"name": "mlflow-env", "channels": ["conda-forge"], "dependencies": [ "python=3.8.15", "pip": [ "catboost==x.y.z" ], >, ], >from mlflow.models import infer_signature train = df.drop_column("target_label") predictions = . # compute model predictions signature = infer_signature(train, predictions)
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements .
A ModelInfo instance that contains the metadata of the logged model.
mlflow.catboost. save_model ( cb_model , path , conda_env = None , code_paths = None , mlflow_model = None , signature : mlflow.models.signature.ModelSignature = None , input_example : Union [ pandas.core.frame.DataFrame , numpy.ndarray , dict , list , csr_matrix , csc_matrix , str , bytes ] = None , pip_requirements = None , extra_pip_requirements = None , metadata = None , ** kwargs ) [source]
Save a CatBoost model to a path on the local file system.
- cb_model – CatBoost model (an instance of CatBoost, CatBoostClassifier, CatBoostRanker, or CatBoostRegressor) to be saved.
- path – Local path where the model is to be saved.
- conda_env – Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env() . If None , a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements() . pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml . The following is an example dictionary representation of a conda environment:
"name": "mlflow-env", "channels": ["conda-forge"], "dependencies": [ "python=3.8.15", "pip": [ "catboost==x.y.z" ], >, ], >from mlflow.models import infer_signature train = df.drop_column("target_label") predictions = . # compute model predictions signature = infer_signature(train, predictions)
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements .
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