--- metadata: - name: generator content: Diplodoc Platform v5.52.0 alternate: - https://catboost.ai/docs/en/concepts/python-reference_catboost_metadata.md - href: en/concepts/python-reference_catboost_metadata.md type: text/markdown title: Markdown version - href: ../llms.txt type: text/markdown title: llms.txt --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # get_metadata Return a proxy object with metadata from the model's internal key-value string storage. This proxy object mimics a Python dictionary with string-only keys and values. All modifying operations write changes directly to the inner C++ model object behind the corresponding class. By default, a trained CatBoost model contains the following metadata keys: - `catboost_version_info` — The CatBoost library version information, including revision details. {% cut "Example" %} #### Example #### Key invoke format ```python print(metadata["catboost_version_info"]) ``` #### Key format example ``` Git info: Commit: 73f2e5e34e8add6f2b6bd22b4c0cc1c0456ec7da Author: 'format:mail ' Summary: Fix a bug caused by difference in indices in CoreML model and source flat feature indices. Canonize GPU CoreML onehot tests. MLTOOLS-2436. Other info: Build by: go Top src dir: /var/lib/go-agent/pipelines/BuildMaster/catboost.git Top build dir: /storage/.ya/build/build_root/zj35/0000e3 Hostname: catboost-builder-linux Host information: Linux catboost-builder-linux 4.4.0-139-generic #165-Ubuntu SMP Wed Oct 24 10:58:50 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux ``` {% endcut %} - `model_guid` — A randomly generated identifier of the trained model. This key is used for model identification purposes. {% cut "Example" %} #### Example #### Key invoke format ```python print(metadata["model_guid"]) ``` #### Key format example ``` 47ebcf93-81d7170a-2e69cf54-804224ed ``` {% endcut %} - `params` — A JSON dictionary with full training parameters serialized to a string. {% cut "Example" %} #### Example #### Key invoke format ```python print(metadata["params"]) ``` #### Key format example ``` {"detailed_profile":false,"boosting_options":{"approx_on_full_history":false,"fold_len_multiplier":2,"fold_permutation_block":0,"boosting_type":"Ordered","iterations":1000,"od_config":{"wait_iterations":20,"type":"None","stop_pvalue":0},"permutation_count":4,"learning_rate":0.02999999933},"metrics":{"objective_metric":{"type":"RMSE","params":{}},"eval_metric":{"type":"RMSE","params":{}},"custom_metrics":[]},"metadata":{},"cat_feature_params":{"store_all_simple_ctr":false,"ctr_leaf_count_limit":18446744073709551615,"simple_ctrs":[{"ctr_binarization":{"border_count":15,"border_type":"Uniform"},"target_binarization":{"border_count":1,"border_type":"MinEntropy"},"prior_estimation":"No","priors":[[0,1],[0.5,1],[1,1]],"ctr_type":"Borders"},{"ctr_binarization":{"border_count":15,"border_type":"Uniform"},"prior_estimation":"No","priors":[[0,1]],"ctr_type":"Counter"}],"counter_calc_method":"SkipTest","one_hot_max_size":2,"max_ctr_complexity":4,"combinations_ctrs":[{"ctr_binarization":{"border_count":15,"border_type":"Uniform"},"target_binarization":{"border_count":1,"border_type":"MinEntropy"},"prior_estimation":"No","priors":[[0,1],[0.5,1],[1,1]],"ctr_type":"Borders"},{"ctr_binarization":{"border_count":15,"border_type":"Uniform"},"prior_estimation":"No","priors":[[0,1]],"ctr_type":"Counter"}],"target_binarization":{"border_count":1,"border_type":"MinEntropy"},"per_feature_ctrs":{}},"logging_level":"Verbose","data_processing_options":{"has_time":false,"allow_const_label":false,"class_names":[],"class_weights":[],"target_border":null,"float_features_binarization":{"border_count":254,"nan_mode":"Min","border_type":"GreedyLogSum"},"classes_count":0,"ignored_features":[]},"loss_function":{"type":"RMSE","params":{}},"tree_learner_options":{"rsm":1,"random_strength":1,"leaf_estimation_iterations":1,"dev_efb_max_buckets":1024,"dev_score_calc_obj_block_size":5000000,"leaf_estimation_backtracking":"AnyImprovement","bayesian_matrix_reg":0.1000000015,"leaf_estimation_method":"Newton","sampling_frequency":"PerTree","model_size_reg":0.5,"bootstrap":{"bagging_temperature":1,"type":"Bayesian"},"efb_max_conflict_fraction":0,"l2_leaf_reg":3,"depth":6},"task_type":"CPU","flat_params":{"verbose":1},"random_seed":0,"system_options":{"thread_count":4,"file_with_hosts":"hosts.txt","node_type":"SingleHost","node_port":0,"used_ram_limit":""}} ``` {% endcut %} - `train_finish_time` — Date and time of model training completion. {% cut "Example" %} #### Example #### Key invoke format ```python print(metadata["train_finish_time"]) ``` #### Key format example ``` 2019-06-04T06:30:13Z ``` {% endcut %} ## Method call format {#call-format} ```python get_metadata() ``` ## Type of return value {#output-format} _catboost._MetadataHashProxy ## Examples {#examples} ```python import numpy as np from catboost import Pool, CatBoost # initialize data train_data = np.random.randint(0, 100, size=(100, 10)) train_label = np.random.randint(0, 1000, size=(100)) # initialize Pool train_pool = Pool(train_data, train_label) # specify the training parameters model = CatBoost() #train the model model.fit(train_pool) # get proxy reference for convenience metadata = model.get_metadata() # set some metadata key metadata['example_key'] = 'example_value' # check if key is in metadata print('needed_key' in metadata) # iterate metadata keys for i in metadata: # for i in meta.keys() would also work print(i) # iterate metadata key-values for i in metadata.items(): print(i) # delete some key del metadata['example_key'] # copy metadata to python dictionary metadata_copy = dict(metadata) ```