专家设置¶
本节介绍启动实验时可使用的专家设置。Driverless AI 在专家设置中提供了多种选项,让您能够自定义实验。使用搜索栏可完善设置列表或查找特定设置。
这些选项的默认值源自 config.toml 文件中的配置选项。请参阅 样本 config.toml 文件 一节,了解更多关于其中每个选项的信息。更改设置的默认值后,会在界面中突出显示,以表明目前未选用默认值。
请注意特征大脑级别:默认情况下,特征大脑将不顾特征而调用任何更佳模型,即使新模型禁用了这些特征。为了完全控制通过在专家设置中进行更改而调用的特征,用户应将**特征大脑级别**选项设置为 0。
- 上传自定义插件
- 从 URL 加载自定义插件
- 官方插件(开源)
- 编辑 TOML 配置
- 实验设置
max_runtime_minutes
max_runtime_minutes_until_abort
time_abort
pipeline-building-recipe
enable_genetic_algorithm
tournament_style
make_python_scoring_pipeline
make_mojo_scoring_pipeline
mojo_for_predictions
reduce_mojo_size
make_pipeline_visualization
benchmark_mojo_latency
mojo_building_timeout
mojo_building_parallelism
kaggle_username
kaggle_key
kaggle_timeout
min_num_rows
reproducibility_level
seed
allow_different_classes_across_fold_splits
save_validation_splits
max_num_classes
max_num_classes_compute_roc
max_num_classes_client_and_gui
roc_reduce_type
max_rows_cm_ga
use_feature_brain_new_experiments
feature_brain_level
feature_brain2
feature_brain3
feature_brain4
feature_brain5
force_model_restart_to_defaults
min_dai_iterations
target_transformer
fixed_num_folds_evolution
fixed_num_folds
fixed_only_first_fold_model
feature_evolution_data_size
final_pipeline_data_size
max_validation_to_training_size_ratio_for_final_ensemble
force_stratified_splits_for_imbalanced_threshold_binary
mli_custom
last_recipe
feature_brain_reset_score
feature_brain_save_every_iteration
which_iteration_brain
refit_same_best_individual
restart_refit_redo_origfs_shift_leak
brain_add_features_for_new_columns
force_model_restart_to_defaults
dump_modelparams_every_scored_indiv
fast_approx_num_trees
fast_approx_do_one_fold
fast_approx_do_one_model
fast_approx_contribs_num_trees
fast_approx_contribs_do_one_fold
fast_approx_contribs_do_one_model
autoviz_recommended_transformation
- 模型设置
enable_constant_model
enable_decision_tree
enable_glm
enable_xgboost_gbm
enable_lightgbm
enable_xgboost_dart
enable_xgboost_rapids
enable_xgboost_rf
enable_xgboost_gbm_dask
enable_xgboost_dart_dask
enable_lightgbm_dask
enable_hyperopt_dask
num_inner_hyperopt_trials_prefinal
num_inner_hyperopt_trials_final
num_hyperopt_individuals_final
optuna_pruner
optuna_sampler
enable_xgboost_hyperopt_callback
enable_lightgbm_hyperopt_callback
enable_tensorflow
enable_grownet
enable_ftrl
enable_rulefit
enable_zero_inflated_models
enable_lightgbm_boosting_types
enable_lightgbm_cat_support
enable_lightgbm_cuda_support
show_constant_model
params_tensorflow
max_nestimators
n_estimators_list_no_early_stopping
min_learning_rate_final
max_learning_rate_final
max_nestimators_feature_evolution_factor
max_abs_score_delta_train_valid
max_rel_score_delta_train_valid
min_learning_rate
max_learning_rate
max_epochs
max_max_depth
max_max_bin
rulefit_max_num_rules
ensemble_meta_learner
fixed_ensemble_level
cross_validate_meta_learner
cross_validate_single_final_model
parameter_tuning_num_models
imbalance_sampling_method
imbalance_sampling_threshold_min_rows_original
imbalance_ratio_sampling_threshold
heavy_imbalance_ratio_sampling_threshold
imbalance_sampling_number_of_bags
imbalance_sampling_max_number_of_bags
imbalance_sampling_max_number_of_bags_feature_evolution
imbalance_sampling_max_multiple_data_size
imbalance_sampling_target_minority_fraction
ftrl_max_interaction_terms_per_degree
enable_bootstrap
tensorflow_num_classes_switch
prediction_intervals
prediction_intervals_alpha
dump_modelparams_every_scored_indiv
- 特征设置
feature_engineering_effort
check_distribution_shift
check_distribution_shift_drop
drop_features_distribution_shift_threshold_auc
check_leakage
drop_features_leakage_threshold_auc
leakage_max_data_size
max_features_importance
enable_wide_rules
orig_features_fs_report
max_rows_fs
max_orig_cols_selected
max_orig_nonnumeric_cols_selected
fs_orig_cols_selected
fs_orig_numeric_cols_selected
fs_orig_nonnumeric_cols_selected
max_relative_cardinality
num_as_cat
max_int_as_cat_uniques
max_fraction_invalid_numeric
nfeatures_max
ngenes_max
features_allowed_by_interpretability
monotonicity_constraints_interpretability_switch
monotonicity_constraints_correlation_threshold
monotonicity_constraints_log_level
monotonicity_constraints_drop_low_correlation_features
monotonicity_constraints_dict
max_feature_interaction_depth
fixed_feature_interaction_depth
enable_target_encoding
cvte_cv_in_cv
enable_lexilabel_encoding
enable_isolation_forest
enable_one_hot_encoding
isolation_forest_nestimators
drop_constant_columns
drop_id_columns
no_drop_features
cols_to_drop
cols_to_force_in
cols_to_group_by
sample_cols_to_group_by
agg_funcs_for_group_by
folds_for_group_by
mutation_mode
dump_varimp_every_scored_indiv
dump_trans_timings
compute_correlation
interaction_finder_gini_rel_improvement_threshold
interaction_finder_return_limit
enable_rapids_transformers
varimp_threshold_at_interpretability_10
stabilize_fs
- 时间序列设置
time_series_recipe
time_series_leaderboard_mode
time_series_leaderboard_periods_per_model
time_series_merge_splits
merge_splits_max_valid_ratio
fixed_size_splits
time_series_validation_fold_split_datetime_boundaries
timeseries_split_suggestion_timeout
holiday_features
holiday_countries
override_lag_sizes
override_ufapt_lag_sizes
override_non_ufapt_lag_sizes
min_lag_size
allow_time_column_as_feature
allow_time_column_as_numeric_feature
datetime_funcs
filter_datetime_funcs
allow_tgc_as_features
allowed_coltypes_for_tgc_as_features
enable_time_unaware_transformers
tgc_only_use_all_groups
tgc_allow_target_encoding
time_series_holdout_preds
time_series_validation_splits
time_series_splits_max_overlap
time_series_max_holdout_splits
mli_ts_fast_approx
mli_ts_fast_approx_contribs
mli_ts_holdout_contribs
time_series_min_interpretability
lags_dropout
prob_lag_non_targets
rolling_test_method
fast_tta_internal
prob_default_lags
prob_lagsinteraction
prob_lagsaggregates
ts_target_trafo
ts_target_trafo_epidemic_params_dict
ts_target_trafo_epidemic_target
ts_lag_target_trafo
ts_target_trafo_lag_size
- NLP 设置
enable_tensorflow_textcnn
enable_tensorflow_textbigru
enable_tensorflow_charcnn
enable_pytorch_nlp
pytorch_nlp_pretrained_models
tensorflow_max_epochs_nlp
enable_tensorflow_nlp_accuracy_switch
pytorch_nlp_fine_tuning_num_epochs
pytorch_nlp_fine_tuning_batch_size
pytorch_nlp_fine_tuning_padding_length
pytorch_nlp_pretrained_models_dir
tensorflow_nlp_pretrained_embeddings_file_path
tensorflow_nlp_pretrained_s3_access_key_id
tensorflow_nlp_pretrained_s3_secret_access_key
tensorflow_nlp_pretrained_embeddings_trainable
text_fraction_for_text_dominated_problem
text_transformer_fraction_for_text_dominated_problem
string_col_as_text_threshold
text_transformers_max_vocabulary_size
- 图像设置
enable_tensorflow_image
tensorflow_image_pretrained_models
tensorflow_image_vectorization_output_dimension
tensorflow_image_fine_tune
tensorflow_image_fine_tuning_num_epochs
tensorflow_image_augmentations
tensorflow_image_batch_size
image_download_timeout
string_col_as_image_max_missing_fraction
string_col_as_image_min_valid_types_fraction
tensorflow_image_use_gpu
- 插件设置
included_transformers
included_models
included_scorers
included_pretransformers
num_pipeline_layers
included_datas
threshold_scorer
prob_add_genes
prob_addbest_genes
prob_prune_genes
prob_perturb_xgb
prob_prune_by_features
skip_transformer_failures
skip_model_failures
detailed_skip_failure_messages_level
notify_failures
acceptance_test_timeout
- 系统设置
exclusive_mode
max_cores
max_fit_cores
use_dask_cluster
max_predict_cores
max_predict_cores_in_dai
batch_cpu_tuning_max_workers
cpu_max_workers
num_gpus_per_experiment
min_num_cores_per_gpu
num_gpus_per_model
num_gpus_for_prediction
gpu_id_start
assumed_simultaneous_dt_forks_munging
max_max_dt_threads_munging
max_dt_threads_munging
max_dt_threads_readwrite
max_dt_threads_stats_openblas
allow_reduce_features_when_failure
reduce_repeats_when_failure
fraction_anchor_reduce_features_when_failure
xgboost_reduce_on_errors_list
lightgbm_reduce_on_errors_list
num_gpus_per_hyperopt_dask
detailed_traces
debug_log
log_system_info_per_experiment
- AutoDoc 设置
make_autoreport
autodoc_report_name
autodoc_template
autodoc_output_type
autodoc_subtemplate_type
autodoc_max_cm_size
autodoc_num_features
autodoc_min_relative_importance
autodoc_include_permutation_feature_importance
autodoc_feature_importance_num_perm
autodoc_feature_importance_scorer
autodoc_pd_max_rows
autodoc_pd_max_runtime
autodoc_out_of_range
autodoc_num_rows
autodoc_population_stability_index
autodoc_population_stability_index_n_quantiles
autodoc_prediction_stats
autodoc_prediction_stats_n_quantiles
autodoc_response_rate
autodoc_response_rate_n_quantiles
autodoc_gini_plot
autodoc_enable_shapley_values
autodoc_data_summary_col_num
autodoc_list_all_config_settings
autodoc_keras_summary_line_length
autodoc_transformer_architecture_max_lines
autodoc_full_architecture_in_appendix
autodoc_coef_table_appendix_results_table
autodoc_coef_table_num_models
autodoc_coef_table_num_folds
autodoc_coef_table_num_coef
autodoc_coef_table_num_classes
autodoc_num_histogram_plots