Progressive Tools =================== This part of the library is designed for progressive learning. make_run ___________ It creates multiple models and calculates the accuracy for each one of them, the size of the train data is repeatedly getting bigger till the dedicated size. =========== ====================== ============= Parameters Datatype Default Value =========== ====================== ============= model_class any AI model class - X_train multidimensional array - y_train 1D array - X_test multidimensional array - y_test 1D array - init_per integer 1 limit_per integer 100 increment integer or float 1 metrics list None average string weighted params dict None =========== ====================== ============= .. note:: init_per must be less than limit_per. These are the valid keywords for metrics: ========= =============== ============================== algo_type metrics keyword sklearn function ========= =============== ============================== clf acc accuracy_score clf f1 f1_score clf hamming hamming_loss clf jaccard jaccard_score clf log log_loss clf mcc matthews_corrcoef clf precision precision_score clf recall recall_score clf zol zero_one_loss reg var explained_variance_score reg max max_error reg var explained_variance_score reg abs mean_absolute_error reg sq mean_squared_error reg rsq root_mean_squared_error reg log mean_squared_log_error reg rlog root_mean_squared_log_error reg medabs median_absolute_error reg poisson mean_poisson_deviance reg gamma mean_gamma_deviance reg per mean_absolute_percentage_error reg d2abs d2_absolute_error_score reg d2pin d2_pinball_score reg d2twe d2_tweedie_score ========= =============== ============================== .. attention:: average value must be valid for sklearn's metrics functions. .. note:: params is for the model, model does not have to be created in default settings, it can be manipulated. ==================== ============== ======== ========= Priority (in return) Returns Datatype Condition ==================== ============== ======== ========= 1 percentage_log list always 2 metrics_log list always ==================== ============== ======== ========= get_best_model _________________ It calculates the optimum dataset size for the model. ================= ======== ============= Parameters Datatype Default Value ================= ======== ============= percentage_log list - metrics_log list - requested_metrics string - ================= ======== ============= ==================== =============== ================ ========= Priority (in return) Returns Datatype Condition ==================== =============== ================ ========= 1 best_percentage integer or float always 2 best_score float always ==================== =============== ================ ========= path_chain _____________ Sometimes, train data can be kept in different files with different sizes. It is preferred when the data is too big to store in RAM. That function is designed for these situations. ============= ====================== ============= Parameters Datatype Default Value ============= ====================== ============= paths list - model_class any AI model class - X_test multidimensional array - y_test 1D array - output_column string - metrics list None average string weighted params dict None ============= ====================== ============= These are the valid keywords for metrics: ========= =============== ============================== algo_type metrics keyword sklearn function ========= =============== ============================== clf acc accuracy_score clf f1 f1_score clf hamming hamming_loss clf jaccard jaccard_score clf log log_loss clf mcc matthews_corrcoef clf precision precision_score clf recall recall_score clf zol zero_one_loss reg var explained_variance_score reg max max_error reg var explained_variance_score reg abs mean_absolute_error reg sq mean_squared_error reg rsq root_mean_squared_error reg log mean_squared_log_error reg rlog root_mean_squared_log_error reg medabs median_absolute_error reg poisson mean_poisson_deviance reg gamma mean_gamma_deviance reg per mean_absolute_percentage_error reg d2abs d2_absolute_error_score reg d2pin d2_pinball_score reg d2twe d2_tweedie_score ========= =============== ============================== .. attention:: average value must be valid for sklearn's metrics functions. .. note:: params is for the model, model does not have to be created in default settings, it can be manipulated. ==================== =========== ======== ========= Priority (in return) Returns Datatype Condition ==================== =========== ======== ========= 1 metrics_log dict always ==================== =========== ======== =========