зеркало из https://github.com/microsoft/LightGBM.git
This commit is contained in:
James Lamb
GitHub
Родитель
163416d2f5
Коммит
ab1eaa832d
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@ -29,3 +29,20 @@
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.LGB_VERBOSITY <- as.integer(
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Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
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)
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# [description]
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# test that every element of 'x' is in 'y'
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#
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# testthat::expect_in() is not available in version of {testthat}
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# built for R 3.6, this is here to support a similar interface on R 3.6
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.expect_in <- function(x, y) {
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if (exists("expect_in")) {
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expect_in(x, y)
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} else {
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missing_items <- x[!(x %in% y)]
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if (length(missing_items) != 0L) {
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error_msg <- paste0("Some expected items not found: ", toString(missing_items))
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stop(error_msg)
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}
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}
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}
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@ -799,37 +799,166 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
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data = matrix(rnorm(500L), nrow = 100L)
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, label = rnorm(100L)
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)
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nrounds <- 4L
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bst <- lgb.train(
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params = list(
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objective = "regression"
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, metric = "l2"
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objective = "mape"
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, metric = c("l2", "mae")
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, num_threads = .LGB_MAX_THREADS
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, seed = 708L
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, data_sample_strategy = "bagging"
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, sub_row = 0.8234
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)
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, data = dtrain
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, nrounds = nrounds
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, nrounds = 3L
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, verbose = .LGB_VERBOSITY
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)
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model_str <- bst$save_model_to_string()
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params_in_file <- .params_from_model_string(model_str = model_str)
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# entries whose values should reflect params passed to lgb.train()
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non_default_param_entries <- c(
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"[objective: mape]"
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# 'l1' was passed in with alias 'mae'
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, "[metric: l2,l1]"
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, "[data_sample_strategy: bagging]"
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, "[seed: 708]"
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# this was passed in with alias 'sub_row'
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, "[bagging_fraction: 0.8234]"
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, "[num_iterations: 3]"
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)
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# entries with default values of params
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default_param_entries <- c(
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"[boosting: gbdt]"
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, "[tree_learner: serial]"
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, "[device_type: cpu]"
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, "[data: ]"
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, "[valid: ]"
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, "[learning_rate: 0.1]"
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, "[num_leaves: 31]"
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, sprintf("[num_threads: %i]", .LGB_MAX_THREADS)
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, "[deterministic: 0]"
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, "[histogram_pool_size: -1]"
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, "[max_depth: -1]"
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, "[min_data_in_leaf: 20]"
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, "[min_sum_hessian_in_leaf: 0.001]"
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, "[pos_bagging_fraction: 1]"
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, "[neg_bagging_fraction: 1]"
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, "[bagging_freq: 0]"
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, "[bagging_seed: 15415]"
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, "[feature_fraction: 1]"
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, "[feature_fraction_bynode: 1]"
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, "[feature_fraction_seed: 32671]"
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, "[extra_trees: 0]"
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, "[extra_seed: 6642]"
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, "[early_stopping_round: 0]"
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, "[first_metric_only: 0]"
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, "[max_delta_step: 0]"
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, "[lambda_l1: 0]"
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, "[lambda_l2: 0]"
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, "[linear_lambda: 0]"
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, "[min_gain_to_split: 0]"
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, "[drop_rate: 0.1]"
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, "[max_drop: 50]"
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, "[skip_drop: 0.5]"
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, "[xgboost_dart_mode: 0]"
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, "[uniform_drop: 0]"
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, "[drop_seed: 20623]"
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, "[top_rate: 0.2]"
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, "[other_rate: 0.1]"
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, "[min_data_per_group: 100]"
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, "[max_cat_threshold: 32]"
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, "[cat_l2: 10]"
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, "[cat_smooth: 10]"
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, "[max_cat_to_onehot: 4]"
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, "[top_k: 20]"
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, "[monotone_constraints: ]"
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, "[monotone_constraints_method: basic]"
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, "[monotone_penalty: 0]"
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, "[feature_contri: ]"
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, "[forcedsplits_filename: ]"
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, "[force_col_wise: 0]"
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, "[force_row_wise: 0]"
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, "[refit_decay_rate: 0.9]"
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, "[cegb_tradeoff: 1]"
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, "[cegb_penalty_split: 0]"
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, "[cegb_penalty_feature_lazy: ]"
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, "[cegb_penalty_feature_coupled: ]"
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, "[path_smooth: 0]"
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, "[interaction_constraints: ]"
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, sprintf("[verbosity: %i]", .LGB_VERBOSITY)
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, "[saved_feature_importance_type: 0]"
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, "[use_quantized_grad: 0]"
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, "[num_grad_quant_bins: 4]"
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, "[quant_train_renew_leaf: 0]"
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, "[stochastic_rounding: 1]"
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, "[linear_tree: 0]"
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, "[max_bin: 255]"
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, "[max_bin_by_feature: ]"
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, "[min_data_in_bin: 3]"
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, "[bin_construct_sample_cnt: 200000]"
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, "[data_random_seed: 2350]"
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, "[is_enable_sparse: 1]"
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, "[enable_bundle: 1]"
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, "[use_missing: 1]"
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, "[zero_as_missing: 0]"
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, "[feature_pre_filter: 1]"
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, "[pre_partition: 0]"
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, "[two_round: 0]"
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, "[header: 0]"
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, "[label_column: ]"
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, "[weight_column: ]"
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, "[group_column: ]"
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, "[ignore_column: ]"
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, "[categorical_feature: ]"
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, "[forcedbins_filename: ]"
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, "[precise_float_parser: 0]"
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, "[parser_config_file: ]"
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, "[objective_seed: 4309]"
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, "[num_class: 1]"
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, "[is_unbalance: 0]"
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, "[scale_pos_weight: 1]"
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, "[sigmoid: 1]"
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, "[boost_from_average: 1]"
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, "[reg_sqrt: 0]"
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, "[alpha: 0.9]"
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, "[fair_c: 1]"
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, "[poisson_max_delta_step: 0.7]"
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, "[tweedie_variance_power: 1.5]"
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, "[lambdarank_truncation_level: 30]"
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, "[lambdarank_norm: 1]"
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, "[label_gain: ]"
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, "[lambdarank_position_bias_regularization: 0]"
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, "[eval_at: ]"
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, "[multi_error_top_k: 1]"
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, "[auc_mu_weights: ]"
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, "[num_machines: 1]"
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, "[local_listen_port: 12400]"
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, "[time_out: 120]"
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, "[machine_list_filename: ]"
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, "[machines: ]"
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, "[gpu_platform_id: -1]"
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, "[gpu_device_id: -1]"
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, "[gpu_use_dp: 0]"
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, "[num_gpu: 1]"
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)
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all_param_entries <- c(non_default_param_entries, default_param_entries)
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# parameters should match what was passed from the R package
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expect_equal(sum(startsWith(params_in_file, "[metric:")), 1L)
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expect_equal(sum(params_in_file == "[metric: l2]"), 1L)
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expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L)
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expect_equal(sum(params_in_file == "[num_iterations: 4]"), 1L)
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expect_equal(sum(startsWith(params_in_file, "[objective:")), 1L)
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expect_equal(sum(params_in_file == "[objective: regression]"), 1L)
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expect_equal(sum(startsWith(params_in_file, "[verbosity:")), 1L)
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expect_equal(sum(params_in_file == sprintf("[verbosity: %i]", .LGB_VERBOSITY)), 1L)
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model_str <- bst$save_model_to_string()
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params_in_file <- .params_from_model_string(model_str = model_str)
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.expect_in(all_param_entries, params_in_file)
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# early stopping should be off by default
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expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L)
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expect_equal(sum(params_in_file == "[early_stopping_round: 0]"), 1L)
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# since save_model_to_string() is used when serializing with saveRDS(), check that parameters all
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# roundtrip saveRDS()/loadRDS() successfully
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rds_file <- tempfile()
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saveRDS(bst, rds_file)
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bst_rds <- readRDS(rds_file)
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model_str <- bst_rds$save_model_to_string()
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params_in_file <- .params_from_model_string(model_str = model_str)
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.expect_in(all_param_entries, params_in_file)
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})
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test_that("early_stopping, num_iterations are stored correctly in model string even with aliases", {
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@ -330,7 +330,7 @@ def gen_parameter_code(
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str_to_write += ' std::string tmp_str = "";\n'
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for x in infos:
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for y in x:
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if "[doc-only]" in y:
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if "[no-automatically-extract]" in y:
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continue
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param_type = y["inner_type"][0]
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name = y["name"][0]
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@ -345,7 +345,7 @@ def gen_parameter_code(
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str_to_write += " std::stringstream str_buf;\n"
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for x in infos:
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for y in x:
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if "[doc-only]" in y or "[no-save]" in y:
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if "[no-save]" in y:
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continue
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param_type = y["inner_type"][0]
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name = y["name"][0]
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@ -5,8 +5,13 @@
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* \note
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* - desc and descl2 fields must be written in reStructuredText format;
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* - nested sections can be placed only at the bottom of parent's section;
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* - [doc-only] tag indicates that only documentation for this param should be generated and all other actions are performed manually;
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* - [no-save] tag indicates that this param should not be saved into a model text representation.
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* - [no-automatically-extract]
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* - do not automatically extract this parameter into a Config property with the same name in Config::GetMembersFromString(). Use if:
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* - specialized extraction logic for this param exists in Config::GetMembersFromString()
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* - [no-save]
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* - this param should not be saved into a model text representation via Config::SaveMembersToString(). Use if:
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* - param is only used by the CLI (especially the "predict" and "convert_model" tasks)
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* - param is related to LightGBM writing files (e.g. "output_model", "save_binary")
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*/
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#ifndef LIGHTGBM_CONFIG_H_
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#define LIGHTGBM_CONFIG_H_
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@ -97,15 +102,15 @@ struct Config {
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#pragma region Core Parameters
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#endif // __NVCC__
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// [no-automatically-extract]
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// [no-save]
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// [doc-only]
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// alias = config_file
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// desc = path of config file
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// desc = **Note**: can be used only in CLI version
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std::string config = "";
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// [no-automatically-extract]
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// [no-save]
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// [doc-only]
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// type = enum
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// default = train
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// options = train, predict, convert_model, refit
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@ -118,7 +123,8 @@ struct Config {
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// desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions
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TaskType task = TaskType::kTrain;
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// [doc-only]
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// [no-automatically-extract]
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// [no-save]
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// type = enum
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// options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg
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// alias = objective_type, app, application, loss
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@ -150,7 +156,8 @@ struct Config {
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// descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
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std::string objective = "regression";
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// [doc-only]
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// [no-automatically-extract]
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// [no-save]
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// type = enum
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// alias = boosting_type, boost
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// options = gbdt, rf, dart
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@ -160,7 +167,7 @@ struct Config {
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// descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations
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std::string boosting = "gbdt";
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// [doc-only]
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// [no-automatically-extract]
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// type = enum
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// options = bagging, goss
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// desc = ``bagging``, Randomly Bagging Sampling
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@ -200,7 +207,8 @@ struct Config {
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// desc = max number of leaves in one tree
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int num_leaves = kDefaultNumLeaves;
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// [doc-only]
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// [no-automatically-extract]
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// [no-save]
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// type = enum
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// options = serial, feature, data, voting
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// alias = tree, tree_type, tree_learner_type
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@ -222,7 +230,8 @@ struct Config {
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// desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
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int num_threads = 0;
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// [doc-only]
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// [no-automatically-extract]
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// [no-save]
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// type = enum
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// options = cpu, gpu, cuda
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// alias = device
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@ -235,7 +244,7 @@ struct Config {
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// desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst#build-gpu-version>`__ to build LightGBM with GPU support
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std::string device_type = "cpu";
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// [doc-only]
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// [no-automatically-extract]
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// alias = random_seed, random_state
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// default = None
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// desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc.
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@ -593,7 +602,6 @@ struct Config {
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// desc = **Note**: can be used only in CLI version
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int snapshot_freq = -1;
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// [no-save]
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// desc = whether to use gradient quantization when training
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// desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``
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// desc = with quantized training, most arithmetics in the training process will be integer operations
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@ -602,21 +610,18 @@ struct Config {
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// desc = *New in version 4.0.0*
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bool use_quantized_grad = false;
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// [no-save]
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// desc = number of bins to quantization gradients and hessians
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// desc = with more bins, the quantized training will be closer to full precision training
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// desc = **Note**: can be used only with ``device_type = cpu``
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// desc = *New in 4.0.0*
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int num_grad_quant_bins = 4;
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// [no-save]
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// desc = whether to renew the leaf values with original gradients when quantized training
|
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// desc = renewing is very helpful for good quantized training accuracy for ranking objectives
|
||||
// desc = **Note**: can be used only with ``device_type = cpu``
|
||||
// desc = *New in 4.0.0*
|
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bool quant_train_renew_leaf = false;
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// [no-save]
|
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// desc = whether to use stochastic rounding in gradient quantization
|
||||
// desc = *New in 4.0.0*
|
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bool stochastic_rounding = true;
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@ -976,7 +981,8 @@ struct Config {
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|||
#pragma region Metric Parameters
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#endif // __NVCC__
|
||||
|
||||
// [doc-only]
|
||||
// [no-automatically-extract]
|
||||
// [no-save]
|
||||
// alias = metrics, metric_types
|
||||
// default = ""
|
||||
// type = multi-enum
|
||||
|
|
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|||
|
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@ -664,12 +664,14 @@ void Config::GetMembersFromString(const std::unordered_map<std::string, std::str
|
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|
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std::string Config::SaveMembersToString() const {
|
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std::stringstream str_buf;
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str_buf << "[data_sample_strategy: " << data_sample_strategy << "]\n";
|
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str_buf << "[data: " << data << "]\n";
|
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str_buf << "[valid: " << Common::Join(valid, ",") << "]\n";
|
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str_buf << "[num_iterations: " << num_iterations << "]\n";
|
||||
str_buf << "[learning_rate: " << learning_rate << "]\n";
|
||||
str_buf << "[num_leaves: " << num_leaves << "]\n";
|
||||
str_buf << "[num_threads: " << num_threads << "]\n";
|
||||
str_buf << "[seed: " << seed << "]\n";
|
||||
str_buf << "[deterministic: " << deterministic << "]\n";
|
||||
str_buf << "[force_col_wise: " << force_col_wise << "]\n";
|
||||
str_buf << "[force_row_wise: " << force_row_wise << "]\n";
|
||||
|
|
@ -722,6 +724,10 @@ std::string Config::SaveMembersToString() const {
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str_buf << "[interaction_constraints: " << interaction_constraints << "]\n";
|
||||
str_buf << "[verbosity: " << verbosity << "]\n";
|
||||
str_buf << "[saved_feature_importance_type: " << saved_feature_importance_type << "]\n";
|
||||
str_buf << "[use_quantized_grad: " << use_quantized_grad << "]\n";
|
||||
str_buf << "[num_grad_quant_bins: " << num_grad_quant_bins << "]\n";
|
||||
str_buf << "[quant_train_renew_leaf: " << quant_train_renew_leaf << "]\n";
|
||||
str_buf << "[stochastic_rounding: " << stochastic_rounding << "]\n";
|
||||
str_buf << "[linear_tree: " << linear_tree << "]\n";
|
||||
str_buf << "[max_bin: " << max_bin << "]\n";
|
||||
str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n";
|
||||
|
|
|
|||
|
|
@ -1534,6 +1534,203 @@ def test_save_load_copy_pickle():
|
|||
assert ret_origin == pytest.approx(ret)
|
||||
|
||||
|
||||
def test_all_expected_params_are_written_out_to_model_text(tmp_path):
|
||||
X, y = make_synthetic_regression()
|
||||
params = {
|
||||
'objective': 'mape',
|
||||
'metric': ['l2', 'mae'],
|
||||
'seed': 708,
|
||||
'data_sample_strategy': 'bagging',
|
||||
'sub_row': 0.8234,
|
||||
'verbose': -1
|
||||
}
|
||||
dtrain = lgb.Dataset(data=X, label=y)
|
||||
gbm = lgb.train(
|
||||
params=params,
|
||||
train_set=dtrain,
|
||||
num_boost_round=3
|
||||
)
|
||||
|
||||
model_txt_from_memory = gbm.model_to_string()
|
||||
model_file = tmp_path / "out.model"
|
||||
gbm.save_model(filename=model_file)
|
||||
with open(model_file, "r") as f:
|
||||
model_txt_from_file = f.read()
|
||||
|
||||
assert model_txt_from_memory == model_txt_from_file
|
||||
|
||||
# entries whose values should reflect params passed to lgb.train()
|
||||
non_default_param_entries = [
|
||||
"[objective: mape]",
|
||||
# 'l1' was passed in with alias 'mae'
|
||||
"[metric: l2,l1]",
|
||||
"[data_sample_strategy: bagging]",
|
||||
"[seed: 708]",
|
||||
# NOTE: this was passed in with alias 'sub_row'
|
||||
"[bagging_fraction: 0.8234]",
|
||||
"[num_iterations: 3]",
|
||||
]
|
||||
|
||||
# entries with default values of params
|
||||
default_param_entries = [
|
||||
"[boosting: gbdt]",
|
||||
"[tree_learner: serial]",
|
||||
"[data: ]",
|
||||
"[valid: ]",
|
||||
"[learning_rate: 0.1]",
|
||||
"[num_leaves: 31]",
|
||||
"[num_threads: 0]",
|
||||
"[deterministic: 0]",
|
||||
"[histogram_pool_size: -1]",
|
||||
"[max_depth: -1]",
|
||||
"[min_data_in_leaf: 20]",
|
||||
"[min_sum_hessian_in_leaf: 0.001]",
|
||||
"[pos_bagging_fraction: 1]",
|
||||
"[neg_bagging_fraction: 1]",
|
||||
"[bagging_freq: 0]",
|
||||
"[bagging_seed: 15415]",
|
||||
"[feature_fraction: 1]",
|
||||
"[feature_fraction_bynode: 1]",
|
||||
"[feature_fraction_seed: 32671]",
|
||||
"[extra_trees: 0]",
|
||||
"[extra_seed: 6642]",
|
||||
"[early_stopping_round: 0]",
|
||||
"[first_metric_only: 0]",
|
||||
"[max_delta_step: 0]",
|
||||
"[lambda_l1: 0]",
|
||||
"[lambda_l2: 0]",
|
||||
"[linear_lambda: 0]",
|
||||
"[min_gain_to_split: 0]",
|
||||
"[drop_rate: 0.1]",
|
||||
"[max_drop: 50]",
|
||||
"[skip_drop: 0.5]",
|
||||
"[xgboost_dart_mode: 0]",
|
||||
"[uniform_drop: 0]",
|
||||
"[drop_seed: 20623]",
|
||||
"[top_rate: 0.2]",
|
||||
"[other_rate: 0.1]",
|
||||
"[min_data_per_group: 100]",
|
||||
"[max_cat_threshold: 32]",
|
||||
"[cat_l2: 10]",
|
||||
"[cat_smooth: 10]",
|
||||
"[max_cat_to_onehot: 4]",
|
||||
"[top_k: 20]",
|
||||
"[monotone_constraints: ]",
|
||||
"[monotone_constraints_method: basic]",
|
||||
"[monotone_penalty: 0]",
|
||||
"[feature_contri: ]",
|
||||
"[forcedsplits_filename: ]",
|
||||
"[refit_decay_rate: 0.9]",
|
||||
"[cegb_tradeoff: 1]",
|
||||
"[cegb_penalty_split: 0]",
|
||||
"[cegb_penalty_feature_lazy: ]",
|
||||
"[cegb_penalty_feature_coupled: ]",
|
||||
"[path_smooth: 0]",
|
||||
"[interaction_constraints: ]",
|
||||
"[verbosity: -1]",
|
||||
"[saved_feature_importance_type: 0]",
|
||||
"[use_quantized_grad: 0]",
|
||||
"[num_grad_quant_bins: 4]",
|
||||
"[quant_train_renew_leaf: 0]",
|
||||
"[stochastic_rounding: 1]",
|
||||
"[linear_tree: 0]",
|
||||
"[max_bin: 255]",
|
||||
"[max_bin_by_feature: ]",
|
||||
"[min_data_in_bin: 3]",
|
||||
"[bin_construct_sample_cnt: 200000]",
|
||||
"[data_random_seed: 2350]",
|
||||
"[is_enable_sparse: 1]",
|
||||
"[enable_bundle: 1]",
|
||||
"[use_missing: 1]",
|
||||
"[zero_as_missing: 0]",
|
||||
"[feature_pre_filter: 1]",
|
||||
"[pre_partition: 0]",
|
||||
"[two_round: 0]",
|
||||
"[header: 0]",
|
||||
"[label_column: ]",
|
||||
"[weight_column: ]",
|
||||
"[group_column: ]",
|
||||
"[ignore_column: ]",
|
||||
"[categorical_feature: ]",
|
||||
"[forcedbins_filename: ]",
|
||||
"[precise_float_parser: 0]",
|
||||
"[parser_config_file: ]",
|
||||
"[objective_seed: 4309]",
|
||||
"[num_class: 1]",
|
||||
"[is_unbalance: 0]",
|
||||
"[scale_pos_weight: 1]",
|
||||
"[sigmoid: 1]",
|
||||
"[boost_from_average: 1]",
|
||||
"[reg_sqrt: 0]",
|
||||
"[alpha: 0.9]",
|
||||
"[fair_c: 1]",
|
||||
"[poisson_max_delta_step: 0.7]",
|
||||
"[tweedie_variance_power: 1.5]",
|
||||
"[lambdarank_truncation_level: 30]",
|
||||
"[lambdarank_norm: 1]",
|
||||
"[label_gain: ]",
|
||||
"[lambdarank_position_bias_regularization: 0]",
|
||||
"[eval_at: ]",
|
||||
"[multi_error_top_k: 1]",
|
||||
"[auc_mu_weights: ]",
|
||||
"[num_machines: 1]",
|
||||
"[local_listen_port: 12400]",
|
||||
"[time_out: 120]",
|
||||
"[machine_list_filename: ]",
|
||||
"[machines: ]",
|
||||
"[gpu_platform_id: -1]",
|
||||
"[gpu_device_id: -1]",
|
||||
"[num_gpu: 1]",
|
||||
]
|
||||
all_param_entries = non_default_param_entries + default_param_entries
|
||||
|
||||
# add device-specific entries
|
||||
#
|
||||
# passed-in force_col_wise / force_row_wise parameters are ignored on CUDA and GPU builds...
|
||||
# https://github.com/microsoft/LightGBM/blob/1d7ee63686272bceffd522284127573b511df6be/src/io/config.cpp#L375-L377
|
||||
if getenv('TASK', '') == 'cuda':
|
||||
device_entries = [
|
||||
"[force_col_wise: 0]",
|
||||
"[force_row_wise: 1]",
|
||||
"[device_type: cuda]",
|
||||
"[gpu_use_dp: 1]"
|
||||
]
|
||||
elif getenv('TASK', '') == 'gpu':
|
||||
device_entries = [
|
||||
"[force_col_wise: 1]",
|
||||
"[force_row_wise: 0]",
|
||||
"[device_type: gpu]",
|
||||
"[gpu_use_dp: 0]"
|
||||
]
|
||||
else:
|
||||
device_entries = [
|
||||
"[force_col_wise: 0]",
|
||||
"[force_row_wise: 0]",
|
||||
"[device_type: cpu]",
|
||||
"[gpu_use_dp: 0]"
|
||||
]
|
||||
|
||||
all_param_entries += device_entries
|
||||
|
||||
# check that model text has all expected param entries
|
||||
for param_str in all_param_entries:
|
||||
assert param_str in model_txt_from_file
|
||||
assert param_str in model_txt_from_memory
|
||||
|
||||
# since Booster.model_to_string() is used when pickling, check that parameters all
|
||||
# roundtrip pickling successfully too
|
||||
gbm_pkl = pickle_and_unpickle_object(gbm, serializer="joblib")
|
||||
model_txt_from_memory = gbm_pkl.model_to_string()
|
||||
model_file = tmp_path / "out-pkl.model"
|
||||
gbm_pkl.save_model(filename=model_file)
|
||||
with open(model_file, "r") as f:
|
||||
model_txt_from_file = f.read()
|
||||
|
||||
for param_str in all_param_entries:
|
||||
assert param_str in model_txt_from_file
|
||||
assert param_str in model_txt_from_memory
|
||||
|
||||
|
||||
def test_pandas_categorical():
|
||||
pd = pytest.importorskip("pandas")
|
||||
np.random.seed(42) # sometimes there is no difference how cols are treated (cat or not cat)
|
||||
|
|
|
|||
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