diff --git "a/m2_mos_v1/lgbm_precipitation_q05.txt" "b/m2_mos_v1/lgbm_precipitation_q05.txt"
new file mode 100644--- /dev/null
+++ "b/m2_mos_v1/lgbm_precipitation_q05.txt"
@@ -0,0 +1,6816 @@
+tree
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+leaf_count=10716 11063 3959 12147 4720 1531 889 1823 212 4728 7209 6607 8080 15662 10830 1461 3001 1237 1839 9796 1528 9071 2684 7237 1035 2225 8201 2837 675 7560 9133 1214 2583 3996 7794 7133 13089 2603 21254 1237 772 12907 2997 4336 4998 5108 4487 309 2723 43146 2564 1246 1118 311314 10339 53470 784 2379 5925 595 710 3422 9234 2743 2351 772 1893 1416 4256 4883 1381 773 11374 507 237 1556 504 5617 20759 416 82494 4124 1156 3260 971 611 3943 47747 5924 1867 2599 4443 2106 12133 1983 7695
+internal_value=0 -0.0312564 -0.0717464 -0.0440426 -0.0197892 -0.0729128 -0.0252477 -0.074305 -0.0651002 -0.0364413 -0.0225176 -0.0212003 -0.0697457 -0.0516783 -0.0340535 -0.0528028 -0.0599902 -0.0393662 -0.0712007 -0.010719 -0.0722398 -0.0622898 -0.0486757 -0.0497187 -0.0266715 -0.0463031 -0.0436087 -0.0394231 -0.057728 -0.0669278 -0.065687 -0.0286293 -0.0278594 -0.014434 -0.0152495 -0.0129871 -0.0410551 -0.00682258 -0.0505298 -0.0639405 -0.0684467 -0.0629307 -0.025677 -0.0642504 -0.0223269 -0.0606493 -0.040599 -0.0317116 -0.0730358 -0.0567247 -0.037782 -0.0476751 -0.0747841 -0.0174198 -0.0735568 -0.0512232 -0.0445918 -0.0303049 -0.0571791 -0.0470034 -0.0544032 -0.0373761 -0.0564498 -0.0481456 -0.0694004 -0.0415618 -0.0651343 -0.0524792 -0.0718648 -0.0668046 -0.0527683 -0.0536227 -0.0357497 -0.0442742 -0.0390117 -0.0574525 -0.0283341 -0.0654682 -0.0318561 -0.0750696 -0.0738425 -0.0353532 -0.0160796 -0.017878 -0.0408828 -0.0726237 -0.0731165 -0.0213021 -0.0463217 -0.0365854 -0.0385899 -0.0437679 -0.0670977 -0.0463048 -0.0295164
+internal_weight=0 235659 724317 111421 124238 682305 77561 473961 23450 55836 63689 59085 208344 55585 47325 42012 14751 23908 158374 46677 127396 13514 40834 8511 30783 18111 14515 4604 23901 30978 13800 23417 28558 23895 28302 21169 22069 22782 32986 8332 20632 7725 21951 21182 7691 12049 2818 15726 113596 7562 13872 2364 450511 16101 102880 31749 5056 14265 3596 11262 15569 12993 3957 10552 9843 4272 49970 26693 49410 6264 7476 22437 11508 2509 1793 716 11542 48554 3253 397932 86618 10390 14570 11310 7848 52579 48636 10260 9076 11001 8402 3929 27795 2756 11691
+internal_count=959976 235659 724317 111421 124238 682305 77561 473961 23450 55836 63689 59085 208344 55585 47325 42012 14751 23908 158374 46677 127396 13514 40834 8511 30783 18111 14515 4604 23901 30978 13800 23417 28558 23895 28302 21169 22069 22782 32986 8332 20632 7725 21951 21182 7691 12049 2818 15726 113596 7562 13872 2364 450511 16101 102880 31749 5056 14265 3596 11262 15569 12993 3957 10552 9843 4272 49970 26693 49410 6264 7476 22437 11508 2509 1793 716 11542 48554 3253 397932 86618 10390 14570 11310 7848 52579 48636 10260 9076 11001 8402 3929 27795 2756 11691
+is_linear=0
+shrinkage=0.08
+
+
+end of trees
+
+feature_importances:
+Column_23=5473
+Column_15=4942
+Column_22=3748
+Column_21=3490
+Column_19=3032
+Column_24=2675
+Column_25=1856
+Column_11=1124
+Column_1=1118
+Column_20=935
+Column_4=934
+Column_13=895
+Column_8=818
+Column_17=555
+Column_16=518
+Column_10=405
+Column_6=303
+Column_3=114
+Column_5=77
+Column_2=58
+Column_7=46
+Column_18=35
+Column_0=28
+Column_12=28
+Column_14=28
+Column_9=13
+Column_28=2
+
+parameters:
+[boosting: gbdt]
+[objective: quantile]
+[metric: quantile]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 350]
+[learning_rate: 0.08]
+[num_leaves: 96]
+[num_threads: 0]
+[seed: 42]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 200]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.8]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 1]
+[bagging_seed: 400]
+[feature_fraction: 0.85]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 30056]
+[extra_trees: 0]
+[extra_seed: 12879]
+[early_stopping_round: 0]
+[early_stopping_min_delta: 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: 17869]
+[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: 175]
+[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: 16083]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.05]
+[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]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:null