diff --git "a/models/model_circulation_pump.txt" "b/models/model_circulation_pump.txt"
new file mode 100644--- /dev/null
+++ "b/models/model_circulation_pump.txt"
@@ -0,0 +1,15087 @@
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+
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+
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+
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+
+Tree=773
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+split_gain=1.22196e-09 1.2152e-07 1.32641e-07 1.74981e-07 7.64993e-08 3.39642e-08 1.83633e-08 1.97521e-08 5.17387e-08
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+
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+
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+
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+
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+
+Tree=780
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+
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+
+
+Tree=782
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+split_gain=2.81624e-11 2.27349e-10
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+decision_type=2 2
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+internal_value=0 -4.01932e-10
+internal_weight=0 162841
+internal_count=167821 162841
+is_linear=0
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+
+
+Tree=783
+num_leaves=2
+num_cat=0
+split_feature=50
+split_gain=2.32164e-11
+threshold=0.073999999999898605
+decision_type=2
+left_child=-1
+right_child=-2
+leaf_value=-3.6493456517442232e-10 0
+leaf_weight=162841 4980
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+internal_value=0
+internal_weight=0
+internal_count=167821
+is_linear=0
+shrinkage=0.0305635
+
+
+end of trees
+
+feature_importances:
+exact_1=2383
+exact_3=623
+max_1=396
+max_192=282
+max_3=272
+min_192=218
+min_1=207
+median_48=196
+median_3=182
+min_12=169
+median_192=168
+std_1=148
+min_3=145
+max_6=145
+median_24=142
+median_96=140
+std_6=132
+max_48=124
+exact_6=123
+max_12=115
+median_12=108
+max_96=107
+median_6=106
+std_3=105
+mean_1=93
+mean_192=88
+std_192=88
+min_96=84
+max_24=81
+mean_6=80
+min_6=79
+std_12=63
+hour_sin=59
+std_96=55
+min_48=54
+exact_12=49
+std_48=44
+min_24=42
+std_24=40
+mean_48=32
+mean_12=26
+mean_3=22
+median_1=21
+mean_24=20
+mean_96=19
+day_of_week_sin=14
+hour=8
+exact_24=8
+month=5
+month_sin=5
+month_cos=4
+hour_cos=2
+exact_48=1
+
+parameters:
+[boosting: gbdt]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1000]
+[learning_rate: 0.0305635]
+[num_leaves: 131]
+[num_threads: 0]
+[seed: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 14]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 7.05293]
+[bagging_fraction: 0.871599]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 7]
+[bagging_seed: 3]
+[feature_fraction: 0.739573]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0.187839]
+[lambda_l2: 2.30074]
+[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: 4]
+[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: 1]
+[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: 5]
+[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]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]