| Previous changeset 31:af0523c606a7 (2019-12-16) Next changeset 33:3ab7af14f1b5 (2020-03-11) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 756f8be9c3cd437e131e6410cd625c24fe078e8c" |
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modified:
main_macros.xml test-data/glm_result07 |
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added:
test-data/lgb_class_model.txt test-data/lgb_prediction_result01.tabular test-data/lgb_regr_model.txt |
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| diff -r af0523c606a7 -r 1a53edc4b438 main_macros.xml --- a/main_macros.xml Mon Dec 16 05:42:39 2019 -0500 +++ b/main_macros.xml Wed Jan 22 08:06:22 2020 -0500 |
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| @@ -220,6 +220,28 @@ </param> </xml> + <!-- LightGBM --> + <xml name="feature_fraction" token_help="LightGBM will randomly select part of the features for each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree."> + <param argument="feature_fraction" type="float" value="1.0" label="Proportion of features to train each tree" help="@HELP@"/> + </xml> + + <xml name="lambda_l1" token_help=" "> + <param argument="lambda_l1" type="float" value="0.0" label="L1 regularization" help="@HELP@"/> + </xml> + + <xml name="lambda_l2" token_help=" "> + <param argument="lambda_l2" type="float" value="0.0" label="L1 regularization" help="@HELP@"/> + </xml> + + <xml name="min_gain_to_split" token_help=" "> + <param argument="min_gain_to_split" type="float" value="0.0" label="Minimal gain to perform split" help="@HELP@"/> + </xml> + + <xml name="min_child_weight" token_help="Minimal sum hessian in one leaf. It can be used to deal with over-fitting."> + <param argument="min_child_weight" type="float" value="0.0" label="Minimal sum hessian in one leaf" help="@HELP@"/> + </xml> + + <!--Parameters--> <xml name="tol" token_default_value="0.0" token_help_text="Early stopping heuristics based on the relative center changes. Set to default (0.0) to disable this convergence detection."> <param argument="tol" type="float" optional="true" value="@DEFAULT_VALUE@" label="Tolerance" help="@HELP_TEXT@"/> |
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| diff -r af0523c606a7 -r 1a53edc4b438 test-data/glm_result07 --- a/test-data/glm_result07 Mon Dec 16 05:42:39 2019 -0500 +++ b/test-data/glm_result07 Wed Jan 22 08:06:22 2020 -0500 |
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| @@ -1,5 +1,5 @@ 86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6093152833692663 91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5963828164943974 --47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.07927429227257943 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.07927429227257948 61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.2621440442022235 -206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.7330414645145749 |
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| diff -r af0523c606a7 -r 1a53edc4b438 test-data/lgb_class_model.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lgb_class_model.txt Wed Jan 22 08:06:22 2020 -0500 |
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| @@ -0,0 +1,151 @@ +tree +version=v3 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=3 +objective=binary sigmoid:1 +feature_names=Column_0 Column_1 Column_2 Column_3 +feature_infos=none none none none +tree_sizes=228 + +Tree=0 +num_leaves=1 +num_cat=0 +split_feature= +split_gain= +threshold= +decision_type= +left_child= +right_child= +leaf_value=-0.40546510810816427 +leaf_weight= +leaf_count= +internal_value= +internal_weight= +internal_count= +shrinkage=1 + + +end of trees + +feature importances: + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.02] +[num_leaves: 32] +[num_threads: 0] +[max_depth: 8] +[min_data_in_leaf: 20] +[min_sum_hessian_in_leaf: 39] +[bagging_fraction: 0.9] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 18467] +[feature_fraction: 0.9] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 26500] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0.04] +[lambda_l2: 0.07] +[min_gain_to_split: 0.02] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 6334] +[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: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: -1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 41] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[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] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[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] + +end of parameters + +pandas_categorical:null |
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| diff -r af0523c606a7 -r 1a53edc4b438 test-data/lgb_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lgb_prediction_result01.tabular Wed Jan 22 08:06:22 2020 -0500 |
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|
| b |
| diff -r af0523c606a7 -r 1a53edc4b438 test-data/lgb_regr_model.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lgb_regr_model.txt Wed Jan 22 08:06:22 2020 -0500 |
| [ |
| b'@@ -0,0 +1,180142 @@\n+tree\n+version=v3\n+num_class=1\n+num_tree_per_iteration=1\n+label_index=0\n+max_feature_idx=16\n+objective=regression\n+feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16\n+feature_infos=none [1:12] [1:31] [35:89] [35:117] [45.100000000000001:77.400000000000006] [41:77] [46:81] [44:79] [28:95] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]\n+tree_sizes=518 530 532 530 531 532 530 534 533 535 535 532 532 535 534 530 534 533 532 533 533 537 534 456 531 456 534 458 458 536 538 532 536 613 538 532 536 458 533 536 613 533 537 536 533 537 534 537 537 536 458 532 535 612 538 537 614 617 615 615 538 539 615 616 617 539 616 536 615 537 615 539 540 618 619 617 538 540 458 539 457 459 461 460 538 616 613 612 614 459 617 542 618 461 541 615 617 540 619 542 618 622 541 618 622 543 541 541 618 619 538 619 617 462 542 620 618 459 618 541 544 457 460 623 619 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631 544 627 468 550 622 629 543 544 548 548 547 467 630 467 549 628 468 547 468 549 627 467 546 546 548 546 630 543 468 544 626 546 545 627 627 548 545 549 548 625 626 631 626 546 625 547 629 547 469 548 545 548 547 469 546 629 547 545 468 549 549 548 544 548 630 545 547 549 467 547 543 630 544 547 467 547 548 627 623 626 465 469 547 547 547 546 629 548 548 546 546 547 545 548 627 467 550 545 548 548 546 545 552 548 546 629 546 547 543 625 548 548 550 545 547 543 545 548 548 548 549 466 547 549 543 548 547 548 628 469 626 546 626 547 545 550 628 549 544 623 552 544 549 468 552 539 546 467 548 630 544 548 545 627 548 549 624 551 551 545 468 549 468 544 548 548 550 546 548 549 628 548 549 628 548 547 548 550 546 546 550 550 548 550 629 547 547 551 551 546 468 550 466 546 547 545 550 549 628 552 550 545 549 551 549 630 630 467 547 549 551 545 624 468 552 547 549 469 548 543 549 546 548 546 624 546 625 469 552 549 630 544 549 551 547 548 543 550 628 545 551 549 549 545 549 551 550 545 468 547 550 550 '..b'98 -0.0264908\n+internal_weight=0 166 98 95\n+internal_count=261 166 98 95\n+shrinkage=0.02\n+\n+\n+Tree=9998\n+num_leaves=5\n+num_cat=0\n+split_feature=6 9 2 1\n+split_gain=0.0814517 0.423897 0.49947 0.560638\n+threshold=69.500000000000014 71.500000000000014 13.500000000000002 4.5000000000000009\n+decision_type=2 2 2 2\n+left_child=1 2 -1 -4\n+right_child=-2 -3 3 -5\n+leaf_value=0.0015287226576175053 0.00082953833355114027 -0.0021162448255171214 0.0011383155735161057 -0.0019402952165041626\n+leaf_weight=73 48 39 41 60\n+leaf_count=73 48 39 41 60\n+internal_value=0 -0.00938209 0.0120356 -0.0341362\n+internal_weight=0 213 174 101\n+internal_count=261 213 174 101\n+shrinkage=0.02\n+\n+\n+Tree=9999\n+num_leaves=5\n+num_cat=0\n+split_feature=5 5 4 9\n+split_gain=0.0787592 0.317423 0.245512 0.291236\n+threshold=70.000000000000014 64.200000000000003 60.500000000000007 45.500000000000007\n+decision_type=2 2 2 2\n+left_child=1 2 3 -1\n+right_child=-2 -3 -4 -5\n+leaf_value=0.00095524065220772586 0.00069620116702481252 -0.001859639079375311 0.0015142907661079197 -0.0011629911884827296\n+leaf_weight=46 62 40 44 69\n+leaf_count=46 62 40 44 69\n+internal_value=0 -0.0108758 0.00956023 -0.015434\n+internal_weight=0 199 159 115\n+internal_count=261 199 159 115\n+shrinkage=0.02\n+\n+\n+end of trees\n+\n+feature importances:\n+Column_9=8322\n+Column_2=6585\n+Column_5=5272\n+Column_4=4915\n+Column_6=4114\n+Column_3=3831\n+Column_1=3507\n+Column_7=2717\n+Column_8=2340\n+\n+parameters:\n+[boosting: gbdt]\n+[objective: regression]\n+[metric: ]\n+[tree_learner: serial]\n+[device_type: cpu]\n+[data: ]\n+[valid: ]\n+[num_iterations: 100]\n+[learning_rate: 0.02]\n+[num_leaves: 32]\n+[num_threads: 0]\n+[max_depth: 8]\n+[min_data_in_leaf: 20]\n+[min_sum_hessian_in_leaf: 39]\n+[bagging_fraction: 0.9]\n+[pos_bagging_fraction: 1]\n+[neg_bagging_fraction: 1]\n+[bagging_freq: 0]\n+[bagging_seed: 18467]\n+[feature_fraction: 0.9]\n+[feature_fraction_bynode: 1]\n+[feature_fraction_seed: 26500]\n+[early_stopping_round: 0]\n+[first_metric_only: 0]\n+[max_delta_step: 0]\n+[lambda_l1: 0.04]\n+[lambda_l2: 0.07]\n+[min_gain_to_split: 0.02]\n+[drop_rate: 0.1]\n+[max_drop: 50]\n+[skip_drop: 0.5]\n+[xgboost_dart_mode: 0]\n+[uniform_drop: 0]\n+[drop_seed: 6334]\n+[top_rate: 0.2]\n+[other_rate: 0.1]\n+[min_data_per_group: 100]\n+[max_cat_threshold: 32]\n+[cat_l2: 10]\n+[cat_smooth: 10]\n+[max_cat_to_onehot: 4]\n+[top_k: 20]\n+[monotone_constraints: ]\n+[feature_contri: ]\n+[forcedsplits_filename: ]\n+[forcedbins_filename: ]\n+[refit_decay_rate: 0.9]\n+[cegb_tradeoff: 1]\n+[cegb_penalty_split: 0]\n+[cegb_penalty_feature_lazy: ]\n+[cegb_penalty_feature_coupled: ]\n+[verbosity: -1]\n+[max_bin: 255]\n+[max_bin_by_feature: ]\n+[min_data_in_bin: 3]\n+[bin_construct_sample_cnt: 200000]\n+[histogram_pool_size: -1]\n+[data_random_seed: 41]\n+[output_model: LightGBM_model.txt]\n+[snapshot_freq: -1]\n+[input_model: ]\n+[output_result: LightGBM_predict_result.txt]\n+[initscore_filename: ]\n+[valid_data_initscores: ]\n+[pre_partition: 0]\n+[enable_bundle: 1]\n+[max_conflict_rate: 0]\n+[is_enable_sparse: 1]\n+[sparse_threshold: 0.8]\n+[use_missing: 1]\n+[zero_as_missing: 0]\n+[two_round: 0]\n+[save_binary: 0]\n+[header: 0]\n+[label_column: ]\n+[weight_column: ]\n+[group_column: ]\n+[ignore_column: ]\n+[categorical_feature: ]\n+[predict_raw_score: 0]\n+[predict_leaf_index: 0]\n+[predict_contrib: 0]\n+[num_iteration_predict: -1]\n+[pred_early_stop: 0]\n+[pred_early_stop_freq: 10]\n+[pred_early_stop_margin: 10]\n+[convert_model_language: ]\n+[convert_model: gbdt_prediction.cpp]\n+[num_class: 1]\n+[is_unbalance: 0]\n+[scale_pos_weight: 1]\n+[sigmoid: 1]\n+[boost_from_average: 1]\n+[reg_sqrt: 0]\n+[alpha: 0.9]\n+[fair_c: 1]\n+[poisson_max_delta_step: 0.7]\n+[tweedie_variance_power: 1.5]\n+[max_position: 20]\n+[lambdamart_norm: 1]\n+[label_gain: ]\n+[metric_freq: 1]\n+[is_provide_training_metric: 0]\n+[eval_at: ]\n+[multi_error_top_k: 1]\n+[num_machines: 1]\n+[local_listen_port: 12400]\n+[time_out: 120]\n+[machine_list_filename: ]\n+[machines: ]\n+[gpu_platform_id: -1]\n+[gpu_device_id: -1]\n+[gpu_use_dp: 0]\n+\n+end of parameters\n+\n+pandas_categorical:null\n' |