# HG changeset patch
# User bgruening
# Date 1579697205 18000
# Node ID f04a2405f3fe869e7d02ec8f6ef789e98ceaa6da
# Parent  a152c1d23379d4827cc3f0a1d54fd60b790bc9f0
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 756f8be9c3cd437e131e6410cd625c24fe078e8c"

diff -r a152c1d23379 -r f04a2405f3fe main_macros.xml
--- a/main_macros.xml	Mon Dec 16 05:25:18 2019 -0500
+++ b/main_macros.xml	Wed Jan 22 07:46:45 2020 -0500
@@ -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@"/>
diff -r a152c1d23379 -r f04a2405f3fe test-data/glm_result07
--- a/test-data/glm_result07	Mon Dec 16 05:25:18 2019 -0500
+++ b/test-data/glm_result07	Wed Jan 22 07:46:45 2020 -0500
@@ -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
diff -r a152c1d23379 -r f04a2405f3fe 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 07:46:45 2020 -0500
@@ -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
diff -r a152c1d23379 -r f04a2405f3fe 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 07:46:45 2020 -0500
@@ -0,0 +1,262 @@
+year	month	day	temp_2	temp_1	average	forecast_noaa	forecast_acc	forecast_under	friend	week_Fri	week_Mon	week_Sat	week_Sun	week_Thurs	week_Tues	week_Wed	predicted
+2016	9	19	68	69	69.7	65	74	71	88	0	1	0	0	0	0	0	71.89319490976423
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diff -r a152c1d23379 -r f04a2405f3fe 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 07:46:45 2020 -0500
@@ -0,0 +1,180142 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=16
+objective=regression
+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
+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]
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+
+Tree=9991
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+
+Tree=9992
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+
+Tree=9993
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+
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+
+Tree=9995
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+
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+
+Tree=9997
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+
+
+Tree=9998
+num_leaves=5
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+split_feature=6 9 2 1
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+threshold=69.500000000000014 71.500000000000014 13.500000000000002 4.5000000000000009
+decision_type=2 2 2 2
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+leaf_weight=73 48 39 41 60
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+
+
+Tree=9999
+num_leaves=5
+num_cat=0
+split_feature=5 5 4 9
+split_gain=0.0787592 0.317423 0.245512 0.291236
+threshold=70.000000000000014 64.200000000000003 60.500000000000007 45.500000000000007
+decision_type=2 2 2 2
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+internal_weight=0 199 159 115
+internal_count=261 199 159 115
+shrinkage=0.02
+
+
+end of trees
+
+feature importances:
+Column_9=8322
+Column_2=6585
+Column_5=5272
+Column_4=4915
+Column_6=4114
+Column_3=3831
+Column_1=3507
+Column_7=2717
+Column_8=2340
+
+parameters:
+[boosting: gbdt]
+[objective: regression]
+[metric: ]
+[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