Mercurial > repos > bgruening > sklearn_discriminant_classifier
comparison test-data/pipeline_params18 @ 31:64b771b1471a draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author | bgruening |
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date | Mon, 16 Dec 2019 05:17:00 -0500 |
parents | |
children | eeaf989f1024 |
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30:18b39ada6f35 | 31:64b771b1471a |
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1 Parameter Value | |
2 * memory memory: None | |
3 @ powertransformer powertransformer: PowerTransformer(copy=True, method='yeo-johnson', standardize=True) | |
4 * steps "steps: [('powertransformer', PowerTransformer(copy=True, method='yeo-johnson', standardize=True)), ('transformedtargetregressor', TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, | |
5 regressor=RandomForestRegressor(bootstrap=True, | |
6 criterion='mse', | |
7 max_depth=None, | |
8 max_features='auto', | |
9 max_leaf_nodes=None, | |
10 min_impurity_decrease=0.0, | |
11 min_impurity_split=None, | |
12 min_samples_leaf=1, | |
13 min_samples_split=2, | |
14 min_weight_fraction_leaf=0.0, | |
15 n_estimators='warn', | |
16 n_jobs=1, | |
17 oob_score=False, | |
18 random_state=10, | |
19 verbose=0, | |
20 warm_start=False), | |
21 transformer=QuantileTransformer(copy=True, | |
22 ignore_implicit_zeros=False, | |
23 n_quantiles=1000, | |
24 output_distribution='uniform', | |
25 random_state=10, | |
26 subsample=100000)))]" | |
27 @ transformedtargetregressor "transformedtargetregressor: TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, | |
28 regressor=RandomForestRegressor(bootstrap=True, | |
29 criterion='mse', | |
30 max_depth=None, | |
31 max_features='auto', | |
32 max_leaf_nodes=None, | |
33 min_impurity_decrease=0.0, | |
34 min_impurity_split=None, | |
35 min_samples_leaf=1, | |
36 min_samples_split=2, | |
37 min_weight_fraction_leaf=0.0, | |
38 n_estimators='warn', | |
39 n_jobs=1, | |
40 oob_score=False, | |
41 random_state=10, | |
42 verbose=0, | |
43 warm_start=False), | |
44 transformer=QuantileTransformer(copy=True, | |
45 ignore_implicit_zeros=False, | |
46 n_quantiles=1000, | |
47 output_distribution='uniform', | |
48 random_state=10, | |
49 subsample=100000))" | |
50 * verbose verbose: False | |
51 @ powertransformer__copy powertransformer__copy: True | |
52 @ powertransformer__method powertransformer__method: 'yeo-johnson' | |
53 @ powertransformer__standardize powertransformer__standardize: True | |
54 @ transformedtargetregressor__check_inverse transformedtargetregressor__check_inverse: True | |
55 @ transformedtargetregressor__func transformedtargetregressor__func: None | |
56 @ transformedtargetregressor__inverse_func transformedtargetregressor__inverse_func: None | |
57 @ transformedtargetregressor__regressor "transformedtargetregressor__regressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, | |
58 max_features='auto', max_leaf_nodes=None, | |
59 min_impurity_decrease=0.0, min_impurity_split=None, | |
60 min_samples_leaf=1, min_samples_split=2, | |
61 min_weight_fraction_leaf=0.0, n_estimators='warn', | |
62 n_jobs=1, oob_score=False, random_state=10, verbose=0, | |
63 warm_start=False)" | |
64 @ transformedtargetregressor__regressor__bootstrap transformedtargetregressor__regressor__bootstrap: True | |
65 @ transformedtargetregressor__regressor__criterion transformedtargetregressor__regressor__criterion: 'mse' | |
66 @ transformedtargetregressor__regressor__max_depth transformedtargetregressor__regressor__max_depth: None | |
67 @ transformedtargetregressor__regressor__max_features transformedtargetregressor__regressor__max_features: 'auto' | |
68 @ transformedtargetregressor__regressor__max_leaf_nodes transformedtargetregressor__regressor__max_leaf_nodes: None | |
69 @ transformedtargetregressor__regressor__min_impurity_decrease transformedtargetregressor__regressor__min_impurity_decrease: 0.0 | |
70 @ transformedtargetregressor__regressor__min_impurity_split transformedtargetregressor__regressor__min_impurity_split: None | |
71 @ transformedtargetregressor__regressor__min_samples_leaf transformedtargetregressor__regressor__min_samples_leaf: 1 | |
72 @ transformedtargetregressor__regressor__min_samples_split transformedtargetregressor__regressor__min_samples_split: 2 | |
73 @ transformedtargetregressor__regressor__min_weight_fraction_leaf transformedtargetregressor__regressor__min_weight_fraction_leaf: 0.0 | |
74 @ transformedtargetregressor__regressor__n_estimators transformedtargetregressor__regressor__n_estimators: 'warn' | |
75 * transformedtargetregressor__regressor__n_jobs transformedtargetregressor__regressor__n_jobs: 1 | |
76 @ transformedtargetregressor__regressor__oob_score transformedtargetregressor__regressor__oob_score: False | |
77 @ transformedtargetregressor__regressor__random_state transformedtargetregressor__regressor__random_state: 10 | |
78 * transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0 | |
79 @ transformedtargetregressor__regressor__warm_start transformedtargetregressor__regressor__warm_start: False | |
80 @ transformedtargetregressor__transformer "transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, | |
81 output_distribution='uniform', random_state=10, | |
82 subsample=100000)" | |
83 @ transformedtargetregressor__transformer__copy transformedtargetregressor__transformer__copy: True | |
84 @ transformedtargetregressor__transformer__ignore_implicit_zeros transformedtargetregressor__transformer__ignore_implicit_zeros: False | |
85 @ transformedtargetregressor__transformer__n_quantiles transformedtargetregressor__transformer__n_quantiles: 1000 | |
86 @ transformedtargetregressor__transformer__output_distribution transformedtargetregressor__transformer__output_distribution: 'uniform' | |
87 @ transformedtargetregressor__transformer__random_state transformedtargetregressor__transformer__random_state: 10 | |
88 @ transformedtargetregressor__transformer__subsample transformedtargetregressor__transformer__subsample: 100000 | |
89 Note: @, params eligible for search in searchcv tool. |