Commit
•
8cfb1f7
1
Parent(s):
2fffc7b
init
Browse files- README.md +186 -0
- config.json +52 -0
- pipeline_model_sklearn.joblib +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
library_name: sklearn
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| 3 |
+
tags:
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| 4 |
+
- sklearn
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| 5 |
+
- skops
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| 6 |
+
- tabular-classification
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| 7 |
+
model_format: pickle
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+
model_file: pipeline_model_sklearn.joblib
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| 9 |
+
widget:
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| 10 |
+
- structuredData:
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| 11 |
+
Age:
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| 12 |
+
- 23
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| 13 |
+
- 47
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| 14 |
+
- 47
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| 15 |
+
BP:
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| 16 |
+
- HIGH
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| 17 |
+
- LOW
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| 18 |
+
- LOW
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| 19 |
+
Cholesterol:
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| 20 |
+
- HIGH
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| 21 |
+
- HIGH
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| 22 |
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- HIGH
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| 23 |
+
K:
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| 24 |
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- 0.031258
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| 25 |
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- 0.056468
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| 26 |
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- 0.068944
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| 27 |
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Na:
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| 28 |
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- 0.792535
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| 29 |
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- 0.739309
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| 30 |
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- 0.697269
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| 31 |
+
Sex:
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| 32 |
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- F
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| 33 |
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- M
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| 34 |
+
- M
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| 35 |
+
---
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| 36 |
+
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| 37 |
+
# Model description
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| 38 |
+
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| 39 |
+
[More Information Needed]
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| 40 |
+
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| 41 |
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## Intended uses & limitations
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| 42 |
+
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| 43 |
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[More Information Needed]
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| 44 |
+
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| 45 |
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## Training Procedure
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| 46 |
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| 47 |
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[More Information Needed]
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| 48 |
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| 49 |
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### Hyperparameters
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| 50 |
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| 51 |
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<details>
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| 52 |
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<summary> Click to expand </summary>
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| 53 |
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| 54 |
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| Hyperparameter | Value |
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| 55 |
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|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 56 |
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| memory | |
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| 57 |
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| steps | [('featureunion', FeatureUnion(transformer_list=[('float32_transform_139955258811312',<br /> Pipeline(steps=[('numpycolumnselector',<br /> NumpyColumnSelector(columns=[1,<br /> 2,<br /> 3])),<br /> ('compressstrings',<br /> CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str',<br /> 'char_str',<br /> 'char_str'],<br /> missing_values_reference_list=['',<br /> '-',<br /> '?',<br /> nan],<br /> misslist_list=[[],<br /> [],<br /> []])),<br /> ('numpyreplacemissingvalues'...<br /> FloatStr2Float(dtypes_list=['float_int_num',<br /> 'float_num',<br /> 'float_num'],<br /> missing_values_reference_list=[])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numimputer',<br /> NumImputer(missing_values=nan,<br /> strategy='median')),<br /> ('optstandardscaler',<br /> OptStandardScaler(use_scaler_flag=False)),<br /> ('float32_transform',<br /> float32_transform())]))])), ('numpypermutearray', NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5])), ('lgbmclassifier', LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33))] |
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| 58 |
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| verbose | False |
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| 59 |
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| featureunion | FeatureUnion(transformer_list=[('float32_transform_139955258811312',<br /> Pipeline(steps=[('numpycolumnselector',<br /> NumpyColumnSelector(columns=[1,<br /> 2,<br /> 3])),<br /> ('compressstrings',<br /> CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str',<br /> 'char_str',<br /> 'char_str'],<br /> missing_values_reference_list=['',<br /> '-',<br /> '?',<br /> nan],<br /> misslist_list=[[],<br /> [],<br /> []])),<br /> ('numpyreplacemissingvalues'...<br /> FloatStr2Float(dtypes_list=['float_int_num',<br /> 'float_num',<br /> 'float_num'],<br /> missing_values_reference_list=[])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numimputer',<br /> NumImputer(missing_values=nan,<br /> strategy='median')),<br /> ('optstandardscaler',<br /> OptStandardScaler(use_scaler_flag=False)),<br /> ('float32_transform',<br /> float32_transform())]))]) |
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| 60 |
+
| numpypermutearray | NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5]) |
|
| 61 |
+
| lgbmclassifier | LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33) |
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| 62 |
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| featureunion__n_jobs | |
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| 63 |
+
| featureunion__transformer_list | [('float32_transform_139955258811312', Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])),<br /> ('compressstrings',<br /> CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str', 'char_str',<br /> 'char_str'],<br /> missing_values_reference_list=['', '-', '?',<br /> nan],<br /> misslist_list=[[], [], []])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numpyreplaceunknown...<br /> 40061271003327253395033901872323469393]],<br /> missing_values_reference_list=['',<br /> '-',<br /> '?',<br /> nan])),<br /> ('boolean2float', boolean2float()),<br /> ('catimputer',<br /> CatImputer(missing_values=nan, strategy='most_frequent')),<br /> ('catencoder',<br /> CatEncoder(categories='auto', dtype=<class 'numpy.float64'>,<br /> encoding='ordinal', handle_unknown='error')),<br /> ('float32_transform', float32_transform())])), ('float32_transform_139955258809968', Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])),<br /> ('floatstr2float',<br /> FloatStr2Float(dtypes_list=['float_int_num', 'float_num',<br /> 'float_num'],<br /> missing_values_reference_list=[])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numimputer',<br /> NumImputer(missing_values=nan, strategy='median')),<br /> ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)),<br /> ('float32_transform', float32_transform())]))] |
|
| 64 |
+
| featureunion__transformer_weights | |
|
| 65 |
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| featureunion__verbose | False |
|
| 66 |
+
| featureunion__float32_transform_139955258811312 | Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])),<br /> ('compressstrings',<br /> CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str', 'char_str',<br /> 'char_str'],<br /> missing_values_reference_list=['', '-', '?',<br /> nan],<br /> misslist_list=[[], [], []])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numpyreplaceunknown...<br /> 40061271003327253395033901872323469393]],<br /> missing_values_reference_list=['',<br /> '-',<br /> '?',<br /> nan])),<br /> ('boolean2float', boolean2float()),<br /> ('catimputer',<br /> CatImputer(missing_values=nan, strategy='most_frequent')),<br /> ('catencoder',<br /> CatEncoder(categories='auto', dtype=<class 'numpy.float64'>,<br /> encoding='ordinal', handle_unknown='error')),<br /> ('float32_transform', float32_transform())]) |
|
| 67 |
+
| featureunion__float32_transform_139955258809968 | Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])),<br /> ('floatstr2float',<br /> FloatStr2Float(dtypes_list=['float_int_num', 'float_num',<br /> 'float_num'],<br /> missing_values_reference_list=[])),<br /> ('numpyreplacemissingvalues',<br /> NumpyReplaceMissingValues(missing_values=[])),<br /> ('numimputer',<br /> NumImputer(missing_values=nan, strategy='median')),<br /> ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)),<br /> ('float32_transform', float32_transform())]) |
|
| 68 |
+
| featureunion__float32_transform_139955258811312__memory | |
|
| 69 |
+
| featureunion__float32_transform_139955258811312__steps | [('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])), ('compressstrings', CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str', 'char_str', 'char_str'],<br /> missing_values_reference_list=['', '-', '?', nan],<br /> misslist_list=[[], [], []])), ('numpyreplacemissingvalues', NumpyReplaceMissingValues(missing_values=[])), ('numpyreplaceunknownvalues', NumpyReplaceUnknownValues(filling_values=nan,<br /> filling_values_list=[nan, nan, nan],<br /> known_values_list=[[170172835760119224333519554008280666130,<br /> 140114708448418632577632402066430035116],<br /> [245397760256243238036686602120338271372,<br /> 87378989482499796866217412016778320776,<br /> 40061271003327253395033901872323469393],<br /> [245397760256243238036686602120338271372,<br /> 40061271003327253395033901872323469393]],<br /> missing_values_reference_list=['', '-', '?', nan])), ('boolean2float', boolean2float()), ('catimputer', CatImputer(missing_values=nan, strategy='most_frequent')), ('catencoder', CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',<br /> handle_unknown='error')), ('float32_transform', float32_transform())] |
|
| 70 |
+
| featureunion__float32_transform_139955258811312__verbose | False |
|
| 71 |
+
| featureunion__float32_transform_139955258811312__numpycolumnselector | NumpyColumnSelector(columns=[1, 2, 3]) |
|
| 72 |
+
| featureunion__float32_transform_139955258811312__compressstrings | CompressStrings(compress_type='hash',<br /> dtypes_list=['char_str', 'char_str', 'char_str'],<br /> missing_values_reference_list=['', '-', '?', nan],<br /> misslist_list=[[], [], []]) |
|
| 73 |
+
| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues | NumpyReplaceMissingValues(missing_values=[]) |
|
| 74 |
+
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues | NumpyReplaceUnknownValues(filling_values=nan,<br /> filling_values_list=[nan, nan, nan],<br /> known_values_list=[[170172835760119224333519554008280666130,<br /> 140114708448418632577632402066430035116],<br /> [245397760256243238036686602120338271372,<br /> 87378989482499796866217412016778320776,<br /> 40061271003327253395033901872323469393],<br /> [245397760256243238036686602120338271372,<br /> 40061271003327253395033901872323469393]],<br /> missing_values_reference_list=['', '-', '?', nan]) |
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| 75 |
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| featureunion__float32_transform_139955258811312__boolean2float | boolean2float() |
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| 76 |
+
| featureunion__float32_transform_139955258811312__catimputer | CatImputer(missing_values=nan, strategy='most_frequent') |
|
| 77 |
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| featureunion__float32_transform_139955258811312__catencoder | CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',<br /> handle_unknown='error') |
|
| 78 |
+
| featureunion__float32_transform_139955258811312__float32_transform | float32_transform() |
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| 79 |
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| featureunion__float32_transform_139955258811312__numpycolumnselector__columns | [1, 2, 3] |
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| 80 |
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| featureunion__float32_transform_139955258811312__compressstrings__activate_flag | True |
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| 81 |
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| featureunion__float32_transform_139955258811312__compressstrings__compress_type | hash |
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| 82 |
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| featureunion__float32_transform_139955258811312__compressstrings__dtypes_list | ['char_str', 'char_str', 'char_str'] |
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| 83 |
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| featureunion__float32_transform_139955258811312__compressstrings__missing_values_reference_list | ['', '-', '?', nan] |
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| 84 |
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| featureunion__float32_transform_139955258811312__compressstrings__misslist_list | [[], [], []] |
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| 85 |
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| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__filling_values | nan |
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| 86 |
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| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__missing_values | [] |
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| 87 |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values | nan |
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| 88 |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values_list | [nan, nan, nan] |
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| 89 |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__known_values_list | [[170172835760119224333519554008280666130, 140114708448418632577632402066430035116], [245397760256243238036686602120338271372, 87378989482499796866217412016778320776, 40061271003327253395033901872323469393], [245397760256243238036686602120338271372, 40061271003327253395033901872323469393]] |
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| 90 |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__missing_values_reference_list | ['', '-', '?', nan] |
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| 91 |
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| featureunion__float32_transform_139955258811312__boolean2float__activate_flag | True |
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| 92 |
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| featureunion__float32_transform_139955258811312__catimputer__activate_flag | True |
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| 93 |
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| featureunion__float32_transform_139955258811312__catimputer__missing_values | nan |
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| 94 |
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| featureunion__float32_transform_139955258811312__catimputer__sklearn_version_family | 1 |
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| 95 |
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| featureunion__float32_transform_139955258811312__catimputer__strategy | most_frequent |
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| 96 |
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| featureunion__float32_transform_139955258811312__catencoder__activate_flag | True |
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| 97 |
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| featureunion__float32_transform_139955258811312__catencoder__categories | auto |
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| 98 |
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| featureunion__float32_transform_139955258811312__catencoder__dtype | <class 'numpy.float64'> |
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| 99 |
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| featureunion__float32_transform_139955258811312__catencoder__encoding | ordinal |
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| 100 |
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| featureunion__float32_transform_139955258811312__catencoder__handle_unknown | error |
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| 101 |
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| featureunion__float32_transform_139955258811312__catencoder__sklearn_version_family | 1 |
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| 102 |
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| featureunion__float32_transform_139955258811312__float32_transform__activate_flag | True |
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| 103 |
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| featureunion__float32_transform_139955258809968__memory | |
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| 104 |
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| featureunion__float32_transform_139955258809968__steps | [('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])), ('floatstr2float', FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],<br /> missing_values_reference_list=[])), ('numpyreplacemissingvalues', NumpyReplaceMissingValues(missing_values=[])), ('numimputer', NumImputer(missing_values=nan, strategy='median')), ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)), ('float32_transform', float32_transform())] |
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| 105 |
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| featureunion__float32_transform_139955258809968__verbose | False |
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| 106 |
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| featureunion__float32_transform_139955258809968__numpycolumnselector | NumpyColumnSelector(columns=[0, 4, 5]) |
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| 107 |
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| featureunion__float32_transform_139955258809968__floatstr2float | FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],<br /> missing_values_reference_list=[]) |
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| 108 |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues | NumpyReplaceMissingValues(missing_values=[]) |
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| 109 |
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| featureunion__float32_transform_139955258809968__numimputer | NumImputer(missing_values=nan, strategy='median') |
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| 110 |
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| featureunion__float32_transform_139955258809968__optstandardscaler | OptStandardScaler(use_scaler_flag=False) |
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| 111 |
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| featureunion__float32_transform_139955258809968__float32_transform | float32_transform() |
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| 112 |
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| featureunion__float32_transform_139955258809968__numpycolumnselector__columns | [0, 4, 5] |
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| 113 |
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| featureunion__float32_transform_139955258809968__floatstr2float__activate_flag | True |
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| 114 |
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| featureunion__float32_transform_139955258809968__floatstr2float__dtypes_list | ['float_int_num', 'float_num', 'float_num'] |
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| 115 |
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| featureunion__float32_transform_139955258809968__floatstr2float__missing_values_reference_list | [] |
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| 116 |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__filling_values | nan |
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| 117 |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__missing_values | [] |
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| 118 |
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| featureunion__float32_transform_139955258809968__numimputer__activate_flag | True |
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| 119 |
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| featureunion__float32_transform_139955258809968__numimputer__missing_values | nan |
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| 120 |
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| featureunion__float32_transform_139955258809968__numimputer__strategy | median |
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| 121 |
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| featureunion__float32_transform_139955258809968__optstandardscaler__use_scaler_flag | False |
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| 122 |
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| featureunion__float32_transform_139955258809968__float32_transform__activate_flag | True |
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| 123 |
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| numpypermutearray__axis | 0 |
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| 124 |
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| numpypermutearray__permutation_indices | [1, 2, 3, 0, 4, 5] |
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| 125 |
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| lgbmclassifier__boosting_type | gbdt |
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| 126 |
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| lgbmclassifier__class_weight | balanced |
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| 127 |
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| lgbmclassifier__colsample_bytree | 1.0 |
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| 128 |
+
| lgbmclassifier__importance_type | split |
|
| 129 |
+
| lgbmclassifier__learning_rate | 0.1 |
|
| 130 |
+
| lgbmclassifier__max_depth | -1 |
|
| 131 |
+
| lgbmclassifier__min_child_samples | 20 |
|
| 132 |
+
| lgbmclassifier__min_child_weight | 0.001 |
|
| 133 |
+
| lgbmclassifier__min_split_gain | 0.0 |
|
| 134 |
+
| lgbmclassifier__n_estimators | 100 |
|
| 135 |
+
| lgbmclassifier__n_jobs | 1 |
|
| 136 |
+
| lgbmclassifier__num_leaves | 31 |
|
| 137 |
+
| lgbmclassifier__objective | |
|
| 138 |
+
| lgbmclassifier__random_state | 33 |
|
| 139 |
+
| lgbmclassifier__reg_alpha | 0.0 |
|
| 140 |
+
| lgbmclassifier__reg_lambda | 0.0 |
|
| 141 |
+
| lgbmclassifier__silent | warn |
|
| 142 |
+
| lgbmclassifier__subsample | 1.0 |
|
| 143 |
+
| lgbmclassifier__subsample_for_bin | 200000 |
|
| 144 |
+
| lgbmclassifier__subsample_freq | 0 |
|
| 145 |
+
|
| 146 |
+
</details>
|
| 147 |
+
|
| 148 |
+
### Model Plot
|
| 149 |
+
|
| 150 |
+
<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('featureunion',FeatureUnion(transformer_list=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),('numimputer',NumImputer(missing_values=nan,strategy='median')),('optstandardscaler',OptStandardScaler(use_scaler_flag=False)),('float32_transform',float32_transform())]))])),('numpypermutearray',NumpyPermuteArray(axis=0,permutation_indices=[1, 2, 3, 0, 4, 5])),('lgbmclassifier',LGBMClassifier(class_weight='balanced', n_jobs=1,random_state=33))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('featureunion',FeatureUnion(transformer_list=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),('numimputer',NumImputer(missing_values=nan,strategy='median')),('optstandardscaler',OptStandardScaler(use_scaler_flag=False)),('float32_transform',float32_transform())]))])),('numpypermutearray',NumpyPermuteArray(axis=0,permutation_indices=[1, 2, 3, 0, 4, 5])),('lgbmclassifier',LGBMClassifier(class_weight='balanced', n_jobs=1,random_state=33))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label sk-toggleable__label-arrow">featureunion: FeatureUnion</label><div class="sk-toggleable__content"><pre>FeatureUnion(transformer_list=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]])),('numpyreplacemissingvalues'...FloatStr2Float(dtypes_list=['float_int_num','float_num','float_num'],missing_values_reference_list=[])),('numpyreplacemissingvalues',NumpyReplaceMissingValues(missing_values=[])),('numimputer',NumImputer(missing_values=nan,strategy='median')),('optstandardscaler',OptStandardScaler(use_scaler_flag=False)),('float32_transform',float32_transform())]))])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>float32_transform_139955258811312</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyColumnSelector</label><div class="sk-toggleable__content"><pre>NumpyColumnSelector(columns=[1, 2, 3])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label sk-toggleable__label-arrow">CompressStrings</label><div class="sk-toggleable__content"><pre>CompressStrings(compress_type='hash',dtypes_list=['char_str', 'char_str', 'char_str'],missing_values_reference_list=['', '-', '?', nan],misslist_list=[[], [], []])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceMissingValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceMissingValues(missing_values=[])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceUnknownValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceUnknownValues(filling_values=nan,filling_values_list=[nan, nan, nan],known_values_list=[[170172835760119224333519554008280666130,140114708448418632577632402066430035116],[245397760256243238036686602120338271372,87378989482499796866217412016778320776,40061271003327253395033901872323469393],[245397760256243238036686602120338271372,40061271003327253395033901872323469393]],missing_values_reference_list=['', '-', '?', nan])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label sk-toggleable__label-arrow">boolean2float</label><div class="sk-toggleable__content"><pre>boolean2float()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label sk-toggleable__label-arrow">CatImputer</label><div class="sk-toggleable__content"><pre>CatImputer(missing_values=nan, strategy='most_frequent')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label sk-toggleable__label-arrow">CatEncoder</label><div class="sk-toggleable__content"><pre>CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',handle_unknown='error')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label sk-toggleable__label-arrow">float32_transform</label><div class="sk-toggleable__content"><pre>float32_transform()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>float32_transform_139955258809968</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyColumnSelector</label><div class="sk-toggleable__content"><pre>NumpyColumnSelector(columns=[0, 4, 5])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label sk-toggleable__label-arrow">FloatStr2Float</label><div class="sk-toggleable__content"><pre>FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],missing_values_reference_list=[])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceMissingValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceMissingValues(missing_values=[])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label sk-toggleable__label-arrow">NumImputer</label><div class="sk-toggleable__content"><pre>NumImputer(missing_values=nan, strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label sk-toggleable__label-arrow">OptStandardScaler</label><div class="sk-toggleable__content"><pre>OptStandardScaler(use_scaler_flag=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label sk-toggleable__label-arrow">float32_transform</label><div class="sk-toggleable__content"><pre>float32_transform()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-53" type="checkbox" ><label for="sk-estimator-id-53" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyPermuteArray</label><div class="sk-toggleable__content"><pre>NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-54" type="checkbox" ><label for="sk-estimator-id-54" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33)</pre></div></div></div></div></div></div></div>
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| 151 |
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| 152 |
+
## Evaluation Results
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| 153 |
+
|
| 154 |
+
[More Information Needed]
|
| 155 |
+
|
| 156 |
+
# How to Get Started with the Model
|
| 157 |
+
|
| 158 |
+
[More Information Needed]
|
| 159 |
+
|
| 160 |
+
# Model Card Authors
|
| 161 |
+
|
| 162 |
+
This model card is written by following authors:
|
| 163 |
+
|
| 164 |
+
[More Information Needed]
|
| 165 |
+
|
| 166 |
+
# Model Card Contact
|
| 167 |
+
|
| 168 |
+
You can contact the model card authors through following channels:
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| 169 |
+
[More Information Needed]
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| 170 |
+
|
| 171 |
+
# Citation
|
| 172 |
+
|
| 173 |
+
Below you can find information related to citation.
|
| 174 |
+
|
| 175 |
+
**BibTeX:**
|
| 176 |
+
```
|
| 177 |
+
[More Information Needed]
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
+
# model_card_authors
|
| 181 |
+
|
| 182 |
+
wenpei
|
| 183 |
+
|
| 184 |
+
# model_description
|
| 185 |
+
|
| 186 |
+
test propose for autoai and hugging face
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config.json
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"Age",
|
| 5 |
+
"Sex",
|
| 6 |
+
"BP",
|
| 7 |
+
"Cholesterol",
|
| 8 |
+
"Na",
|
| 9 |
+
"K"
|
| 10 |
+
],
|
| 11 |
+
"environment": [
|
| 12 |
+
"scikit-learn=1.1.1"
|
| 13 |
+
],
|
| 14 |
+
"example_input": {
|
| 15 |
+
"Age": [
|
| 16 |
+
23,
|
| 17 |
+
47,
|
| 18 |
+
47
|
| 19 |
+
],
|
| 20 |
+
"BP": [
|
| 21 |
+
"HIGH",
|
| 22 |
+
"LOW",
|
| 23 |
+
"LOW"
|
| 24 |
+
],
|
| 25 |
+
"Cholesterol": [
|
| 26 |
+
"HIGH",
|
| 27 |
+
"HIGH",
|
| 28 |
+
"HIGH"
|
| 29 |
+
],
|
| 30 |
+
"K": [
|
| 31 |
+
0.031258,
|
| 32 |
+
0.056468,
|
| 33 |
+
0.068944
|
| 34 |
+
],
|
| 35 |
+
"Na": [
|
| 36 |
+
0.792535,
|
| 37 |
+
0.739309,
|
| 38 |
+
0.697269
|
| 39 |
+
],
|
| 40 |
+
"Sex": [
|
| 41 |
+
"F",
|
| 42 |
+
"M",
|
| 43 |
+
"M"
|
| 44 |
+
]
|
| 45 |
+
},
|
| 46 |
+
"model": {
|
| 47 |
+
"file": "pipeline_model_sklearn.joblib"
|
| 48 |
+
},
|
| 49 |
+
"model_format": "pickle",
|
| 50 |
+
"task": "tabular-classification"
|
| 51 |
+
}
|
| 52 |
+
}
|
pipeline_model_sklearn.joblib
ADDED
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bac82d7169cff814dadb256acadba89badd04c25c4ac2f21535cc1c6aa9a5c16
|
| 3 |
+
size 476674
|