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fix .0 suffix #445

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Feb 22, 2020
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fix .0 suffix #445

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7 changes: 5 additions & 2 deletions src/DotNetBridge/NativeDataInterop.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -213,8 +213,11 @@ private static unsafe void SendViewToNativeAsDataFrame(IChannel ch, EnvironmentB
}
else
{
for (int i = 0; i < nSlots; i++)
AddUniqueName(name + "." + i, ref nameIndices, ref nameUtf8Bytes);
if (nSlots == 1)
AddUniqueName(name, ref nameIndices, ref nameUtf8Bytes);
else
for (int i = 0; i < nSlots; i++)
AddUniqueName(name + "." + i, ref nameIndices, ref nameUtf8Bytes);
}
}
else
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96 changes: 51 additions & 45 deletions src/python/tests_extended/test_export_to_onnx.py
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Expand Up @@ -35,7 +35,7 @@
from nimbusml.preprocessing.missing_values import Filter, Handler, Indicator
from nimbusml.preprocessing.normalization import Binner, GlobalContrastRowScaler, LpScaler
from nimbusml.preprocessing.schema import (ColumnConcatenator, TypeConverter,
ColumnDuplicator, ColumnSelector)
ColumnDuplicator, ColumnSelector, PrefixColumnConcatenator)
from nimbusml.preprocessing.text import CharTokenizer, WordTokenizer
from nimbusml.timeseries import (IidSpikeDetector, IidChangePointDetector,
SsaSpikeDetector, SsaChangePointDetector,
Expand Down Expand Up @@ -186,6 +186,7 @@
Loader(columns={'ImgPath': 'Path'}),
PixelExtractor(columns={'ImgPixels': 'ImgPath'}),
]),
'PrefixColumnConcatenator': PrefixColumnConcatenator(columns={'Features': 'Sepal_'}),
'Resizer': Pipeline([
Loader(columns={'ImgPath': 'Path'}),
Resizer(image_width=227, image_height=227,
Expand Down Expand Up @@ -264,71 +265,76 @@
}

EXPECTED_RESULTS = {
'AveragedPerceptronBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'AveragedPerceptronBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'CharTokenizer': {'cols': [('SentimentText_Transform.%03d' % i, 'SentimentText_Transform.%03d' % i)
for i in range(0, 422)]},
'ColumnDuplicator': {'cols': [('dup', 'dup.0')]},
'ColumnConcatenator': {'num_cols': 11, 'cols': 0},
'ColumnDuplicator': {'cols': [('dup', 'dup')]},
'ColumnSelector': {
'num_cols': 2,
'cols': [('Sepal_Width', 'Sepal_Width.0'), ('Sepal_Length', 'Sepal_Length.0')]
'cols': [('Sepal_Width', 'Sepal_Width'), ('Sepal_Length', 'Sepal_Length')]
},
#'EnsembleClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
#'EnsembleRegressor': {'cols': [('Score', 'Score.0')]},
'FastForestBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'FastForestRegressor': {'cols': [('Score', 'Score.0')]},
'FastLinearBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'FastLinearClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'FastLinearRegressor': {'cols': [('Score', 'Score.0')]},
'FastTreesBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'FastTreesRegressor': {'cols': [('Score', 'Score.0')]},
'FastTreesTweedieRegressor': {'cols': [('Score', 'Score.0')]},
'FromKey': {'cols': [('Sepal_Length', 'Sepal_Length.0'), ('Label', 'Label.0')]},
#'EnsembleClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
#'EnsembleRegressor': {'cols': [('Score', 'Score')]},
'FastForestBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'FastForestRegressor': {'cols': [('Score', 'Score')]},
'FastLinearBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'FastLinearClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'FastLinearRegressor': {'cols': [('Score', 'Score')]},
'FastTreesBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'FastTreesRegressor': {'cols': [('Score', 'Score')]},
'FastTreesTweedieRegressor': {'cols': [('Score', 'Score')]},
'FromKey': {'cols': [('Sepal_Length', 'Sepal_Length'), ('Label', 'Label')]},
'GlobalContrastRowScaler': {'cols': [
('normed_columns.Petal_Length', 'normed_columns.0'),
('normed_columns.Sepal_Width', 'normed_columns.1'),
('normed_columns.Sepal_Length', 'normed_columns.2')
('normed_columns.Petal_Length', 'normed_columns.Petal_Length'),
('normed_columns.Sepal_Width', 'normed_columns.Sepal_Width'),
('normed_columns.Sepal_Length', 'normed_columns.Sepal_Length')
]},
'Handler': {'cols': [
('NewVals.NewVals', 'NewVals.0'),
('NewVals.IsMissing.NewVals', 'NewVals.1')
('NewVals.NewVals', 'NewVals.NewVals'),
('NewVals.IsMissing.NewVals', 'NewVals.IsMissing.NewVals')
]},
'Indicator': {'cols': [('Has_Nan', 'Has_Nan.0')]},
'KMeansPlusPlus': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'LightGbmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'LightGbmClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'LightGbmRanker': {'cols': [('Score', 'Score.0')]},
'LightGbmRegressor': {'cols': [('Score', 'Score.0')]},
'LinearSvmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'LogisticRegressionBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'LogisticRegressionClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'Indicator': {'cols': [('Has_Nan', 'Has_Nan')]},
'KMeansPlusPlus': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LightGbmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LightGbmClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LightGbmRanker': {'cols': [('Score', 'Score')]},
'LightGbmRegressor': {'cols': [('Score', 'Score')]},
'LinearSvmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LogisticRegressionBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LogisticRegressionClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LpScaler': {'cols': [
('normed_columns.Petal_Length', 'normed_columns.0'),
('normed_columns.Sepal_Width', 'normed_columns.1'),
('normed_columns.Sepal_Length', 'normed_columns.2')
('normed_columns.Petal_Length', 'normed_columns.Petal_Length'),
('normed_columns.Sepal_Width', 'normed_columns.Sepal_Width'),
('normed_columns.Sepal_Length', 'normed_columns.Sepal_Length')
]},
'MeanVarianceScaler': {'cols': list(zip(
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa'],
['Sepal_Length.0', 'Sepal_Width.0', 'Petal_Length.0', 'Petal_Width.0', 'Setosa.0']
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa']
))},
'MinMaxScaler': {'cols': list(zip(
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa'],
['Sepal_Length.0', 'Sepal_Width.0', 'Petal_Length.0', 'Petal_Width.0', 'Setosa.0']
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa']
))},
#'MutualInformationSelector',
'NGramFeaturizer': {'num_cols': 273, 'cols': 0},
'NaiveBayesClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'NaiveBayesClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'OneHotVectorizer': {'cols': list(zip(
['education_str.0-5yrs', 'education_str.6-11yrs', 'education_str.12+ yrs'],
['education_str.0', 'education_str.1', 'education_str.2']
['education_str.0-5yrs', 'education_str.6-11yrs', 'education_str.12+ yrs']
))},
'OnlineGradientDescentRegressor': {'cols': [('Score', 'Score.0')]},
'OrdinaryLeastSquaresRegressor': {'cols': [('Score', 'Score.0')]},
'OnlineGradientDescentRegressor': {'cols': [('Score', 'Score')]},
'OrdinaryLeastSquaresRegressor': {'cols': [('Score', 'Score')]},
'PcaTransformer': {'num_cols': 9, 'cols': 0},
'PoissonRegressionRegressor': {'cols': [('Score', 'Score.0')]},
'SgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'SymSgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel.0')]},
'ToKey': {'cols': [('edu_1', 'edu_1.0'), ('parity_1', 'parity_1.0')]},
'TypeConverter': {'cols': [('group', 'group.0')]},
'PoissonRegressionRegressor': {'cols': [('Score', 'Score')]},
'PrefixColumnConcatenator': {'cols': [
('Features.Sepal_Length', 'Features.Sepal_Length'),
('Features.Sepal_Width', 'Features.Sepal_Width')
]},
'SgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'SymSgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'ToKey': {'cols': [('edu_1', 'edu_1'), ('parity_1', 'parity_1')]},
'TypeConverter': {'cols': [('group', 'group')]},
'WordTokenizer': {'num_cols': 73, 'cols': 0}
}

Expand Down Expand Up @@ -575,8 +581,8 @@ def test_export_to_onnx(estimator, class_name):
for entry_point in entry_points:
class_name = entry_point['NewName']

# if not class_name in ['NGramFeaturizer']:
# continue
# if not class_name in ['CharTokenizer']:
# continue

print('\n===========> %s' % class_name)

Expand Down