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Add OneVsRest support to export to onnx tests and increase test coverage #446

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Feb 24, 2020
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Add OneVsRest support to export to onnx tests and increase test coverage #446

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99 changes: 54 additions & 45 deletions src/python/tests_extended/test_export_to_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,10 @@
from nimbusml.feature_extraction.text import NGramFeaturizer
from nimbusml.feature_extraction.text.extractor import Ngram
from nimbusml.feature_selection import CountSelector, MutualInformationSelector
from nimbusml.linear_model import FastLinearBinaryClassifier
from nimbusml.linear_model import (AveragedPerceptronBinaryClassifier,
FastLinearBinaryClassifier,
LinearSvmBinaryClassifier)
from nimbusml.multiclass import OneVsRestClassifier
from nimbusml.naive_bayes import NaiveBayesClassifier
from nimbusml.preprocessing import (TensorFlowScorer, FromKey, ToKey,
DateTimeSplitter, OnnxRunner)
Expand Down Expand Up @@ -75,6 +78,7 @@
infert_df.rename(columns={'case': 'Label'}, inplace=True)

infert_onehot_df = (OneHotVectorizer() << 'education_str').fit_transform(infert_df)
infert_onehot_df['Label'] = infert_onehot_df['Label'].astype(np.uint32)

# rank group carrier price Class dep_day nbr_stops duration
# 0 2 1 AA 240 3 1 0 12.0
Expand Down Expand Up @@ -118,6 +122,8 @@
}

INSTANCES = {
'AveragedPerceptronBinaryClassifier': AveragedPerceptronBinaryClassifier(
feature=['education_str.0-5yrs', 'education_str.6-11yrs', 'education_str.12+ yrs']),
'Binner': Binner(num_bins=3),
'CharTokenizer': CharTokenizer(columns={'SentimentText_Transform': 'SentimentText'}),
'ColumnConcatenator': ColumnConcatenator(columns={'Features': [
Expand Down Expand Up @@ -174,12 +180,28 @@
slots_in_output=2) # only accept one column
]),
'NaiveBayesClassifier': NaiveBayesClassifier(feature=['Sepal_Width', 'Sepal_Length']),
'NGramFeaturizer': NGramFeaturizer(word_feature_extractor=Ngram(),
'NGramFeaturizer': NGramFeaturizer(word_feature_extractor=Ngram(),
char_feature_extractor=Ngram(),
keep_diacritics=True,
columns={ 'features': ['SentimentText']}),
'OneHotHashVectorizer': OneHotHashVectorizer(columns=['education_str']),
'OneHotVectorizer': OneHotVectorizer(columns=['education_str']),
'OneVsRestClassifier(AveragedPerceptronBinaryClassifier)': \
OneVsRestClassifier(AveragedPerceptronBinaryClassifier(),
use_probabilities=True,
feature=['age',
'education_str.0-5yrs',
'education_str.6-11yrs',
'education_str.12+ yrs'],
label='induced'),
'OneVsRestClassifier(LinearSvmBinaryClassifier)': \
OneVsRestClassifier(LinearSvmBinaryClassifier(),
use_probabilities=True,
feature=['age',
'education_str.0-5yrs',
'education_str.6-11yrs',
'education_str.12+ yrs'],
label='induced'),
'PcaAnomalyDetector': PcaAnomalyDetector(rank=3),
'PcaTransformer': PcaTransformer(rank=2),
'PixelExtractor': Pipeline([
Expand Down Expand Up @@ -238,6 +260,7 @@
'GlobalContrastRowScaler': iris_df.astype(np.float32),
'Handler': iris_with_nan_df,
'Indicator': iris_with_nan_df,
'LightGbmBinaryClassifier': iris_binary_df,
'LightGbmRanker': gen_tt_df,
'LinearSvmBinaryClassifier': iris_binary_df,
'Loader': image_paths_df,
Expand All @@ -251,6 +274,8 @@
'OneHotHashVectorizer': infert_df,
'OneHotVectorizer': infert_df,
'OnlineGradientDescentRegressor': iris_regression_df,
'OneVsRestClassifier(AveragedPerceptronBinaryClassifier)': infert_onehot_df,
'OneVsRestClassifier(LinearSvmBinaryClassifier)': infert_onehot_df,
'OrdinaryLeastSquaresRegressor': iris_regression_df,
'PcaAnomalyDetector': iris_no_label_df,
'PcaTransformer': iris_regression_df,
Expand All @@ -266,10 +291,9 @@

EXPECTED_RESULTS = {
'AveragedPerceptronBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'CharTokenizer': {'cols': [('SentimentText_Transform.%03d' % i, 'SentimentText_Transform.%03d' % i)
for i in range(0, 422)]},
'CharTokenizer': {'num_cols': 424, 'cols': 0},
'ColumnConcatenator': {'num_cols': 11, 'cols': 0},
'ColumnDuplicator': {'cols': [('dup', 'dup')]},
'ColumnDuplicator': {'num_cols': 7, 'cols': 0},
'ColumnSelector': {
'num_cols': 2,
'cols': [('Sepal_Width', 'Sepal_Width'), ('Sepal_Length', 'Sepal_Length')]
Expand All @@ -284,17 +308,10 @@
'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.Petal_Length'),
('normed_columns.Sepal_Width', 'normed_columns.Sepal_Width'),
('normed_columns.Sepal_Length', 'normed_columns.Sepal_Length')
]},
'Handler': {'cols': [
('NewVals.NewVals', 'NewVals.NewVals'),
('NewVals.IsMissing.NewVals', 'NewVals.IsMissing.NewVals')
]},
'Indicator': {'cols': [('Has_Nan', 'Has_Nan')]},
'FromKey': {'num_cols': 6, 'cols': 0},
'GlobalContrastRowScaler': {'num_cols': 12, 'cols': 0},
'Handler': {'num_cols': 8, 'cols': 0},
'Indicator': {'num_cols': 7, 'cols': 0},
'KMeansPlusPlus': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LightGbmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LightGbmClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
Expand All @@ -303,38 +320,26 @@
'LinearSvmBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LogisticRegressionBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LogisticRegressionClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'LpScaler': {'cols': [
('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', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa']
))},
'MinMaxScaler': {'cols': list(zip(
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa'],
['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width', 'Setosa']
))},
#'MutualInformationSelector',
'LpScaler': {'num_cols': 10, 'cols': 0},
'MeanVarianceScaler': {'num_cols': 5, 'cols': 0},
'MinMaxScaler': {'num_cols': 5, 'cols': 0},
'MutualInformationSelector': {'num_cols': 8, 'cols': 0},
'NGramFeaturizer': {'num_cols': 273, 'cols': 0},
'NaiveBayesClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'OneHotVectorizer': {'cols': list(zip(
['education_str.0-5yrs', 'education_str.6-11yrs', 'education_str.12+ yrs'],
['education_str.0-5yrs', 'education_str.6-11yrs', 'education_str.12+ yrs']
))},
'OneHotVectorizer': {'num_cols': 12, 'cols': 0},
'OneVsRestClassifier(AveragedPerceptronBinaryClassifier)': \
{'cols': [('PredictedLabel', 'PredictedLabel')]},
'OneVsRestClassifier(LinearSvmBinaryClassifier)': \
{'cols': [('PredictedLabel', 'PredictedLabel')]},
'OnlineGradientDescentRegressor': {'cols': [('Score', 'Score')]},
'OrdinaryLeastSquaresRegressor': {'cols': [('Score', 'Score')]},
'PcaTransformer': {'num_cols': 9, 'cols': 0},
'PoissonRegressionRegressor': {'cols': [('Score', 'Score')]},
'PrefixColumnConcatenator': {'cols': [
('Features.Sepal_Length', 'Features.Sepal_Length'),
('Features.Sepal_Width', 'Features.Sepal_Width')
]},
'PrefixColumnConcatenator': {'num_cols': 8, 'cols': 0},
'SgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'SymSgdBinaryClassifier': {'cols': [('PredictedLabel', 'PredictedLabel')]},
'ToKey': {'cols': [('edu_1', 'edu_1'), ('parity_1', 'parity_1')]},
'TypeConverter': {'cols': [('group', 'group')]},
'ToKey': {'num_cols': 12, 'cols': 0},
'TypeConverter': {'num_cols': 8, 'cols': 0},
'WordTokenizer': {'num_cols': 73, 'cols': 0}
}

Expand Down Expand Up @@ -571,6 +576,10 @@ def test_export_to_onnx(estimator, class_name):

manifest_diff = os.path.join(script_dir, '..', 'tools', 'manifest_diff.json')
entry_points = load_json(manifest_diff)['EntryPoints']
entry_points.extend([
{'NewName': 'OneVsRestClassifier(AveragedPerceptronBinaryClassifier)'},
{'NewName': 'OneVsRestClassifier(LinearSvmBinaryClassifier)'}
])
entry_points = sorted(entry_points, key=lambda ep: ep['NewName'])

exportable_estimators = set()
Expand All @@ -581,21 +590,21 @@ def test_export_to_onnx(estimator, class_name):
for entry_point in entry_points:
class_name = entry_point['NewName']

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

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

if class_name in SKIP:
print("skipped")
continue

mod = __import__('nimbusml.' + entry_point['Module'],
fromlist=[str(class_name)])

if class_name in INSTANCES:
estimator = INSTANCES[class_name]
else:
mod = __import__('nimbusml.' + entry_point['Module'],
fromlist=[str(class_name)])

the_class = getattr(mod, class_name)
estimator = the_class()

Expand Down