def test_pipeline_with_no_columns_raise(self):
trainData = pd.DataFrame({
"Sentiment": [0, 1, 1, 0, 1, 1],
"SentimentText": [
"this is train ", "review ", "sentence ", "an apple",
"sentence 22", "another one one one"
]
})
ppl = Pipeline([
NGramFeaturizer(word_feature_extractor=n_gram()),
LightGbmClassifier()
])
assert ppl is not None
# Bug 147697
info = ppl.get_fit_info(trainData[["SentimentText"]],
trainData["Sentiment"])
assert len(info) == 2
assert len(info[0]) == 3
with self.assertRaises(RuntimeError):
# Message
# System.InvalidOperationException:
# 'LightGBM Error, code is -1, error message is
# 'Cannot construct Dataset since there are not useful features.
# It should be at least two unique rows.
# If the num_row (num_data) is small,
# you can set min_data=1 and min_data_in_bin=1 to fix this.
# Otherwise please make sure you are using the right dataset.'
ppl.fit(trainData[["SentimentText"]], trainData["Sentiment"])
def test_clone_sweep(self):
# grid search, then clone pipeline and grid search again
# results should be same
np.random.seed(0)
(X_train, y_train) = get_X_y(train_file,
label_column,
sep=',',
encoding='utf-8')
(X_test, y_test) = get_X_y(test_file,
label_column,
sep=',',
encoding='utf-8')
cat = OneHotHashVectorizer() << categorical_columns
learner = FastTreesBinaryClassifier(number_of_trees=100,
number_of_leaves=5)
pipe = Pipeline(steps=[('cat', cat), ('learner', learner)])
param_grid = dict(learner__number_of_trees=[1, 5, 10])
grid = GridSearchCV(pipe, param_grid)
grid.fit(X_train, y_train)
pipe1 = pipe.clone()
grid1 = GridSearchCV(pipe1, param_grid)
grid1.fit(X_train, y_train)
assert grid.best_params_[
'learner__number_of_trees'] == grid1.best_params_[
'learner__number_of_trees']
def test_test(self):
transformed_data, transformed_data_df = transform_data()
fl = FastLinearRegressor(
feature=[
'parity',
'in',
'sp',
'stratum'],
label='age')
flpipe = Pipeline([fl])
flpipe.fit(transformed_data)
metrics, scores = flpipe.test(transformed_data, output_scores=True)
metrics_df, scores_df = flpipe.test(
transformed_data_df, output_scores=True)
assert_array_equal(scores, scores_df)
assert_array_equal(metrics, metrics_df)
flpipe.fit(
transformed_data_df.drop(
'age',
axis=1),
transformed_data_df['age'])
metrics, scores = flpipe.test(transformed_data, output_scores=True)
metrics_df, scores_df = flpipe.test(
transformed_data_df, output_scores=True)
assert_array_equal(scores, scores_df)
assert_array_equal(metrics, metrics_df)
def test_get_fit_info_fastl(self):
train_file = get_dataset("airquality").as_filepath()
schema = DataSchema.read_schema(train_file)
data = FileDataStream(train_file, schema)
pipeline = Pipeline([
Filter(columns=['Ozone']),
FastLinearRegressor(feature=['Solar_R', 'Temp'], label='Ozone')
])
info = pipeline.get_fit_info(data)
exp = [{
'name':
None,
'outputs':
['Unnamed0', 'Ozone', 'Solar_R', 'Wind', 'Temp', 'Month', 'Day'],
'schema_after':
['Unnamed0', 'Ozone', 'Solar_R', 'Wind', 'Temp', 'Month', 'Day'],
'type':
'start'
}, {
'inputs': ['Ozone'],
'name':
'Filter',
'outputs': ['Ozone'],
'schema_after':
['Unnamed0', 'Ozone', 'Solar_R', 'Wind', 'Temp', 'Month', 'Day'],
'type':
'transform'
}]
for el in info[0]:
if 'operator' in el:
del el['operator']
self.assertEqual(exp, info[0][:2])
def test_pipeline_info(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
yy=[1.1, 2.2, 1.24, 3.4, 3.4]))
exp = Pipeline([
MeanVarianceScaler() << {
'new_y': 'yy'
},
OneHotVectorizer() << ['workclass', 'education'],
Drop() << 'yy',
FastLinearRegressor() << {
'Feature': ['workclass', 'education'],
Role.Label: 'new_y'
}
])
infos = exp.get_fit_info(df)[0]
for inf in infos:
if 'operator' in inf:
del inf['operator']
exp = [{
'name': None,
'schema_after': ['education', 'workclass', 'yy'],
'type': 'start',
'outputs': ['education', 'workclass', 'yy']
}, {
'name': 'TypeConverter',
'inputs': ['yy'],
'outputs': ['new_y'],
'schema_after': ['education', 'workclass', 'yy', 'new_y'],
'type': 'transform'
}, {
'name': 'MeanVarianceScaler',
'inputs': ['new_y'],
'type': 'transform',
'outputs': ['new_y'],
'schema_after': ['education', 'workclass', 'yy', 'new_y']
}, {
'name': 'OneHotVectorizer',
'inputs': ['workclass', 'education'],
'type': 'transform',
'outputs': ['workclass', 'education'],
'schema_after': ['education', 'workclass', 'yy', 'new_y']
}, {
'name': 'ColumnDropper',
'type': 'transform',
'schema_after': ['education', 'workclass', 'new_y'],
'inputs': ['education', 'workclass', 'yy', 'new_y'],
'outputs': ['education', 'workclass', 'new_y']
}, {
'name': 'FastLinearRegressor',
'inputs': ['Feature:education,workclass', 'Label:new_y'],
'type': 'regressor',
'outputs': ['Score'],
'schema_after': ['Score']
}]
if infos != exp:
raise Exception(infos)
def test_syntax12_mixed2(self):
X = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
weight=[10., 1., 1., 1., 1.],
y=[1.1, 2.2, 1.24, 3.4, 3.4]))
exp = Pipeline(
[
OneHotVectorizer(
columns=[
'workclass', 'education']),
Concat(
columns={
'Feature': ['workclass', 'education']}),
FastTreesRegressor(
num_trees=5, feature='Feature', weight='weight') << {
Role.Label: 'y'}])
exp.fit(X, verbose=0)
assert exp.nodes[-1].feature_column_ == 'Feature'
assert exp.nodes[-1].label_column_ == 'y'
assert exp.nodes[-1].weight_column_ == 'weight'
# y is required here as well as weight.
# It is replaced by fakes values.
# The test does not fail but the weight is not taken into account.
X['y'] = -5
X['weight'] = -5
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert list(prediction.columns) == ['Score']
assert prediction.shape == (5, 1)
def test_syntax8_label(self):
df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
yy=[1.1, 2.2, 1.24, 3.4, 3.4]))
X = df.drop('yy', axis=1)
exp = Pipeline([
MeanVarianceScaler() << {'new_y': 'yy'},
OneHotVectorizer() << ['workclass', 'education'],
Drop() << 'yy',
FastLinearRegressor() << {'Feature': ['workclass', 'education'],
Role.Label: 'new_y'}
])
exp.fit(df, verbose=0)
assert exp.nodes[-1].feature_column_ == 'Features'
assert exp.nodes[-1].label_column_ == 'new_y'
# The pipeline requires it now as it is transformed all along.
X['yy'] = 0.0
prediction = exp.predict(X, verbose=0)
assert isinstance(prediction, pandas.DataFrame)
assert list(prediction.columns) == ['Score']
assert prediction.shape == (5, 1)
if prediction['Score'].min() < 0.4:
raise Exception(prediction)
if prediction['Score'].max() > 2.00:
raise Exception(prediction)
def nimbus_pred(model_path, test_set_path):
X = pd.read_csv(test_set_path)
X['c'] = X['c'].astype("category")
p = Pipeline()
p.load_model(model_path)
pred = p.predict(X)
print(pred)
def test_syntax4_dict(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'edu1': 'education'
},
OneHotHashVectorizer() << {
'edu2': 'education'
},
OneHotVectorizer(max_num_terms=2) << {
'wki': 'workclass'
},
Concat() << {
'Inputs': ['edu1', 'edu2', 'wki']
},
FastLinearBinaryClassifier(max_iterations=1) << 'Inputs'
])
exp.fit(X, y)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(
prediction.columns)) == ['PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_ensemble_supports_get_fit_info(self):
df = pd.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
yy=[1.1, 2.2, 1.24, 3.4, 3.4]))
col_info = {'Feature': ['workclass', 'education'], Role.Label: 'new_y'}
r1 = OrdinaryLeastSquaresRegressor(normalize="Yes") << col_info
r2 = OnlineGradientDescentRegressor(normalize="Yes") << col_info
r3 = LightGbmRegressor(normalize="Yes") << col_info
pipeline = Pipeline([
MeanVarianceScaler() << {'new_y': 'yy'},
OneHotVectorizer() << ['workclass', 'education'],
ColumnDropper() << 'yy',
VotingRegressor(estimators=[r1, r2, r3], combiner='Average')
])
info = pipeline.get_fit_info(df)
last_info_node = info[0][-1]
self.assertEqual(last_info_node['inputs'],
['Feature:education,workclass', 'Label:new_y'])
self.assertEqual(last_info_node['name'], 'VotingRegressor')
self.assertTrue(isinstance(last_info_node['operator'], VotingRegressor))
self.assertEqual(last_info_node['outputs'], ['Score'])
self.assertEqual(last_info_node['schema_after'], ['Score'])
self.assertEqual(last_info_node['type'], 'regressor')
def test_syntax6_change_role(self):
# REVIEW: the pipeline drops all columns but one -->
# nimbusml still thinks the Features are eduction, workclass
# and does not automatically detects that the only remaining
# columns should play that role
# (maybe because the label column is here too even though
# the only remaining column without a role is Features).
df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {'f1': 'education'},
OneHotHashVectorizer() << {'f2': 'education'},
OneHotVectorizer(max_num_terms=2) << {'f3': 'workclass'},
Concat() << {'Features': ['f%d' % i for i in range(1, 4)]},
Drop() << ['education', 'workclass', 'f1', 'f2', 'f3'],
FastLinearBinaryClassifier(maximum_number_of_iterations=1) << ['Features']
])
exp.fit(X, y, verbose=0)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(prediction.columns)) == [
'PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_ensemble_supports_user_defined_transforms(self):
test2_df = test_df.copy(deep=True)
test2_df = test2_df.append(pd.DataFrame({'c1': [9, 11], 'c2': [1, 1]}))
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r1.fit(train_df)
result1 = r1.predict(test2_df)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r2.fit(train_df)
result2 = r2.predict(test2_df)
r3 = LightGbmRegressor(**lgbmArgs)
r3.fit(train_df)
result3 = r3.predict(test2_df)
r1 = OrdinaryLeastSquaresRegressor(**olsrArgs)
r2 = OnlineGradientDescentRegressor(**ogdArgs)
r3 = LightGbmRegressor(**lgbmArgs)
pipeline = Pipeline([
RangeFilter(min=0, max=10, columns='c1'),
VotingRegressor(estimators=[r1, r2, r3], combiner='Average')
])
pipeline.fit(train_df)
result4 = pipeline.predict(test2_df)
self.assertEqual(len(result4), 3)
average1 = (result1[0] + result2[0] + result3[0]) / 3
average2 = (result1[1] + result2[1] + result3[1]) / 3
average3 = (result1[2] + result2[2] + result3[2]) / 3
self.assertAlmostEqual(average1, result4.loc[0, 'Score'], places=5)
self.assertAlmostEqual(average2, result4.loc[1, 'Score'], places=5)
self.assertAlmostEqual(average3, result4.loc[2, 'Score'], places=5)
def test_predictor_loaded_from_zip_has_feature_contributions(self):
features = ['age', 'education-num', 'hours-per-week']
model_nimbusml = FastLinearBinaryClassifier(feature=features)
model_nimbusml.fit(train, label)
fc = model_nimbusml.get_feature_contributions(test)
# Save the model to zip
model_filename = 'nimbusml_model.zip'
model_nimbusml.save_model(model_filename)
# Load the model from zip
model_nimbusml_zip = Pipeline()
model_nimbusml_zip.load_model(model_filename)
fc_zip = model_nimbusml_zip.get_feature_contributions(test)
assert [
'FeatureContributions.' + feature in fc_zip.columns
for feature in features
]
assert [
fc['FeatureContributions.' + feature].equals(
fc_zip['FeatureContributions.' + feature])
for feature in features
]
os.remove(model_filename)
def test_PcaTransformer_int(self):
df_ = get_dataset("infert").as_df()
res = {}
dt = {}
for ty in (int, float):
df = df_.copy()
df['age'] = df['age'].astype(ty)
df['parity'] = df['parity'].astype(ty)
df['spontaneous'] = df['spontaneous'].astype(ty)
df['stratum'] = df['stratum'].astype(ty)
X = ['age', 'parity', 'spontaneous', 'stratum']
pipe = Pipeline([
ColumnConcatenator() << {
'X': X
},
PcaTransformer(rank=3) << 'X'
])
y = pipe.fit_transform(df[X], verbose=0)
res[ty] = y.sum().sum()
dt[ty] = list(y.dtypes)
vals = list(res.values())
assert_almost_equal(vals[0], vals[1])
dt = list(dt.values())
dt[0].sort()
dt[1].sort()
assert dt[0] != dt[1]
def test_metrics_evaluate_clusterer(self):
np.random.seed(0)
df = get_dataset("iris").as_df()
df.drop(['Species'], inplace=True, axis=1)
df.Label = [1 if x == 1 else 0 for x in df.Label]
X_train, X_test, y_train, y_test = \
train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
lr = KMeansPlusPlus(n_clusters=2, initialization_algorithm="Random")
e = Pipeline([lr])
e.fit(X_train, y_train.to_frame(), verbose=0)
metrics, _ = e.test(X_test, y_test)
# if abs(metrics['NMI'][0] - 0.7) >= 0.15:
# raise AssertionError("NMI loss should be %f not %f" % \
# (0.7, metrics['NMI'][0]))
# if abs(metrics['AvgMinScore'][0] - 0.014) >= 0.015:
# raise AssertionError("AvgMinScore should be %f not %f" % (\
# 0.014, metrics['AvgMinScore'][0]))
assert_almost_equal(metrics['NMI'][0],
0.7,
decimal=0,
err_msg="NMI loss should be %s" % 0.7)
assert_almost_equal(metrics['AvgMinScore'][0],
0.032,
decimal=2,
err_msg="AvgMinScore should be %s" % 0.014)
def test_syntax4_fail(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'edu1': 'education'
},
OneHotHashVectorizer() << {
'edu2': 'education'
},
OneHotVectorizer(max_num_terms=2) << {
'wki': 'workclass'
},
FastLinearBinaryClassifier(max_iterations=1) <<
['edu1', 'edu2', 'wki']
])
try:
exp.fit(X, y)
assert False
except RuntimeError as e:
assert "ConcatTransform() << {'Input': ['edu1', 'edu2', 'wki']}" \
in str(e)
def test_syntax10_weights(self):
df = pandas.DataFrame(dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
weight=[1., 1., 1., 2., 1.],
y=[1.1, 2.2, 1.24, 3.4, 3.4]))
X = df.drop(['y', 'weight'], axis=1)
y = df['y']
w = df['weight']
exp = Pipeline([
OneHotVectorizer() << ['workclass', 'education'],
FastLinearRegressor()
])
exp.fit(X, y, weight=w, verbose=0)
assert exp.nodes[-1].feature_column == 'Features'
assert exp.nodes[-1].label_column == 'y'
assert exp.nodes[-1].weight_column == 'weight'
X['weight'] = -5
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert list(prediction.columns) == ['Score']
assert prediction.shape == (5, 1)
if prediction['Score'].min() < 1.:
raise Exception(prediction)
if prediction['Score'].max() > 3.6:
raise Exception(prediction)
if len(set(prediction['Score'])) < 4:
raise Exception(prediction)
def test_syntax4_fail2(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'edu1': 'education'
},
OneHotHashVectorizer() << {
'edu2': 'education'
},
OneHotVectorizer(max_num_terms=2) << {
'wki': 'workclass'
},
FastLinearBinaryClassifier(max_iterations=1) <<
['edu1', 'edu4', 'wki']
])
try:
exp.fit(X, y)
raise AssertionError("The test should not reach this line.")
except Exception as e:
assert "Feature column 'edu4' not found" in str(e)
def test_syntax7_rename(self):
# Error message are usually not informative enough.
# Missing column --> no indication of other columns.
# Error is (one transform should handle it)
# 'The label column 'y' of the training data has a data type
# not suitable for binary classification: Vec<Key<U4, 0-1>, 2>.
# Type must be Bool, R4, R8 or Key with two classes.
df = pandas.DataFrame(
dict(
education=[
'A', 'B', 'A', 'B', 'A'], workclass=[
'X', 'X', 'Y', 'Y', 'Y'], y=[
'red', 'white', 'red', 'white', 'white']))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << 'y',
OneHotVectorizer() << ['workclass', 'education'],
TypeConverter(result_type='R4') << {'yi': 'y'},
Drop() << 'y',
FastLinearBinaryClassifier(max_iterations=1) << 'yi'
])
exp.fit(X, y, verbose=0)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert list(prediction.columns) == ['Score']
assert prediction.shape == (5, 1)
assert prediction.min() > 0.01
assert prediction.max() < 0.05
def test_syntax5_regular_expression(self):
# REVIEW: not implemented yet
# The best would be to handle regular expression inside nimbusml.
# It could be handled in entrypoint.py just before calling nimbusml.
# It can be handled inside Pipeline if it is aware of
# the input schema.
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'f1': 'education'
},
OneHotHashVectorizer() << {
'f2': 'education'
},
OneHotVectorizer(max_num_terms=2) << {
'f3': 'workclass'
},
Concat() << {
'Features': 'f[0-9]+'
},
FastLinearBinaryClassifier(max_iterations=1) << 'Features'
])
exp.fit(X, y)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(
prediction.columns)) == ['PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_unpickled_pipeline_has_feature_contributions(self):
features = ['age', 'education-num', 'hours-per-week']
model_nimbusml = Pipeline(
steps=[FastLinearBinaryClassifier(feature=features)])
model_nimbusml.fit(train, label)
fc = model_nimbusml.get_feature_contributions(test)
# Save with pickle
pickle_filename = get_temp_file(suffix='.p')
with open(pickle_filename, 'wb') as f:
pickle.dump(model_nimbusml, f)
# Unpickle model
with open(pickle_filename, "rb") as f:
model_nimbusml_pickle = pickle.load(f)
fc_pickle = model_nimbusml_pickle.get_feature_contributions(test)
assert ['FeatureContributions.' + feature in fc_pickle.columns
for feature in features]
assert [fc['FeatureContributions.' + feature].equals(
fc_pickle['FeatureContributions.' + feature])
for feature in features]
os.remove(pickle_filename)
def test_syntax6_regular_expression(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'f1': 'education'
},
OneHotHashVectorizer() << {
'f2': 'education'
},
OneHotVectorizer(max_num_terms=2) << {
'f3': 'workclass'
},
Concat() << {
'Features': ['f%d' % i for i in range(1, 4)]
},
Drop() << '~Features',
FastLinearBinaryClassifier(max_iterations=1)
])
exp.fit(X, y)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(
prediction.columns)) == ['PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_pipeline_pca(self):
X = numpy.array([[1.0, 2, 3], [2, 3, 4], [3, 4, 5]])
exp = Pipeline([PcaTransformer(rank=2)])
infos = exp.get_fit_info(X)[0]
for inf in infos:
if 'operator' in inf:
del inf['operator']
exp = [{
'name': None,
'schema_after': ['F0', 'F1', 'F2'],
'type': 'start',
'outputs': ['F0', 'F1', 'F2']
}, {
'name': 'TypeConverter',
'inputs': ['F0', 'F1', 'F2'],
'type': 'transform',
'outputs': ['F0', 'F1', 'F2'],
'schema_after': ['F0', 'F1', 'F2']
}, {
'name': 'PcaTransformer',
'inputs': ['temp_'],
'type': 'transform',
'outputs': ['temp_'],
'schema_after': ['F0', 'F1', 'F2', 'temp_']
}]
# This id depends on id(node), different at each execution.
infos[-1]["inputs"] = ["temp_"]
# This id depends on id(node), different at each execution.
infos[-1]["outputs"] = ["temp_"]
# This id depends on id(node), different at each execution.
infos[-1]["schema_after"][-1] = ["temp_"]
self.assertTrue(any(x != y for x, y in zip(exp, infos)))
def test_syntax11_learner(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
exp = Pipeline([
OneHotVectorizer() << {
'edu1': 'education'
},
OneHotHashVectorizer() << {
'edu2': 'education'
},
FastLinearBinaryClassifier(max_iterations=1) << {
'Features': ['edu1', 'edu2'],
Role.Label: 'y'
}
])
exp.fit(df)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(
prediction.columns)) == ['PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_datetime_column_parsed_from_string(self):
dates = ["2018-01-02", "2018-02-01"]
df = pd.DataFrame({'c1': dates, 'c2': [3, 4]})
file_name = get_temp_file('.csv')
df.to_csv(file_name)
df = pd.read_csv(file_name, parse_dates=['c1'], index_col=0)
self.assertEqual(df.dtypes[0], np.dtype('datetime64[ns]'))
pipeline = Pipeline(steps=[Handler(columns={'c2': 'c2'})])
result = pipeline.fit_transform(df)
self.assertEqual(result.loc[0, 'c1'].year, 2018)
self.assertEqual(result.loc[0, 'c1'].month, 1)
self.assertEqual(result.loc[0, 'c1'].day, 2)
self.assertEqual(result.loc[0, 'c1'].hour, 0)
self.assertEqual(result.loc[0, 'c1'].minute, 0)
self.assertEqual(result.loc[0, 'c1'].second, 0)
self.assertEqual(result.loc[1, 'c1'].year, 2018)
self.assertEqual(result.loc[1, 'c1'].month, 2)
self.assertEqual(result.loc[1, 'c1'].day, 1)
self.assertEqual(result.loc[1, 'c1'].hour, 0)
self.assertEqual(result.loc[1, 'c1'].minute, 0)
self.assertEqual(result.loc[1, 'c1'].second, 0)
self.assertEqual(len(result), 2)
self.assertEqual(result.dtypes[0], np.dtype('datetime64[ns]'))
os.remove(file_name)
def test_syntax3(self):
df = pandas.DataFrame(
dict(education=['A', 'B', 'A', 'B', 'A'],
workclass=['X', 'X', 'Y', 'Y', 'Y'],
y=[1, 0, 1, 0, 0]))
X = df.drop('y', axis=1)
y = df['y']
exp = Pipeline([
OneHotVectorizer() << {
'edu1': 'education'
},
OneHotHashVectorizer() << 'education',
OneHotVectorizer(max_num_terms=2) << 'workclass',
# Currently the learner does not use edu1
# unless it is specified explicitely so nimbusml
# does not do what the syntax implicetely tells.
# We need to modify either the bridge to look into
# every available column at one step.
FastLinearBinaryClassifier(max_iterations=1)
])
exp.fit(X, y)
prediction = exp.predict(X)
assert isinstance(prediction, pandas.DataFrame)
assert sorted(list(
prediction.columns)) == ['PredictedLabel', 'Probability', 'Score']
assert prediction.shape == (5, 3)
def test_model_summary_not_supported(self):
for learner in learners_not_supported:
pipeline = Pipeline(
[OneHotVectorizer() << categorical_columns, learner])
train_stream = FileDataStream(train_file, schema=file_schema)
pipeline.fit(train_stream, label_column)
assert_raises(TypeError, pipeline.summary)
def test_metrics_evaluate_regressor(self):
np.random.seed(0)
df = get_dataset("iris").as_df()
df.drop(['Species'], inplace=True, axis=1)
df.Label = [1 if x == 1 else 0 for x in df.Label]
X_train, X_test, y_train, y_test = \
train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
lr = FastTreesRegressor()
e = Pipeline([lr])
e.fit(X_train, y_train.to_frame(), verbose=0)
metrics, _ = e.test(X_test, y_test)
# TODO: debug flucations, and increase decimal precision on checks
assert_almost_equal(metrics['L1(avg)'][0],
0.107,
decimal=1,
err_msg="L1 loss should be %s" % 0.107)
assert_almost_equal(metrics['L2(avg)'][0],
0.0453,
decimal=1,
err_msg="L2(avg) should be %s" % 0.0453)
assert_almost_equal(metrics['Loss-fn(avg)'][0],
0.0453,
decimal=1,
err_msg="Loss-fn(avg)loss should be %s" % 0.0453)
def test_globalcontrastrowscaler(self):
in_df = pd.DataFrame(
data=dict(Sepal_Length=[2.5, 1, 2.1, 1.0],
Sepal_Width=[.75, .9, .8, .76],
Petal_Length=[0, 2.5, 2.6, 2.4],
Species=["setosa", "viginica", "setosa", 'versicolor']))
in_df.iloc[:, 0:3] = in_df.iloc[:, 0:3].astype(np.float32)
# generate two new Columns - Petal_Normed and Sepal_Normed
concat = ColumnConcatenator() << {
'concated_columns':
['Petal_Length', 'Sepal_Width', 'Sepal_Length']
}
# Performs a global contrast normalization on input values:
# Y = (s * X - M) / D, where s is a scale, M is mean and D is either
# L2 norm or standard deviation
normed = GlobalContrastRowScaler() << {
'normed_columns': 'concated_columns'
}
pipeline = Pipeline([concat, normed])
out_df = pipeline.fit_transform(in_df)
cols = [
'concated_columns.' + s
for s in ['Sepal_Length', 'Sepal_Width', 'Petal_Length']
]
cols.extend([
'normed_columns.' + s
for s in ['Sepal_Length', 'Sepal_Width', 'Petal_Length']
])
sum = out_df[cols].sum().sum()
assert_greater(sum, 17.309, "sum should be greater than %s" % 17.309)
assert_less(sum, 17.3102, "sum should be less than %s" % 17.31)
def test_lpscaler_automatically_converts_to_single(self):
in_df = pd.DataFrame(
data=dict(Sepal_Length=[2.5, 1, 2.1, 1.0],
Sepal_Width=[.75, .9, .8, .76],
Petal_Length=[0, 2.5, 2.6, 2.4],
Species=["setosa", "viginica", "setosa", 'versicolor']))
in_df.iloc[:, 0:3] = in_df.iloc[:, 0:3].astype(np.float64)
src_cols = ['Sepal_Length', 'Sepal_Width', 'Petal_Length']
pipeline = Pipeline([
ColumnConcatenator() << {
'concat': src_cols
},
LpScaler() << {
'norm': 'concat'
}
])
out_df = pipeline.fit_transform(in_df)
cols = ['concat.' + s for s in src_cols]
cols.extend(['norm.' + s for s in src_cols])
sum = out_df[cols].sum().sum()
sum_range = (23.24, 23.25)
assert_greater(sum, sum_range[0],
"sum should be greater than %s" % sum_range[0])
assert_less(sum, sum_range[1],
"sum should be less than %s" % sum_range[1])
