Remove scikit-learn testing module from normal flow

src/python/nimbusml/tests/pipeline/test_uci_adult.py

@@ -6,14 +6,15 @@
 import tempfile
 import unittest
 
+import numpy as np
 from nimbusml import FileDataStream
 from nimbusml import Pipeline
 from nimbusml.datasets import get_dataset
 from nimbusml.ensemble import FastTreesBinaryClassifier
 from nimbusml.feature_extraction.categorical import OneHotVectorizer
 from nimbusml.linear_model import FastLinearBinaryClassifier
-from nimbusml.utils import check_accuracy, get_X_y
-from sklearn.utils.testing import assert_raises_regex, assert_equal, assert_true
+from nimbusml.utils import get_X_y
+from sklearn.utils.testing import assert_raises_regex, assert_equal, assert_true, assert_greater
 
 train_file = get_dataset("uciadult_train").as_filepath()
 test_file = get_dataset("uciadult_test").as_filepath()
@@ -32,6 +33,15 @@
               'col=sex:TX:7 col=native-country-region:TX:8 header+'
 label_column = 'label'
 
+def check_accuracy(test_file, label_column, predictions, threshold, sep=','):
+    (test, label) = get_X_y(test_file, label_column, sep=sep)
+    accuracy = np.mean(label[label_column].values ==
+                       predictions.ix[:, 'PredictedLabel'].values)
+    assert_greater(
+        accuracy,
+        threshold,
+        "accuracy should be greater than %s" %
+        threshold)
 
 class TestUciAdult(unittest.TestCase):
 

src/python/nimbusml/tests/scikit/test_uci_adult_scikit.py

@@ -16,14 +16,14 @@
 from nimbusml.linear_model import FastLinearBinaryClassifier
 from nimbusml.linear_model import LogisticRegressionClassifier
 from nimbusml.preprocessing.normalization import MeanVarianceScaler
-from nimbusml.utils import check_accuracy_scikit, get_X_y
+from nimbusml.utils import get_X_y
 from sklearn.base import clone
 from sklearn.datasets import load_iris
 from sklearn.decomposition import PCA
 from sklearn.model_selection import GridSearchCV
 from sklearn.pipeline import Pipeline, FeatureUnion
 from sklearn.preprocessing import OneHotEncoder
-from sklearn.utils.testing import assert_equal
+from sklearn.utils.testing import assert_equal, assert_greater
 
 try:
     from pandas.testing import assert_frame_equal
@@ -45,6 +45,19 @@
     'native-country-region']
 selected_features = ['age', 'education-num']
 
+def check_accuracy_scikit(
+        test_file,
+        label_column,
+        predictions,
+        threshold,
+        sep=','):
+    (test, label) = get_X_y(test_file, label_column, sep=sep)
+    accuracy = np.mean(label[label_column].values == predictions.values)
+    assert_greater(
+        accuracy,
+        threshold,
+        "accuracy should be greater than %s" %
+        threshold)
 
 class TestUciAdultScikit(unittest.TestCase):
 

src/python/nimbusml/utils/__init__.py

@@ -1,5 +1,4 @@
-from .utils import get_X_y, evaluate_binary_classifier, check_accuracy, \
-    check_accuracy_scikit, load_img, ColumnSelector
+from .utils import get_X_y, evaluate_binary_classifier, load_img, ColumnSelector
 
 try:
     from inspect import signature
@@ -9,8 +8,6 @@
 __all__ = [
     'get_X_y',
     'evaluate_binary_classifier',
-    'check_accuracy',
-    'check_accuracy_scikit',
     'load_img',
     'ColumnSelector',
     'signature'

src/python/nimbusml/utils/utils.py

@@ -12,12 +12,9 @@
 import pandas as pd
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.metrics import roc_auc_score
-from sklearn.utils.testing import assert_greater
-
-
-# select columns from DataFrame insize a pipeline
 
 
+# select columns from DataFrame inside a pipeline
 class ColumnSelector(BaseEstimator, TransformerMixin):
     def __init__(self, columns, ravel=False):
         self.columns = columns
@@ -106,30 +103,4 @@ def evaluate_binary_classifier(target, predicted, probabilities=None):
     auc_score = None
     if probabilities is not None:
         auc_score = roc_auc_score(target, probabilities)
-    return (accuracy, auc_score)
-
-
-def check_accuracy(test_file, label_column, predictions, threshold, sep=','):
-    (test, label) = get_X_y(test_file, label_column, sep=sep)
-    accuracy = np.mean(label[label_column].values ==
-                       predictions.ix[:, 'PredictedLabel'].values)
-    assert_greater(
-        accuracy,
-        threshold,
-        "accuracy should be greater than %s" %
-        threshold)
-
-
-def check_accuracy_scikit(
-        test_file,
-        label_column,
-        predictions,
-        threshold,
-        sep=','):
-    (test, label) = get_X_y(test_file, label_column, sep=sep)
-    accuracy = np.mean(label[label_column].values == predictions.values)
-    assert_greater(
-        accuracy,
-        threshold,
-        "accuracy should be greater than %s" %
-        threshold)
+    return (accuracy, auc_score)