
import random

from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss

import tensorflow as tf
from tensorflow.python.platform import googletest

from skflow.io import *
import skflow

class BaseTest(googletest.TestCase):
    def test_pandas_data_classification(self):
        if HAS_PANDAS:
            random.seed(42)
            iris = datasets.load_iris()
            data = DataFrame(iris.data)
            labels = DataFrame(iris.target)
            classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
            classifier.fit(data, labels)
            score = accuracy_score(classifier.predict(data), labels)
            self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))

if __name__ == '__main__':
    tf.test.main()


import random

from sklearn import datasets
from sklearn.metrics import accuracy_score

import tensorflow as tf
from tensorflow.python.platform import googletest

from skflow.io import *
import skflow

class BaseTest(googletest.TestCase):
    def test_pandas_data_classification(self):
        if HAS_PANDAS:
            random.seed(42)
            iris = datasets.load_iris()
            data = DataFrame(iris.data)
            labels = DataFrame(iris.target)
            classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
            classifier.fit(data, labels)
            score = accuracy_score(classifier.predict(data), labels)
            self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
        else:
            print("No pandas installed. test_pandas_data_classification skipped.")

if __name__ == '__main__':
    tf.test.main()

Print when pandas not installed and removed unnecessary imports
Print when pandas not installed and removed unnecessary imports

  
  import random
  
  from sklearn import datasets
- from sklearn.metrics import accuracy_score, mean_squared_error, log_loss
+ from sklearn.metrics import accuracy_score
  
  import tensorflow as tf
  from tensorflow.python.platform import googletest
  
  from skflow.io import *
  import skflow
  
  class BaseTest(googletest.TestCase):
      def test_pandas_data_classification(self):
          if HAS_PANDAS:
              random.seed(42)
              iris = datasets.load_iris()
              data = DataFrame(iris.data)
              labels = DataFrame(iris.target)
              classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
              classifier.fit(data, labels)
              score = accuracy_score(classifier.predict(data), labels)
              self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
+         else:
+             print("No pandas installed. test_pandas_data_classification skipped.")
  
  if __name__ == '__main__':
      tf.test.main()
  
Print when pandas not installed and removed unnecessary imports
## Code Before:

import random

from sklearn import datasets
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss

import tensorflow as tf
from tensorflow.python.platform import googletest

from skflow.io import *
import skflow

class BaseTest(googletest.TestCase):
    def test_pandas_data_classification(self):
        if HAS_PANDAS:
            random.seed(42)
            iris = datasets.load_iris()
            data = DataFrame(iris.data)
            labels = DataFrame(iris.target)
            classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
            classifier.fit(data, labels)
            score = accuracy_score(classifier.predict(data), labels)
            self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))

if __name__ == '__main__':
    tf.test.main()

## Instruction:
Print when pandas not installed and removed unnecessary imports
## Code After:

import random

from sklearn import datasets
from sklearn.metrics import accuracy_score

import tensorflow as tf
from tensorflow.python.platform import googletest

from skflow.io import *
import skflow

class BaseTest(googletest.TestCase):
    def test_pandas_data_classification(self):
        if HAS_PANDAS:
            random.seed(42)
            iris = datasets.load_iris()
            data = DataFrame(iris.data)
            labels = DataFrame(iris.target)
            classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
            classifier.fit(data, labels)
            score = accuracy_score(classifier.predict(data), labels)
            self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
        else:
            print("No pandas installed. test_pandas_data_classification skipped.")

if __name__ == '__main__':
    tf.test.main()
