
import unittest
from unittest import TestCase

from sklearn.ensemble import RandomForestClassifier

from ..Classifier import Classifier
from ...language.PHP import PHP


class RandomForestClassifierPHPTest(PHP, Classifier, TestCase):

    def setUp(self):
        super(RandomForestClassifierPHPTest, self).setUp()
        self.mdl = RandomForestClassifier(n_estimators=100, random_state=0)

    def tearDown(self):
        super(RandomForestClassifierPHPTest, self).tearDown()

    @unittest.skip('The generated code would be too large.')
    def test_existing_features_w_digits_data(self):
        pass

    @unittest.skip('The generated code would be too large.')
    def test_random_features_w_digits_data(self):
        pass


import unittest
from unittest import TestCase

from sklearn.ensemble import RandomForestClassifier

from ..Classifier import Classifier
from ...language.PHP import PHP


class RandomForestClassifierPHPTest(PHP, Classifier, TestCase):

    def setUp(self):
        super(RandomForestClassifierPHPTest, self).setUp()
        self.mdl = RandomForestClassifier(n_estimators=20, random_state=0)

    def tearDown(self):
        super(RandomForestClassifierPHPTest, self).tearDown()

Reduce the number of trees
Reduce the number of trees

  
  import unittest
  from unittest import TestCase
  
  from sklearn.ensemble import RandomForestClassifier
  
  from ..Classifier import Classifier
  from ...language.PHP import PHP
  
  
  class RandomForestClassifierPHPTest(PHP, Classifier, TestCase):
  
      def setUp(self):
          super(RandomForestClassifierPHPTest, self).setUp()
-         self.mdl = RandomForestClassifier(n_estimators=100, random_state=0)
+         self.mdl = RandomForestClassifier(n_estimators=20, random_state=0)
  
      def tearDown(self):
          super(RandomForestClassifierPHPTest, self).tearDown()
  
-     @unittest.skip('The generated code would be too large.')
-     def test_existing_features_w_digits_data(self):
-         pass
- 
-     @unittest.skip('The generated code would be too large.')
-     def test_random_features_w_digits_data(self):
-         pass
- 
Reduce the number of trees
## Code Before:

import unittest
from unittest import TestCase

from sklearn.ensemble import RandomForestClassifier

from ..Classifier import Classifier
from ...language.PHP import PHP


class RandomForestClassifierPHPTest(PHP, Classifier, TestCase):

    def setUp(self):
        super(RandomForestClassifierPHPTest, self).setUp()
        self.mdl = RandomForestClassifier(n_estimators=100, random_state=0)

    def tearDown(self):
        super(RandomForestClassifierPHPTest, self).tearDown()

    @unittest.skip('The generated code would be too large.')
    def test_existing_features_w_digits_data(self):
        pass

    @unittest.skip('The generated code would be too large.')
    def test_random_features_w_digits_data(self):
        pass

## Instruction:
Reduce the number of trees
## Code After:

import unittest
from unittest import TestCase

from sklearn.ensemble import RandomForestClassifier

from ..Classifier import Classifier
from ...language.PHP import PHP


class RandomForestClassifierPHPTest(PHP, Classifier, TestCase):

    def setUp(self):
        super(RandomForestClassifierPHPTest, self).setUp()
        self.mdl = RandomForestClassifier(n_estimators=20, random_state=0)

    def tearDown(self):
        super(RandomForestClassifierPHPTest, self).tearDown()
