Patch randomness in eval

Author: BlankCheng

data/raw/f_243_indraneil.py

@@ -57,7 +57,7 @@ class TestCases(unittest.TestCase):
 
     def setUp(self):
         # Set up the test file path
-        self.temp_dir = tempfile.gettempdir()
+        self.temp_dir = tempfile.mkdtemp()
         self.test_file_path = f"{self.temp_dir}/test_log.json"
 
     def tearDown(self):

data/raw/f_245_indraneil.py

@@ -61,6 +61,7 @@ def f_1238(obj_list, attr, num_bins=30, seed=0):
 class TestCases(unittest.TestCase):
     def test_case_1(self):
         # Input 1: Simple list of objects with integer values from 0 to 9
+        random.seed(1)
         obj_list = [Object(value=i) for i in range(10)]
         ax = f_1238(obj_list, 'value')
 
@@ -73,6 +74,7 @@ def test_case_1(self):
 
     def test_case_2(self):
         # Input 2: List of objects with random Gaussian values
+        random.seed(2)
         obj_list = [Object() for _ in range(100)]
         ax = f_1238(obj_list, 'value', seed=77)
 
@@ -87,6 +89,7 @@ def test_case_2(self):
 
     def test_case_3(self):
         # Input 3: List of objects with fixed value
+        random.seed(3)
         obj_list = [Object(value=5) for _ in range(50)]
         ax = f_1238(obj_list, 'value', seed=4)
 
@@ -116,6 +119,7 @@ def test_case_4(self):
 
     def test_case_5(self):
         # Input 5: Large list of objects
+        random.seed(5)
         obj_list = [Object(value=random.gauss(0, 5)) for _ in range(1000)]
         ax = f_1238(obj_list, 'value')
 

data/raw/f_247_indraneil.py

@@ -28,7 +28,7 @@ def f_247(data, save_plot=False, plot_path=None):
 
     Example:
     >>> import tempfile
-    >>> temp_dir = tempfile.gettempdir()
+    >>> temp_dir = tempfile.mkdtemp()
     >>> f_247([('A', 1, 1, 1), ('B', 2, 2, 2)], save_plot=True, plot_path=f"{temp_dir}/temp_plot.png")[0]
     array([[ 8.66025404e-01,  4.09680598e-17],
            [-8.66025404e-01,  4.09680598e-17]])

data/raw/f_2695_junda_james.py

@@ -66,13 +66,14 @@ class TestCases(unittest.TestCase):
     def setUp(self):
         # Create a dummy image for testing
         np.random.seed(42)
-        self.dummy_img_path = os.path.join(tempfile.gettempdir(), 'test_image.jpg')
+        self.dummy_img_path = os.path.join(tempfile.mkdtemp(), 'test_image.jpg')
         dummy_img = np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)
         cv2.imwrite(self.dummy_img_path, dummy_img)
 
     def tearDown(self):
         # Cleanup the dummy image
-        os.remove(self.dummy_img_path)
+        if os.path.exists(self.dummy_img_path):
+            os.remove(self.dummy_img_path)
 
     def test_valid_input(self):
         def dummy_onpick(event):

data/raw/f_2724_hanhu.py

@@ -5,15 +5,15 @@
 def f_2726(X, y, n_splits, batch_size, epochs):
     """
     Trains a simple neural network on provided data using k-fold cross-validation.
-    The network has one hidden layer with 50 neurons and ReLU activation, and
+    The network has one hidden layer with 20 neurons and ReLU activation, and
     an output layer with sigmoid activation for binary classification.
 
     Parameters:
         X (numpy.array): The input data.
         y (numpy.array): The target data.
         n_splits (int): The number of splits for k-fold cross-validation. Default is 5.
         batch_size (int): The size of the batch used during training. Default is 32.
-        epochs (int): The number of epochs for training the model. Default is 10.
+        epochs (int): The number of epochs for training the model. Default is 1.
 
     Returns:
         list: A list containing the training history of the model for each fold. Each history
@@ -47,7 +47,7 @@ def f_2726(X, y, n_splits, batch_size, epochs):
         y_train, y_test = y[train_index], y[test_index]
 
         model = tf.keras.models.Sequential([
-            tf.keras.layers.Dense(50, activation='relu'),
+            tf.keras.layers.Dense(20, activation='relu'),
             tf.keras.layers.Dense(1, activation='sigmoid')
         ])
 
@@ -70,7 +70,7 @@ def setUp(self):
         self.y = np.random.randint(0, 2, 100)
         self.n_splits = 5
         self.batch_size = 32
-        self.epochs = 10
+        self.epochs = 1
 
     def test_return_type(self):
         """Test that the function returns a list."""
@@ -101,9 +101,9 @@ def test_effect_of_different_batch_sizes(self):
 
     def test_effect_of_different_epochs(self):
         """Test function behavior with different epochs."""
-        for epochs in [5, 20]:
-            result = f_2726(self.X, self.y, self.n_splits, self.batch_size, epochs)
-            self.assertEqual(len(result), self.n_splits)  # Validating function execution
+        epochs=5
+        result = f_2726(self.X, self.y, self.n_splits, self.batch_size, epochs)
+        self.assertEqual(len(result), self.n_splits)  # Validating function execution
 
 
 def run_tests():

data/raw/f_289_indraneil.py

@@ -23,7 +23,7 @@ def f_289(file_path, regex_pattern=r'\(.+?\)|\w+|[\W_]+'):
     
     Example:
     >>> import tempfile
-    >>> temp_dir = tempfile.gettempdir()
+    >>> temp_dir = tempfile.mkdtemp()
     >>> file_path = os.path.join(temp_dir, 'data.csv')
     >>> with open(file_path, 'w', newline='') as file:
     ...     writer = csv.writer(file)
@@ -52,7 +52,7 @@ def f_289(file_path, regex_pattern=r'\(.+?\)|\w+|[\W_]+'):
 
 
 class TestCases(unittest.TestCase):
-    base_tmp_dir = tempfile.gettempdir()
+    base_tmp_dir = tempfile.mkdtemp()
     test_data_dir = f"{base_tmp_dir}/test"
 
     def setUp(self):

data/raw/f_290_indraneil.py

@@ -22,7 +22,7 @@ def f_290(file_path: str, regex_pattern=r'\(.+?\)|\w') -> dict:
     
     Example:
     >>> import tempfile
-    >>> temp_dir = tempfile.gettempdir()
+    >>> temp_dir = tempfile.mkdtemp()
     >>> file_path = os.path.join(temp_dir, 'sample_data.json')
     >>> with open(file_path, 'w') as file:
     ...     json.dump({'content': 'This is a (sample) text with some (matches) and characters.'}, file)

data/raw/f_294_indraneil.py

@@ -54,7 +54,7 @@ class TestCases(unittest.TestCase):
 
     def setUp(self):
         self.extensions = ['*.txt', '*.md', '*.csv']
-        self.base_tmp_dir = tempfile.gettempdir()
+        self.base_tmp_dir = tempfile.mkdtemp()
         self.test_directory = f"{self.base_tmp_dir}/test/"
         os.makedirs(self.test_directory, exist_ok=True)
 
@@ -70,9 +70,9 @@ def setUp(self):
         # Write the sample data to files
         for filename, content in sample_files_data.items():
             with (
-                open(os.path.join(self.test_directory, filename), 'w')
-                if os.path.exists(os.path.join(self.test_directory, filename))
-                else open(os.path.join(self.test_directory, filename), 'x')
+                    open(os.path.join(self.test_directory, filename), 'w')
+                    if os.path.exists(os.path.join(self.test_directory, filename))
+                    else open(os.path.join(self.test_directory, filename), 'x')
             ) as file:
                 file.write(content)
         return super().setUp()

data/raw/f_321_indraneil.py

@@ -48,7 +48,7 @@ def f_321(file_path):
 class TestCases(unittest.TestCase):
     def setUp(self):
         # Preparing sample JSON data for testing
-        self.base_tmp_dir = tempfile.gettempdir()
+        self.base_tmp_dir = tempfile.mkdtemp()
         self.test_data_folder = f"{self.base_tmp_dir}/test"
         os.makedirs(self.test_data_folder, exist_ok=True)
 

data/raw/f_367_jenny.py

@@ -6,7 +6,7 @@ def f_367(file_path="data.csv", columns=["A", "B", "C"]):
     """
     Read a CSV file into a Pandas DataFrame, convert numeric values into floats,and draw a line chart of data in the specified columns.
     In addition, compute the cube-root of the data.
-    
+
     Parameters:
     - file_path (str): Path to the CSV file. Default is 'data.csv'.
     - columns (list of str): List of column names from the data to plot.
@@ -17,7 +17,7 @@ def f_367(file_path="data.csv", columns=["A", "B", "C"]):
         - DataFrame: A pandas DataFrame of the data in the CSV file.
         - Axes: A matplotlib Axes object showing the plotted data.
         - Series: A pandas Series containing the cube-root of the data.
-        
+
     Requirements:
     - pandas
     - numpy
@@ -31,7 +31,7 @@ def f_367(file_path="data.csv", columns=["A", "B", "C"]):
     >>> ax
     <matplotlib.axes._subplots.AxesSubplot object at 0x7f24b00f4a90>
     >>> croot
-    0    1.0    
+    0    1.0
     """
     df = pd.read_csv(file_path, dtype=float)
     ax = df[columns].plot()
@@ -46,6 +46,11 @@ def f_367(file_path="data.csv", columns=["A", "B", "C"]):
 import os
 
 
+def round_dict(d, digits):
+    return {k: {i: round(v, digits) for i, v in subdict.items()} for k, subdict in
+            d.items()}
+
+
 class TestCases(unittest.TestCase):
     def setUp(self):
         self.test_dir = tempfile.TemporaryDirectory()
@@ -87,8 +92,13 @@ def test_case_1(self):
         self.assertTrue((df["A"].tolist() == [1, 2, 3]))
         self.assertTrue((df["B"].tolist() == [4, 5, 6]))
         self.assertTrue((df["C"].tolist() == [7, 8, 9]))
-        self.assertEqual(croot.to_dict(), {'A': {0: 1.0, 1: 1.2599210498948734, 2: 1.4422495703074083}, 'B': {0: 1.5874010519681996, 1: 1.7099759466766968, 2: 1.8171205928321394}, 'C': {0: 1.9129311827723894, 1: 2.0, 2: 2.080083823051904}})
-        
+        rounded_croot = round_dict(croot.to_dict(), 6)
+        self.assertEqual(rounded_croot,
+                         {'A': {0: 1.0, 1: 1.259921, 2: 1.44225},
+                          'B': {0: 1.587401, 1: 1.709976,
+                                2: 1.817121},
+                          'C': {0: 1.912931, 1: 2.0, 2: 2.080084}})
+
     def test_case_2(self):
         file_path = self.temp_files["int"]
         with self.assertRaises(KeyError):
@@ -104,8 +114,14 @@ def test_case_3(self):
         self.assertTrue(df["IntColumn"].equals(pd.Series([1.0, 2.0, 3.0])))
         self.assertTrue(df["FloatColumn"].equals(pd.Series([1.1, 2.2, 3.3])))
         self.assertTrue(df["StringColumn"].equals(pd.Series([4.0, 5.0, 6.0])))
-        self.assertEqual(croot.to_dict(), {'IntColumn': {0: 1.0, 1: 1.2599210498948734, 2: 1.4422495703074083}, 'FloatColumn': {0: 1.0322801154563672, 1: 1.300591446851387, 2: 1.4888055529538275}, 'StringColumn': {0: 1.5874010519681996, 1: 1.7099759466766968, 2: 1.8171205928321394}})
-        
+        rounded_croot = round_dict(croot.to_dict(), 6)
+        self.assertEqual(rounded_croot, {
+            'IntColumn': {0: 1.0, 1: 1.259921, 2: 1.44225},
+            'FloatColumn': {0: 1.03228, 1: 1.300591,
+                            2: 1.488806},
+            'StringColumn': {0: 1.587401, 1: 1.709976,
+                             2: 1.817121}})
+
     def test_case_4(self):
         file_path = self.temp_files["varied_invalid"]
         with self.assertRaises(Exception):