Added test for additive invariance check and function docstrings

Author: tirthajyoti

Pytest/test_linear_model.py

@@ -8,9 +8,14 @@
 from os import path
 import sklearn
 from sklearn.model_selection import train_test_split
+import pytest
+import math
 
 
 def random_data_constructor(noise_mag=1.0):
+    """
+    Random data constructor utility for tests
+    """
     num_points = 100
     X = 10*np.random.random(size=num_points)
     y = 2*X+3+2*noise_mag*np.random.normal(size=num_points)
@@ -19,6 +24,9 @@ def random_data_constructor(noise_mag=1.0):
 #-------------------------------------------------------------------
 
 def fixed_data_constructor():
+    """
+    Fixed data constructor utility for tests
+    """
     num_points = 100
     X = np.linspace(1,10,num_points)
     y = 2*X+3
@@ -27,11 +35,14 @@ def fixed_data_constructor():
 #-------------------------------------------------------------------
 
 def test_model_return_object():
+    """
+    Tests the returned object of the modeling function
+    """
     X,y = random_data_constructor()
     scores = train_linear_model(X,y)
 
     #=================================
-    # TEST SUITES
+    # TEST SUITE
     #=================================
     # Check the return object type
     assert isinstance(scores, dict)
@@ -43,11 +54,14 @@ def test_model_return_object():
 #-------------------------------------------------------------------
 
 def test_model_return_vals():
+    """
+    Tests for the returned values of the modeling function
+    """
     X,y = random_data_constructor()
     scores = train_linear_model(X,y)
 
     #=================================
-    # TEST SUITES
+    # TEST SUITE
     #=================================
     # Check returned scores' type
     assert isinstance(scores['Train-score'], float)
@@ -61,12 +75,15 @@ def test_model_return_vals():
 #-------------------------------------------------------------------
 
 def test_model_save_load():
+    """
+    Tests for the model saving process
+    """
     X,y = random_data_constructor()
     filename = 'testing'
     _ = train_linear_model(X,y, filename=filename)
 
     #=================================
-    # TEST SUITES
+    # TEST SUITE
     #=================================
     # Check the model file is created/saved in the directory
     assert path.exists('testing.sav')
@@ -78,6 +95,9 @@ def test_model_save_load():
 #-------------------------------------------------------------------
 
 def test_loaded_model_works():
+    """
+    Tests if the loading of the model works correctly
+    """
     X,y = fixed_data_constructor()
     if len(X.shape) == 1:
         X = X.reshape(-1,1)
@@ -88,7 +108,7 @@ def test_loaded_model_works():
     loaded_model = load('testing.sav')
 
     #=================================
-    # TEST SUITES
+    # TEST SUITE
     #=================================
     # Check that test and train scores are perfectly equal to 1.0
     assert scores['Train-score'] == 1.0
@@ -101,6 +121,9 @@ def test_loaded_model_works():
 #-------------------------------------------------------------------
 
 def test_model_works_data_range_sign_change():
+    """
+    Tests for functionality with data scaled high and low
+    """
     # Small-valued data
     X,y = fixed_data_constructor()
     X = 1.0e-9*X
@@ -146,6 +169,9 @@ def test_model_works_data_range_sign_change():
 #-------------------------------------------------------------------
 
 def test_noise_impact():
+    """
+    Tests functionality with low and high noise data and expected change in the R^2 score
+    """
     X,y = random_data_constructor(noise_mag=0.5)
     filename = 'testing'
     scores_low_noise = train_linear_model(X,y, filename=filename)
@@ -160,13 +186,45 @@ def test_noise_impact():
 
 #-------------------------------------------------------------------
 
+def test_additive_invariance():
+    """
+    Tests additive invariance 
+    i.e. adding constant numbers to X or y array does not change the model coefficients
+    """
+    X,y = random_data_constructor(noise_mag=0.5)
+    filename = 'testing'
+    
+    _ = train_linear_model(X,y, filename=filename)
+    m = load('testing.sav')
+    coeff_no_additive = float(m.coef_)
+
+    X = X + 100
+    _ = train_linear_model(X,y, filename=filename)
+    m = load('testing.sav')
+    coeff_X_additive = float(m.coef_)
+
+    y = y - 100
+    _ = train_linear_model(X,y, filename=filename)
+    m = load('testing.sav')
+    coeff_y_additive = float(m.coef_)
+
+    # Check that model coefficients for default and additive data are same (very close)
+    # Note the use of math.isclose function
+    assert math.isclose(coeff_no_additive, coeff_X_additive, rel_tol=1e-6)
+    assert math.isclose(coeff_no_additive, coeff_y_additive, rel_tol=1e-6)
+
+#-------------------------------------------------------------------
+
 def test_wrong_input_raises_assertion():
+    """
+    Tests for various assertion cheks written in the modeling function
+    """
     X,y = random_data_constructor()
     filename = 'testing'
     scores = train_linear_model(X,y, filename=filename)
 
     #=================================
-    # TEST SUITES
+    # TEST SUITE
     #=================================
     # Test that it handles the case of: X is a string
     msg = train_linear_model('X',y)
@@ -195,4 +253,31 @@ def test_wrong_input_raises_assertion():
     X = X.reshape(10,10)
     msg = train_linear_model(X,y, filename='testing')
     assert isinstance(msg, AssertionError)
-    assert msg.args[0] == "Row numbers of X and y data must be identical"
+    assert msg.args[0] == "Row numbers of X and y data must be identical"
+
+#-------------------------------------------------------------------
+
+def test_raised_exception():
+    """
+    Tests for raised exception with pytest.raises context manager
+    """
+    # ValueError
+    with pytest.raises(ValueError):
+        # Insert a np.nan into the X array
+        X,y = random_data_constructor()    
+        X[1] = np.nan
+        filename = 'testing'
+        scores = train_linear_model(X,y, filename=filename)
+        # Insert a np.nan into the y array
+        X,y = random_data_constructor()
+        y[1] = np.nan
+        filename = 'testing'
+        scores = train_linear_model(X,y, filename=filename)
+        
+    with pytest.raises(ValueError) as exception:
+        # Insert a string into the X array
+        X,y = random_data_constructor()
+        X[1] = "A string"
+        filename = 'testing'
+        scores = train_linear_model(X,y, filename=filename)
+        assert "could not convert string to float" in str(exception.value)