@@ -33,9 +33,6 @@ def test_default_boston(self):
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if self .__class__ == BaseRegressionComponentTest :
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return
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- fixture = self .res ["default_boston" ]
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- places = self .res .get ("default_boston_places" , 7 )
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-
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for _ in range (2 ):
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with ignore_warnings (regressor_warnings ):
@@ -46,20 +43,23 @@ def test_default_boston(self):
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score = sklearn .metrics .r2_score (y_true = targets , y_pred = predictions )
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+ # Special treatment for Gaussian Process Regression
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if "default_boston_le_ge" in self .res :
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- # Special treatment for Gaussian Process Regression
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- self .assertLessEqual (score , self . res [ "default_boston_le_ge" ][ 0 ] )
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- self .assertGreaterEqual (score , self . res [ "default_boston_le_ge" ][ 1 ] )
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+ upper , lower = self . res [ "default_boston_le_ge" ]
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+ self .assertLessEqual (score , upper )
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+ self .assertGreaterEqual (score , lower )
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else :
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+ fixture = self .res ["default_boston" ]
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+ places = self .res .get ("default_boston_places" , 7 )
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+
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if score < - 1e10 :
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- print (f"score = { score } , fixture = { fixture } " )
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score = np .log (- score )
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fixture = np .log (- fixture )
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self .assertAlmostEqual (fixture , score , places )
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- if self . res . get ( "boston_n_calls" ) :
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+ if "boston_n_calls" in self . res :
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self .assertEqual (self .res ["boston_n_calls" ], n_calls )
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def test_default_boston_iterative_fit (self ):
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