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Change name of fitness function instance in tests

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This commit is contained in:
Rishabh Garg
2021-07-19 21:26:53 +05:30
parent 071f223346
commit 89c3d85f6e
1 changed files with 51 additions and 65 deletions
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src/mlpack/tests/decision_tree_regressor_test.cpp
+51 -65
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@@ -77,9 +77,8 @@ TEST_CASE("MSEGainPerfectTest", "[DecisionTreeRegressorTest]")
arma::rowvec responses;
responses.ones(10);
MSEGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(0.0).margin(1e-5));
MSEGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(0.0).margin(1e-5));
}
/**
@@ -90,12 +89,9 @@ TEST_CASE("MSEGainEmptyTest", "[DecisionTreeRegressorTest]")
arma::rowvec weights = arma::ones<arma::rowvec>(10);
arma::rowvec responses;
MSEGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(0.0).margin(1e-5));
REQUIRE(Gain.Evaluate<true>(responses, weights) ==
Approx(0.0).margin(1e-5));
MSEGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(0.0).margin(1e-5));
REQUIRE(f.Evaluate<true>(responses, weights) == Approx(0.0).margin(1e-5));
}
/**
@@ -111,10 +107,9 @@ TEST_CASE("MSEGainHandCalculation", "[DecisionTreeRegressorTest]")
const double gain = -27.08999;
const double weightedGain = -27.53960;
MSEGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(gain).margin(1e-5));
REQUIRE(Gain.Evaluate<true>(responses, weights) ==
MSEGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(gain).margin(1e-5));
REQUIRE(f.Evaluate<true>(responses, weights) ==
Approx(weightedGain).margin(1e-5));
}
@@ -127,9 +122,8 @@ TEST_CASE("MADGainPerfectTest", "[DecisionTreeRegressorTest]")
arma::rowvec responses;
responses.ones(10);
MADGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(0.0).margin(1e-5));
MADGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(0.0).margin(1e-5));
}
/**
@@ -148,8 +142,8 @@ TEST_CASE("MADGainNormalTest", "[DecisionTreeRegressorTest")
theoreticalGain /= (double) responses.n_elem;
// Calculated gain.
MADGain Gain;
const double calculatedGain = Gain.Evaluate<false>(responses, weights);
MADGain f;
const double calculatedGain = f.Evaluate<false>(responses, weights);
REQUIRE(calculatedGain == Approx(theoreticalGain).margin(1e-5));
}
@@ -162,12 +156,9 @@ TEST_CASE("MADGainEmptyTest", "[DecisionTreeRegressorTest]")
arma::rowvec weights = arma::ones<arma::rowvec>(10);
arma::rowvec responses;
MADGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(0.0).margin(1e-5));
REQUIRE(Gain.Evaluate<true>(responses, weights) ==
Approx(0.0).margin(1e-5));
MADGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(0.0).margin(1e-5));
REQUIRE(f.Evaluate<true>(responses, weights) == Approx(0.0).margin(1e-5));
}
/**
@@ -183,10 +174,9 @@ TEST_CASE("MADGainHandCalculation", "[DecisionTreeRegressorTest]")
const double gain = -4.1;
const double weightedGain = -3.8592;
MADGain Gain;
REQUIRE(Gain.Evaluate<false>(responses, weights) ==
Approx(gain).margin(1e-5));
REQUIRE(Gain.Evaluate<true>(responses, weights) ==
MADGain f;
REQUIRE(f.Evaluate<false>(responses, weights) == Approx(gain).margin(1e-5));
REQUIRE(f.Evaluate<true>(responses, weights) ==
Approx(weightedGain).margin(1e-5));
}
@@ -213,14 +203,13 @@ TEST_CASE("AllCategoricalSplitSimpleSplitTest_", "[DecisionTreeRegressorTest]")
AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
bestGain, predictor, 2, responses, weights, 3, 1e-7, splitInfo, aux,
Gain);
bestGain, predictor, 2, responses, weights, 3, 1e-7, splitInfo, aux, f);
const double weightedGain =
AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictor, 2,
responses, weights, 3, 1e-7, splitInfo, aux, Gain);
responses, weights, 3, 1e-7, splitInfo, aux, f);
// Make sure that a split was made.
REQUIRE(gain > bestGain);
@@ -246,11 +235,10 @@ TEST_CASE("AllCategoricalSplitMinSamplesTest_", "[DecisionTreeRegressorTest]")
AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
bestGain, predictors, 4, responses, weights, 4, 1e-7, splitInfo, aux,
Gain);
bestGain, predictors, 4, responses, weights, 4, 1e-7, splitInfo, aux, f);
// Make sure it's not split.
REQUIRE(gain == DBL_MAX);
@@ -279,14 +267,14 @@ TEST_CASE("AllCategoricalSplitNoGainTest_", "[DecisionTreeRegressorTest]")
AllCategoricalSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = AllCategoricalSplit<MSEGain>::SplitIfBetter<false>(
bestGain, predictors, 10, responses, weights, 10, 1e-7,
splitInfo, aux, Gain);
splitInfo, aux, f);
const double weightedGain =
AllCategoricalSplit<MSEGain>::SplitIfBetter<true>(bestGain, predictors,
10, responses, weights, 10, 1e-7, splitInfo, aux, Gain);
10, responses, weights, 10, 1e-7, splitInfo, aux, f);
// Make sure that there was no split.
REQUIRE(gain == DBL_MAX);
@@ -311,13 +299,13 @@ TEST_CASE("BestBinaryNumericSplitSimpleSplitTest_",
BestBinaryNumericSplit<MADGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MADGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MADGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = BestBinaryNumericSplit<MADGain>::SplitIfBetter<false>(
bestGain, predictors, responses, weights, 3, 1e-7, splitInfo, aux, Gain);
bestGain, predictors, responses, weights, 3, 1e-7, splitInfo, aux, f);
const double weightedGain =
BestBinaryNumericSplit<MADGain>::SplitIfBetter<true>(bestGain, predictors,
responses, weights, 3, 1e-7, splitInfo, aux, Gain);
responses, weights, 3, 1e-7, splitInfo, aux, f);
// Make sure that a split was made.
REQUIRE(gain > bestGain);
@@ -348,14 +336,14 @@ TEST_CASE("BestBinaryNumericSplitMinSamplesTest_",
BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
bestGain, predictors, responses, weights, 8, 1e-7, splitInfo, aux, Gain);
bestGain, predictors, responses, weights, 8, 1e-7, splitInfo, aux, f);
// This should make no difference because it won't split at all.
const double weightedGain =
BestBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain,
predictors, responses, weights, 8, 1e-7, splitInfo, aux, Gain);
predictors, responses, weights, 8, 1e-7, splitInfo, aux, f);
// Make sure that no split was made.
REQUIRE(gain == DBL_MAX);
@@ -383,11 +371,10 @@ TEST_CASE("BestBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
BestBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = BestBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
bestGain, predictors, responses, weights, 10, 1e-7, splitInfo, aux,
Gain);
bestGain, predictors, responses, weights, 10, 1e-7, splitInfo, aux, f);
// Make sure there was no split.
REQUIRE(gain == DBL_MAX);
@@ -409,13 +396,13 @@ TEST_CASE("RandomBinaryNumericSplitAlwaysSplit_",
RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
bestGain, values, responses, weights, 1, 1e-7, splitInfo, aux, Gain);
bestGain, values, responses, weights, 1, 1e-7, splitInfo, aux, f);
const double weightedGain =
RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
responses, weights, 1, 1e-7, splitInfo, aux, Gain);
responses, weights, 1, 1e-7, splitInfo, aux, f);
// Make sure that split was made.
REQUIRE(gain != DBL_MAX);
@@ -437,14 +424,14 @@ TEST_CASE("RandomBinaryNumericSplitMinSamplesTest_",
RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
bestGain, values, responses, weights, 8, 1e-7, splitInfo, aux, Gain);
bestGain, values, responses, weights, 8, 1e-7, splitInfo, aux, f);
// This should make no difference because it won't split at all.
const double weightedGain =
RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<true>(bestGain, values,
responses, weights, 8, 1e-7, splitInfo, aux, Gain);
responses, weights, 8, 1e-7, splitInfo, aux, f);
// Make sure that no split was made.
REQUIRE(gain == DBL_MAX);
@@ -472,11 +459,10 @@ TEST_CASE("RandomBinaryNumericSplitNoGainTest_", "[DecisionTreeRegressorTest]")
RandomBinaryNumericSplit<MSEGain>::AuxiliarySplitInfo aux;
// Call the method to do the splitting.
MSEGain Gain;
const double bestGain = Gain.Evaluate<false>(responses, weights);
MSEGain f;
const double bestGain = f.Evaluate<false>(responses, weights);
const double gain = RandomBinaryNumericSplit<MSEGain>::SplitIfBetter<false>(
bestGain, values, responses, weights, 10, 1e-7, splitInfo, aux, Gain,
true);
bestGain, values, responses, weights, 10, 1e-7, splitInfo, aux, f, true);
// Make sure there was no split.
REQUIRE(gain == DBL_MAX);