def test_log1p_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6623502468),
ast.NumVal(0.6683497987)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1405181490),
ast.NumVal(0.1453602134)),
ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def test_log1p_exp_output_transform():
estimator = lgb.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6622623010380544),
ast.NumVal(0.6684065452877841)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1404975120475147),
ast.NumVal(0.14535916856709272)), ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
function Log1p([double] $x) {
if ($x -eq 0.0) { return 0.0 }
if ($x -eq -1.0) { return [double]::NegativeInfinity }
if ($x -lt -1.0) { return [double]::NaN }
[double] $xAbs = [math]::Abs($x)
if ($xAbs -lt 0.5 * [double]::Epsilon) { return $x }
if ((($x -gt 0.0) -and ($x -lt 1e-8))
-or (($x -gt -1e-9) -and ($x -lt 0.0))) {
return $x * (1.0 - $x * 0.5)
}
if ($xAbs -lt 0.375) {
[double[]] $coeffs = @(
0.10378693562743769800686267719098e+1,
-0.13364301504908918098766041553133e+0,
0.19408249135520563357926199374750e-1,
-0.30107551127535777690376537776592e-2,
0.48694614797154850090456366509137e-3,
-0.81054881893175356066809943008622e-4,
0.13778847799559524782938251496059e-4,
-0.23802210894358970251369992914935e-5,
0.41640416213865183476391859901989e-6,
-0.73595828378075994984266837031998e-7,
0.13117611876241674949152294345011e-7,
-0.23546709317742425136696092330175e-8,
0.42522773276034997775638052962567e-9,
-0.77190894134840796826108107493300e-10,
0.14075746481359069909215356472191e-10,
-0.25769072058024680627537078627584e-11,
0.47342406666294421849154395005938e-12,
-0.87249012674742641745301263292675e-13,
0.16124614902740551465739833119115e-13,
-0.29875652015665773006710792416815e-14,
0.55480701209082887983041321697279e-15,
-0.10324619158271569595141333961932e-15)
return $x * (1.0 - $x * (Chebyshev-Broucke ($x / 0.375) $coeffs))
}
return [math]::Log(1.0 + $x)
}
function Chebyshev-Broucke([double] $x, [double[]] $coeffs) {
[double] $b2 = [double] $b1 = [double] $b0 = 0.0
[double] $x2 = $x * 2
for ([int] $i = $coeffs.Length - 1; $i -ge 0; --$i) {
$b2 = $b1
$b1 = $b0
$b0 = $x2 * $b1 - $b2 + $coeffs[$i]
}
return ($b0 - $b2) * 0.5
}
function Score([double[]] $InputVector) {
return Log1p $(2.0)
}
"""
interpreter = PowershellInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
score <- function(input) {
return(log1p(2.0))
}
"""
interpreter = RInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
fn score(input: Vec<f64>) -> f64 {
f64::ln_1p(2.0_f64)
}
"""
interpreter = RustInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
function score(input) {
return Math.log1p(2.0);
}
"""
interpreter = JavascriptInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
import math
def score(input):
return math.log1p(2.0)
"""
interpreter = PythonInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
interpreter = GoInterpreter()
expected_code = """
import "math"
func score(input []float64) float64 {
return math.Log1p(2.0)
}"""
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
<?php
function score(array $input) {
return log1p(2.0);
}
"""
interpreter = PhpInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
module Model where
score :: [Double] -> Double
score input =
log1p (2.0)
log1p :: Double -> Double
log1p x
| x == 0 = 0
| x == -1 = -1 / 0
| x < -1 = 0 / 0
| x' < m_epsilon * 0.5 = x
| (x > 0 && x < 1e-8) || (x > -1e-9 && x < 0)
= x * (1 - x * 0.5)
| x' < 0.375 = x * (1 - x * chebyshevBroucke (x / 0.375) coeffs)
| otherwise = log (1 + x)
where
m_epsilon = encodeFloat (signif + 1) expo - 1.0
where (signif, expo) = decodeFloat (1.0::Double)
x' = abs x
coeffs = [
0.10378693562743769800686267719098e+1,
-0.13364301504908918098766041553133e+0,
0.19408249135520563357926199374750e-1,
-0.30107551127535777690376537776592e-2,
0.48694614797154850090456366509137e-3,
-0.81054881893175356066809943008622e-4,
0.13778847799559524782938251496059e-4,
-0.23802210894358970251369992914935e-5,
0.41640416213865183476391859901989e-6,
-0.73595828378075994984266837031998e-7,
0.13117611876241674949152294345011e-7,
-0.23546709317742425136696092330175e-8,
0.42522773276034997775638052962567e-9,
-0.77190894134840796826108107493300e-10,
0.14075746481359069909215356472191e-10,
-0.25769072058024680627537078627584e-11,
0.47342406666294421849154395005938e-12,
-0.87249012674742641745301263292675e-13,
0.16124614902740551465739833119115e-13,
-0.29875652015665773006710792416815e-14,
0.55480701209082887983041321697279e-15,
-0.10324619158271569595141333961932e-15]
chebyshevBroucke i = fini . foldr step (0, 0, 0)
where
step k (b0, b1, _) = ((k + i * 2 * b0 - b1), b0, b1)
fini (b0, _, b2) = (b0 - b2) * 0.5
"""
interpreter = HaskellInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
interpreter = interpreters.CInterpreter()
expected_code = """
#include <math.h>
double score(double * input) {
return log1p(2.0);
}"""
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
interpreter = JavaInterpreter()
expected_code = """
public class Model {
public static double score(double[] input) {
return Math.log1p(2.0);
}
}"""
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
let private chebyshevBroucke i coeffs =
let step k (b0, b1, _) = ((k + i * 2.0 * b0 - b1), b0, b1)
let fini (b0, _, b2) = (b0 - b2) * 0.5
fini (List.foldBack step coeffs (0.0, 0.0, 0.0))
let private log1p x =
let x' = abs x
let chebCoeffs = [
0.10378693562743769800686267719098e+1;
-0.13364301504908918098766041553133e+0;
0.19408249135520563357926199374750e-1;
-0.30107551127535777690376537776592e-2;
0.48694614797154850090456366509137e-3;
-0.81054881893175356066809943008622e-4;
0.13778847799559524782938251496059e-4;
-0.23802210894358970251369992914935e-5;
0.41640416213865183476391859901989e-6;
-0.73595828378075994984266837031998e-7;
0.13117611876241674949152294345011e-7;
-0.23546709317742425136696092330175e-8;
0.42522773276034997775638052962567e-9;
-0.77190894134840796826108107493300e-10;
0.14075746481359069909215356472191e-10;
-0.25769072058024680627537078627584e-11;
0.47342406666294421849154395005938e-12;
-0.87249012674742641745301263292675e-13;
0.16124614902740551465739833119115e-13;
-0.29875652015665773006710792416815e-14;
0.55480701209082887983041321697279e-15;
-0.10324619158271569595141333961932e-15]
match x with
| 0.0 -> 0.0
| -1.0 -> -infinity
| i when i < -1.0 -> nan
| i when x' < System.Double.Epsilon * 0.5
-> x
| i when (x > 0.0 && x < 1e-8) || (x > -1e-9 && x < 0.0)
-> x * (1.0 - x * 0.5)
| i when x' < 0.375 ->
x * (1.0 - x * chebyshevBroucke (x / 0.375) chebCoeffs)
| _ -> log (1.0 + x)
let score (input : double list) =
log1p (2.0)
"""
interpreter = FSharpInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_fallback_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
interpreter = PythonInterpreter()
interpreter.log1p_function_name = NotImplemented
expected_code = """
import math
def score(input):
var1 = 2.0
var2 = (1.0) + (var1)
var3 = (var2) - (1.0)
if (var3) == (0.0):
var0 = var1
else:
var0 = ((var1) * (math.log(var2))) / (var3)
return var0
"""
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
using static System.Math;
namespace ML {
public static class Model {
public static double Score(double[] input) {
return Log1p(2.0);
}
private static double Log1p(double x) {
if (x == 0.0)
return 0.0;
if (x == -1.0)
return double.NegativeInfinity;
if (x < -1.0)
return double.NaN;
double xAbs = Abs(x);
if (xAbs < 0.5 * double.Epsilon)
return x;
if ((x > 0.0 && x < 1e-8) || (x > -1e-9 && x < 0.0))
return x * (1.0 - x * 0.5);
if (xAbs < 0.375) {
double[] coeffs = {
0.10378693562743769800686267719098e+1,
-0.13364301504908918098766041553133e+0,
0.19408249135520563357926199374750e-1,
-0.30107551127535777690376537776592e-2,
0.48694614797154850090456366509137e-3,
-0.81054881893175356066809943008622e-4,
0.13778847799559524782938251496059e-4,
-0.23802210894358970251369992914935e-5,
0.41640416213865183476391859901989e-6,
-0.73595828378075994984266837031998e-7,
0.13117611876241674949152294345011e-7,
-0.23546709317742425136696092330175e-8,
0.42522773276034997775638052962567e-9,
-0.77190894134840796826108107493300e-10,
0.14075746481359069909215356472191e-10,
-0.25769072058024680627537078627584e-11,
0.47342406666294421849154395005938e-12,
-0.87249012674742641745301263292675e-13,
0.16124614902740551465739833119115e-13,
-0.29875652015665773006710792416815e-14,
0.55480701209082887983041321697279e-15,
-0.10324619158271569595141333961932e-15};
return x * (1.0 - x * ChebyshevBroucke(x / 0.375, coeffs));
}
return Log(1.0 + x);
}
private static double ChebyshevBroucke(double x, double[] coeffs) {
double b0, b1, b2, x2;
b2 = b1 = b0 = 0.0;
x2 = x * 2;
for (int i = coeffs.Length - 1; i >= 0; --i) {
b2 = b1;
b1 = b0;
b0 = x2 * b1 - b2 + coeffs[i];
}
return (b0 - b2) * 0.5;
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def _log1p_exp_transform(self, expr):
return ast.Log1pExpr(ast.ExpExpr(expr))
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
def score(input)
log1p(2.0)
end
def log1p(x)
if x == 0.0
return 0.0
end
if x == -1.0
return -Float::INFINITY
end
if x < -1.0
return Float::NAN
end
x_abs = x.abs
if x_abs < 0.5 * Float::EPSILON
return x
end
if (x > 0.0 && x < 1e-8) || (x > -1e-9 && x < 0.0)
return x * (1.0 - x * 0.5)
end
if x_abs < 0.375
coeffs = [
0.10378693562743769800686267719098e+1,
-0.13364301504908918098766041553133e+0,
0.19408249135520563357926199374750e-1,
-0.30107551127535777690376537776592e-2,
0.48694614797154850090456366509137e-3,
-0.81054881893175356066809943008622e-4,
0.13778847799559524782938251496059e-4,
-0.23802210894358970251369992914935e-5,
0.41640416213865183476391859901989e-6,
-0.73595828378075994984266837031998e-7,
0.13117611876241674949152294345011e-7,
-0.23546709317742425136696092330175e-8,
0.42522773276034997775638052962567e-9,
-0.77190894134840796826108107493300e-10,
0.14075746481359069909215356472191e-10,
-0.25769072058024680627537078627584e-11,
0.47342406666294421849154395005938e-12,
-0.87249012674742641745301263292675e-13,
0.16124614902740551465739833119115e-13,
-0.29875652015665773006710792416815e-14,
0.55480701209082887983041321697279e-15,
-0.10324619158271569595141333961932e-15]
return x * (1.0 - x * chebyshev_broucke(x / 0.375, coeffs))
end
return Math.log(1.0 + x)
end
def chebyshev_broucke(x, coeffs)
b2 = b1 = b0 = 0.0
x2 = x * 2
coeffs.reverse_each do |i|
b2 = b1
b1 = b0
b0 = x2 * b1 - b2 + i
end
(b0 - b2) * 0.5
end
"""
interpreter = RubyInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
) == 0
assert ast.count_exprs(
ast.BinNumExpr(ast.NumVal(1), ast.NumVal(2), ast.BinNumOpType.ADD),
exclude_list={ast.BinNumExpr}
) == 2
EXPR_WITH_ALL_EXPRS = ast.BinVectorNumExpr(
ast.BinVectorExpr(
ast.VectorVal([
ast.AbsExpr(ast.NumVal(-2)),
ast.AtanExpr(ast.NumVal(2)),
ast.ExpExpr(ast.NumVal(2)),
ast.LogExpr(ast.NumVal(2)),
ast.Log1pExpr(ast.NumVal(2)),
ast.SigmoidExpr(ast.NumVal(2)),
ast.SqrtExpr(ast.NumVal(2)),
ast.PowExpr(ast.NumVal(2), ast.NumVal(3)),
ast.TanhExpr(ast.NumVal(1)),
ast.BinNumExpr(
ast.NumVal(0),
ast.FeatureRef(0),
ast.BinNumOpType.ADD)
]),
ast.IdExpr(
ast.SoftmaxExpr([
ast.NumVal(1),
ast.NumVal(2),
ast.NumVal(3),
ast.NumVal(4),
def test_log1p_expr():
expr = ast.Log1pExpr(ast.NumVal(2.0))
expected_code = """
Module Model
Function ChebyshevBroucke(ByVal x As Double, _
ByRef coeffs() As Double) As Double
Dim b2 as Double
Dim b1 as Double
Dim b0 as Double
Dim x2 as Double
b2 = 0.0
b1 = 0.0
b0 = 0.0
x2 = x * 2
Dim i as Integer
For i = UBound(coeffs) - 1 To 0 Step -1
b2 = b1
b1 = b0
b0 = x2 * b1 - b2 + coeffs(i)
Next i
ChebyshevBroucke = (b0 - b2) * 0.5
End Function
Function Log1p(ByVal x As Double) As Double
If x = 0.0 Then
Log1p = 0.0
Exit Function
End If
If x = -1.0 Then
On Error Resume Next
Log1p = -1.0 / 0.0
Exit Function
End If
If x < -1.0 Then
On Error Resume Next
Log1p = 0.0 / 0.0
Exit Function
End If
Dim xAbs As Double
xAbs = Math.Abs(x)
If xAbs < 0.5 * 4.94065645841247e-324 Then
Log1p = x
Exit Function
End If
If (x > 0.0 AND x < 1e-8) OR (x > -1e-9 AND x < 0.0) Then
Log1p = x * (1.0 - x * 0.5)
Exit Function
End If
If xAbs < 0.375 Then
Dim coeffs(22) As Double
coeffs(0) = 0.10378693562743769800686267719098e+1
coeffs(1) = -0.13364301504908918098766041553133e+0
coeffs(2) = 0.19408249135520563357926199374750e-1
coeffs(3) = -0.30107551127535777690376537776592e-2
coeffs(4) = 0.48694614797154850090456366509137e-3
coeffs(5) = -0.81054881893175356066809943008622e-4
coeffs(6) = 0.13778847799559524782938251496059e-4
coeffs(7) = -0.23802210894358970251369992914935e-5
coeffs(8) = 0.41640416213865183476391859901989e-6
coeffs(9) = -0.73595828378075994984266837031998e-7
coeffs(10) = 0.13117611876241674949152294345011e-7
coeffs(11) = -0.23546709317742425136696092330175e-8
coeffs(12) = 0.42522773276034997775638052962567e-9
coeffs(13) = -0.77190894134840796826108107493300e-10
coeffs(14) = 0.14075746481359069909215356472191e-10
coeffs(15) = -0.25769072058024680627537078627584e-11
coeffs(16) = 0.47342406666294421849154395005938e-12
coeffs(17) = -0.87249012674742641745301263292675e-13
coeffs(18) = 0.16124614902740551465739833119115e-13
coeffs(19) = -0.29875652015665773006710792416815e-14
coeffs(20) = 0.55480701209082887983041321697279e-15
coeffs(21) = -0.10324619158271569595141333961932e-15
Log1p = x * (1.0 - x * ChebyshevBroucke(x / 0.375, coeffs))
Exit Function
End If
Log1p = Math.log(1.0 + x)
End Function
Function Score(ByRef inputVector() As Double) As Double
Score = Log1p(2.0)
End Function
End Module
"""
interpreter = VisualBasicInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)