def test_pow_expr():
expr = ast.PowExpr(ast.NumVal(2.0), ast.NumVal(3.0))
expected_code = """
using static System.Math;
namespace ML {
public static class Model {
public static double Score(double[] input) {
return Pow(2.0, 3.0);
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_tanh_expr():
expr = ast.TanhExpr(ast.NumVal(2.0))
expected_code = """
using static System.Math;
namespace ML {
public static class Model {
public static double Score(double[] input) {
return Tanh(2.0);
}
}
}
"""
interpreter = CSharpInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_nested_condition():
left = ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.NumVal(1), ast.NumVal(1), ast.CompOpType.EQ),
ast.NumVal(1), ast.NumVal(2)), ast.NumVal(2), ast.BinNumOpType.ADD)
bool_test = ast.CompExpr(ast.NumVal(1), left, ast.CompOpType.EQ)
expr_nested = ast.IfExpr(bool_test, ast.FeatureRef(2), ast.NumVal(2))
expr = ast.IfExpr(bool_test, expr_nested, ast.NumVal(2))
expected_code = """
namespace ML {
public static class Model {
public static double Score(double[] input) {
double var0;
double var1;
if ((1.0) == (1.0)) {
var1 = 1.0;
} else {
var1 = 2.0;
}
if ((1.0) == ((var1) + (2.0))) {
double var2;
if ((1.0) == (1.0)) {
var2 = 1.0;
} else {
var2 = 2.0;
}
if ((1.0) == ((var2) + (2.0))) {
var0 = input[2];
} else {
var0 = 2.0;
}
} else {
var0 = 2.0;
}
return var0;
}
}
}
"""
interpreter = CSharpInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_bin_num_expr():
expr = ast.BinNumExpr(
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-2),
ast.BinNumOpType.DIV), ast.NumVal(2),
ast.BinNumOpType.MUL)
expected_code = """
namespace ML {
public static class Model {
public static double Score(double[] input) {
return ((input[0]) / (-2.0)) * (2.0);
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_reused_expr():
reused_expr = ast.ExpExpr(ast.NumVal(1.0), to_reuse=True)
expr = ast.BinNumExpr(reused_expr, reused_expr, ast.BinNumOpType.DIV)
expected_code = """
using static System.Math;
namespace ML {
public static class Model {
public static double Score(double[] input) {
double var0;
var0 = Exp(1.0);
return (var0) / (var0);
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_dependable_condition():
left = ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.NumVal(1),
ast.NumVal(1),
ast.CompOpType.EQ),
ast.NumVal(1),
ast.NumVal(2)),
ast.NumVal(2),
ast.BinNumOpType.ADD)
right = ast.BinNumExpr(ast.NumVal(1), ast.NumVal(2), ast.BinNumOpType.DIV)
bool_test = ast.CompExpr(left, right, ast.CompOpType.GTE)
expr = ast.IfExpr(bool_test, ast.NumVal(1), ast.FeatureRef(0))
expected_code = """
namespace ML {
public static class Model {
public static double Score(double[] input) {
double var0;
double var1;
if ((1) == (1)) {
var1 = 1;
} else {
var1 = 2;
}
if (((var1) + (2)) >= ((1) / (2))) {
var0 = 1;
} else {
var0 = input[0];
}
return var0;
}
}
}
"""
interpreter = CSharpInterpreter()
utils.assert_code_equal(interpreter.interpret(expr), expected_code)
def test_if_expr():
expr = ast.IfExpr(
ast.CompExpr(ast.NumVal(1), ast.FeatureRef(0), ast.CompOpType.EQ),
ast.NumVal(2), ast.NumVal(3))
expected_code = """
namespace ML {
public static class Model {
public static double Score(double[] input) {
double var0;
if ((1.0) == (input[0])) {
var0 = 2.0;
} else {
var0 = 3.0;
}
return var0;
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
def test_sigmoid_expr():
expr = ast.SigmoidExpr(ast.NumVal(2.0))
expected_code = """
using static System.Math;
namespace ML {
public static class Model {
public static double Score(double[] input) {
return Sigmoid(2.0);
}
private static double Sigmoid(double x) {
if (x < 0.0) {
double z = Exp(x);
return z / (1.0 + z);
}
return 1.0 / (1.0 + Exp(-x));
}
}
}
"""
interpreter = CSharpInterpreter()
assert_code_equal(interpreter.interpret(expr), expected_code)
class CSharpExecutor(BaseExecutor):
target_exec_dir = None
project_name = "test_model"
def __init__(self, model):
self.model = model
self.interpreter = CSharpInterpreter()
assembler_cls = get_assembler_cls(model)
self.model_ast = assembler_cls(model).assemble()
def predict(self, X):
exec_args = [
str(self.target_exec_dir / self.project_name),
*map(utils.format_arg, X)
]
return utils.predict_from_commandline(exec_args)
@classmethod
def prepare_global(cls, **kwargs):
super().prepare_global(**kwargs)
if cls.target_exec_dir is None:
cls.target_exec_dir = cls._global_tmp_dir / "bin"
subprocess.call([
"dotnet", "new", "console", "--output",
str(cls._global_tmp_dir), "--name", cls.project_name,
"--language", "C#"
])
def prepare(self):
if self.model_ast.output_size > 1:
print_code = EXECUTE_AND_PRINT_VECTOR
else:
print_code = EXECUTE_AND_PRINT_SCALAR
executor_code = EXECUTOR_CODE_TPL.format(print_code=print_code)
model_code = self.interpreter.interpret(self.model_ast)
model_file_name = self._global_tmp_dir / "Model.cs"
executor_file_name = self._global_tmp_dir / "Program.cs"
utils.write_content_to_file(model_code, model_file_name)
utils.write_content_to_file(executor_code, executor_file_name)
subprocess.call([
"dotnet", "build",
str(self._global_tmp_dir / f"{self.project_name}.csproj"),
"--output",
str(self.target_exec_dir)
])
def test_multi_output():
expr = ast.IfExpr(
ast.CompExpr(ast.NumVal(1), ast.NumVal(1), ast.CompOpType.EQ),
ast.VectorVal([ast.NumVal(1), ast.NumVal(2)]),
ast.VectorVal([ast.NumVal(3), ast.NumVal(4)]))
expected_code = """
namespace ML {
public static class Model {
public static double[] Score(double[] input) {
double[] var0;
if ((1.0) == (1.0)) {
var0 = new double[2] {1.0, 2.0};
} else {
var0 = new double[2] {3.0, 4.0};
}
return var0;
}
}
}
"""
interpreter = CSharpInterpreter()
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 __init__(self, model):
self.model = model
self.interpreter = CSharpInterpreter()
assembler_cls = get_assembler_cls(model)
self.model_ast = assembler_cls(model).assemble()