diff --git a/.travis.yml b/.travis.yml index d84f27e2..d7c897e3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,7 @@ python: env: - TEST=API - TEST=E2E LANG="c_lang or python or java or go_lang or javascript or php or haskell or ruby" - - TEST=E2E LANG="c_sharp or visual_basic or powershell" + - TEST=E2E LANG="powershell" - TEST=E2E LANG="r_lang or dart" before_install: diff --git a/m2cgen/interpreters/c/interpreter.py b/m2cgen/interpreters/c/interpreter.py index 8c5a2ee6..be1f13ae 100644 --- a/m2cgen/interpreters/c/interpreter.py +++ b/m2cgen/interpreters/c/interpreter.py @@ -17,9 +17,9 @@ class CInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mul_vector_number", } - exponent_function_name = "exp" +# exponent_function_name = "exp" power_function_name = "pow" - sqrt_function_name = "sqrt" +# sqrt_function_name = "sqrt" tanh_function_name = "tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/c_sharp/interpreter.py b/m2cgen/interpreters/c_sharp/interpreter.py index 6e32b763..19c8038b 100644 --- a/m2cgen/interpreters/c_sharp/interpreter.py +++ b/m2cgen/interpreters/c_sharp/interpreter.py @@ -18,9 +18,9 @@ class CSharpInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "MulVectorNumber", } - exponent_function_name = "Exp" +# exponent_function_name = "Exp" power_function_name = "Pow" - sqrt_function_name = "Sqrt" +# sqrt_function_name = "Sqrt" tanh_function_name = "Tanh" def __init__(self, namespace="ML", class_name="Model", indent=4, diff --git a/m2cgen/interpreters/dart/interpreter.py b/m2cgen/interpreters/dart/interpreter.py index 34d658e3..121fd071 100644 --- a/m2cgen/interpreters/dart/interpreter.py +++ b/m2cgen/interpreters/dart/interpreter.py @@ -21,9 +21,9 @@ class DartInterpreter(ImperativeToCodeInterpreter, bin_depth_threshold = 465 - exponent_function_name = "exp" +# exponent_function_name = "exp" power_function_name = "pow" - sqrt_function_name = "sqrt" +# sqrt_function_name = "sqrt" tanh_function_name = "tanh" with_tanh_expr = False diff --git a/m2cgen/interpreters/go/interpreter.py b/m2cgen/interpreters/go/interpreter.py index 95ca0ee2..0b246682 100644 --- a/m2cgen/interpreters/go/interpreter.py +++ b/m2cgen/interpreters/go/interpreter.py @@ -16,9 +16,9 @@ class GoInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mulVectorNumber", } - exponent_function_name = "math.Exp" +# exponent_function_name = "math.Exp" power_function_name = "math.Pow" - sqrt_function_name = "math.Sqrt" +# sqrt_function_name = "math.Sqrt" tanh_function_name = "math.Tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/haskell/interpreter.py b/m2cgen/interpreters/haskell/interpreter.py index 577aa8af..569eea69 100644 --- a/m2cgen/interpreters/haskell/interpreter.py +++ b/m2cgen/interpreters/haskell/interpreter.py @@ -16,8 +16,8 @@ class HaskellInterpreter(ToCodeInterpreter, ast.BinNumOpType.MUL: "mulVectorNumber", } - exponent_function_name = "exp" - sqrt_function_name = "sqrt" +# exponent_function_name = "exp" +# sqrt_function_name = "sqrt" tanh_function_name = "tanh" def __init__(self, module_name="Model", indent=4, function_name="score", diff --git a/m2cgen/interpreters/java/interpreter.py b/m2cgen/interpreters/java/interpreter.py index 01b4d41a..8913a071 100644 --- a/m2cgen/interpreters/java/interpreter.py +++ b/m2cgen/interpreters/java/interpreter.py @@ -24,9 +24,9 @@ class JavaInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mulVectorNumber", } - exponent_function_name = "Math.exp" +# exponent_function_name = "Math.exp" power_function_name = "Math.pow" - sqrt_function_name = "Math.sqrt" +# sqrt_function_name = "Math.sqrt" tanh_function_name = "Math.tanh" def __init__(self, package_name=None, class_name="Model", indent=4, diff --git a/m2cgen/interpreters/javascript/interpreter.py b/m2cgen/interpreters/javascript/interpreter.py index 09fb05e7..d545d847 100644 --- a/m2cgen/interpreters/javascript/interpreter.py +++ b/m2cgen/interpreters/javascript/interpreter.py @@ -19,9 +19,9 @@ class JavascriptInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mulVectorNumber", } - exponent_function_name = "Math.exp" +# exponent_function_name = "Math.exp" power_function_name = "Math.pow" - sqrt_function_name = "Math.sqrt" +# sqrt_function_name = "Math.sqrt" tanh_function_name = "Math.tanh" def __init__(self, indent=4, function_name="score", diff --git a/m2cgen/interpreters/php/interpreter.py b/m2cgen/interpreters/php/interpreter.py index 06d0416b..984c6853 100644 --- a/m2cgen/interpreters/php/interpreter.py +++ b/m2cgen/interpreters/php/interpreter.py @@ -17,9 +17,9 @@ class PhpInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mulVectorNumber", } - exponent_function_name = "exp" +# exponent_function_name = "exp" power_function_name = "pow" - sqrt_function_name = "sqrt" +# sqrt_function_name = "sqrt" tanh_function_name = "tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/powershell/interpreter.py b/m2cgen/interpreters/powershell/interpreter.py index 3b75ab8c..9789a5a8 100644 --- a/m2cgen/interpreters/powershell/interpreter.py +++ b/m2cgen/interpreters/powershell/interpreter.py @@ -18,9 +18,9 @@ class PowershellInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "Mul-Vector-Number", } - exponent_function_name = "[math]::Exp" +# exponent_function_name = "[math]::Exp" power_function_name = "[math]::Pow" - sqrt_function_name = "[math]::Sqrt" +# sqrt_function_name = "[math]::Sqrt" tanh_function_name = "[math]::Tanh" def __init__(self, indent=4, function_name="Score", *args, **kwargs): @@ -48,16 +48,6 @@ def interpret(self, expr): return self._cg.code - def interpret_exp_expr(self, expr, **kwargs): - nested_result = self._do_interpret(expr.expr, **kwargs) - return self._cg.math_function_invocation( - self.exponent_function_name, nested_result) - - def interpret_sqrt_expr(self, expr, **kwargs): - nested_result = self._do_interpret(expr.expr, **kwargs) - return self._cg.math_function_invocation( - self.sqrt_function_name, nested_result) - def interpret_tanh_expr(self, expr, **kwargs): nested_result = self._do_interpret(expr.expr, **kwargs) return self._cg.math_function_invocation( diff --git a/m2cgen/interpreters/python/interpreter.py b/m2cgen/interpreters/python/interpreter.py index e884ad75..c54fe04e 100644 --- a/m2cgen/interpreters/python/interpreter.py +++ b/m2cgen/interpreters/python/interpreter.py @@ -10,9 +10,9 @@ class PythonInterpreter(ImperativeToCodeInterpreter, # 60 raises MemoryError for some SVM models with RBF kernel. bin_depth_threshold = 55 - exponent_function_name = "math.exp" +# exponent_function_name = "math.exp" power_function_name = "math.pow" - sqrt_function_name = "math.sqrt" +# sqrt_function_name = "math.sqrt" tanh_function_name = "math.tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/r/interpreter.py b/m2cgen/interpreters/r/interpreter.py index 67d959d1..2bdfcce8 100644 --- a/m2cgen/interpreters/r/interpreter.py +++ b/m2cgen/interpreters/r/interpreter.py @@ -22,8 +22,8 @@ class RInterpreter(ImperativeToCodeInterpreter, ast_size_check_frequency = 2 ast_size_per_subroutine_threshold = 200 - exponent_function_name = "exp" - sqrt_function_name = "sqrt" +# exponent_function_name = "exp" +# sqrt_function_name = "sqrt" tanh_function_name = "tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/ruby/interpreter.py b/m2cgen/interpreters/ruby/interpreter.py index 92c402df..6e2c4dec 100644 --- a/m2cgen/interpreters/ruby/interpreter.py +++ b/m2cgen/interpreters/ruby/interpreter.py @@ -17,8 +17,8 @@ class RubyInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "mul_vector_number", } - exponent_function_name = "Math.exp" - sqrt_function_name = "Math.sqrt" +# exponent_function_name = "Math.exp" +# sqrt_function_name = "Math.sqrt" tanh_function_name = "Math.tanh" def __init__(self, indent=4, function_name="score", *args, **kwargs): diff --git a/m2cgen/interpreters/visual_basic/interpreter.py b/m2cgen/interpreters/visual_basic/interpreter.py index 767e2461..c0fade08 100644 --- a/m2cgen/interpreters/visual_basic/interpreter.py +++ b/m2cgen/interpreters/visual_basic/interpreter.py @@ -17,7 +17,7 @@ class VisualBasicInterpreter(ImperativeToCodeInterpreter, ast.BinNumOpType.MUL: "MulVectorNumber", } - exponent_function_name = "Math.Exp" +# exponent_function_name = "Math.Exp" tanh_function_name = "Tanh" with_tanh_expr = False diff --git a/tests/e2e/test_e2e.py b/tests/e2e/test_e2e.py index 2e2904e8..35dfc57d 100644 --- a/tests/e2e/test_e2e.py +++ b/tests/e2e/test_e2e.py @@ -37,7 +37,7 @@ # Set of helper functions to make parametrization less verbose. -def regression(model, test_fraction=0.02): +def regression(model, test_fraction=0.2): return ( model, utils.get_regression_model_trainer(test_fraction), @@ -258,241 +258,241 @@ def regression_bounded(model, test_fraction=0.02): kernel="cosine", random_state=RANDOM_SEED)), # Sklearn Linear Regression - regression(linear_model.ARDRegression()), - regression(linear_model.BayesianRidge()), - regression(linear_model.ElasticNet(random_state=RANDOM_SEED)), - regression(linear_model.ElasticNetCV(random_state=RANDOM_SEED)), - regression(linear_model.HuberRegressor()), - regression(linear_model.Lars()), - regression(linear_model.LarsCV()), - regression(linear_model.Lasso(random_state=RANDOM_SEED)), - regression(linear_model.LassoCV(random_state=RANDOM_SEED)), - regression(linear_model.LassoLars()), - regression(linear_model.LassoLarsCV()), - regression(linear_model.LassoLarsIC()), - regression(linear_model.LinearRegression()), - regression(linear_model.OrthogonalMatchingPursuit()), - regression(linear_model.OrthogonalMatchingPursuitCV()), - regression(linear_model.PassiveAggressiveRegressor( - random_state=RANDOM_SEED)), - regression(linear_model.RANSACRegressor( - base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS), - random_state=RANDOM_SEED)), - regression(linear_model.Ridge(random_state=RANDOM_SEED)), - regression(linear_model.RidgeCV()), - regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)), - regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)), +# regression(linear_model.ARDRegression()), +# regression(linear_model.BayesianRidge()), +# regression(linear_model.ElasticNet(random_state=RANDOM_SEED)), +# regression(linear_model.ElasticNetCV(random_state=RANDOM_SEED)), +# regression(linear_model.HuberRegressor()), +# regression(linear_model.Lars()), +# regression(linear_model.LarsCV()), +# regression(linear_model.Lasso(random_state=RANDOM_SEED)), +# regression(linear_model.LassoCV(random_state=RANDOM_SEED)), +# regression(linear_model.LassoLars()), +# regression(linear_model.LassoLarsCV()), +# regression(linear_model.LassoLarsIC()), +# regression(linear_model.LinearRegression()), +# regression(linear_model.OrthogonalMatchingPursuit()), +# regression(linear_model.OrthogonalMatchingPursuitCV()), +# regression(linear_model.PassiveAggressiveRegressor( +# random_state=RANDOM_SEED)), +# regression(linear_model.RANSACRegressor( +# base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS), +# random_state=RANDOM_SEED)), +# regression(linear_model.Ridge(random_state=RANDOM_SEED)), +# regression(linear_model.RidgeCV()), +# regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)), +# regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)), # Statsmodels Linear Regression - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(fit_constrained=dict(constraints=(np.eye( - utils.get_binary_classification_model_trainer() - .X_train.shape[-1])[0], [1]))))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - family=sm.families.Binomial( - sm.families.links.cloglog())), - fit=dict(maxiter=2)))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - family=sm.families.Binomial( - sm.families.links.logit())), - fit=dict(maxiter=2)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.Gaussian( - sm.families.links.identity()))))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.Gaussian( - sm.families.links.inverse_power()))))), - regression_bounded(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - family=sm.families.InverseGaussian( - sm.families.links.inverse_squared()))))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.NegativeBinomial( - sm.families.links.nbinom())), - fit=dict(maxiter=2)))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.Poisson( - sm.families.links.log())), - fit=dict(maxiter=2)))), - classification_binary(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.Poisson( - sm.families.links.sqrt())), - fit=dict(maxiter=2)))), - regression_bounded(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - family=sm.families.Tweedie( - sm.families.links.Power(-3)))))), - regression_bounded(utils.StatsmodelsSklearnLikeWrapper( - sm.GLM, - dict(init=dict( - fit_intercept=True, family=sm.families.Tweedie( - sm.families.links.Power(2)))))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLS, - dict(init=dict(sigma=np.eye( - len(utils.get_regression_model_trainer().y_train)) + 1)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLS, - dict(init=dict(sigma=np.eye( - len(utils.get_regression_model_trainer().y_train)) + 1), - fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLSAR, - dict(init=dict(fit_intercept=True, rho=3)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLSAR, - dict(iterative_fit=dict(maxiter=2)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.GLSAR, - dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.OLS, - dict(init=dict(fit_intercept=True)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.OLS, - dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), - regression(utils.StatsmodelsSklearnLikeWrapper( - ProcessMLE, - dict(init=dict(exog_scale=np.ones( - (len(utils.get_regression_model_trainer().y_train), 2)), - exog_smooth=np.ones( - (len(utils.get_regression_model_trainer().y_train), 2)), - exog_noise=np.ones( - (len(utils.get_regression_model_trainer().y_train), 2)), - time=np.kron( - np.ones( - len(utils.get_regression_model_trainer().y_train) // 3), - np.arange(3)), - groups=np.kron( - np.arange( - len(utils.get_regression_model_trainer().y_train) // 3), - np.ones(3))), - fit=dict(maxiter=2)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.QuantReg, - dict(init=dict(fit_intercept=True)))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.WLS, - dict(init=dict(fit_intercept=True, weights=np.arange( - len(utils.get_regression_model_trainer().y_train)))))), - regression(utils.StatsmodelsSklearnLikeWrapper( - sm.WLS, - dict(init=dict(fit_intercept=True, weights=np.arange( - len(utils.get_regression_model_trainer().y_train))), - fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(fit_constrained=dict(constraints=(np.eye( +# utils.get_binary_classification_model_trainer() +# .X_train.shape[-1])[0], [1]))))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# family=sm.families.Binomial( +# sm.families.links.cloglog())), +# fit=dict(maxiter=2)))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# family=sm.families.Binomial( +# sm.families.links.logit())), +# fit=dict(maxiter=2)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.Gaussian( +# sm.families.links.identity()))))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.Gaussian( +# sm.families.links.inverse_power()))))), +# regression_bounded(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# family=sm.families.InverseGaussian( +# sm.families.links.inverse_squared()))))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.NegativeBinomial( +# sm.families.links.nbinom())), +# fit=dict(maxiter=2)))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.Poisson( +# sm.families.links.log())), +# fit=dict(maxiter=2)))), +# classification_binary(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.Poisson( +# sm.families.links.sqrt())), +# fit=dict(maxiter=2)))), +# regression_bounded(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# family=sm.families.Tweedie( +# sm.families.links.Power(-3)))))), +# regression_bounded(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLM, +# dict(init=dict( +# fit_intercept=True, family=sm.families.Tweedie( +# sm.families.links.Power(2)))))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLS, +# dict(init=dict(sigma=np.eye( +# len(utils.get_regression_model_trainer().y_train)) + 1)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLS, +# dict(init=dict(sigma=np.eye( +# len(utils.get_regression_model_trainer().y_train)) + 1), +# fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLSAR, +# dict(init=dict(fit_intercept=True, rho=3)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLSAR, +# dict(iterative_fit=dict(maxiter=2)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.GLSAR, +# dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.OLS, +# dict(init=dict(fit_intercept=True)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.OLS, +# dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# ProcessMLE, +# dict(init=dict(exog_scale=np.ones( +# (len(utils.get_regression_model_trainer().y_train), 2)), +# exog_smooth=np.ones( +# (len(utils.get_regression_model_trainer().y_train), 2)), +# exog_noise=np.ones( +# (len(utils.get_regression_model_trainer().y_train), 2)), +# time=np.kron( +# np.ones( +# len(utils.get_regression_model_trainer().y_train) // 3), +# np.arange(3)), +# groups=np.kron( +# np.arange( +# len(utils.get_regression_model_trainer().y_train) // 3), +# np.ones(3))), +# fit=dict(maxiter=2)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.QuantReg, +# dict(init=dict(fit_intercept=True)))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.WLS, +# dict(init=dict(fit_intercept=True, weights=np.arange( +# len(utils.get_regression_model_trainer().y_train)))))), +# regression(utils.StatsmodelsSklearnLikeWrapper( +# sm.WLS, +# dict(init=dict(fit_intercept=True, weights=np.arange( +# len(utils.get_regression_model_trainer().y_train))), +# fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))), # Lightning Linear Regression - regression(light_reg.AdaGradRegressor(random_state=RANDOM_SEED)), - regression(light_reg.CDRegressor(random_state=RANDOM_SEED)), - regression(light_reg.FistaRegressor()), - regression(light_reg.SAGARegressor(random_state=RANDOM_SEED)), - regression(light_reg.SAGRegressor(random_state=RANDOM_SEED)), - regression(light_reg.SDCARegressor(random_state=RANDOM_SEED)), +# regression(light_reg.AdaGradRegressor(random_state=RANDOM_SEED)), +# regression(light_reg.CDRegressor(random_state=RANDOM_SEED)), +# regression(light_reg.FistaRegressor()), +# regression(light_reg.SAGARegressor(random_state=RANDOM_SEED)), +# regression(light_reg.SAGRegressor(random_state=RANDOM_SEED)), +# regression(light_reg.SDCARegressor(random_state=RANDOM_SEED)), # Sklearn Linear Classifiers - classification(linear_model.LogisticRegression( - random_state=RANDOM_SEED)), - classification(linear_model.LogisticRegressionCV( - random_state=RANDOM_SEED)), - classification(linear_model.PassiveAggressiveClassifier( - random_state=RANDOM_SEED)), - classification(linear_model.Perceptron( - random_state=RANDOM_SEED)), - classification(linear_model.RidgeClassifier( - random_state=RANDOM_SEED)), - classification(linear_model.RidgeClassifierCV()), - classification(linear_model.SGDClassifier( - random_state=RANDOM_SEED)), - - classification_binary(linear_model.LogisticRegression( - random_state=RANDOM_SEED)), - classification_binary(linear_model.LogisticRegressionCV( - random_state=RANDOM_SEED)), - classification_binary(linear_model.PassiveAggressiveClassifier( - random_state=RANDOM_SEED)), - classification_binary(linear_model.Perceptron( - random_state=RANDOM_SEED)), - classification_binary(linear_model.RidgeClassifier( - random_state=RANDOM_SEED)), - classification_binary(linear_model.RidgeClassifierCV()), - classification_binary(linear_model.SGDClassifier( - random_state=RANDOM_SEED)), +# classification(linear_model.LogisticRegression( +# random_state=RANDOM_SEED)), +# classification(linear_model.LogisticRegressionCV( +# random_state=RANDOM_SEED)), +# classification(linear_model.PassiveAggressiveClassifier( +# random_state=RANDOM_SEED)), +# classification(linear_model.Perceptron( +# random_state=RANDOM_SEED)), +# classification(linear_model.RidgeClassifier( +# random_state=RANDOM_SEED)), +# classification(linear_model.RidgeClassifierCV()), +# classification(linear_model.SGDClassifier( +# random_state=RANDOM_SEED)), + +# classification_binary(linear_model.LogisticRegression( +# random_state=RANDOM_SEED)), +# classification_binary(linear_model.LogisticRegressionCV( +# random_state=RANDOM_SEED)), +# classification_binary(linear_model.PassiveAggressiveClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(linear_model.Perceptron( +# random_state=RANDOM_SEED)), +# classification_binary(linear_model.RidgeClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(linear_model.RidgeClassifierCV()), +# classification_binary(linear_model.SGDClassifier( +# random_state=RANDOM_SEED)), # Lightning Linear Classifiers - classification(light_clf.AdaGradClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.CDClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.CDClassifier( - penalty="l1/l2", multiclass=True, random_state=RANDOM_SEED)), - classification(light_clf.FistaClassifier()), - classification(light_clf.FistaClassifier(multiclass=True)), - classification(light_clf.SAGAClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.SAGClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.SDCAClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.SGDClassifier( - random_state=RANDOM_SEED)), - classification(light_clf.SGDClassifier( - multiclass=True, random_state=RANDOM_SEED)), - - classification_binary(light_clf.AdaGradClassifier( - random_state=RANDOM_SEED)), - classification_binary(light_clf.CDClassifier( - random_state=RANDOM_SEED)), - classification_binary(light_clf.FistaClassifier()), - classification_binary(light_clf.SAGAClassifier( - random_state=RANDOM_SEED)), - classification_binary(light_clf.SAGClassifier( - random_state=RANDOM_SEED)), - classification_binary(light_clf.SDCAClassifier( - random_state=RANDOM_SEED)), - classification_binary(light_clf.SGDClassifier( - random_state=RANDOM_SEED)), +# classification(light_clf.AdaGradClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.CDClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.CDClassifier( +# penalty="l1/l2", multiclass=True, random_state=RANDOM_SEED)), +# classification(light_clf.FistaClassifier()), +# classification(light_clf.FistaClassifier(multiclass=True)), +# classification(light_clf.SAGAClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.SAGClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.SDCAClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.SGDClassifier( +# random_state=RANDOM_SEED)), +# classification(light_clf.SGDClassifier( +# multiclass=True, random_state=RANDOM_SEED)), + +# classification_binary(light_clf.AdaGradClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(light_clf.CDClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(light_clf.FistaClassifier()), +# classification_binary(light_clf.SAGAClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(light_clf.SAGClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(light_clf.SDCAClassifier( +# random_state=RANDOM_SEED)), +# classification_binary(light_clf.SGDClassifier( +# random_state=RANDOM_SEED)), # Decision trees - regression(tree.DecisionTreeRegressor(**TREE_PARAMS)), - regression(tree.ExtraTreeRegressor(**TREE_PARAMS)), +# regression(tree.DecisionTreeRegressor(**TREE_PARAMS)), +# regression(tree.ExtraTreeRegressor(**TREE_PARAMS)), - classification(tree.DecisionTreeClassifier(**TREE_PARAMS)), - classification(tree.ExtraTreeClassifier(**TREE_PARAMS)), +# classification(tree.DecisionTreeClassifier(**TREE_PARAMS)), +# classification(tree.ExtraTreeClassifier(**TREE_PARAMS)), - classification_binary(tree.DecisionTreeClassifier(**TREE_PARAMS)), - classification_binary(tree.ExtraTreeClassifier(**TREE_PARAMS)), +# classification_binary(tree.DecisionTreeClassifier(**TREE_PARAMS)), +# classification_binary(tree.ExtraTreeClassifier(**TREE_PARAMS)), # Random forest - regression(ensemble.ExtraTreesRegressor(**FOREST_PARAMS)), - regression(ensemble.RandomForestRegressor(**FOREST_PARAMS)), +# regression(ensemble.ExtraTreesRegressor(**FOREST_PARAMS)), +# regression(ensemble.RandomForestRegressor(**FOREST_PARAMS)), - classification(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)), - classification(ensemble.RandomForestClassifier(**FOREST_PARAMS)), +# classification(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)), +# classification(ensemble.RandomForestClassifier(**FOREST_PARAMS)), - classification_binary(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)), - classification_binary( - ensemble.RandomForestClassifier(**FOREST_PARAMS)), +# classification_binary(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)), +# classification_binary( +# ensemble.RandomForestClassifier(**FOREST_PARAMS)), ], # Following is the list of extra tests for languages/models which are