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Added solver to feols and created new test file for it (#513)
* Added solver to feols and created new test file for it * Updated test file and feos class with new solver * Adding solver to feiv and fepois, and upding test_solver file * update tests * move solve_ols to Feols class, delete in other methods; add docstrings * fix tests except for Attribute error Fepois has no attribute _N * Update fepois_.py add reshape to delta_new * fix tests * add scipy.sparse.linalg.lsqr solver --------- Co-authored-by: Alexander Fischer <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,165 @@ | ||
| import numpy as np | ||
| import pytest | ||
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| import pyfixest as pf | ||
| from pyfixest.estimation.feiv_ import Feiv | ||
| from pyfixest.estimation.feols_ import Feols | ||
| from pyfixest.estimation.fepois_ import Fepois | ||
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| @pytest.fixture() | ||
| def data_feols(): | ||
| data = pf.get_data().dropna() | ||
| Y = data["Y"].to_numpy().reshape((-1, 1)) | ||
| X = data[["X1", "X2"]].to_numpy() | ||
| Z = data[["Z1", "X2"]].to_numpy() | ||
| weights = ( | ||
| data["weights"].to_numpy().reshape((-1, 1)) | ||
| ) # Define the variable "weights" | ||
| return X, Y, Z, weights | ||
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| @pytest.fixture() | ||
| def data_fepois(): | ||
| data = pf.get_data(model="Fepois").dropna() | ||
| Y = data["Y"].to_numpy().reshape((-1, 1)) | ||
| X = data[["X1", "X2"]].to_numpy() | ||
| weights = ( | ||
| data["weights"].to_numpy().reshape((-1, 1)) | ||
| ) # Define the variable "weights" | ||
| return X, Y, weights | ||
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| def test_solver_fepois(data_fepois): | ||
| """ | ||
| Test the equivalence of different solvers for the fepois class. | ||
| This function initializes an object with test data and compares the results | ||
| obtained from two different solvers: np.linalg.lstsq | ||
| and np.linalg.solve. It asserts that | ||
| the results are identical or very close within a tolerance. | ||
| """ | ||
| X, Y, weights = data_fepois | ||
| obj = Fepois( | ||
| X=X, | ||
| Y=Y, | ||
| weights=weights, | ||
| coefnames=["X1", "X2"], | ||
| collin_tol=1e-08, | ||
| weights_name="weights", | ||
| weights_type="aweights", | ||
| solver="np.linalg.solve", | ||
| fe=None, | ||
| drop_singletons=False, | ||
| ) | ||
| # Use np.linalg.lstsq solver | ||
| obj._solver = "np.linalg.lstsq" | ||
| obj.get_fit() | ||
| beta_hat_lstsq = obj._beta_hat.copy() | ||
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| # Use np.linalg.solve solver | ||
| X, Y, weights = data_fepois | ||
| obj = Fepois( | ||
| X=X, | ||
| Y=Y, | ||
| weights=weights, | ||
| coefnames=["X1", "X2"], | ||
| collin_tol=1e-08, | ||
| weights_name="weights", | ||
| weights_type="aweights", | ||
| solver="np.linalg.solve", | ||
| fe=None, | ||
| drop_singletons=False, | ||
| ) | ||
| obj._solver = "np.linalg.solve" | ||
| obj.get_fit() | ||
| beta_hat_solve = obj._beta_hat.copy() | ||
| # Assert that the results are identical or very close within a tolerance | ||
| np.testing.assert_array_almost_equal( | ||
| beta_hat_lstsq, beta_hat_solve, decimal=6, err_msg="Solvers' results differ" | ||
| ) | ||
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| def test_solver_feiv(data_feols): | ||
| """ | ||
| Test the equivalence of different solvers for the feiv class. | ||
| This function initializes an object with test data and compares the results | ||
| obtained from two different solvers: np.linalg.lstsq | ||
| and np.linalg.solve. It asserts that | ||
| the results are identical or very close within a tolerance. | ||
| """ | ||
| X, Y, Z, weights = data_feols | ||
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| obj = Feiv( | ||
| Y=Y, | ||
| X=X, | ||
| Z=Z, | ||
| endogvar=X[:, 0].reshape((-1, 1)), | ||
| weights=weights, | ||
| coefnames_x=["X1", "X2"], | ||
| collin_tol=1e-08, | ||
| weights_name="weights", | ||
| weights_type="aweights", | ||
| solver="np.linalg.solve", | ||
| coefnames_z=["Z1"], | ||
| ) | ||
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| # Use np.linalg.lstsq solver | ||
| obj._solver = "np.linalg.lstsq" | ||
| obj.get_fit() | ||
| beta_hat_lstsq = obj._beta_hat.copy() | ||
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| # Use np.linalg.solve solver | ||
| obj._solver = "np.linalg.solve" | ||
| obj.get_fit() | ||
| beta_hat_solve = obj._beta_hat.copy() | ||
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| # Assert that the results are identical or very close within a tolerance | ||
| np.testing.assert_array_almost_equal( | ||
| beta_hat_lstsq, beta_hat_solve, decimal=6, err_msg="Solvers' results differ" | ||
| ) | ||
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| def test_solver_feols(data_feols): | ||
| """ | ||
| Test the equivalence of different solvers for the feols class. | ||
| This function initializes an object with test data and compares the results | ||
| obtained from two different solvers: np.linalg.lstsq | ||
| and np.linalg.solve. It asserts that | ||
| the results are identical or very close within a tolerance. | ||
| """ | ||
| X, Y, _, weights = data_feols | ||
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| obj = Feols( | ||
| X=X, | ||
| Y=Y, | ||
| weights=weights, | ||
| collin_tol=1e-08, | ||
| coefnames=["X1", "X2"], | ||
| weights_name="weights", | ||
| weights_type="aweights", | ||
| solver="np.linalg.solve", | ||
| ) | ||
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| # Use np.linalg.lstsq solver | ||
| obj._solver = "np.linalg.lstsq" | ||
| obj.get_fit() | ||
| beta_hat_lstsq = obj._beta_hat.copy() | ||
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| # Use np.linalg.solve solver | ||
| obj._solver = "np.linalg.solve" | ||
| obj.get_fit() | ||
| beta_hat_solve = obj._beta_hat.copy() | ||
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| # scipy.sparse.linalg.lsqr solver | ||
| obj._solver = "scipy.sparse.linalg.lsqr" | ||
| obj.get_fit() | ||
| beta_hat_lsqr = obj._beta_hat.copy() | ||
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| # Assert that the results are identical or very close within a tolerance | ||
| np.testing.assert_array_almost_equal( | ||
| beta_hat_lstsq, beta_hat_solve, decimal=6, err_msg="Solvers' results differ" | ||
| ) | ||
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| np.testing.assert_array_almost_equal( | ||
| beta_hat_lstsq, beta_hat_lsqr, decimal=6, err_msg="Solvers' results differ" | ||
| ) |