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АнгелинаАнгелина
Ангелина
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refactor: update tests
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9 files changed

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‎tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_eigenvalue_based.py‎

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -4,7 +4,7 @@
44
import pytest
55
from scipy.stats import expon, uniform
66

7-
from src.estimators.semiparametric.nm_semiparametric_estimator import NMSemiParametricEstimator
7+
from src.estimators.semiparametric.nm_semiparametric_estimator import NMSemiparEstim
88
from src.generators.nm_generator import NMGenerator
99
from src.mixtures.nm_mixture import NormalMeanMixtures
1010

@@ -28,7 +28,7 @@ def test_sigma_estimation_eigenvalue_based_expon_single(
2828
* (np.log(np.log(sample_len))) ** 0.6
2929
)
3030

31-
estimator = NMSemiParametricEstimator(
31+
estimator = NMSemiparEstim(
3232
"sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
3333
)
3434
est = estimator.estimate(sample)
@@ -54,7 +54,7 @@ def test_sigma_estimation_eigenvalue_based_expon_1(
5454
* (np.log(np.log(sample_len))) ** 0.6
5555
)
5656

57-
estimator = NMSemiParametricEstimator(
57+
estimator = NMSemiparEstim(
5858
"sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
5959
)
6060
est = estimator.estimate(sample)
@@ -80,7 +80,7 @@ def test_sigma_estimation_eigenvalue_based_expon_2(
8080
* (np.log(np.log(sample_len))) ** 0.6
8181
)
8282

83-
estimator = NMSemiParametricEstimator(
83+
estimator = NMSemiparEstim(
8484
"sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
8585
)
8686
est = estimator.estimate(sample)
@@ -106,7 +106,7 @@ def test_sigma_estimation_eigenvalue_based_expon_3(
106106
* (np.log(np.log(sample_len))) ** 0.6
107107
)
108108

109-
estimator = NMSemiParametricEstimator(
109+
estimator = NMSemiparEstim(
110110
"sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
111111
)
112112
est = estimator.estimate(sample)
@@ -131,7 +131,7 @@ def test_sigma_estimation_eigenvalue_based_uniform(
131131
* (np.log(np.log(sample_len))) ** 0.6
132132
)
133133

134-
estimator = NMSemiParametricEstimator(
134+
estimator = NMSemiparEstim(
135135
"sigma_estimation_eigenvalue", {"k": k, "l": l, "eps": eps, "search_area": search_area}
136136
)
137137
est = estimator.estimate(sample)

‎tests/algorithms/nm_algorithms/semiparametric_sigma_estimation/test_sigma_estimation_empirical.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
import pytest
44
from scipy.stats import expon, uniform
55

6-
from src.estimators.semiparametric.nm_semiparametric_estimator import NMSemiParametricEstimator
6+
from src.estimators.semiparametric.nm_semiparametric_estimator import NMSemiparEstim
77
from src.generators.nm_generator import NMGenerator
88
from src.mixtures.nm_mixture import NormalMeanMixtures
99

@@ -20,7 +20,7 @@ def test_sigma_estimation_empirical_expon(self, real_sigma: float, sample_len: i
2020
answer_list = []
2121
for alpha in [round(x, 5) for x in [i * 0.0001 for i in range(1, 10000)]]:
2222
t = math.sqrt(alpha * math.log(sample_len)) / (2 * search_area)
23-
estimator = NMSemiParametricEstimator("sigma_estimation_laplace", {"t": t})
23+
estimator = NMSemiparEstim("sigma_estimation_laplace", {"t": t})
2424
est = estimator.estimate(sample)
2525
left = (est.value**2 - real_sigma**2) ** 0.5
2626
right = (
@@ -39,7 +39,7 @@ def test_sigma_estimation_empirical_uniform(self, real_sigma: float, sample_len:
3939
M = 10
4040
for alpha in [round(x, 4) for x in [i * 0.0001 for i in range(1, 10000)]]:
4141
t = math.sqrt(alpha * math.log(sample_len)) / (2 * search_area)
42-
estimator = NMSemiParametricEstimator("sigma_estimation_laplace", {"t": t})
42+
estimator = NMSemiparEstim("sigma_estimation_laplace", {"t": t})
4343
est = estimator.estimate(sample)
4444
left = abs(est.value**2 - real_sigma**2) ** 0.5
4545
right = 4 * M * search_area / math.sqrt(alpha * math.log(sample_len))

‎tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_g_estimation_given_mu.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
import pytest
44
from scipy.stats import expon, gamma
55

6-
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiParametricEstimator
6+
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiparEstim
77
from src.generators.nmv_generator import NMVGenerator
88
from src.mixtures.nmv_mixture import NormalMeanVarianceMixtures
99

@@ -18,7 +18,7 @@ def test_g_estimation_expon(self, x_data) -> None:
1818

1919
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=given_mu, distribution=expon)
2020
sample = NMVGenerator().canonical_generate(mixture, n)
21-
estimator = NMVSemiParametricEstimator(
21+
estimator = NMVSemiparEstim(
2222
"density_estim_inv_mellin_quad_int", {"x_data": x_data, "u_value": 7.6, "v_value": 0.9}
2323
)
2424
est = estimator.estimate(sample)

‎tests/algorithms/nmv_algorithms/semiparametric_g_estimation/test_post_widder.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
from mpmath import ln
44
from scipy.stats import expon, gamma
55

6-
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiParametricEstimator
6+
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiparEstim
77
from src.generators.nmv_generator import NMVGenerator
88
from src.mixtures.nmv_mixture import NormalMeanVarianceMixtures
99

@@ -20,7 +20,7 @@ def test_post_widder_expon(self, mu, sigma, degree, sample_size) -> None:
2020
sample = NMVGenerator().classical_generate(mixture, sample_size)
2121
x_data = np.linspace(0.5, 10.0, 30)
2222

23-
estimator = NMVSemiParametricEstimator(
23+
estimator = NMVSemiparEstim(
2424
"density_estim_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
2525
)
2626
est = estimator.estimate(sample)
@@ -43,7 +43,7 @@ def test_post_widder_gamma(self, mu, sigma, degree, sample_size, a) -> None:
4343
sample = NMVGenerator().classical_generate(mixture, sample_size)
4444
x_data = np.linspace(0.5, 10.0, 30)
4545

46-
estimator = NMVSemiParametricEstimator(
46+
estimator = NMVSemiparEstim(
4747
"density_estim_post_widder", {"x_data": x_data, "mu": mu, "sigma": sigma, "n": degree}
4848
)
4949
est = estimator.estimate(sample)

‎tests/algorithms/nmv_algorithms/semiparametric_mu_estimation/test_semiparametric_mu_estimation.py‎

Lines changed: 17 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
import pytest
44
from scipy.stats import expon, gamma, halfnorm, pareto
55

6-
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiParametricEstimator
6+
from src.estimators.semiparametric.nmv_semiparametric_estimator import NMVSemiparEstim
77
from src.generators.nmv_generator import NMVGenerator
88
from src.mixtures.nmv_mixture import NormalMeanVarianceMixtures
99

@@ -15,47 +15,47 @@ class TestSemiParametricMuEstimation:
1515
def test_mu_estimation_expon_no_parameters(self, real_mu: float) -> None:
1616
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
1717
sample = self.generator.canonical_generate(mixture, 10000)
18-
estimator = NMVSemiParametricEstimator("mu_estimation")
18+
estimator = NMVSemiparEstim("mu_estimation")
1919
est = estimator.estimate(sample)
2020
assert abs(real_mu - est.value) < 1 and est.success is True
2121

2222
@pytest.mark.parametrize("real_mu", [10**i for i in range(0, -10, -2)])
2323
def test_mu_estimation_expon_no_parameters_small(self, real_mu: float) -> None:
2424
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
2525
sample = self.generator.canonical_generate(mixture, 10000)
26-
estimator = NMVSemiParametricEstimator("mu_estimation")
26+
estimator = NMVSemiparEstim("mu_estimation")
2727
est = estimator.estimate(sample)
2828
assert abs(real_mu - est.value) < 1 and est.success is True
2929

3030
@pytest.mark.parametrize("real_mu", [i for i in range(-3, 3)])
3131
def test_mu_estimation_pareto_no_parameters(self, real_mu: float) -> None:
3232
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=pareto(2.62))
3333
sample = self.generator.canonical_generate(mixture, 50000)
34-
estimator = NMVSemiParametricEstimator("mu_estimation")
34+
estimator = NMVSemiparEstim("mu_estimation")
3535
est = estimator.estimate(sample)
3636
assert abs(real_mu - est.value) < 1 and est.success is True
3737

3838
@pytest.mark.parametrize("real_mu", [10**i for i in range(0, -10, -2)])
3939
def test_mu_estimation_pareto_no_parameters_small(self, real_mu: float) -> None:
4040
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=pareto(2.62))
4141
sample = self.generator.canonical_generate(mixture, 50000)
42-
estimator = NMVSemiParametricEstimator("mu_estimation")
42+
estimator = NMVSemiparEstim("mu_estimation")
4343
est = estimator.estimate(sample)
4444
assert abs(real_mu - est.value) < 1 and est.success is True
4545

4646
@pytest.mark.parametrize("real_mu", [i for i in range(-3, 3)])
4747
def test_mu_estimation_halfnorm_no_parameters(self, real_mu: float) -> None:
4848
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=halfnorm)
4949
sample = self.generator.canonical_generate(mixture, 10000)
50-
estimator = NMVSemiParametricEstimator("mu_estimation")
50+
estimator = NMVSemiparEstim("mu_estimation")
5151
est = estimator.estimate(sample)
5252
assert abs(real_mu - est.value) < 1 and est.success is True
5353

5454
@pytest.mark.parametrize("real_mu", [i for i in range(-3, 3)])
5555
def test_mu_estimation_gamma_no_parameters(self, real_mu: float) -> None:
5656
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=gamma(2))
5757
sample = self.generator.canonical_generate(mixture, 10000)
58-
estimator = NMVSemiParametricEstimator("mu_estimation")
58+
estimator = NMVSemiparEstim("mu_estimation")
5959
est = estimator.estimate(sample)
6060
assert abs(real_mu - est.value) < 1 and est.success is True
6161

@@ -64,7 +64,7 @@ def test_mu_estimation_expon_1_parameter_m_positive(self, params: dict) -> None:
6464
real_mu = 1
6565
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
6666
sample = self.generator.canonical_generate(mixture, 10000)
67-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
67+
estimator = NMVSemiparEstim("mu_estimation", params)
6868
est = estimator.estimate(sample)
6969
assert abs(real_mu - est.value) < 1 and est.success is True
7070

@@ -73,7 +73,7 @@ def test_mu_estimation_expon_1_parameter_m_negative(self, params: dict) -> None:
7373
real_mu = -1
7474
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
7575
sample = self.generator.canonical_generate(mixture, 10000)
76-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
76+
estimator = NMVSemiparEstim("mu_estimation", params)
7777
est = estimator.estimate(sample)
7878
assert abs(real_mu - est.value) < 1 and est.success is True
7979

@@ -84,7 +84,7 @@ def test_mu_estimation_expon_1_parameter_max_iterations(self, params: dict) -> N
8484
real_mu = 1
8585
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
8686
sample = self.generator.canonical_generate(mixture, 10000)
87-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
87+
estimator = NMVSemiparEstim("mu_estimation", params)
8888
est = estimator.estimate(sample)
8989
assert abs(real_mu - est.value) < 1 and est.success is True
9090

@@ -93,7 +93,7 @@ def test_mu_estimation_expon_1_parameter_m_is_best_estimation(self, params: dict
9393
real_mu = 10
9494
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
9595
sample = self.generator.canonical_generate(mixture, 10000)
96-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
96+
estimator = NMVSemiparEstim("mu_estimation", params)
9797
est = estimator.estimate(sample)
9898
assert abs(est.value == params["m"]) and est.success is False
9999

@@ -104,7 +104,7 @@ def test_mu_estimation_expon_2_parameters_tol_positive(self, params: dict) -> No
104104
real_mu = 1
105105
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
106106
sample = self.generator.canonical_generate(mixture, 10000)
107-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
107+
estimator = NMVSemiparEstim("mu_estimation", params)
108108
est = estimator.estimate(sample)
109109
assert abs(real_mu - est.value) < 1 and est.success is True
110110

@@ -115,7 +115,7 @@ def test_mu_estimation_expon_2_parameters_tol_negative(self, params: dict) -> No
115115
real_mu = -1
116116
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
117117
sample = self.generator.canonical_generate(mixture, 10000)
118-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
118+
estimator = NMVSemiparEstim("mu_estimation", params)
119119
est = estimator.estimate(sample)
120120
assert abs(real_mu - est.value) < 1 and est.success is True
121121

@@ -132,7 +132,7 @@ def test_mu_estimation_expon_3_parameters_omega_positive(self, params: dict) ->
132132
real_mu = 1
133133
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
134134
sample = self.generator.canonical_generate(mixture, 10000)
135-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
135+
estimator = NMVSemiparEstim("mu_estimation", params)
136136
est = estimator.estimate(sample)
137137
assert abs(real_mu - est.value) < 1 and est.success is True
138138

@@ -149,7 +149,7 @@ def test_mu_estimation_expon_3_parameters_omega_negative(self, params: dict) ->
149149
real_mu = -1
150150
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
151151
sample = self.generator.canonical_generate(mixture, 10000)
152-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
152+
estimator = NMVSemiparEstim("mu_estimation", params)
153153
est = estimator.estimate(sample)
154154
assert abs(real_mu - est.value) < 1 and est.success is True
155155

@@ -168,7 +168,7 @@ def test_mu_estimation_expon_3_parameters_all_positive(self, params: dict) -> No
168168
real_mu = 1
169169
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
170170
sample = self.generator.canonical_generate(mixture, 10000)
171-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
171+
estimator = NMVSemiparEstim("mu_estimation", params)
172172
est = estimator.estimate(sample)
173173
assert abs(real_mu - est.value) < 1 and est.success is True
174174

@@ -187,6 +187,6 @@ def test_mu_estimation_expon_3_parameters_all_negative(self, params: dict) -> No
187187
real_mu = -1
188188
mixture = NormalMeanVarianceMixtures("canonical", alpha=0, mu=real_mu, distribution=expon)
189189
sample = self.generator.canonical_generate(mixture, 10000)
190-
estimator = NMVSemiParametricEstimator("mu_estimation", params)
190+
estimator = NMVSemiparEstim("mu_estimation", params)
191191
est = estimator.estimate(sample)
192192
assert abs(real_mu - est.value) < 1 and est.success is True

‎tests/algorithms/nmv_algorithms/semiparametric_mu_estimation/test_validate_kwargs.py‎

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
import numpy as np
44
import pytest
55

6-
from src.algorithms.semiparam_algorithms.nvm_semi_param_algorithms.mu_estimation import SemiParametricMuEstimation
6+
from src.procedures.semiparametric.nvm_semi_param_algorithms.mu_estimation import NMVEstimationMu
77

88

99
def _test_omega(x: float) -> float:
@@ -18,20 +18,20 @@ class TestValidateKwargs:
1818
)
1919
def test_set_default_params_len_1_value_error_m(self, params: dict) -> None:
2020
with pytest.raises(ValueError, match="Expected positive integer as parameter m"):
21-
SemiParametricMuEstimation()._validate_kwargs(**params)
21+
NMVEstimationMu()._validate_kwargs(**params)
2222

2323
@pytest.mark.parametrize(
2424
"params",
2525
[{"tolerance": -1}, {"tolerance": -0.5}, {"tolerance": 0}, {"tolerance": []}, {"tolerance": "str"}],
2626
)
2727
def test_set_default_params_len_1_value_error_tolerance(self, params: dict) -> None:
2828
with pytest.raises(ValueError, match="Expected positive float as parameter tolerance"):
29-
SemiParametricMuEstimation()._validate_kwargs(**params)
29+
NMVEstimationMu()._validate_kwargs(**params)
3030

3131
@pytest.mark.parametrize("params", [{"omega": 1}, {"omega": []}, {"omega": "str"}, {"omega": ()}])
3232
def test_set_default_params_len_3_value_error_omega(self, params: dict) -> None:
3333
with pytest.raises(ValueError, match="Expected callable object as parameter omega"):
34-
SemiParametricMuEstimation()._validate_kwargs(**params)
34+
NMVEstimationMu()._validate_kwargs(**params)
3535

3636
@pytest.mark.parametrize(
3737
"params",
@@ -47,7 +47,7 @@ def test_set_default_params_len_3_value_error_omega(self, params: dict) -> None:
4747
)
4848
def test_set_default_params_len_1_value_error_max_iterations(self, params: dict) -> None:
4949
with pytest.raises(ValueError, match="Expected positive integer as parameter max_iterations"):
50-
SemiParametricMuEstimation()._validate_kwargs(**params)
50+
NMVEstimationMu()._validate_kwargs(**params)
5151

5252
@pytest.mark.parametrize(
5353
"params",
@@ -62,7 +62,7 @@ def test_set_default_params_len_1_value_error_max_iterations(self, params: dict)
6262
)
6363
def test_set_default_params_len_2_value_error_m(self, params: dict) -> None:
6464
with pytest.raises(ValueError, match="Expected positive integer as parameter m"):
65-
SemiParametricMuEstimation()._validate_kwargs(**params)
65+
NMVEstimationMu()._validate_kwargs(**params)
6666

6767
@pytest.mark.parametrize(
6868
"params",
@@ -73,7 +73,7 @@ def test_set_default_params_len_2_value_error_m(self, params: dict) -> None:
7373
],
7474
)
7575
def test_set_default_params_len_3_correct(self, params: dict) -> None:
76-
SemiParametricMuEstimation()._validate_kwargs(**params)
76+
NMVEstimationMu()._validate_kwargs(**params)
7777

7878
@pytest.mark.parametrize(
7979
"params",
@@ -84,17 +84,17 @@ def test_set_default_params_len_3_correct(self, params: dict) -> None:
8484
],
8585
)
8686
def test_set_default_params_len_4_correct(self, params: dict) -> None:
87-
SemiParametricMuEstimation()._validate_kwargs(**params)
87+
NMVEstimationMu()._validate_kwargs(**params)
8888

8989
@pytest.mark.parametrize(
9090
"params", [{"m": 100, "tolerance": 1 / 10}, {"m": 1000, "tolerance": 10**-9}, {"m": 1, "tolerance": 1}]
9191
)
9292
def test_set_default_params_len_2_correct(self, params: dict) -> None:
93-
SemiParametricMuEstimation()._validate_kwargs(**params)
93+
NMVEstimationMu()._validate_kwargs(**params)
9494

9595
@pytest.mark.parametrize("params", [{"m": 100}, {"m": 1000}, {"m": 1}])
9696
def test_set_default_params_len_1_correct(self, params: dict) -> None:
97-
SemiParametricMuEstimation()._validate_kwargs(**params)
97+
NMVEstimationMu()._validate_kwargs(**params)
9898

9999
@pytest.mark.parametrize(
100100
"params",
@@ -105,14 +105,14 @@ def test_set_default_params_len_1_correct(self, params: dict) -> None:
105105
],
106106
)
107107
def test_init_set_default_params_len_3_correct(self, params: dict) -> None:
108-
SemiParametricMuEstimation(np.array([1]), **params)
108+
NMVEstimationMu(np.array([1]), **params)
109109

110110
@pytest.mark.parametrize(
111111
"params", [{"m": 100, "tolerance": 1 / 10}, {"m": 1000, "tolerance": 10**-9}, {"m": 1, "tolerance": 1}]
112112
)
113113
def test_init_set_default_params_len_2_correct(self, params: dict) -> None:
114-
SemiParametricMuEstimation(np.array([1]), **params)
114+
NMVEstimationMu(np.array([1]), **params)
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@pytest.mark.parametrize("params", [{"m": 100}, {"m": 1000}, {"m": 1}])
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def test_init_set_default_params_len_1_correct(self, params: dict) -> None:
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SemiParametricMuEstimation(np.array([1]), **params)
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NMVEstimationMu(np.array([1]), **params)

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