Merge pull request #44 from PySATL/api/renaming-of-statistical-procedures

Api/renaming of statistical procedures

Author: plidan123

README.md

@@ -120,7 +120,7 @@ Computes given integral with the limits of integration being 0 and 1 using Rando
 
 ### Usage:
 ```Python
-from src.algorithms.support_algorithms.rqmc import RQMC
+from src.procedures.support.rqmc import RQMC
 rqmc = RQMC(lambda x: x**3 - x**2 + 1, error_tolerance=1e-5)
 ```
 So `rqmc()[0]` is estimated integral value and `rqmc()[1]` is current error tolerance.

src/algorithms/__init__.py

@@ -2,7 +2,7 @@
 
 from numpy import _typing
 
-from src.algorithms import ALGORITHM_REGISTRY
+from src.procedures import ALGORITHM_REGISTRY
 from src.estimators.estimate_result import EstimateResult
 from src.register.algorithm_purpose import AlgorithmPurpose
 
@@ -14,7 +14,7 @@ class AbstractEstimator:
         algorithm_name: A string indicating chosen algorithm.
         params: A dictionary of algorithm parameters.
         estimate_result: Estimation result.
-        _registry: Registry that contains classes of all algorithms.
+        _registry: Registry that contains classes of all procedures.
         _purpose: Defines purpose of algorithm, one of the registry key.
     """
 
@@ -47,7 +47,7 @@ def set_params(self, algorithm_name: str, params: dict | None = None) -> Self:
         return self
 
     def get_available_algorithms(self) -> list[str]:
-        """Get all algorithms that can be used for current estimator class"""
+        """Get all procedures that can be used for current estimator class"""
         return [key[0] for key in self._registry.register_of_names.keys() if key[1] == self._purpose]
 
     def estimate(self, sample: _typing.ArrayLike) -> EstimateResult:

src/estimators/abstract_estimator.py

@@ -4,7 +4,7 @@
 from src.estimators.estimate_result import EstimateResult
 
 
-class AbstractSemiParametricEstimator(AbstractEstimator):
+class AbstractSemiparEstim(AbstractEstimator):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
 

src/estimators/semiparametric/abstract_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NMSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NMSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NM_SEMIPARAMETRIC

src/estimators/semiparametric/nm_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NMVSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NMVSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NMV_SEMIPARAMETRIC

src/estimators/semiparametric/nmv_semiparametric_estimator.py

@@ -1,11 +1,11 @@
 from numpy import _typing
 
 from src.estimators.estimate_result import EstimateResult
-from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiParametricEstimator
+from src.estimators.semiparametric.abstract_semiparametric_estimator import AbstractSemiparEstim
 from src.register.algorithm_purpose import AlgorithmPurpose
 
 
-class NVSemiParametricEstimator(AbstractSemiParametricEstimator):
+class NVSemiparEstim(AbstractSemiparEstim):
     def __init__(self, algorithm_name: str, params: dict | None = None) -> None:
         super().__init__(algorithm_name, params)
         self._purpose = AlgorithmPurpose.NV_SEMIPARAMETRIC

src/estimators/semiparametric/nv_semiparametric_estimator.py

@@ -7,8 +7,8 @@
 from scipy.stats import rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.integrator import Integrator
-from src.algorithms.support_algorithms.rqmc import RQMCIntegrator  # default integrator
+from src.procedures.support.integrator import Integrator
+from src.procedures.support.rqmc import RQMCIntegrator  # default integrator
 
 class AbstractMixtures(metaclass=ABCMeta):
     """Base class for Mixtures"""

src/mixtures/abstract_mixture.py

@@ -6,8 +6,9 @@
 from scipy.stats import norm, rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.integrator import Integrator
-from src.algorithms.support_algorithms.rqmc import RQMCIntegrator
+from src.procedures.support.integrator import Integrator
+from src.procedures.support.rqmc import RQMCIntegrator
+from src.procedures.support.log_rqmc import LogRQMC
 from src.mixtures.abstract_mixture import AbstractMixtures
 
 @dataclass

src/mixtures/nm_mixture.py

@@ -7,9 +7,9 @@
 from scipy.stats import norm, rv_continuous
 from scipy.stats.distributions import rv_frozen
 
-from src.algorithms.support_algorithms.integrator import Integrator
-from src.algorithms.support_algorithms.rqmc import RQMCIntegrator
-from src.algorithms.support_algorithms.log_rqmc import LogRQMC
+from src.procedures.support.integrator import Integrator
+from src.procedures.support.rqmc import RQMCIntegrator
+from src.procedures.support.log_rqmc import LogRQMC
 from src.mixtures.abstract_mixture import AbstractMixtures
 
 @dataclass