renaming models to be less verbose

tfm000 · Nov 4, 2023 · 413cd6d · 413cd6d
1 parent 1544070
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diff --git a/examples/copula_examples/copula_example.py b/examples/copula_examples/copula_example.py
@@ -15,10 +15,10 @@
 my_params = (my_chi, my_psi, my_loc, my_shape, my_gamma)
 
 # generating multivariate hyperbolic random variables
-from sklarpy.multivariate import multivariate_hyperbolic
+from sklarpy.multivariate import mvt_hyperbolic
 
 num_generate: int = 1000
-rvs: np.ndarray = multivariate_hyperbolic.rvs(num_generate, my_params)
+rvs: np.ndarray = mvt_hyperbolic.rvs(num_generate, my_params)
 rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Process A', 'Process B'],
                                     dtype=float)
 

diff --git a/examples/multivariate_examples/multivariate_example.py b/examples/multivariate_examples/multivariate_example.py
@@ -15,18 +15,17 @@
 my_mvn_params: tuple = (my_mu, my_cov)
 
 # generating multivariate random normal variables
-from sklarpy.multivariate import multivariate_normal
+from sklarpy.multivariate import mvt_normal
 
-rvs: np.ndarray = multivariate_normal.rvs(1000, my_mvn_params)
+rvs: np.ndarray = mvt_normal.rvs(1000, my_mvn_params)
 rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Wife Age', 'Husband Age'],
                                     dtype=float)
 
 # fitting a symmetric hyperbolic dist to our generated data using
 # Maximum Likelihood Estimation
-from sklarpy.multivariate import multivariate_sym_hyperbolic
+from sklarpy.multivariate import mvt_shyperbolic
 
-fitted_msh = multivariate_sym_hyperbolic.fit(rvs_df, method='mle',
-                                             show_progress=True)
+fitted_msh = mvt_shyperbolic.fit(rvs_df, method='mle', show_progress=True)
 
 # printing our fitted parameters
 print(fitted_msh.params.to_dict)
@@ -48,7 +47,7 @@
 # distribution of the same type
 from sklarpy import load
 
-loaded_msh_params = load('multivariate_sym_hyperbolic.pickle')
-param_fitted_msh = multivariate_sym_hyperbolic.fit(params=loaded_msh_params)
+loaded_msh_params = load('mvt_shyperbolic.pickle')
+param_fitted_msh = mvt_shyperbolic.fit(params=loaded_msh_params)
 print(param_fitted_msh.params.to_dict)
 param_fitted_msh.pdf_plot(axes_names=rvs_df.columns)
diff --git a/sklarpy/copulas/README.md b/sklarpy/copulas/README.md
@@ -6,23 +6,23 @@ Examples of how to use these models can be found in the `examples` folder.
 
 ## <u> SklarPy Copula Models </u>
 
- | Family         | Name                              | Dimensions   | SklarPy Model                  |
-|----------------|-----------------------------------|--------------|--------------------------------|
-| Normal Mixture | Normal / Gaussian                 | Multivariate | gaussian_copula                | 
-| Normal Mixture | Student-T                         | Multivariate | student_t_copula               |
-| Normal Mixture | Skewed-T                          | Multivariate | skewed_t_copula                |
-| Normal Mixture | Generalized Hyperbolic            | Multivariate | gen_hyperbolic_copula          |
-| Normal Mixture | Symmetric Generalized Hyperbolic  | Multivariate | sym_gen_hyperbolic_copula      |
-| Normal Mixture | Hyperbolic                        | Multivariate | hyperbolic_copula              |
-| Normal Mixture | Symmetric Hyperbolic              | Multivariate | sym_hyperbolic_copula          |
-| Normal Mixture | Normal-Inverse Gaussian (NIG)     | Multivariate | nig_copula                     |
-| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | sym_nig_copula                 |
-| Normal Mixture | Marginal Hyperbolic               | Multivariate | marginal_hyperbolic_copula     |
-| Normal Mixture | Symmetric Marginal Hyperbolic     | Multivariate | sym_marginal_hyperbolic_copula |
-| Archimedean    | Clayton                           | Multivariate | clayton_copula                 |
-| Archimedean    | Gumbel                            | Multivariate | gumbel_copula                  |
-| Archimedean    | Frank                             | Bivariate    | frank_copula                   |
-| Numerical      | Gaussian KDE                      | Multivariate | gaussian_kde_copula            |
+ | Family         | Name                              | Dimensions   | SklarPy Model       |
+|----------------|-----------------------------------|--------------|---------------------|
+| Normal Mixture | Normal / Gaussian                 | Multivariate | gaussian_copula     | 
+| Normal Mixture | Student-T                         | Multivariate | student_t_copula    |
+| Normal Mixture | Skewed-T                          | Multivariate | skewed_t_copula     |
+| Normal Mixture | Generalized Hyperbolic            | Multivariate | gh_copula           |
+| Normal Mixture | Symmetric Generalized Hyperbolic  | Multivariate | sgh_copula          |
+| Normal Mixture | Hyperbolic                        | Multivariate | hyperbolic_copula   |
+| Normal Mixture | Symmetric Hyperbolic              | Multivariate | shyperbolic_copula  |
+| Normal Mixture | Normal-Inverse Gaussian (NIG)     | Multivariate | nig_copula          |
+| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | snig_copula         |
+| Normal Mixture | Marginal Hyperbolic               | Multivariate | mh_copula           |
+| Normal Mixture | Symmetric Marginal Hyperbolic     | Multivariate | smh_copula          |
+| Archimedean    | Clayton                           | Multivariate | clayton_copula      |
+| Archimedean    | Gumbel                            | Multivariate | gumbel_copula       |
+| Archimedean    | Frank                             | Bivariate    | frank_copula        |
+| Numerical      | Gaussian KDE                      | Multivariate | gaussian_kde_copula |
 
 ### Implementation Status
 - [x] Normal Mixture 

diff --git a/sklarpy/copulas/__init__.py b/sklarpy/copulas/__init__.py
@@ -1,8 +1,7 @@
 from sklarpy.copulas.marginal_fitter import MarginalFitter
-from sklarpy.copulas.distributions import gaussian_copula, \
-    gaussian_kde_copula, gen_hyperbolic_copula, marginal_hyperbolic_copula, \
-    hyperbolic_copula, nig_copula, skewed_t_copula, student_t_copula, \
-    sym_gen_hyperbolic_copula, sym_marginal_hyperbolic_copula, \
-    sym_hyperbolic_copula, sym_nig_copula, clayton_copula, gumbel_copula, \
-    frank_copula
+from sklarpy.copulas.distributions import (
+    gaussian_copula, gaussian_kde_copula, gh_copula, mh_copula,
+    hyperbolic_copula, nig_copula, skewed_t_copula, student_t_copula,
+    sgh_copula, smh_copula, shyperbolic_copula, snig_copula, clayton_copula,
+    gumbel_copula, frank_copula)
 from sklarpy.copulas.distributions_map import distributions_map
diff --git a/sklarpy/copulas/distributions.py b/sklarpy/copulas/distributions.py
@@ -1,95 +1,81 @@
 # Contains code for building copula models
-from sklarpy.copulas._distributions._gaussian import \
-    gaussian_copula_gen
-from sklarpy.copulas._distributions._gaussian_kde import \
-    gaussian_kde_copula_gen
-from sklarpy.copulas._distributions._generalized_hyperbolic import \
-    gen_hyperbolic_copula_gen
-from sklarpy.copulas._distributions._hyperbolics import \
-    marginal_hyperbolic_copula_gen, hyperbolic_copula_gen, nig_copula_gen
+from sklarpy.copulas._distributions._gaussian import gaussian_copula_gen
+from sklarpy.copulas._distributions._gaussian_kde import (
+    gaussian_kde_copula_gen)
+from sklarpy.copulas._distributions._generalized_hyperbolic import (
+    gen_hyperbolic_copula_gen)
+from sklarpy.copulas._distributions._hyperbolics import (
+    marginal_hyperbolic_copula_gen, hyperbolic_copula_gen, nig_copula_gen)
 from sklarpy.copulas._distributions._skewed_t import skewed_t_copula_gen
 from sklarpy.copulas._distributions._student_t import student_t_copula_gen
-from sklarpy.copulas._distributions._symmetric_generalized_hyperbolic import \
-    sym_gen_hyperbolic_copula_gen
-from sklarpy.copulas._distributions._symmetric_hyperbolics import \
-    sym_marginal_hyperbolic_copula_gen, sym_hyperbolic_copula_gen, \
-    sym_nig_copula_gen
-from sklarpy.copulas._distributions._archimedean import clayton_copula_gen, \
-    gumbel_copula_gen, frank_copula_gen
-
-from sklarpy.multivariate import multivariate_normal, \
-    multivariate_gaussian_kde, multivariate_gen_hyperbolic, \
-    multivariate_marginal_hyperbolic, multivariate_hyperbolic, \
-    multivariate_nig, multivariate_student_t, multivariate_skewed_t,\
-    multivariate_sym_gen_hyperbolic, multivariate_sym_marginal_hyperbolic, \
-    multivariate_sym_hyperbolic, multivariate_sym_nig
-
-from sklarpy.multivariate.distributions import multivariate_clayton, \
-    multivariate_gumbel, bivariate_frank
-
-__all__ = ['gaussian_copula', 'gaussian_kde_copula', 'gen_hyperbolic_copula',
-           'marginal_hyperbolic_copula', 'hyperbolic_copula', 'nig_copula',
-           'skewed_t_copula', 'student_t_copula', 'sym_gen_hyperbolic_copula',
-           'sym_marginal_hyperbolic_copula', 'sym_hyperbolic_copula',
-           'sym_nig_copula','clayton_copula', 'gumbel_copula', 'frank_copula']
+from sklarpy.copulas._distributions._symmetric_generalized_hyperbolic import (
+    sym_gen_hyperbolic_copula_gen)
+from sklarpy.copulas._distributions._symmetric_hyperbolics import (
+    sym_marginal_hyperbolic_copula_gen, sym_hyperbolic_copula_gen,
+    sym_nig_copula_gen)
+from sklarpy.copulas._distributions._archimedean import (
+    clayton_copula_gen, gumbel_copula_gen, frank_copula_gen)
+
+from sklarpy.multivariate import (
+    mvt_normal, mvt_gaussian_kde, mvt_gh, mvt_mh, mvt_hyperbolic, mvt_nig,
+    mvt_student_t, mvt_skewed_t, mvt_sgh, mvt_smh, mvt_shyperbolic, mvt_snig)
+
+from sklarpy.multivariate.distributions import (
+    mvt_clayton, mvt_gumbel, bvt_frank)
+
+__all__ = ['gaussian_copula', 'gaussian_kde_copula', 'gh_copula', 'mh_copula',
+           'hyperbolic_copula', 'nig_copula', 'skewed_t_copula',
+           'student_t_copula', 'sgh_copula', 'smh_copula',
+           'shyperbolic_copula', 'snig_copula', 'clayton_copula',
+           'gumbel_copula', 'frank_copula']
 
 ###############################################################################
 # Numerical/Non-Parametric
 ###############################################################################
 gaussian_kde_copula: gaussian_kde_copula_gen = \
     gaussian_kde_copula_gen(
-        name="gaussian_kde", mv_object=multivariate_gaussian_kde)
+        name="gaussian_kde", mv_object=mvt_gaussian_kde)
 
 ###############################################################################
 # Parametric
 ###############################################################################
 gaussian_copula: gaussian_copula_gen = \
-    gaussian_copula_gen(name="gaussian", mv_object=multivariate_normal)
+    gaussian_copula_gen(name="gaussian", mv_object=mvt_normal)
 
-gen_hyperbolic_copula: gen_hyperbolic_copula_gen = \
-    gen_hyperbolic_copula_gen(
-        name="gen_hyperbolic", mv_object=multivariate_gen_hyperbolic)
+gh_copula: gen_hyperbolic_copula_gen = gen_hyperbolic_copula_gen(
+    name="gh", mv_object=mvt_gh)
 
-marginal_hyperbolic_copula: marginal_hyperbolic_copula_gen = \
-    marginal_hyperbolic_copula_gen(
-        name="marginal_hyperbolic", mv_object=multivariate_marginal_hyperbolic)
+mh_copula: marginal_hyperbolic_copula_gen = marginal_hyperbolic_copula_gen(
+    name="mh", mv_object=mvt_mh)
 
-hyperbolic_copula: hyperbolic_copula_gen = \
-    hyperbolic_copula_gen(
-        name="hyperbolic", mv_object=multivariate_hyperbolic)
+hyperbolic_copula: hyperbolic_copula_gen = hyperbolic_copula_gen(
+    name="hyperbolic", mv_object=mvt_hyperbolic)
 
-nig_copula: nig_copula_gen = \
-    nig_copula_gen(name="nig", mv_object=multivariate_nig)
+nig_copula: nig_copula_gen = nig_copula_gen(name="nig", mv_object=mvt_nig)
 
-skewed_t_copula: skewed_t_copula_gen = \
-    skewed_t_copula_gen(
-        name="skewed_t", mv_object=multivariate_skewed_t)
+skewed_t_copula: skewed_t_copula_gen = skewed_t_copula_gen(
+    name="skewed_t", mv_object=mvt_skewed_t)
 
-student_t_copula: student_t_copula_gen = \
-    student_t_copula_gen(
-        name="student_t", mv_object=multivariate_student_t)
+student_t_copula: student_t_copula_gen = student_t_copula_gen(
+    name="student_t", mv_object=mvt_student_t)
 
-sym_gen_hyperbolic_copula: sym_gen_hyperbolic_copula_gen = \
-    sym_gen_hyperbolic_copula_gen(
-        name="sym_gen_hyperbolic", mv_object=multivariate_sym_gen_hyperbolic)
+sgh_copula: sym_gen_hyperbolic_copula_gen = sym_gen_hyperbolic_copula_gen(
+    name="sgh", mv_object=mvt_sgh)
 
-sym_marginal_hyperbolic_copula: sym_marginal_hyperbolic_copula_gen = \
-    sym_marginal_hyperbolic_copula_gen(
-        name="sym_marginal_hyperbolic",
-        mv_object=multivariate_sym_marginal_hyperbolic)
+smh_copula: sym_marginal_hyperbolic_copula_gen = \
+    sym_marginal_hyperbolic_copula_gen(name="smh", mv_object=mvt_smh)
 
-sym_hyperbolic_copula: sym_hyperbolic_copula_gen = \
-    sym_hyperbolic_copula_gen(name="sym_hyperbolic",
-                              mv_object=multivariate_sym_hyperbolic)
+shyperbolic_copula: sym_hyperbolic_copula_gen = sym_hyperbolic_copula_gen(
+    name="shyperbolic", mv_object=mvt_shyperbolic)
 
-sym_nig_copula: sym_nig_copula_gen = \
-    sym_nig_copula_gen(name="sym_nig", mv_object=multivariate_sym_nig)
+snig_copula: sym_nig_copula_gen = sym_nig_copula_gen(
+    name="snig", mv_object=mvt_snig)
 
 clayton_copula: clayton_copula_gen = \
-    clayton_copula_gen(name="clayton", mv_object=multivariate_clayton)
+    clayton_copula_gen(name="clayton", mv_object=mvt_clayton)
 
 gumbel_copula: gumbel_copula_gen = \
-    gumbel_copula_gen(name="gumbel", mv_object=multivariate_gumbel)
+    gumbel_copula_gen(name="gumbel", mv_object=mvt_gumbel)
 
 frank_copula: frank_copula_gen = \
-    frank_copula_gen(name="frank", mv_object=bivariate_frank)
+    frank_copula_gen(name="frank", mv_object=bvt_frank)
diff --git a/sklarpy/copulas/distributions_map.py b/sklarpy/copulas/distributions_map.py
@@ -9,16 +9,16 @@
     'gumbel_copula',
     'frank_copula',
     'gaussian_copula',
-    'gen_hyperbolic_copula',
-    'marginal_hyperbolic_copula',
+    'gh_copula',
+    'mh_copula',
     'hyperbolic_copula',
     'nig_copula',
     'skewed_t_copula',
     'student_t_copula',
-    'sym_gen_hyperbolic_copula',
-    'sym_marginal_hyperbolic_copula',
-    'sym_hyperbolic_copula',
-    'sym_nig_copula',
+    'sgh_copula',
+    'smh_copula',
+    'shyperbolic_copula',
+    'snig_copula',
 )
 
 ###############################################################################

diff --git a/sklarpy/multivariate/README.md b/sklarpy/multivariate/README.md
@@ -6,20 +6,20 @@ Examples of how to use these models can be found in the `examples` folder.
 
 ## <u> SklarPy Multivariate Models </u>
 
- | Family         | Name                              | Dimensions   | SklarPy Model                        |
-|----------------|-----------------------------------|--------------|--------------------------------------|
-| Normal Mixture | Normal / Gaussian                 | Multivariate | multivariate_normal                  | 
-| Normal Mixture | Student-T                         | Multivariate | multivariate_student_t               |
-| Normal Mixture | Skewed-T                          | Multivariate | multivariate_skewed_t                |
-| Normal Mixture | Generalized Hyperbolic            | Multivariate | multivariate_gen_hyperbolic          |
-| Normal Mixture | Symmetric Generalized Hyperbolic  | Multivariate | multivariate_sym_gen_hyperbolic      |
-| Normal Mixture | Hyperbolic                        | Multivariate | multivariate_hyperbolic              |
-| Normal Mixture | Symmetric Hyperbolic              | Multivariate | multivariate_sym_hyperbolic          |
-| Normal Mixture | Normal-Inverse Gaussian (NIG)     | Multivariate | multivariate_nig                     |
-| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | multivariate_sym_nig                 |
-| Normal Mixture | Marginal Hyperbolic               | Multivariate | multivariate_marginal_hyperbolic     |
-| Normal Mixture | Symmetric Marginal Hyperbolic     | Multivariate | multivariate_sym_marginal_hyperbolic |
-| Numerical      | Gaussian KDE                      | Multivariate | multivariate_gaussian_kde            |
+ | Family         | Name                              | Dimensions   | SklarPy Model    |
+|----------------|-----------------------------------|--------------|------------------|
+| Normal Mixture | Normal / Gaussian                 | Multivariate | mvt_normal       | 
+| Normal Mixture | Student-T                         | Multivariate | mvt_student_t    |
+| Normal Mixture | Skewed-T                          | Multivariate | mvt_skewed_t     |
+| Normal Mixture | Generalized Hyperbolic            | Multivariate | mvt_gh           |
+| Normal Mixture | Symmetric Generalized Hyperbolic  | Multivariate | mvt_sgh          |
+| Normal Mixture | Hyperbolic                        | Multivariate | mvt_hyperbolic   |
+| Normal Mixture | Symmetric Hyperbolic              | Multivariate | mvt_shyperbolic  |
+| Normal Mixture | Normal-Inverse Gaussian (NIG)     | Multivariate | mvt_nig          |
+| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | mvt_snig         |
+| Normal Mixture | Marginal Hyperbolic               | Multivariate | mvt_mh           |
+| Normal Mixture | Symmetric Marginal Hyperbolic     | Multivariate | mvt_smh          |
+| Numerical      | Gaussian KDE                      | Multivariate | mvt_gaussian_kde |
 
 ### Implementation Status
 - [x] Normal Mixture 

diff --git a/sklarpy/multivariate/__init__.py b/sklarpy/multivariate/__init__.py
@@ -1,8 +1,5 @@
-from sklarpy.multivariate.distributions import multivariate_gaussian_kde, \
-    multivariate_gen_hyperbolic, multivariate_marginal_hyperbolic, \
-    multivariate_hyperbolic, multivariate_nig, multivariate_normal, \
-    multivariate_skewed_t, multivariate_student_t, \
-    multivariate_sym_gen_hyperbolic, multivariate_sym_marginal_hyperbolic, \
-    multivariate_sym_hyperbolic, multivariate_sym_nig
+from sklarpy.multivariate.distributions import (
+    mvt_gaussian_kde, mvt_gh, mvt_mh, mvt_hyperbolic, mvt_nig, mvt_normal,
+    mvt_skewed_t, mvt_student_t, mvt_sgh, mvt_smh, mvt_shyperbolic, mvt_snig)
 
 from sklarpy.multivariate.distributions_map import distributions_map
diff --git a/sklarpy/multivariate/_params/_archimedean.py b/sklarpy/multivariate/_params/_archimedean.py
@@ -1,11 +1,11 @@
 # Contains code for holding Archimedean copula parameters
 from sklarpy._utils import Params
 
-__all__ = ['MultivariateClaytonParams', 'MultivariateGumbelParams',
-           'BivariateFrankParams']
+__all__ = ['MvtClaytonParams', 'MvtGumbelParams',
+           'BvtFrankParams']
 
 
-class MultivariateArchimedeanParamsBase(Params):
+class MvtArchimedeanParamsBase(Params):
     """Base class containing the fitted parameters of an Archimedean Copula."""
     @property
     def theta(self) -> float:
@@ -32,13 +32,13 @@ def d(self) -> int:
         return self.to_dict['d']
 
 
-class MultivariateClaytonParams(MultivariateArchimedeanParamsBase):
+class MvtClaytonParams(MvtArchimedeanParamsBase):
     """Contains the fitted parameters of a Clayton Copula."""
 
 
-class MultivariateGumbelParams(MultivariateArchimedeanParamsBase):
+class MvtGumbelParams(MvtArchimedeanParamsBase):
     """Contains the fitted parameters of a Gumbel Copula."""
 
 
-class BivariateFrankParams(MultivariateArchimedeanParamsBase):
+class BvtFrankParams(MvtArchimedeanParamsBase):
     """Contains the fitted parameters of a Frank Copula."""
diff --git a/sklarpy/multivariate/_params/_gaussian_kde.py b/sklarpy/multivariate/_params/_gaussian_kde.py
@@ -1,10 +1,10 @@
 # Contains code for holding Gaussian KDE parameters.
 from sklarpy._utils import Params
 
-__all__ = ['MultivariateGaussianKDEParams']
+__all__ = ['MvtGaussianKDEParams']
 
 
-class MultivariateGaussianKDEParams(Params):
+class MvtGaussianKDEParams(Params):
     """Contains the fitted parameters of a Multivariate Gaussian KDE
     distribution."""
     @property

diff --git a/sklarpy/multivariate/_params/_generalized_hyperbolic.py b/sklarpy/multivariate/_params/_generalized_hyperbolic.py
@@ -2,14 +2,14 @@
 import numpy as np
 
 from sklarpy.multivariate._params._symmetric_generalized_hyperbolic import \
-    MultivariateSymGenHyperbolicParams
+    MvtSGHParams
 from sklarpy.multivariate._distributions._generalized_hyperbolic import \
     multivariate_gen_hyperbolic_gen
 
-__all__ = ['MultivariateGenHyperbolicParams']
+__all__ = ['MvtGHParams']
 
 
-class MultivariateGenHyperbolicParams(MultivariateSymGenHyperbolicParams):
+class MvtGHParams(MvtSGHParams):
     """Contains the fitted parameters of a Multivariate Generalized Hyperbolic
     distribution."""
     _DIST_GENERATOR = multivariate_gen_hyperbolic_gen