store random seed as an object

copulas/__init__.py

@@ -32,20 +32,14 @@ def set_random_state(random_state, set_model_random_state):
     """
     original_state = np.random.get_state()
 
-    if isinstance(random_state, int):
-        random_state = np.random.RandomState(seed=random_state).get_state()
-    elif isinstance(random_state, np.random.RandomState):
-        random_state = random_state.get_state()
-    elif not isinstance(random_state, tuple):
-        raise TypeError(f'RandomState {random_state} is an unexpected type. '
-                        'Expected to be int, np.random.RandomState, or tuple.')
-
-    np.random.set_state(random_state)
+    np.random.set_state(random_state.get_state())
 
     try:
         yield
     finally:
-        set_model_random_state(np.random.get_state())
+        current_random_state = np.random.RandomState()
+        current_random_state.set_state(np.random.get_state())
+        set_model_random_state(current_random_state)
         np.random.set_state(original_state)
 
 
@@ -68,6 +62,29 @@ def wrapper(self, *args, **kwargs):
     return wrapper
 
 
+def validate_random_state(random_state):
+    """Validate random state argument.
+
+    Args:
+        random_state (int, numpy.random.RandomState, tuple, or None):
+            Seed or RandomState for the random generator.
+
+    Output:
+        numpy.random.RandomState
+    """
+    if random_state is None:
+        return None
+
+    if isinstance(random_state, int):
+        return np.random.RandomState(seed=random_state)
+    elif isinstance(random_state, np.random.RandomState):
+        return random_state
+    else:
+        raise TypeError(
+            f'`random_state` {random_state} expected to be an int '
+            'or `np.random.RandomState` object.')
+
+
 def get_instance(obj, **kwargs):
     """Create new instance of the ``obj`` argument.
 

copulas/bivariate/base.py

@@ -8,7 +8,7 @@
 from scipy import stats
 from scipy.optimize import brentq
 
-from copulas import EPSILON, NotFittedError, random_state
+from copulas import EPSILON, NotFittedError, random_state, validate_random_state
 from copulas.bivariate.utils import split_matrix
 
 
@@ -113,7 +113,7 @@ def __init__(self, copula_type=None, random_state=None):
             random_state (int, np.random.RandomState, or None): Seed or RandomState
                 for the random generator.
         """
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     def check_theta(self):
         """Validate the computed theta against the copula specification.
@@ -352,7 +352,7 @@ def set_random_state(self, random_state):
             random_state (int, np.random.RandomState, or None): Seed or RandomState
                 for the random generator.
         """
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     @random_state
     def sample(self, n_samples):

copulas/datasets.py

@@ -4,7 +4,7 @@
 import pandas as pd
 from scipy import stats
 
-from copulas import set_random_state
+from copulas import set_random_state, validate_random_state
 
 
 def _dummy_fn(state):
@@ -27,7 +27,7 @@ def sample_bivariate_age_income(size=1000, seed=42):
         pandas.DataFrame:
             DataFrame with two columns, ``age`` and ``income``.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         age = stats.beta.rvs(a=2.0, b=6.0, loc=18, scale=100, size=size)
         income = np.log(age) * 100
         income += np.random.normal(loc=np.log(age) / 100, scale=10, size=size)
@@ -58,7 +58,7 @@ def sample_trivariate_xyz(size=1000, seed=42):
         pandas.DataFrame:
             DataFrame with three columns, ``x``, ``y`` and ``z``.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         x = stats.beta.rvs(a=0.1, b=0.1, size=size)
         y = stats.beta.rvs(a=0.1, b=0.5, size=size)
         return pd.DataFrame({
@@ -84,7 +84,7 @@ def sample_univariate_bernoulli(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(np.random.random(size=size) < 0.3).astype(float)
 
 
@@ -104,7 +104,7 @@ def sample_univariate_bimodal(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         bernoulli = sample_univariate_bernoulli(size, seed)
         mode1 = np.random.normal(size=size) * bernoulli
         mode2 = np.random.normal(size=size, loc=10) * (1.0 - bernoulli)
@@ -125,7 +125,7 @@ def sample_univariate_uniform(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(4.0 * np.random.random(size=size) - 1.0)
 
 
@@ -142,7 +142,7 @@ def sample_univariate_normal(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(np.random.normal(size=size, loc=1.0))
 
 
@@ -159,7 +159,7 @@ def sample_univariate_degenerate(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(np.full(size, np.random.random()))
 
 
@@ -176,7 +176,7 @@ def sample_univariate_exponential(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(np.random.exponential(size=size) + 3.0)
 
 
@@ -193,7 +193,7 @@ def sample_univariate_beta(size=1000, seed=42):
         pandas.Series:
             Series with the sampled values.
     """
-    with set_random_state(seed, _dummy_fn):
+    with set_random_state(validate_random_state(seed), _dummy_fn):
         return pd.Series(stats.beta.rvs(a=3, b=1, loc=4, size=size))
 
 

copulas/multivariate/base.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from copulas import NotFittedError, get_instance
+from copulas import NotFittedError, get_instance, validate_random_state
 
 
 class Multivariate(object):
@@ -13,7 +13,7 @@ class Multivariate(object):
     fitted = False
 
     def __init__(self, random_state=None):
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     def fit(self, X):
         """Fit the model to table with values from multiple random variables.
@@ -116,7 +116,7 @@ def set_random_state(self, random_state):
             random_state (int, np.random.RandomState, or None):
                 Seed or RandomState for the random generator.
         """
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     def sample(self, num_rows=1):
         """Sample values from this model.

copulas/multivariate/gaussian.py

@@ -9,7 +9,8 @@
 from scipy import stats
 
 from copulas import (
-    EPSILON, check_valid_values, get_instance, get_qualified_name, random_state, store_args)
+    EPSILON, check_valid_values, get_instance, get_qualified_name, random_state, store_args,
+    validate_random_state)
 from copulas.multivariate.base import Multivariate
 from copulas.univariate import Univariate
 
@@ -33,7 +34,7 @@ class GaussianMultivariate(Multivariate):
 
     @store_args
     def __init__(self, distribution=DEFAULT_DISTRIBUTION, random_state=None):
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
         self.distribution = distribution
 
     def __repr__(self):

copulas/multivariate/vine.py

@@ -7,7 +7,9 @@
 import numpy as np
 import pandas as pd
 
-from copulas import EPSILON, check_valid_values, get_qualified_name, random_state, store_args
+from copulas import (
+    EPSILON, check_valid_values, get_qualified_name, random_state, store_args,
+    validate_random_state)
 from copulas.bivariate.base import Bivariate, CopulaTypes
 from copulas.multivariate.base import Multivariate
 from copulas.multivariate.tree import Tree, get_tree
@@ -73,7 +75,7 @@ def __init__(self, vine_type, random_state=None):
                 'produce wrong results. Please use Python 3.5, 3.6 or 3.7'
             )
 
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
         self.vine_type = vine_type
         self.u_matrix = None
 

copulas/univariate/base.py

@@ -6,7 +6,9 @@
 
 import numpy as np
 
-from copulas import NotFittedError, get_instance, get_qualified_name, random_state, store_args
+from copulas import (
+    NotFittedError, get_instance, get_qualified_name, random_state, store_args,
+    validate_random_state)
 from copulas.univariate.selection import select_univariate
 
 
@@ -85,7 +87,7 @@ def _select_candidates(cls, parametric=None, bounded=None):
     def __init__(self, candidates=None, parametric=None, bounded=None, random_state=None,
                  selection_sample_size=None):
         self.candidates = candidates or self._select_candidates(parametric, bounded)
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
         self.selection_sample_size = selection_sample_size
 
     @classmethod
@@ -364,7 +366,7 @@ def set_random_state(self, random_state):
             random_state (int, np.random.RandomState, or None):
                 Seed or RandomState for the random generator.
         """
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     def sample(self, n_samples=1):
         """Sample values from this model.
@@ -498,7 +500,7 @@ def __init__(self, random_state=None):
             random_state (int, np.random.RandomState, or None): seed
                 or RandomState for random generator.
         """
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
 
     def probability_density(self, X):
         """Compute the probability density for each point in X.

copulas/univariate/gaussian_kde.py

@@ -4,7 +4,7 @@
 from scipy.special import ndtr
 from scipy.stats import gaussian_kde
 
-from copulas import EPSILON, random_state, store_args
+from copulas import EPSILON, random_state, store_args, validate_random_state
 from copulas.optimize import bisect, chandrupatla
 from copulas.univariate.base import BoundedType, ParametricType, ScipyModel
 
@@ -29,7 +29,7 @@ class GaussianKDE(ScipyModel):
 
     @store_args
     def __init__(self, sample_size=None, random_state=None, bw_method=None, weights=None):
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
         self._sample_size = sample_size
         self.bw_method = bw_method
         self.weights = weights

copulas/univariate/truncated_gaussian.py

@@ -4,7 +4,7 @@
 from scipy.optimize import fmin_slsqp
 from scipy.stats import truncnorm
 
-from copulas import EPSILON, store_args
+from copulas import EPSILON, store_args, validate_random_state
 from copulas.univariate.base import BoundedType, ParametricType, ScipyModel
 
 
@@ -20,7 +20,7 @@ class TruncatedGaussian(ScipyModel):
 
     @store_args
     def __init__(self, minimum=None, maximum=None, random_state=None):
-        self.random_state = random_state
+        self.random_state = validate_random_state(random_state)
         self.min = minimum
         self.max = maximum
 

tests/unit/bivariate/test_base.py

@@ -19,13 +19,13 @@ def setUp(self):
     def test___init__random_state(self):
         """If random_state is passed as argument, will be set as attribute."""
         # Setup
-        random_seed = 'random_seed'
+        random_seed = 42
 
         # Run
         instance = Bivariate(copula_type=CopulaTypes.CLAYTON, random_state=random_seed)
 
         # Check
-        assert instance.random_state == 'random_seed'
+        assert instance.random_state is not None
 
     def test_from_dict(self):
         """From_dict sets the values of a dictionary as attributes of the instance."""
@@ -130,4 +130,4 @@ def test_set_random_state(self):
         instance.set_random_state(3)
 
         # Check
-        assert instance.random_state == 3
+        assert instance.random_state is not None

tests/unit/multivariate/test_base.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from copulas.multivariate.base import Multivariate
 
 
@@ -12,4 +14,4 @@ def test_set_random_state(self):
         instance.set_random_state(3)
 
         # Check
-        assert instance.random_state == 3
+        assert isinstance(instance.random_state, np.random.RandomState)