Refactor RandomVariable

.github/workflows/pylint_check.py

@@ -4,8 +4,11 @@
 PYPROJECTTOML_FILE = Path("pyproject.toml")
 GLOBAL_DISABLES = {
     "invalid-name",
+    "fixme",
+    "bad-continuation",
     "no-else-raise",
     "no-else-return",
+    "no-member",
 }
 
 

benchmarks/ivpsolve.py

@@ -3,12 +3,12 @@
 """
 import numpy as np
 from probnum.diffeq import lotkavolterra, probsolve_ivp
-from probnum.prob import RandomVariable, Dirac
+from probnum.random_variables import Dirac
 
 
 def load_lotkavolterra():
     """Load LV system as a basic IVP."""
-    initrv = RandomVariable(distribution=Dirac(np.array([20, 20])))
+    initrv = Dirac(np.array([20, 20]))
     return lotkavolterra(
         timespan=[0, 0.55], initrv=initrv, params=(0.5, 0.05, 0.5, 0.05)
     )

benchmarks/random_variables.py

@@ -0,0 +1,104 @@
+"""
+Benchmarks for random variables.
+"""
+
+import numpy as np
+
+from probnum import random_variables as rvs
+import probnum.linalg.linops as linops
+
+# Module level variables
+rv_names = [
+    "univar_normal",
+    "multivar_normal",
+    "matrixvar_normal",
+    "symmatrixvar_normal",
+    "operatorvar_normal",
+]
+
+
+def get_randvar(rv_name):
+    """
+    Return a random variable for a given distribution name
+    """
+    # Distribution Means and Covariances
+    spd_mat = np.array(
+        [
+            [2.3, -2.3, 3.5, 4.2, 1.8],
+            [-2.3, 3.0, -3.5, -4.8, -1.9],
+            [3.5, -3.5, 6.9, 5.8, 0.8],
+            [4.2, -4.8, 5.8, 10.1, 6.3],
+            [1.8, -1.9, 0.8, 6.3, 12.1],
+        ]
+    )
+    mean_0d = np.random.rand()
+    mean_1d = np.random.rand(5)
+    mean_2d_mat = spd_mat
+    mean_2d_linop = linops.MatrixMult(spd_mat)
+    cov_0d = np.random.rand() + 10 ** -12
+    cov_1d = spd_mat
+    cov_2d_kron = linops.Kronecker(A=spd_mat, B=spd_mat)
+    cov_2d_symkron = linops.SymmetricKronecker(A=spd_mat)
+
+    if rv_name == "univar_normal":
+        randvar = rvs.Normal(mean=mean_0d, cov=cov_0d)
+    elif rv_name == "multivar_normal":
+        randvar = rvs.Normal(mean=mean_1d, cov=cov_1d)
+    elif rv_name == "matrixvar_normal":
+        randvar = rvs.Normal(mean=mean_2d_mat, cov=cov_2d_kron)
+    elif rv_name == "symmatrixvar_normal":
+        randvar = rvs.Normal(mean=mean_2d_mat, cov=cov_2d_symkron)
+    elif rv_name == "operatorvar_normal":
+        randvar = rvs.Normal(mean=mean_2d_linop, cov=cov_2d_symkron)
+
+    return randvar
+
+
+class Functions:
+    """
+    Benchmark various functions of distributions.
+    """
+
+    param_names = ["randvar", "property"]
+    params = [rv_names, ["pdf", "logpdf", "cdf", "logcdf"]]
+
+    def setup(self, randvar, property):
+        self.randvar = get_randvar(rv_name=randvar)
+        self.eval_point = np.random.uniform(size=self.randvar.shape)
+        self.quantile = np.random.uniform(size=self.randvar.shape)
+
+    def time_distr_functions(self, randvar, property):
+        """Times evaluation of the pdf, logpdf, cdf and logcdf."""
+        try:
+            if property == "pdf":
+                self.randvar.pdf(x=self.eval_point)
+            elif property == "logpdf":
+                self.randvar.logpdf(x=self.eval_point)
+            elif property == "cdf":
+                self.randvar.cdf(x=self.quantile)
+            elif property == "logcdf":
+                self.randvar.logcdf(x=self.quantile)
+        except NotImplementedError:
+            pass
+
+
+class Sampling:
+    """
+    Benchmark sampling routines for various distributions.
+    """
+
+    param_names = ["randvar"]
+    params = [rv_names]
+
+    def setup(self, randvar):
+        np.random.seed(42)
+        self.n_samples = 1000
+        self.randvar = get_randvar(rv_name=randvar)
+
+    def time_sample(self, randvar):
+        """Times sampling from this distribution."""
+        self.randvar.sample(self.n_samples)
+
+    def peakmem_sample(self, randvar):
+        """Peak memory of sampling process."""
+        self.randvar.sample(self.n_samples)

docs/source/index.rst

@@ -48,7 +48,7 @@ To learn how to use ProbNum check out the `quickstart guide <introduction/quicks
    :maxdepth: 1
    :caption: API Documentation
 
-   public_api/prob
+   public_api/probnum
    public_api/linalg
    public_api/quad
    public_api/diffeq

docs/source/public_api/probnum.rst

@@ -0,0 +1,7 @@
+probnum
+=======
+
+probnum.random_variables
+************************
+.. automodapi:: probnum.random_variables
+	:no-heading: