Resolve pylint benchmarks (#178)

* resolved pylint complaints in distribution benchmarks

* cleaned up pylint messages in linearsolver, too

* pulled out random spd matrix

* finalised distribution and linearsolver

* blacked results

* black messed up my pylint ignore

* removed benchmark directory from tox pylint ignore

* blacked

* re-fix ivpsolver and linearsolver

* added pylint ignores, renamed property into method

* renamed random spd matrix

* globally ignored missing-function-docstring

* documented benchmark change in tox file

* blacked

* mini docstring updates

benchmarks/benchmark_utils.py

@@ -0,0 +1,14 @@
+"""Reocurring constants and methods for benchmarking functions."""
+
+import numpy as np
+
+
+SPD_MATRIX_5x5 = 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],
+    ]
+)

benchmarks/ivpsolve.py

@@ -21,10 +21,12 @@ class IVPSolve:
     params = [["eks0", "ekf0"], ["with", "without"], ["ibm4", "ioup4", "matern92"]]
 
     def setup(self, method, precond, prior):
+        # pylint: disable=attribute-defined-outside-init,invalid-name,unused-argument
         self.ivp = load_lotkavolterra()
         self.stepsize = 1e-2
 
     def time_solve(self, method, precond, prior):
+        # pylint: disable=missing-function-docstring
         precond_step = self.stepsize if precond == "with" else 1.0
         probsolve_ivp(
             self.ivp,
@@ -35,6 +37,7 @@ def time_solve(self, method, precond, prior):
         )
 
     def peakmem_solve(self, method, precond, prior):
+        # pylint: disable=missing-function-docstring
         precond_step = self.stepsize if precond == "with" else 1.0
         probsolve_ivp(
             self.ivp,

benchmarks/linearsolvers.py

@@ -7,6 +7,7 @@
 import scipy.sparse
 
 from probnum.linalg import problinsolve
+from benchmarks.benchmark_utils import SPD_MATRIX_5x5
 
 
 def load_poisson_linear_system():
@@ -21,59 +22,63 @@ def load_poisson_linear_system():
 
     Linear system resulting from discretization on an elliptic grid.
     """
+    # pylint: disable=invalid-name
     fpath = os.path.join(os.path.dirname(__file__), "../tests/resources")
     A = scipy.sparse.load_npz(file=fpath + "/matrix_poisson.npz")
     f = np.load(file=fpath + "/rhs_poisson.npy")
     return A, f
 
 
 class LinSolve:
-    """
-    Benchmark solving a linear system.
-    """
+    """Benchmark solving a linear system."""
 
     param_names = ["system"]
     params = [["sparse", "dense"]]  # , "large-scale"]
 
     def setup(self, system):
+        # pylint: disable=attribute-defined-outside-init,invalid-name
+
         # Seed
         np.random.seed(42)
 
         if system == "sparse":
-            self.A, self.b = load_poisson_linear_system()
+            (
+                self.A,
+                self.b,
+            ) = load_poisson_linear_system()
         elif system == "dense":
-            self.A = 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],
-                ]
-            )
+            self.A = SPD_MATRIX_5x5
             self.b = np.random.normal(size=self.A.shape[0])
         elif system == "large-scale":
             self.A = None
             self.b = None
 
     def time_solve(self, system):
-        # Solve linear system
-        self.xhat, self.Ahat, self.Ainvhat, _ = problinsolve(A=self.A, b=self.b)
+        """Time solving a linear system"""
+        # pylint: disable=unused-argument
+        problinsolve(A=self.A, b=self.b)
 
     def mem_solve(self, system):
-        # Solve linear system
-        self.xhat, self.Ahat, self.Ainvhat, _ = problinsolve(A=self.A, b=self.b)
+        """Time solving a linear system"""
+        # pylint: disable=unused-argument
+        problinsolve(A=self.A, b=self.b)
 
     def peakmem_solve(self, system):
-        # Solve linear system
-        self.xhat, self.Ahat, self.Ainvhat, _ = problinsolve(A=self.A, b=self.b)
+        """Time solving a linear system"""
+        # pylint: disable=unused-argument
+        problinsolve(A=self.A, b=self.b)
 
 
 class PosteriorDist:
+    """Benchmark sampling from the posterior distribution."""
+
     param_names = ["output"]
     params = [["solution", "matrix", "matrix_inverse"]]
 
     def setup(self, output):
+        # pylint: disable=attribute-defined-outside-init
+        # pylint: disable=unused-argument, invalid-name
+
         # Sparse system
         self.A, self.b = load_poisson_linear_system()
 
@@ -84,6 +89,7 @@ def setup(self, output):
         self.n_samples = 10
 
     def time_sample(self, output):
+        """Time sampling from the posterior distribution"""
         if output == "solution":
             self.xhat.sample(self.n_samples)
         elif output == "matrix":

benchmarks/random_variables.py

@@ -7,8 +7,10 @@
 from probnum import random_variables as rvs
 import probnum.linalg.linops as linops
 
+from benchmarks.benchmark_utils import SPD_MATRIX_5x5
+
 # Module level variables
-rv_names = [
+RV_NAMES = [
     "univar_normal",
     "multivar_normal",
     "matrixvar_normal",
@@ -18,27 +20,17 @@
 
 
 def get_randvar(rv_name):
-    """
-    Return a random variable for a given distribution 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)
+    mean_2d_mat = SPD_MATRIX_5x5
+    mean_2d_linop = linops.MatrixMult(SPD_MATRIX_5x5)
     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)
+    cov_1d = SPD_MATRIX_5x5
+    cov_2d_kron = linops.Kronecker(A=SPD_MATRIX_5x5, B=SPD_MATRIX_5x5)
+    cov_2d_symkron = linops.SymmetricKronecker(A=SPD_MATRIX_5x5)
 
     if rv_name == "univar_normal":
         randvar = rvs.Normal(mean=mean_0d, cov=cov_0d)
@@ -55,50 +47,55 @@ def get_randvar(rv_name):
 
 
 class Functions:
-    """
-    Benchmark various functions of distributions.
-    """
+    """Benchmark various functions of random variables."""
+
+    param_names = ["randvar", "method"]
+    params = [RV_NAMES, ["pdf", "logpdf", "cdf", "logcdf"]]
 
-    param_names = ["randvar", "property"]
-    params = [rv_names, ["pdf", "logpdf", "cdf", "logcdf"]]
+    def setup(self, randvar, method):
+        # pylint: disable=unused-argument,attribute-defined-outside-init,missing-function-docstring
 
-    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):
+    def time_distr_functions(self, randvar, method):
         """Times evaluation of the pdf, logpdf, cdf and logcdf."""
+        # pylint: disable=unused-argument
+
         try:
-            if property == "pdf":
+            if method == "pdf":
                 self.randvar.pdf(x=self.eval_point)
-            elif property == "logpdf":
+            elif method == "logpdf":
                 self.randvar.logpdf(x=self.eval_point)
-            elif property == "cdf":
+            elif method == "cdf":
                 self.randvar.cdf(x=self.quantile)
-            elif property == "logcdf":
+            elif method == "logcdf":
                 self.randvar.logcdf(x=self.quantile)
         except NotImplementedError:
             pass
 
 
 class Sampling:
-    """
-    Benchmark sampling routines for various distributions.
-    """
+    """Benchmark sampling routines for various distributions."""
 
     param_names = ["randvar"]
-    params = [rv_names]
+    params = [RV_NAMES]
 
     def setup(self, randvar):
+        # pylint: disable=unused-argument,attribute-defined-outside-init,missing-function-docstring
         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."""
+        # pylint: disable=unused-argument
+
         self.randvar.sample(self.n_samples)
 
     def peakmem_sample(self, randvar):
         """Peak memory of sampling process."""
+        # pylint: disable=unused-argument
+
         self.randvar.sample(self.n_samples)

tox.ini

@@ -62,5 +62,5 @@ commands =
          pylint src/probnum/quad --disable="attribute-defined-outside-init,too-few-public-methods,redefined-builtin,arguments-differ,unused-argument,missing-module-docstring"
          pylint src/probnum/random_variables --disable="missing-function-docstring"
          pylint src/probnum/utils --disable="duplicate-code,missing-module-docstring,missing-function-docstring"
+         pylint benchmarks --disable="missing-function-docstring"  # not a work in progress, but final
          pylint tests --disable="line-too-long,duplicate-code,missing-class-docstring,unnecessary-pass,unused-variable,protected-access,attribute-defined-outside-init,no-self-use,abstract-class-instantiated,too-many-arguments,too-many-instance-attributes,too-many-locals,unused-argument,fixme,missing-module-docstring,missing-function-docstring"
-         pylint benchmarks --disable="attribute-defined-outside-init,unused-argument,redefined-builtin,missing-class-docstring,duplicate-code,missing-function-docstring"