style(linting): fixed liniting errors

Author: mwong009

.github/workflows/linting.yml

@@ -10,6 +10,7 @@ jobs:
       - uses: psf/black@stable
         with:
           src: "."
+          version: "23.11.0"
 
   isort:
     runs-on: ubuntu-latest

poetry.lock

@@ -1,5 +1,10 @@
 """
-The code snippet is a part of the PyCMTensor expressions module. It defines a base class for parsing and manipulating Aesara tensor expressions. The class provides methods for parsing a tensor expression to remove parentheses and tensor operators, and returns a clean list of keywords found in the expression. It also defines a base class for expression objects, which includes overloaded operators for tensor operations such as addition, subtraction, multiplication, division, and comparison.
+The code snippet is a part of the PyCMTensor expressions module. It defines a base 
+class for parsing and manipulating Aesara tensor expressions. The class provides 
+methods for parsing a tensor expression to remove parentheses and tensor operators, and 
+returns a clean list of keywords found in the expression. It also defines a base class 
+for expression objects, which includes overloaded operators for tensor operations such 
+as addition, subtraction, multiplication, division, and comparison.
 """
 from typing import Union
 
@@ -51,7 +56,8 @@ def __init__(self, expression=None):
         """Base class for parsing and manipulating Aesara tensor expressions.
 
         Args:
-            expression (TensorVariable, optional): The tensor expression to parse. Defaults to None.
+            expression (TensorVariable, optional): The tensor expression to parse.
+            Defaults to None.
         """
         if expression is not None:
             self.expression = str(pprint(expression))
@@ -153,7 +159,7 @@ def __mul__(self, other):
             return self() * other()
         else:
             raise TypeError(
-                f"__mul__ {other} must be a TensorVariable or TensorShared Variable object"
+                f"__mul__ {other} must be a TensorVariable or TensorShared Variable"
             )
 
     def __rmul__(self, other):
@@ -399,8 +405,10 @@ def __init__(self, name: str, draw_type: str, n_draws: int):
 
         Args:
             name (str): The name of the RandomDraw object.
-            draw_type (str): The distribution of the draw. Can be "normal", "lognormal", "gumbel", "exponential", "gamma", or "poisson".
-            n_draws (int): The number of draws, which determines the size of the shared tensor.
+            draw_type (str): The distribution of the draw. Can be "normal",
+            "lognormal", "gumbel", "exponential", "gamma", or "poisson".
+            n_draws (int): The number of draws, which determines the size of the shared
+            tensor.
 
         Raises:
             NotImplementedError: If an unsupported draw_type is provided.
@@ -447,7 +455,8 @@ def __init__(self, name, size, value=None):
         Args:
             name (str): The name of the parameter.
             size (Union[tuple,list]): The size of the array in 1 dimension.
-            value (numpy.ndarray): The initial values of the parameter. If `None` is given, it defaults to `0`.
+            value (numpy.ndarray): The initial values of the parameter. If `None` is
+            given, it defaults to `0`.
         """
         Param.__init__(self, name, lb=None, ub=None)
 
@@ -496,7 +505,8 @@ def __init__(self, name, size, value=None, init_type=None):
         Args:
             name (str): name of the parameter
             size (Union[tuple,list]): size of the array
-            value (numpy.ndarray): initial values of the parameter. Defaults to `random.uniform(-0.1, 0.1, size)`
+            value (numpy.ndarray): initial values of the parameter. Defaults to `random.
+            uniform(-0.1, 0.1, size)`
             init_type (str): initialization type, see notes
 
         Note:
@@ -510,8 +520,14 @@ def __init__(self, name, size, value=None, init_type=None):
             * `"glorot"`: initialization method that maintains the variance for
               symmetric activation functions, e.g. sigm, tanh [^2]
 
-            [^1] He, K., Zhang, X., Ren, S. and Sun, J., 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).
-            [^2] Glorot, X. and Bengio, Y., 2010, March. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256). JMLR Workshop and Conference Proceedings.
+            [^1] He, K., Zhang, X., Ren, S. and Sun, J., 2015. Delving deep into
+            rectifiers: Surpassing human-level performance on imagenet classification.
+            In Proceedings of the IEEE international conference on computer vision (pp.
+            1026-1034).
+            [^2] Glorot, X. and Bengio, Y., 2010, March. Understanding the difficulty
+            of training deep feedforward neural networks. In Proceedings of the
+            thirteenth international conference on artificial intelligence and
+            statistics (pp. 249-256). JMLR Workshop and Conference Proceedings.
 
         !!! example
             Specifying a weight array:

pycmtensor/expressions.py

@@ -18,7 +18,8 @@
 ```
 
 ## Inputs
-- `level` (int): The level of the logger to be set. It can be one of the predefined levels: `DEBUG`, `INFO`, `WARNING`, `ERROR`, or `CRITICAL`.
+- `level` (int): The level of the logger to be set. It can be one of the predefined 
+  levels: `DEBUG`, `INFO`, `WARNING`, `ERROR`, or `CRITICAL`.
 
 ## Outputs
 - None

pycmtensor/logger.py

@@ -37,7 +37,8 @@ def __init__(self):
             bhhh_matrix (numpy.ndarray): the 3-D bhhh matrix where the 1st dimension is
                 the length of the dataset and the last 2 dimensions are the matrix for
                 each data observation
-            statistics_graph (dict): a dictionary containing the learning_rate, training, and validation statistics
+            statistics_graph (dict): a dictionary containing the learning_rate,
+                training, and validation statistics
             betas (dict): a dictionary containing the Beta coefficients
             params (dict): a dictionary containing all model coefficients
         """
@@ -99,7 +100,8 @@ def AIC(self):
         return 2.0 * (k - self.best_loglikelihood)
 
     def BIC(self):
-        """Bayesian information criterion, adjusted for the number of parameters and number of training samples
+        """Bayesian information criterion, adjusted for the number of parameters and
+           number of training samples
 
         Returns:
             (float): the BIC of the model

pycmtensor/results.py

@@ -3,14 +3,21 @@
 This module contains methods for calculating the statistics of the estimated parameters.
 
 Functions:
-- variance_covariance(hessian): Computes the variance covariance matrix given the Hessian.
-- rob_variance_covariance(hessian, bhhh): Computes the robust variance covariance matrix given the Hessian and BHHH matrices.
-- t_test(stderr, params): Computes the statistical t-test of the estimated parameters and the standard errors.
-- p_value(stderr, params): Computes the p-value (statistical significance) of the estimated parameters using the two-tailed normal distribution.
-- stderror(hessian, params): Calculates the standard error of the estimated parameters given the Hessian matrix.
-- rob_stderror(hessian, bhhh, params): Calculates the robust standard error of the estimated parameters given the Hessian and BHHH matrices.
+- variance_covariance(hessian): Computes the variance covariance matrix given the 
+  Hessian.
+- rob_variance_covariance(hessian, bhhh): Computes the robust variance covariance 
+  matrix given the Hessian and BHHH matrices.
+- t_test(stderr, params): Computes the statistical t-test of the estimated parameters 
+  and the standard errors.
+- p_value(stderr, params): Computes the p-value (statistical significance) of the 
+  estimated parameters using the two-tailed normal distribution.
+- stderror(hessian, params): Calculates the standard error of the estimated parameters 
+  given the Hessian matrix.
+- rob_stderror(hessian, bhhh, params): Calculates the robust standard error of the 
+  estimated parameters given the Hessian and BHHH matrices.
 - correlation_matrix(hessian): Computes the correlation matrix from the Hessian matrix.
-- rob_correlation_matrix(hessian, bhhh): Computes the robust correlation matrix from the Hessian and BHHH matrices.
+- rob_correlation_matrix(hessian, bhhh): Computes the robust correlation matrix from  
+  the Hessian and BHHH matrices.
 """
 import numpy as np
 from scipy import stats
@@ -37,7 +44,9 @@ def variance_covariance(hessian):
 
     !!! notes
 
-        The variance covariance matrix is calculated by taking the inverse of the (negative) hessian matrix. If the inverse is undefined, returns a zero or a large finite number.
+        The variance covariance matrix is calculated by taking the inverse of the
+        (negative) hessian matrix. If the inverse is undefined, returns a zero or a
+        large finite number.
 
         $$
         varcovar = -H^{-1}
@@ -47,7 +56,8 @@ def variance_covariance(hessian):
 
 
 def rob_variance_covariance(hessian, bhhh):
-    """computes the robust variance covariance matrix given the Hessian and the BHHH matrices
+    """computes the robust variance covariance matrix given the Hessian and the BHHH
+       matrices
 
     Args:
         hessian (numpy.ndarray): the hessian matrix
@@ -83,7 +93,9 @@ def t_test(stderr, params):
 
 
 def p_value(stderr, params):
-    """computes the p-value (statistical significance) of the estimated parameter using the two-tailed normal distribution, where p-value=$2(1-\\phi(|t|)$, $\\phi$ is the cdf of the normal distribution
+    """computes the p-value (statistical significance) of the estimated parameter using
+       the two-tailed normal distribution, where p-value=$2(1-\\phi(|t|)$, $\\phi$ is
+       the cdf of the normal distribution
 
     Args:
         stderr (pandas.Series): standard errors
@@ -128,7 +140,8 @@ def stderror(hessian, params):
 
 
 def rob_stderror(hessian, bhhh, params):
-    """calculates the robust standard error of the estimated parameter given the hessian and the bhhh matrices
+    """calculates the robust standard error of the estimated parameter given the
+       hessian and the bhhh matrices
 
     Args:
         hessian (numpy.ndarray): the hessian matrix

pycmtensor/statistics.py

@@ -36,6 +36,13 @@ optional = true
 pytest = ">=7.4.3"
 pytest-cov = ">=4.1.0"
 
+[tool.poetry.group.lint]
+optional = true
+
+[tool.poetry.group.lint.dependencies]
+black = ">=22.1.0"
+isort = ">=5.9.3"
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"