add logging

alvaromc317 · Aug 22, 2020 · aaa21d4 · aaa21d4
1 parent ec96cce
commit aaa21d4
Show file tree

Hide file tree

Showing 4 changed files with 35 additions and 30 deletions.
diff --git a/asgl/asgl.py b/asgl/asgl.py
@@ -1,3 +1,4 @@
+import sys
 import functools
 import itertools
 import logging
@@ -7,7 +8,7 @@
 import numpy as np
 from sklearn.metrics import mean_absolute_error, median_absolute_error, mean_squared_error
 
-logger = logging.getLogger(__name__)
+logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 class ASGL:
@@ -67,7 +68,7 @@ def __model_checker(self):
         if self.model in self.valid_models:
             return True
         else:
-            logger.error(f'{self.model} is not a valid model. Valid models are {self.valid_models}')
+            logging.error(f'{self.model} is not a valid model. Valid models are {self.valid_models}')
             return False
 
     def __penalization_checker(self):
@@ -83,8 +84,8 @@ def __penalization_checker(self):
         if (self.penalization in self.valid_penalizations) or (self.penalization is None):
             return True
         else:
-            logger.error(f'{self.penalization} is not a valid penalization. '
-                         f'Valid penalizations are {self.valid_penalizations} or None')
+            logging.error(f'{self.penalization} is not a valid penalization. '
+                          f'Valid penalizations are {self.valid_penalizations} or None')
             return False
 
     def __dtype_checker(self):
@@ -182,7 +183,7 @@ def __preprocessing_itertools_param(self, lambda_vector, alpha_vector, lasso_wei
             param = itertools.product(lambda_vector, alpha_vector, lasso_weights_list, gl_weights_list)
         else:
             param = None
-            logger.error(f'Error preprocessing input parameters')
+            logging.error(f'Error preprocessing input parameters')
         param = list(param)
         return param
 
@@ -193,7 +194,7 @@ def __preprocessing(self):
         """
         # Run the input_checker to verify that the inputs have the correct format
         if self.__input_checker() is False:
-            logger.error('incorrect input parameters')
+            logging.error('incorrect input parameters')
             raise ValueError('incorrect input parameters')
         # Defines param as None for the unpenalized model
         if self.penalization is None:
@@ -270,7 +271,7 @@ def unpenalized_solver(self, x, y):
                 except (ValueError, cvxpy.error.SolverError):
                     continue
         if problem.status in ["infeasible", "unbounded"]:
-            logger.warning('Optimization problem status failure')
+            logging.warning('Optimization problem status failure')
         beta_sol = beta_var.value
         beta_sol[np.abs(beta_sol) < self.tol] = 0
         return [beta_sol]
@@ -316,11 +317,11 @@ def lasso(self, x, y, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = beta_var.value
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
-        logger.debug('Function finished without errors')
+        logging.debug('Function finished without errors')
         return beta_sol_list
 
     def gl(self, x, y, group_index, param):
@@ -376,7 +377,7 @@ def gl(self, x, y, group_index, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = np.concatenate([b.value for b in beta_var], axis=0)
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
@@ -439,7 +440,7 @@ def sgl(self, x, y, group_index, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = np.concatenate([b.value for b in beta_var], axis=0)
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
@@ -486,11 +487,11 @@ def alasso(self, x, y, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = beta_var.value
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
-        logger.debug('Function finished without errors')
+        logging.debug('Function finished without errors')
         return beta_sol_list
 
     def agl(self, x, y, group_index, param):
@@ -546,7 +547,7 @@ def agl(self, x, y, group_index, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = np.concatenate([b.value for b in beta_var], axis=0)
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
@@ -611,7 +612,7 @@ def asgl(self, x, y, group_index, param):
                     except (ValueError, cvxpy.error.SolverError):
                         continue
             if problem.status in ["infeasible", "unbounded"]:
-                logger.warning('Optimization problem status failure')
+                logging.warning('Optimization problem status failure')
             beta_sol = np.concatenate([b.value for b in beta_var], axis=0)
             beta_sol[np.abs(beta_sol) < self.tol] = 0
             beta_sol_list.append(beta_sol)
@@ -695,7 +696,7 @@ def predict(self, x_new):
         if self.intercept:
             x_new = np.c_[np.ones(x_new.shape[0]), x_new]
         if x_new.shape[1] != len(self.coef_[0]):
-            logger.error('Model dimension and new data dimension does not match')
+            logging.error('Model dimension and new data dimension does not match')
             raise ValueError('Model dimension and new data dimension does not match')
         # Store predictions in a list
         prediction_list = []
@@ -717,7 +718,7 @@ def _num_parameters(self):
         """
         # Run the input_checker to verify that the inputs have the correct format
         if self.__input_checker() is False:
-            logger.error('incorrect input parameters')
+            logging.error('incorrect input parameters')
             raise ValueError('incorrect input parameters')
         if self.penalization is None:
             # See meaning of each element in the "else" result statement.
@@ -755,7 +756,7 @@ def _retrieve_parameters_idx(self, param_index):
         if param_index > n_models:
             string = f'param_index should be smaller or equal than the number of models solved. n_models={n_models}, ' \
                      f'param_index={param_index}'
-            logger.error(string)
+            logging.error(string)
             raise ValueError(string)
         # If penalization is None, all parameters are set to None
         if self.penalization is None:
@@ -822,7 +823,7 @@ def error_calculator(y_true, prediction_list, error_type="MSE", tau=None):
     if error_type not in valid_error_types:
         raise ValueError(f'invalid error type. Valid error types are {error_dict.keys()}')
     if y_true.shape[0] != len(prediction_list[0]):
-        logger.error('Dimension of test data does not match dimension of prediction')
+        logging.error('Dimension of test data does not match dimension of prediction')
         raise ValueError('Dimension of test data does not match dimension of prediction')
     # For each prediction, store the error associated to that prediction in a list
     error_list = []

diff --git a/asgl/cv.py b/asgl/cv.py
@@ -1,12 +1,13 @@
 import logging
+import sys
 
 import numpy as np
 from sklearn.model_selection import KFold
 
 from . import asgl
 from . import weights
 
-logger = logging.getLogger(__name__)
+logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 class CvGeneralClass(asgl.ASGL):
@@ -73,8 +74,9 @@ def __init__(self, model, penalization, intercept=True, tol=1e-5, lambda1=1, alp
         """
         # CvGeneralClass
         super().__init__(model, penalization, intercept, tol, lambda1, alpha, tau, lasso_weights, gl_weights, parallel,
-                         num_cores, solver, max_iters, weight_technique, weight_tol, lasso_power_weight, gl_power_weight,
-                         variability_pct, lambda1_weights, spca_alpha, spca_ridge_alpha, error_type, random_state)
+                         num_cores, solver, max_iters, weight_technique, weight_tol, lasso_power_weight,
+                         gl_power_weight, variability_pct, lambda1_weights, spca_alpha, spca_ridge_alpha, error_type,
+                         random_state)
         # Relative to cross validation / train validate / test
         self.nfolds = nfolds
 
@@ -122,8 +124,9 @@ def __init__(self, model, penalization, intercept=True, tol=1e-5, lambda1=1, alp
                  random_state=None, train_pct=0.05, validate_pct=0.05, train_size=None, validate_size=None):
 
         super().__init__(model, penalization, intercept, tol, lambda1, alpha, tau, lasso_weights, gl_weights, parallel,
-                         num_cores, solver, max_iters, weight_technique, weight_tol, lasso_power_weight, gl_power_weight,
-                         variability_pct, lambda1_weights, spca_alpha, spca_ridge_alpha, error_type, random_state)
+                         num_cores, solver, max_iters, weight_technique, weight_tol, lasso_power_weight,
+                         gl_power_weight, variability_pct, lambda1_weights, spca_alpha, spca_ridge_alpha, error_type,
+                         random_state)
         # Relative to / train validate / test
         self.train_pct = train_pct
         self.validate_pct = validate_pct
@@ -195,7 +198,7 @@ def train_test_split(nrows, train_size=None, train_pct=0.7, random_state=None):
         train_size = int(round(nrows * train_pct))
     # Check that nrows is larger than train_size
     if nrows < train_size:
-        logger.error(f'Train size is too large. Input number of rows:{nrows}, current train_size: {train_size}')
+        logging.error(f'Train size is too large. Input number of rows:{nrows}, current train_size: {train_size}')
     # List of 2 elements of size train_size, remaining_size (test)
     split_index = np.split(data_index, [train_size])
     train_idx, test_idx = [elt for elt in split_index]

diff --git a/asgl/weights.py b/asgl/weights.py
@@ -1,4 +1,5 @@
 import logging
+import sys
 
 import numpy as np
 from sklearn.cross_decomposition import PLSRegression
@@ -7,7 +8,7 @@
 
 from .asgl import ASGL
 
-logger = logging.getLogger(__name__)
+logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 class WEIGHTS:
@@ -200,6 +201,6 @@ def fit(self, x, y=None, group_index=None):
         else:
             lasso_weights = None
             gl_weights = None
-            logger.error(f'Not a valid penalization for weight calculation. Valid penalizations '
-                         f'are {self.valid_penalizations}')
+            logging.error(f'Not a valid penalization for weight calculation. Valid penalizations '
+                          f'are {self.valid_penalizations}')
         return lasso_weights, gl_weights
diff --git a/setup.py b/setup.py
@@ -9,13 +9,13 @@
 
 setup(
     name='asgl',
-    version='v1.0.1',
+    version='v1.0.2',
     author='Alvaro Mendez Civieta',
     author_email='[email protected]',
     license='GNU General Public License',
     zip_safe=False,
     url='https://github.com/alvaromc317/asgl',
-    dowload_url='https://github.com/alvaromc317/asgl/archive/v1.0.1.tar.gz',
+    dowload_url='https://github.com/alvaromc317/asgl/archive/v1.0.2.tar.gz',
     description='A regression solver for linear and quantile regression models and lasso based penalizations',
     long_description=long_description,
     long_description_content_type='text/markdown',