0.24.3

formatting, new fcts: show_exp_table, show_res_table

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.24.2"
+version = "0.24.3"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/hyperparameters/values.py

@@ -187,7 +187,8 @@ def get_dict_with_levels_and_types(fun_control: Dict[str, Any], v: Dict[str, Any
     Examples:
             >>> from spotpython.hyperdict.light_hyper_dict import LightHyperDict
                 from spotpython.hyperparameters.values import get_default_hyperparameters_as_array
-                from spotpython.hyperparameters.values import assign_values, get_var_name, iterate_dict_values, convert_keys, get_dict_with_levels_and_types, get_core_model_from_name, add_core_model_to_fun_control
+                from spotpython.hyperparameters.values import (assign_values, get_var_name, iterate_dict_values,
+                    convert_keys, get_dict_with_levels_and_types, get_core_model_from_name, add_core_model_to_fun_control)
                 import pprint
                 core_model_name="light.regression.NNLinearRegressor"
                 hyperdict=LightHyperDict
@@ -508,40 +509,6 @@ def get_tuned_architecture(spot_tuner, force_minX=False) -> dict:
     return config
 
 
-def return_conf_list_from_var_dict(
-    var_dict: Dict[str, np.ndarray],
-    fun_control: Dict[str, Union[List[str], str]],
-    default: bool = False,
-) -> List[Dict[str, Union[int, float]]]:
-    """Return a list of configurations from a dictionary of variables.
-
-    This function takes a dictionary of variables and a dictionary of function control as input arguments.
-    It performs similar steps as generate_one_config_from_var_dict() but returns a list of dictionaries
-    of hyper parameter values.
-
-    Args:
-        var_dict (dict): A dictionary where keys are variable names and values are numpy arrays.
-        fun_control (dict): A dictionary which (at least) has an entry with the following key:
-            "var_type" (list): A list of variable types. If the entry is not "num" the corresponding
-            value will be converted to the type "int".
-
-    Returns:
-        list: A list of dictionaries of hyper parameter values. Transformations are applied to the values.
-
-    Examples:
-        >>> import numpy as np
-        >>> from spotpython.hyperparameters.values import return_conf_list_from_var_dict
-        >>> var_dict = {'a': np.array([1, 3, 5]), 'b': np.array([2, 4, 6])}
-        >>> fun_control = {'var_type': ['int', 'int']}
-        >>> return_conf_list_from_var_dict(var_dict, fun_control)
-        [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}, {'a': 5, 'b': 6}]
-    """
-    conf_list = []
-    for values in generate_one_config_from_var_dict(var_dict, fun_control, default=default):
-        conf_list.append(values)
-    return conf_list
-
-
 def modify_boolean_hyper_parameter_levels(fun_control, hyperparameter, levels) -> None:
     """
     This function modifies the levels of a boolean hyperparameter in the fun_control dictionary.

src/spotpython/spot/spot.py

@@ -361,7 +361,7 @@ def __init__(
 
         # save experiment move here (spotpython >= v0.24.1)
         if self.fun_control.get("save_experiment"):
-            self.save_experiment(verbosity=self.verbosity)        
+            self.save_experiment(verbosity=self.verbosity)
 
         logger.setLevel(self.log_level)
         logger.info(f"Starting the logger at level {self.log_level} for module {__name__}:")
@@ -1139,7 +1139,7 @@ def save_experiment(self, filename=None, path=None, overwrite=True, unpickleable
         # Ensure we don't accidentally try to pickle unpicklable components
         self._close_and_del_spot_writer()
         self._remove_logger_handlers()
-        
+
         S = self._get_pickle_safe_spot_tuner(unpickleables=unpickleables, verbosity=verbosity)
 
         # Determine the filename based on PREFIX if not provided

src/spotpython/utils/eda.py

@@ -45,6 +45,75 @@ def get_stars(input_list) -> list:
     return output_list
 
 
+def show_exp_table(fun_control: dict, tablefmt="github") -> str:
+    """Generates a table with the design variables and their bounds.
+        Can be used for the experiment design, which was not run yet.
+    Args:
+        fun_control (dict):
+            A dictionary with function design variables.
+    Returns:
+        (str):
+            a table with the design variables, their default values, and their bounds.
+            Use the `print` function to display the table.
+    """
+    default_values = get_default_values(fun_control)
+    defaults = list(default_values.values())
+    tab = tabulate(
+        {
+            "name": get_var_name(fun_control),
+            "type": get_var_type(fun_control),
+            "default": defaults,
+            "lower": get_bound_values(fun_control, "lower", as_list=True),
+            "upper": get_bound_values(fun_control, "upper", as_list=True),
+            "transform": get_transform(fun_control),
+        },
+        headers="keys",
+        tablefmt=tablefmt,
+    )
+    return tab
+
+
+def show_res_table(spot: object = None, tablefmt="github") -> str:
+    """
+    Generates a table with the design variables and their bounds,
+    after the run was completed.
+
+    Args:
+        spot (object):
+            A spot object. Defaults to None.
+    Returns:
+        (str):
+            a table with the design variables, their default values, their bounds,
+            the value and the importance of each hyperparameter.
+            Use the `print` function to display the table.
+    """
+    fun_control = spot.fun_control
+    default_values = get_default_values(fun_control)
+    defaults = list(default_values.values())
+    res = spot.print_results(print_screen=False, dict=fun_control)
+    tuned = [item[1] for item in res]
+    importance = spot.get_importance()
+    stars = get_stars(importance)
+    tab = tabulate(
+        {
+            "name": get_var_name(fun_control),
+            "type": get_var_type(fun_control),
+            "default": defaults,
+            "lower": get_bound_values(fun_control, "lower", as_list=True),
+            "upper": get_bound_values(fun_control, "upper", as_list=True),
+            "tuned": tuned,
+            "transform": get_transform(fun_control),
+            "importance": importance,
+            "stars": stars,
+        },
+        headers="keys",
+        numalign="right",
+        floatfmt=("", "", "", "", "", "", "", ".2f"),
+        tablefmt=tablefmt,
+    )
+    return tab
+
+
 def gen_design_table(fun_control: dict, spot: object = None, tablefmt="github") -> str:
     """Generates a table with the design variables and their bounds.
     Args:

src/spotpython/utils/file.py

@@ -5,10 +5,6 @@
 import json
 import sys
 import importlib
-from spotpython.utils.eda import gen_design_table
-from spotpython.utils.init import setup_paths
-import copy
-import pprint
 
 
 # from torch.utils.tensorboard import SummaryWriter
@@ -214,11 +210,10 @@ def load_and_run_spot_python_experiment(PREFIX=None, filename=None) -> object:
 
     """
     S = load_experiment(PREFIX=PREFIX, filename=filename)
-    S.run()    
+    S.run()
     return S
 
 
-
 def load_dict_from_file(coremodel, dirname="userModel"):
     """Loads a dictionary from a json file.