ExpDesign: Add LHS space-filling algorithm

Also add parameters for random.

Example:

--exp-design lhs(42,100)

Will sample 100 points using maxmin design, and the seed 42

Very usefull to quickly get the influence of the factors

npf/expdesign/fullexp.py

@@ -16,6 +16,7 @@ def __init__(self, vlist, overriden):
  newList = []
  l = v.makeValues()
 
+ # For every of those new values we duplicate all existing runs
  for nvalue in l:
  for ovalue in self.expanded:
  z = ovalue.copy()

npf/expdesign/randomexp.py

@@ -1,11 +1,14 @@
-from random import shuffle
-
+import random
 from npf.expdesign.fullexp import FullVariableExpander
 
 
 class RandomVariableExpander(FullVariableExpander):
  """Same as BruteVariableExpander but shuffle the series to test"""
 
- def __init__(self, vlist, overriden):
+ def __init__(self, vlist, overriden, seed, n_iter):
  super().__init__(vlist, overriden)
- shuffle(self.expanded)
+
+ random.seed(seed)
+ random.shuffle(self.expanded)
+ if n_iter > 0:
+ self.expanded = self.expanded[:n_iter]

npf/sections/__init__.py

@@ -1,7 +1,9 @@
 from collections import OrderedDict
 import re
+import time
 from typing import List, Set
 from npf.expdesign.fullexp import FullVariableExpander
+from npf.expdesign.lhsexp import LHSVariableExpander
 from npf.expdesign.randomexp import RandomVariableExpander
 from npf.expdesign.zltexp import ZLTVariableExpander
 from npf.repository import Repository
@@ -200,19 +202,52 @@ def replace_all(self, value):
  values.append(SectionVariable.replace_variables(v, value))
  return values
 
- def expand(self, results={}, method="full", overriden=set()):
- if method == "shuffle" or method == "rand" or method == "random":
- return RandomVariableExpander(self.vlist)
+ def expand(self, results=None, method="full", overriden=set()):
+ """Expands variables based on the specified method.
+
+ This method determines how to expand the variables in the list based on the provided method.
+ It can return different types of variable expanders depending on the method specified, including
+ random shuffling or specific ZLT expansions.
+
+ Args:
+ results (dict, optional): A dictionary of previous results for iterative methods. Defaults to an empty dictionary.
+ method (str, optional): The method to use for expansion. Can be "full", "shuffle", "rand", "random", or variations of "zl". Defaults to "full".
+ overriden (set, optional): A set of overridden variables. Defaults to an empty set.
+
+ Returns:
+ VariableExpander: An instance of the appropriate variable expander based on the method specified.
+
+ """
+
+ if results is None:
+ results = {}
+ a = method.find("(")
+ params = []
+
+ if a > 0:
+ params = method[a + 1:-1].split(",")
+ if method == "shuffle" or method.startswith("rand"):
+ if len(params) >= 1:
+ seed = int(params[0])
+ else:
+ print("WARNING: Using a time-based seed. Please set the seed with --exp-design random(42)")
+ seed = time.time()
+ return RandomVariableExpander(self.vlist, overriden, seed = seed, n_iter = int(params[1]) if len(params) >= 2 else -1)
+ elif method.startswith("lhs"):
+ if len(params) >= 1:
+ seed = int(params[0])
+ else:
+ print("WARNING: Using a time-based seed. Please set the seed with --exp-design lhs(42)")
+ seed = time.time()
+ return LHSVariableExpander(self.vlist, overriden, seed = seed, n_iter = int(params[1]) if len(params) >= 2 else -1)
  elif "zl" in method.lower():
  if method.lower().startswith("allzl"):
- params = method[7:-1].split(",")
- all = True
+ all_lower = True
  perc = method.lower()[5] == 'p'
  else:
- params = method[4:-1].split(",")
- all = False
+ all_lower = False
  perc = method.lower()[2] == 'p'
- return ZLTVariableExpander(self.vlist, overriden=overriden, results=results, input=params[0], output=params[1], margin=1.01 if len(params) == 2 else float(params[2]), all=all, perc=perc)
+ return ZLTVariableExpander(self.vlist, overriden=overriden, results=results, input=params[0], output=params[1], margin=1.01 if len(params) == 2 else float(params[2]), all=all_lower, perc=perc)
 
  else:
  return FullVariableExpander(self.vlist, overriden)

npf/statistics.py

@@ -167,6 +167,7 @@ def printline(n):
  else:
  interaction_matrix.loc[factors[0],result_type] = interaction_pvalues[interaction]
 
+ print("")
  print("P-value of ANOVA (low p-value indicates a probable interaction):")
  print(interaction_matrix.fillna(""))
  ax = sn.heatmap(interaction_matrix, cmap="viridis", fmt=".2f", annot=True)

setup.py

@@ -34,7 +34,8 @@
  'jinja2',
  'spellwise',
  'seaborn',
- 'statsmodels'
+ 'statsmodels',
+ 'scikit-optimize'
  ]
 
 setuptools.setup(