master-thesis/EvolutionaryAlgorithm/EvolutionaryAlgorithm.py

1157 lines
34 KiB
Python

#!/usr/bin/env python3
# coding: utf-8
# # Evolutionary Algorithm
# the Evolutionary Algorithm that is supposed to create new recipes based on the Recipe Matrices that are created during the *Recipe Analysis* step.
#
# The Population of the Evolutional Algorithm consists of a set of recipe trees. Each Recipe Tree consists of several Nodes where each node is of one of the following Types:
#
# * **Ingredient Node:**
# these are the leaf nodes. Containing an ingredient. The score is determined by the actions, that are applied if you follow up the path. At the Moment it measures how many duplicate actions are applied.
# * **Action Node:**
# An Action that is applied on it's child and this child's subtree. Score indicates the average likelihood that this action is applied on the ingredients inside the subtree
# * **Mix Node:**
# Mixing ingredients together. This is also the only Node that can have more than one child. The score is the average of all pairwise likelihoods that two ingredients are mixed togethter
import sys
sys.path.append("../")
sys.path.append("../RecipeAnalysis/")
import settings
import pycrfsuite
import json
import db.db_settings as db_settings
from db.database_connection import DatabaseConnection
from Tagging.conllu_generator import ConlluGenerator
from Tagging.crf_data_generator import *
from RecipeAnalysis.Recipe import Ingredient
import ea_tools
from difflib import SequenceMatcher
import numpy as np
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.subplots import make_subplots
init_notebook_mode(connected=True)
from graphviz import Digraph
import itertools
import random
import plotly.io as pio
pio.renderers.default = "jupyterlab"
from IPython.display import Markdown, HTML, display
from tqdm.autonotebook import tqdm
from copy import deepcopy
def gaussian(x, mu, sig):
return 1./(np.sqrt(2.*np.pi)*sig)*np.exp(-np.power((x - mu)/sig, 2.)/2)
# ## load adjacency matrices
import dill
m_act = dill.load(open("../RecipeAnalysis/m_act.dill", "rb"))
m_mix = dill.load(open("../RecipeAnalysis/m_mix.dill", "rb"))
m_base_act = dill.load(open("../RecipeAnalysis/m_base_act.dill", "rb"))
m_base_mix = dill.load(open("../RecipeAnalysis/m_base_mix.dill", "rb"))
m_grouped_mix = dill.load(open("../RecipeAnalysis/m_grouped_mix_raw.dill", "rb"))
m_grouped_act = dill.load(open("../RecipeAnalysis/m_grouped_act_raw.dill", "rb"))
m_grouped_base_act = dill.load(open("../RecipeAnalysis/m_grouped_base_act_raw.dill", "rb"))
#m_act.apply_threshold(3)
#m_mix.apply_threshold(3)
#m_base_act.apply_threshold(5)
#m_base_mix.apply_threshold(5)
#c_act = m_act.get_csr()
#c_mix = m_mix.get_csr()
#c_base_act = m_base_act.get_csr()
#c_base_mix = m_base_mix.get_csr()
m_act.compile()
m_mix.compile()
m_base_act.compile()
m_base_mix.compile()
m_grouped_mix.compile()
m_grouped_act.compile()
m_grouped_base_act.compile()
c_act = m_act._csr
c_mix = m_mix._csr
c_base_act = m_base_act._csr
c_base_mix = m_base_mix._csr
actions = m_act.get_labels()[0]
base_ingredients = m_base_mix.get_labels()
sym_label_buffer = {}
fw_label_buffer = {}
bw_label_buffer = {}
# ### helper functions for adjacency matrices
def get_sym_adjacent(key, m, c):
index = m._label_index[key]
i1 = c[index,:].nonzero()[1]
i2 = c[:,index].nonzero()[0]
i = np.concatenate((i1,i2))
if m in sym_label_buffer:
names = sym_label_buffer[m][i]
else:
names = np.array(m.get_labels())
sym_label_buffer[m] = names
names = names[i]
counts = np.concatenate((c[index, i1].toarray().flatten(), c[i2, index].toarray().flatten()))
s = np.argsort(-counts)
return names[s], counts[s]
def get_forward_adjacent(key, m, c):
index = m._x_label_index[key]
i = c[index,:].nonzero()[1]
if m in fw_label_buffer:
names = fw_label_buffer[m][i]
else:
names = np.array(m._y_labels)
fw_label_buffer[m] = names
names = names[i]
counts = c[index, i].toarray().flatten()
s = np.argsort(-counts)
return names[s], counts[s]
def get_backward_adjacent(key, m, c):
index = m._y_label_index[key]
i = c[:,index].nonzero()[0]
if m in bw_label_buffer:
names = bw_label_buffer[m][i]
else:
names = np.array(m._x_labels)
bw_label_buffer[m] = names
names = names[i]
counts = c[i, index].toarray().flatten()
s = np.argsort(-counts)
return names[s], counts[s]
def sym_sum(key, m, c):
return np.sum(get_sym_adjacent(key,m,c)[1])
def fw_sum(key, m, c):
return np.sum(get_forward_adjacent(key,m,c)[1])
def bw_sum(key, m, c):
return np.sum(get_backward_adjacent(key,m,c)[1])
# #### different score functions
# ##### normalizations
def fw_normalization_factor(key, m, c, quotient_func):
ia = m._x_label_index[key]
occurances = c[ia,:].nonzero()[1]
return 1. / quotient_func(c[ia,occurances].toarray())
def bw_normalization_factor(key, m, c, quotient_func):
ib = m._y_label_index[key]
occurances = c[:,ib].nonzero()[0]
return 1. / quotient_func(c[occurances,ib].toarray())
def sym_normalization_factor(key, m, c, quotient_func):
ii = m._label_index[key]
fw_occurances = c[ii,:].nonzero()[1]
bw_occurances = c[:,ii].nonzero()[0]
return 1. / quotient_func(np.concatenate(
[c[ii,fw_occurances].toarray().flatten(),
c[bw_occurances,ii].toarray().flatten()]
))
def sym_p_a_given_b(key_a, key_b, m, c, quot_func = np.max):
ia = m._label_index[key_a]
ib = m._label_index[key_b]
v = c[ia,ib] + c[ib,ia]
return v * sym_normalization_factor(key_b, m, c, quot_func)
def fw_p_a_given_b(key_a, key_b, m, c, quot_func = np.max):
ia = m._x_label_index[key_a]
ib = m._y_label_index[key_b]
v = c[ia,ib]
return v * bw_normalization_factor(key_b, m, c, quot_func)
def bw_p_a_given_b(key_a, key_b, m, c, quot_func = np.max):
ia = m._y_label_index[key_a]
ib = m._x_label_index[key_b]
v = c[ib,ia]
return v * fw_normalization_factor(key_b, m, c, quot_func)
def sym_score(key_a, key_b, m, c):
ia = m._label_index[key_a]
ib = m._label_index[key_b]
v = c[ia,ib] + c[ib,ia]
if v == 0:
return 0
return max((v/sym_sum(key_a, m, c)), (v/sym_sum(key_b, m, c)))
def asym_score(key_a, key_b, m, c):
ia = m._x_label_index[key_a]
ib = m._y_label_index[key_b]
v = c[ia,ib]
if v == 0:
return 0
return max(v/fw_sum(key_a, m, c), v/bw_sum(key_b, m, c))
def p_ingredient_unprepared(base_ing):
ing = Ingredient(base_ing)
base_sum = sym_sum(base_ing, m_base_mix, c_base_mix)
specialized_sum = sym_sum(ing.to_json(), m_mix, c_mix)
return specialized_sum / base_sum
def p_heat(base_ing):
heat_actions = ["heat","cook","simmer","bake"]
heat_sum = 0
m
# ## Recipe Tree
# ### Tree Node Base Class
class RecipeTreeNode(object):
id = 0
def __init__(self, name, constant=False, single_child=False):
self._constant = constant
self._name = name
self._parent = None
self._id = str(RecipeTreeNode.id)
RecipeTreeNode.id += 1
self._single_child = single_child
if self._single_child:
self._child = None
def child():
return self._child
def remove_child(c):
assert c == self._child
self._child._parent = None
self._child = None
def childs():
c = self.child()
if c is None:
return set()
return set([c])
def add_child(n):
self._child = n
n._parent = self
self.child = child
self.childs = childs
self.add_child = add_child
self.remove_child = remove_child
else:
self._childs = set()
def childs():
return self._childs
def add_child(n):
self._childs.add(n)
n._parent = self
def remove_child(c):
assert c in self._childs
c._parent = None
self._childs.remove(c)
self.childs = childs
self.add_child = add_child
self.remove_child = remove_child
def parent(self):
return self._parent
def root(self):
if self._parent is None:
return self
return self._parent.root()
def name(self):
return self._name
def traverse(self):
l = []
for c in self.childs():
l += c.traverse()
return [self] + l
def traverse_ingredients(self):
ingredient_set = []
for c in self.childs():
ingredient_set += c.traverse_ingredients()
return ingredient_set
def remove(self):
p = self.parent()
childs = self.childs().copy()
assert p is None or not (len(childs) > 1 and p._single_child)
for c in childs:
self.remove_child(c)
if p is not None:
p.remove_child(self)
if self._single_child and self._child is not None and p._name == self._child._name:
# two adjacent nodes with same name would remain after deletion.
# merge them! (by adding the child's childs to our parent instead of our childs)
childs = self._child.childs()
self._child.remove()
for c in childs:
p.add_child(c)
def insert_before(self, n):
p = self._parent
if p is not None:
p.remove_child(self)
p.add_child(n)
n.add_child(self)
def mutate(self):
n_node = self.n_node_mutate_options()
n_edge = self.n_edge_mutate_options()
choice = random.choice(range(n_node + n_edge))
if choice < n_node:
self.mutate_node()
else:
self.mutate_edges()
def mutate_edges(self):
ings = self.traverse_ingredients()
ing = random.choice(ings)
a, w = get_backward_adjacent(ing._base_ingredient, m_base_act, c_base_act)
action = random.choices(a, w)[0]
self.insert_before(ActionNode(action))
def mutate_node(self):
raise NotImplementedError
def n_node_mutate_options(self):
return 0 if self._constant else 1
def n_edge_mutate_options(self):
n = 1 if self._parent is not None else 0
return n
def n_mutate_options(self):
return self.n_edge_mutate_options() + self.n_node_mutate_options()
def dot_node(self, dot):
raise NotImplementedError()
def dot(self, d=None):
if d is None:
d = Digraph()
self.dot_node(d)
else:
self.dot_node(d)
if self._parent is not None:
d.edge(self._parent._id, self._id)
for c in self.childs():
c.dot(d)
return d
def serialize(self):
r = {}
r['type'] = str(self.__class__.__name__)
r['id'] = self._id
r['parent'] = self._parent._id if self._parent is not None else None
r['name'] = self._name
r['childs'] = [c._id for c in self.childs()]
r['constant'] = self._constant
r['single_child'] = self._single_child
return r
def node_score(self):
raise NotImplementedError()
# ### Mix Node
# For the Node Score: just make a simple lookup whether this combination is seen or not. So the node Score is defined as:
#
class MixNode(RecipeTreeNode):
def __init__(self, constant=False):
super().__init__("mix", constant, single_child=False)
def dot_node(self, dot):
dot.node(self._id, label=f"< <B>{self._name}</B><BR/>node score: {self.node_score():.4f}>", shape="diamond", style="filled", color="#d5e8d4")
def split(self, set_above, set_below, node_between):
assert len(set_above.difference(self.childs())) == 0
assert len(set_below.difference(self.childs())) == 0
n_above = MixNode()
n_below = MixNode()
p = self.parent()
for c in self.childs().copy():
self.remove_child(c)
self.remove()
for c in set_below:
n_below.add_child(c)
for c in set_above:
n_above.add_child(c)
n_above.add_child(node_between)
node_between.add_child(n_below)
if p is not None:
p.add_child(n_above)
# test whether the mix nodes are useless
if len(n_above.childs()) == 1:
n_above.remove()
if len(n_below.childs()) == 1:
n_below.remove()
def n_node_mutate_options(self):
return 0 if self._constant or len(self.childs()) <= 2 else len(self.childs())
def mutate_node(self):
childs = self.childs()
if len(childs) <= 2:
print("Warning: cannot modify mix node")
return
childs = random.sample(childs, len(childs))
n = random.choice(range(1, len(childs)-1))
between_node = ActionNode(random.choice(actions))
self.split(set(childs[:n]), set(childs[n:]), between_node)
def node_score(self):
child_ingredients = [c.traverse_ingredients() for c in self.childs()]
tmp_set = set()
cumulative_sets = []
pairwise_tuples = []
for c in child_ingredients:
if len(tmp_set) > 0:
cumulative_sets.append(tmp_set)
pairwise_tuples += [x for x in itertools.product(tmp_set, c)]
tmp_set = tmp_set.union(set(c))
s_base = 0
s = 0
for ing_a, ing_b in pairwise_tuples:
try:
#s_base += sym_score(ing_a._base_ingredient, ing_b._base_ingredient, m_base_mix, c_base_mix)
#s += sym_score(ing_a.to_json(), ing_b.to_json(), m_mix, c_mix)
# old method:
#p1 = sym_p_a_given_b(ing_a.to_json(), ing_b.to_json(), m_mix, c_mix)
#p2 = sym_p_a_given_b(ing_b.to_json(), ing_a.to_json(), m_mix, c_mix)
#s += 0.5 * p1 + 0.5 * p2
ia = m_mix._label_index[ing_a.to_json()]
ib = m_mix._label_index[ing_b.to_json()]
if c_mix[ia,ib] > 0 or c_mix[ib,ia] > 0:
s += 1
except KeyError as e:
pass
#s_base /= len(pairwise_tuples)
s /= len(pairwise_tuples)
#return 0.5 * (s_base + s)
return s
# ### Ingredient Node Class
class IngredientNode(RecipeTreeNode):
def __init__(self, name, constant=False):
super().__init__(name, constant, single_child=True)
def get_actions(self):
a_list = []
n = self.parent()
while n is not None:
if type(n) == ActionNode:
a_list.append(n.name())
n = n.parent()
return a_list
def mutate_node(self):
self._name = random.choice(base_ingredients)
#TODO: change w.r.t. mixing probabilities
def traverse_ingredients(self):
return [Ingredient(self._name)]
def node_score(self):
actions = self.get_actions()
if len(actions) == 0:
if p_ingredient_unprepared(self._name) < 0.2:
return 0
return 1
seen_actions = set()
n_duplicates = 0
for act in actions:
if act in seen_actions:
n_duplicates += 1
else:
seen_actions.add(act)
duplicate_actions_score = len(seen_actions) / len(actions)
return duplicate_actions_score
def dot_node(self, dot):
dot.node(self._id, label=f"< <B>{self._name}</B><BR/>node score:{self.node_score():.4f}>", shape="box", style="filled", color="#ffe6cc")
# ### Action Node Class
class ActionNode(RecipeTreeNode):
def __init__(self, name, constant=False):
super().__init__(name, constant, single_child=True)
def n_node_mutate_options(self):
# beacause we can change or remove ourselve!
return 0 if self._constant else 2
def mutate_node(self):
if random.choice(range(2)) == 0:
# change action
self._name = random.choice(actions)
else:
# delete
self.remove()
def traverse_ingredients(self):
ingredient_set = super().traverse_ingredients()
for ing in ingredient_set:
ing.apply_action(self._name)
return ingredient_set
def node_score(self):
ings = self.child().traverse_ingredients()
s = 0
for ing in ings:
try:
#score = asym_score(self._name, ing.to_json(), m_act, c_act)
#base_score = asym_score(self._name, ing._base_ingredient, m_base_act, c_base_act)
score = fw_p_a_given_b(self._name, ing._base_ingredient, m_base_act, c_base_act)
s += score
except KeyError as e:
pass
return s / len(ings)
def dot_node(self, dot):
dot.node(self._id, label=f"< <B>{self._name}</B><BR/>node score: {self.node_score():.4f}>", shape="ellipse", style="filled", color="#dae8fc")
# ### Tree Class
class Tree(object):
@staticmethod
def build_initial_tree(ingredients: list, main_ingredients: list, max_n = 4, wheel_turns = 2):
'''
# get action sets for ingredients
possible_actions = {}
for ing in ingredients:
action_set, action_weights = m_base_act.get_backward_adjacent(ing)
possible_actions[ing] = set(action_set.tolist()[:5])
# now find actions with the same subset
ings_for_acts = {}
for ing, acts in possible_actions.items():
for a in acts:
if a not in ings_for_acts:
ings_for_acts[a] = set()
ings_for_acts[a].add(ing)
'''
# choose randomly an action for each ingredient by the "wheel of fortune" method
actions_for_ing = {}
for ing in ingredients:
actions_for_ing[ing] = set()
action_set, action_weights = m_base_act.get_backward_adjacent(ing)
for i in range(wheel_turns):
action = ea_tools.wheel_of_fortune_selection(action_set[:max_n], action_weights[:max_n])
actions_for_ing[ing].add(action)
#print(ing, action)
ings_for_acts = {}
for ing, acts in actions_for_ing.items():
for a in acts:
if a not in ings_for_acts:
ings_for_acts[a] = set()
ings_for_acts[a].add(ing)
# now looking for the largest subset and choose one of them randomly
action_keys = np.array(list(ings_for_acts.keys()))
set_lengths = np.array([len(ings_for_acts[a]) for a in action_keys])
# sort lengths
sorted_length_indices = np.argsort(-set_lengths)
# now perform the following steps:
# * go through all unprocessed ingredients
# * for each ing: find largest action set that is suitable
# * perform this action on all it's ingredients.
# * continue until no ingredient is left
unprocessed_ings = set(ingredients)
unprocessed_actions = set(ings_for_acts.keys())
ingredient_nodes = {}
# create ingredient nodes:
for ing in ingredients:
ingredient_nodes[ing] = IngredientNode(ing, constant=True)
i = 0
while len(unprocessed_ings) > 0:
# select random ingredient:
ing = np.random.choice(list(unprocessed_ings))
sorted_actions = action_keys[sorted_length_indices]
selected_action = None
for action in sorted_actions:
if ing in ings_for_acts[action]:
selected_action = action
break
# found best action. apply it to all matching ingredients
if selected_action is not None:
matching_ingredients = ings_for_acts[selected_action]
# debugging:
'''
print(f"choose {selected_action}")
print(f"matching ingredients {matching_ingredients}")
'''
if len(matching_ingredients) == 1:
ing = list(matching_ingredients)[0]
ing_node = ingredient_nodes[ing].root()
action_node = ActionNode(selected_action)
action_node.add_child(ing_node)
unprocessed_ings.remove(ing)
#display(action_node.dot())
else:
nodes_to_mix = set()
mix_node = MixNode()
action_node = ActionNode(selected_action)
action_node.add_child(mix_node)
for ing in matching_ingredients:
nodes_to_mix.add(ingredient_nodes[ing].root())
if ing in unprocessed_ings:
unprocessed_ings.remove(ing)
for node in nodes_to_mix:
mix_node.add_child(node)
#display(action_node.dot())
# debugging:
'''
tmp = set([n.root() for n in ingredient_nodes.values()])
print(f"iteration {i}:")
for n in tmp:
print(n.name())
display(n.dot())
'''
i += 1
root_layer = set([n.root() for n in ingredient_nodes.values()])
root_layer_without_parents = []
for node in root_layer:
if node.parent() is None:
root_layer_without_parents.append(node)
if len(root_layer_without_parents) == 1:
return root_layer_without_parents[0]
root_node = MixNode()
for r in root_layer_without_parents:
root_node.add_child(r)
return root_node
@staticmethod
def find_ingredients(constant_ingredients, min_additional:int, max_additional:int, top_ings:int=3):
'''
create an initial set of ingredients, based on given constant ingredients.
min_additional and max_additional gives the range of ingredients that are added to our set
'''
seen_items = set(constant_ingredients)
items = []
scores = []
for ing in constant_ingredients:
# find best matching ingredients
best_items = []
best_scores = []
candidates, weights = m_base_mix.get_adjacent(ing)
i = 0
while i < len(candidates) and len(best_items) < top_ings:
if candidates[i] not in seen_items:
best_items.append(candidates[i])
best_scores.append(weights[i])
i += 1
items.append(best_items)
scores.append(best_scores)
#TODO: error handling if too few options are availabale!
additional_ingredients = ea_tools.combined_wheel_of_fortune_selection(items,
scores,
np.random.randint(
min_additional,
max_additional + 1
))
return list(constant_ingredients) + list(additional_ingredients)
@staticmethod
def from_ingredients(ingredients: list, main_ingredients: list, additional_ings=0):
root = None
constant_ingredients = ingredients
if additional_ings > 0:
ingredients = Tree.find_ingredients(ingredients, min_additional=0, max_additional=additional_ings)
root = Tree.build_initial_tree(ingredients)
# mark initial ingredient nodes as constant:
nodes = root.traverse()
for node in nodes:
if type(node) == IngredientNode:
if node.name() in constant_ingredients:
node._constant = True
return Tree(root)
@staticmethod
def from_serialization(s):
def empty_node(raw_n):
if raw_n['type'] == "MixNode":
node = MixNode(raw_n['constant'])
elif raw_n['type'] == "IngredientNode":
node = IngredientNode(raw_n['name'], raw_n['constant'])
elif raw_n['type'] == "ActionNode":
node = ActionNode(raw_n['name'], raw_n['constant'])
else:
print("unknown node detected")
return
return node
nodes = {}
for n in s:
nodes[n['id']] = empty_node(n)
for n in s:
childs = n['childs']
id = n['id']
for c in childs:
nodes[id].add_child(nodes[c])
return Tree(nodes[s[0]['id']])
def __init__(self, root):
# create a dummy entry node
self._root = RecipeTreeNode("root", single_child=True)
self._root.add_child(root)
def root(self):
return self._root.child()
def mutate(self):
nodes = self.root().traverse()
weights = [n.n_mutate_options() for n in nodes]
n = random.choices(nodes, weights)[0]
n.mutate()
def dot(self):
return self.root().dot()
def serialize(self):
return [n.serialize() for n in self.root().traverse()]
def structure_score(self):
n_duplicates = 0
def collect_scores(self):
self._mix_scores = []
self._act_scores = []
self._ing_scores = []
nodes = self.root().traverse()
self._n_mix_nodes = 0
self._n_act_nodes = 0
self._n_ing_nodes = 0
s = 0
for n in nodes:
if type(n) == MixNode:
self._mix_scores.append(n.node_score())
self._n_mix_nodes += 1
if type(n) == ActionNode:
self._act_scores.append(n.node_score())
self._n_act_nodes += 1
if type(n) == IngredientNode:
self._ing_scores.append(n.node_score())
self._n_ing_nodes += 1
self._n_duplicates = 0
seen_actions = set()
for n in nodes:
if type(n) == ActionNode:
if n.name() in seen_actions:
self._n_duplicates += 1
else:
seen_actions.add(n.name())
self._mix_scores = np.array(self._mix_scores)
self._act_scores = np.array(self._act_scores)
self._ing_scores = np.array(self._ing_scores)
def mix_scores(self):
return self._mix_scores
def action_scores(self):
return self._act_scores
def ing_scores(self):
return self._ing_scores
def bounds_mix_scores(self):
nonzeros = self._mix_scores[self._mix_scores > 0]
if len(nonzeros) > 0:
mmax = np.max(nonzeros)
mmin = np.min(nonzeros)
return mmin, mmax
else:
return None, None
def bounds_act_scores(self):
if len(self._act_scores) == 0:
return None, None
nonzeros = self._act_scores[self._act_scores > 0]
if len(nonzeros) > 0:
mmax = np.max(nonzeros)
mmin = np.min(nonzeros)
return mmin, mmax
else:
return None, None
def normalized_mix_scores(self, min, max):
if (max != min):
normalized = (self._mix_scores - min)/(max-min)
normalized[normalized <= 0] = 0
return normalized
else:
return None
def normalized_act_scores(self, min, max):
if len(self._act_scores) == 0 or max == min:
return None
normalized = (self._act_scores - min)/(max-min)
normalized[normalized <= 0] = 0
return normalized
def copy(self):
return Tree.from_serialization(self.serialize())
# ## Population
class Population(object):
def __init__(self, start_ingredients, main_ingredients, n_population = 10, max_additional_ings=0):
self.population = [Tree.from_ingredients(start_ingredients, main_ingredients, additional_ings=max_additional_ings) for i in range(n_population)]
self._n = n_population
self._mix_min = None
self._mix_max = None
self._act_min = None
self._act_max = None
self._mix_scores = None
self._act_scores = None
self._scores = None
def mutate(self):
for tree in self.population.copy():
t_clone = tree.copy()
t_clone.mutate()
self.population.append(t_clone)
def pairwise_competition(self):
new_population = []
indices = list(range(len(self.population)))
random.shuffle(indices)
for i in range(len(self.population) // 2):
i_a = indices[2*i]
i_b = indices[2*i+1]
if self._scores[i_a] > self._scores[i_b]:
new_population.append(self.population[i_a])
else:
new_population.append(self.population[i_b])
self.population = new_population
def hold_best(self, n=10):
sorted_indices = np.argsort(-self._scores)
self.population = np.array(self.population)[sorted_indices[:n]].tolist()
def analyse_scores(self):
for tree in self.population:
min, max = tree.bounds_mix_scores()
if min is not None and max is not None:
if self._mix_min is None or min < self._mix_min:
self._mix_min = min
if self._mix_max is None or max > self._mix_max:
self._mix_max = max
min, max = tree.bounds_act_scores()
if min is not None and max is not None:
if self._act_min is None or min < self._act_min:
self._act_min = min
if self._act_max is None or max > self._act_max:
self._act_max = max
def single_score(self, mix_scores, act_scores, ing_scores):
if mix_scores is None or act_scores is None or ing_scores is None:
return 0
score = (0.5 * np.average(mix_scores) + 0.5 * np.average(act_scores)) * np.average(ing_scores)
# judging also how many actions we have. So far use a gaussian with mean at number of ingredients
score *= gaussian(len(act_scores), len(mix_scores), 1)
return score
def collect_scores(self):
for tree in tqdm(self.population, desc="evaluate population scores", leave=False):
tree.collect_scores()
self.analyse_scores()
if self._mix_min is not None and self._mix_max is not None:
self._mix_scores = [t.normalized_mix_scores(self._mix_min, self._mix_max) for t in self.population]
else:
# if no normalization can be done, all values are the same or 0.
# in this case just fill in zeros as score
self._mix_scores = [np.zeros(shape=t._mix_scores.shape) for t in self.population]
if self._act_min is not None and self._act_max is not None:
self._act_scores = [t.normalized_act_scores(self._act_min, self._act_max) for t in self.population]
else:
self._act_scores = [np.zeros(shape=t._act_scores) for t in self.population]
self._scores = []
for i in range(len(self._mix_scores)):
#print (self._mix_scores[i], self._act_scores[i])
if self._act_scores is None or self._mix_scores is None or self._act_scores[i] is None:
self._scores.append(0)
continue
s = self.single_score(self._mix_scores[i], self._act_scores[i], self.population[i].ing_scores())
self._scores.append(s)
self._scores = np.array(self._scores)
def run(self, n=50):
for i in tqdm(range(n), desc="run evolutionary cycles"):
self.mutate()
self.mutate()
self.collect_scores()
#self.pairwise_competition()
#self.collect_scores()
self.hold_best(self._n)
def plot_population(self, collect_scores=True):
if (collect_scores):
self.collect_scores()
#print(self._mix_scores)
#print(self._act_scores)
#print(self._scores)
for i, t in enumerate(self.population):
if (collect_scores):
display(self._scores[i])
display(t.root().dot())
# ## Run Evolutionary Algorithm
#p = Population(["bacon", "tomato", "onion"],['noodle'], max_additional_ings=6)
#p_ingredient_unprepared(list(p.population[0].root().childs())[0]._name) < 0.2
#p.run(100)
#p.plot_population(collect_scores=False)