def get_ing_type(self):
"""returns a dictionary of all of the ingredients in a cluster that
are sorted based on their type.
Looks in the dictionary where key=cluste number and value =
each ingredient. Appends each to the correct type.
#do something here where it makes the ingredient type based on the
#global type_dictionary, that has the ingredients categorized by type
#and then grabs at the ingredients that are in the particular cluster
#dictionary.
"""
thisclusterings = config_db.getter()[1][self.seed_cluster]
alltypes = config_db.getter()[2]
allingrs = config_db.getter()[3]
ingr_sorted_type = {}
this_cluster_types = {}
for ingr in thisclusterings:
ingr = ingr.replace(" ","_")
for alltypes in allingrs[ingr]:
this_cluster_types.setdefault(alltypes,[]).append(ingr)
return this_cluster_types
def populate_markov_meal(self):
"""Populates the meal skeleton for Markov meals.
Randomly selects an ingredient from the Markov chain
associated with random seed ingredient. Checks if the meal
skeleton is complete. Returns meal if it is, keeps going if
it's not. If more than 20 items have been generated, and the
meal is still not complete, it picks a new random seed
ingredient and uses that as the base for the Markov
chain. Currently an bi-gram.
"""
ingredient_by_type = config_db.getter()[3]
meal = {"protein":"", "vegetable":"", "starch":""}
needs_more = True
my_ingredient = self.markov_seed_ingr
counter = 0
while needs_more:
my_list_of_types = ingredient_by_type[my_ingredient]
my_first_type = my_list_of_types[0]
meal[my_first_type] = my_ingredient
my_ingredient = random.choice(self.markov_chain_dict[self.markov_seed_ingr])
counter +=1
if counter == 20:
counter = 1
my_ingredient = random.choice(self.markov_chain_dict.keys())
if meal['protein'] != "" and meal['vegetable']!="" and meal['starch']!="":
needs_more = False
return meal
def testShouldGiveBackListOfPotentiallyGlobalVariables(self):
"""test getter"""
cluster_test = {1: [1]}
cluster_ingredients={1:[u'tomato', u'steak', u'potato']}
test_type_ingredient = {u'vegetable': [u'tomato'], u'protein': [u'steak'], u'starch': [u'potato']}
test_ingredient_types = {u'tomato': [u'vegetable'], u'steak': [u'protein'], u'potato': [u'starch']}
test_ingredient_ingredients = {u'potato': [u'tomato', u'steak', u'tomato', u'steak', u'tomato', u'steak']}
test_getter = [cluster_test, cluster_ingredients, test_type_ingredient, test_ingredient_types,test_ingredient_ingredients]
self.assertEqual(config_db.getter(),test_getter)
def generate_markov_recipe(self):
"""RM method that creates Markov chain meals.
Uses config getter to get the Markov chain dictionary, picks a
seed ingredient, calls a function that fills out the rest of
the meal based on the seed ingredient, formats the meal to
remove the underscores, and then returns the completed Markov
meal.
"""
self.markov_chain_dict = config_db.getter()[4]
self.markov_seed_ingr = random.choice(self.markov_chain_dict.keys())
self.markov_meal = self.populate_markov_meal()
self.meal = self.markov_meal
display_meal = self.nounder()
return self.meal
def generate_random_recipe(self):
"""Generates the meal for the random meals.
Gets a list of types with every associated ingredient from the
entire recipe database. Calls the meal skeleton-filling
method. Checks to see if meal is completed. Passes it through
a formatter to take out underscores. Returns completed meal.
note: congfig_db2 => {protein: chicken, steak}
"""
self.all_types = config_db.getter()[2]
while True:
self.meal = self.make_random_meal()
complete_meal = self.check_meal()
display_meal = self.nounder()
if complete_meal:
return self.meal
break
def generate_kmeans_recipe(self):
"""Generates the k-means meals.
Gets a dictionary filled with clusters and associated
ingredients. Randomly chooses a cluster. Sorts ingredients of
said cluster into types. Calls method that fills the meal
skeleton. Checks to see if the meal is complete. Calls
function to remove underscores. Returns completed meal.
"""
while True:
self.cluster = config_db.getter()[0].keys()
self.seed_cluster = random.choice(self.cluster)
self.ingr_sorted_type = self.get_ing_type()
self.meal = self.make_kmeans_meal()
complete_meal = self.check_meal()
display_meal = self.nounder()
if complete_meal:
return self.meal
break
def testShouldGiveBackListOfPotentiallyGlobalVariables(self):
"""test getter"""
cluster_test = {1: [1]}
cluster_ingredients = {1: [u'tomato', u'steak', u'potato']}
test_type_ingredient = {
u'vegetable': [u'tomato'],
u'protein': [u'steak'],
u'starch': [u'potato']
}
test_ingredient_types = {
u'tomato': [u'vegetable'],
u'steak': [u'protein'],
u'potato': [u'starch']
}
test_ingredient_ingredients = {
u'potato':
[u'tomato', u'steak', u'tomato', u'steak', u'tomato', u'steak']
}
test_getter = [
cluster_test, cluster_ingredients, test_type_ingredient,
test_ingredient_types, test_ingredient_ingredients
]
self.assertEqual(config_db.getter(), test_getter)