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How to use list_mappings method in tempest

Best Python code snippet using tempest_python

recipe_adaptation.py

Source:recipe_adaptation.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3"""4Created on Tue Mar 24 17:29:00 20205@author: Andrea Morales GarzÃ³n6Adaptation of recipes 7This file pre-processes the ingredients of the recipe, and looks for a mapping to the database.8The properties of the food are consulted to see if there is an incompatibility9with the restriction, and a new mapping is made that allows to map the ingredient10to one of those that are allowed.11 12"""13from gensim.models import KeyedVectors14from gensim.models.phrases import Phraser15from gensim.parsing.preprocessing import preprocess_string, remove_stopwords, stem_text16import gensim17import pandas as pd18import numpy as np19from fjaccard import fjaccard_extended20import ast21import random22import json23# load language models24CUSTOM_FILTERS = [remove_stopwords, stem_text]25CUSTOM_FILTERS_2 = [remove_stopwords]26modelo_guardado = KeyedVectors.load("../models/v2/modelo3")27bigram_loaded = Phraser.load("../models/v2/my_bigram_model.pkl")28df_idiet = pd.read_excel("../data/inglesa-labeled_reducida_vegan_terminada.xlsx")29def funcc(tex):30    x = 'Yes'31    return x32df_idiet['no_restriction'] = df_idiet['Main food description'].apply(funcc)33#%%34# apply word embedding text preprocessing to the food to get their representation35def preprocess_recipe(text_recipe, create_tokens = True, main_ingredient = False):36    if main_ingredient:37        pos = text_recipe.find(',')38        if pos != -1:39            text_recipe = text_recipe[0:(pos+1)]40            41    if create_tokens:42        tokens = list(gensim.utils.tokenize(text_recipe, lower=True))43        44    else:45        tokens = text_recipe46        47    tokens = preprocess_string(" ".join(tokens), [remove_stopwords, stem_text]) 48    sentenc = list(bigram_loaded[tokens])49    50    return sentenc51#%%52# Function to mapping ingredient to food database53def get_DB_equivalent(idiet_item_list, bd_names_ , bd_names_mapping , model, number_instances = False):54    55    # get similarity between ingredient and dataset foods56    vector_similarity = np.zeros(len(bd_names_mapping))57    for i,item in enumerate(bd_names_mapping):58        s = fjaccard_extended(idiet_item_list, item, modelo_guardado)59        vector_similarity[i] = s60    # identify the most accurate61    sorted_list = np.argsort(vector_similarity)62    # get the bestx mappings (in this case we obtain 10)63    if number_instances == True:64        most_similar = [ bd_names_[sorted_list[i]] for i in range(10) ]65        value_most_similar = [ vector_similarity[sorted_list[i]] for i in range(10) ]66        67        # inf distance value is infinity or very high, we consider there is no similar ingredient in the database68        for i,value in enumerate(value_most_similar):69            if value == np.Infinity:70                most_similar[i] = 'No matches'71            if value > 44.0:72                most_similar[i]  = 'No matches'73    else:74        most_similar = bd_names_[sorted_list[0]]75        value_most_similar = vector_similarity[sorted_list[0]]    76        if value_most_similar == np.Infinity:77            most_similar = 'No matches'78    alternatives = [ bd_names_[sorted_list[i]] for i in range(10) ]79    print(alternatives)80    return most_similar, value_most_similar, alternatives81#%%82def get_accurate_mapping(i,model,bigram,db,nutritional_data,bd_names_processed):83    ingredient_processed = preprocess_recipe(i)84    print(ingredient_processed)85    mapping_idiet, value_mapping, alternatives_  = get_DB_equivalent(ingredient_processed,bd_names_=db,bd_names_mapping=bd_names_processed,model=model,number_instances=False)86    mapping = nutritional_data[nutritional_data['Main food description'] == mapping_idiet]87    88    return mapping, mapping_idiet, value_mapping, alternatives_ 89#%%90def get_adapted_recipe(recipe,model,bigram,db,nutritional_data,restriction):91    92    # preprocessing recipe data93    bd_names_processed = [preprocess_recipe(x) for x in db]94    95    96    list_recipe_ingredients = []97    # get recipe ingredients98    recipe_ingredients = recipe['ingredients']99    100    for i in recipe_ingredients:101        print("......")102        print(i)103        104        # for each ingredient in recipe, we search for the most accurate mapping105        mapping, mapping_idiet, value_mapping, others= get_accurate_mapping(i,model,bigram,db,nutritional_data,bd_names_processed)106        modified_action = 'no'107        adapted_ing = ""108        109        if mapping_idiet != 'No matches':110            111            mapping = mapping.iloc[0]112            if mapping[restriction] == "No" and value_mapping <0.9:113                print("entra")114                modified_db = nutritional_data[nutritional_data[restriction] == "Yes"]115                bd_names_mod = list(modified_db['Main food description'])116                bd_names_mod_processed = [preprocess_recipe(x) for x in bd_names_mod] 117                mapping, adapted_ing, value_mapping, others = get_accurate_mapping(i,model,bigram,bd_names_mod,modified_db,bd_names_mod_processed)118                modified_action = 'yes'119                120            if value_mapping > 0.9:121                value_mapping = None122        else:123            value_mapping = None124            125        list_recipe_ingredients.append({'name':i, 'modified':modified_action,'adapted':adapted_ing, 'others':others})126    return list_recipe_ingredients127                128#%%  129# ejemplo130usda_ingredients = pd.read_excel("../data/inglesa-labeled_reducida_vegan_terminada.xlsx")131bd_names = list(usda_ingredients['Main food description'])132df_rcp = pd.read_csv('../data/RAW_recipes-foodcom-kaggle.csv')133dict_rcp = df_rcp.to_dict('records')134##135random.seed(12345)136xx = random.sample(dict_rcp,20)137#%%138    139# order results (used for preference type of adaptations)140    141def ordenar(list_mappings,nutrient):142    list_mappings = list_mappings[2:5]143    print(len(list_mappings))144    valores_nutricionales = np.zeros(len(list_mappings))145    for i,map_ in enumerate(list_mappings):146        print(map_)147        item = usda_ingredients[usda_ingredients['Main food description'] == map_].iloc[0]148        print(item['Main food description'])149        if item[nutrient] == 'Tr':150            valores_nutricionales[i] = 0151        elif item[nutrient] != 'N':152            valores_nutricionales[i] = item[nutrient]153            print(valores_nutricionales[i])154        else: 155            valores_nutricionales[i] = np.nan156            157        print(valores_nutricionales)158        159    mean_value = np.nanmean(valores_nutricionales)160    valores_nutricionales = np.where(valores_nutricionales==np.nan, mean_value, valores_nutricionales) 161    print(mean_value)162    sorted_list = np.argsort(valores_nutricionales)163    print(sorted_list)164    res_ordenado = [list_mappings[i] for i in sorted_list ]165    return res_ordenado166    167        168list_prueba = ['Allspice, ground','Lime juice cordial, diluted','Langoustine, boiled']      169r_list = ordenar(list_prueba, 'Energy (kcal) (kcal)')  170#%%171def get_recipe_adaptation(recipe_,adaptation_type='no_restriction', kind='preferences', order=False):172    173    print(recipe_['name'])174    print(recipe_['ingredients'])175    rp_ = {"id":recipe_176           ['id'],'name':recipe_["name"],"ingredients":ast.literal_eval(recipe_["ingredients"]), "steps":ast.literal_eval(recipe_['steps']), 'description':recipe_['description'], 'type':kind}177    x = get_adapted_recipe(recipe=rp_,model=modelo_guardado,bigram=bigram_loaded,db=bd_names,nutritional_data=usda_ingredients,restriction=adaptation_type)178        179    180    if order:181        for i,element in enumerate(x):182            x[i]['others'] = ordenar(element['others'],'Energy (kcal) (kcal)')183        184        185    rp_['ingredients'] = x186    187    188    189    with open('../res/'+kind+'/data_recipe_'+rp_['name']+'.json', 'w') as fp:190        json.dump(rp_, fp)191    192    print("--")193    194    195#%%196for i in list(xx):197    print("-------------------------------------")198    get_recipe_adaptation(i,adaptation_type='no_restriction', kind='light',order=True)199    ...

3_list_of_depths.py

Source:3_list_of_depths.py

1class Node:2    def __init__(self, value:int, children:list=[]):3        self.value = value4        self.children = children5class LlNode:6    def __init__(self, value:int, next_node=None):7        self.value = value8        self.next = next_node9def create_tree(array:list) -> Node:10    if not array:11        return None12    mid_index = len(array)//213    14    left_child = create_tree(array[:mid_index])15    right_child = create_tree(array[mid_index+1:])16    r = Node(array[mid_index], [left_child, right_child])17    return r18def print_link_list(node:LlNode):19    while node:20        print(node.value)21        node = node.next22def find_depths(tree_root:Node) -> list:23    list_mapping = []24    queue = []25    queue.append(tree_root)26    level_counter = 027    while len(queue) > 0:28        next_queue = []29        list_mapping.append(LlNode(None))30        while len(queue) > 0:31            r = queue.pop()32            33            next_queue.extend([i for i in r.children if i])34            35            ll_element = LlNode(r.value)36            ll_element.next = list_mapping[level_counter].next37            list_mapping[level_counter].next = ll_element38        39        level_counter += 140        queue = next_queue41    return list_mapping42    43    44def main():45    46    values = [0,1,2,3,4,5,6,7,8,9]47    tree_root = create_tree(values)48    list_mappings = find_depths(tree_root)49    50    for i in list_mappings:51        print("list")52        print_link_list(i)53if __name__ == '__main__':...

dict_to_xml.py

Source:dict_to_xml.py

1from xml.dom.minidom import Document2class DictToXML(object):3    default_list_item_name = "item"4    def __init__(self, structure, list_mappings={}):5        self.doc = Document()6        if len(structure) == 1:7            rootName = str(list(structure.keys())[0])8            self.root = self.doc.createElement(rootName)9            self.list_mappings = list_mappings10            self.doc.appendChild(self.root)11            self.build(self.root, structure[rootName])12    def build(self, father, structure):13        if type(structure) == dict:14            for k in structure:15                tag = self.doc.createElement(k)16                father.appendChild(tag)17                self.build(tag, structure[k])18        elif type(structure) == list:19            tag_name = self.default_list_item_name20            if father.tagName in self.list_mappings:21                tag_name = self.list_mappings[father.tagName]22            for l in structure:23                tag = self.doc.createElement(tag_name)24                self.build(tag, l)25                father.appendChild(tag)26        else:27            data = str(structure)28            tag = self.doc.createTextNode(data)29            father.appendChild(tag)30    def display(self):31        print(self.doc.toprettyxml(indent="  "))32    def get_string(self):...

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