emo_utils.py

import csv
import numpy as np
import emoji
import pandas as pd

def read_glove_vecs(glove_file):
    with open(glove_file,'r', encoding='cp437') as f:
        words = set()
        word_to_vec_map = {}
        for line in f:
            line = line.strip().split()
            curr_word = line[0]
            words.add(curr_word)
            word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)
        
        i = 1
        words_to_index = {}
        index_to_words = {}
        for w in sorted(words):
            words_to_index[w] = i
            index_to_words[i] = w
            i = i + 1
    return words_to_index, index_to_words, word_to_vec_map

def softmax(x):
    """Compute softmax values for each sets of scores in x."""
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()


def read_csv(filename = 'data/emojify_data.csv'):
    phrase = []
    emoji = []

    with open (filename) as csvDataFile:
        csvReader = csv.reader(csvDataFile)

        for row in csvReader:
            phrase.append(row[0])
            emoji.append(row[1])

    X = np.asarray(phrase)
    Y = np.asarray(emoji, dtype=int)

    return X, Y

def convert_to_one_hot(Y, C):
    Y = np.eye(C)[Y.reshape(-1)]
    return Y


emoji_dictionary = {"0": "\u2764\uFE0F",    # :heart: prints a black instead of red heart depending on the font
                    "1": ":baseball:",
                    "2": ":smile:",
                    "3": ":disappointed:",
                    "4": ":fork_and_knife:"}

def label_to_emoji(label):
    """
    Converts a label (int or string) into the corresponding emoji code (string) ready to be printed
    """
    return emoji.emojize(emoji_dictionary[str(label)], use_aliases=True)
              
    
def print_predictions(X, pred):
    print()
    for i in range(X.shape[0]):
        print(X[i], label_to_emoji(int(pred[i])))
        
    
def get_emoji(X, W, b, word_to_vec_map):
    m = X.shape[0]
    pred = np.zeros((m, 1))
    
    for j in range(m):                     
        words = X[j].lower().split()
        avg = np.zeros((50,))
        for w in words:
            avg += word_to_vec_map[w]
        avg = avg/len(words)
        Z = np.dot(W, avg) + b
        A = softmax(Z)
        pred[j] = np.argmax(A)
            
    print_predictions(X,pred)