nlp-lab/Project/naive_approach/naive_approach.py

# coding: utf-8

# In[1]:


import pandas as pd
from IPython.display import clear_output, Markdown, Math
import ipywidgets as widgets
import os
import unicodedata as uni
import numpy as np
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import wordnet
import math
import pprint


# # Naive Approach
table = pd.read_csv('../Tools/emoji_descriptions.csv')

#######################
# Helper functions
#######################

def stemming(messages):
    stemmed_messages = []
    ps = PorterStemmer()
    for m in messages:
        words = word_tokenize(m)
        sm = []
        for w in words:
            sm.append(ps.stem(w))
        m = (" ").join(sm)
        stemmed_messages.append(m)
    return stemmed_messages


# * compare words to emoji descriptions
def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng'):
    
    tokenized_sentence = word_tokenize(sentence)
    n = len(tokenized_sentence)
    l = table.shape[0]
    matrix_list = []
    
    for index, row in table.iterrows():
        emoji_tokens = word_tokenize(row[description_key])
        m = len(emoji_tokens)

        mat = np.zeros(shape=(m,n))
        for i in range(len(emoji_tokens)):
            for j in range(len(tokenized_sentence)):
                syn1 = wordnet.synsets(emoji_tokens[i],lang=lang)
                if len(syn1) == 0:
                    continue
                w1 = syn1[0]
                #print(j, tokenized_sentence)
                syn2 = wordnet.synsets(tokenized_sentence[j], lang=lang)
                if len(syn2) == 0:
                    continue
                w2 = syn2[0]
                val = w1.wup_similarity(w2)
                if val is None:
                    continue
                mat[i,j] = val
        #print(row['character'], mat)
        matrix_list.append(mat)
            
    return matrix_list
    
    
###########################
#Functions to be called from main script
###########################
    

# load and preprocess data
# emojis_to_consider can be either a list or "all"
def prepareData(stemming=False, emojis_to_consider="all"):

    table.head()
    
    if(stemming):
        table['description'] = stemming(table['description'])
    
    #collect the emojis
    lookup = {}
    emoji_set = []
    for index, row in table.iterrows():
        if(emojis_to_consider=="all" or (type(emojis_to_consider)==list and row['character'] in emojis_to_consider)):
            lookup[index] = row['character']
            emoji_set.append(row['character'])

    emoji_set = set(emoji_set)
    
    return lookup

# make a prediction for an input sentence
def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", description_key='description', lang = 'eng', n=10, t=0.9):

    result = evaluate_sentence(sentence, table, description_key, lang)
    
    if(criteria=="summed"):
        indexes = np.argsort([-np.sum(x) for x in result])[0:n]
    elif (criteria=="max_val"):
        indexes = np.argsort([-np.max(x) for x in result])[0:n]
    elif(criteria=="avg"):
        indexes = np.argsort([-np.mean(x) for x in result])[0:n]
    else:
        indexes= np.argsort([-len(np.where(x>t)[0]) / (x.shape[0] * x.shape[1]) for x in result])[0:n] 
    
    if(emojis_to_consider!="all"):
        for i in indexes:
            if (i not in lookup):
                indexes = np.delete(indexes, [i])
    
    # build a result table
    table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ]
      
    table_frame = pd.DataFrame(table_array, columns=[criteria, 'description'])
    
    #display(table_frame)
    
    return list(table_frame[criteria])

#predict("I like to travel by train", description_key='description' , lang='eng')