129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
# coding: utf-8
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# In[1]:
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import pandas as pd
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from IPython.display import clear_output, Markdown, Math
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import ipywidgets as widgets
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import os
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import unicodedata as uni
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import numpy as np
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from nltk.stem import PorterStemmer
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from nltk.tokenize import sent_tokenize, word_tokenize
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from nltk.corpus import wordnet
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import math
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import pprint
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# # Naive Approach
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table = pd.read_csv('../Tools/emoji_descriptions.csv')
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#######################
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# Helper functions
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#######################
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def stemming(messages):
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stemmed_messages = []
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ps = PorterStemmer()
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for m in messages:
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words = word_tokenize(m)
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sm = []
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for w in words:
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sm.append(ps.stem(w))
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m = (" ").join(sm)
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stemmed_messages.append(m)
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return stemmed_messages
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# * compare words to emoji descriptions
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def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng'):
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tokenized_sentence = word_tokenize(sentence)
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n = len(tokenized_sentence)
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l = table.shape[0]
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matrix_list = []
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for index, row in table.iterrows():
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emoji_tokens = word_tokenize(row[description_key])
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m = len(emoji_tokens)
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mat = np.zeros(shape=(m,n))
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for i in range(len(emoji_tokens)):
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for j in range(len(tokenized_sentence)):
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syn1 = wordnet.synsets(emoji_tokens[i],lang=lang)
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if len(syn1) == 0:
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continue
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w1 = syn1[0]
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#print(j, tokenized_sentence)
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syn2 = wordnet.synsets(tokenized_sentence[j], lang=lang)
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if len(syn2) == 0:
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continue
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w2 = syn2[0]
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val = w1.wup_similarity(w2)
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if val is None:
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continue
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mat[i,j] = val
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#print(row['character'], mat)
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matrix_list.append(mat)
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return matrix_list
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###########################
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#Functions to be called from main script
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###########################
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# load and preprocess data
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# emojis_to_consider can be either a list or "all"
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def prepareData(stemming=False, emojis_to_consider="all"):
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table.head()
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if(stemming):
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table['description'] = stemming(table['description'])
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#collect the emojis
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lookup = {}
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emoji_set = []
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for index, row in table.iterrows():
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if(emojis_to_consider=="all" or (type(emojis_to_consider)==list and row['character'] in emojis_to_consider)):
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lookup[index] = row['character']
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emoji_set.append(row['character'])
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emoji_set = set(emoji_set)
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return lookup
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# make a prediction for an input sentence
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def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", description_key='description', lang = 'eng', n=10, t=0.9):
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result = evaluate_sentence(sentence, table, description_key, lang)
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if(criteria=="summed"):
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indexes = np.argsort([-np.sum(x) for x in result])[0:n]
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elif (criteria=="max_val"):
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indexes = np.argsort([-np.max(x) for x in result])[0:n]
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elif(criteria=="avg"):
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indexes = np.argsort([-np.mean(x) for x in result])[0:n]
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else:
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indexes= np.argsort([-len(np.where(x>t)[0]) / (x.shape[0] * x.shape[1]) for x in result])[0:n]
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if(emojis_to_consider!="all"):
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for i in indexes:
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if (i not in lookup):
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indexes = np.delete(indexes, [i])
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# build a result table
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table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ]
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table_frame = pd.DataFrame(table_array, columns=[criteria, 'description'])
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#display(table_frame)
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return list(table_frame[criteria])
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#predict("I like to travel by train", description_key='description' , lang='eng')
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