Naive Approach in Python Modul
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								Project/naive_approach/naive_approach.py
									
									
									
									
									
										Normal file
									
								
							| @ -0,0 +1,129 @@ | ||||
| # 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 | ||||
|  | ||||
|  | ||||
| ####################### | ||||
| # 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 = pd.read_csv('../Tools/emoji_descriptions.csv') | ||||
|     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, table | ||||
|  | ||||
| # make a prediction for an input sentence | ||||
| def predict(sentence, lookup, table, 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') | ||||
|  | ||||
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