Datenstruktur zu Dictionary geändert
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		| @ -19,6 +19,12 @@ import pprint | ||||
| # # Naive Approach | ||||
| table = pd.read_csv('../Tools/emoji_descriptions.csv') | ||||
|  | ||||
| ##Store table in the format: | ||||
| ## { index: [emoji, description]} | ||||
| tableDict = {} | ||||
| for index, row in table.iterrows(): | ||||
|     tableDict.update({index: [row['character'], row['description']]}) | ||||
|  | ||||
| ####################### | ||||
| # Helper functions | ||||
| ####################### | ||||
| @ -37,15 +43,15 @@ def stemming(messages): | ||||
|  | ||||
|  | ||||
| # * compare words to emoji descriptions | ||||
| def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng', emojis_to_consider="all"): | ||||
| def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', emojis_to_consider="all"): | ||||
|      | ||||
|     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]) | ||||
|     for index in tableDict.keys(): | ||||
|         emoji_tokens = word_tokenize(tableDict[index][1]) | ||||
|         m = len(emoji_tokens) | ||||
|  | ||||
|         mat = np.zeros(shape=(m,n)) | ||||
| @ -79,23 +85,24 @@ def evaluate_sentence(sentence, table, description_key = 'description', lang = ' | ||||
| # emojis_to_consider can be either a list or "all" | ||||
| def prepareData(stemming=False): | ||||
|     if(stemming): | ||||
|         table['description'] = stemming(table['description']) | ||||
|         for index in tableDict.keys(): | ||||
|             tableDict[index][1] = stemming(tableDict[index][1]) | ||||
|      | ||||
|     #collect the emojis | ||||
|     lookup = {} | ||||
|     emoji_set = [] | ||||
|     for index, row in table.iterrows(): | ||||
|         lookup[index] = row['character'] | ||||
|         emoji_set.append(row['character']) | ||||
|     for index in tableDict.keys(): | ||||
|         lookup[index] = tableDict[index][0] | ||||
|         emoji_set.append(tableDict[index][0]) | ||||
|  | ||||
|     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): | ||||
| def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", lang = 'eng', n=10, t=0.9): | ||||
|  | ||||
|     result = evaluate_sentence(sentence, table, description_key, lang, emojis_to_consider=emojis_to_consider) | ||||
|     result = evaluate_sentence(sentence, lang, emojis_to_consider=emojis_to_consider) | ||||
|      | ||||
|     try: | ||||
|         if(criteria=="summed"): | ||||
| @ -118,20 +125,22 @@ def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", de | ||||
|                     results2.append(results[i]) | ||||
|             indexes = indexes2 | ||||
|             results = results2 | ||||
|              | ||||
|         indexes = indexes[0:n] | ||||
|         results = results[0:n] | ||||
|          | ||||
|         # build a result table | ||||
|         table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ] | ||||
|         table_array = [lookup[indexes[i]] for i in range(n) ] | ||||
|            | ||||
|         table_frame = pd.DataFrame(table_array, columns=[criteria, 'description']) | ||||
|         #table_frame = pd.DataFrame(table_array, columns=[criteria, 'description']) | ||||
|          | ||||
|         #display(table_frame) | ||||
|          | ||||
|         return list(table_frame[criteria]), results | ||||
|         return table_array, results | ||||
|      | ||||
|     except ZeroDivisionError as err: | ||||
|         print("There seems to be a problem with the input format. Please enter a nonempty string") | ||||
|         return [], [] | ||||
|  | ||||
|  | ||||
| #predict("I like to travel by train", description_key='description' , lang='eng') | ||||
|  | ||||
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