def fit(self,
text: Union[str, Iterable[str], Iterable[Data], pd.DataFrame],
category: Union[str, Iterable[str]] = None) -> TextClassifier:
'''learn probabilities for tokens extracted by the given text'''
data = DataSet.FromAny(text, category)
categories = []
tokens = {}
values = []
for d in data:
categories.append((d.category, d.score))
for token in d.tokens:
tokens[token] = 1
values.append((d.table, d.score))
self.total_documents += 1
tokens = list(tokens)
self.__add_category(categories)
self.__add_token(tokens)
data_values = [[1 if t in v[0] else 0 for t in tokens] + [v[1]]
for v in values]
tokens.append(Data.CATEGORY_NAME)
data_values = pd.DataFrame(data_values, columns=tokens)
self.model.fit(data_values, Data.CATEGORY_NAME)
return self
def fit(self, text: Union[str, Iterable[str], Iterable[Data], pd.DataFrame], category: Union[str, Iterable[str]]=None) -> NaiveBayes:
'''learn probabilities for tokens extracted by the given text'''
data = DataSet.FromAny(text, category)
for d in data:
# ensure we have defined the c category
self.__add_category(d.category)
# update our count of how many documents mapped to this category
self.documents[d.category] += 1
# update the total number of documents we have learned from
self.total_documents += 1
# Update our vocabulary and our word frequency count for this category
for token, frequency in d.table.items():
# add this word to our vocabulary if not already existing
self.__add_token(token)
# update the frequency information for this word in this category
if token not in self.word_frequency[d.category]: self.word_frequency[d.category][token] = frequency
else: self.word_frequency[d.category][token] += frequency
# update the count of all words we have seen mapped to this category
self.word_count[d.category] += frequency
return self
