def __init__(self,
df,
category_col,
parsed_col,
feats_from_spacy_doc=FeatsFromSpacyDoc()):
'''
Parameters
----------
df : pd.DataFrame
contains category_col, and parse_col, were parsed col is entirely spacy docs
category_col : str
name of category column in df
parsed_col : str
name of spacy parsed column in df
feats_from_spacy_doc : FeatsFromSpacyDoc
'''
self._df = df.reset_index()
self._category_col = category_col
self._parsed_col = parsed_col
self._category_idx_store = IndexStore()
self._X_factory = CSRMatrixFactory()
self._mX_factory = CSRMatrixFactory()
self._term_idx_store = IndexStore()
self._metadata_idx_store = IndexStore()
self._feats_from_spacy_doc = feats_from_spacy_doc
class CorpusFromParsedDocuments(object):
def __init__(self,
df,
category_col,
parsed_col,
feats_from_spacy_doc=FeatsFromSpacyDoc()):
'''
Parameters
----------
df : pd.DataFrame
contains category_col, and parse_col, were parsed col is entirely spacy docs
category_col : str
name of category column in df
parsed_col : str
name of spacy parsed column in df
feats_from_spacy_doc : FeatsFromSpacyDoc
'''
self._df = df.reset_index()
self._category_col = category_col
self._parsed_col = parsed_col
self._category_idx_store = IndexStore()
self._X_factory = CSRMatrixFactory()
self._mX_factory = CSRMatrixFactory()
self._term_idx_store = IndexStore()
self._metadata_idx_store = IndexStore()
self._feats_from_spacy_doc = feats_from_spacy_doc
def build(self):
'''Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus
'''
self._y = self._get_y_and_populate_category_idx_store()
self._df.apply(self._add_to_x_factory, axis=1)
self._X = self._X_factory.set_last_row_idx(len(self._y) -
1).get_csr_matrix()
self._mX = self._mX_factory.set_last_row_idx(len(self._y) -
1).get_csr_matrix()
return ParsedCorpus(self._df, self._X, self._mX, self._y,
self._term_idx_store, self._category_idx_store,
self._metadata_idx_store, self._parsed_col,
self._category_col)
def _get_y_and_populate_category_idx_store(self):
return np.array(self._df[self._category_col].apply(
self._category_idx_store.getidx))
def _add_to_x_factory(self, row):
parsed_text = row[self._parsed_col]
for term, count in self._feats_from_spacy_doc.get_feats(
parsed_text).items():
term_idx = self._term_idx_store.getidx(term)
self._X_factory[row.name, term_idx] = count
for meta, val in self._feats_from_spacy_doc.get_doc_metadata(
parsed_text).items():
meta_idx = self._metadata_idx_store.getidx(meta)
self._mX_factory[row.name, meta_idx] = val
def init_term_doc_matrix_variables():
y = []
X_factory = CSRMatrixFactory()
mX_factory = CSRMatrixFactory()
category_idx_store = IndexStore()
term_idx_store = IndexStore()
metadata_idx_store = IndexStore()
return X_factory, mX_factory, category_idx_store, \
term_idx_store, metadata_idx_store, y
def test_main(self):
index_store = IndexStore()
self.assertEqual(index_store.getidx('a'), 0)
self.assertEqual(index_store.getidx('b'), 1)
self.assertEqual(index_store.getidx('a'), 0)
self.assertEqual(index_store.getval(0), 'a')
self.assertEqual(index_store.getval(1), 'b')
self.assertTrue('a' in index_store)
self.assertFalse('c' in index_store)
self.assertEqual(set(index_store.values()), set(['a', 'b']))
self.assertFalse(0 in index_store)
self.assertTrue(index_store.hasidx(0))
self.assertFalse(index_store.hasidx(2))
self.assertEqual(index_store.getnumvals(), 2)
self.assertEqual(list(index_store.items()), [(0, 'a'), (1, 'b')])
def test_getidxstrict(self):
index_store = IndexStore()
self.assertEqual(index_store.getidx('a'), 0)
self.assertEqual(index_store.getidx('b'), 1)
self.assertEqual(index_store.getidx('a'), 0)
with self.assertRaises(KeyError):
index_store.getidxstrict('c')
def _build_from_category_spacy_doc_iter(self, category_doc_iter):
'''
Parameters
----------
category_doc_iter : iterator of (string category name, spacy.tokens.doc.Doc) pairs
Returns
----------
t : TermDocMatrix
'''
term_idx_store = IndexStore()
category_idx_store = IndexStore()
metadata_idx_store = IndexStore()
X, mX, y = self._get_features_and_labels_from_documents_and_indexes(
category_doc_iter, category_idx_store, term_idx_store,
metadata_idx_store)
return TermDocMatrix(X,
mX,
y,
term_idx_store=term_idx_store,
category_idx_store=category_idx_store,
metadata_idx_store=metadata_idx_store)
def test_batch_delete(self):
index_store = IndexStore()
self.assertEqual(index_store.getidx('a'), 0)
self.assertEqual(index_store.getidx('b'), 1)
self.assertEqual(index_store.getidx('c'), 2)
self.assertEqual(index_store.getidx('d'), 3)
with self.assertRaises(KeyError):
new_idx_store = index_store.batch_delete_vals(['e', 'c'])
new_idx_store = index_store.batch_delete_vals(['b', 'c'])
self.assertEqual(new_idx_store.getidx('a'), 0)
self.assertEqual(new_idx_store.getidx('c'), 2)
self.assertEqual(new_idx_store.getidx('e'), 3)
self.assertEqual(index_store.getidx('d'), 3)
self.assertEqual(index_store.getidx('c'), 2)
self.assertEqual(index_store.getidx('b'), 1)
self.assertEqual(index_store.getidx('a'), 0)
with self.assertRaises(ValueError):
new_idx_store = index_store.batch_delete_idx([5, 1])
new_idx_store = index_store.batch_delete_idx([2, 1])
self.assertEqual(new_idx_store.getidx('a'), 0)
self.assertEqual(new_idx_store.getidx('c'), 2)
self.assertEqual(new_idx_store.getidx('e'), 3)
def test_batch_delete_extra(self):
index_store = IndexStore()
self.assertEqual(index_store.getidx('a'), 0)
self.assertEqual(index_store.getidx('b'), 1)
self.assertEqual(index_store.getidx('c'), 2)
self.assertEqual(index_store.getidx('d'), 3)
self.assertEqual(index_store.getidx('e'), 4)
self.assertEqual(index_store.getidx('f'), 5)
del_idxstore = index_store.batch_delete_vals(['b', 'e'])
self.assertEqual(list(del_idxstore.items()), [(0, 'a'), (1, 'c'),
(2, 'd'), (3, 'f')])
del_idxstore2 = del_idxstore.batch_delete_vals([])
self.assertEqual(list(del_idxstore.items()),
list(del_idxstore2.items()))
def build_from_category_whitespace_delimited_text(category_text_iter):
'''
Parameters
----------
category_text_iter iterator of (string category name, one line per sentence, whitespace-delimited text) pairs
Returns
-------
TermDocMatrix
'''
y = []
X_factory = CSRMatrixFactory()
term_idx_store = IndexStore()
category_idx_store = IndexStore()
mX_factory = CSRMatrixFactory()
metadata_idx_store = IndexStore()
for doci, (category, text) in enumerate(category_text_iter):
y.append(category_idx_store.getidx(category))
term_freq = Counter()
for sent in text.strip(string.punctuation).lower().split('\n'):
unigrams = []
for tok in sent.strip().split():
unigrams.append(tok)
bigrams = list(map(' '.join, zip(unigrams[:-1], unigrams[1:])))
for term in unigrams + bigrams:
term_freq[term_idx_store.getidx(term)] += 1
for word_idx, freq in term_freq.items():
X_factory[doci, word_idx] = freq
metadata_idx_store = IndexStore()
return TermDocMatrix(X=X_factory.get_csr_matrix(),
mX=mX_factory.get_csr_matrix(),
y=np.array(y),
term_idx_store=term_idx_store,
metadata_idx_store=metadata_idx_store,
category_idx_store=category_idx_store)
def term_group_freq_df(self, group_col):
# type: (str) -> pd.DataFrame
'''
Returns a dataframe indexed on the number of groups a term occured in.
Parameters
----------
group_col
Returns
-------
pd.DataFrame
'''
group_idx_store = IndexStore()
X = self._X
group_idx_to_cat_idx, row_group_cat \
= self._get_group_docids_and_index_store(X, group_col, group_idx_store)
newX = self._change_document_type_in_matrix(X, row_group_cat)
newX = self._make_all_positive_data_ones(newX)
category_row = newX.tocoo().row
for group_idx, cat_idx in group_idx_to_cat_idx.items():
category_row[category_row == group_idx] = cat_idx
catX = self._change_document_type_in_matrix(newX, category_row)
return self._term_freq_df_from_matrix(catX)