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_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()
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 add_doc_names_as_metadata(self, doc_names):
'''
:param doc_names: array-like[str], document names of reach document
:return: Corpus-like object with doc names as metadata. If two documents share the same name
(doc number) will be appended to their names.
'''
if len(doc_names) != self.get_num_docs():
raise Exception("The parameter doc_names contains %s elements. "
"It should have %s elements, one per document." %
(len(doc_names), self.get_num_docs()))
doc_names_counter = collections.Counter(np.array(doc_names))
metafact = CSRMatrixFactory()
metaidxstore = IndexStore()
doc_id_uses = collections.Counter()
for i in range(self.get_num_docs()):
doc_id = doc_names[i]
if doc_names_counter[doc_id] > 1:
doc_id_uses[doc_id] += 1
doc_name_idx = metaidxstore.getidx(
'%s (%s)' % (doc_id, doc_id_uses[doc_id]))
else:
doc_name_idx = metaidxstore.getidx(doc_id)
metafact[i, i] = doc_name_idx
return self.add_metadata(metafact.get_csr_matrix(), metaidxstore)
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 test_getidxstrictbatch(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)
self.assertEqual(index_store.getidxstrictbatch(['b', 'f', 'b', 'a']),
[1, 5, 1, 0])
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 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_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 add_doc_names_as_metadata(self, doc_names):
'''
:param doc_names: array-like[str], document names of reach document
:return: Corpus-like object with doc names as metadata. If two documents share the same name
(doc number) will be appended to their names.
'''
if len(doc_names) != self.get_num_docs():
raise Exception("The parameter doc_names contains %s elements. "
"It should have %s elements, one per document." % (len(doc_names), self.get_num_docs()))
doc_names_counter = collections.Counter(np.array(doc_names))
metafact = CSRMatrixFactory()
metaidxstore = IndexStore()
doc_id_uses = collections.Counter()
for i in range(self.get_num_docs()):
doc_id = doc_names[i]
if doc_names_counter[doc_id] > 1:
doc_id_uses[doc_id] += 1
doc_name_idx = metaidxstore.getidx('%s (%s)' % (doc_id, doc_id_uses[doc_id]))
else:
doc_name_idx = metaidxstore.getidx(doc_id)
metafact[i, i] = doc_name_idx
return self.add_metadata(metafact.get_csr_matrix(), metaidxstore)
class CorpusFromFeatureDict(object): def __init__(self, df, category_col, text_col, feature_col, metadata_col=None, parsed_col=None): ''' 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 convention_df text_col : str The name of the column which contains each document's raw text. feature_col : str name of column in convention_df with a feature dictionary metadata_col : str, optional name of column in convention_df with a meatadata dictionary parsed_col : str, optional name of column in convention_df with parsed strings ''' self._df = df.reset_index() self._category_col = category_col self._text_col = text_col self._feature_col = feature_col self._parsed_col = parsed_col self._metadata_col = metadata_col self._category_idx_store = IndexStore() self._X_factory = CSRMatrixFactory() self._mX_factory = CSRMatrixFactory() self._term_idx_store = IndexStore() self._metadata_idx_store = IndexStore() 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() if self._parsed_col is not None and self._parsed_col in self._df: 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) else: return CorpusDF(self._df, self._X, self._mX, self._y, self._text_col, self._term_idx_store, self._category_idx_store, self._metadata_idx_store) 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): for feat, count in row[self._feature_col].items(): feat_idx = self._term_idx_store.getidx(feat) self._X_factory[row.name, feat_idx] = count if self._metadata_col in self._df: for meta, count in row[self._metadata_col].items(): meta_idx = self._metadata_idx_store.getidx(meta) self._mX_factory[row.name, meta_idx] = count def _make_new_term_doc_matrix(self, new_X, new_mX, new_y, new_term_idx_store, new_category_idx_store, new_metadata_idx_store, new_y_mask): if self._parsed_col is not None and self._parsed_col in self._df: return ParsedCorpus(self._df[new_y_mask], new_X, new_mX, new_y, new_term_idx_store, new_category_idx_store, new_metadata_idx_store, self._parsed_col, self._category_col) else: return CorpusDF(self._df[new_y_mask], new_X, new_mX, new_y, self._text_col, new_term_idx_store, new_category_idx_store, new_metadata_idx_store, self._df[self._text_col][new_y_mask])
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 convention_df parsed_col : str name of spacy parsed column in convention_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 _make_new_term_doc_matrix(self, new_X, new_mX, new_y, new_term_idx_store, new_category_idx_store, new_metadata_idx_store, new_y_mask): return ParsedCorpus(self._df[new_y_mask], new_X, new_mX, new_y, new_term_idx_store, new_category_idx_store, new_metadata_idx_store, self._parsed_col, self._category_col)
class OffsetCorpusFactory(object):
def __init__(self,
df,
parsed_col,
feat_and_offset_getter,
category_col=None):
'''
Parameters
----------
df : pd.DataFrame
contains category_col, and parse_col, were parsed col is entirely spacy docs
parsed_col : str
name of spacy parsed column in convention_df
feats_from_spacy_doc : FeatsFromSpacyDoc
category_col : str, Optional
name of category column in df; if None, all category names will be '_'
'''
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._feat_and_offset_getter = feat_and_offset_getter
self._term_offsets = {}
self._metadata_offsets = {}
def build(self):
'''Constructs the term doc matrix.
Returns
-------
scattertext.ParsedCorpus.ParsedCorpus
'''
self._ensure_category_col_is_in_df()
y = self._get_y_and_populate_category_idx_store(
self._df[self._category_col])
self._df.apply(self._add_to_x_factory, axis=1)
self._mX = self._mX_factory.set_last_row_idx(len(y) -
1).get_csr_matrix()
return OffsetCorpus(
df=self._df,
X=self._X_factory.set_last_row_idx(len(y) - 1).get_csr_matrix(),
mX=self._mX_factory.set_last_row_idx(len(y) - 1).get_csr_matrix(),
y=self._get_y_and_populate_category_idx_store(
self._df[self._category_col]),
term_idx_store=self._term_idx_store,
category_idx_store=self._category_idx_store,
metadata_idx_store=self._metadata_idx_store,
parsed_col=self._parsed_col,
category_col=self._category_col,
term_offsets=self._term_offsets,
metadata_offsets=self._metadata_offsets)
def _ensure_category_col_is_in_df(self):
if self._category_col not in self._df:
self._category_col = 'Category'
while self._category_col in self._df:
self._category_col = 'Category_' + ''.join(
np.random.choice(string.ascii_letters) for _ in range(5))
def _get_y_and_populate_category_idx_store(self, categories):
return np.array(categories.apply(self._category_idx_store.getidx))
def _add_to_x_factory(self, row):
parsed_text = row[self._parsed_col]
for term, (count,
offsets) in self._feat_and_offset_getter.get_term_offsets(
parsed_text):
term_idx = self._term_idx_store.getidx(term)
self._X_factory[row.name, term_idx] = count
if offsets is not None:
self._term_offsets.setdefault(term, {}).setdefault(
row.name, []).extend(offsets)
for meta, (
val, offsets
) in self._feat_and_offset_getter.get_metadata_offsets(parsed_text):
meta_idx = self._metadata_idx_store.getidx(meta)
self._mX_factory[row.name, meta_idx] = val
if offsets is not None:
self._metadata_offsets.setdefault(meta, {}).setdefault(
row.name, []).extend(offsets)