def __init__(self,
df,
X,
mX,
y,
term_idx_store,
category_idx_store,
metadata_idx_store,
parsed_col,
category_col,
unigram_frequency_path=None):
'''
Parameters
----------
convention_df pd.DataFrame, contains parsed_col and metadata
X, csr_matrix
mX csr_matrix
y, np.array
term_idx_store, IndexStore
category_idx_store, IndexStore
parsed_col str, column in convention_df containing parsed documents
category_col str, columns in convention_df containing category
unigram_frequency_path str, None by default, path of unigram counts file
'''
self._df = df
self._parsed_col = parsed_col
self._category_col = category_col
Corpus.__init__(self, X, mX, y, term_idx_store, category_idx_store,
metadata_idx_store, self._df[self._parsed_col],
unigram_frequency_path)
def __init__(self,
df,
X,
mX,
y,
text_col,
term_idx_store,
category_idx_store,
metadata_idx_store,
unigram_frequency_path=None):
'''
Parameters
----------
X : csr_matrix
term document matrix
mX : csr_matrix
metadata-document matrix
y : np.array
category index array
term_idx_store : IndexStore
Term indices
category_idx_store : IndexStore
Catgory indices
metadata_idx_store : IndexStore
Document metadata indices
text_col: np.array or pd.Series
Raw texts
unigram_frequency_path : str or None
Path to term frequency file.
'''
self._df = df
self._text_col = text_col
Corpus.__init__(self, X, mX, y, term_idx_store, category_idx_store,
metadata_idx_store, df[text_col],
unigram_frequency_path)
def __init__(self,
df,
X,
mX,
y,
term_idx_store,
category_idx_store,
metadata_idx_store,
parsed_col,
category_col,
unigram_frequency_path=None):
'''
Parameters
----------
convention_df pd.DataFrame, contains parsed_col and metadata
X, csr_matrix
mX csr_matrix
y, np.array
term_idx_store, IndexStore
category_idx_store, IndexStore
parsed_col str, column in convention_df containing parsed documents
category_col str, columns in convention_df containing category
unigram_frequency_path str, None by default, path of unigram counts file
'''
self._df = df
self._parsed_col = parsed_col
self._category_col = category_col
Corpus.__init__(self, X, mX, y, term_idx_store, category_idx_store,
metadata_idx_store,
self._df[self._parsed_col],
unigram_frequency_path)
def _apply_pipeline_and_get_build_instance(self, X_factory, mX_factory,
category_idx_store, df,
parse_pipeline, term_idx_store,
metadata_idx_store, y):
df.apply(parse_pipeline.parse, axis=1)
y = np.array(y)
X, mX = build_sparse_matrices(y, X_factory, mX_factory)
raw_texts = df[self._text_col]
return Corpus(X, mX, y, term_idx_store, category_idx_store,
metadata_idx_store, raw_texts)
def __init__(self,
df,
X,
mX,
y,
text_col,
term_idx_store,
category_idx_store,
metadata_idx_store,
unigram_frequency_path=None):
'''
Parameters
----------
X : csr_matrix
term document matrix
mX : csr_matrix
metadata-document matrix
y : np.array
category index array
term_idx_store : IndexStore
Term indices
category_idx_store : IndexStore
Catgory indices
metadata_idx_store : IndexStore
Document metadata indices
text_col: np.array or pd.Series
Raw texts
unigram_frequency_path : str or None
Path to term frequency file.
'''
self._df = df
self._text_col = text_col
Corpus.__init__(self,
X,
mX,
y,
term_idx_store,
category_idx_store,
metadata_idx_store,
df[text_col],
unigram_frequency_path)