class DataGround:
"""DataGround is used to store the corpus-level information"""
def __init__(self, K=100, n_early_stop=30, use_default_dict=False,
dict_file=None, min_tf=5, min_df=2, max_dict_len=8000,
stem=False):
self.K = K
self.n_early_stop = n_early_stop
if use_default_dict:
self.dictionary = Dictionary(dict_file=default_dict)
elif dict_file:
self.dictionary = Dictionary(dict_file=dict_file)
else:
self.dictionary = None
self._min_tf = min_tf
self._min_df = min_df
self._max_dict_len = max_dict_len
self._stem = stem
def load_data(self, corpus):
if self.dictionary is not None:
self.lda_model = LDA(dictionary=self.dictionary,
K=self.K,
n_early_stop=self.n_early_stop)
self.lda_model.fit(corpus)
else:
self.lda_model = LDA(K=self.K, n_early_stop=self.n_early_stop)
self.lda_model.fit(corpus,
min_df=self._min_df,
min_tf=self._min_tf,
max_dict_len=self._max_dict_len,
stem=self._stem)
self.dictionary = self.lda_model.dictionary
self.target_subset_ids = range(int(self.lda_model.M))
def set_target_subset(self, selected_ids):
self.target_subset_ids = selected_ids
def subset_w_mi(self, with_dictionary=False):
subset_w_mi = [self.lda_model.w_mi[x] for x in self.target_subset_ids]
if with_dictionary:
return (subset_w_mi, self.dictionary)
else:
return subset_w_mi
def subset_z_mi(self):
lda_model = self.lda_model
z_mi = []
ids = self.target_subset_ids
for doc_id in ids:
start = lda_model.I_m[doc_id]
end = start + lda_model.N_m[doc_id]
z_mi.append(lda_model.Z[start:end])
return z_mi
def load_data(self, corpus):
if self.dictionary is not None:
self.lda_model = LDA(dictionary=self.dictionary,
K=self.K,
n_early_stop=self.n_early_stop)
self.lda_model.fit(corpus)
else:
self.lda_model = LDA(K=self.K, n_early_stop=self.n_early_stop)
self.lda_model.fit(corpus,
min_df=self._min_df,
min_tf=self._min_tf,
max_dict_len=self._max_dict_len,
stem=self._stem)
self.dictionary = self.lda_model.dictionary
self.target_subset_ids = range(int(self.lda_model.M))
