def generate_gibbs_states_plots(self,
states_path: str,
cat: str = "likelihood"):
new_dir = Tools.get_path(states_path, f"{cat}_plots")
if Tools.path_exists(new_dir):
print("Plots found, skipping..")
return
Tools.initialise_directory(new_dir)
with Tools.scan_directory(states_path) as outputs:
for i, output in enumerate(outputs):
try:
state_file = Tools.get_path(output.path, "state.log")
df = pd.read_csv(filepath_or_buffer=state_file,
delim_whitespace=True,
index_col="iter")
ax = sns.lineplot(x=df.index, y=cat, data=df)
ax.margins(x=0)
name = output.name
fig = ax.get_figure()
fig.savefig(Tools.get_path(states_path, f"{cat}_plots",
f"{name}.png"),
dpi=300,
bbox_incehs="tight",
format="png")
fig.clf()
print(f"{i}")
except FileNotFoundError:
print(f"→ Skipping {output.name}")
def _generate_lda_c_corpus(self):
""" Convert a group of files LDA_C corpus and store it on disk"""
bow_corpus, id2word_map, plain_docs = self._convert_corpus_to_bow()
# Sterialise into LDA_C and store on disk
output_dir = Tools.get_path(
self.input_docs_path,
f"lda_c_format_{self.hdp_eta:0.1f}_{self.hdp_gamma_s:0.1f}",
f"_{self.hdp_alpha_s:0.1f}_common_{self.drop_uncommon}")
Tools.initialise_directory(output_dir)
save_location = Tools.get_path(output_dir, f"{self.lda_c_fname}.dat")
bleicorpus.BleiCorpus.serialize(fname=save_location,
corpus=bow_corpus,
id2word=id2word_map)
return plain_docs, bow_corpus
def _invoke_gibbs_hdp(self):
"""Invoke Gibbs hdp posterior inference on the corpus"""
path_executable = Tools.get_path(self.hdp_path, "hdp.exe")
param_data = Tools.get_path(
self.input_docs_path,
f"lda_c_format_{self.hdp_eta:0.1f}_{self.hdp_gamma_s:0.1f}",
f"_{self.hdp_alpha_s:0.1f}_common_{self.drop_uncommon}",
f"{self.lda_c_fname}.dat")
param_directory = Tools.get_path(self.input_docs_path,
self.hdp_output_directory)
# Prepare the output directory
Tools.initialise_directory(param_directory)
if self.hdp_seed is not None and self.hdp_seed > 0:
ret = s.run([
path_executable, "--algorithm", "train", "--data", param_data,
"--directory", param_directory, "--max_iter",
str(self.hdp_iterations), "--sample_hyper",
"yes" if self.hdp_hyper_sampling else "no", "--save_lag", "-1",
"--eta",
str(self.hdp_eta), "--random_seed",
str(self.hdp_seed), "--gamma_a",
str(self.hdp_gamma_s), "--alpha_a",
str(self.hdp_alpha_s)
],
check=True,
capture_output=True,
text=True)
else:
ret = s.run([
path_executable, "--algorithm", "train", "--data", param_data,
"--directory", param_directory, "--max_iter",
str(self.hdp_iterations), "--sample_hyper",
"yes" if self.hdp_hyper_sampling else "no", "--save_lag", "-1",
"--eta",
str(self.hdp_eta), "--gamma_a",
str(self.hdp_gamma_s), "--alpha_a",
str(self.hdp_alpha_s)
],
check=True,
capture_output=True,
text=True)
return ret.stdout
