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import pickle
import Levenshtein
import numpy as np
import pandas as pd
import plotly.figure_factory as ff
import config
def get_statistics_for_sample(start_msg, end_msg, row=None):
edit_ops = Levenshtein.editops(start_msg, end_msg)
n_deletes = sum([1 if op == "delete" else 0 for op, _, _ in edit_ops])
n_inserts = sum([1 if op == "insert" else 0 for op, _, _ in edit_ops])
n_replaces = sum([1 if op == "replace" else 0 for op, _, _ in edit_ops])
n_changes = n_deletes + n_inserts + n_replaces
n_deletes += n_replaces
n_inserts += n_replaces
return {
"deletions": n_deletes,
"insertions": n_inserts,
"changes": n_changes,
"deletions_norm": n_deletes / len(start_msg),
"insertions_norm": n_inserts / len(end_msg),
"changes_norm": n_changes / len(end_msg),
"lendiff": abs(len(start_msg) - len(end_msg)),
"editdist": row["editdist"] if row is not None else Levenshtein.distance(start_msg, end_msg),
}
def get_statistics_for_row(row):
if "commit_msg_start" in row:
start = row["commit_msg_start"]
else:
start = row["G_text"]
if "commit_msg_end" in row:
end = row["commit_msg_end"]
else:
end = row["E_text"]
return get_statistics_for_sample(start, end, row=row)
def get_statistics_for_df(df: pd.DataFrame):
stats = [get_statistics_for_row(row) for _, row in df.iterrows()]
assert len(stats) > 0
return {stat_name: np.asarray([e[stat_name] for e in stats]) for stat_name in stats[0]}
def build_plotly_chart(stat_golden, stat_e2s, stat_s2e, stat_e2s_s2e, stat_name):
hist_data = [
stat_golden,
stat_e2s,
stat_s2e,
stat_e2s_s2e,
np.concatenate((stat_e2s, stat_s2e, stat_e2s_s2e), axis=0),
]
group_labels = ["Golden", "e2s", "s2e", "e2s+s2e", "Synthetic"]
fig = ff.create_distplot(hist_data, group_labels, bin_size=0.05, show_rug=False, show_hist=False)
fig.update_layout(title_text=stat_name)
with open(config.OUTPUT_CHARTS_DIR / f"{stat_name}_data.pkl", "wb") as f:
pickle.dump(hist_data, f)
return fig
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