import math import multiprocessing import pathlib import numpy import orjson import pandas import seaborn import tqdm import typer from loguru import logger from matplotlib import pyplot as plt app = typer.Typer() GB = 2**30 def read_lines_jsonl(file_name, chunk_size=GB // 2): with open(file_name, "rb") as file_handle: buffer = b"" while True: chunk = file_handle.read(chunk_size) if not chunk: break lines = (buffer + chunk).split(b"\n") for line in lines[:-1]: yield line.strip() buffer = lines[-1] def compute_subreddit_score( subs: pathlib.Path, comments: pathlib.Path, stats_out: pathlib.Path ): submission_data = {"authors": set(), "submissions": 0, "media": 0} logger.debug(f"Gather Subs: {subs}") for line in read_lines_jsonl(subs): sub_data = orjson.loads(line) if sub_data["author"]: submission_data["authors"].add(sub_data["author"]["name"]) submission_data["submissions"] += 1 if not sub_data["text"]: submission_data["media"] += 1 logger.debug(f"Done Gather Subs for: {subs}") submission_data["authors"] = len(submission_data["authors"]) comment_data = { "authors": set(), "comments": 0, } logger.debug(f"Gather Comments: {comments}") for line in read_lines_jsonl(comments): sub_data = orjson.loads(line) if sub_data["author"]: comment_data["authors"].add(sub_data["author"]["name"]) comment_data["comments"] += 1 comment_data["authors"] = len(comment_data["authors"]) # Asked ChatGPT for formula advice... engagement = comment_data["comments"] / submission_data["submissions"] richness = (submission_data["media"] / submission_data["submissions"]) ** 2 diversity = ( comment_data["authors"] + submission_data["authors"] ) / submission_data["submissions"] wrapped = orjson.dumps( { "submission": submission_data, "comment": comment_data, "qscore": { "engagement": engagement, "richness": richness, "diversity": diversity, "compound": engagement * richness * diversity, }, } ) stats_out.write_bytes(wrapped) logger.debug(f"{stats_out.name}: {wrapped}") def err_cb(err): logger.exception(err) @app.command() def compute_scores(output_path:pathlib.Path): # pathlib.Path("subreddits_M700") with multiprocessing.Pool(processes=32) as pool: fns = [] for sub in [ i for i in output_path.iterdir() if i.stem.endswith("_Submission") ]: root_sub = sub.with_stem(sub.stem[: -len("_Submission")]) comments = root_sub.with_stem(root_sub.stem + "_Comments") if sub.exists() and comments.exists(): stats = root_sub.with_stem(root_sub.stem + "_Scores") fns.append( pool.apply_async( compute_subreddit_score, args=( sub, comments, stats, ), error_callback=err_cb, ) ) else: logger.warning(f"Mismatched: {sub} {comments}") sub.unlink() if sub.exists() else None comments.unlink() if comments.exists() else None [i.wait() for i in fns] @app.command() def makefilter(merged_stats: pathlib.Path, output_file: pathlib.Path, mode="text"): reddits = [] with open(merged_stats, "rb") as f: for stats in tqdm.tqdm(f): stats_data = orjson.loads(stats) if "qscore" not in stats_data: logger.warning(f"{stats} did not have any qscores.") continue qscores: dict = stats_data["qscore"] if mode == "text": # Baseline author filters if ( stats_data["submission"]["authors"] < 70 or stats_data["comment"]["authors"] < 20 or stats_data["submission"]["submissions"] < 450 or stats_data["comment"]["comments"] < 585 ): continue # QScores if qscores["engagement"] < 1.05: # Low amount of engagement continue elif qscores["engagement"] > 50: # Excessive engagement. continue elif qscores["compound"] < 0.05: # Close to 0 compound is probably not worth continue elif qscores["richness"] < 0.01 or qscores["richness"] > 0.95: # low richness means it's probably mostly text. # # High richness means almost or mostly images. continue elif qscores["diversity"] < 0.05 or qscores["diversity"] > 5: # Too little diversity: Too many submission authors, not enough comment authors # > 2: Too many comment authors, not enough submission authors. continue reddits.append(stats_data) elif mode == "media": # For media, we don't care too much about a lot of stats for text and # more interested about raw media stuff. # Biased richness score for images image_bias_richness = math.sqrt(math.sqrt(qscores["richness"])) if ( stats_data["submission"]["authors"] < 70 or stats_data["comment"]["authors"] < 20 or stats_data["submission"]["submissions"] < 450 or stats_data["comment"]["comments"] < 585 ): continue if qscores["engagement"] < 0.5: # Low amount of engagement continue elif qscores["engagement"] > 50: # Excessive engagement. continue elif qscores["compound"] < 0.05: # Close to 0 compound is probably not worth continue elif image_bias_richness < 0.15 or image_bias_richness > 0.95: # low richness means it's probably mostly text. # # High richness means almost or mostly images. continue reddits.append(stats_data) output_file.write_bytes( b"\n".join([orjson.dumps(reddit) for reddit in reddits]) ) @app.command() def merge_stats(folder: pathlib.Path, output_file: pathlib.Path): with open(output_file, "wb") as fp: scores = [i for i in folder.iterdir() if i.stem.endswith("_Scores")] for stats in tqdm.tqdm(scores): stats_data = orjson.loads(stats.read_bytes()) if "qscore" not in stats_data: logger.warning(f"{stats} did not have any qscores.") continue fp.write( orjson.dumps( {"file": stats.name, **stats_data}, option=orjson.OPT_APPEND_NEWLINE, ) ) @app.command() def plot(file: pathlib.Path): total_stats = {} with open(file, "rb") as f: for stats in tqdm.tqdm(f): stats = orjson.loads(stats) if "qscore" in stats: for key, value in stats["qscore"].items(): vv_stats = total_stats.setdefault(key, []) vv_stats.append(value) total_stats[key] = vv_stats for key, value in stats["submission"].items(): key = f"submissions_{key}" vv_stats = total_stats.setdefault(key, []) vv_stats.append(value) total_stats[key] = vv_stats for key, value in stats["submission"].items(): key = f"submissions_{key}" vv_stats = total_stats.setdefault(key, []) vv_stats.append(value) total_stats[key] = vv_stats for key, value in stats["comment"].items(): key = f"comments_{key}" vv_stats = total_stats.setdefault(key, []) vv_stats.append(value) total_stats[key] = vv_stats for key in total_stats.keys(): if key == "richness": total_stats[key] = [i for i in total_stats[key] if i > 0 and i < 10] elif key.startswith(("submission", "comment")): total_stats[key] = [i for i in total_stats[key] if i > 0 and i < 100_000] else: total_stats[key] = [i for i in total_stats[key] if i > 0 and i < 100] df = pandas.DataFrame.from_dict( {k: v for k, v in total_stats.items() if k == key} ) if key == "richness": fg = seaborn.displot(df, x=key, bins=500, log_scale=(False, False)) elif key.startswith(("submission", "comment")): fg = seaborn.displot(df, x=key, bins=50, log_scale=(False, False)) else: fg = seaborn.displot(df, x=key, bins=500, log_scale=(False, False)) nuarr = numpy.array(total_stats[key]) percentiles = [ numpy.percentile(nuarr, 95), numpy.percentile(nuarr, 90), numpy.percentile(nuarr, 50), numpy.percentile(nuarr, 10), numpy.percentile(nuarr, 5), ] plt.axvline(x=percentiles[0], color="cyan") plt.axvline(x=percentiles[1], color="blue") print(percentiles, "pct for", key) print("Save fig") fg.savefig(f"test-{key}.png", dpi=120) if __name__ == "__main__": app()