speech-maturity-tests/votes_distrib.py

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt 
import seaborn as sns

import argparse

def order(x,l):
    if x == "NO-LABEL":
        return 2
    if x in ["NA","Junk"]:
        return 1
    else:
        return -sorted(l, reverse=True).index(x)

parser = argparse.ArgumentParser()
parser.add_argument("--key")
parser.add_argument("--threads-per-chain", type=int, default=4)
parser.add_argument("--normalize", action="store_true", default=False)
args = parser.parse_args()

majority_col = f"majority_label_{args.key}"
individual_col = f"labels_{args.key}"

df = pd.concat([
    pd.read_csv("speech-maturity-dataset/data/babblecor/babblecor.csv"),
    pd.read_csv("speech-maturity-dataset/data/maturity1/maturity1.csv"),
    pd.read_csv("speech-maturity-dataset/data/maturity2/maturity2.csv"),
])
df["clip_id"] = df.index.astype(int)+1

df.dropna(axis=0, subset=[majority_col], inplace=True)

df[individual_col] = df[individual_col].str.split(",")
df["n_responses"] = df[individual_col].map(len)
df = df.explode(individual_col)

df = df.groupby(["clip_id",individual_col]).agg(
    n=("child_id", "count"),
    majority_label=(majority_col, "first"),
    n_responses=("n_responses", "first")
).reset_index()

df = df.pivot(index="clip_id", columns=individual_col,values="n").fillna(0)

print(df)

data = {
    "N": len(df),
    "C": len(df.columns),
    "votes": df.values.astype(int),
}

from cmdstanpy import CmdStanModel

model = CmdStanModel(
    stan_file=f"votes_distrib.stan",
    cpp_options={"STAN_THREADS": "TRUE"},
)
fit = model.sample(
    data=data,
    chains=2,
    threads_per_chain=args.threads_per_chain,
    iter_sampling=1000,
    iter_warmup=250,
    show_console=True,
)

vars = fit.stan_variables()
samples = {}
for (k, v) in vars.items():
    samples[k] = v

np.savez('votes_distrib.npz', **samples)

# if args.normalize:
#     df["n"] /= df["n_responses"]

# # df = df.pivot(index=["clip_id"], columns=individual_col, values="n").reset_index()
# df = df.groupby([individual_col, "n"]).agg(
#     clip_id = ("n", "count")
# )
# df.to_csv(f"votes_distrib_{args.key}.csv")