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@@ -1 +1,62 @@
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-/annex/objects/MD5E-s1459--58acfe400b29daf088dd4488aaa81c39.py
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+import pandas as pd
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+import numpy as np
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+from matplotlib import pyplot as plt
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+import seaborn as sns
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+
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+
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+df = pd.read_csv("data/maturity2/maturity2.csv")
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+df = df[df["selection"]=="key_chi"]
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+df["child_age"] /= 365
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+
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+df = df[~df["majority_label_age"].isin(["Junk",""])]
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+
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+df = df.groupby(["child_id", "child_age", "majority_label_age"]).agg(
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+ n=("majority_label_age", "count")
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+)
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+
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+df = df.reset_index()
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+df = df.pivot(columns="majority_label_age", index=["child_id","child_age"], values="n")
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+df["ratio"] = df["Baby"]/(df["Baby"]+df["Child"])
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+
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+df = df.reset_index()
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+df = df[df["ratio"]>=0]
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+
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+from cmdstanpy import CmdStanModel
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+
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+data = {
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+ "N": len(df),
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+ "age": df["child_age"].astype(float).values,
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+ "ratio": df["ratio"].astype(float).values
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+}
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+
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+model = CmdStanModel(
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+ stan_file=f"model.stan",
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+)
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+fit = model.sample(
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+ data=data,
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+ chains=4,
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+ threads_per_chain=1,
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+ iter_sampling=2000,
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+ iter_warmup=500,
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+ show_console=True,
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+)
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+
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+vars = fit.stan_variables()
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+samples = {}
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+for (k, v) in vars.items():
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+ samples[k] = v
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+
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+m = samples["lp"].max()
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+q = np.exp(samples["lp"]-m).mean(axis=0)
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+p = q/q.sum()
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+
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+fig, ax1 = plt.subplots()
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+ax1.scatter(df["child_age"], df["ratio"], label="Data")
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+ax1.set_xlabel("Key child age (in years)")
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+ax1.set_ylabel("Baby/(Baby+Child)")
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+ax2 = ax1.twinx()
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+ax2.plot(np.linspace(0,5,len(p)),q, color="black", label="Threshold probability density")
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+
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+fig.legend()
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+
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+fig.savefig("output/ratio_vs_age.png", bbox_inches="tight")
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