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@@ -0,0 +1,46 @@
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+import pandas as pd
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+import numpy as np
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+
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+from scipy.stats import binom
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+
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+import matplotlib
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+from matplotlib import pyplot as plt
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+matplotlib.use("pgf")
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+matplotlib.rcParams.update(
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+ {
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+ "pgf.texsystem": "xelatex",
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+ "font.family": "serif",
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+ "font.serif": "Times New Roman",
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+ "text.usetex": True,
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+ "pgf.rcfonts": False,
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+ }
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+)
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+
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+cats = ["Experiment", "Phenomenology", "Theory"]
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+colors = ['#4daf4a', '#ff7f00', '#377eb8']
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+
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+accuracy = pd.read_csv("output/category_prediction/accuracy_per_period.csv").sort_values("year_group")
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+accuracy = accuracy[accuracy["year_group"]<8]
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+
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+fig, ax = plt.subplots(1,1)
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+for i in range(3):
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+ ci = ([],[])
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+ for row in accuracy.to_dict(orient="records"):
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+ low,high = binom.ppf([0.025, 0.975], row[f"count_{i}"], row[f"accurate_{i}"], loc=0)
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+ ci[0].append(low/row[f"count_{i}"])
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+ ci[1].append(high/row[f"count_{i}"])
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+
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+ ax.scatter(accuracy["year_group"], accuracy[f"accurate_{i}"], color=colors[i], label=cats[i])
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+ ax.errorbar(accuracy["year_group"], accuracy[f"accurate_{i}"], yerr=(ci[0]-accuracy[f"accurate_{i}"], accuracy[f"accurate_{i}"]-ci[1]), color=colors[i], ls="none")
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+
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+ ax.plot(accuracy["year_group"], accuracy[f"dummy_accurate_{i}"], color=colors[i], ls="dashed")
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+
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+ax.set_xticks(accuracy["year_group"].tolist())
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+ax.set_xticklabels([f"{1980+i*5}-{1980+(i+1)*5-1}" for i in accuracy["year_group"].tolist()], rotation=45, ha='right')
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+
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+ax.set_ylabel("Accuracy")
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+
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+ax.legend()
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+fig.savefig("plots/category_prediction_stability.eps", bbox_inches="tight")
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+fig.savefig("plots/category_prediction_stability.png", bbox_inches="tight")
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+plt.show()
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