adaptation_specialization_material/plots/change_effects_summary.py

from cProfile import label
import numpy as np
import pandas as pd
from scipy.stats import entropy
from sklearn.linear_model import LinearRegression

from matplotlib import pyplot as plt
import matplotlib
matplotlib.use("pgf")
matplotlib.rcParams.update(
    {
        "pgf.texsystem": "xelatex",
        "font.family": "serif",
        "font.serif": "Times New Roman",
        "text.usetex": True,
        "pgf.rcfonts": False,
    }
)
plt.rcParams["text.latex.preamble"].join([
        r"\usepackage{amsmath}",              
        r"\setmainfont{amssymb}",
])

import argparse
from os.path import join as opj, exists
import pickle

parser = argparse.ArgumentParser()
parser.add_argument("--input")
args = parser.parse_args()

topics = pd.read_csv(opj(args.input, "topics.csv"))
junk = topics["label"].str.contains("Junk")
topics = topics[~junk]["label"].tolist()

n_topics = len(topics)

labels = [
    "Intellectual capital (diversity)",
    "Social capital (diversity)",
    "Social capital (power)",
    "Stable affiliation",
    "Academic age",
    "Productivity (co-authored)",
    "Productivity (solo-authored)",
]
labels = [f"\\textbf{{{label}}}" for label in labels]
labels += topics
n_vars = len(labels)

label_position = {label: i for i, label in enumerate(labels)}

names = [
    "beta_int_div", "beta_soc_div", "beta_soc_cap", "beta_stable", "beta_age", "beta_productivity", "beta_productivity_solo"
]

nice_names = {
    "change": "Change score ($c_a$)",
    "disruption": "Disruption score ($d_a$)",
    "entered": "Entered a new research area",
    "exited": "Exited a research area"
}

def get_effects(metric, diversity, power):
    filename = opj(args.input, f"samples_{metric}_{diversity}_{power}.npz")
    if not exists(filename):
        print(f"samples not found: {filename}")
        return pd.DataFrame([])
    
    samples = np.load(filename)

    mu = np.array([samples[name].mean() for name in names] + [(samples["beta_x"][:,i]*samples["tau"]).mean() for i in range(n_topics)])
    low = np.array([np.quantile(samples[name], q=0.05/2) for name in names] + [np.quantile(samples["beta_x"][:,i]*samples["tau"], q=0.05/2) for i in range(n_topics)])
    up = np.array([np.quantile(samples[name], q=1-0.05/2) for name in names] + [np.quantile(samples["beta_x"][:,i]*samples["tau"], q=1-0.05/2) for i in range(n_topics)])
    sig = up*low>0
    sign = mu>0

    prob = np.array([(samples[name]*np.sign(samples[name].mean())<0).mean() for name in names] + [((samples["beta_x"][:,i]*np.sign(samples["beta_x"][:,i].mean()))<0).mean() for i in range(n_topics)])
    
    vars = []
    model = None
    if diversity == "entropy":
        model = "Reference" if power=="magnitude" else "$P=\\text{Brokerage}$"
    else:
        model = "$D=\\text{Stirling}$"
    
    for i in range(n_vars):
        plus = up[i]-mu[i]
        minus = mu[i]-low[i]
        sign_char = "+" if sign[i] else ""

        s = (f"{mu[i]:.2g}").replace("-", "")
        if len(s)<5 and "e" not in s:
            if sig[i]:
                string = f"$\\bm{{{sign_char}{mu[i]:.2g}}}\\substack{{+{plus:.2g} \\\\ -{minus:.2g}}}$"
            else:
                string = f"${sign_char}{mu[i]:.2g}\\substack{{+{plus:.2g} \\\\ -{minus:.2g}}}$"
        else:
            if sig[i]:
                string = f"$\\bm{{{sign_char}{mu[i]:.1g}}}\\substack{{+{plus:.1g} \\\\ -{minus:.1g}}}$"
            else:
                string = f"${sign_char}{mu[i]:.1g}\\substack{{+{plus:.1g} \\\\ -{minus:.1g}}}$"

        vars.append({
            "Dep. variable": nice_names[metric],
            "Model": model,
            "mu": mu[i],
            "low": low[i],
            "up": up[i],
            "sig": sig[i]>0,
            "Predictor": labels[i],
            "string": string,
        })

    print(metric, model)

    return pd.DataFrame(vars)

vars = []
metrics = ["change", "disruption"]

for metric in metrics:
    vars.append(get_effects(metric, "entropy", "magnitude"))
    vars.append(get_effects(metric, "stirling", "magnitude"))
    vars.append(get_effects(metric, "entropy", "brokerage"))

vars = pd.concat(vars)

print(vars)
vars = vars.pivot(columns=["Dep. variable", "Model"], index="Predictor", values="string")
vars.sort_index(key=lambda x: x.map(label_position), inplace=True)

latex = vars.to_latex(
    escape=False,
    multicolumn_format="c",
    caption="Effect of each variable on (a) the change score and (b) the disruption score for each model. The reference model uses entropy as the diversity measure $D$ and the magnitude of intellectual capital as a measure of power $P$. Values indicate the mean posterior effect size and the 95\\% credible interval. Significant effects are shown in bold.",
    label="table:summary_change_disruption",
    position="H"
)

latex = latex.replace("\\\nHadrons", "\\\n\\hline Hadrons")
latex = latex.replace("\\begin{tabular}", "\\renewcommand{\\arraystretch}{2}\\fontsize{6}{7}\\selectfont\\begin{tabular}")
latex = latex.replace("\\end{tabular}", "\\end{tabular}\\normalsize\\renewcommand{\\arraystretch}{1}")

with open(opj(args.input, f"summary_change_disruption.tex"), "w+") as fp:
    fp.write(latex)

vars = []
metrics = ["entered", "exited"]

for metric in metrics:
    vars.append(get_effects(metric, "entropy", "magnitude"))
    vars.append(get_effects(metric, "stirling", "magnitude"))
    vars.append(get_effects(metric, "entropy", "brokerage"))

vars = pd.concat(vars)

print(vars)
vars = vars.pivot(columns=["Dep. variable", "Model"], index="Predictor", values="string")
vars.sort_index(key=lambda x: x.map(label_position), inplace=True)

latex = vars.to_latex(
    escape=False,
    multicolumn_format="c",
    caption="Effect of each variable on (a) the probability of having entered a new research area and (b) the probability of having exited a research area, for each model. The reference model uses entropy as the diversity measure $D$ and the magnitude of intellectual capital as a measure of power $P$. Values indicate the mean posterior effect size and the 95\\% credible interval. Significant effects are shown in bold.",
    label="table:summary_entered_exited",
    position="H"
)

latex = latex.replace("\\\nHadrons", "\\\n\\hline Hadrons")
latex = latex.replace("\\begin{tabular}", "\\renewcommand{\\arraystretch}{2}\\fontsize{6}{7}\\selectfont\\begin{tabular}")
latex = latex.replace("\\end{tabular}", "\\end{tabular}\\normalsize\\renewcommand{\\arraystretch}{1}")

with open(opj(args.input, f"summary_entered_exited.tex"), "w+") as fp:
    fp.write(latex)