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+import pandas as pd
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+import numpy as np
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+import tomotopy as tp
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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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+from adjustText import adjust_text
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+
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+import textwrap
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+
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+from sklearn.manifold import TSNE
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+
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+articles = pd.read_parquet("inspire-harvest/database/articles.parquet")[["article_id", "pacs_codes", "categories"]]
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+articles["article_id"] = articles["article_id"].astype(int)
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+
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+topics = pd.read_parquet("output/hep-ct-75-0.1-0.001-130000-20/topics_0.parquet")
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+topics["article_id"] = topics["article_id"].astype(int)
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+topics["topics"] = topics["probs"]
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+topics = topics.merge(articles, how="inner", left_on = "article_id", right_on = "article_id")
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+topics["categories"] = topics["categories"].map(
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+ lambda l: (
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+ [x in l for x in ["Theory-HEP", "Phenomenology-HEP", "Experiment-HEP"]]
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+ )
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+)
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+
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+X = np.stack(topics["topics"].values)
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+Y = np.stack(topics["categories"].values).astype(int)
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+
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+cat_topic_mean = np.zeros((Y.shape[1], X.shape[1]))
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+for i in range(3):
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+ cat_topic_mean[i] = X[Y[:,i]==1,:].mean(axis=0)
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+
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+topic_main_category = cat_topic_mean.argmax(axis=0).astype(int)
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+
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+usages = pd.read_csv('output/supersymmetry_usages.csv')
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+usages = usages.groupby("term").agg(topic=("topic", lambda x: x.tolist()))
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+susy_topics = usages.loc["supersymmetry"]["topic"] + [t for t in usages.loc["susy"]["topic"] if t not in usages.loc["supersymmetry"]["topic"]]
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+
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+descriptions = pd.read_csv("output/hep-ct-75-0.1-0.001-130000-20/descriptions.csv")
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+labels = descriptions.loc[susy_topics]["description"].tolist()
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+
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+edges = np.array([False]*len(topic_main_category))
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+edges[susy_topics]=True
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+edges = ["black" if edge else "none" for edge in edges]
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+
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+mdl = tp.CTModel.load("output/hep-ct-75-0.1-0.001-130000-20/model")
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+correlations = mdl.get_correlations()
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+
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+
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+colors=['#377eb8', '#ff7f00', '#4daf4a']
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+cats=["Theory", "Phenomenology", "Experiment"]
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+
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+tsne = TSNE(n_components=2, metric="precomputed", random_state=714, perplexity=40)
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+points = tsne.fit_transform(1-correlations)
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+
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+from sklearn.linear_model import LinearRegression
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+reg = LinearRegression()
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+reg.fit(points, topic_main_category)
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+angle = np.arctan(reg.coef_[0]/reg.coef_[1])-np.pi/2
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+m = np.array([[np.cos(angle), np.sin(angle)], [-np.sin(angle),np.cos(angle)]])
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+points=points@m
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+
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+fig, axes = plt.subplots(nrows=2,ncols=1,sharex=True,gridspec_kw={"height_ratios": [5, 1]},figsize=[6.4,5])
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+
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+for i, cat in enumerate(cats):
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+ axes[0].scatter(
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+ points[topic_main_category==i,0],
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+ points[topic_main_category==i,1],
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+ color=colors[i],
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+ label=cat,
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+ edgecolors=[edges[i[0]] for i in np.argwhere(topic_main_category==i) if i!=np.nan]
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+ )
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+
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+texts = []
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+
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+for i,topic in enumerate(susy_topics):
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+ texts.append(
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+ axes[0].annotate(
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+ labels[i],
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+ xy=(points[topic,0],points[topic,1]),
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+ xytext=(points[topic,0],points[topic,1]+0.25),
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+ size="small"
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+ )
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+ )
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+
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+adjust_text(texts,ax=axes[0])
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+
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+import seaborn as sns
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+sns.kdeplot(data=[points[topic_main_category==i,0] for i in range(3)],ax=axes[1],legend=False)
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+
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+plt.subplots_adjust(wspace=0, hspace=0)
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+
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+for i in range(2):
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+ axes[i].set_xticklabels([])
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+ axes[i].set_yticklabels([])
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+
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+fig.legend()
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+plt.savefig(f"plots/topics_tsne.eps", bboxes_inches="tight")
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+plt.savefig(f"plots/topics_tsne.png", bboxes_inches="tight")
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