diff --git a/research/legacy_skatervaluation/battlelatex/tables.py b/research/legacy_skatervaluation/battlelatex/tables.py index 513d202f..263414cd 100644 --- a/research/legacy_skatervaluation/battlelatex/tables.py +++ b/research/legacy_skatervaluation/battlelatex/tables.py @@ -11,9 +11,9 @@ def leaderboard_to_latex(leaderboard, caption, label, max_rows=42): '_emp':' empirical ', '_ppo':' ', '_r':' r=0.', - '_g025': ' \gamma=0.5', - '_g050':' \gamma=0.5', - '_g100': ' \gamma=1', + '_g025': ' \\gamma=0.5', + '_g050':' \\gamma=0.5', + '_g100': ' \\gamma=1', '_l010': ' r_l=0.01', '_l020': ' r_l=0.02', '_l050': ' r_l=0.05', diff --git a/research/legacy_skatervaluation/battleutil/interpretingelo.py b/research/legacy_skatervaluation/battleutil/interpretingelo.py index df7e0145..e8010cf9 100644 --- a/research/legacy_skatervaluation/battleutil/interpretingelo.py +++ b/research/legacy_skatervaluation/battleutil/interpretingelo.py @@ -89,7 +89,7 @@ def infer_categorical_and_ordinal(elos): for wd in name_words: for r_old, r_new in MODEL_KEY_REPLACEMENTS.items(): wd = wd.replace(r_old, r_new) - wd_head = re.search("[^\d]*", wd).group() + wd_head = re.search("[^\\d]*", wd).group() if wd == wd_head: if wd not in MODEL_KEYS_NOT_USED: categorical.add(wd_head) @@ -111,7 +111,7 @@ def make_model_regs_elo(elos, categorical, ordinal): if wd in categorical: pair = (wd+'_hot',1) else: - wd_head = re.search("[^\d]*", wd).group() + wd_head = re.search("[^\\d]*", wd).group() if wd_head in categorical: pair = (wd+'_hot',1) elif wd_head in ordinal: diff --git a/research/legacy_skatervaluation/schurcomparisonutil/schurportpaperutils.py b/research/legacy_skatervaluation/schurcomparisonutil/schurportpaperutils.py index fb9442e0..20f704b0 100644 --- a/research/legacy_skatervaluation/schurcomparisonutil/schurportpaperutils.py +++ b/research/legacy_skatervaluation/schurcomparisonutil/schurportpaperutils.py @@ -60,7 +60,7 @@ def moment_plot(moments: dict, sty='go'): normalized_moments = dict([(k, v / moments['g000']) for k, v in moments.items()]) moment_plot(moments=normalized_moments, sty=sty) - plt.title('Portfolio variance as $\gamma$ is varied') + plt.title('Portfolio variance as $\\gamma$ is varied') full_xlabel = xlabel + ' benefit=' + str(round(np.mean(bps))) + ' bps' plt.xlabel(full_xlabel) plt.show()