label_n=LABEL_N): pattern = f'dp_{DP_PROBES_NAME}_{METRIC}.csv' for p in param_path.rglob(pattern): s = pd.read_csv(p, index_col=0, squeeze=True) n = 1 y_mean = s.values h = np.zeros((len(s))) # margin of error # collect for comparison figure summary = (s.index.values, y_mean, h, label, n) summaries.append(summary) if not summaries: raise RuntimeError(f'Did not find csv files matching {pattern}') # plot comparison fig = make_summary_fig( summaries, ylabel=Y_LABEL, title=TITLE, log_y=LOG_Y, ylims=Y_LIMS, xlims=X_LIMS, figsize=FIG_SIZE, legend_loc='best', # vline=200_000, # legend_labels=['reverse age-ordered', 'age-ordered'], # palette_ids=[0, 1], # re-assign colors to each line ) fig.show()
print(f'--------------------- End section {p.name}') print() # sort data summaries = sorted(summaries, key=lambda s: s[1][-1], reverse=True) if not summaries: raise SystemExit('No data found') # print to console for s in summaries: _, y_mean, y_std, label, n = s print(label) print(y_mean) print(y_std) print() # plot fig = make_summary_fig( summaries, Y_LABEL, title=TITLE, palette_ids=PALETTE_IDS, figsize=FIG_SIZE, ylims=Y_LIMS, legend_labels=LABELS, vlines=V_LINES, plot_max_lines=PLOT_MAX_LINES, plot_max_line=PLOT_MAX_LINE, legend_loc='best', ) fig.show()
concatenated_df = pd.concat(series_list, axis=1) y_mean = concatenated_df.mean(axis=1).values.flatten() y_sem = sem(concatenated_df.values, axis=1) h = y_sem * t.ppf((1 + CONFIDENCE) / 2, n - 1) # margin of error return concatenated_df.index.values, y_mean, h, lb, n summaries = [] project_name = __name__ for param_path, label in gen_param_paths(project_name, param2requests, param2default, runs_path=RUNS_PATH, ludwig_data_path=LUDWIG_DATA_PATH, label_n=LABEL_N): summary = make_summary(param_path, label, f'*_{PROBES_NAME}_js.csv') summaries.append(summary) fig = make_summary_fig( summaries, ylabel='Noun ' + Y_LABEL, title='', log_y=LOG_Y, ylims=Y_LIMS, figsize=FIG_SIZE, legend_loc='best', vline=200_000, palette_ids=[0, 1], # re-assign colors to each line ) fig.show()