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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| import numpy as np | ||
| from pandas import DataFrame | ||
| import pylab as pl | ||
| #%% Figure 4b and GRID statistics | ||
| with np.load('/mnt/ceph/neuro/DataForPublications/DATA_PAPER_ELIFE/ALL_RECORDS_GRID_FINAL.npz') as ld: | ||
| records = ld['records'][()] | ||
| records = [list(rec) for rec in records] | ||
| records = [rec[:5]+[float(rr) for rr in rec[5:]] for rec in records] | ||
| #%% Max of all datasets | ||
| df = DataFrame(records) | ||
| df.columns = ['name', 'gr_snr', 'grid_rval', 'grid_max_prob_rej', 'grid_thresh_CNN', 'recall', | ||
| 'precision', 'f1_score'] | ||
| best_res = df.groupby(by=['name']) | ||
| best_res = best_res.describe() | ||
| max_caiman_batch = best_res['f1_score']['max'] | ||
| print(max_caiman_batch) | ||
| print(max_caiman_batch.mean()) | ||
| print(max_caiman_batch.std()) | ||
| #%% | ||
| df = DataFrame(records) | ||
| df.columns = ['name', 'gr_snr', 'grid_rval', 'grid_max_prob_rej', 'grid_thresh_CNN','recall', 'precision', 'f1_score'] | ||
| best_res = df.groupby(by=['gr_snr', 'grid_rval', 'grid_max_prob_rej', 'grid_thresh_CNN']) | ||
| best_res = best_res.describe() | ||
| print(best_res.loc[:, 'f1_score'].max()) | ||
| #%% | ||
| df = DataFrame(records) | ||
| df.columns = ['name', 'gr_snr', 'grid_rval', 'grid_max_prob_rej', 'grid_thresh_CNN','recall', 'precision', 'f1_score'] | ||
| best_res = df.groupby(by=['gr_snr', 'grid_rval', 'grid_max_prob_rej', 'grid_thresh_CNN']) | ||
| best_res = best_res.describe() | ||
| pars = best_res.loc[:, 'f1_score'].idxmax()['mean'] | ||
| print(pars) | ||
| df_result = df[((df['gr_snr'] == pars[0]) & (df['grid_rval'] == pars[1]) & (df['grid_max_prob_rej'] == pars[2]) & (df['grid_thresh_CNN'] == pars[3]))] | ||
|
|
||
| print(df_result.sort_values(by='name')[['name','precision','recall','f1_score']]) | ||
| print(df_result.mean()) | ||
| print(df_result.std()) | ||
|
|
||
| #%% | ||
| df_result = df_result.sort_values(by='name') | ||
| max_res = df.groupby(by=['name']) | ||
| max_res = max_res.describe() | ||
| max_res = max_res.sort_values(by='name') | ||
| max_res = max_res['f1_score']['max'] | ||
| df_result['f1_score_max'] = max_res.values | ||
| min_res = df.groupby(by=['name']) | ||
| min_res = min_res.describe() | ||
| min_res = min_res.sort_values(by='name') | ||
| min_res = min_res['f1_score']['min'] | ||
| df_result['f1_score_min'] = min_res.values | ||
|
|
||
|
|
||
| names = ['N.03.00.t', | ||
| 'N.04.00.t', | ||
| 'YST', | ||
| 'N.00.00', | ||
| 'N.02.00', | ||
| 'N.01.01', | ||
| 'K53', | ||
| 'J115', | ||
| 'J123'] | ||
|
|
||
| idx_sort = np.argsort(names) | ||
| df_result['L1_f1'] = np.array([np.nan, np.nan, 0.78, np.nan, 0.89, 0.8, 0.89, 0.85, np.nan])[idx_sort] # Human 1 | ||
| df_result['L2_f1'] = np.array([0.9, 0.69, 0.9, 0.92, 0.87, 0.89, 0.92, 0.93, 0.83])[idx_sort] # Human 2 | ||
| df_result['L3_f1'] = np.array([0.85, 0.75, 0.82, 0.83, 0.84, 0.78, 0.93, 0.94, 0.9])[idx_sort] # Human 3 | ||
| df_result['L4_f1'] = np.array([0.78, 0.87, 0.79, 0.87, 0.82, 0.75, 0.83, 0.83, 0.91])[idx_sort] | ||
|
|
||
| df_result['f1_score_CaImAn_online'] = np.array([0.74213836, 0.71713147, 0.78541374, 0.77562327, 0.69266771, | ||
| 0.74285714, 0.80835509, 0.78950077, 0.83573487])[idx_sort] | ||
| #%% | ||
| ax = df_result.plot(x='name', y=['f1_score', 'f1_score_CaImAn_online','L4_f1','L3_f1','L2_f1','L1_f1'], xticks=range(len(df_result)), | ||
| kind='bar', color=[[1,0,0],[0,0,1],[.5,.5,.5],[.6,.6,.6],[.7,.7,.7],[.8,.8,.8]]) | ||
|
|
||
| ax.set_xticklabels(df_result.name, rotation=45) | ||
| pl.legend(['CaImAn batch','CaImAn online','L4','L3','L2','L1']) | ||
| # ax.set_xticklabels(df_result.name) | ||
| # pl.xlabel('Dataset') | ||
| pl.ylabel('F1 score') | ||
| pl.ylim([0.55,0.95]) | ||
| params_display = { | ||
| 'downsample_ratio': .2, | ||
| 'thr_plot': 0.8 | ||
| } | ||
|
|
||
| pl.rcParams['pdf.fonttype'] = 42 | ||
| font = {'family': 'Arial', | ||
| 'weight': 'regular', | ||
| 'size': 20} | ||
| pl.rc('font', **font) | ||
| pl.close() | ||
| #%% | ||
| pl.figure() | ||
| online_F1_max = np.array([0.75316456, 0.71713147, 0.79427083, 0.79733333, 0.718529, | ||
| 0.76, 0.84371328, 0.81400438, 0.83965015])[idx_sort] | ||
| all_labels = np.vstack([df_result['L1_f1'],df_result['L2_f1'], df_result['L3_f1'] , df_result['L4_f1']]) | ||
| mean_labels = np.nanmean(all_labels,0).T | ||
| df_cm = DataFrame({'Human average':mean_labels,'CaImAn online max':online_F1_max, 'CaImAn online avg': df_result['f1_score_CaImAn_online'].values | ||
| ,'CaImAn batch max':max_caiman_batch,'CaImAn batch avg': df_result['f1_score'].values}) | ||
| df_cm.plot(kind='bar') |