plt=pl pearsonabs=copy.deepcopy(pearson) for metrique in pearson: for lstm in list(pearson[metrique].keys()): for celltype in list(pearson[metrique][lstm].keys()): pearsonabs[metrique][lstm][celltype]=np.abs(pearson[metrique][lstm][celltype]) plt.figure() plt.hist(pearsonabs['deltV']['lstm_1']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm1, métrique delta V') #plt.savefig('pearsonabs_lstm1_output_deltav.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltV']['lstm_1']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm1, métrique delta V') #plt.savefig('pearsonabs_lstm1_cellstates_deltav.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltV']['lstm_2']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm2, métrique delta V') #plt.savefig('pearsonabs_lstm2_output_deltav.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltV']['lstm_2']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm2, métrique delta V') #plt.savefig('pearsonabs_lstm2_cellstates_deltav.pdf') #plt.close() #delta C plt.figure() plt.hist(pearsonabs['deltC']['lstm_1']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm1, métrique delta C') #plt.savefig('pearsonabs_lstm1_output_deltac.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltC']['lstm_1']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm1, métrique delta C') #plt.savefig('pearsonabs_lstm1_cellstates_deltac.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltC']['lstm_2']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm2, métrique delta C') #plt.savefig('pearsonabs_lstm2_output_deltac.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['deltC']['lstm_2']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm2, métrique delta C') #plt.savefig('pearsonabs_lstm2_cellstates_deltac.pdf') #plt.close() #prop V plt.figure() plt.hist(pearsonabs['propV']['lstm_1']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm1, métrique prop V') #plt.savefig('pearsonabs_lstm1_output_propv.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['propV']['lstm_1']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm1, métrique prop V') #plt.savefig('pearsonabs_lstm1_cellstates_propv.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['propV']['lstm_2']['outputs']) plt.title('Coef de corrélation des cellules output de la lstm2, métrique prop V') #plt.savefig('pearsonabs_lstm2_output_propv.pdf') #plt.close() plt.figure() plt.hist(pearsonabs['propV']['lstm_2']['cell_states']) plt.title('Coef de corrélation des cellules mémoire de la lstm2, métrique prop V') #plt.savefig('pearsonabs_lstm2_cellstates_propv.pdf') #plt.close()
layer_list=['lstm_1', 'lstm_2', 'lstm_2', 'lstm_2'] celltype_list=['outputs', 'outputs', 'outputs' , 'outputs'] # 'outputs','cell_states': ind_list=[3, 4, 116, 115, 121] metrique_list=['deltC', 'propV', 'nPVI_V', 'rPVI_C'] for layer, celltype, ind, metrique in zip(layer_list, celltype_list, ind_list, metrique_list): met_arr=[] y=[] c=[] for file in list_files: #metrique_ind=metrique_inds[metrique] #met_arr.append(dfiles[file][metrique_ind]) met_arr.append(dmetrics[file][metrique]) audio=d_match[file].split('.')[0] y.append(datay[audio][layer][celltype][ind]) c.append(c_lang[d_lang[file]]) y=np.array(y).astype(np.float) #HACK for legend from matplotlib.lines import Line2D custom_lines = [Line2D([0], [0], c=f'C{i}') for i in range(len(c_lang))] pl.figure(figsize=(8,6)) pl.scatter(met_arr, y, c=c) pl.xlabel(metrique) pl.legend(custom_lines, c_lang) pl.ylabel(f'{layer} {celltype} unit {ind}')
for metrique in metrique_keys: print(f'std {metrique} : {metric_arrays[metrique].std()}')