def dp_accuracy(): logging.basicConfig(level=10) # 5 for more stuff CL = CL3 animal = 66 session = 60 room =[[-55,55],[-55,55]] bin_size = 5 K = 50 # Segment length used to calculate firing rates CL.delt_t = K*.02 cluster_profile = 0 label = 'Task' cl_prof_name, good_clusters = get_good_clusters(cluster_profile) try: adat = try_cache('One big data structure') correct_dp = adat[CL.name][animal][session][cl_prof_name][bin_size][label][K] logging.info('Got data from Cache.cache.') except: logging.info('Calculating classifications...') CL.delt_t=K fn, trigger_tm = load_mux(animal, session) vl = load_vl(animal,fn) cls = {tetrode:load_cl(animal,fn,tetrode) for tetrode in range(1,17)} label_l = vl['Task'] t_cells = count_cells(vl,cls,trigger_tm,good_clusters) logging.info('About to generate population vector.') #X, Y = gpv(vl, t_cells, label_l, K) s=time() X, Y = gpv(vl, t_cells, label_l, K, bin_size, room) logging.info('%i population vectors generated in %.3f.',X.shape[0],time()-s) Y = Y.reshape([-1]) correct_dp = check_classifier(range(X.shape[0]),range(X.shape[0]), X, Y, CL, room, bin_size) # Accuracy meter plt.figure() plt.hist(correct_dp,normed=True) plt.xlabel('Accuracy') tt = '%s, K: %i, ClPr: %s, Label:%s'%(CL.name,K,cl_prof_name, label) plt.title(tt) msg = [] for i in [1,50,75,90,95,99]: perc = 1.0*np.sum(correct_dp > i/100.0)/len(correct_dp)*100.0 msg.append('>%i%%: %.1f%%'%(i,perc)) msg = '\n'.join(msg) plt.xlim([0,1]) xcoord = plt.xlim()[0] + (plt.xlim()[1]-plt.xlim()[0])*.1 ycoord = plt.ylim()[0] + (plt.ylim()[1]-plt.ylim()[0])*.5 plt.text(xcoord,ycoord,msg) plt.show()
def run(Folds): # Toggle-able parameters #CLs = [CL2,CL6,CL5] #CLs = [CL6, CL7] CLs = [CL10] Ks = np.arange(10, 200, 20) # Segment length used to calculate firing rates # Sort of toggle-able parameters #animal_sess_combs = [(66,60),(70,8),(70,10),(66,61)] animal_sess_combs = [(66, 60)] #good_trials = try_cache('Good trials') #animal_sess_combs = [(animal,session) for animal in range(65,74) # for session in good_trials[animal]] bin_sizes = [5] label = 'Task' exceptions = [] cl_profs = [0] # Not really toggle-able parameters room = [[-55, 55], [-55, 55]] cache = try_cache('One big data structure for %i folds' % (Folds, )) adat = ({} if cache is None else cache) for animal, session in animal_sess_combs: fn, trigger_tm = load_mux(animal, session) vl = load_vl(animal, fn) cls = { tetrode: load_cl(animal, fn, tetrode) for tetrode in range(1, 17) } if label == 'Task': label_l = vl['Task'] else: raise Exception('Not implemented yet.') for clust_prof in cl_profs: cl_prof_name, good_clusters = get_good_clusters(clust_prof) t_cells = count_cells(vl, cls, trigger_tm, good_clusters) for bin_size, K in product(bin_sizes, Ks): cached = np.zeros(len(CLs)) for CL in CLs: i = CLs.index(CL) try: raise Exception adat[CL.name][animal][session][cl_prof_name][bin_size][ label][K] cached[i] = True except: cached[i] = False if np.sum(cached) == len(CLs): print 'Everything already cached' continue # Everything is already cached! logging.info('About to generate population vector.') X, Y = gpv(vl, t_cells, label_l, K, bin_size, room) # The main data stricture dps = {CL: [] for CL in CLs if CL not in cached} if Folds > 0: kf = cross_validation.KFold(len(Y), n_folds=Folds, shuffle=True) else: kf = [(range(len(Y)), range(len(Y)))] for train_index, test_index in kf: logging.warning('Training/testing: %i/%i', len(train_index), len(test_index)) for CL in CLs: if cached[CLs.index(CL)]: continue logging.warning('%s, %i seg, (%i, %i)', CL.name, K, animal, session) if (CL, clust_prof) in exceptions: continue CL.delt_t = K correct_dp = check_classifier(train_index, test_index, X, Y, CL, room, bin_size) dps[CL].extend(correct_dp.tolist()) for CL in CLs: if cached[CLs.index(CL)]: continue to_add = np.array(dps[CL]).reshape([-1]) add(adat, CL.name, animal, session, cl_prof_name, bin_size, label, K, to_add) store_in_cache('One big data structure for %i folds' % (Folds, ), adat)
def run(Folds): # Toggle-able parameters #CLs = [CL2,CL6,CL5] #CLs = [CL6, CL7] CLs = [CL10] Ks = np.arange(10,200,20) # Segment length used to calculate firing rates # Sort of toggle-able parameters #animal_sess_combs = [(66,60),(70,8),(70,10),(66,61)] animal_sess_combs = [(66,60)] #good_trials = try_cache('Good trials') #animal_sess_combs = [(animal,session) for animal in range(65,74) # for session in good_trials[animal]] bin_sizes = [5] label = 'Task' exceptions = [] cl_profs = [0] # Not really toggle-able parameters room = [[-55,55],[-55,55]] cache = try_cache('One big data structure for %i folds'%(Folds,)) adat = ({} if cache is None else cache) for animal, session in animal_sess_combs: fn, trigger_tm = load_mux(animal, session) vl = load_vl(animal,fn) cls = {tetrode:load_cl(animal,fn,tetrode) for tetrode in range(1,17)} if label == 'Task': label_l = vl['Task'] else: raise Exception('Not implemented yet.') for clust_prof in cl_profs: cl_prof_name, good_clusters = get_good_clusters(clust_prof) t_cells = count_cells(vl,cls,trigger_tm,good_clusters) for bin_size, K in product(bin_sizes,Ks): cached = np.zeros(len(CLs)) for CL in CLs: i = CLs.index(CL) try: raise Exception adat[CL.name][animal][session][cl_prof_name][bin_size][label][K] cached[i] = True except: cached[i] = False if np.sum(cached) == len(CLs): print 'Everything already cached' continue # Everything is already cached! logging.info('About to generate population vector.') X, Y = gpv(vl, t_cells, label_l, K, bin_size, room) # The main data stricture dps = {CL:[] for CL in CLs if CL not in cached} if Folds >0: kf = cross_validation.KFold(len(Y),n_folds=Folds,shuffle=True) else: kf = [(range(len(Y)),range(len(Y)))] for train_index, test_index in kf: logging.warning('Training/testing: %i/%i',len(train_index),len(test_index)) for CL in CLs: if cached[CLs.index(CL)]: continue logging.warning('%s, %i seg, (%i, %i)',CL.name, K, animal, session) if (CL,clust_prof) in exceptions: continue CL.delt_t = K correct_dp = check_classifier(train_index,test_index,X,Y,CL, room, bin_size) dps[CL].extend(correct_dp.tolist()) for CL in CLs: if cached[CLs.index(CL)]: continue to_add = np.array(dps[CL]).reshape([-1]) add(adat, CL.name, animal, session, cl_prof_name, bin_size, label, K, to_add) store_in_cache('One big data structure for %i folds'%(Folds,),adat)