def upper_bound(fm, nr_subs=None, scale_factor=1): """ compute the inter-subject consistency upper bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1 scale_factor : the scale factor of the FDMs. Default is 1. Returns: A list of scores; the list contains one dictionary for each measure. Each dictionary contains one key for each category and corresponding values is an array with scores for each subject. """ nr_subs_total = len(np.unique(fm.SUBJECTINDEX)) if not nr_subs: nr_subs = nr_subs_total - 1 assert (nr_subs < nr_subs_total) # initialize output structure; every measure gets one dict with # category numbers as keys and numpy-arrays as values intersub_scores = [] for measure in range(len(measures.scores)): res_dict = {} result_vectors = [ np.empty(nr_subs_total) + np.nan for _ in np.unique(fm.category) ] res_dict.update(zip(np.unique(fm.category), result_vectors)) intersub_scores.append(res_dict) #compute inter-subject scores for every stimulus, with leave-one-out #over subjects for fm_cat in fm.by_field('category'): cat = fm_cat.category[0] for (sub_counter, sub) in enumerate(np.unique(fm_cat.SUBJECTINDEX)): image_scores = [] for fm_single in fm_cat.by_field('filenumber'): predicting_subs = (np.setdiff1d( np.unique(fm_single.SUBJECTINDEX), [sub])) np.random.shuffle(predicting_subs) predicting_subs = predicting_subs[0:nr_subs] predicting_fm = fm_single[(ismember(fm_single.SUBJECTINDEX, predicting_subs))] predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor=scale_factor) except RuntimeError: predicting_fdm = None image_scores.append( measures.prediction_scores(predicting_fdm, predicted_fm)) for (measure, score) in enumerate(nanmean(image_scores, 0)): intersub_scores[measure][cat][sub_counter] = score return intersub_scores
def test_prediction_scores(self): measures.set_scores([measures.roc_model, measures.kldiv_model, measures.nss_model]) fm = fixmat.TestFixmatFactory(categories = [1,2,3], filenumbers = [1,2,3,4,5,6], subjectindices = [1, 2, 3, 4, 5, 6], params = {'pixels_per_degree':10, 'image_size':[100,500]}) fdm = fixmat.compute_fdm(fm) measures.set_scores([measures.roc_model, measures.kldiv_model, measures.nss_model]) scores = measures.prediction_scores(fdm, fm) self.assertEquals(len(scores), 3) measures.set_scores([measures.roc_model]) scores = measures.prediction_scores(fdm, fm) self.assertEquals(len(scores), 1) measures.set_scores([measures.roc_model, measures.kldiv_model, measures.nss_model]) scores = measures.prediction_scores(fdm, fm) self.assertEquals(len(scores), 3)
def upper_bound(fm, nr_subs = None, scale_factor = 1): """ compute the inter-subject consistency upper bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1 scale_factor : the scale factor of the FDMs. Default is 1. Returns: A list of scores; the list contains one dictionary for each measure. Each dictionary contains one key for each category and corresponding values is an array with scores for each subject. """ nr_subs_total = len(np.unique(fm.SUBJECTINDEX)) if not nr_subs: nr_subs = nr_subs_total - 1 assert (nr_subs < nr_subs_total) # initialize output structure; every measure gets one dict with # category numbers as keys and numpy-arrays as values intersub_scores = [] for measure in range(len(measures.scores)): res_dict = {} result_vectors = [np.empty(nr_subs_total) + np.nan for _ in np.unique(fm.category)] res_dict.update(zip(np.unique(fm.category), result_vectors)) intersub_scores.append(res_dict) #compute inter-subject scores for every stimulus, with leave-one-out #over subjects for fm_cat in fm.by_field('category'): cat = fm_cat.category[0] for (sub_counter, sub) in enumerate(np.unique(fm_cat.SUBJECTINDEX)): image_scores = [] for fm_single in fm_cat.by_field('filenumber'): predicting_subs = (np.setdiff1d(np.unique( fm_single.SUBJECTINDEX),[sub])) np.random.shuffle(predicting_subs) predicting_subs = predicting_subs[0:nr_subs] predicting_fm = fm_single[ (ismember(fm_single.SUBJECTINDEX, predicting_subs))] predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor = scale_factor) except NoFixations: predicting_fdm = None image_scores.append(measures.prediction_scores( predicting_fdm, predicted_fm)) for (measure, score) in enumerate(nanmean(image_scores, 0)): intersub_scores[measure][cat][sub_counter] = score return intersub_scores
def lower_bound(fm, nr_subs=None, nr_imgs=None, scale_factor=1): """ Compute the spatial bias lower bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1 nr_imgs : the number of images used for prediction. If given, the same number will be used for every category. If not given, leave-one-out will be used in all categories. scale_factor : the scale factor of the FDMs. Default is 1. Returns: A list of spatial bias scores; the list contains one dictionary for each measure. Each dictionary contains one key for each category and corresponding values is an array with scores for each subject. """ nr_subs_total = len(np.unique(fm.SUBJECTINDEX)) if nr_subs is None: nr_subs = nr_subs_total - 1 assert (nr_subs < nr_subs_total) # initialize output structure; every measure gets one dict with # category numbers as keys and numpy-arrays as values sb_scores = [] for measure in range(len(measures.scores)): res_dict = {} result_vectors = [ np.empty(nr_subs_total) + np.nan for _ in np.unique(fm.category) ] res_dict.update(zip(np.unique(fm.category), result_vectors)) sb_scores.append(res_dict) # compute mean spatial bias predictive power for all subjects in all # categories for fm_cat in fm.by_field('category'): cat = fm_cat.category[0] nr_imgs_cat = len(np.unique(fm_cat.filenumber)) if not nr_imgs: nr_imgs_current = nr_imgs_cat - 1 else: nr_imgs_current = nr_imgs assert (nr_imgs_current < nr_imgs_cat) for (sub_counter, sub) in enumerate(np.unique(fm.SUBJECTINDEX)): image_scores = [] for fm_single in fm_cat.by_field('filenumber'): # Iterating by field filenumber makes filenumbers # in fm_single unique: Just take the first one to get the # filenumber for this fixmat fn = fm_single.filenumber[0] predicting_subs = (np.setdiff1d(np.unique(fm_cat.SUBJECTINDEX), [sub])) np.random.shuffle(predicting_subs) predicting_subs = predicting_subs[0:nr_subs] predicting_fns = (np.setdiff1d(np.unique(fm_cat.filenumber), [fn])) np.random.shuffle(predicting_fns) predicting_fns = predicting_fns[0:nr_imgs_current] predicting_fm = fm_cat[ (ismember(fm_cat.SUBJECTINDEX, predicting_subs)) & (ismember(fm_cat.filenumber, predicting_fns))] predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor=scale_factor) except RuntimeError: predicting_fdm = None image_scores.append( measures.prediction_scores(predicting_fdm, predicted_fm)) for (measure, score) in enumerate(nanmean(image_scores, 0)): sb_scores[measure][cat][sub_counter] = score return sb_scores
def intersubject_scores(fm, category, predicting_filenumbers, predicting_subjects, predicted_filenumbers, predicted_subjects, controls=True, scale_factor=1): """ Calculates how well the fixations from a set of subjects on a set of images can be predicted with the fixations from another set of subjects on another set of images. The prediction is carried out by computing a fixation density map from fixations of predicting_subjects subjects on predicting_images images. Prediction accuracy is assessed by measures.prediction_scores. Parameters fm : fixmat instance category : int Category from which the fixations are taken. predicting_filenumbers : list List of filenumbers used for prediction, i.e. images where fixations for the prediction are taken from. predicting_subjects : list List of subjects whose fixations on images in predicting_filenumbers are used for the prediction. predicted_filenumnbers : list List of images from which the to be predicted fixations are taken. predicted_subjects : list List of subjects used for evaluation, i.e subjects whose fixations on images in predicted_filenumbers are taken for evaluation. controls : bool, optional If True (default), n_predict subjects are chosen from the fixmat. If False, 1000 fixations are randomly generated and used for testing. scale_factor : int, optional specifies the scaling of the fdm. Default is 1. Returns auc : area under the roc curve for sets of actuals and controls true_pos_rate : ndarray Rate of true positives for every given threshold value. All values appearing in actuals are taken as thresholds. Uses lower sum interpolation. false_pos_rate : ndarray See true_pos_rate but for false positives. """ predicting_fm = fm[(ismember(fm.SUBJECTINDEX, predicting_subjects)) & (ismember(fm.filenumber, predicting_filenumbers)) & (fm.category == category)] predicted_fm = fm[(ismember(fm.SUBJECTINDEX, predicted_subjects)) & (ismember(fm.filenumber, predicted_filenumbers)) & (fm.category == category)] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor=scale_factor) except RuntimeError: predicting_fdm = None if controls == True: fm_controls = fm[(ismember(fm.SUBJECTINDEX, predicted_subjects)) & ( (ismember(fm.filenumber, predicted_filenumbers)) != True) & (fm.category == category)] return measures.prediction_scores(predicting_fdm, predicted_fm, controls=(fm_controls.y, fm_controls.x)) return measures.prediction_scores(predicting_fdm, predicted_fm, controls=None)
def lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1): """ Compute the spatial bias lower bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1 nr_imgs : the number of images used for prediction. If given, the same number will be used for every category. If not given, leave-one-out will be used in all categories. scale_factor : the scale factor of the FDMs. Default is 1. Returns: A list of spatial bias scores; the list contains one dictionary for each measure. Each dictionary contains one key for each category and corresponding values is an array with scores for each subject. """ nr_subs_total = len(np.unique(fm.SUBJECTINDEX)) if nr_subs is None: nr_subs = nr_subs_total - 1 assert (nr_subs < nr_subs_total) # initialize output structure; every measure gets one dict with # category numbers as keys and numpy-arrays as values sb_scores = [] for measure in range(len(measures.scores)): res_dict = {} result_vectors = [np.empty(nr_subs_total) + np.nan for _ in np.unique(fm.category)] res_dict.update(zip(np.unique(fm.category),result_vectors)) sb_scores.append(res_dict) # compute mean spatial bias predictive power for all subjects in all # categories for fm_cat in fm.by_field('category'): cat = fm_cat.category[0] nr_imgs_cat = len(np.unique(fm_cat.filenumber)) if not nr_imgs: nr_imgs_current = nr_imgs_cat - 1 else: nr_imgs_current = nr_imgs assert(nr_imgs_current < nr_imgs_cat) for (sub_counter, sub) in enumerate(np.unique(fm.SUBJECTINDEX)): image_scores = [] for fm_single in fm_cat.by_field('filenumber'): # Iterating by field filenumber makes filenumbers # in fm_single unique: Just take the first one to get the # filenumber for this fixmat fn = fm_single.filenumber[0] predicting_subs = (np.setdiff1d(np.unique( fm_cat.SUBJECTINDEX), [sub])) np.random.shuffle(predicting_subs) predicting_subs = predicting_subs[0:nr_subs] predicting_fns = (np.setdiff1d(np.unique( fm_cat.filenumber), [fn])) np.random.shuffle(predicting_fns) predicting_fns = predicting_fns[0:nr_imgs_current] predicting_fm = fm_cat[ (ismember(fm_cat.SUBJECTINDEX, predicting_subs)) & (ismember(fm_cat.filenumber, predicting_fns))] predicted_fm = fm_single[fm_single.SUBJECTINDEX == sub] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor = scale_factor) except NoFixations: predicting_fdm = None image_scores.append(measures.prediction_scores(predicting_fdm, predicted_fm)) for (measure, score) in enumerate(nanmean(image_scores, 0)): sb_scores[measure][cat][sub_counter] = score return sb_scores
def intersubject_scores(fm, category, predicting_filenumbers, predicting_subjects, predicted_filenumbers, predicted_subjects, controls = True, scale_factor = 1): """ Calculates how well the fixations from a set of subjects on a set of images can be predicted with the fixations from another set of subjects on another set of images. The prediction is carried out by computing a fixation density map from fixations of predicting_subjects subjects on predicting_images images. Prediction accuracy is assessed by measures.prediction_scores. Parameters fm : fixmat instance category : int Category from which the fixations are taken. predicting_filenumbers : list List of filenumbers used for prediction, i.e. images where fixations for the prediction are taken from. predicting_subjects : list List of subjects whose fixations on images in predicting_filenumbers are used for the prediction. predicted_filenumnbers : list List of images from which the to be predicted fixations are taken. predicted_subjects : list List of subjects used for evaluation, i.e subjects whose fixations on images in predicted_filenumbers are taken for evaluation. controls : bool, optional If True (default), n_predict subjects are chosen from the fixmat. If False, 1000 fixations are randomly generated and used for testing. scale_factor : int, optional specifies the scaling of the fdm. Default is 1. Returns auc : area under the roc curve for sets of actuals and controls true_pos_rate : ndarray Rate of true positives for every given threshold value. All values appearing in actuals are taken as thresholds. Uses lower sum interpolation. false_pos_rate : ndarray See true_pos_rate but for false positives. """ predicting_fm = fm[ (ismember(fm.SUBJECTINDEX, predicting_subjects)) & (ismember(fm.filenumber, predicting_filenumbers)) & (fm.category == category)] predicted_fm = fm[ (ismember(fm.SUBJECTINDEX,predicted_subjects)) & (ismember(fm.filenumber,predicted_filenumbers))& (fm.category == category)] try: predicting_fdm = compute_fdm(predicting_fm, scale_factor = scale_factor) except NoFixations: predicting_fdm = None if controls == True: fm_controls = fm[ (ismember(fm.SUBJECTINDEX, predicted_subjects)) & ((ismember(fm.filenumber, predicted_filenumbers)) != True) & (fm.category == category)] return measures.prediction_scores(predicting_fdm, predicted_fm, controls = (fm_controls.y, fm_controls.x)) return measures.prediction_scores(predicting_fdm, predicted_fm, controls = None)