Merge branch 'main' of github.com:ronboger/protein-conformal into main

analyze_protein_vec_results.ipynb

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clean_selection/README.md

@@ -0,0 +1 @@
+For using retrieval-conditioned conformal prediction on the CLEAN model, we assume you have followed the steps to install the CLEAN package (link github repo). From there, after loading the pre-trained models and datasets, copy the `data/` directory into the `clean_selection` folder and all methods should run off the fly. 

clean_selection/analyze_clean_hierarchical_loss_protein_vec.ipynb

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clean_selection/clean_utils.py

@@ -0,0 +1,216 @@
+import csv
+from CLEAN.utils import *
+from CLEAN.distance_map import *
+from CLEAN.evaluate import *
+from CLEAN.model import LayerNormNet
+from sklearn.metrics import precision_score, recall_score, \
+    roc_auc_score, accuracy_score, f1_score, average_precision_score
+from tqdm import tqdm
+import numpy as np
+import torch
+import pandas as pd
+import pickle
+
+def get_true_labels_test(file_name, test_idx: None):
+    result = open(file_name+'.csv', 'r')
+    csvreader = csv.reader(result, delimiter='\t')
+    all_label = set()
+    true_label_dict = {}
+    header = True
+    count = 0
+    for row in csvreader:
+        # don't read the header
+        if header is False:
+            count += 1
+            true_ec_lst = row[1].split(';')
+            true_label_dict[row[0]] = true_ec_lst
+            for ec in true_ec_lst:
+                if test_idx is not None and count - 1 in test_idx:
+                    all_label.add(ec)
+                elif test_idx is None: ## add all EC labels
+                    all_label.add(ec)
+                else:
+                    continue
+        if header:
+            header = False
+    true_label = [true_label_dict[i] for i in true_label_dict.keys()]
+    if test_idx is not None:
+        true_label = [true_label[i] for i in test_idx]
+    return true_label, all_label
+
+
+def infer_conformal(train_data, test_data, thresh, report_metrics = False, 
+                 pretrained=True, model_name=None, test_idx=None, name_id="1"):    
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda:0" if use_cuda else "cpu")
+    dtype = torch.float32
+    id_ec_train, ec_id_dict_train = get_ec_id_dict('./data/' + train_data + '.csv')
+    id_ec_test, _ = get_ec_id_dict('./data/' + test_data + '.csv')
+    # load checkpoints
+    # NOTE: change this to LayerNormNet(512, 256, device, dtype) 
+    # and rebuild with [python build.py install]
+    # if inferencing on model trained with supconH loss
+    model = LayerNormNet(512, 128, device, dtype)
+    
+    if pretrained:
+        try:
+            checkpoint = torch.load('./data/pretrained/'+ train_data +'.pth', map_location=device)
+        except FileNotFoundError as error:
+            raise Exception('No pretrained weights for this training data')
+    else:
+        try:
+            checkpoint = torch.load('./data/model/'+ model_name +'.pth', map_location=device)
+        except FileNotFoundError as error:
+            raise Exception('No model found!')
+            
+    model.load_state_dict(checkpoint)
+    model.eval()
+    # load precomputed EC cluster center embeddings if possible
+    if train_data == "split70":
+        emb_train = torch.load('./data/pretrained/70.pt', map_location=device)
+    elif train_data == "split100":
+        emb_train = torch.load('./data/pretrained/100.pt', map_location=device)
+    else:
+        emb_train = model(esm_embedding(ec_id_dict_train, device, dtype))
+        
+    emb_test = model_embedding_test(id_ec_test, model, device, dtype)
+    eval_dist = get_dist_map_test(emb_train, emb_test, ec_id_dict_train, id_ec_test, device, dtype)
+    seed_everything()
+    eval_df = pd.DataFrame.from_dict(eval_dist)
+    ensure_dirs("./results")
+    out_filename = "results/" +  test_data + name_id
+    if test_idx is None:
+        idx = [i for i in range(len(id_ec_test))]
+        write_conformal_choices(eval_df, out_filename, threshold=thresh, test_idx=idx)
+    else:
+        write_conformal_choices(eval_df, out_filename, threshold=thresh, test_idx=test_idx)
+    if report_metrics:
+        pred_label = get_pred_labels(out_filename, pred_type='_conformal')
+        pred_probs = get_pred_probs(out_filename, pred_type='_conformal')
+        true_label, all_label = get_true_labels_test('./data/' + test_data, test_idx=test_idx if test_idx is not None else None)
+        pre, rec, f1, roc, acc = get_eval_metrics(
+            pred_label, pred_probs, true_label, all_label)
+        print("############ EC calling results using conformal calibration on randomly shuffled test set ############")
+        print('-' * 75)
+        print(f'>>> total samples: {len(true_label)} | total ec: {len(all_label)} \n'
+            f'>>> precision: {pre:.3} | recall: {rec:.3}'
+            f'| F1: {f1:.3} | AUC: {roc:.3} ')
+        print('-' * 75)
+
+
+## In theory, we should be able to use the lambda we find on the raw eval distance map,
+## slice the test set out of it, and pass it into a small method, infer_confromal
+## that will take in the eval_df and the lambda, and write the choices using this method,
+## then report all the metrics we want.
+def write_conformal_choices(df, csv_name, threshold, test_idx: list):
+    """
+    df: dataframe containing the distances between the test set and the EC centroids
+    csv_name: name of the csv file to write the choices to
+    threshold: threshold to use for the choices (euclidean distance by default, so <=)
+    test_idx: list of indices of the test set within the dataframe. this is how we splice the columns
+            to get the ones we want to test on, not the ones calibrated on.
+    """
+    out_file = open(csv_name + '_conformal.csv', 'w', newline='')
+    csvwriter = csv.writer(out_file, delimiter=',')
+    dists = []
+    for col in df.iloc[:, test_idx].columns:
+        ec = []
+        dist_lst = []
+        ## grsb EC numbers bounded by threshold
+        smallest_dists_thresh = df[col][df[col] <= threshold]
+        for i in range(len(smallest_dists_thresh)):
+            EC_i = smallest_dists_thresh.index[i]
+            dist_i = smallest_dists_thresh[i]
+            dist_str = "{:.4f}".format(dist_i)
+            dist_lst.append(dist_i)
+            ec.append('EC:' + str(EC_i) + '/' + dist_str)
+        ec.insert(0, col)
+        dists.append(dist_lst)
+        csvwriter.writerow(ec)
+    return dists
+
+
+## Below code is taken from CLEAN/evaluate.py, but modified to take in the test_idx and only eval on that
+
+def infer_maxsep(train_data, test_data, report_metrics = False, 
+                 pretrained=True, model_name=None, gmm = None, test_idx=None):
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda:0" if use_cuda else "cpu")
+    dtype = torch.float32
+    id_ec_train, ec_id_dict_train = get_ec_id_dict('./data/' + train_data + '.csv')
+    id_ec_test, _ = get_ec_id_dict('./data/' + test_data + '.csv')
+    # load checkpoints
+    # NOTE: change this to LayerNormNet(512, 256, device, dtype) 
+    # and rebuild with [python build.py install]
+    # if inferencing on model trained with supconH loss
+    model = LayerNormNet(512, 128, device, dtype)
+    
+    if pretrained:
+        try:
+            checkpoint = torch.load('./data/pretrained/'+ train_data +'.pth', map_location=device)
+        except FileNotFoundError as error:
+            raise Exception('No pretrained weights for this training data')
+    else:
+        try:
+            checkpoint = torch.load('./data/model/'+ model_name +'.pth', map_location=device)
+        except FileNotFoundError as error:
+            raise Exception('No model found!')
+            
+    model.load_state_dict(checkpoint)
+    model.eval()
+    # load precomputed EC cluster center embeddings if possible
+    if train_data == "split70":
+        emb_train = torch.load('./data/pretrained/70.pt', map_location=device)
+    elif train_data == "split100":
+        emb_train = torch.load('./data/pretrained/100.pt', map_location=device)
+    else:
+        emb_train = model(esm_embedding(ec_id_dict_train, device, dtype))
+        
+    emb_test = model_embedding_test(id_ec_test, model, device, dtype)
+    eval_dist = get_dist_map_test(emb_train, emb_test, ec_id_dict_train, id_ec_test, device, dtype)
+    seed_everything()
+    eval_df = pd.DataFrame.from_dict(eval_dist)
+    ensure_dirs("./results")
+    out_filename = "results/" +  test_data + "test_idx"
+    if test_idx is None:
+        idx = [i for i in range(len(id_ec_test))]
+        write_max_sep_choices_test(eval_df, out_filename, gmm=gmm, test_idx=idx)
+    else:
+        write_max_sep_choices_test(eval_df, out_filename, gmm=gmm, test_idx=test_idx)
+    if report_metrics:
+        pred_label = get_pred_labels(out_filename, pred_type='_maxsep')
+        pred_probs = get_pred_probs(out_filename, pred_type='_maxsep')
+        true_label, all_label = get_true_labels_test('./data/' + test_data, test_idx=test_idx if test_idx is not None else None)
+        pre, rec, f1, roc, acc = get_eval_metrics(
+            pred_label, pred_probs, true_label, all_label)
+        print("############ EC calling results using maximum separation on randomly shuffled test set ############")
+        print('-' * 75)
+        print(f'>>> total samples: {len(true_label)} | total ec: {len(all_label)} \n'
+            f'>>> precision: {pre:.3} | recall: {rec:.3}'
+            f'| F1: {f1:.3} | AUC: {roc:.3} ')
+        print('-' * 75)
+
+
+def write_max_sep_choices_test(df, csv_name, test_idx, first_grad=True, use_max_grad=False, gmm = None):
+    out_file = open(csv_name + '_maxsep.csv', 'w', newline='')
+    csvwriter = csv.writer(out_file, delimiter=',')
+    all_test_EC = set()
+    for col in df.iloc[:, test_idx].columns:
+        ec = []
+        smallest_10_dist_df = df[col].nsmallest(10)
+        dist_lst = list(smallest_10_dist_df)
+        max_sep_i = maximum_separation(dist_lst, first_grad, use_max_grad)
+        for i in range(max_sep_i+1):
+            EC_i = smallest_10_dist_df.index[i]
+            dist_i = smallest_10_dist_df[i]
+            if gmm != None:
+                gmm_lst = pickle.load(open(gmm, 'rb'))
+                dist_i = infer_confidence_gmm(dist_i, gmm_lst)
+            dist_str = "{:.4f}".format(dist_i)
+            all_test_EC.add(EC_i)
+            ec.append('EC:' + str(EC_i) + '/' + dist_str)
+        ec.insert(0, col)
+        csvwriter.writerow(ec)
+    return
+
+

clean_selection/get_clean_dists.ipynb

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clean_selection/process_clean_ec.ipynb

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ec/analyze_ec_hierarchical_loss_protein_vec.ipynb

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ec/process_pfam_ec.ipynb

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protein_conformal/__init__.py

@@ -0,0 +1 @@
+import os, sys; sys.path.append(os.path.dirname(os.path.realpath(__file__)))

protein_conformal/scope_utils.py

@@ -0,0 +1,350 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ipdb
+from sklearn.isotonic import IsotonicRegression
+import seaborn as sns
+from scipy.stats import binom, norm
+
+def get_sims_labels(data, partial=False):
+    sims = []
+    labels = []
+    for query in data:
+        similarity = query['S_i']
+        sims += similarity.tolist()
+        if partial:
+            #labels_to_append = np.logical_or.reduce(query['partial'], axis=1).tolist()
+            # NOTE: no need to do the above for scope - i already handle the pre-processing
+            labels_to_append = query['partial']
+        else:
+            labels_to_append = query['exact']
+        labels += labels_to_append
+    return sims, labels
+
+def get_arbitrary_attribute(data, attribute: str):
+    # get an arbitrary attribute from the data
+    attributes = []
+    for query in data:
+        attribute = query[attribute]
+        attributes += attribute.tolist()
+    return attributes
+
+    
+def get_thresh(data, alpha):
+    # conformal risk control
+    all_sim_exact = []
+    for query in data:
+        idx = query['exact']
+        similarity = query['S_i']
+        sims_to_append = similarity[idx]
+        all_sim_exact += list(sims_to_append)
+        n = len(all_sim_exact)
+        if n > 0:
+            lhat = np.quantile(all_sim_exact, np.maximum(alpha-(1-alpha)/n, 0), interpolation='lower')
+        else:
+            lhat = 0
+    return lhat
+
+# Bentkus p value
+def bentkus_p_value(r_hat,n,alpha):
+    return binom.cdf(np.ceil(n*r_hat),n,alpha/np.e)
+
+# def clt_p_value(r_hat,n,alpha):
+
+def clt_p_value(r_hat, std_hat, n, alpha):
+    z = (r_hat - alpha) / (std_hat / np.sqrt(n))
+    p_value = norm.cdf(z)
+    return p_value
+    
+def percentage_of_discoveries(sims, labels, lam):
+    # FDR: Number of false matches / number of matches
+    total_discoveries = (sims >= lam).sum(axis=1)
+    return total_discoveries.mean() / len(labels) # or sims.shape[1]
+
+def risk(sims, labels, lam):
+    # FDR: Number of false matches / number of matches
+    total_discoveries = (sims >= lam).sum(axis=1)
+    false_discoveries = ((1-labels)*(sims >= lam)).sum(axis=1)
+    total_discoveries = np.maximum(total_discoveries, 1)
+    return (false_discoveries/total_discoveries).mean()
+
+def calculate_false_negatives(sims, labels, lam):
+    # FNR: Number of false non-matches / number of non-matches
+    total_non_matches = labels.sum(axis=1)
+    false_non_matches = (labels & (sims < lam)).sum(axis=1)
+    total_non_matches = np.maximum(total_non_matches, 1)
+    return (false_non_matches/total_non_matches).mean()
+
+def risk_no_empties(sims, labels, lam):
+    # FDR: Number of false matches / number of matches
+    total_discoveries = (sims >= lam).sum(axis=1)
+    false_discoveries = ((1-labels)*(sims >= lam)).sum(axis=1)
+    idx = total_discoveries > 0
+    total_discoveries = total_discoveries[idx]
+    false_discoveries = false_discoveries[idx]
+    return (false_discoveries/total_discoveries).mean()
+
+def std_loss(sims, labels, lam):
+    # FDR: Number of false matches / number of matches
+    total_discoveries = (sims >= lam).sum(axis=1)
+    false_discoveries = ((1-labels)*(sims >= lam)).sum(axis=1)
+    total_discoveries = np.maximum(total_discoveries, 1)
+    return (false_discoveries/total_discoveries).std()
+
+def get_thresh_FDR(labels, sims, alpha, delta=0.5, N=5000):
+    # FDR control with LTT
+    # labels = np.stack([query['exact'] for query in data], axis=0)
+    # sims = np.stack([query['S_i'] for query in data], axis=0)
+    print(f"sims.max: {sims.max()}")
+    n = len(labels)
+    lambdas = np.linspace(sims.min(),sims.max(),N)
+    risks = np.array( [risk(sims, labels, lam) for lam in lambdas] )
+    stds = np.array( [std_loss(sims, labels, lam) for lam in lambdas] )
+    #pvals = np.array( [bentkus_p_value(r,n,alpha) for r in risks] )
+    pvals = np.array( [clt_p_value(r,s,n,alpha) for r, s in zip(risks, stds)] )
+    below = pvals <= delta
+    # Pick the smallest lambda such that all lambda above it have p-value below delta
+    pvals_satisfy_condition = np.array([ np.all(below[i:])for i in range(N) ])
+    lhat = lambdas[np.argmax(pvals_satisfy_condition)]
+    print(f"lhat: {lhat}")
+    print(f"risk: {risk(sims, labels, lhat)}")
+    return lhat
+
+def get_isotone_regression(data):
+    sims, labels = get_sims_labels(data, partial=True)
+    ir = IsotonicRegression(out_of_bounds='clip')
+    ir.fit(sims, labels)
+    return ir
+
+def scope_hierarchical_loss(y_sccs, y_hat_sccs):
+    """
+    Find the common ancestor of two sets of SCCs (0 if family, 1 if superfamily, 2 if fold, 3 if class)
+    """
+    y_sccs, y_hat_sccs = y_sccs.split('.'), y_hat_sccs.split('.')
+    first_non_matching_index = next((i for i, (x, y) in enumerate(zip(y_sccs, y_hat_sccs)) if x != y), len(y_sccs))
+
+    loss = len(y_sccs) - first_non_matching_index # ex if the first mismatch is at idx 2 (0-indexed), that means that the second last label (superfamily) is wrong, which is a loss of two (wrong family & superfamily)
+    exact = True if len(y_sccs) == first_non_matching_index else False
+
+    return loss, exact
+
+"""
+def scope_hierarchical_loss(y_sccs, y_hat_sccs, slack = 0):
+
+    #Find the common ancestor of two sets of SCCs (0 if family, 1 if superfamily, 2 if fold, 3 if class)
+
+    # Find the common ancestor of the two sets of SCCs
+    y_sccs, y_hat_sccs = y_sccs.split('.'), y_hat_sccs.split('.')
+    assert len(y_sccs) == len(y_hat_sccs) == 4
+
+    loss, count = None, 0
+    while loss is None:
+        if y_sccs[-1] != y_hat_sccs[-1]:
+            count += 1
+            y_sccs.pop()
+            y_hat_sccs.pop()
+        else:
+            break
+    loss = count - slack
+    
+    return loss == 0
+"""
+    
+
+def get_scope_dict(true_test_idcs, test_df, lookup_idcs, lookup_df, D, I):
+    """
+    true_test_idcs: indices of the test set within the scope dataframe
+
+    test_df: dataframe containing the test set (indices are the same as true_test_idcs)
+
+    lookup_idcs: indices of the lookup set within the larger scope dataframe
+
+    lookup_df: dataframe containing the lookup set (indices are the same as lookup_idcs)
+
+    D: distances matrix (400 x 14777 or test x lookup by default)
+
+    I: indices matrix (400 x 14777 or test x lookup by default)
+
+    NOTE: Indices computed by FAISS are not the same as the indices of the dataframe, so 
+    we use the lookup_idcs list to map FAISS indices in I to the indices of the dataframe
+    """
+
+    near_ids = []
+    min_sim = np.min(D)
+    max_sim = np.max(D)
+
+    for i in range(len(true_test_idcs)):
+        test_id = test_df.loc[true_test_idcs[i], 'sid']
+        test_sccs = test_df.loc[true_test_idcs[i], 'sccs']
+        query_ids = [lookup_df.loc[lookup_idcs[j], 'sid'] for j in I[i]]
+        exact_loss = [scope_hierarchical_loss(test_sccs, lookup_df.loc[lookup_idcs[j], 'sccs']) for j in I[i]]
+        # grab the 2nd element in the tuple belonging to each element of exact_loss as mask_exact
+        mask_exact = [x[1] for x in exact_loss]
+        loss = [x[0] for x in exact_loss]
+        
+        # define mask_partial as 1 for any element of loss that is <=1 (tolerate retrieving homolog with diff family but same superfamily)
+        mask_partial = [l <= 1 for l in loss]
+
+        # create a row of size len(lookup_df) where each element is the sum of all entries in S_i until that index
+        sum = np.cumsum(D[i])
+        norm_sim = (D[i] - min_sim) / (max_sim - min_sim) # convert similarities into a probability space (0, 1) based on (min_sim, max_sim)
+        #mask_exact = [test_sccs == lookup_df.loc[lookup_idcs[j], 'sccs'] for j in I[i]]
+
+        sum_norm_s_i = np.cumsum(norm_sim)
+        near_ids.append({
+            'test_id': test_id,
+            'query_ids': query_ids,
+            #'meta_query': meta_query,
+            'loss' : loss,
+            'exact': mask_exact,
+            'partial': mask_partial,
+            'S_i': D[i],
+            'Sum_i' : sum,
+            'Norm_S_i' : norm_sim,
+            'Sum_Norm_S_i': sum_norm_s_i,
+            'I_i': I[i]
+        })
+    return near_ids
+
+def validate_lhat(data, lhat):
+    total_missed = 0
+    total_missed_partial = 0
+    total_exact = 0
+    total_inexact_identified = 0
+    total_identified = 0
+    total_partial = 0
+    total_partial_identified = 0
+    for query in data:
+        idx = query['exact']
+        # if partial has multiple rows, we want to take the logical or of all of them. Otherwise just set it to the single row
+        # check if there is one or more rows
+        # query['partial'] = np.array(query['partial'])
+        if len(np.array(query['partial']).shape) > 1:
+            idx_partial = np.logical_or.reduce(query['partial'], axis=1)
+        else:
+            idx_partial = query['partial']
+        
+        sims = query['S_i']
+        sims_exact = sims[idx]
+        sims_partial = sims[idx_partial]
+        total_missed += (sims_exact < lhat).sum()
+
+        # TODO: are there any divisions by zero here?
+        total_missed_partial += (sims_partial < lhat).sum()
+        total_partial_identified += (sims_partial >= lhat).sum()
+        total_partial += len(sims_partial)
+
+        total_exact += len(sims_exact)
+        total_inexact_identified += (sims[~np.array(idx)] >= lhat).sum()
+        total_identified += (sims >= lhat).sum()
+    return total_missed/total_exact, total_inexact_identified/total_identified, total_missed_partial/total_partial, total_partial_identified/total_identified
+
+
+def load_database(lookup_database):
+    """
+    lookup_database: NxM matrix of embeddings
+    """
+    # On the fly FAISS import to prevent installation issues
+    import faiss
+
+    # Build an indexed database
+    d = lookup_database.shape[1]
+    index = faiss.IndexFlatIP(d)
+    faiss.normalize_L2(lookup_database)
+    index.add(lookup_database)
+
+    return index
+
+def get_thresh_hierarchical(data, lambdas, alpha):
+    # get the worst case loss
+    wc_loss = max([np.sum(x['loss']) for x in data])
+    wc_loss = wc_loss / 14777
+    loss_thresh = alpha - (wc_loss - alpha)/len(data) # normalize by size of calib set
+    losses = []
+    best_lam = None
+    for lam in lambdas:
+        per_lam_loss = get_hierarchical_loss(data, lam)
+        if per_lam_loss > loss_thresh:
+            break
+        best_lam = lam
+        losses.append(per_lam_loss)
+    print("worst case loss: " + str(wc_loss))
+    print("Loss threshold: " + str(loss_thresh))
+    print("Best lambda: " + str(best_lam))
+    print("Loss of best lambda: " + str(losses[-1]))
+
+    return best_lam, losses
+
+def get_hierarchical_loss(data_, lambda_):
+    losses = []
+    for query in data_:
+        thresh_idx = query['Sum_Norm_S_i'] <= lambda_
+        if np.sum(thresh_idx) == 0:
+            loss = 0
+        else:
+            loss = np.sum(np.asarray(query['loss'])[thresh_idx]) / np.sum(thresh_idx) # NOTE: have fixed denominator, but alpha has to change
+        losses.append(loss) # average over all queries
+    return np.mean(losses)
+
+
+def get_thresh_max_hierarchical(data, lambdas, alpha, sim="cosine"):
+    # get the worst case loss
+    wc_loss = 4 # in the max case, the max_loss is simply retrieving a protein with different class
+    loss_thresh = alpha - (wc_loss - alpha)/len(data) # normalize by size of calib set
+    losses = []
+    best_lam = None
+    if sim == "cosine":
+        ## reverse lambdas and return list
+        lambdas = list(reversed(lambdas))
+    for lam in lambdas: # start from the largest lambda since we are dealing with raw similarity scores
+        per_lam_loss = get_hierarchical_max_loss(data, lam, sim=sim)
+        if per_lam_loss > loss_thresh:
+            break
+        best_lam = lam
+        losses.append(per_lam_loss)
+    print("worst case loss: " + str(wc_loss))
+    print("Loss threshold: " + str(loss_thresh))
+    print("Best lambda: " + str(best_lam))
+    print("Loss of best lambda: " + str(losses[-1]))
+
+    return best_lam, losses
+
+def get_hierarchical_max_loss(data_, lambda_, sim="cosine"):
+    losses = []
+    for query in data_:
+        if sim == "cosine":
+            thresh_idx = query['S_i'] >= lambda_
+        else:
+            thresh_idx = query['S_i'] <= lambda_
+        if np.sum(thresh_idx) == 0:
+            loss = 0
+        else:
+            loss = np.max(np.asarray(query['loss'])[thresh_idx]) # monotonic loss
+            #loss = np.sum(np.asarray(query['loss'])[thresh_idx]) / np.sum(thresh_idx) # NOTE: have fixed denominator, but alpha has to change
+        losses.append(loss) # average over all queries
+    return np.mean(losses)
+
+
+def query(index, queries, k=10):
+    # On the fly FAISS import to prevent installation issues
+    import faiss
+
+    # Search indexed database
+    faiss.normalize_L2(queries)
+    D, I = index.search(queries, k)
+
+    return (D, I)
+
+def build_scope_tree(list_sccs):
+    """
+    Build a scope tree from a list of SCCs
+    """
+    tree = {}
+    for sccs in list_sccs:
+        sccs = sccs.split('.')
+        node = tree
+        for s in sccs:
+            if s not in node:
+                node[s] = {}
+            node = node[s]
+    return tree

scope/analyze_scope_hierarchical_loss_protein_vec.ipynb

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scope/analyze_scope_protein_vec.ipynb

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scope/test_scope_conformal_retrieval.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8f4006f8108efcd9cb99a08cabde82ccfda250dc4807f7ff5fbe4a9aaccbe90
+size 3232293

setup.py

@@ -0,0 +1,5 @@
+from setuptools import setup, find_packages
+setup(
+    name = 'protein_conformal',
+    packages = find_packages(),
+)