def split_cv_all_goterms(ann_obj, folds=5, seed=None, **kwargs):
"""
Split the positives and negatives into folds across all GO terms
*seed*: the seed used by the random number generator when generating the folds. If None, the np.random RandomState will be used
*returns*: a list of tuples containing the (train pos, train neg, test pos, test neg)
"""
ann_matrix = ann_obj.ann_matrix
goids, prots = ann_obj.goids, ann_obj.prots
print(
"Splitting all annotations into %d folds by splitting each GO terms annotations into folds, and then combining them"
% (folds))
# TODO there must be a better way to do this than getting the folds in each go term separately
# but thi at least ensures that each GO term has evenly split annotations
# list of tuples containing the (train pos, train neg, test pos, test neg)
ann_matrix_folds = []
for i in range(folds):
train_ann_mat = sparse.lil_matrix(ann_matrix.shape, dtype=np.float)
test_ann_mat = sparse.lil_matrix(ann_matrix.shape, dtype=np.float)
ann_matrix_folds.append((train_ann_mat, test_ann_mat))
for i in trange(ann_matrix.shape[0]):
goid = goids[i]
positives, negatives = alg_utils.get_goid_pos_neg(ann_matrix, i)
# if there are less positives or negatives than there are folds, this will give an error
if len(positives) < folds or len(negatives) < folds:
continue
# print("%d positives, %d negatives for goterm %s" % (len(positives), len(negatives), goid))
# split the set of positives and the set of negatives into K folds separately
kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
kf.get_n_splits(positives)
kf.get_n_splits(negatives)
fold = 0
# now combine the positive and negative sets into a single array, and store it in the corresponding training or testing matrix
for (pos_train_idx,
pos_test_idx), (neg_train_idx,
neg_test_idx) in zip(kf.split(positives),
kf.split(negatives)):
train_pos, test_pos = positives[pos_train_idx], positives[
pos_test_idx]
train_neg, test_neg = negatives[neg_train_idx], negatives[
neg_test_idx]
train_ann_mat, test_ann_mat = ann_matrix_folds[fold]
fold += 1
# build an array of positive and negative assignments and set it in corresponding annotation matrix
for pos, neg, mat in [(train_pos, train_neg, train_ann_mat),
(test_pos, test_neg, test_ann_mat)]:
pos_neg_arr = np.zeros(len(prots))
pos_neg_arr[list(pos)] = 1
pos_neg_arr[list(neg)] = -1
mat[i] = pos_neg_arr
return ann_matrix_folds
def run_cv_all_goterms(
alg_runners, ann_obj, folds=5, num_reps=1,
cv_seed=None, **kwargs):
"""
Split the positives and negatives into folds across all GO terms
and then run the algorithms on those folds.
Algorithms are all run on the same split of data.
*num_reps*: Number of times to repeat cross-validation.
An output file will be written for each repeat
*cv_seed*: Seed to use for the random number generator when splitting the annotations into folds
If *num_reps* > 1, the seed will be incremented by 1 each time
"""
ann_matrix = ann_obj.ann_matrix
goids, prots = ann_obj.goids, ann_obj.prots
# set the cv_seed if specified
# 2019-06-26 BUG: If there are a different number of terms, or the order of the terms changed, then the results won't be the same
#if cv_seed is not None:
# print("\nSetting the Random State seed to %d" % (cv_seed))
# np.random.seed(cv_seed)
# first check to see if the algorithms have already been run
# and if the results should be overwritten
if kwargs['forcealg'] is True or len(goids) == 1:
# runners_to_run is a list of runners for each repitition
runners_to_run = {i: alg_runners for i in range(1,num_reps+1)}
else:
runners_to_run = {}
# a different file is stored for each repitition, so check each one
for rep in range(1,num_reps+1):
curr_runners_to_run = []
curr_seed = cv_seed
if curr_seed is not None:
# add the current repitition number to the seed
curr_seed += rep-1
for run_obj in alg_runners:
out_file = "%s/cv-%dfolds-rep%d%s%s.txt" % (
run_obj.out_dir, folds, rep,
"-seed%s"%curr_seed if curr_seed is not None else "", run_obj.params_str)
if os.path.isfile(out_file):
print("%s already exists. Use --forcealg to overwite" % (out_file))
else:
curr_runners_to_run.append(run_obj)
runners_to_run[rep] = curr_runners_to_run
# repeat the CV process the specified number of times
for rep in range(1,num_reps+1):
if len(runners_to_run[rep]) == 0:
continue
curr_seed = cv_seed
if curr_seed is not None:
# add the current repitition number to the seed
curr_seed += rep-1
# split the annotation matrix into training and testing matrices K times
ann_matrix_folds = split_cv_all_goterms(ann_obj, folds=folds, seed=curr_seed, **kwargs)
for run_obj in runners_to_run[rep]:
# because each fold contains a different set of positives, and combined they contain all positives,
# store all of the prediction scores from each fold in a matrix
combined_fold_scores = sparse.lil_matrix(ann_matrix.shape, dtype=np.float)
for curr_fold, (train_ann_mat, test_ann_mat) in enumerate(ann_matrix_folds):
print("* "*20)
print("Fold %d" % (curr_fold+1))
# change the annotation matrix to the current fold
curr_ann_obj = setup.Sparse_Annotations(train_ann_mat, goids, prots)
# replace the ann_obj in the runner with the current fold's annotations
run_obj.ann_obj = curr_ann_obj
run_obj.train_mat = train_ann_mat
run_obj.test_mat = test_ann_mat
#alg_runners = run_eval_algs.setup_runners([alg], alg_settings, net_obj, curr_ann_obj, **kwargs)
# now setup the inputs for the runners
run_obj.setupInputs()
# run the alg
run_obj.run()
# parse the outputs. Only needed for the algs that write output files
run_obj.setupOutputs()
# store only the scores of the test (left out) positives and negatives
for i in range(len(goids)):
test_pos, test_neg = alg_utils.get_goid_pos_neg(test_ann_mat, i)
curr_goid_scores = run_obj.goid_scores[i].toarray().flatten()
curr_comb_scores = combined_fold_scores[i].toarray().flatten()
curr_comb_scores[test_pos] = curr_goid_scores[test_pos]
curr_comb_scores[test_neg] = curr_goid_scores[test_neg]
combined_fold_scores[i] = curr_comb_scores
# replace the goid_scores in the runner to combined_fold_scores to evaluate
run_obj.goid_scores = combined_fold_scores
#curr_goids = dag_goids if alg == 'birgrank' else goids
# now evaluate the results and write to a file
out_file = "%s/cv-%dfolds-rep%d%s%s.txt" % (
run_obj.out_dir, folds, rep,
"-seed%s"%curr_seed if curr_seed is not None else "", run_obj.params_str)
utils.checkDir(os.path.dirname(out_file))
eval_utils.evaluate_ground_truth(
run_obj, ann_obj, out_file,
#non_pos_as_neg_eval=opts.non_pos_as_neg_eval,
alg=run_obj.name, append=False, **kwargs)
print("Finished running cross-validation")
return
def weight_SWSN(ann_matrix,
sparse_nets=None,
normalized_nets=None,
net_names=None,
out_file=None,
nodes=None,
verbose=False):
"""
*normalized_nets*: list of networks stored as scipy sparse matrices. Should already be normalized
"""
# UPDATED: normalize the networks
if sparse_nets is not None:
print("Normalizing the networks")
normalized_nets = []
for net in sparse_nets:
normalized_nets.append(_net_normalize(net))
elif normalized_nets is None:
print("No networks given. Nothing to do")
return None, 0
if len(normalized_nets) == 1:
print("Only one network given to weight_SWSN. Nothing to do.")
total_time = 0
return sparse_nets[0], total_time
if verbose:
print("Removing rows with 0 annotations/positives")
utils.print_memory_usage()
# remove rows with 0 annotations/positives
empty_rows = []
for i in range(ann_matrix.shape[0]):
pos, neg = alg_utils.get_goid_pos_neg(ann_matrix, i)
# the combineWeightsSWSN method doesn't seem to
# work if there's only 1 positive
if len(pos) <= 1 or len(neg) <= 1:
empty_rows.append(i)
# don't modify the original annotation matrix to keep the rows matching the GO ids
curr_ann_mat = delete_rows_csr(ann_matrix.tocsr(), empty_rows)
if verbose:
utils.print_memory_usage()
print("Weighting networks for %d different GO terms" %
(curr_ann_mat.shape[0]))
print("Running simultaneous weights with specific negatives")
start_time = time.process_time()
alpha, indices = combineNetworksSWSN(curr_ann_mat,
normalized_nets,
verbose=verbose)
# print out the computed weights for each network
if net_names is not None:
print("network weights:")
#print("\tnetworks chosen: %s" % (', '.join([net_names[i] for i in indices])))
weights = defaultdict(int)
for i in range(len(alpha)):
weights[net_names[indices[i]]] = alpha[i]
weights_table = ["%0.3e" % weights[net] for net in net_names]
print('\t'.join(net_names))
print('\t'.join(weights_table))
# now add the networks together with the alpha weight applied
weights_list = [0] * len(normalized_nets)
weights_list[indices[0]] = alpha[0]
combined_network = alpha[0] * normalized_nets[indices[0]]
for i in range(1, len(alpha)):
combined_network += alpha[i] * normalized_nets[indices[i]]
weights_list[indices[i]] = alpha[i]
total_time = time.process_time() - start_time
if out_file is not None:
# replace the .txt if present
out_file = out_file.replace('.txt', '.npz')
utils.checkDir(os.path.dirname(out_file))
print("\twriting combined network to %s" % (out_file))
sp.save_npz(out_file, combined_network)
# also write the node ids so it's easier to access
# TODO figure out a better way to store this
node2idx_file = out_file + "-node-ids.txt"
print("\twriting node ids to %s" % (node2idx_file))
with open(node2idx_file, 'w') as out:
out.write(''.join("%s\t%s\n" % (n, i)
for i, n in enumerate(nodes)))
# write the alpha/weight of the networks as well
net_weight_file = out_file + "-net-weights.txt"
print("\twriting network weights to %s" % (net_weight_file))
with open(net_weight_file, 'w') as out:
out.write(''.join("%s\t%s\n" % (net_names[idx], str(alpha[i]))
for i, idx in enumerate(indices)))
return combined_network, total_time, weights_list
def run(run_obj):
"""
This script performs logistic regression by building a classifier for each term in the ontology
"""
params_results = run_obj.params_results
P, alg, params = run_obj.P, run_obj.name, run_obj.params
# get the labels matrix and transpose it to have label names as columns
ann_mat = run_obj.ann_matrix
max_iter = params['num_iter']
# see if train and test annotation matrices from the cross validation pipeline exist
# if not, set train and test to the original annotation matrix itself
if run_obj.train_mat is not None and run_obj.test_mat is not None:
print("Performing cross validation")
run_obj.cv = True
train_mat = run_obj.train_mat
test_mat = run_obj.test_mat
else:
run_obj.cv = False
train_mat = ann_mat
test_mat = ann_mat
# stores the scores for all the terms
scores = sparse.lil_matrix(ann_mat.shape,
dtype=np.float) # dim: term x genes
for term in tqdm(run_obj.goids_to_run):
idx = run_obj.hpoidx[term]
# compute the train gene indices of the annotations for the given label
train_pos, train_neg = alg_utils.get_goid_pos_neg(train_mat, idx)
train_set = sorted(list(set(train_pos) | set(train_neg)))
if len(train_pos) == 0:
print("Skipping term, 0 positive examples")
continue
if run_obj.cv:
# if cross validation, then obtain the test gene set on which classifier should be tested
test_pos, test_neg = alg_utils.get_goid_pos_neg(test_mat, idx)
test_set = set(test_pos) | set(test_neg)
test_set = sorted(list(test_set))
else:
# set all unlabeled genes to the test set
test_set = sorted(
list(set(run_obj.protidx.values()) - set(train_set)))
# obtain the feature vector only for the genes in the training set
X_train = P[train_set, :]
# obtain the feature vector only for the genes in the testing set
X_test = P[test_set, :]
# obtain the labels matrix corresponding to genes in the training set
y_train = train_mat.transpose()[train_set, :]
y_train = sparse.lil_matrix(y_train)
# get the column of training data for the given label
lab = y_train[:, idx].toarray().flatten()
# now train the model on the constructed training data and the column of labels
clf = logReg.training(X_train, lab, max_iter)
# make predictions on the constructed training set
predict = logReg.testing(clf, X_test)
predict = predict.tolist()
# get the current scores for the given label term in the current fold
curr_score = scores[idx].toarray().flatten()
# for the test indices of the current label, set the scores
curr_score[test_set] = predict
curr_score[train_pos] = 1
# add the scores produced by predicting on the current label of test set to a combined score matrix
scores[idx] = curr_score
run_obj.goid_scores = scores
run_obj.params_results = params_results
def leave_out_taxon(t,
ann_obj,
species_to_uniprot_idx,
eval_ann_obj=None,
keep_ann=False,
non_pos_as_neg_eval=False,
eval_goterms_with_left_out_only=False,
oracle=False,
num_test_cutoff=10,
**kwargs):
"""
Training positives are removed from testing positives, and train pos and neg are removed from test neg
I don't remove training negatives from testing positives, because not all algorithms use negatives
*t*: species to be left out. If t is None or 'all', then no species will be left out, and keep_ann must be True.
*eval_ann_obj*:
*eval_goterms_with_left_out_only*: if eval_ann_obj is given and keep_ann is False,
only evaluate GO terms that have at least 2% of annotations.
Useful to speed-up processing for term-based algorithms
*oracle*: remove train negatives that are actually test positives
*num_test_cutoff*: minimum number of annotations for each go term in the left-out species
"""
if t == "all":
t = None
# leave this taxon out by removing its annotations
# rather than a dictionary, build a matrix
ann_matrix, goids, prots = ann_obj.ann_matrix, ann_obj.goids, ann_obj.prots
train_ann_mat = sparse.lil_matrix(ann_matrix.shape, dtype=np.float)
test_ann_mat = sparse.lil_matrix(ann_matrix.shape, dtype=np.float)
sp_goterms = []
#skipped_eval_no_left_out_ann = 0
for idx, goid in enumerate(goids):
pos, neg = alg_utils.get_goid_pos_neg(ann_matrix, idx)
ann_pos = set(list(pos))
ann_neg = set(list(neg))
# first setup the training annotations (those used as positives/negatives for the algorithm)
if keep_ann:
train_pos = ann_pos
train_neg = ann_neg
else:
train_pos = ann_pos - species_to_uniprot_idx[t]
train_neg = ann_neg - species_to_uniprot_idx[t]
eval_pos = ann_pos.copy()
eval_neg = ann_neg.copy()
# setup the testing annotations (those used when evaluating the performance)
if eval_ann_obj is not None:
if goid not in eval_ann_obj.goid2idx:
eval_pos, eval_neg = set(), set()
else:
eval_pos, eval_neg = alg_utils.get_goid_pos_neg(
eval_ann_obj.ann_matrix, eval_ann_obj.goid2idx[goid])
eval_pos = set(list(eval_pos))
eval_neg = set(list(eval_neg))
# if this species has little-to-no annotations that are being left-out, then we can skip it
#if not keep_ann and eval_goterms_with_left_out_only:
## If the percentage of left-out ann is less than 2%, then skip it
#if (len(ann_pos) - len(train_pos)) / float(len(train_pos)) < .02:
# skipped_eval_no_left_out_ann += 1
# continue
if t is None:
test_pos = eval_pos
test_neg = eval_neg
if non_pos_as_neg_eval:
# everything minus the positives
test_neg = set(prots) - test_pos
else:
test_pos = eval_pos & species_to_uniprot_idx[t]
# UPDATE 2018-06-27: Only evaluate the species prots as negatives, not all prots
if non_pos_as_neg_eval:
test_neg = species_to_uniprot_idx[t] - eval_pos
test_neg.discard(None)
else:
test_neg = eval_neg & species_to_uniprot_idx[t]
# UPDATE 2018-06-30: Remove test positives/negatives that are part of the training positives/negatives
# don't remove test positives if its a training negative because not all algorithms use negatives
test_pos -= train_pos
if oracle:
train_neg -= test_pos
test_neg -= train_pos | train_neg
# build an array of the scores and set it in the goid sparse matrix of scores
# UPDATE 2019-07: Some algorithms are node-based and could benefit from the extra annotations
pos_neg_arr = np.zeros(len(prots))
pos_neg_arr[list(train_pos)] = 1
pos_neg_arr[list(train_neg)] = -1
train_ann_mat[idx] = pos_neg_arr
pos_neg_arr = np.zeros(len(prots))
pos_neg_arr[list(test_pos)] = 1
pos_neg_arr[list(test_neg)] = -1
test_ann_mat[idx] = pos_neg_arr
# UPDATE 2018-10: Add a cutoff on both the # of training positive and # of test pos
if len(train_pos) < num_test_cutoff or len(test_pos) < num_test_cutoff or \
(len(train_neg) == 0 or len(test_neg) == 0):
continue
sp_goterms.append(goid)
#if eval_ann_matrix is not None and not keep_ann and eval_goterms_with_left_out_only:
# print("\t%d goterms skipped_eval_no_left_out_ann (< 0.02 train ann in the left-out species)" % (skipped_eval_no_left_out_ann))
return train_ann_mat.tocsr(), test_ann_mat.tocsr(), sp_goterms
def weight_SWSN(ann_matrix,
sparse_nets,
net_names=None,
out_file=None,
nodes=None):
""" TODO DOC
"""
if len(sparse_nets) == 1:
print("Only one network given to weight_SWSN. Nothing to do.")
total_time = 0
return sparse_nets[0], total_time
# remove rows with 0 annotations/positives
empty_rows = []
for i in range(ann_matrix.shape[0]):
pos, neg = alg_utils.get_goid_pos_neg(ann_matrix, i)
# the combineWeightsSWSN method doesn't seem to
# work if there's only 1 positive
if len(pos) <= 1 or len(neg) <= 1:
empty_rows.append(i)
# don't modify the original annotation matrix to keep the rows matching the GO ids
curr_ann_mat = delete_rows_csr(ann_matrix.tocsr(), empty_rows)
# normalize the networks
print("Normalizing the networks")
normalized_nets = []
for net in sparse_nets:
normalized_nets.append(_net_normalize(net))
print("Weighting networks for %d different GO terms" %
(curr_ann_mat.shape[0]))
print("Running simultaneous weights with specific negatives")
start_time = time.process_time()
alpha, indices = combineNetworksSWSN(curr_ann_mat, normalized_nets)
if net_names is not None:
print("\tnetworks chosen: %s" %
(', '.join([net_names[i] for i in indices])))
# now add the networks together with the alpha weight applied
combined_network = alpha[0] * sparse_nets[indices[0]]
for i in range(1, len(alpha)):
combined_network += alpha[i] * sparse_nets[indices[i]]
total_time = time.process_time() - start_time
if out_file is not None:
# replace the .txt if present
out_file = out_file.replace('.txt', '.npz')
utils.checkDir(os.path.dirname(out_file))
print("\twriting combined network to %s" % (out_file))
sparse.save_npz(out_file, combined_network)
# also write the node ids so it's easier to access
# TODO figure out a better way to store this
node2idx_file = out_file + "-node-ids.txt"
print("\twriting node ids to %s" % (node2idx_file))
with open(node2idx_file, 'w') as out:
out.write(''.join("%s\t%s\n" % (n, i)
for i, n in enumerate(nodes)))
# write the alpha/weight of the networks as well
net_weight_file = out_file + "-net-weights.txt"
print("\twriting network weights to %s" % (net_weight_file))
with open(net_weight_file, 'w') as out:
out.write(''.join("%s\t%s\n" % (net_names[idx], str(alpha[i]))
for i, idx in enumerate(indices)))
return combined_network, total_time
def setup_post_to_graphspace(config_map,
selected_goid,
alg='fastsinksource',
name_postfix='',
tags=None,
taxon=None,
goid_summary_file=None,
num_neighbors=1,
nodes_to_post=None,
**kwargs):
input_settings, alg_settings, \
output_settings, out_pref, kwargs = \
plot_utils.setup_variables(
config_map, **kwargs)
input_dir = input_settings['input_dir']
dataset = input_settings['datasets'][0]
for arg in [
'ssn_target_only', 'ssn_target_ann_only', 'ssn_only',
'string_target_only', 'string_nontarget_only',
'limit_to_taxons_file', 'add_target_taxon', 'oracle_weights',
'rem_neg_neighbors', 'youngs_neg', 'sp_leaf_terms_only'
]:
kwargs[arg] = dataset.get(arg)
uniprot_taxon_file = "%s/%s" % (input_dir, dataset['taxon_file'])
# don't need it since we are re-running the alg anyway
# # predictions file:
# results_dir = "%s/%s/%s" % (
# output_settings['output_dir'], dataset['net_version'], dataset['exp_name'])
# alg_params = alg_settings[alg]
# combos = [dict(zip(alg_params.keys(), val))
# for val in itertools.product(
# *(alg_params[param] for param in alg_params))]
# # TODO allow for multiple
# if len(combos) > 1:
# print("%d combinations for %s. Using the first one" % (len(combos), alg))
# param_combo = combos[0]
# # first get the parameter string for this runner
# params_str = runner.get_runner_params_str(alg, dataset, param_combo)
# prec_rec_str = "prec-rec%s-%s" % (taxon, selected_goid)
# exp_type = 'loso'
# pred_file = "%s/%s/%s%s%s%s.txt" % (results_dir, alg, exp_type, params_str, kwargs.get('postfix',''), prec_rec_str)
# if not os.path.isfile(pred_file):
# print("\tPredictions file not found: %s. Quitting" % (pred_file))
# sys.exit(1)
# print("\treading %s" % (pred_file))
# df = pd.read_csv(pred_file, sep='\t')
# print(df.head())
out_dir = "outputs/viz/graphspace/%s-%s/" % (dataset['net_version'].split(
'/')[-1], dataset['exp_name'].split('/')[-1])
os.makedirs(out_dir, exist_ok=True)
print("storing net and ann files to %s" % (out_dir))
# TODO allow posting without STRING
net_obj, new_net_obj, ann_obj, eval_ann_obj, species_to_uniprot_idx = \
load_net_ann_datasets(
out_dir, taxon,
dataset, input_settings, alg_settings,
uniprot_taxon_file, **kwargs)
W = new_net_obj.W
prots = ann_obj.prots
# also run the alg to get the full prediction scores
# TODO get them from a file?
alg_settings = {alg: alg_settings[alg]}
alg_settings[alg]['should_run'] = [True]
kwargs['verbose'] = True
alg_runners = run_eval_algs.setup_runners(alg_settings, new_net_obj,
ann_obj,
output_settings['output_dir'],
**kwargs)
run_obj = alg_runners[0]
run_obj.goids_to_run = [selected_goid]
train_ann_mat, test_ann_mat, sp_goterms = eval_loso.leave_out_taxon(
taxon,
ann_obj,
species_to_uniprot_idx,
eval_ann_obj=eval_ann_obj,
**kwargs)
# now run the loso evaluation for this term, and get the scores back
eval_loso.run_and_eval_algs(run_obj,
ann_obj,
train_ann_mat,
test_ann_mat,
taxon=taxon,
**kwargs)
term_scores = np.ravel(
run_obj.goid_scores[ann_obj.goid2idx[selected_goid]].toarray())
print("top 10 scores for %s, %s:" % (taxon, selected_goid))
taxon_prots_idx = list(species_to_uniprot_idx[taxon])
taxon_prots = [prots[i] for i in taxon_prots_idx]
taxon_term_scores = term_scores[taxon_prots_idx]
print('\n'.join(["%s\t%0.4e" % (
ann_obj.prots[taxon_prots_idx[i]], taxon_term_scores[i]) \
for i in np.argsort(taxon_term_scores)[::-1][:10]]))
pos_neg_file = "%s/%s" % (input_dir, dataset['pos_neg_file'])
#selected_goid = "15643" # toxic substance binding
#selected_goid = "9405" # pathogenesis
#selected_goid = "98754" # detoxification
selected_goname = None
# build a dictionary of the evidencecode for each prot
uniprot_to_evidencecode = defaultdict(set)
annotated_prots = set()
neg_prots = set()
if goid_summary_file is None:
goid_summary_file = pos_neg_file.replace("bp-", '').replace("mf-", '')
if '-list' in pos_neg_file:
goid_summary_file = goid_summary_file.replace(
"-list", "-summary-stats")
elif '.gz' in pos_neg_file:
goid_summary_file = goid_summary_file.replace(
".tsv.gz", "-summary-stats.tsv")
else:
goid_summary_file = goid_summary_file.replace(
".tsv", "-summary-stats.tsv")
df_summary = pd.read_csv(goid_summary_file, sep='\t')
goid_names = dict(zip(df_summary['GO term'], df_summary['GO term name']))
#goid_num_anno = dict(zip(df_summary['GO term'], df_summary['# positive examples']))
print("GO name: %s" % (goid_names[selected_goid]))
selected_goname = goid_names[selected_goid].replace(' ', '-')[0:20]
# load the GAIN propagation to get the evidence code
ev_codes_file = dataset.get('ev_codes_file')
if ev_codes_file is not None:
for orf, goid, goname, hierarchy, evidencecode, annotation_type in utils.readColumns(
ev_codes_file, 1, 2, 3, 4, 5, 6):
if selected_goid[:3] == "GO:":
goid = "GO:" + "0" * (7 - len(goid)) + goid
if goid != selected_goid:
continue
selected_goname = goname.replace(' ', '-')[0:20]
if annotation_type != '1':
continue
uniprot_to_evidencecode[orf].add(evidencecode)
# limit it to the current taxon
if taxon is not None:
print("Getting species of each prot from %s" % (uniprot_taxon_file))
#print("Limiting the prots to those for taxon %s (%s)" % (taxon, selected_species[taxon]))
print("Limiting the prots to those for taxon %s" % (taxon))
# for each of the 19 species, leave out their annotations
# and see how well we can retrieve them
uniprot_to_species = utils.readDict(uniprot_taxon_file, 1, 2)
if taxon not in species_to_uniprot_idx:
print("Error: taxon ID '%d' not found" % (taxon))
sys.exit()
# also limit the proteins to those in the network
print("\t%d prots for taxon %s." % (len(taxon_prots_idx), taxon))
goid_idx = ann_obj.goid2idx[selected_goid]
pos, neg = alg_utils.get_goid_pos_neg(train_ann_mat, goid_idx)
non_taxon_annotated_prots = set([prots[i] for i in pos])
non_taxon_neg_prots = set([prots[i] for i in neg])
print("\t%d non-taxon pos, %d non-taxon neg" %
(len(non_taxon_annotated_prots), len(non_taxon_neg_prots)))
pos, neg = alg_utils.get_goid_pos_neg(test_ann_mat, goid_idx)
annotated_prots = set([prots[i] for i in pos])
neg_prots = set([prots[i] for i in neg])
print("\t%d taxon pos, %d taxon neg" %
(len(annotated_prots), len(neg_prots)))
print("\t%d annotated prots for %s (%s)" %
(len(annotated_prots), selected_goname, selected_goid))
#conf_cutoff = 0.2
conf_cutoff = -1
predicted_prots = set()
ranks = {}
scores = {}
first_zero_rank = None
for i, idx in enumerate(np.argsort(taxon_term_scores)[::-1]):
rank = i + 1
prot = prots[taxon_prots_idx[idx]]
predicted_prots.add(prot)
score = taxon_term_scores[idx]
scores[prot] = score
if taxon is not None:
ranks[prot] = rank
if score == 0 and first_zero_rank is None:
first_zero_rank = rank
else:
ranks[prot] = rank
# move the score between 0 and 1 if it's genemania (normally between -1 and 1)
# as the score is used to set the opacity
# TODO fix genemania
#if alg == "genemania":
# pred_cut_conf[gene] = local_conf
# local_conf = ((float(local_conf) - -1) / float(1--1)) * (1-0) + 0
#pred_local_conf[gene] = local_conf
print("\t%d prots with a score" % (len(taxon_term_scores)))
print("Rank of first zero score: %d" % (first_zero_rank))
print("Ranks of left-out positives:")
for gene in sorted(annotated_prots, key=ranks.get):
print("%s\t%d" % (gene, ranks[gene]))
print("Including top 30 ranked-proteins of left-out species")
top_30 = sorted(set(taxon_prots) & set(ranks.keys()), key=ranks.get)[:30]
if ev_codes_file is not None:
print("Evidence codes of top 30:")
for i, gene in enumerate(top_30):
if gene in uniprot_to_evidencecode:
print("%s\t%s\t%s" % (i, gene, uniprot_to_evidencecode[gene]))
top_30 = set(top_30)
if taxon is not None:
print(
"Getting the induced subgraph of the neighbors of the %d annotated nodes"
% (len(annotated_prots)))
prededges = set()
if nodes_to_post is not None:
print("Getting neighbors of %s" % (', '.join(nodes_to_post)))
nodes_to_add_neighbors = set(nodes_to_post)
else:
nodes_to_add_neighbors = annotated_prots.copy() | top_30
node2idx = ann_obj.node2idx
for i in range(opts.num_neighbors):
#print("Adding neighbors %d" % (i+1))
curr_nodes_to_add_neighbors = nodes_to_add_neighbors.copy()
nodes_to_add_neighbors = set()
print("adding %sneighbors of %d nodes" %
("positive ", len(curr_nodes_to_add_neighbors)))
for u in curr_nodes_to_add_neighbors:
#neighbors = set(nx.all_neighbors(G, u))
neighbors = set(
[prots[v] for v in get_mat_neighbors(W, node2idx[u])])
if opts.node_to_post is None:
# UPDATE 2018-10: try adding just the positive neighbors of the node
# TODO make this a command-line option
neighbors = neighbors & (non_taxon_annotated_prots
| annotated_prots | top_30)
#if len(neighbors) > 15 and nodes_to_post is None:
# print("\tskipping adding neighbors of %s. len(neighbors): %d" % (u, len(neighbors)))
# continue
nodes_to_add_neighbors.update(neighbors)
prededges.update(set([(u, v) for v in neighbors]))
else:
print(
"Getting the induced subgraph of the %d annotated and %d predicted proteins"
% (len(annotated_prots), len(predicted_prots)))
print("not yet implemented. quitting")
sys.exit()
# prededges = set(G.subgraph(annotated_prots.union(predicted_prots)).edges())
prededges = set([tuple(sorted((u, v))) for u, v in prededges])
# TODO I should also show the disconnected nodes
prednodes = set([n for edge in prededges for n in edge])
print("\t%d nodes, %d edges" % (len(prednodes), len(prededges)))
if len(prededges) > 1000 or len(prednodes) > 500:
print("\nToo many nodes/edges. Not posting to GraphSpace. Quitting")
sys.exit()
#graph_attr_file = ""
#graph_attr, attr_desc = readGraphAttr()
# add the edge weight from the network to attr_desc which will be used for the popup
# set the edges as the neighbors of the annotated genes
#prededges = set()
# get the induced subgraph of the annotated nodes and predicted nodes
#for n in func_prots:
# if not G.has_node(n):
# continue
# for neighbor in G.neighbors(n):
# prededges.add((n, neighbor))
graph_attr = {n: {} for n in prednodes}
attr_desc = {n: {} for n in prednodes}
print("Reading gene names and species for each protein from %s" %
(uniprot_taxon_file))
#prot_species = utils.readDict(uniprot_taxon_file, 1, 2)
uniprot_to_gene = utils.readDict(uniprot_taxon_file, 1, 4)
# there can be multiple gene names. Just show the first one for now
uniprot_to_gene = {
n: gene.split(' ')[0]
for n, gene in uniprot_to_gene.items()
}
node_labels = {}
print("building graphspace object")
# get the abbreviation of the species names
species_names, net_taxons = eval_loso.get_selected_species(
species_to_uniprot_idx, kwargs['limit_to_taxons_file'])
sp_abbrv = {
t: ''.join(subs[0] for subs in sp_name.split(' ')[:2])
for t, sp_name in species_names.items()
}
# for each node, add the prediction values
for n in tqdm(prednodes):
# set the name of the node to be the gene name and add the k to the label
gene_name = uniprot_to_gene.get(n, n)
curr_taxon = uniprot_to_species[n]
species_short_name = sp_abbrv[curr_taxon]
# add the species to the front of the gene name
label = "%s-%s" % (species_short_name, gene_name)
uniprot_to_gene[n] = label
#node_labels[n] = "%s\n%d" % (label, min(ranks[n], 43)) if n in annotated_prots else label
node_labels[n] = "%s\n%d" % (
label, ranks[n] if ranks[n] < first_zero_rank else
first_zero_rank) if n in taxon_prots else label
# maybe put the labels below the nodes?
# helps with visualizing the background opacity
graph_attr[n]['text-valign'] = 'bottom'
# add the strain name to the popup
attr_desc[n]['Strain'] = species_names[curr_taxon]
if n in predicted_prots:
# don't need to normalize because the confidence values are already between 0 and 1
if taxon and (n in non_taxon_annotated_prots
or n in non_taxon_neg_prots):
pass
else:
# UPDATE: use the node rank instead of the node score
#graph_attr[n]['background-opacity'] = pred_local_conf[n]
if n not in ranks:
graph_attr[n]['background-opacity'] = scores[n]
else:
#graph_attr[n]['background-opacity'] = scores[n]
graph_attr[n]['background-opacity'] = max([
0.9 - (ranks[n] / float(first_zero_rank)),
float(scores[n])
])
attr_desc[n]["%s rank" % (alg_names[alg])] = ranks[n]
attr_desc[n]["%s prediction score" %
(alg_names[alg])] = "%0.4f" % (scores[n])
#elif n in annotated_prots or (taxon and (n in non_taxon_annotated_prots or n in non_taxon_neg_prots)) \
# or n in neg_prots:
#if n in pred_local_conf:
# graph_attr[n]['background-opacity'] = pred_local_conf[n]
# attr_desc[n]["Local prediction confidence"] = pred_local_conf[n]
# also add the annotation to the popup
if n in uniprot_to_evidencecode:
codes = uniprot_to_evidencecode[n]
# TODO add bullet points to the list
#attr_desc[n]["Evidence code"] = ''.join(["%s (%s)\n" % (c, evidence_code_name[c]) for c in codes])
# order it by exp, comp, then elec
evidence_codes = ''.join([
"<li>%s (%s)</li>" % (c, evidence_code_name[c]) for c in codes
if evidence_code_type[c] == 'experimental'
])
evidence_codes += ''.join([
"<li>%s (%s)</li>" % (c, evidence_code_name[c]) for c in codes
if evidence_code_type[c] == 'computational'
])
evidence_codes += ''.join([
"<li>%s (%s)</li>" % (c, evidence_code_name[c]) for c in codes
if evidence_code_type[c] == 'electronic'
])
attr_desc[n]["Evidence code"] = "<ul>%s</ul>" % (evidence_codes)
# set the width of the edges by the network weight
edge_weights = defaultdict(float)
for u, v in tqdm(prededges):
e = (u, v)
if e not in attr_desc:
attr_desc[e] = {}
if e not in graph_attr:
graph_attr[e] = {}
#attr_desc[e]["edge weight"] = G.adj[u][v]]['weight']
if net_obj.multi_net:
#attr_desc[e]["Final edge weight"] = "%0.1f" % (W[node2idx[u]][:,node2idx[v]].A.flatten()[0])
edge_type_weights = []
# add the weights for the individual string networks
for i in range(len(net_obj.net_names)):
net_name = net_obj.net_names[i]
net_name = "SSN (E-value <= 0.1)" if 'eval-e0_1' in net_name else net_name
net = net_obj.sparse_networks[i]
w = net[node2idx[u]][:, node2idx[v]].A.flatten()[0]
if w != 0:
#attr_desc[e][net_name] = "%0.1f" % (w)
edge_type_weights.append("<li>%s: %0.1f</li>" %
(net_name, w))
edge_weights[e] += w * net_obj.swsn_weights[i]
attr_desc[e]["Edge weights by type"] = "<ul>%s</ul>" % (''.join(
sorted(edge_type_weights)))
else:
attr_desc[e]["Edge weight"] = "%0.1f" % (
W[node2idx[u]][:, node2idx[v]].A.flatten()[0])
# make the edges somewhat opaque for a better visual style
graph_attr[e]['opacity'] = 0.7
# set the width of the edges by the network weight
#edge_weights = {(u,v): float(W[node2idx[u]][:,node2idx[v]].A.flatten()[0]) for u,v in prededges}
for e, w in edge_weights.items():
attr_desc[e]["Final edge weight"] = "%0.1f" % (w)
# TODO set the min and max as parameters or something
#max_weight = 180
if net_obj.multi_net:
max_weight = net_obj.swsn_weights[0] * 180
print(max_weight)
else:
max_weight = 180
for e in edge_weights:
if edge_weights[e] > max_weight:
edge_weights[e] = max_weight
graph_attr = gs.set_edge_width(prededges,
edge_weights,
graph_attr,
a=1,
b=12,
min_weight=1,
max_weight=max_weight)
H = nx.Graph()
H.add_edges_from(prededges)
# see which DB the edge came from to set the edge color
print("Getting the edge type from networks")
if net_obj.multi_net:
print("\tFrom both STRING and SEQ_SIM")
seq_sim_edges = set()
for u, v in prededges:
# get the SSN weight of this edge. Should be the first network
net = net_obj.sparse_networks[0]
w = net[node2idx[u]][:, node2idx[v]].A.flatten()[0]
if w != 0:
# these are all undirected, so just store the sorted version
u, v = tuple(sorted((u, v)))
# give these the default color
graph_attr[(u, v)]['color'] = edge_type_color['default']
seq_sim_edges.add((u, v))
# string_edges = set()
# temp_version = '2017_10-string'
# net = f_settings.NETWORK_template % (temp_version, temp_version)
# for u,v in utils.readColumns(net, 1, 2):
# #if (u,v) not in prededges:
# if not H.has_edge(u,v):
# continue
# # give these the default color
# u,v = tuple(sorted((u,v)))
# graph_attr[(u,v)]['color'] = edge_type_color['string']
# string_edges.add((u,v))
string_edges = prededges.difference(seq_sim_edges)
print("\t%d edges from seq-sim, %d edges from STRING" %
(len(seq_sim_edges), len(string_edges)))
# set the color to STRING if it didn't come from sequence similarity
for e in string_edges:
#if 'color' not in graph_attr[e]:
graph_attr[e]['color'] = edge_type_color['string']
#elif 'STRING' in f_settings.NETWORK_VERSION_INPUTS[version]:
# for e in graph_attr:
# graph_attr[e]['color'] = edge_type_color['string']
else:
for e in graph_attr:
graph_attr[e]['color'] = edge_type_color['default']
# apply the evidence code style to each protein
for n in prednodes:
if n in annotated_prots:
graph_attr[n]['color'] = node_type_color['annotation']
elif taxon and n in non_taxon_annotated_prots:
graph_attr[n]['color'] = node_type_color['non-taxon-annotation']
elif taxon and n in non_taxon_neg_prots:
graph_attr[n]['color'] = node_type_color[
'non-taxon-neg-annotation']
elif n in neg_prots:
graph_attr[n]['color'] = node_type_color['neg-annotation']
elif n in predicted_prots:
graph_attr[n]['color'] = node_type_color['prediction']
if n in uniprot_to_evidencecode:
curr_style = ""
for evidencecode in uniprot_to_evidencecode[n]:
curr_type = evidence_code_type[evidencecode]
if curr_type == "experimental":
curr_style = annotation_type_styles[curr_type]
break
elif curr_style == "computational":
continue
else:
curr_style = annotation_type_styles[curr_type]
graph_attr[n].update(curr_style)
# temporary fix to get the non-target positive examples
if n in non_taxon_annotated_prots:
graph_attr[n].update(annotation_type_styles['experimental'])
# TODO build the popups here. That way the popup building logic can be separated from the
# GSGraph building logic
popups = {}
prednodes = set([n for edge in prededges for n in edge])
for n in prednodes:
popups[n] = gs.buildNodePopup(n, attr_val=attr_desc)
for u, v in prededges:
popups[(u, v)] = gs.buildEdgePopup(u,
v,
node_labels=uniprot_to_gene,
attr_val=attr_desc)
# Now post to graphspace!
print("Building GraphSpace graph")
G = gs.constructGraph(prededges,
node_labels=node_labels,
graph_attr=graph_attr,
popups=popups)
# TODO add an option to build the 'graph information' tab legend/info
# build the 'Graph Information' metadata
#desc = gs.buildGraphDescription(opts.edges, opts.net)
desc = ''
metadata = {'description': desc, 'tags': [], 'title': ''}
if tags is not None:
metadata['tags'] = tags
G.set_data(metadata)
if 'graph_exp_name' in dataset:
graph_exp_name = dataset['graph_exp_name']
else:
graph_exp_name = "%s-%s" % (dataset['exp_name'].split('/')[-1],
dataset['net_version'].split('/')[-1])
graph_name = "%s-%s-%s-%s%s" % (selected_goname, selected_goid, alg,
graph_exp_name, name_postfix)
G.set_name(graph_name)
# rather than call it from here and repeat all the options, return G, and then call this after
#post_graph_to_graphspace(G, opts.username, opts.password, opts.graph_name, apply_layout=opts.apply_layout, layout_name=opts.layout_name,
# group=opts.group, make_public=opts.make_public)
return G, graph_name
def run(run_obj):
params_results = run_obj.params_results
P, alg, params = run_obj.P, run_obj.name, run_obj.params
# get the labels matrix and transpose it to have label names as columns
ann_mat = run_obj.ann_matrix
max_iter = params['num_iter']
if run_obj.train_mat is not None and run_obj.test_mat is not None:
print("Performing Cross validation")
run_obj.cv = True
train_mat = run_obj.train_mat
test_mat = run_obj.test_mat
else:
run_obj.cv = False
train_mat = ann_mat
test_mat = ann_mat
# stores the scores for all the terms
scores = sparse.lil_matrix(ann_mat.shape,
dtype=np.float) # dim: term x genes
for term in tqdm(run_obj.goids_to_run):
idx = run_obj.hpoidx[term]
# get the training positive, negative sets for current fold
train_pos, train_neg = alg_utils.get_goid_pos_neg(train_mat, idx)
train_set = sorted(list(set(train_pos) | set(train_neg)))
if len(train_pos) == 0:
print("Skipping term, 0 positive examples")
continue
if run_obj.cv:
# if cross validation, then obtain the test gene set on which classifier should be tested
test_pos, test_neg = alg_utils.get_goid_pos_neg(test_mat, idx)
test_set = set(test_pos) | set(test_neg)
test_set = sorted(list(test_set))
else:
# set all unlabeled genes to the test set
test_set = sorted(
list(set(run_obj.protidx.values()) - set(train_set)))
# obtain the feature vector only for the genes in the training set
X_train = P[train_set, :]
# obtain the feature vector only for the genes in the testing set
X_test = P[test_set, :]
# obtain the labels matrix corresponding to genes in the training set
y_train = train_mat.transpose()[train_set, :]
y_train = sparse.lil_matrix(y_train)
classifier = svm.training(X_train, y_train[:, idx].toarray().flatten(),
max_iter)
score_testSet = svm.testing(classifier, X_test)
predict = score_testSet.tolist()
# get the current scores for the given term in current fold
curr_score = scores[idx].toarray().flatten()
# for the test indices of the current label, set the scores
curr_score[test_set] = predict
curr_score[train_pos] = 1
# add the scores produced by predicting on the current label of test set to a combined score matrix
scores[idx] = curr_score
run_obj.goid_scores = scores
run_obj.params_results = params_results
def run(run_obj):
"""
Function to run FastSinkSource, FastSinkSourcePlus, Local and LocalPlus
*goids_to_run*: goids for which to run the method.
Must be a subset of the goids present in the ann_obj
"""
params_results = run_obj.params_results
# make sure the goid_scores matrix is reset
# because if it isn't empty, overwriting the stored scores seems to be time consuming
goid_scores = sp.lil_matrix(run_obj.ann_matrix.shape, dtype=np.float)
goid_rank_stats = {}
P, alg, params = run_obj.P, run_obj.name, run_obj.params
print("Running %s with these parameters: %s" % (alg, params))
# run FastSinkSource on each GO term individually
#for i in trange(run_obj.ann_matrix.shape[0]):
#goid = run_obj.goids[i]
for goid in tqdm(run_obj.goids_to_run):
idx = run_obj.ann_obj.goid2idx[goid]
# get the row corresponding to the current goids annotations
y = run_obj.ann_matrix[idx, :]
positives = (y > 0).nonzero()[1]
negatives = (y < 0).nonzero()[1]
# if this method uses positive examples only, then remove the negative examples
if alg in ["fastsinksourceplus", "sinksourceplus", "localplus"]:
negatives = None
if run_obj.net_obj.weight_gmw is True:
start_time = time.process_time()
# weight the network for each GO term individually
W, process_time = run_obj.net_obj.weight_GMW(y.toarray()[0], goid)
P = alg_utils.normalizeGraphEdgeWeights(W,
ss_lambda=params.get(
'lambda', None))
params_results['%s_weight_time' %
(alg)] += time.process_time() - start_time
a, max_iters = params['alpha'], params['max_iters']
compare_ranks = params['compare_ranks']
# rank_all is a T/F option, but 'rank_pos_neg' will be the test/left-out ann matrix
# from which we can get the left-out pos/neg for this term
rank_all, rank_pos_neg = params['rank_all'], params['rank_pos_neg']
if sp.issparse(rank_pos_neg):
pos, neg = alg_utils.get_goid_pos_neg(rank_pos_neg, idx)
rank_pos_neg = (set(pos), set(neg))
elif rank_pos_neg is True:
print("ERROR: rank_pos_neg must be the test_ann_mat")
sys.exit()
# now actually run the algorithm
ss_obj = ss_bounds.SinkSourceBounds(P,
positives,
negatives=negatives,
max_iters=max_iters,
a=a,
rank_all=rank_all,
rank_pos_neg=rank_pos_neg,
verbose=run_obj.kwargs.get(
'verbose', False))
scores_arr = ss_obj.runSinkSourceBounds()
process_time, update_time, iters, comp = ss_obj.get_stats()
if run_obj.kwargs.get('verbose', False) is True:
tqdm.write("\t%s converged after %d iterations " % (alg, iters) +
"(%0.4f sec) for %s" % (process_time, goid))
if compare_ranks:
# compare how long it takes for the ranks to match the previous run
tqdm.write(
"\tRepeating the run, but comparing the ranks from the previous run at each iteration"
)
# keep only the nodes with a non-zero score
scores = {n: s for n, s in enumerate(scores_arr) if s > 0}
# ranks is a list containing the ranked order of nodes.
# The node with the highest score is first, the lowest is last
if rank_pos_neg is not None:
pos_neg_nodes = rank_pos_neg[0] | rank_pos_neg[1]
ranks = [
n for n in sorted(set(scores.keys()) & pos_neg_nodes,
key=scores.get,
reverse=True)
]
else:
ranks = [
n for n in sorted(scores, key=scores.get, reverse=True)
]
# left off top-k for now
#ranks = ranks[:k] if self.rank_topk is True else ranks
ss_obj = ss_bounds.SinkSourceBounds(P,
positives,
negatives=negatives,
max_iters=max_iters,
a=a,
rank_all=rank_all,
rank_pos_neg=rank_pos_neg,
ranks_to_compare=ranks,
verbose=run_obj.kwargs.get(
'verbose', False))
ss_obj.runSinkSourceBounds()
rank_stats = [
"%d\t%d\t%0.4e\t%d\t%d\t%0.2e\t%0.2e\t%0.4f\t%0.4f\t%0.4f" %
(len(positives), i + 1, ss_obj.kendalltau_list[i],
ss_obj.num_unranked_list[i],
ss_obj.max_unranked_stretch_list[i], ss_obj.max_d_list[i],
ss_obj.UB_list[i], ss_obj.eval_stats_list[i][0],
ss_obj.eval_stats_list[i][1], ss_obj.eval_stats_list[i][2])
for i in range(ss_obj.num_iters)
]
goid_rank_stats[goid] = rank_stats
#rank_fh.write(''.join("%s%s\t%d\t%d\t%0.6f\t%d\t%d\t%0.4e\t%0.4e\t%0.4f\t%0.4f\t%0.4f\t%0.4f\n" % (
# goid, "\t%s"%self.taxon if self.taxon is not None else "", len(positives), i+1, ss_squeeze.kendalltau_list[i],
# ss_squeeze.num_unranked_list[i], ss_squeeze.max_unranked_stretch_list[i], ss_squeeze.max_d_list[i], ss_squeeze.UB_list[i],
# ss_squeeze.eval_stats_list[i][0], ss_squeeze.eval_stats_list[i][1], ss_squeeze.eval_stats_list[i][2], ss_squeeze.eval_stats_list[i][3])
# for i in range(ss_squeeze.num_iters)))
## if they're different dimensions, then set the others to zeros
#if len(scores_arr) < goid_scores.shape[1]:
# scores_arr = np.append(scores_arr, [0]*(goid_scores.shape[1] - len(scores_arr)))
# limit the scores to the target nodes
if len(run_obj.target_prots) != len(scores_arr):
mask = np.ones(len(scores_arr), np.bool)
mask[run_obj.target_prots] = 0
scores_arr[mask] = 0
goid_scores[idx] = scores_arr
# make sure 0s are removed
#goid_scores.eliminate_zeros()
# also keep track of the time it takes for each of the parameter sets
alg_name = "%s%s" % (alg, run_obj.params_str)
#params_results["%s_wall_time"%alg_name] += wall_time
params_results["%s_process_time" % alg_name] += process_time
params_results["%s_update_time" % alg_name] += update_time
run_obj.goid_scores = goid_scores.tocsr()
run_obj.params_results = params_results
run_obj.goid_rank_stats = goid_rank_stats
return
