def phenotype_imputation(data, config):
'''
Function to impute the labels on II based on the classifier learned on I.
Parameters
----------
data : an object of class Dataset that contains: genotypes, covariates,
labels and information about random folds
config : an object of class ConfigState. It contains the user-entered
parameters in a YAML format.
See the config_file parameter in the main script for more details.
'''
# Parameters for this task
num_folds = data.num_folds
task_name = "phenotype_imputation"
n_estimators = config.get_entry(task_name, "n_estimators")
romans_trn = config.get_entry(task_name, "romans_used_for_learning")
romans_tst = config.get_entry(task_name, "romans_used_for_imputing")
# Iterate through the folds:
i = 0
size_of_two = find_vec_entries_that_contain(data.folds[:,0], romans_tst).shape[0]
soft_labels = np.zeros((size_of_two, num_folds))
X_scaled = preprocessing.scale(data.clin_covariate.transpose()).transpose()
fpr = dict()
tpr = dict()
thres = dict()
roc_auc = np.zeros(num_folds)
for fold in data.folds.transpose():
logging.info("Fold=%d" % (i + 1))
sel_trn = find_vec_entries_that_contain(fold,[romans_trn])
sel_tst = find_vec_entries_that_contain(fold,[romans_tst])
model = BaggingClassifier(base_estimator=linear_model.LogisticRegression(),
n_estimators=n_estimators, max_samples=0.632,
# for small set I n_estimators=n_estimators, max_samples=0.8,
max_features=5,
bootstrap=True, bootstrap_features=True, oob_score=False,
# for small set I bootstrap=False, bootstrap_features=True, oob_score=False,
n_jobs=1, random_state=None, verbose=0)
model.fit(X_scaled[:,sel_trn].transpose(), data.labels[:,sel_trn].transpose())
soft_labels[:,i] = model.predict_proba(X_scaled[:,sel_tst].transpose())[:,1]
fpr[i], tpr[i], thres[i] = metrics.roc_curve(data.labels[0,sel_tst], soft_labels[:,i])
roc_auc[i] = metrics.auc(fpr[i], tpr[i])
i+=1
# Save the output of this task
config.save_variable(task_name, "%f", soft_labels=soft_labels, roc_auc=roc_auc)
def univ_feature_sel(data, config):
'''
Do univariate feature selection
'''
# Parameters
task_name = "univ_feature_sel"
romans_trn_gold = config.get_entry(task_name, "golden_romans_used_for_learning")
romans_trn_silver = config.get_entry(task_name, "silver_romans_used_for_learning")
# Load the output of the previous task(s)
soft_labels = config.load_variable("phenotype_imputation", "soft_labels")
# ---------------------------
feature_ranking = np.ones((data.folds.shape[1], data.genotype.shape[1]))
feature_pval = np.ones((data.folds.shape[1], data.genotype.shape[1]))
# Iterate through the folds:
i = 0
for fold in data.folds.transpose():
logging.info("Fold=%d" % (i + 1))
sel_trn_gold = find_vec_entries_that_contain(fold, romans_trn_gold)
sel_trn_silver = find_vec_entries_that_contain(fold, romans_trn_silver)
sel_trn = np.concatenate([sel_trn_gold, sel_trn_silver])
trn_labels = np.concatenate([data.labels[0,sel_trn_gold], soft_labels[range(len(sel_trn_silver)),i]])
j = 0
# ---------------
for genotype_snp in data.genotype.transpose():
feature_pval[i,j] = pearsonr(genotype_snp[sel_trn], trn_labels)[1]
j+=1
# ---------------
i+=1
feature_ranking = feature_pval.argsort()
# ---------------------------
# Save the output of this task
config.save_variable(task_name, "%d", feature_ranking=feature_ranking)
