def score1B(sim, J_pred):
J_true = len(sim.phi)
return score1B_base(J_true, J_pred)
def score1B(sim, est):
J_true = len(sim.phi)
J_pred = len(est.phi)
return score1B_base(J_true, J_pred)
def score1B(sim, cluster_table):
J_true = len(sim.phi)
J_pred = len(cluster_table)
return score1B_base(J_true, J_pred)
def score1B(sim, loci_table):
J_true = len(sim.phi)
J_pred = loci_table.ccube_ccf_mean.nunique()
return score1B_base(J_true, J_pred)
row_list.append(J_true)
input_filename = '{}/input_t.tsv'.format(folder_path)
input_df = pd.read_csv(input_filename, sep='\t')
row_list.append(input_df.purity.mean())
row_list.append(info_df.perc_dip.mean())
row_list.append(folder_path.split('_')[-1])
row_list.append(info_df.median_depth.mean())
(ll_ratio, pval, J_pred, phi_pred_values, weights_pred,
pred_cluster_assign, pred_subclonal, pred_profile, pred_signatures,
pred_signatures_mut, est_dist, runtime) = \
method_function_dict[method](folder_path)
row_list.append(J_pred)
row_list.append(ll_ratio)
row_list.append(pval)
if J_pred is not None:
row_list.append(score1B_base(J_true, J_pred))
else:
row_list.append(np.nan)
if phi_pred_values is not None:
row_list.append(
score1C_base(phi_true_values, phi_pred_values, weights_true,
weights_pred))
else:
row_list.append(np.nan)
if pred_cluster_assign is not None:
ordered_table = pd.merge(pred_cluster_assign,
true_cluster_assign,
on='mutation_id',
how='inner')
if len(true_cluster_assign) < 20000:
row_list.append(
def score1B(sim, loci_table):
J_true = len(sim.phi)
J_pred = loci_table[loci_table.cluster > 0].cluster.nunique()
return score1B_base(J_true, J_pred)