"""
Measures of prediction accuracy
"""
from __future__ import (
print_function,
division,
absolute_import,
)
import logging
import sklearn.metrics
import numpy
import scipy
from .regression_target import from_ic50
[docs]def make_scores(
ic50_y,
ic50_y_pred,
sample_weight=None,
threshold_nm=500,
max_ic50=50000):
"""
Calculate AUC, F1, and Kendall Tau scores.
Parameters
-----------
ic50_y : float list
true IC50s (i.e. affinities)
ic50_y_pred : float list
predicted IC50s
sample_weight : float list [optional]
threshold_nm : float [optional]
max_ic50 : float [optional]
Returns
-----------
dict with entries "auc", "f1", "tau"
"""
y_pred = from_ic50(ic50_y_pred, max_ic50)
try:
auc = sklearn.metrics.roc_auc_score(
ic50_y <= threshold_nm,
y_pred,
sample_weight=sample_weight)
except ValueError as e:
logging.warning(e)
auc = numpy.nan
try:
f1 = sklearn.metrics.f1_score(
ic50_y <= threshold_nm,
ic50_y_pred <= threshold_nm,
sample_weight=sample_weight)
except ValueError as e:
logging.warning(e)
f1 = numpy.nan
try:
tau = scipy.stats.kendalltau(ic50_y_pred, ic50_y)[0]
except ValueError as e:
logging.warning(e)
tau = numpy.nan
return dict(
auc=auc,
f1=f1,
tau=tau)