"""Metrics for estimating performance of drug discovery methods implemented in ODDT"""
from math import ceil
import numpy as np
from sklearn.metrics import roc_curve as roc, roc_auc_score as roc_auc, auc, mean_squared_error
__all__ = ['roc', 'auc', 'roc_auc', 'roc_log_auc', 'enrichment_factor', 'random_roc_log_auc', 'rmse']
[docs]def rmse(y_true, y_pred):
"""Compute Root Mean Squared Error (RMSE)
Parameters
----------
y_true : array-like of shape = [n_samples] or [n_samples, n_outputs]
Ground truth (correct) target values.
y_pred : array-like of shape = [n_samples] or [n_samples, n_outputs]
Estimated target values.
Returns
-------
rmse : float
A positive floating point value (the best value is 0.0).
"""
return np.sqrt(mean_squared_error(y_true, y_pred))
[docs]def enrichment_factor(y_true, y_score, percentage=1, pos_label=None):
"""Computes enrichment factor for given percentage, i.e. EF_1% is enrichment factor for first percent of given samples.
Parameters
----------
y_true : array, shape=[n_samples]
True binary labels, in range {0,1} or {-1,1}. If positive label is different than 1, it must be explicitly defined.
y_score : array, shape=[n_samples]
Scores for tested series of samples
percentage : int or float
The percentage for which EF is being calculated
pos_label: int
Positive label of samples (if other than 1)
Returns
-------
ef : float
Enrichment Factor for given percenage
"""
if pos_label is None:
pos_label = 1
labels = y_true == pos_label
# calculate fraction of positve labels
n_perc = ceil(float(percentage)/100.*len(labels))
return labels[:n_perc].sum()/n_perc*100
[docs]def roc_log_auc(y_true, y_score, pos_label=None, log_min=0.001, log_max=1.):
"""Computes area under semi-log ROC for random distribution.
Parameters
----------
y_true : array, shape=[n_samples]
True binary labels, in range {0,1} or {-1,1}. If positive label is different than 1, it must be explicitly defined.
y_score : array, shape=[n_samples]
Scores for tested series of samples
pos_label: int
Positive label of samples (if other than 1)
log_min : float (default=0.001)
Minimum logarithm value for estimating AUC
log_max : float (default=1.)
Maximum logarithm value for estimating AUC.
Returns
-------
auc : float
semi-log ROC AUC
"""
fpr, tpr, t = roc(y_true, y_score, pos_label=pos_label)
idx = (fpr >= log_min) & (fpr <= log_max)
log_fpr = 1-np.log10(fpr[idx])/np.log10(log_min)
return auc(log_fpr, tpr[idx])
[docs]def random_roc_log_auc(log_min=0.001, log_max=1.):
"""Computes area under semi-log ROC for random distribution.
Parameters
----------
log_min : float (default=0.001)
Minimum logarithm value for estimating AUC
log_max : float (default=1.)
Maximum logarithm value for estimating AUC.
Returns
-------
auc : float
semi-log ROC AUC for random distribution
"""
return (log_max-log_min)/(np.log(10)*np.log10(log_max/log_min))