mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
from __future__ import division
|
|
import numpy as np
|
|
|
|
__author__ = "Eric Chiang"
|
|
__email__ = "eric[at]yhathq.com"
|
|
|
|
"""
|
|
|
|
Measurements inspired by Philip Tetlock's "Expert Political Judgment"
|
|
|
|
Equations take from Yaniv, Yates, & Smith (1991):
|
|
"Measures of Descrimination Skill in Probabilistic Judgement"
|
|
|
|
"""
|
|
|
|
|
|
def calibration(prob,outcome,n_bins=10):
|
|
"""Calibration measurement for a set of predictions.
|
|
|
|
When predicting events at a given probability, how far is frequency
|
|
of positive outcomes from that probability?
|
|
NOTE: Lower scores are better
|
|
|
|
prob: array_like, float
|
|
Probability estimates for a set of events
|
|
|
|
outcome: array_like, bool
|
|
If event predicted occurred
|
|
|
|
n_bins: int
|
|
Number of judgement categories to prefrom calculation over.
|
|
Prediction are binned based on probability, since "descrete"
|
|
probabilities aren't required.
|
|
|
|
"""
|
|
prob = np.array(prob)
|
|
outcome = np.array(outcome)
|
|
|
|
c = 0.0
|
|
# Construct bins
|
|
judgement_bins = np.arange(n_bins + 1) / n_bins
|
|
# Which bin is each prediction in?
|
|
bin_num = np.digitize(prob,judgement_bins)
|
|
for j_bin in np.unique(bin_num):
|
|
# Is event in bin
|
|
in_bin = bin_num == j_bin
|
|
# Predicted probability taken as average of preds in bin
|
|
predicted_prob = np.mean(prob[in_bin])
|
|
# How often did events in this bin actually happen?
|
|
true_bin_prob = np.mean(outcome[in_bin])
|
|
# Squared distance between predicted and true times num of obs
|
|
c += np.sum(in_bin) * ((predicted_prob - true_bin_prob) ** 2)
|
|
return c / len(prob)
|
|
|
|
def discrimination(prob,outcome,n_bins=10):
|
|
"""Discrimination measurement for a set of predictions.
|
|
|
|
For each judgement category, how far from the base probability
|
|
is the true frequency of that bin?
|
|
NOTE: High scores are better
|
|
|
|
prob: array_like, float
|
|
Probability estimates for a set of events
|
|
|
|
outcome: array_like, bool
|
|
If event predicted occurred
|
|
|
|
n_bins: int
|
|
Number of judgement categories to prefrom calculation over.
|
|
Prediction are binned based on probability, since "descrete"
|
|
probabilities aren't required.
|
|
|
|
"""
|
|
prob = np.array(prob)
|
|
outcome = np.array(outcome)
|
|
|
|
d = 0.0
|
|
# Base frequency of outcomes
|
|
base_prob = np.mean(outcome)
|
|
# Construct bins
|
|
judgement_bins = np.arange(n_bins + 1) / n_bins
|
|
# Which bin is each prediction in?
|
|
bin_num = np.digitize(prob,judgement_bins)
|
|
for j_bin in np.unique(bin_num):
|
|
in_bin = bin_num == j_bin
|
|
true_bin_prob = np.mean(outcome[in_bin])
|
|
# Squared distance between true and base times num of obs
|
|
d += np.sum(in_bin) * ((true_bin_prob - base_prob) ** 2)
|
|
return d / len(prob)
|