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