def test_get_hit_rate():
hit, miss, aborted = ([0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 1,
0], [0, 0, 1, 1, 0, 0])
result = get_hit_rate(hit=hit,
miss=miss,
aborted=aborted,
sliding_window=3)
np.testing.assert_allclose(result, [0, .5, 1 / 3, 2 / 3])
def get_rolling_performance_df(self):
# Indices to build trial metrics dataframe:
trials_index = self.trials.index
not_aborted_index = \
self.trials[np.logical_not(self.trials.aborted)].index
# Initialize dataframe:
performance_metrics_df = pd.DataFrame(index=trials_index)
# Reward rate:
performance_metrics_df['reward_rate'] = \
pd.Series(self.get_reward_rate(), index=self.trials.index)
# Hit rate raw:
hit_rate_raw = get_hit_rate(hit=self.trials.hit,
miss=self.trials.miss,
aborted=self.trials.aborted)
performance_metrics_df['hit_rate_raw'] = \
pd.Series(hit_rate_raw, index=not_aborted_index)
# Hit rate with trial count correction:
hit_rate = get_trial_count_corrected_hit_rate(
hit=self.trials.hit,
miss=self.trials.miss,
aborted=self.trials.aborted)
performance_metrics_df['hit_rate'] = \
pd.Series(hit_rate, index=not_aborted_index)
# False-alarm rate raw:
false_alarm_rate_raw = \
get_false_alarm_rate(
false_alarm=self.trials.false_alarm,
correct_reject=self.trials.correct_reject,
aborted=self.trials.aborted)
performance_metrics_df['false_alarm_rate_raw'] = \
pd.Series(false_alarm_rate_raw, index=not_aborted_index)
# False-alarm rate with trial count correction:
false_alarm_rate = \
get_trial_count_corrected_false_alarm_rate(
false_alarm=self.trials.false_alarm,
correct_reject=self.trials.correct_reject,
aborted=self.trials.aborted)
performance_metrics_df['false_alarm_rate'] = \
pd.Series(false_alarm_rate, index=not_aborted_index)
# Rolling-dprime:
rolling_dprime = get_rolling_dprime(hit_rate, false_alarm_rate)
performance_metrics_df['rolling_dprime'] = \
pd.Series(rolling_dprime, index=not_aborted_index)
return performance_metrics_df
def test_rolling_dprime_unit():
hit, miss, false_alarm, correct_reject, aborted = ([0, 0, 1, 0, 0,
1], [1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0,
0], [0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0])
hr = get_hit_rate(hit=hit, miss=miss, aborted=aborted, sliding_window=3)
far = get_false_alarm_rate(false_alarm=false_alarm,
correct_reject=correct_reject,
aborted=aborted,
sliding_window=3)
result = get_rolling_dprime(hr, far)
np.testing.assert_allclose(result,
[NaN, NaN, NaN, 2.326348, 4.652696, 4.652696])
def test_rolling_dprime_integration_legacy(mock_rolling_dprime_fixture):
sliding_window = 100
hit = mock_rolling_dprime_fixture.hit
miss = mock_rolling_dprime_fixture.miss
false_alarm = mock_rolling_dprime_fixture.false_alarm
correct_reject = mock_rolling_dprime_fixture.correct_reject
aborted = mock_rolling_dprime_fixture.aborted
hr = get_hit_rate(hit=hit,
miss=miss,
aborted=aborted,
sliding_window=sliding_window)
cr = get_false_alarm_rate(false_alarm=false_alarm,
correct_reject=correct_reject,
aborted=aborted,
sliding_window=sliding_window)
dprime = get_rolling_dprime(hr, cr)
assert dprime[2] == 4.6526957480816815