Merge pull request #416 from oughtinc/fix-scaling-for-logistic-fit

Fix scaling for logistic fit

ergo/platforms/metaculus/question/continuous.py

@@ -322,7 +322,10 @@ def get_submission_from_samples(
 
         normalized_samples = self.scale.normalize_points(samples)
         _dist = dist.LogisticMixture.from_samples(
-            normalized_samples, fixed_params={"num_components": 3}, verbose=verbose
+            normalized_samples,
+            fixed_params={"num_components": 3},
+            verbose=verbose,
+            scale=Scale(0, 1),
         )
         return self.prepare_logistic_mixture(_dist)
 

tests/conftest.py

@@ -44,6 +44,18 @@ def logistic_mixture():
     )
 
 
+@pytest.fixture(scope="module")
+def smooth_logistic_mixture():
+    xscale = Scale(1, 1000000.0)
+    return LogisticMixture(
+        components=[
+            Logistic(loc=400000, s=100000, scale=xscale),
+            Logistic(loc=700000, s=50000, scale=xscale),
+        ],
+        probs=[0.8, 0.2],
+    )
+
+
 @pytest.fixture(scope="module")
 def logistic_mixture10():
     xscale = Scale(-20, 40)

tests/test_metaculus.py

@@ -158,6 +158,23 @@ def test_get_questions_question_status(metaculus):
     ).all()
 
 
+def test_submission_from_samples_smooth(metaculus_questions, smooth_logistic_mixture):
+    samples = np.array([smooth_logistic_mixture.sample() for _ in range(5000)])
+    fit_mixture = metaculus_questions.continuous_linear_open_question.get_submission_from_samples(
+        samples
+    )
+    normalized_samples_from_fit_mixture = [fit_mixture.sample() for _ in range(5000)]
+    mixture_samples = metaculus_questions.continuous_linear_open_question.denormalize_samples(
+        normalized_samples_from_fit_mixture
+    )
+    assert float(np.mean(samples)) == pytest.approx(
+        float(np.mean(mixture_samples)), rel=0.1
+    )
+    assert float(np.var(samples)) == pytest.approx(
+        float(np.var(mixture_samples)), rel=0.2
+    )
+
+
 @pytest.mark.xfail(reason="Fitting doesn't reliably work yet #219")
 def test_submission_from_samples_linear(metaculus_questions, logistic_mixture_samples):
     normalized_mixture = metaculus_questions.continuous_linear_open_question.get_submission_from_samples(