build: bump version and update __init__.py

Author: uriahf

pyproject.toml

@@ -15,7 +15,7 @@ dependencies = [
     "statsmodels>=0.14.0",
 ]
 name = "rtichoke"
-version = "0.1.25"
+version = "0.1.26"
 description = "interactive visualizations for performance of predictive models"
 readme = "README.md"
 

src/rtichoke/__init__.py

@@ -32,6 +32,7 @@
 
 from rtichoke.calibration.calibration import (
     create_calibration_curve as create_calibration_curve,
+    create_calibration_curve_times as create_calibration_curve_times,
 )
 
 from rtichoke.utility.decision import (

src/rtichoke/calibration/__init__.py

@@ -1,6 +1,7 @@
 """
 Subpackage for Calibration
 """
+
 from .calibration import create_calibration_curve, create_calibration_curve_times
 
 __all__ = ["create_calibration_curve", "create_calibration_curve_times"]

src/rtichoke/calibration/calibration.py

@@ -2,8 +2,8 @@
 import polars as pl
 from polarstate import predict_aj_estimates
 from polarstate import prepare_event_table
-from typing import Dict
 from collections.abc import Sequence
+from rtichoke.processing.transforms import assign_and_explode_polars
 
 
 def create_aj_data(

src/rtichoke/processing/adjustments.py

@@ -1,7 +1,7 @@
 import numpy as np
 import polars as pl
 from typing import Dict, Union
-from collections.abc import Sequence
+from rtichoke.processing.combinations import create_breaks_values
 
 
 def add_cutoff_strata(data: pl.DataFrame, by: float, stratified_by) -> pl.DataFrame:
@@ -697,4 +697,4 @@ def _turn_cumulative_aj_to_performance_data(
         .alias("ppcr"),
     )
 
-    return performance_data
+    return performance_data

src/rtichoke/processing/transforms.py

@@ -1,8 +1,7 @@
-
 import numpy as np
-import polars as pl
 from rtichoke.calibration.calibration import create_calibration_curve
 
+
 def test_create_calibration_curve_smooth():
     probs = {"model_1": np.linspace(0, 1, 100)}
     reals = np.random.randint(0, 2, 100)

tests/test_calibration.py

@@ -1,14 +1,15 @@
-import pytest
 import numpy as np
-import polars as pl
 from rtichoke.calibration import create_calibration_curve_times
 
+
 def test_create_calibration_curve_times():
     probs = {"model_1": np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])}
     reals = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
     times = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
     fixed_time_horizons = [5, 10]
-    heuristics_sets = [{"censoring_heuristic": "excluded", "competing_heuristic": "excluded"}]
+    heuristics_sets = [
+        {"censoring_heuristic": "excluded", "competing_heuristic": "excluded"}
+    ]
 
     fig = create_calibration_curve_times(
         probs,

tests/test_calibration_times.py

@@ -3,64 +3,95 @@
 from polars.testing import assert_frame_equal
 from rtichoke.calibration.calibration import _apply_heuristics_and_censoring
 
+
 @pytest.fixture
 def sample_data():
-    return pl.DataFrame({
-        "real": [1, 0, 2, 1, 2, 0, 1],
-        "time": [1, 2, 3, 8, 9, 10, 12],
-    })
+    return pl.DataFrame(
+        {
+            "real": [1, 0, 2, 1, 2, 0, 1],
+            "time": [1, 2, 3, 8, 9, 10, 12],
+        }
+    )
+
 
 def test_competing_as_negative_logic(sample_data):
     # Heuristics that shouldn't change data before horizon
-    result = _apply_heuristics_and_censoring(sample_data, 15, "adjusted", "adjusted_as_negative")
+    result = _apply_heuristics_and_censoring(
+        sample_data, 15, "adjusted", "adjusted_as_negative"
+    )
     # Competing events at times 3 and 9 should become 0.
-    expected = pl.DataFrame({
-        "real": [1, 0, 0, 1, 0, 0, 1],
-        "time": [1, 2, 3, 8, 9, 10, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 0, 1, 0, 0, 1],
+            "time": [1, 2, 3, 8, 9, 10, 12],
+        }
+    )
     assert_frame_equal(result, expected)
 
+
 def test_admin_censoring(sample_data):
-    result = _apply_heuristics_and_censoring(sample_data, 7, "adjusted", "adjusted_as_negative")
+    result = _apply_heuristics_and_censoring(
+        sample_data, 7, "adjusted", "adjusted_as_negative"
+    )
     # Admin censoring for times > 7. Competing event at time=3 becomes 0.
-    expected = pl.DataFrame({
-        "real": [1, 0, 0, 0, 0, 0, 0],
-        "time": [1, 2, 3, 8, 9, 10, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 0, 0, 0, 0, 0],
+            "time": [1, 2, 3, 8, 9, 10, 12],
+        }
+    )
     assert_frame_equal(result, expected)
 
+
 def test_censoring_excluded(sample_data):
-    result = _apply_heuristics_and_censoring(sample_data, 10, "excluded", "adjusted_as_negative")
+    result = _apply_heuristics_and_censoring(
+        sample_data, 10, "excluded", "adjusted_as_negative"
+    )
     # Excludes censored at times 2, 10. Admin censors time > 10. Competing at 3,9 -> 0.
-    expected = pl.DataFrame({
-        "real": [1, 0, 1, 0, 0],
-        "time": [1, 3, 8, 9, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 1, 0, 0],
+            "time": [1, 3, 8, 9, 12],
+        }
+    )
     assert_frame_equal(result.sort("time"), expected.sort("time"))
 
+
 def test_competing_excluded(sample_data):
     result = _apply_heuristics_and_censoring(sample_data, 10, "adjusted", "excluded")
     # Excludes competing at 3, 9. Admin censors time > 10.
-    expected = pl.DataFrame({
-        "real": [1, 0, 1, 0, 0],
-        "time": [1, 2, 8, 10, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 1, 0, 0],
+            "time": [1, 2, 8, 10, 12],
+        }
+    )
     assert_frame_equal(result.sort("time"), expected.sort("time"))
 
+
 def test_competing_as_negative(sample_data):
-    result = _apply_heuristics_and_censoring(sample_data, 10, "adjusted", "adjusted_as_negative")
+    result = _apply_heuristics_and_censoring(
+        sample_data, 10, "adjusted", "adjusted_as_negative"
+    )
     # Competing at 3,9 -> 0. Admin censors time > 10.
-    expected = pl.DataFrame({
-        "real": [1, 0, 0, 1, 0, 0, 0],
-        "time": [1, 2, 3, 8, 9, 10, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 0, 1, 0, 0, 0],
+            "time": [1, 2, 3, 8, 9, 10, 12],
+        }
+    )
     assert_frame_equal(result, expected)
 
+
 def test_competing_as_composite(sample_data):
-    result = _apply_heuristics_and_censoring(sample_data, 10, "adjusted", "adjusted_as_composite")
+    result = _apply_heuristics_and_censoring(
+        sample_data, 10, "adjusted", "adjusted_as_composite"
+    )
     # Competing at 3,9 -> 1. Admin censors time > 10.
-    expected = pl.DataFrame({
-        "real": [1, 0, 1, 1, 1, 0, 0],
-        "time": [1, 2, 3, 8, 9, 10, 12],
-    })
+    expected = pl.DataFrame(
+        {
+            "real": [1, 0, 1, 1, 1, 0, 0],
+            "time": [1, 2, 3, 8, 9, 10, 12],
+        }
+    )
     assert_frame_equal(result, expected)