represent sampling intervals as Interval objects

CHANGELOG.md

@@ -9,5 +9,6 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ### Changed
 - Update workflow to use ubuntu-latest instances from github actions. ([#8](httpps://github.com/neuro-galaxy/torch_brain/pull/8))
-
+- Simplify Dataset interface by removing the `include` dictionnary and allowing to directly load selection from a configuration file. ([#10](https://github.com/neuro-galaxy/torch_brain/pull/10))
+- Sampling intervals are now represented as `Interval` objects. ([#11](https://github.com/neuro-galaxy/torch_brain/pull/11))
 ### Fixed

tests/test_sampler.py

@@ -3,6 +3,8 @@
 import numpy as np
 import torch
 
+from temporaldata import Interval
+
 from torch_brain.data.sampler import (
     SequentialFixedWindowSampler,
     RandomFixedWindowSampler,
@@ -29,7 +31,9 @@ def samples_in_interval_dict(samples, interval_dict):
             sum(
                 [
                     (s.start >= start) and (s.end <= end)
-                    for start, end in allowed_intervals
+                    for start, end in zip(
+                        allowed_intervals.start, allowed_intervals.end
+                    )
                 ]
             )
             == 1
@@ -42,14 +46,18 @@ def samples_in_interval_dict(samples, interval_dict):
 def test_sequential_sampler():
     sampler = SequentialFixedWindowSampler(
         interval_dict={
-            "session1": [
-                (0.0, 2.0),
-                (3.0, 4.5),
-            ],  # 3
-            "session2": [(0.1, 1.25), (2.5, 5.0), (15.0, 18.7)],  # 7
-            "session3": [
-                (1000.0, 1002.0),
-            ],  # 2
+            "session1": Interval(
+                start=np.array([0.0, 3.0]),
+                end=np.array([2.0, 4.5]),
+            ),
+            "session2": Interval(
+                start=np.array([0.1, 2.5, 15.0]),
+                end=np.array([1.25, 5.0, 18.7]),
+            ),
+            "session3": Interval(
+                start=np.array([1000.0]),
+                end=np.array([1002.0]),
+            ),
         },
         window_length=1.1,
         step=0.75,
@@ -84,14 +92,18 @@ def test_sequential_sampler():
 def test_random_sampler():
 
     interval_dict = {
-        "session1": [
-            (0.0, 2.0),
-            (3.0, 4.5),
-        ],  # 3
-        "session2": [(0.1, 1.25), (2.5, 5.0), (15.0, 18.7)],  # 7
-        "session3": [
-            (1000.0, 1002.0),
-        ],  # 2
+        "session1": Interval(
+            start=np.array([0.0, 3.0]),
+            end=np.array([2.0, 4.5]),
+        ),  # 3
+        "session2": Interval(
+            start=np.array([0.1, 2.5, 15.0]),
+            end=np.array([1.25, 5.0, 18.7]),
+        ),  # 7
+        "session3": Interval(
+            start=np.array([1000.0]),
+            end=np.array([1002.0]),
+        ),  # 2
     }
 
     sampler = RandomFixedWindowSampler(
@@ -135,12 +147,18 @@ def test_random_sampler():
 
 def test_trial_sampler():
     interval_dict = {
-        "session1": [
-            (0.0, 2.0),
-            (3.0, 4.5),
-        ],
-        "session2": [(0.1, 1.25), (2.5, 5.0), (15.0, 18.7)],
-        "session3": [(1000.0, 1002.0), (1002.0, 1003.0)],
+        "session1": Interval(
+            start=np.array([0.0, 3.0]),
+            end=np.array([2.0, 4.5]),
+        ),
+        "session2": Interval(
+            start=np.array([0.1, 2.5, 15.0]),
+            end=np.array([1.25, 5.0, 18.7]),
+        ),
+        "session3": Interval(
+            start=np.array([1000.0, 1002.0]),
+            end=np.array([1002.0, 1003.0]),
+        ),
     }
 
     sampler = TrialSampler(

torch_brain/data/dataset.py

@@ -315,21 +315,22 @@ def get_session_data(self, session_id: str):
         return data
 
     def get_sampling_intervals(self):
-        r"""Returns a dictionary of interval-list for each session.
-        Each interval-list is a list of tuples (start, end) for each interval. This
-        represents the intervals that can be sampled from each session.
+        r"""Returns a dictionary of sampling intervals for each session.
+        This represents the intervals that can be sampled from each session.
 
-        Note that these intervals will change depending on the split.
+        Note that these intervals will change depending on the split. If no split is
+        provided, the full domain of the data is used.
         """
-        interval_dict = {}
+        sampling_intervals_dict = {}
         for session_id in self.session_dict.keys():
-            intervals = getattr(self._data_objects[session_id], f"{self.split}_domain")
+            sampling_domain = (
+                f"{self.split}_domain" if self.split is not None else "domain"
+            )
+            intervals = getattr(self._data_objects[session_id], sampling_domain)
             sampling_intervals_modifier_code = self.session_dict[session_id][
                 "config"
             ].get("sampling_intervals_modifier", None)
-            if sampling_intervals_modifier_code is None:
-                interval_dict[session_id] = list(zip(intervals.start, intervals.end))
-            else:
+            if sampling_intervals_modifier_code is not None:
                 local_vars = {
                     "data": copy.deepcopy(self._data_objects[session_id]),
                     "sampling_intervals": intervals,
@@ -351,10 +352,8 @@ def get_sampling_intervals(self):
                     raise type(e)(error_message) from e
 
                 sampling_intervals = local_vars.get("sampling_intervals")
-                interval_dict[session_id] = list(
-                    zip(sampling_intervals.start, sampling_intervals.end)
-                )
-        return interval_dict
+            sampling_intervals_dict[session_id] = sampling_intervals
+        return sampling_intervals_dict
 
     def get_session_config_dict(self):
         r"""Returns configs for each session in the dataset as a dictionary."""

torch_brain/data/sampler.py

@@ -4,6 +4,8 @@
 from functools import cached_property
 
 import torch
+from temporaldata import Interval
+
 
 from torch_brain.data.dataset import DatasetIndex
 
@@ -33,7 +35,7 @@ class RandomFixedWindowSampler(torch.utils.data.Sampler):
     def __init__(
         self,
         *,
-        interval_dict: Dict[str, List[Tuple[float, float]]],
+        interval_dict: Dict[str, Interval],
         window_length: float,
         generator: Optional[torch.Generator],
         drop_short: bool = True,
@@ -49,7 +51,7 @@ def _estimated_len(self):
         total_short_dropped = 0.0
 
         for session_name, sampling_intervals in self.interval_dict.items():
-            for start, end in sampling_intervals:
+            for start, end in zip(sampling_intervals.start, sampling_intervals.end):
                 interval_length = end - start
                 if interval_length < self.window_length:
                     if self.drop_short:
@@ -81,7 +83,7 @@ def __iter__(self):
 
         indices = []
         for session_name, sampling_intervals in self.interval_dict.items():
-            for start, end in sampling_intervals:
+            for start, end in zip(sampling_intervals.start, sampling_intervals.end):
                 interval_length = end - start
                 if interval_length < self.window_length:
                     if self.drop_short:
@@ -177,7 +179,7 @@ def _indices(self) -> List[DatasetIndex]:
         total_short_dropped = 0.0
 
         for session_name, sampling_intervals in self.interval_dict.items():
-            for start, end in sampling_intervals:
+            for start, end in zip(sampling_intervals.start, sampling_intervals.end):
                 interval_length = end - start
                 if interval_length < self.window_length:
                     if self.drop_short:
@@ -252,7 +254,7 @@ def __iter__(self):
         all_intervals = [
             (session_id, start, end)
             for session_id, intervals in self.interval_dict.items()
-            for start, end in intervals
+            for start, end in zip(intervals.start, intervals.end)
         ]
 
         indices = [