Merge remote-tracking branch 'origin/main' into batched-inference-and-padding

Author: stes

cebra/__init__.py

@@ -92,11 +92,11 @@ def __getattr__(key):
 
         return CEBRA
     elif key == "KNNDecoder":
-        from cebra.integrations.sklearn.decoder import KNNDecoder
+        from cebra.integrations.sklearn.decoder import KNNDecoder  # noqa: F811
 
         return KNNDecoder
     elif key == "L1LinearRegressor":
-        from cebra.integrations.sklearn.decoder import L1LinearRegressor
+        from cebra.integrations.sklearn.decoder import L1LinearRegressor  # noqa: F811
 
         return L1LinearRegressor
     elif not key.startswith("_"):

cebra/data/datasets.py

@@ -22,13 +22,15 @@
 """Pre-defined datasets."""
 
 import types
-from typing import List, Tuple, Union
+from typing import List, Literal, Optional, Tuple, Union
 
 import numpy as np
 import numpy.typing as npt
 import torch
 
 import cebra.data as cebra_data
+import cebra.helper as cebra_helper
+from cebra.data.datatypes import Offset
 
 
 class TensorDataset(cebra_data.SingleSessionDataset):
@@ -64,26 +66,52 @@ def __init__(self,
                  neural: Union[torch.Tensor, npt.NDArray],
                  continuous: Union[torch.Tensor, npt.NDArray] = None,
                  discrete: Union[torch.Tensor, npt.NDArray] = None,
-                 offset: int = 1,
+                 offset: Offset = Offset(0, 1),
                  device: str = "cpu"):
         super().__init__(device=device)
-        self.neural = self._to_tensor(neural, torch.FloatTensor).float()
-        self.continuous = self._to_tensor(continuous, torch.FloatTensor)
-        self.discrete = self._to_tensor(discrete, torch.LongTensor)
+        self.neural = self._to_tensor(neural, check_dtype="float").float()
+        self.continuous = self._to_tensor(continuous, check_dtype="float")
+        self.discrete = self._to_tensor(discrete, check_dtype="int")
         if self.continuous is None and self.discrete is None:
             raise ValueError(
                 "You have to pass at least one of the arguments 'continuous' or 'discrete'."
             )
         self.offset = offset
 
-    def _to_tensor(self, array, check_dtype=None):
+    def _to_tensor(
+            self,
+            array: Union[torch.Tensor, npt.NDArray],
+            check_dtype: Optional[Literal["int",
+                                          "float"]] = None) -> torch.Tensor:
+        """Convert :py:func:`numpy.array` to :py:class:`torch.Tensor` if necessary and check the dtype.
+
+        Args:
+            array: Array to check.
+            check_dtype: If not `None`, list of dtypes to which the values in `array`
+                must belong to. Defaults to None.
+
+        Returns:
+            The `array` as a :py:class:`torch.Tensor`.
+        """
         if array is None:
             return None
         if isinstance(array, np.ndarray):
             array = torch.from_numpy(array)
         if check_dtype is not None:
-            if not isinstance(array, check_dtype):
-                raise TypeError(f"{type(array)} instead of {check_dtype}.")
+            if check_dtype not in ["int", "float"]:
+                raise ValueError(
+                    f"check_dtype must be 'int' or 'float', got {check_dtype}")
+            if (check_dtype == "int" and not cebra_helper._is_integer(array)
+               ) or (check_dtype == "float" and
+                     not cebra_helper._is_floating(array)):
+                raise TypeError(
+                    f"Array has type {array.dtype} instead of {check_dtype}.")
+        if cebra_helper._is_floating(array):
+            array = array.float()
+        if cebra_helper._is_integer(array):
+            # NOTE(stes): Required for standardizing number format on
+            # windows machines.
+            array = array.long()
         return array
 
     @property

cebra/data/helper.py

@@ -94,10 +94,15 @@ class OrthogonalProcrustesAlignment:
 
     For each dataset, the data and labels to align the data on is provided.
 
-    1. The ``top_k`` indexes of the labels to align (``label``) that are the closest to the labels of the reference dataset (``ref_label``) are selected and used to sample from the dataset to align (``data``).
-    2. ``data`` and ``ref_data`` (the reference dataset) are subsampled to the same number of samples ``subsample``.
-    3. The orthogonal mapping is computed, using :py:func:`scipy.linalg.orthogonal_procrustes`, on those subsampled datasets.
-    4. The resulting orthongonal matrix ``_transform`` can be used to map the original ``data`` to the ``ref_data``.
+    1. The ``top_k`` indexes of the labels to align (``label``) that are the closest to
+       the labels of the reference dataset (``ref_label``) are selected and used to sample
+       from the dataset to align (``data``).
+    2. ``data`` and ``ref_data`` (the reference dataset) are subsampled to the same number
+       of samples ``subsample``.
+    3. The orthogonal mapping is computed, using :py:func:`scipy.linalg.orthogonal_procrustes`,
+       on those subsampled datasets.
+    4. The resulting orthongonal matrix ``_transform`` can be used to map the original ``data``
+       to the ``ref_data``.
 
     Note:
         ``data`` and ``ref_data`` can be of different sample size (axis 0) but **must** have the same number

cebra/data/load.py

@@ -663,7 +663,8 @@ def load(
         - if no key is provided, the first data structure found upon iteration of the collection will be loaded;
         - if a key is provided, it needs to correspond to an existing item of the collection;
         - if a key is provided, the data value accessed needs to be a data structure;
-        - the function loads data for only one data structure, even if the file contains more. The function can be called again with the corresponding key to get the other ones.
+        - the function loads data for only one data structure, even if the file contains more. The function can be
+          called again with the corresponding key to get the other ones.
 
     Args:
         file: The path to the given file to load, in a supported format.

cebra/data/single_session.py

@@ -358,7 +358,6 @@ def __post_init__(self):
         #            here might be sub-optimal. The final behavior should be determined after
         #            e.g. integrating the FAISS dataloader back in.
         super().__post_init__()
-        index = self.index.to(self.device)
 
         if self.conditional != "time_delta":
             raise NotImplementedError(
@@ -368,8 +367,7 @@ def __post_init__(self):
         self.time_distribution = cebra.distributions.TimeContrastive(
             time_offset=self.time_offset,
             num_samples=len(self.dataset.neural),
-            device=self.device,
-        )
+            device=self.device)
         self.behavior_distribution = cebra.distributions.TimedeltaDistribution(
             self.dataset.continuous_index, self.time_offset, device=self.device)
 

cebra/datasets/__init__.py

@@ -98,8 +98,6 @@ def get_datapath(path: str = None) -> str:
     from cebra.datasets.monkey_reaching import *
     from cebra.datasets.synthetic_data import *
 except ModuleNotFoundError as e:
-    import warnings
-
     warnings.warn(f"Could not initialize one or more datasets: {e}. "
                   f"For using the datasets, consider installing the "
                   f"[datasets] extension via pip.")

cebra/datasets/allen/ca_movie.py

@@ -22,11 +22,15 @@
 """Allen pseudomouse Ca dataset.
 
 References:
-    *Deitch, Daniel, Alon Rubin, and Yaniv Ziv. "Representational drift in the mouse visual cortex." Current biology 31.19 (2021): 4327-4339.
-    *de Vries, Saskia EJ, et al. "A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex." Nature neuroscience 23.1 (2020): 138-151.
-    *https://github.com/zivlab/visual_drift
-    *http://observatory.brain-map.org/visualcoding
-
+    * Deitch, Daniel, Alon Rubin, and Yaniv Ziv.
+      "Representational drift in the mouse visual cortex."
+      Current biology 31.19 (2021): 4327-4339.
+    * de Vries, Saskia EJ, et al.
+      "A large-scale standardized physiological survey reveals functional
+      organization of the mouse visual cortex."
+      Nature neuroscience 23.1 (2020): 138-151.
+    * https://github.com/zivlab/visual_drift
+    * http://observatory.brain-map.org/visualcoding
 """
 
 import pathlib

cebra/datasets/allen/ca_movie_decoding.py

@@ -22,11 +22,15 @@
 """Allen pseudomouse Ca decoding dataset with train/test split.
 
 References:
-    *Deitch, Daniel, Alon Rubin, and Yaniv Ziv. "Representational drift in the mouse visual cortex." Current biology 31.19 (2021): 4327-4339.
-    *de Vries, Saskia EJ, et al. "A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex." Nature neuroscience 23.1 (2020): 138-151.
-    *https://github.com/zivlab/visual_drift
-    *http://observatory.brain-map.org/visualcoding
-
+    * Deitch, Daniel, Alon Rubin, and Yaniv Ziv.
+      "Representational drift in the mouse visual cortex."
+      Current biology 31.19 (2021): 4327-4339.
+    * de Vries, Saskia EJ, et al.
+      "A large-scale standardized physiological survey reveals functional
+      organization of the mouse visual cortex."
+      Nature neuroscience 23.1 (2020): 138-151.
+    * https://github.com/zivlab/visual_drift
+    * http://observatory.brain-map.org/visualcoding
 """
 
 import pathlib
@@ -243,11 +247,6 @@ def _convert_to_nums(string):
 
         return pseudo_mouse
 
-        pseudo_mouse = np.vstack(
-            [get_neural_data(num_movie, mice) for mice in list_mice])
-
-        return pseudo_mouse
-
     def __len__(self):
         return self.neural.size(0)
 

cebra/datasets/allen/combined.py

@@ -22,13 +22,19 @@
 """Joint Allen pseudomouse Ca/Neuropixel datasets.
 
 References:
-    *Deitch, Daniel, Alon Rubin, and Yaniv Ziv. "Representational drift in the mouse visual cortex." Current biology 31.19 (2021): 4327-4339.
-    *de Vries, Saskia EJ, et al. "A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex." Nature neuroscience 23.1 (2020): 138-151.
-    *https://github.com/zivlab/visual_drift
-    *http://observatory.brain-map.org/visualcoding
-    *https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html
-    *Siegle, Joshua H., et al. "Survey of spiking in the mouse visual system reveals functional hierarchy." Nature 592.7852 (2021): 86-92.
-
+    * Deitch, Daniel, Alon Rubin, and Yaniv Ziv.
+      "Representational drift in the mouse visual cortex."
+      Current Biology 31.19 (2021): 4327-4339.
+    * de Vries, Saskia EJ, et al.
+      "A large-scale standardized physiological survey reveals functional
+      organization of the mouse visual cortex."
+      Nature Neuroscience 23.1 (2020): 138-151.
+    * https://github.com/zivlab/visual_drift
+    * http://observatory.brain-map.org/visualcoding
+    * https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html
+    * Siegle, Joshua H., et al.
+      "Survey of spiking in the mouse visual system reveals functional hierarchy."
+      Nature 592.7852 (2021): 86-92.
 """
 
 import cebra.data

cebra/datasets/allen/make_neuropixel.py

@@ -192,11 +192,12 @@ def read_neuropixel(
                     "intervals/natural_movie_one_presentations/start_time"][...]
                 end_time = d[
                     "intervals/natural_movie_one_presentations/stop_time"][...]
-                timeseries = d[
-                    "intervals/natural_movie_one_presentations/timeseries"][...]
-                timeseries_index = d[
-                    "intervals/natural_movie_one_presentations/timeseries_index"][
-                        ...]
+                # NOTE(stes): never used. leaving here for future reference
+                #timeseries = d[
+                #    "intervals/natural_movie_one_presentations/timeseries"][...]
+                #timeseries_index = d[
+                #    "intervals/natural_movie_one_presentations/timeseries_index"][
+                #        ...]
                 session_no = d["identifier"][...].item()
                 spike_time_index = d["units/spike_times_index"][...]
                 spike_times = d["units/spike_times"][...]
@@ -266,14 +267,14 @@ def read_neuropixel(
             "neural": sessions_dic,
             "frames": session_frames
         },
-        Path(args.save_path) /
+        pathlib.Path(args.save_path) /
         f"neuropixel_sessions_{int(args.sampling_rate)}_filtered.jl",
     )
     jl.dump(
         {
             "neural": pseudo_mice,
             "frames": pseudo_mice_frames
         },
-        Path(args.save_path) /
+        pathlib.Path(args.save_path) /
         f"neuropixel_pseudomouse_{int(args.sampling_rate)}_filtered.jl",
     )

cebra/datasets/allen/single_session_ca.py

@@ -19,14 +19,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-"""Allen single mouse dataset.
+"""
+Allen single mouse dataset.
 
 References:
-    *Deitch, Daniel, Alon Rubin, and Yaniv Ziv. "Representational drift in the mouse visual cortex." Current biology 31.19 (2021): 4327-4339.
-    *de Vries, Saskia EJ, et al. "A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex." Nature neuroscience 23.1 (2020): 138-151.
-    *https://github.com/zivlab/visual_drift
-    *http://observatory.brain-map.org/visualcoding
+    * Deitch, Daniel, Alon Rubin, and Yaniv Ziv.
+      "Representational drift in the mouse visual cortex."
+      Current Biology 31.19 (2021): 4327-4339.
+
+    * de Vries, Saskia EJ, et al.
+      "A large-scale standardized physiological survey reveals functional
+      organization of the mouse visual cortex."
+      Nature Neuroscience 23.1 (2020): 138-151.
 
+    * https://github.com/zivlab/visual_drift
+    * http://observatory.brain-map.org/visualcoding
 """
 import pathlib
 
@@ -113,7 +120,7 @@ def __getitem__(self, index):
     "allen-movie1-ca-single-session-corrupt-{session_id}",
     session_id=range(len(_SINGLE_SESSION_CA)),
 )
-class SingleSessionAllenCa(cebra.data.SingleSessionDataset):
+class SingleSessionAllenCaCorrupted(cebra.data.SingleSessionDataset):
     """A corrupted single mouse 30Hz calcium events dataset during the allen MOVIE1 stimulus.
 
     A dataset of a single mouse 30Hz calcium events from the excitatory neurons in the primary visual cortex
@@ -352,7 +359,7 @@ def __init__(self, repeat_no, split_flag):
     repeat_no=[9],
     split_flag=["train", "test"],
 )
-class SingleSessionAllenCaDecoding(cebra.data.SingleSessionDataset):
+class SingleSessionAllenCaDecodingCorrupted(cebra.data.SingleSessionDataset):
     """A corrupted single mouse 30Hz calcium events dataset during the allen MOVIE1 stimulus with train/test splits.
 
     A dataset of a single mouse 30Hz calcium events from the excitatory neurons

cebra/datasets/gaussian_mixture.py

@@ -27,9 +27,12 @@
 
 import cebra.data
 import cebra.io
+from cebra.datasets import get_datapath
 from cebra.datasets import parametrize
 from cebra.datasets import register
 
+_DEFAULT_DATADIR = get_datapath()
+
 
 @register("continuous-gaussian-mixture")
 @parametrize(

cebra/datasets/generate_synthetic_data.py

@@ -30,7 +30,7 @@
 import joblib as jl
 import keras
 import numpy as np
-import poisson
+import poisson as poisson_utils
 import scipy.stats
 import tensorflow as tf
 
@@ -228,7 +228,7 @@ def refractory_poisson(x):
         flattened_lam = lam_true.flatten()
         x = np.zeros_like(flattened_lam)
         for i, rate in enumerate(flattened_lam):
-            neuron = poisson.PoissonNeuron(
+            neuron = poisson_utils.PoissonNeuron(
                 spike_rate=rate * args.scale,
                 num_repeats=1,
                 time_interval=args.time_interval,

cebra/datasets/hippocampus.py

@@ -160,14 +160,16 @@ def decode(self, x_train, y_train, x_test, y_test):
 class SingleRatTrialSplitDataset(SingleRatDataset):
     """A single rat hippocampus tetrode recording while the rat navigates on a linear track with 3-fold splits.
 
-    Neural data is spike counts binned into 25ms time window and the behavior is position and the running direction (left, right) of a rat.
-    The behavior label is structured as 3D array consists of position, right, and left.
-    The neural and behavior recordings are parsed into trials (a round trip from one end of the track) and the trials are split into a train, valid and test set with k=3 nested cross validation.
+    Neural data is spike counts binned into 25ms time window and the behavior is position and the running
+    direction (left, right) of a rat. The behavior label is structured as 3D array consists of position,
+    right, and left. The neural and behavior recordings are parsed into trials (a round trip from one end
+    of the track) and the trials are split into a train, valid and test set with k=3 nested cross validation.
 
     Args:
         name: The name of a rat to use. Choose among 'achilles', 'buddy', 'cicero' and 'gatsby'.
         split_no: The `k` for k-fold split. Choose among 0, 1, 2.
-        split: The split to use. Choose among 'train', 'valid', 'test', 'all', and 'wo_test'(all trials except test split).
+        split: The split to use. Choose among 'train', 'valid', 'test', 'all', and 'wo_test'
+            (all trials except test split).
 
     """
 
@@ -281,13 +283,16 @@ class MultipleRatsTrialSplitDataset(cebra.data.DatasetCollection):
     """4 rats hippocampus tetrode recording while the rat navigates on a linear track with 3-fold splits.
 
     Neural and behavior recordings of 4 rats.
-    For each rat, neural data is spike counts binned into 25ms time window and the behavior is position and the running direction (left, right) of a rat.
+    For each rat, neural data is spike counts binned into 25ms time window and the behavior is position
+    and the running direction (left, right) of a rat.
     The behavior label is structured as 3D array consists of position, right, and left.
-    Neural and behavior recordings of each rat are parsed into trials (a round trip from one end of the track) and the trials are split into a train, valid and test set with k=3 nested cross validation.
+    Neural and behavior recordings of each rat are parsed into trials (a round trip from one end of the track)
+    and the trials are split into a train, valid and test set with k=3 nested cross validation.
 
     Args:
         split_no: The `k` for k-fold split. Choose among 0, 1, and 2.
-        split: The split to use. Choose among 'train', 'valid', 'test', 'all', and 'wo_test'(all trials except test split).
+        split: The split to use. Choose among 'train', 'valid', 'test', 'all', and 'wo_test'
+            (all trials except test split).
 
     """