Autodownload synthetic data (#68)

cebra/datasets/__init__.py

@@ -86,6 +86,7 @@ def get_datapath(path: str = None) -> str:
     from cebra.datasets.gaussian_mixture import *
     from cebra.datasets.hippocampus import *
     from cebra.datasets.monkey_reaching import *
+    from cebra.datasets.synthetic_data import *
 except ModuleNotFoundError as e:
     import warnings
 

cebra/datasets/synthetic_data.py

@@ -0,0 +1,122 @@
+#
+# (c) All rights reserved. ECOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE,
+# Switzerland, Laboratory of Prof. Mackenzie W. Mathis (UPMWMATHIS) and
+# original authors: Steffen Schneider, Jin H Lee, Mackenzie W Mathis. 2023.
+#
+# Source code:
+# https://github.com/AdaptiveMotorControlLab/CEBRA
+#
+# Please see LICENSE.md for the full license document:
+# https://github.com/AdaptiveMotorControlLab/CEBRA/LICENSE.md
+#
+
+import os
+
+import joblib
+
+import cebra.data
+from cebra.datasets import get_datapath
+from cebra.datasets import parametrize
+
+_DEFAULT_DATADIR = get_datapath()
+
+synthetic_data_urls = {
+    "continuous_label_refractory_poisson": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668815?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "fcd92bd283c528d5294093190f55ceba"
+    },
+    "continuous_label_t": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668818?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "a6e76f274da571568fd2a4bf4cf48b66"
+    },
+    "continuous_label_uniform": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668821?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "e67400e77ac009e8c9bc958aa5151973"
+    },
+    "continuous_label_laplace": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668824?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "41d7ce4ce8901ae7a5136605ac3f5ffb"
+    },
+    "continuous_label_poisson": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668827?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "a789828f9cca5f3faf36d62ebc4cc8a1"
+    },
+    "continuous_label_gaussian": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668830?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "18d66a2020923e2cd67d2264d20890aa"
+    },
+    "continuous_poisson_gaussian_noise": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668833?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "1a51461820c24a5bcaddaff3991f0ebe"
+    },
+    "sim_100d_poisson_cont_label": {
+        "url":
+            "https://figshare.com/ndownloader/files/41668836?private_link=7439c5302e99db36eebb",
+        "checksum":
+            "306b9c646e7b76a52cfd828612d700cb"
+    }
+}
+
+
+@parametrize(
+    "continuous-label-{name}",
+    name=["t", "uniform", "laplace", "poisson", "gaussian"],
+)
+class SyntheticData(cebra.data.SingleSessionDataset):
+    """
+    Synthetic datasets with poisson, gaussian, laplace, uniform,
+    and t noise during generative process.
+    """
+
+    def __init__(self, name, root=_DEFAULT_DATADIR, download=True):
+
+        name = f"continuous_label_{name}"
+        location = os.path.join(root, "synthetic")
+        file_path = os.path.join(location, f"{name}.jl")
+
+        super().__init__(download=download,
+                         data_url=synthetic_data_urls[name]["url"],
+                         data_checksum=synthetic_data_urls[name]["checksum"],
+                         location=location,
+                         file_name=f"{name}.jl")
+
+        data = joblib.load(file_path)
+        self.data = data  #NOTE: making it backwards compatible with synth notebook.
+        self.name = name
+        self.neural = self.data['z']
+        self.latents = self.data['x']
+        self.u = self.data['u']
+        self.lam = self.data['lam']
+
+    @property
+    def input_dimension(self):
+        return self.neural.size(1)
+
+    @property
+    def continuous_index(self):
+        return self.index
+
+    def __getitem__(self, index):
+        """Return [ No.Samples x Neurons x 10 ]"""
+        index = self.expand_index(index)
+        return self.neural[index].transpose(2, 1)
+
+    def __len__(self):
+        return len(self.neural)
+
+    def __repr__(self):
+        return f"SyntheticData(name: {self.name}, shape: {self.neural.shape})"