Merge branch 'main' into c2st

.gitignore

@@ -148,4 +148,6 @@ figures/
 
 .idea/
 
-secrets.py
+secrets.py
+
+results/

README.md

@@ -13,6 +13,7 @@ conda activate labproject
 
 # install labproject package with dependencies
 python3 -m pip install --upgrade pip
+cd labproject
 pip install -e ".[dev,docs]"
 
 # install pre-commit hooks for black auto-formatting

configs/conf_default.yaml

@@ -1,4 +1,5 @@
 
+exp_log_name: "default" # optional but recommended
 data: "random" 
 experiments: ["ScaleDimKL"]
 n: 10000

docs/api.md

@@ -11,6 +11,12 @@ Here all functions will be documented that are part of the public API of the lab
     options:
       heading_level: 4
 
+### Gaussian Wasserstein
+
+::: labproject.metrics.gaussian_squared_wasserstein
+    options:
+      heading_level: 4
+
 ### Sliced Wasserstein
 
 ::: labproject.metrics.sliced_wasserstein

labproject/experiments.py

@@ -1,6 +1,7 @@
 import torch
-from metrics import sliced_wasserstein_distance, gaussian_kl_divergence
-from plotting import plot_scaling_metric_dimensionality
+from .metrics import sliced_wasserstein_distance, gaussian_kl_divergence
+from .plotting import plot_scaling_metric_dimensionality
+import pickle
 
 
 class Experiment:
@@ -9,32 +10,46 @@ def __init__(self):
 
     def run_experiment(self, metric, dataset1, dataset2):
         raise NotImplementedError("Subclasses must implement this method")
-    
+
     def plot_experiment(self):
         raise NotImplementedError("Subclasses must implement this method")
-
+
+    def log_results(self, results, log_path):
+        raise NotImplementedError("Subclasses must implement this method")
+
 
 class ScaleDim(Experiment):
-    
+
     def __init__(self, metric_name, metric_fn, min_dim=1, max_dim=1000, step=100):
         self.metric_name = metric_name
         self.metric_fn = metric_fn
         self.dimensionality = list(range(min_dim, max_dim, step))
         super().__init__()
-    
+
     def run_experiment(self, dataset1, dataset2):
         distances = []
         for d in self.dimensionality:
             distances.append(self.metric_fn(dataset1[:, :d], dataset2[:, :d]))
         return self.dimensionality, distances
-    
+
     def plot_experiment(self, dimensionality, distances, dataset_name):
-        plot_scaling_metric_dimensionality(dimensionality, distances, self.metric_name, dataset_name)
-
+        plot_scaling_metric_dimensionality(
+            dimensionality, distances, self.metric_name, dataset_name
+        )
+
+    def log_results(self, results, log_path):
+        """
+        Save the results to a file.
+        """
+        with open(log_path, "wb") as f:
+            pickle.dump(results, f)
+
+
 class ScaleDimKL(ScaleDim):
     def __init__(self):
         super().__init__("KL", gaussian_kl_divergence, min_dim=2)
-
+
+
 class ScaleDimSW(ScaleDim):
     def __init__(self):
-        super().__init__("Sliced Wasserstein", sliced_wasserstein_distance)
+        super().__init__("Sliced Wasserstein", sliced_wasserstein_distance)