Added some basic structure

src/treeffuser/sampling.py

@@ -0,0 +1,3 @@
+"""
+Something like here: https://github.com/yang-song/score_sde_pytorch/blob/main/sampling.py
+"""

src/treeffuser/score_modesl.py

@@ -0,0 +1,16 @@
+"""
+This file should contain a general abstraction of the score models and
+should function as a wrapper for different models we might want to use.
+
+tThe idea is to "hide" the particular tree we want to use so that
+we can easily switch between different models without having to change
+the rest of the code.
+"""
+
+import abc
+
+
+class ScoreModel(abc.ABC):
+    @abc.abstractmethod
+    def score(self, data):
+        pass

src/treeffuser/sde.py

@@ -0,0 +1,3 @@
+"""
+Similar to https://github.com/yang-song/score_sde_pytorch/blob/main/sde_lib.py
+"""

src/treeffuser/treeffusser.py

@@ -0,0 +1,30 @@
+"""
+This should be the main file corresponding to the project.
+"""
+
+from sklearn.base import BaseEstimator
+
+
+class Treeffuser(BaseEstimator):
+
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def fit(self, X, y):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def sample(self, X):
+        pass
+
+    def likelihood(self, X, y):
+        """
+        Something that computes the log-likelihood of the model.
+        """
+
+    def pred_distribution(self, X):
+        """
+        Maybe the CDF?
+        """