Fix bug in tree solver that was causing wrong fits

wise_pizza/slicer.py

@@ -117,7 +117,7 @@ def fit(
         @param min_segments: Minimum number of segments to find
         @param max_segments: Maximum number of segments to find, defaults to min_segments
         @param min_depth: Minimum number of dimension to constrain in segment definition
-        @param max_depth: Maximum number of dimension to constrain in segment definition
+        @param max_depth: Maximum number of dimensions to constrain in segment definition; also max depth pf tree in tree solver
         @param solver: Valid values are "lasso" (default), "tree" (for non-overlapping segments), "omp", or "lp"
         @param verbose: If set to a truish value, lots of debug info is printed to console
         @param force_dim: To add dim
@@ -287,7 +287,8 @@ def fit(
             # assert wgt == wgts[i]
             s["orig_i"] = i
             s["coef"] = self.reg.coef_[i]
-            s["impact"] = np.abs(s["coef"]) * (np.abs(this_vec) * self.weights).sum()
+            # TODO: does not taking the abs of coef here break time series?
+            s["impact"] = s["coef"] * (np.abs(this_vec) * self.weights).sum()
             s["avg_impact"] = s["impact"] / sum(self.weights)
             s["total"] = (self.totals * dummy).sum()
             s["seg_size"] = wgt

wise_pizza/solve/fitter.py

@@ -18,10 +18,12 @@ def fit_predict(self, X, y, sample_weight=None):
         return self.predict(X)
 
     def error(self, X, y, sample_weight=None):
+        # Error is chosen so that it's minimized by the weighted mean of y
         err = y - self.predict(X)
+        errsq = err**2
         if sample_weight is not None:
-            err *= sample_weight
-        return np.nansum(err**2)
+            errsq *= sample_weight
+        return np.nansum(errsq)
 
 
 class AverageFitter(Fitter):

wise_pizza/solve/tree.py

@@ -69,7 +69,7 @@ def __init__(
         dim_split: Optional[Dict[str, List]] = None,
         depth: int = 0,
     ):
-        self.df = df
+        self.df = df.copy()
         self.fitter = fitter
         self.dims = dims
         self._best_submodels = None
@@ -85,7 +85,7 @@ def error(self):
             self.model = copy.deepcopy(self.fitter)
             self.model.fit(
                 X=self.df[self.dims],
-                y=self.df["totals"],
+                y=self.df["__avg"],
                 sample_weight=self.df["weights"],
             )
         return self.model.error(