Model selection (#63)

* 1. Selection of model for predict method.
2. Subset max shape changed to 2 to make some models working.

* Update base.py

Tests error fixed

bamt/networks/base.py

@@ -607,7 +607,8 @@ def wrapper():
     def predict(self,
                 test: pd.DataFrame,
                 parall_count: int = 1,
-                progress_bar: bool = True) -> Dict[str,
+                progress_bar: bool = True,
+                models_dir: Optional[str] = None) -> Dict[str,
                                                    Union[List[str],
                                                          List[int],
                                                          List[float]]]:
@@ -630,7 +631,7 @@ def predict(self,
 
         from joblib import Parallel, delayed
 
-        def wrapper(bn, test: pd.DataFrame, columns: List[str]):
+        def wrapper(bn, test: pd.DataFrame, columns: List[str], models_dir: str):
             preds = {column_name: list() for column_name in columns}
 
             if len(test) == 1:
@@ -639,7 +640,7 @@ def wrapper(bn, test: pd.DataFrame, columns: List[str]):
                     for n, key in enumerate(columns):
                         try:
                             sample = bn.sample(
-                                1, evidence=test_row, predict=True, progress_bar=False)
+                                1, evidence=test_row, predict=True, progress_bar=False, models_dir=models_dir)
                             if sample.empty:
                                 preds[key].append(np.nan)
                                 continue
@@ -670,10 +671,10 @@ def wrapper(bn, test: pd.DataFrame, columns: List[str]):
 
         if progress_bar:
             processed_list = Parallel(n_jobs=parall_count)(delayed(wrapper)(
-                self, test.loc[[i]], columns) for i in tqdm(test.index, position=0, leave=True))
+                self, test.loc[[i]], columns, models_dir) for i in tqdm(test.index, position=0, leave=True))
         else:
             processed_list = Parallel(n_jobs=parall_count)(
-                delayed(wrapper)(self, test.loc[[i]], columns) for i in test.index)
+                delayed(wrapper)(self, test.loc[[i]], columns, models_dir) for i in test.index)
 
         for i in range(test.shape[0]):
             curr_pred = processed_list[i]

bamt/nodes/conditional_gaussian_node.py

@@ -50,7 +50,7 @@ def fit_parameters(
                 mask = (mask) & (data[col] == val)
             new_data = data[mask]
             key_comb = [str(x) for x in comb]
-            if new_data.shape[0] > 0:
+            if new_data.shape[0] > 1:
                 if self.cont_parents:
                     model = self.regressor
                     model.fit(new_data[self.cont_parents].values,

tests/sendingRegressors.py

@@ -1,6 +1,5 @@
 # import json
 
-# import bamt.networks as Nets
 from bamt.networks.hybrid_bn import HybridBN
 import bamt.preprocessors as preprocessors