refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step (#228)

* refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step

* fix(Linter): ran

* fix(Tests): syntax change

* fix(Inference): runs now again

* fix(Linter): ran

training/directional_training.py

@@ -1,6 +1,7 @@
 import pandas as pd
 
 from transformations.base import Transformation
+from utils.evaluate import evaluate_predictions
 
 from .types import TrainingOutcome
 from training.train_model import train_model
@@ -9,6 +10,7 @@ from training.walk_forward import walk_forward_process_transformations
 from typing import Optional
 from config.types import Config
 from models.base import Model
+import pprint
 
 
 def train_directional_model(
@@ -45,17 +47,30 @@ def train_directional_model(
         sliding_window_size=config.sliding_window_size,
         retrain_every=config.retrain_every,
         from_index=from_index,
-        no_of_classes=config.no_of_classes,
         level="primary",
-        output_stats=config.mode == "training",
         transformations_over_time=transformations_over_time,
         model_over_time=preloaded_training_step.model_over_time
         if preloaded_training_step
         else None,
     )
-    if config.mode == "training":
-        print(training_outcome.stats)
+
+    stats = (
+        evaluate_predictions(
+            forward_returns=forward_returns,
+            y_pred=training_outcome.predictions,
+            y_true=y,
+            discretize_func=config.labeling.get_discretize_function(),
+            labels=config.labeling.get_labels(),
+            transaction_costs=config.transaction_costs,
+        )
+        if config.mode == "training"
+        else None
+    )
+
+    if stats is not None:
+        pp = pprint.PrettyPrinter(depth=2)
+        pp.pprint(stats)
 
     return TrainingOutcome(
-        **vars(training_outcome), transformations=transformations_over_time
+        **vars(training_outcome), transformations=transformations_over_time, stats=stats
     )