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

labeling/labellers/utils.py

@@ -1,6 +1,8 @@
 import pandas as pd
 from data_loader.types import ForwardReturnSeries
 from labeling.types import EventsDataFrame
+from typing import Callable
+import numpy as np
 
 
 def create_forward_returns(series: pd.Series, period: int) -> ForwardReturnSeries:
@@ -22,3 +24,31 @@ def purge_overlapping_events(events: EventsDataFrame) -> EventsDataFrame:
             last_event_end = row["end"]
     events.drop(indicies_to_remove, inplace=True)
     return events
+
+
+def discretize_binary(x):
+    return 1 if x > 0 else -1
+
+
+def discretize_binary_zero_one(x):
+    return 1 if x > 0 else 0
+
+
+def discretize_threeway(x):
+    return 0 if x == 0 else 1 if x > 0 else -1
+
+
+def discretize_threeway_threshold(threshold: float) -> Callable:
+    def discretize(current_value):
+        lower_threshold = -threshold
+        upper_threshold = threshold
+        if np.isnan(current_value):
+            return np.nan
+        elif current_value <= lower_threshold:
+            return -1
+        elif current_value > lower_threshold and current_value < upper_threshold:
+            return 0
+        else:
+            return 1
+
+    return discretize