feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml

tests/test_evaluation.py

@@ -4,6 +4,7 @@ import pandas as pd
 from training.walk_forward import walk_forward_train, walk_forward_inference
 from models.base import Model
 from utils.evaluate import evaluate_predictions
+from sklearn.base import BaseEstimator, ClassifierMixin
 
 no_of_rows = 100
 
@@ -29,7 +30,8 @@ def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
 
     return X, y
 
-class EvenOddStubModel(Model):
+
+class EvenOddStubModel(BaseEstimator, ClassifierMixin, Model):
     '''
     A deteministic model that can predict the future with 100% accuracy
     It verifies that the X[n][any_column] == 1 if n is even,
@@ -49,14 +51,10 @@ class EvenOddStubModel(Model):
             assert y[i] == -1 if X[i][0] == 1 else 1
 
     def predict(self, X):
-        return (-1 if X[0][0] == 1 else 1, np.array([]))
+        return np.array([-1 if row[0] == 1 else 1 for row in X])
 
-    def clone(self):
-        return self
-
-
-    def initialize_network(self, input_dim: int, output_dim: int):
-        pass
+    def predict_proba(self, X):
+        return np.array([[row[0] + 1, 0] for row in X])
 
 
 def test_evaluation():

tests/test_walk_forward.py

@@ -2,6 +2,7 @@ import numpy as np
 import pandas as pd
 from training.walk_forward import walk_forward_train, walk_forward_inference
 from models.base import Model
+from sklearn.base import BaseEstimator, ClassifierMixin
 
 no_of_rows = 100
 
@@ -27,7 +28,7 @@ def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Serie
 
 
 
-class IncrementingStubModel(Model):
+class IncrementingStubModel(Model, BaseEstimator, ClassifierMixin):
     '''
     A deteministic model that can predict the future with 100% accuracy
     It verifies that the X[n][any_column]+1 == y[n]
@@ -48,13 +49,10 @@ class IncrementingStubModel(Model):
             assert X[i][0] + 1 == y[i]
 
     def predict(self, X):
-        return (X[0][0] + 1, np.array([]))
+        return np.array([row[0] + 1 for row in X])
 
-    def clone(self):
-        return self
-    
-    def initialize_network(self, input_dim: int, output_dim: int):
-        pass
+    def predict_proba(self, X):
+        return np.array([[row[0] + 1, 0] for row in X])
     
 def test_walk_forward_train_test():
     X, y = __generate_incremental_test_data(no_of_rows)