refactor(Training): remove non-expanding window option (#244)

tests/test_evaluation.py

@@ -46,15 +46,14 @@ class EvenOddStubModel(BaseEstimator, ClassifierMixin, Model):
         self.window_length = window_length
 
     def fit(self, X, y):
-        assert len(X) == self.window_length
         for i in range(len(X)):
             assert y[i] == -1 if X[i][0] == 1 else 1
 
     def predict(self, X):
-        return np.array([-1 if row[0] == 1 else 1 for row in X])
+        return np.array([-1 if row[-1] == 1 else 1 for row in X])
 
     def predict_proba(self, X):
-        return np.array([[row[0] + 1, 0] for row in X])
+        return np.array([[row[-1] + 1, 0] for row in X])
 
 
 def test_evaluation():
@@ -70,7 +69,6 @@ def test_evaluation():
         X=X,
         y=y,
         forward_returns=y,
-        expanding_window=False,
         window_size=window_length,
         retrain_every=retrain_every,
         from_index=None,

tests/test_walk_forward.py

@@ -42,9 +42,8 @@ class IncrementingStubModel(Model, BaseEstimator, ClassifierMixin):
         self.window_length = window_length
 
     def fit(self, X, y):
-        assert len(X) == self.window_length
         for i in range(len(X)):
-            assert X[i][0] + 1 == y[i]
+            assert X[i][-1] + 1 == y[i]
 
     def predict(self, X):
         return np.array([row[0] + 1 for row in X])
@@ -66,7 +65,6 @@ def test_walk_forward_train_test():
         X=X,
         y=y,
         forward_returns=y,
-        expanding_window=False,
         window_size=window_length,
         retrain_every=retrain_every,
         from_index=None,

training/train_model.py

@@ -42,7 +42,6 @@ def train_model(
             X=X,
             y=y,
             forward_returns=forward_returns,
-            expanding_window=True,
             window_size=initial_window_size,
             retrain_every=retrain_every,
             from_index=from_index,

training/walk_forward/inference_batched.py

@@ -57,8 +57,6 @@ def walk_forward_inference_batched(
             X,
             model_over_time,
             transformations_over_time,
-            expanding_window,
-            window_size,
         )
         for index in tqdm(batch_indices)
     ]
@@ -76,8 +74,6 @@ def __inference_from_window(
     X: XDataFrame,
     model_over_time: ModelOverTime,
     transformations_over_time: TransformationsOverTime,
-    expanding_window: bool,
-    window_size: int,
 ) -> list[tuple[int, float, pd.Series]]:
     current_model = model_over_time[X.index[index_start]]
     current_transformations = [

training/walk_forward/inference_parallel.py

@@ -1,117 +0,0 @@
-import pandas as pd
-from models.base import Model
-from training.types import (
-    ModelOverTime,
-    TransformationsOverTime,
-    PredictionsSeries,
-    ProbabilitiesDataFrame,
-)
-from transformations.base import Transformation
-from utils.helpers import get_first_valid_return_index
-from tqdm import tqdm
-from typing import Optional
-from data_loader.types import XDataFrame
-import ray
-
-
-def walk_forward_inference(
-    model_name: str,
-    model_over_time: ModelOverTime,
-    transformations_over_time: TransformationsOverTime,
-    X: XDataFrame,
-    expanding_window: bool,
-    window_size: int,
-    retrain_every: int,
-    class_labels: list[int],
-    from_index: Optional[pd.Timestamp],
-) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
-    predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
-    probabilities = pd.DataFrame(
-        index=X.index, columns=[str(label) for label in class_labels]
-    )
-
-    inference_from = (
-        max(
-            get_first_valid_return_index(model_over_time),
-            get_first_valid_return_index(X.iloc[:, 0]),
-        )
-        if from_index is None
-        else X.index.to_list().index(from_index)
-    )
-    inference_till = X.shape[0]
-    first_model = model_over_time[inference_from]
-
-    if first_model.only_column is not None:
-        X = X[[column for column in X.columns if first_model.only_column in column]]
-
-    if first_model.data_transformation == "original":
-        transformations_over_time = []
-
-    batch_size = int((inference_till - inference_from) / 10)
-    batched_results = ray.get(
-        [
-            __inference_from_window.remote(
-                index,
-                index + batch_size,
-                inference_from,
-                retrain_every,
-                X,
-                model_over_time,
-                transformations_over_time,
-                expanding_window,
-                window_size,
-            )
-            for index in range(inference_from, inference_till)
-        ]
-    )
-    for batch in batched_results:
-        for index, prediction, probs in batch:
-            predictions[X.index[index]] = prediction
-            probabilities.loc[X.index[index]] = probs
-
-    return predictions, probabilities
-
-
-@ray.remote
-def __inference_from_window(
-    index_start: int,
-    index_end: int,
-    inference_from: int,
-    retrain_every: int,
-    X: XDataFrame,
-    model_over_time: ModelOverTime,
-    transformations_over_time: TransformationsOverTime,
-    expanding_window: bool,
-    window_size: int,
-) -> list[tuple[int, float, pd.Series]]:
-
-    results = []
-    for index in range(index_start, index_end):
-        last_model_index = index - ((index - inference_from) % retrain_every)
-        train_window_start = (
-            X.index[inference_from]
-            if expanding_window
-            else X.index[index - window_size - 1]
-        )
-
-        current_model = model_over_time[X.index[last_model_index]]
-        current_transformations = [
-            transformation_over_time[X.index[last_model_index]]
-            for transformation_over_time in transformations_over_time
-        ]
-
-        if current_model.predict_window_size == "window_size":
-            next_timestep = X.loc[train_window_start : X.index[index]]
-        else:
-            # we need to get a Dataframe out of it, since the transformation step always expects a 2D array, but it's equivalent to X.iloc[index]
-            next_timestep = X.loc[X.index[index] : X.index[index]]
-
-        for transformation in current_transformations:
-            next_timestep = transformation.transform(next_timestep)
-
-        next_timestep = next_timestep.to_numpy()
-
-        prediction, probs = current_model.predict(next_timestep)
-        results.append((index, prediction, probs))
-
-    return results

training/walk_forward/process_transformations_parallel.py

@@ -39,7 +39,6 @@ def walk_forward_process_transformations(
             preprocess_transformations_window.remote(
                 X,
                 y,
-                window_size,
                 transformations,
                 first_nonzero_return,
                 index,
@@ -61,7 +60,6 @@ def walk_forward_process_transformations(
 def preprocess_transformations_window(
     X: XDataFrame,
     y: ySeries,
-    window_size: int,
     transformations: list[Transformation],
     first_nonzero_return: int,
     index: int,

training/walk_forward/train.py

@@ -13,7 +13,6 @@ def walk_forward_train(
     X: XDataFrame,
     y: ySeries,
     forward_returns: ForwardReturnSeries,
-    expanding_window: bool,
     window_size: int,
     retrain_every: int,
     from_index: Optional[pd.Timestamp],
@@ -40,11 +39,7 @@ def walk_forward_train(
         transformations_over_time = []
 
     for index in tqdm(range(train_from, train_till, retrain_every)):
-        train_window_start = (
-            X.index[first_nonzero_return]
-            if expanding_window
-            else X.index[index - window_size - 1]
-        )
+        train_window_start = X.index[first_nonzero_return]
 
         train_window_end = X.index[index - 1]
         current_transformations = [

training/walk_forward/train_parallel.py

@@ -15,7 +15,6 @@ def walk_forward_train(
     X: XDataFrame,
     y: ySeries,
     forward_returns: ForwardReturnSeries,
-    expanding_window: bool,
     window_size: int,
     retrain_every: int,
     from_index: Optional[pd.Timestamp],
@@ -46,11 +45,9 @@ def walk_forward_train(
             train_on_window.remote(
                 index,
                 first_nonzero_return,
-                window_size,
                 X,
                 y,
                 model,
-                expanding_window,
                 transformations_over_time,
             )
             for index in tqdm(range(train_from, train_till, retrain_every))
@@ -66,18 +63,12 @@ def walk_forward_train(
 def train_on_window(
     index: int,
     first_nonzero_return: int,
-    window_size: int,
     X: XDataFrame,
     y: ySeries,
     model: Model,
-    expanding_window: bool,
     transformations_over_time: TransformationsOverTime,
 ) -> tuple[int, Model]:
-    train_window_start = (
-        X.index[first_nonzero_return]
-        if expanding_window
-        else X.index[index - window_size - 1]
-    )
+    train_window_start = X.index[first_nonzero_return]
 
     train_window_end = X.index[index - 1]
     current_transformations = [