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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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training/train_model.py
+52 -43
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@@ -1,69 +1,78 @@
import pandas as pd
from typing import Literal, Optional
from training.walk_forward import walk_forward_train, walk_forward_inference, walk_forward_inference_batched
from training.walk_forward import (
walk_forward_train,
walk_forward_inference,
walk_forward_inference_batched,
)
from utils.evaluate import evaluate_predictions
from models.base import Model
from .types import ModelOverTime, TransformationsOverTime, TrainingOutcome
def train_model(
ticker_to_predict: str,
X: pd.DataFrame,
y: pd.Series,
forward_returns: pd.Series,
model: Model,
expanding_window: bool,
sliding_window_size: int,
retrain_every: int,
from_index: Optional[pd.Timestamp],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
level: str,
output_stats: bool,
transformations_over_time: TransformationsOverTime,
model_over_time: Optional[ModelOverTime]
) -> TrainingOutcome:
ticker_to_predict: str,
X: pd.DataFrame,
y: pd.Series,
forward_returns: pd.Series,
model: Model,
expanding_window: bool,
sliding_window_size: int,
retrain_every: int,
from_index: Optional[pd.Timestamp],
no_of_classes: Literal["two", "three-balanced", "three-imbalanced"],
level: str,
output_stats: bool,
transformations_over_time: TransformationsOverTime,
model_over_time: Optional[ModelOverTime],
) -> TrainingOutcome:
if model_over_time is None:
print("Train model")
model_over_time = walk_forward_train(
model = model,
X = X,
y = y,
forward_returns = forward_returns,
expanding_window = expanding_window,
window_size = sliding_window_size,
retrain_every = retrain_every,
from_index = from_index,
transformations_over_time = transformations_over_time,
model=model,
X=X,
y=y,
forward_returns=forward_returns,
expanding_window=expanding_window,
window_size=sliding_window_size,
retrain_every=retrain_every,
from_index=from_index,
transformations_over_time=transformations_over_time,
)
levelname = ("_" + level) if level == 'meta' else ""
levelname = ("_" + level) if level == "meta" else ""
if model_over_time is None:
model_id = "model_" + model.name + "_" + ticker_to_predict + levelname
model_id = "model_" + model.name + "_" + ticker_to_predict + levelname
else:
model_id = model_over_time.name
inference_function = walk_forward_inference if from_index is not None else walk_forward_inference_batched
predictions, probabilities = inference_function(
model_name = model_id,
model_over_time= model_over_time,
transformations_over_time = transformations_over_time,
X = X,
expanding_window = expanding_window,
window_size = sliding_window_size,
retrain_every = retrain_every,
from_index = from_index,
inference_function = (
walk_forward_inference
if from_index is not None
else walk_forward_inference_batched
)
predictions, probabilities = inference_function(
model_name=model_id,
model_over_time=model_over_time,
transformations_over_time=transformations_over_time,
X=X,
expanding_window=expanding_window,
window_size=sliding_window_size,
retrain_every=retrain_every,
from_index=from_index,
)
assert len(predictions) == len(y)
if output_stats:
stats = evaluate_predictions(
forward_returns = forward_returns,
y_pred = predictions,
y_true = y,
forward_returns=forward_returns,
y_pred=predictions,
y_true=y,
no_of_classes=no_of_classes,
discretize=True
discretize=True,
)
else:
stats = None
return TrainingOutcome(model_id, predictions, probabilities, stats, model_over_time)
return TrainingOutcome(model_id, predictions, probabilities, stats, model_over_time)