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refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classification later) (#182)

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* refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classes later)

* fix(Training): removed mistakenly left in `method` parameter
This commit is contained in:
Mark Aron Szulyovszky
2022-01-23 17:15:08 +01:00
committed by GitHub
parent 516c8bcc87
commit 5c4a5b0cf1
12 changed files with 11 additions and 44 deletions
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utils/evaluate.py
+4 -14
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@@ -12,19 +12,15 @@ def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.002) ->
return (signal * returns) - costs
def __preprocess(target_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, method: Literal['classification', 'regression'], no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
def __preprocess(target_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
y_pred.name = 'y_pred'
target_returns.name = 'target_returns'
df = pd.concat([y_pred, target_returns],axis=1).dropna()
discretize_func = get_discretize_function(no_of_classes)
# make sure that we evaluate binary/three-way predictions even if the model is a regression
if method == 'regression':
df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
df['sign_true'] = df.target_returns.apply(discretize_func)
else:
df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
df['sign_true'] = y_true
df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
df['sign_true'] = y_true
df['result'] = backtest(df.target_returns, df.sign_pred)
@@ -35,7 +31,6 @@ def evaluate_predictions(
target_returns: pd.Series,
y_pred: pd.Series,
y_true: pd.Series,
method: Literal['classification', 'regression'],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
print_results: bool,
discretize: bool = False,
@@ -45,14 +40,9 @@ def evaluate_predictions(
evaluate_from = first_nonzero_return + 1
target_returns = pd.Series(target_returns[evaluate_from:])
if method == 'regression':
# if there are lots of zeros in the ground truth returns, probably something is wrong, but we can tolerate a couple of days of missing data.
is_zero = target_returns[target_returns == 0]
assert len(is_zero) < 15
y_pred = pd.Series(y_pred[evaluate_from:])
df = __preprocess(target_returns, y_pred, y_true, method, no_of_classes, discretize)
df = __preprocess(target_returns, y_pred, y_true, no_of_classes, discretize)
scorecard = pd.Series()