mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
f85ee6bb9c
* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction * fix(Evaluate): print results * fix(Evaluate): make sure we have numerical stability in returns * fix(Inference): only output and print stats in training mode * fix(Evaluate): don't add miniscule amount to result
103 lines
4.5 KiB
Python
103 lines
4.5 KiB
Python
from typing import Literal, Callable
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from quantstats.stats import skew, sortino
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from utils.metrics import probabilistic_sharpe_ratio, sharpe_ratio
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from utils.helpers import get_first_valid_return_index
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import pandas as pd
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import numpy as np
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from data_loader.types import ForwardReturnSeries, ySeries
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from training.types import Stats, WeightsSeries
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def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.002) -> pd.Series:
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delta_pos = signal.diff(1).abs().fillna(0.)
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costs = transaction_cost * delta_pos
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return (signal * returns) - costs
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def __preprocess(forward_returns: ForwardReturnSeries, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
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y_pred.name = 'y_pred'
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forward_returns.name = 'forward_returns'
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df = pd.concat([y_pred, forward_returns],axis=1).dropna()
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discretize_func = get_discretize_function(no_of_classes)
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# make sure that we evaluate binary/three-way predictions even if the model is a regression
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df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
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df['sign_true'] = y_true
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df['result'] = backtest(df.forward_returns, df.sign_pred)
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return df
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def evaluate_predictions(
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forward_returns: ForwardReturnSeries,
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y_pred: WeightsSeries,
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y_true: ySeries,
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
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discretize: bool = False,
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) -> Stats:
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# ignore the predictions until we see a non-zero returns (and definitely skip the first sliding_window_size)
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evaluate_from = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(y_pred))
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forward_returns = pd.Series(forward_returns[evaluate_from:])
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y_pred = pd.Series(y_pred[evaluate_from:])
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df = __preprocess(forward_returns, y_pred, y_true, no_of_classes, discretize)
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scorecard = dict()
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def count_non_zero(series: pd.Series) -> int:
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return len(series[series != 0])
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no_of_samples = count_non_zero(df.y_pred)
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scorecard['no_of_samples'] = no_of_samples
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sharpe = sharpe_ratio(df.result)
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scorecard['sharpe'] = sharpe
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benchmark_sharpe = sharpe_ratio(df.forward_returns)
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scorecard['benchmark_sharpe'] = benchmark_sharpe
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scorecard['prob_sharpe'] = probabilistic_sharpe_ratio(sharpe, benchmark_sharpe, no_of_samples)
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scorecard['sortino'] = sortino(df.result)
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scorecard['skew'] = skew(df.result)
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labels = [1, -1] if no_of_classes == 'two' else [1, -1, 0]
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avg_type = 'weighted' if no_of_classes == 'two' else 'macro'
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if discretize == True:
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scorecard['accuracy'] = accuracy_score(df.sign_true, df.sign_pred) * 100
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scorecard['recall'] = recall_score(df.sign_true, df.sign_pred, labels = labels, average=avg_type)
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scorecard['precision'] = precision_score(df.sign_true, df.sign_pred, labels = labels, average=avg_type)
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scorecard['f1_score'] = f1_score(df.sign_true, df.sign_pred, labels = labels, average=avg_type)
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scorecard['edge'] = df.result.mean()
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scorecard['noise'] = df.y_pred.diff().abs().mean()
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scorecard['edge_to_noise'] = scorecard['edge'] / (scorecard['noise'] + 0.00001)
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if discretize == True:
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for index, row in df.sign_true.value_counts().iteritems():
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scorecard['sign_true_ratio_' + str(index)] = row / len(df.sign_true)
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for index, row in df.sign_pred.value_counts().iteritems():
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scorecard['sign_pred_ratio_' + str(index)] = row / len(df.sign_pred)
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scorecard = {k: round(float(v), 3) for k, v in scorecard.items()}
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return scorecard
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def __discretize_binary(x): return 1 if x > 0 else -1
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def __discretize_threeway(x): return 0 if x == 0 else 1 if x > 0 else -1
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def discretize_threeway_threshold(threshold: float) -> Callable:
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def discretize(current_value):
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lower_threshold = -threshold
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upper_threshold = threshold
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if np.isnan(current_value):
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return np.nan
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elif current_value <= lower_threshold:
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return -1
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elif current_value > lower_threshold and current_value < upper_threshold:
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return 0
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else:
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return 1
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return discretize
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def get_discretize_function(no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']) -> Callable:
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return __discretize_binary if no_of_classes == 'two' else __discretize_threeway
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