mirror of
https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
567cd5e9f0
* refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step * fix(Linter): ran * fix(Tests): syntax change * fix(Inference): runs now again * fix(Linter): ran
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
from ..types import EventLabeller, EventsDataFrame, ReturnSeries
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import pandas as pd
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from .utils import create_forward_returns
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from typing import Callable
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class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
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time_horizon: int
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def __init__(self, time_horizon: int):
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self.time_horizon = time_horizon
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def label_events(
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self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries
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) -> EventsDataFrame:
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forward_returns = create_forward_returns(returns, self.time_horizon)
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cutoff_point = returns.index[-self.time_horizon]
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event_start_times[event_start_times < cutoff_point]
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event_candidates = forward_returns[event_start_times]
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def get_bins_threeway(x):
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bins = pd.qcut(event_candidates, 4, retbins=True, duplicates="drop")[1]
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if len(bins) != 5:
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# if we don't have enough data for the quantiles, we'll need to add hard-coded values
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lower_bound = bins[0]
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upper_bound = bins[-1]
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bins = [lower_bound] + [-0.02, 0.0, 0.02] + [upper_bound]
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return bins
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bins = get_bins_threeway(event_candidates)
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def map_class_threeway(current_value):
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lower_threshold = bins[1]
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upper_threshold = bins[3]
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if 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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labels = event_candidates.map(map_class_threeway)
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events = pd.DataFrame(
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{
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"start": event_start_times,
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"end": event_start_times + pd.Timedelta(minutes=self.time_horizon * 5),
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"label": labels,
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"returns": forward_returns[event_start_times],
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}
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)
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return events
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def get_labels(self) -> list[int]:
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return [-1, 0, 1]
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def get_discretize_function(self) -> Callable:
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raise NotImplementedError
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