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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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labeling/labellers/fixed_time_three_class_imbalanced.py
+18 -10
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@@ -2,6 +2,7 @@ from ..types import EventLabeller, EventsDataFrame, ReturnSeries, ForwardReturnS
import pandas as pd
from .utils import create_forward_returns
class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
time_horizon: int
@@ -9,7 +10,9 @@ class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
def __init__(self, time_horizon: int):
self.time_horizon = time_horizon
def label_events(self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries) -> tuple[EventsDataFrame, ForwardReturnSeries]:
def label_events(
self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries
) -> tuple[EventsDataFrame, ForwardReturnSeries]:
forward_returns = create_forward_returns(returns, self.time_horizon)
cutoff_point = returns.index[-self.time_horizon]
@@ -17,7 +20,7 @@ class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
event_candidates = forward_returns[event_start_times]
def get_bins_threeway(x):
bins = pd.qcut(event_candidates, 4, retbins=True, duplicates = 'drop')[1]
bins = pd.qcut(event_candidates, 4, retbins=True, duplicates="drop")[1]
if len(bins) != 5:
# if we don't have enough data for the quantiles, we'll need to add hard-coded values
@@ -25,6 +28,7 @@ class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
upper_bound = bins[-1]
bins = [lower_bound] + [-0.02, 0.0, 0.02] + [upper_bound]
return bins
bins = get_bins_threeway(event_candidates)
def map_class_threeway(current_value):
@@ -36,13 +40,17 @@ class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
return 0
else:
return 1
labels = event_candidates.map(map_class_threeway)
return (pd.DataFrame({
'start': event_start_times,
'end': event_start_times + pd.Timedelta(days=self.time_horizon),
'label': labels,
'returns': forward_returns[event_start_times]
}), forward_returns[event_start_times])
return (
pd.DataFrame(
{
"start": event_start_times,
"end": event_start_times + pd.Timedelta(days=self.time_horizon),
"label": labels,
"returns": forward_returns[event_start_times],
}
),
forward_returns[event_start_times],
)