mirror of
https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
27 lines
856 B
Python
27 lines
856 B
Python
import pandas as pd
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import numpy as np
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from scipy.stats import shapiro
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def get_close_low_high(df: pd.DataFrame) -> tuple[pd.Series, pd.Series, pd.Series]:
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close = df["close"]
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low = df["low"]
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high = df["high"]
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return close, low, high
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def apply_log_if_necessary_series(series: pd.Series, name: str) -> pd.Series:
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values = series.to_numpy()
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no_of_unique_values = np.unique(values)
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if len(no_of_unique_values) < 4:
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return series
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is_normal = shapiro(values).pvalue > 0.05
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if not is_normal:
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# print("Applying log to column: " + column)
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min_value = np.min(series)
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series = (series + min_value).apply(lambda x: np.log(x))
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is_normal_after_log = shapiro(series).pvalue > 0.05
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if not is_normal_after_log:
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print("Failed to normalize column: ", name)
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return series
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