mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import pandas as pd
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import numpy as np
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import os
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import string
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import random
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from itertools import dropwhile
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def get_files_from_dir(path: str) -> list[str]:
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return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]
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def get_first_valid_return_index(series: pd.Series) -> int:
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double_nested_results = np.where(np.logical_and(series != 0, np.logical_not(pd.isna(series))))
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if len(double_nested_results) == 0:
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return 0
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nested_result = double_nested_results[0]
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if len(nested_result) == 0:
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return 0
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return nested_result[0]
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def get_last_non_na_index(series: pd.Series, index: int) -> int:
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return next(dropwhile(lambda x: pd.isna(x[1]), enumerate(reversed(series[:index+1]))))[0]
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def flatten(list_of_lists: list) -> list:
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return [item for sublist in list_of_lists for item in sublist]
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def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.Series:
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if df.shape[1] == 0:
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return df
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mean_df = df.iloc[:,0]
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weights = df.loc[weights_source]
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for i, row in df.iterrows():
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if i == weights_source: continue
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mean_df.loc[i] = (row * weights).sum() / df.loc[weights_source].sum()
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return mean_df
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def drop_columns_if_exist(df: pd.DataFrame, columns: list) -> pd.DataFrame:
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for column in columns:
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if column in df.columns:
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df = df.drop(column, axis=1)
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return df
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def random_string(n: int) -> str:
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return ''.join(random.choices(string.ascii_uppercase + string.digits, k=n))
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def equal_except_nan(row: pd.Series):
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if np.isnan(row.iloc[0]) or np.isnan(row.iloc[1]):
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return np.nan
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if row.iloc[0] == row.iloc[1]:
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return 1.
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else:
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return 0.
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def drop_until_first_valid_index(df: pd.DataFrame, series: pd.Series) -> tuple[pd.DataFrame, pd.Series]:
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first_valid_index = max(get_first_valid_return_index(df.iloc[:,0]), get_first_valid_return_index(series))
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return df.iloc[first_valid_index:], series.iloc[first_valid_index:] |