drift/utils/helpers.py

import pandas as pd
import numpy as np
import os
import string
import random
from typing import Union
from config.config import Config


def get_files_from_dir(path: str) -> list[str]:
    return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]

def get_first_valid_return_index(series: pd.Series) -> int:
    double_nested_results = np.where(np.logical_and(series != 0, np.logical_not(pd.isna(series))))
    if len(double_nested_results) == 0:
        return 0
    nested_result = double_nested_results[0]
    if len(nested_result) == 0:
        return 0
    return nested_result[0]

def has_enough_samples_to_train(X: pd.DataFrame, y: pd.Series, config: Config) -> bool:
    first_valid_index = get_first_valid_return_index(X.iloc[:,0])
    samples_to_train = len(y) - first_valid_index
    return samples_to_train > config.sliding_window_size_base + config.sliding_window_size_meta_labeling + 100

def flatten(list_of_lists: list) -> list:
    return [item for sublist in list_of_lists for item in sublist]

def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.Series:
    if df.shape[1] == 0:
        return df
    mean_df = df.iloc[:,0]
    weights = df.loc[weights_source]

    for i, row in df.iterrows():
        if i == weights_source: continue
        mean_df.loc[i] = (row * weights).sum() / df.loc[weights_source].sum()

    return mean_df

def deduplicate_indexes(df: pd.DataFrame) -> pd.DataFrame: return df[~df.index.duplicated(keep='last')]

def drop_columns_if_exist(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    for column in columns:
        if column in df.columns:
            df = df.drop(column, axis=1)
    return df

def random_string(n: int) -> str:
    return ''.join(random.choices(string.ascii_uppercase + string.digits, k=n))

def equal_except_nan(row: pd.Series):
    if np.isnan(row.iloc[0]) or np.isnan(row.iloc[1]):
        return np.nan
    if row.iloc[0] == row.iloc[1]:
        return 1.
    else:
        return 0.

def drop_until_first_valid_index(df: pd.DataFrame, series: pd.Series) -> tuple[pd.DataFrame, pd.Series]:
    first_valid_index = max(get_first_valid_return_index(df.iloc[:,0]), get_first_valid_return_index(series))
    return df.iloc[first_valid_index:], series.iloc[first_valid_index:]