utils/forecasting_utils.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 2024-07-19 20:32:30 Friday

@author: Nikhil Kapila
"""

import numpy as np
import pandas as pd

def sliding_windows(data:pd.DataFrame, lookback:int=30)->tuple[np.array, np.array, pd.Index]:
  X, y, timestamps = [], [], []
  for i in range(len(data) - lookback):
    X.append(data.iloc[i:i + lookback].values)
    y.append(data.iloc[i + lookback])
    timestamps.append(data.index[i + lookback])
  return np.array(X), np.array(y), pd.Index(timestamps)

def resampler(df, time='h'):
  times = ['h', 'm', 'd']
  if time not in times: raise ValueError
  return df.resample(time).sum()

def df_from_np(values, timestamps, value_col='predicted'):
   if len(values.shape) == 2: values1 = values.flatten()
   return pd.DataFrame({
     'timestamp': timestamps, 
     f'{value_col}': values
   })