gnurio

---
name: value-of-information
description: >
  Calculate the Expected Value of Information (EVI / EVPI) to determine whether
  gathering more data before a decision is worth the cost. Use this skill
  whenever the user is deciding whether to do research, run a study, conduct a
  survey, buy a report, or collect more data before acting. Trigger on: "is this
  research worth it?", "should we do market research?", "is the survey worth
  buying?", any mention of EVPI, EVI, EOL, Expected Opportunity Loss, or Value
  of Information, or any cost-benefit question about information gathering. Also

gnurio

import argparse
import json
import logging
import os
import re
import shutil
from concurrent.futures import ProcessPoolExecutor, as_completed
from dataclasses import dataclass
from datetime import datetime
from typing import Any, Callable, Dict, List, Literal, Optional, Tuple

gnurio

import pandas as pd
from sklearn.preprocessing import LabelEncoder,OneHotEncoder

def decode_encode(colname):

    '''
    (str) -> (DataFrame)

    Returns a Data Frame with the column given to it transformed into a One-hot encoded set of columns

gnurio

import pandas as pd
from scipy.stats import mode

grouped_mod = df.pivot_table(values='The metric you need the mode for',columns='Category',aggfunc=mode)