STAT 39000: Project 10 — Spring 2022

#!/scratch/brown/kamstut/tdm/apps/jupyter/kernels/f2021-s2022/.venv/bin/python

import sys
import argparse
import pandas as pd
import random
import torch
from pathlib import Path
import uuid


class Regression(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.beta0 = torch.nn.Parameter(torch.tensor(5, requires_grad=True, dtype=torch.float))
        self.beta1 = torch.nn.Parameter(torch.tensor(4, requires_grad=True, dtype=torch.float))
        self.beta2 = torch.nn.Parameter(torch.tensor(3, requires_grad=True, dtype=torch.float))

    def forward(self, x):
        return self.beta0 + self.beta1*x + self.beta2*x**2


def get_point_estimates(x_train, y_train, x_test):

    model = Regression()
    learning_rate = .0003

    num_epochs = 10000
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
    mseloss = torch.nn.MSELoss(reduction='mean')

    # resample data
    resampled_idxs = random.choices(range(75), k=75)
    xr_train = torch.tensor(x_train[resampled_idxs], requires_grad=True, dtype=torch.float).reshape(75)

    for _ in range(num_epochs):
        # set to training mode -- note this does not _train_ anything
        model.train()

        # calculate the predictions / forward pass
        y_predictions = model(xr_train)

        # calculate the MSE
        mse = mseloss(y_train[resampled_idxs], y_predictions)

        # calculate the partial derivatives / backwards step
        mse.backward()

        # update our parameters
        optimizer.step()

        # zero out the gradients
        optimizer.zero_grad()

    # get predictions
    predictions = pd.DataFrame(data={"predictions": model(x_test).detach().numpy()})

    return(predictions)


def main():
    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers(help="possible commands", dest="command")
    bootstrap_parser = subparsers.add_parser("bootstrap", help="")
    bootstrap_parser.add_argument("n", type=int, help="number of set of point estimates for predictions to output")
    bootstrap_parser.add_argument("-o", "--output", help="directory to output file(s) to")

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()

    if args.command == "bootstrap":

        dat = pd.read_csv("/depot/datamine/data/sim/train.csv")
        x_train = torch.tensor(dat['x'].to_numpy(), dtype=torch.float)
        y_train = torch.tensor(dat['y'].to_numpy(), dtype=torch.float)

        dat = pd.read_csv("/depot/datamine/data/sim/test.csv")
        x_test = torch.tensor(dat['x'].to_numpy(), dtype=torch.float)

        for _ in range(args.n):
            estimates = get_point_estimates(x_train, y_train, x_test)
            estimates.to_parquet(f"{Path(args.output) / str(uuid.uuid4())}.parquet")

if __name__ == "__main__":
	main()

#!/bin/bash
#SBATCH --account=datamine              # Queue
#SBATCH --job-name=kevinsjob          # Job name
#SBATCH --mail-type=END,FAIL          # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=kamstut@purdue.edu     # Where to send mail
#SBATCH --time=00:30:00
#SBATCH --ntasks=10                   # Number of tasks (total)
#SBATCH -o /dev/null                  # Output to dev null
#SBATCH -e /dev/null                  # Error to dev null

arr=(/depot/datamine/data/coco/unlabeled2017/*)

for((i=0; i < ${#arr[@]}; i+=12500))
do
    part=( "${arr[@]:i:12500}" )
    srun -A datamine --exclusive -n 1 --mem-per-cpu=200 module use /scratch/brown/kamstut/tdm/opt/modulefiles; module load libffi/3.4.2; $HOME/hash1.py hash --output $CLUSTER_SCRATCH/p4output/ ${part[*]} &
done

wait