btseytlin

import os
import base64
import shlex
from pathlib import Path
from dataclasses import dataclass
from typing import Any

import click
import runpod
from dotenv import load_dotenv, dotenv_values, find_dotenv

btseytlin

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
import transformers
from datasets import load_dataset
from torchvision.transforms import (

btseytlin

btseytlin

from sir_models.utils import eval_on_select_dates_and_k_days_ahead
from sir_models.utils import smape
from sklearn.metrics import mean_absolute_error

K = 30
last_day = train_subset.date.iloc[-1] - pd.to_timedelta(K, unit='D')
eval_dates = pd.date_range(start='2020-06-01', end=last_day)[::20]

def eval_hidden_moscow(train_df, t, train_t, eval_t):
    weights = {

btseytlin

from sir_models.fitters import HiddenCurveFitter
from sir_models.models import SEIRHidden
stepwize_size = 60
weights = {
    'I': 0.25,
    'R': 0.25,
    'D': 0.5,
}
model = SEIRHidden(stepwise_size=stepwize_size)
fitter = HiddenCurveFitter(

btseytlin

def smape_resid_transform(true, pred, eps=1e-5):
    return (true - pred) / (np.abs(true) + np.abs(pred) + eps)

class HiddenCurveFitter(BaseFitter):
...
    def residual(self, params, t_vals, data, model):
        model.params = params

        initial_conditions = model.get_initial_conditions(data)

btseytlin

def sigmoid(x, xmin, xmax, a, b, c, r):
    x_scaled = (x - xmin) / (xmax - xmin)
    out = (a * np.exp(c * r) + b * np.exp(r * x_scaled)) / (np.exp(c * r) + np.exp(x_scaled * r))
    return out


def stepwise_soft(t, coefficients, r=20, c=0.5):
    t_arr = np.array(list(coefficients.keys()))

    min_index = np.min(t_arr)

btseytlin

model = BarebonesSEIR()
model.params = model.get_fit_params()
train_initial_conditions = model.get_initial_conditions(train_subset)
train_t = np.arange(len(train_subset))
(S, E, I, R, D) = model.predict(train_t, train_initial_conditions)
plt.figure(figsize=(10, 7))
plt.plot(train_subset.date, train_subset['total_dead'], label='ground truth')
plt.plot(train_subset.date, D, label='predicted', color='black', linestyle='dashed' )
plt.legend()
plt.title('Total deaths')

btseytlin

class BarebonesSEIR:
    def __init__(self, params=None):
        self.params = params

    def get_fit_params(self):
        params = lmfit.Parameters()
        params.add("population", value=12_000_000, vary=False)
        params.add("epidemic_started_days_ago", value=10, vary=False)
        params.add("r0", value=4, min=3, max=5, vary=True)
        params.add("alpha", value=0.0064, min=0.005, max=0.0078, vary=True)  # CFR

btseytlin

df_smoothed = df.rolling(7).mean().round(5)
df_smoothed.columns = [col + '_ma7' for col in df_smoothed.columns]

full_df = pd.concat([df, df_smoothed], axis=1)
for column in full_df.columns:
    if column.endswith('_ma7'):
        original_column = column.strip('_ma7')
        full_df[column] = full_df[column].fillna(full_df[original_column])