Boris Tseitlin btseytlin

Claude Code Transcript

2026-04-19 23:03

Base directory for this skill: /Users//Documents/Steelman/knowledge-base/.claude/skills/llm-wiki

2026-04-19 23:03

Base directory for this skill: /Users//Documents/Steelman/knowledge-base/.claude/skills/llm-wiki

	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

	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 (

	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 = {

	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(

	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)

	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)

	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')