btseytlin

version: '2'
services:
  ansible:
    build: .
    volumes:
      - ./:/ansible/exploitation/
    network_mode: host

btseytlin

def batchnorm_forward(x, gamma, beta, bn_param):
    """
    Forward pass for batch normalization.

    During training the sample mean and (uncorrected) sample variance are
    computed from minibatch statistics and used to normalize the incoming data.
    During training we also keep an exponentially decaying running mean of the
    mean and variance of each feature, and these averages are used to normalize
    data at test-time.

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])

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

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

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

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

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

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