WillianFuks

python3.8 -m venv .env
source .env/bin/activate
pip install django django-sslserver djwto requests
django-admin startproject djwto_project .
python manage.py makemigrations
python manage.py migrate

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# ./djwto_project/urls.py
from django.urls import path, include


urlpatterns = [
    path('', include('djwto.urls')),
]

WillianFuks

import tensorflow_probability as tfp
from causalimpact.misc import standardize


normed_data, mu_sig = standardize(data)
obs_data = normed_data['BTC-USD'].loc[:'2020-10-14'].astype(np.float32)
design_matrix = pd.concat(
    [normed_data.loc[pre_period[0]: pre_period[1]], normed_data.loc[post_period[0]: post_period[1]]]
).astype(np.float32).iloc[:, 1:]
linear_level = tfp.sts.LocalLinearTrend(observed_time_series=obs_data)

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from typing import Dict

import tensorflow_probability as tfp
import matplotlib.pyplot as plt
import numpy as np


def plot_components(index, one_step_dists: Dict[str, tfp.distributions.Distribution],
                    forecast_dists: Dict[str, tfp.distributions.Distribution],
                    mu_sig=None):

WillianFuks

import datetime
import pandas_datareader as pdr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


btc_data = pdr.get_data_yahoo(['BTC-USD'], 
                              start=datetime.datetime(2018, 1, 1), 
                              end=datetime.datetime(2020, 12, 3))['Close']

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observed_stddev, observed_initial = (tf.convert_to_tensor(value=1, dtype=tf.float32),
                                     tf.convert_to_tensor(value=0., dtype=tf.float32))
level_scale_prior = tfd.LogNormal(loc=tf.math.log(0.05 * observed_stddev), scale=1, name='level_scale_prior')
initial_state_prior = tfd.MultivariateNormalDiag(loc=observed_initial[..., tf.newaxis],
                                                 scale_diag=(tf.abs(observed_initial) + observed_stddev)[..., tf.newaxis],
                                                 name='initial_level_prior')
ll_ssm = tfp.sts.LocalLevelStateSpaceModel(100, initial_state_prior=initial_state_prior, level_scale=level_scale_prior.sample())
ll_ssm_sample = np.squeeze(ll_ssm.sample().numpy())

x0 = 100 * np.random.rand(100)

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import pandas as pd
from causalimpact import CausalImpact


data = pd.read_csv('https://raw.githubusercontent.com/WillianFuks/tfcausalimpact/master/tests/fixtures/arma_data.csv')[['y', 'X']]
data.iloc[70:77, 0] += np.arange(7, 0, -1)
pre_period = [0, 69]
post_period = [70, 99]

ci = CausalImpact(data, pre_period, post_period)

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow_probability as tfp


x = np.random.rand(30)
y = 2.2 * x + np.random.rand(30)
data = pd.DataFrame({'X': x, 'y': y}, dtype=np.float32)
obs_data = data['y'].iloc[:20]

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dist = tfd.JointDistributionNamed(dict(
    Operation=tfd.LogNormal(1, 0.5),
    Marketing=tfd.LogNormal(1, 0.5),
    Sales=lambda Operation, Marketing: (
        tfd.Normal(tf.abs(Operation) * 1.2 + tf.abs(Marketing) * 1.4, 0.5)
    )
))
df = pd.DataFrame(dist.sample(1000))

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