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probml
GitHub Repository: probml/pyprobml
 Path: blob/master/deprecated/notebooks/causal_impact_tfp.ipynb
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Kernel: Python 3

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Setup

!pip install tfcausalimpact
Collecting tfcausalimpact Downloading tfcausalimpact-0.0.9.tar.gz (34 kB) Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from tfcausalimpact) (2.11.3) Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from tfcausalimpact) (1.1.5) Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (from tfcausalimpact) (2.7.0) Requirement already satisfied: tensorflow-probability>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from tfcausalimpact) (0.15.0) Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from tfcausalimpact) (3.2.2) Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>=0.14.0->tfcausalimpact) (1.3.0) Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>=0.14.0->tfcausalimpact) (1.19.5) Requirement already satisfied: 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%matplotlib inline

import sys
import os


# sys.path.append(os.path.abspath('../'))


import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_probability as tfp
import matplotlib.pyplot as plt
from causalimpact import CausalImpact


tfd = tfp.distributions
plt.rcParams["figure.figsize"] = [15, 10]

Data

# This is modified from an example presented in Google's R code.
# https://google.github.io/CausalImpact/CausalImpact.html#creating-an-example-dataset
# The true generative process is y = 1.2 * x0 + 0.8 * x1 + 0 * x2 + random_walk
# where x2 is an irrelevant input variable.
# We simulate the impact of an intervention at I=70 by adding an offset of 10 (decreasing to 0) to the outcome y.

T = 100
I = 70

observed_stddev, observed_initial = (
    tf.convert_to_tensor(value=0.1, dtype=tf.float32),
    tf.convert_to_tensor(value=0.0, dtype=tf.float32),
)
level_scale_prior = tfd.LogNormal(loc=tf.math.log(0.0001 * observed_stddev), scale=1, name="level_scale_prior")
# 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(
    T, initial_state_prior=initial_state_prior, level_scale=level_scale_prior.sample()
)
ll_ssm_sample = np.squeeze(ll_ssm.sample().numpy())


np.random.seed(0)
x0 = 1 * np.random.rand(T)
x1 = 2 * np.random.rand(T)
x2 = 3 * np.random.rand(T)

y = 2 * x0 + 3 * x1 + ll_ssm_sample

# intervention
offset = 5
# y[I:] += offset
duration = 10
y[I : (I + duration)] += np.linspace(offset, 0, num=duration)  # decreasing effect

data = pd.DataFrame({"x0": x0, "x1": x1, "x2": x2, "y": y}, columns=["y", "x0", "x1", "x2"])
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data.plot()
plt.axvline(I - 1, linestyle="--", color="k")
plt.legend()
plt.savefig("causal-impact-data.pdf")
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Fit default model

pre_period = [0, I - 1]
post_period = [I, T - 1]
ci = CausalImpact(data, pre_period, post_period)
WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass. 
print(ci.model_samples.keys())
# We sample 100 times from the variational posterior of each parameter
# https://github.com/WillianFuks/tfcausalimpact/blob/master/causalimpact/model.py#L378

for name, values in ci.model_samples.items():
    print(f"{name}: {values.numpy().mean(axis=0)}")
odict_keys(['observation_noise_scale', 'LocalLevel/_level_scale', 'SparseLinearRegression/_global_scale_variance', 'SparseLinearRegression/_global_scale_noncentered', 'SparseLinearRegression/_local_scale_variances', 'SparseLinearRegression/_local_scales_noncentered', 'SparseLinearRegression/_weights_noncentered']) observation_noise_scale: 0.34998977184295654 LocalLevel/_level_scale: 0.010700251907110214 SparseLinearRegression/_global_scale_variance: 1.5136687755584717 SparseLinearRegression/_global_scale_noncentered: 1.5066444873809814 SparseLinearRegression/_local_scale_variances: [1.0874447 2.730983 0.8268008] SparseLinearRegression/_local_scales_noncentered: [1.2025315 1.7309521 0.44093436] SparseLinearRegression/_weights_noncentered: [ 1.4927632 1.945029 -0.10319362] 
ci.plot(show=False)
fig = plt.gcf()
fig.savefig("causal-impact-inferences.pdf")
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print(ci.summary())
Posterior Inference {Causal Impact} Average Cumulative Actual 4.21 126.28 Prediction (s.d.) 3.34 (0.18) 100.14 (5.26) 95% CI [3.0, 3.69] [90.03, 110.63] Absolute effect (s.d.) 0.87 (0.18) 26.14 (5.26) 95% CI [0.52, 1.21] [15.65, 36.25] Relative effect (s.d.) 26.1% (5.25%) 26.1% (5.25%) 95% CI [15.63%, 36.2%] [15.63%, 36.2%] Posterior tail-area probability p: 0.0 Posterior prob. of a causal effect: 100.0% For more details run the command: print(impact.summary('report'))