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Update readme example #7358

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Jun 15, 2024
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Update readme example #7358

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10a91a2
Add minimal pymc example
HarshvirSandhu Apr 26, 2024
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Add experiment background info
HarshvirSandhu May 2, 2024
7f66672
Update variable names
HarshvirSandhu May 2, 2024
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Update variable names
HarshvirSandhu May 7, 2024
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Add summary outputs
HarshvirSandhu May 12, 2024
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Update example outputs and test that it works
kiramclean Jun 15, 2024
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Update variable names
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@HarshvirSandhu
HarshvirSandhu committed May 2, 2024
commit 7f66672cf8b6df8b4f756682a6509fe8aa57eeb2
36 changes: 17 additions & 19 deletions README.rst
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Expand Up @@ -37,11 +37,10 @@ Features
Linear Regression Example
==========================

**Background**

Plant growth can be influenced by multiple factors, and understanding these relationships is crucial for optimizing agricultural practices.

In this experiment, we aim to predict the growth of a plant based on three different environmental variables.
Imagine we conduct an experiment to predict the growth of a plant based on different environmental variables.

.. code-block:: python

Expand All @@ -53,9 +52,9 @@ In this experiment, we aim to predict the growth of a plant based on three diffe
x_data = pm.draw(x_dist, random_seed=seed)

# Independent Variables:
# Sunlight Hours (X1): Number of hours the plant is exposed to sunlight daily.
# Water Amount (X2): Daily water amount given to the plant (in milliliters).
# Soil Nitrogen Content (X3): Percentage of nitrogen content in the soil.
# Sunlight Hours: Number of hours the plant is exposed to sunlight daily.
# Water Amount: Daily water amount given to the plant (in milliliters).
# Soil Nitrogen Content: Percentage of nitrogen content in the soil.


# Dependent Variable:
Expand All @@ -65,7 +64,7 @@ In this experiment, we aim to predict the growth of a plant based on three diffe
# Define coordinate values for all dimensions of the data
coords={
"trial": range(100),
"features": ["X1", "X2", "X3"],
"features": ["sunlight hours", "water amount", "soil nitrogen"],
}

# Define generative model
Expand All @@ -80,7 +79,7 @@ In this experiment, we aim to predict the growth of a plant based on three diffe
mu = x @ betas

# Likelihood
y = pm.Normal("y", mu, sigma, dims=["trial"])
plant_growth = pm.Normal("plant growth (z-scored)", mu, sigma, dims="trial")


# Generating data from model by fixing parameters
Expand All @@ -90,11 +89,11 @@ In this experiment, we aim to predict the growth of a plant based on three diffe
}
with pm.do(generative_model, fixed_parameters) as synthetic_model:
idata = pm.sample_prior_predictive(random_seed=seed) # Sample from prior predictive distribution.
synthetic_y = idata.prior["y"].sel(draw=0, chain=0)
synthetic_y = idata.prior["plant growth (z-scored)"].sel(draw=0, chain=0)


# Infer parameters conditioned on observed data
with pm.observe(generative_model, {"y": synthetic_y}) as inference_model:
with pm.observe(generative_model, {"plant growth (z-scored)": synthetic_y}) as inference_model:
idata = pm.sample(random_seed=seed)

summary = pm.stats.summary(idata, var_names=["betas", "sigma"]))
Expand All @@ -103,15 +102,14 @@ In this experiment, we aim to predict the growth of a plant based on three diffe

From the summary, we can see that the mean of the inferred parameters are very close to the fixed parameters

=================== ====== ===== ======== ========= =========== ========= ========== ========== =======
Params mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat
=================== ====== ===== ======== ========= =========== ========= ========== ========== =======
betas[X1] 4.972 0.054 4.866 5.066 0.001 0.001 3003 1257 1
betas[X2] 19.963 0.051 19.872 20.062 0.001 0.001 3112 1658 1
betas[X3] 1.994 0.055 1.899 2.107 0.001 0.001 3221 1559 1
sigma 0.511 0.037 0.438 0.575 0.001 0 2945 1522 1
=================== ====== ===== ======== ========= =========== ========= ========== ========== =======

===================== ====== ===== ======== ========= =========== ========= ========== ========== =======
Params mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat
===================== ====== ===== ======== ========= =========== ========= ========== ========== =======
betas[sunlight hours] 4.972 0.054 4.866 5.066 0.001 0.001 3003 1257 1
betas[water amount] 19.963 0.051 19.872 20.062 0.001 0.001 3112 1658 1
betas[soil nitrogen] 1.994 0.055 1.899 2.107 0.001 0.001 3221 1559 1
sigma 0.511 0.037 0.438 0.575 0.001 0 2945 1522 1
===================== ====== ===== ======== ========= =========== ========= ========== ========== =======

.. code-block:: python

Expand All @@ -137,7 +135,7 @@ sigma 0.511 0.037 0.438 0.575 0.001 0
with pm.do(
inference_model,
{inference_model["betas"]: inference_model["betas"] * [1, 1, 0]},
) as heat_death_model:
) as new_model:
new_predictions = pm.sample_posterior_predictive(
idata,
predictions=True,
Expand Down