Converting Dataframes to PyTrees¶

Numpyro models expect data in the form of a pytrees: nested lists or dictionaries of multidimensional arrays. But datasets usually don't start that way: we usually start by processing them as a DataFrame. How do we convert one representation to another?

Let's make this more concrete. Consider the following dataset:

In [1]:
import os
os.environ["JAX_PLATFORMS"] = "cpu"
import numpyro
numpyro.set_host_device_count(4)
import numpyro.distributions as dist
from numpyro import handlers
import pandas as pd
import numpy as np
import pyro_util
import arviz as az
In [22]:
df = pd.DataFrame({'a': [1,2,1,2,1], 'b': [0,0,1,1,2], 'c': np.random.randn(5), 'd': np.random.randn(5)})
In [23]:
df
Out[23]:
a b c d
0 1 0 -0.006010 -0.364595
1 2 0 -0.065718 -0.680357
2 1 1 -1.602073 -0.677900
3 2 1 0.216857 -0.128295
4 1 2 -1.128998 1.151890

Let's say we assume that there's a different latent mean for each value of $a$. We might write the following plated model:

In [24]:
def model(c, d, mask):
    with numpyro.plate("a", 2):
        means = numpyro.sample("mu", dist.Normal(0, 10))
        with numpyro.plate("b", 3):
            numpyro.sample("c", dist.Normal(means, 1), obs=c, obs_mask=mask)
            numpyro.sample("d", dist.Normal(means, 1), obs=d, obs_mask=mask)

But we don't have the c, d, and mask tensors needed for this model yet. We have to convert the dataframe into the appropriate pair of multidimensional arrays.

First, we can group the dataframe by the plate names.

In [25]:
cols = ['c', 'd']
In [26]:
plates = ['a', 'b']
In [27]:
grouped = df.groupby(plates)[cols].first()

Next, we have to add NaN entries to the dataframe to give it a full rectangular shape.

In [28]:
gdf = grouped.reindex(index=pd.MultiIndex.from_product(grouped.index.levels))
In [29]:
gdf
Out[29]:
c d
a b
1 0 -0.006010 -0.364595
1 -1.602073 -0.677900
2 -1.128998 1.151890
2 0 -0.065718 -0.680357
1 0.216857 -0.128295
2 NaN NaN

Now, we can reshape the dataframe's values into a multidimensional array.

In [30]:
reshaped = np.reshape(gdf.values, (*gdf.index.levshape, len(cols)))

Finally, we can split the array into the two columns we need.

In [31]:
list(reshaped.transpose())
Out[31]:
[array([[-0.00601012, -0.06571758],
        [-1.60207272,  0.21685683],
        [-1.12899816,         nan]]),
 array([[-0.36459546, -0.68035653],
        [-0.67789992, -0.12829523],
        [ 1.15189047,         nan]])]

We can generate the obs_mask for numpyro by checking for nan values. Together, this gives us the functions below (which also handle the special case of a single group).

In [32]:
def to_tensor(df: pd.DataFrame, cols: list[str], groups: list[str]):
    """Convert `df` to a tensor for which every dimension but the last enumerates possible values
    of a different covariate in `groups`. Also returns the associated index.
    """
    gdf = df.groupby(groups)[cols].first()
    if len(groups) > 1:
        ix = pd.MultiIndex.from_product(gdf.index.levels)
        gdf = gdf.reindex(index=ix)
        shape = gdf.index.levshape
    else:
        shape = gdf.index.shape
    return np.reshape(gdf.values, (*shape, -1)), gdf.index
In [33]:
def to_pytree(df: pd.DataFrame, cols: list[str], groups: list[str]):
    """Convert `df` into a list of multidimensional arrays: one per element of
   `cols`. The dimensions of each result tensor enumerate over possible values of
   the index variables in `groups`. The final element of the returned list is a
   mask picking out values in the original dataframe. This is useful when specifying
   `obs` and `obs_mask` in `numpyro.sample` statements.
   """
    reshaped, _ = to_tensor(df, cols, groups)
    results = list(reshaped.transpose())
    results.append(~np.isnan(results[0]))
    return results

Using it looks like this:

In [34]:
to_pytree(df, cols, plates)
Out[34]:
[array([[-0.00601012, -0.06571758],
        [-1.60207272,  0.21685683],
        [-1.12899816,         nan]]),
 array([[-0.36459546, -0.68035653],
        [-0.67789992, -0.12829523],
        [ 1.15189047,         nan]]),
 array([[ True,  True],
        [ True,  True],
        [ True, False]])]
In [35]:
mcmc = pyro_util.fit_nuts(model, *to_pytree(df, cols, plates))

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In [36]:
result = az.from_numpyro(mcmc)

As expected, the posterior values of mu center around zero.

In [37]:
az.plot_posterior(result, var_names=['mu']);
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For future posts, I'll be importing this to_pytree function from my pyro_util module.