# Stateful transforms¶

There’s a subtle problem that sometimes bites people when working with formulas. Suppose that I have some numerical data called x, and I would like to center it before fitting. The obvious way would be to write:

y ~ I(x - np.mean(x))  # BROKEN! Don't do this!


or, even better we could package it up into a function:

In [1]: def naive_center(x):  # BROKEN! don't use!
...:     x = np.asarray(x)
...:     return x - np.mean(x)
...:


and then write our formula like:

y ~ naive_center(x)


Why is this a bad idea? Let’s set up an example.

In [2]: import numpy as np

In [3]: from patsy import dmatrix, build_design_matrices, incr_dbuilder

In [4]: data = {"x": [1, 2, 3, 4]}


Now we can build a design matrix and see what we get:

In [5]: mat = dmatrix("naive_center(x)", data)

In [6]: mat
Out[6]:
DesignMatrix with shape (4, 2)
Intercept  naive_center(x)
1             -1.5
1             -0.5
1              0.5
1              1.5
Terms:
'Intercept' (column 0)
'naive_center(x)' (column 1)


Those numbers look correct, and in fact they are correct. If all we’re going to do with this model is call dmatrix() once, then everything is fine – which is what makes this problem so insidious.

Often we want to do more with a model than this. For instance, we might find some new data, and want to feed it into our model to make predictions. To do this, though, we first need to reapply the same transformation, like so:

In [7]: new_data = {"x": [5, 6, 7, 8]}

# Broken!
In [8]: build_design_matrices([mat.design_info], new_data)[0]
Out[8]:
DesignMatrix with shape (4, 2)
Intercept  naive_center(x)
1             -1.5
1             -0.5
1              0.5
1              1.5
Terms:
'Intercept' (column 0)
'naive_center(x)' (column 1)


So it’s clear what’s happened here – Patsy has centered the new data, just like it centered the old data. But if you think about what this means statistically, it makes no sense. According to this, the new data point where x is 5 will behave exactly like the old data point where x is 1, because they both produce the same input to the actual model.

The problem is what it means to apply “the same transformation”. Here, what we really want to do is to subtract the mean of the original data from the new data.

Patsy’s solution is called a stateful transform. These look like ordinary functions, but they perform a bit of magic to remember the state of the original data, and use it in transforming new data. Several useful stateful transforms are included out of the box, including one called center().

Using center() instead of naive_center() produces the same correct result for our original matrix. It’s used in exactly the same way:

In [9]: fixed_mat = dmatrix("center(x)", data)

In [10]: fixed_mat
Out[10]:
DesignMatrix with shape (4, 2)
Intercept  center(x)
1       -1.5
1       -0.5
1        0.5
1        1.5
Terms:
'Intercept' (column 0)
'center(x)' (column 1)


But if we then feed in our new data, we also get out the correct result:

# Correct!
In [11]: build_design_matrices([fixed_mat.design_info], new_data)[0]
Out[11]:
DesignMatrix with shape (4, 2)
Intercept  center(x)
1        2.5
1        3.5
1        4.5
1        5.5
Terms:
'Intercept' (column 0)
'center(x)' (column 1)


Another situation where we need some stateful transform magic is when we are working with data that is too large to fit into memory at once. To handle such cases, Patsy allows you to set up a design matrix while working our way incrementally through the data. But if we use naive_center() when building a matrix incrementally, then it centers each chunk of data, not the data as a whole. (Of course, depending on how your data is distributed, this might end up being just similar enough for you to miss the problem until it’s too late.)

In [12]: data_chunked = [{"x": data["x"][:2]},
....:                 {"x": data["x"][2:]}]
....:

In [13]: dinfo = incr_dbuilder("naive_center(x)", lambda: iter(data_chunked))

# Broken!
In [14]: np.row_stack([build_design_matrices([dinfo], chunk)[0]
....:               for chunk in data_chunked])
....:
Out[14]:
array([[ 1. , -0.5],
[ 1. ,  0.5],
[ 1. , -0.5],
[ 1. ,  0.5]])


But if we use the proper stateful transform, this just works:

In [15]: dinfo = incr_dbuilder("center(x)", lambda: iter(data_chunked))

# Correct!
In [16]: np.row_stack([build_design_matrices([dinfo], chunk)[0]
....:               for chunk in data_chunked])
....:
Out[16]:
array([[ 1. , -1.5],
[ 1. , -0.5],
[ 1. ,  0.5],
[ 1. ,  1.5]])


Note

Under the hood, the way this works is that incr_dbuilder() iterates through the data once to calculate the mean, and then we use build_design_matrices() to iterate through it a second time creating our design matrix. While taking two passes through a large data set may be slow, there’s really no other way to accomplish what the user asked for. The good news is that Patsy is smart enough to make only the minimum number of passes necessary. For example, in our example with naive_center() above, incr_dbuilder() would not have done a full pass through the data at all. And if you have multiple stateful transforms in the same formula, then Patsy will process them in parallel in a single pass.

And, of course, we can use the resulting DesignInfo object for prediction as well:

# Correct!
In [17]: build_design_matrices([dinfo], new_data)[0]
Out[17]:
DesignMatrix with shape (4, 2)
Intercept  center(x)
1        2.5
1        3.5
1        4.5
1        5.5
Terms:
'Intercept' (column 0)
'center(x)' (column 1)


In fact, Patsy’s stateful transform handling is clever enough that it can support arbitrary mixing of stateful transforms with other Python code. E.g., if center() and spline() were both stateful transforms, then even a silly a formula like this will be handled 100% correctly:

y ~ I(spline(center(x1)) + center(x2))


However, it isn’t perfect – there are two things you have to be careful of. Let’s put them in red:

Warning

If you are unwise enough to ignore this section, write a function like naive_center above, and use it in a formula, then Patsy will not notice. If you use that formula with incr_dbuilders() or for predictions, then you will just silently get the wrong results. We have a plan to detect such cases, but it isn’t implemented yet (and in any case can never be 100% reliable). So be careful!

Warning

Even if you do use a “real” stateful transform like center() or standardize(), still have to make sure that Patsy can “see” that you are using such a transform. Currently the rule is that you must access the stateful transform function using a simple, bare variable reference, without any dots or other lookups:

dmatrix("y ~ center(x)", data)  # okay
asdf = patsy.center
dmatrix("y ~ asdf(x)", data)  # okay
dmatrix("y ~ patsy.center(x)", data)  # BROKEN! DON'T DO THIS!
funcs = {"center": patsy.center}
dmatrix("y ~ funcs['center'](x)", data)  # BROKEN! DON'T DO THIS!


## Builtin stateful transforms¶

There are a number of builtin stateful transforms beyond center(); see stateful transforms in the API reference for a complete list.

## Defining a stateful transform¶

You can also easily define your own stateful transforms. The first step is to define a class which fulfills the stateful transform protocol. The lifecycle of a stateful transform object is as follows:

1. An instance of your type will be constructed.
2. memorize_chunk() will be called one or more times.
3. memorize_finish() will be called once.
4. transform() will be called one or more times, on either the same or different data to what was initially passed to memorize_chunk(). You can trust that any non-data arguments will be identical between calls to memorize_chunk() and transform().

And here are the methods and call signatures you need to define:

class patsy.stateful_transform_protocol
__init__()

It must be possible to create an instance of the class by calling the constructor with no arguments.

memorize_chunk(*args, **kwargs)

Update any internal state, based on the data passed into memorize_chunk.

memorize_finish()

Do any housekeeping you want to do between the last call to memorize_chunk() and the first call to transform(). For example, if you are computing some summary statistic that cannot be done incrementally, then your memorize_chunk() method might just store the data that’s passed in, and then memorize_finish() could compute the summary statistic and delete the stored data to free up the associated memory.

transform(*args, **kwargs)

This method should transform the input data passed to it. It should be deterministic, and it should be “point-wise”, in the sense that when passed an array it performs an independent transformation on each data point that is not affected by any other data points passed to transform().

Then once you have created your class, pass it to stateful_transform() to create a callable stateful transform object suitable for use inside or outside formulas.

Here’s a simple example of how you might implement a working version of center() (though it’s less robust and featureful than the real builtin):

class MyExampleCenter(object):
def __init__(self):
self._total = 0
self._count = 0
self._mean = None

def memorize_chunk(self, x):
self._total += np.sum(x)
self._count += len(x)

def memorize_finish(self):
self._mean = self.total * 1. / self._count

def transform(self, x):
return x - self._mean

my_example_center = patsy.stateful_transform(MyExampleCenter)
print(my_example_center(np.array([1, 2, 3])))


But of course, if you come up with any useful ones, please let us know so we can incorporate them into patsy itself!