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Library for compiling trained SKLearn models into abstract expressions suitable for further compilation into executable code in various languages.

Project description

SKompiler: Translate trained SKLearn models to executable code in other languages

The package provides a tool for transforming trained SKLearn models into other forms, such as SQL queries, Excel formulas or Sympy expressions (which, in turn, can be translated to code in a variety of languages, such as C, Javascript, Rust, Julia, etc).


The simplest way to install the package is via pip:

$ pip install SKompiler[full]

Note that the [full] option includes the installations of sympy, sqlalchemy and astor, which are necessary if you plan to convert SKompiler's expressions to sympy expressions (which, in turn, can be compiled to many other languages) or to SQLAlchemy expressions (which can be further translated to different SQL dialects) or to Python source code. If you do not need this functionality (say, you only need the raw SKompiler expressions or perhaps only the SQL conversions without the sympy ones), you may avoid the forced installation of all optional dependencies by simply writing

$ pip install SKompiler

(you are free to install any of the required extra dependencies, via separate calls to pip install, of course)


Introductory example

Let us start by walking through a simple example. We begin by training a model on a simple dataset, e.g.:

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
X, y = load_iris(True)
m = RandomForestClassifier(n_estimators=3, max_depth=3).fit(X, y)

Suppose we need to express the logic of m.predict in SQLite. Here is how we can achieve that:

from skompiler import skompile
expr = skompile(m.predict)
sql ='sqlalchemy/sqlite')

Voila, the value of the sql variable is a super-long expression which looks like

CASE WHEN ((CASE WHEN (x3 <= 2.449999988079071) THEN 1.0 ELSE CASE WHEN
... 100 lines or so ...
THEN 1 ELSE 2 END as y

It corresponds to the m.predict computation. Let us check how we can use it in a query. We import the data into an in-memory SQLite database:

import sqlalchemy as sa
import pandas as pd
conn = sa.create_engine('sqlite://').connect()
df = pd.DataFrame(X, columns=['x1', 'x2', 'x3', 'x4']).reset_index()
df.to_sql('data', conn)

query the data using the generated expression:

results = pd.read_sql('select {0} from data'.format(sql), conn)

and verify that the results match:

assert (results.values.ravel() == m.predict(X).ravel()).all()

Note that the generated SQL expression uses names x1, x2, x3 and x4 to refer to the input variables. You may choose different input variable names by writing:

skompile(m.predict, ['a', 'b', 'c', 'd']).to('sqlalchemy/sqlite')

Multiple outputs

Now let us try to generate code for m.predict_proba:

expr = skompile(m.predict_proba)'sqlalchemy/sqlite')

The generated query is different from the previous one. Firstly, it is of the form

... as y1, ... as y2, ... as y3

The reason for that is that m.predict_proba produces three values - the probabilities of each class, and this is reflected in the SQL. You may, of course, provide different names to the outputs instead of y1,y2,y3:'sqlalchemy/sqlite', assign_to=['a','b','c'])

You may obtain a list of three separate expressions without the as .. parts at all:'sqlalchemy/sqlite', assign_to=None)

or request only the probability of the first class as a single ... as y2 expression:'sqlalchemy/sqlite', component=1, assign_to='y2')

Multi-stage code

You might have noted that the SQL code for predict was significantly longer than the code for predict_proba. Why so? Because

predict(x) = argmax(predict_proba(x))

There is, however, no single argmax function in SQL, hence it has to be faked using approximately the following logic:

predict(x) = if predict_proba(x)[0] == max(predict_proba(x)) then 0
                else if predict_proba(x)[1] == max(predict_proba(x)) then 1
                else 2

Note that the values of predict_proba in this expression must be expanded (and thus the computation repeated) multiple times. This problem could be overcome by performing computation in several steps, saving and reusing intermediate values, rather than doing everything within a single expression. In SQL this can bbe done with the help of with expressions:

with proba as (
     select [predict_proba computation] from data
max as (
     select [max computation] from proba
argmax as (
     select [argmax computation] from ...

To generate this type of SQL, specify multistage=True:'sqlalchemy/sqlite', multistage=True,

Note that while in single-expression mode you only get a single column expression, which you need to wrap in the relevant SELECT .. FROM .. statement, in multistage mode the whole query is generated for you. For that reason you need to provide the name of the source table as well as the key column.

The effect on the query size can be quite significant:

> 15558
len('sqlalchemy/sqlite', multistage=True))
> 2574

The multi-stage translation is especially important if you need to generate Excel code, because Excel does not support formulas longer than 8196 characters. If you need to port complex models, splitting them up is therefore the only way. For fun's sake, let us work through an example.

Suppose we have a decently complicated model, which would not fit into a cell as a single expression:

m = RandomForestClassifier(n_estimators=500, max_depth=10).fit(X, y)
import sys
> 934431

How can we evaluate it via Excel? Start by copying our sample data to clipboard and pasting it into Excel:


When I paste this into a new worksheet, the first row of the data occupies cells C2, D2, E2 and F2. We need to take this information when compiling the model:

expr = skompile(m.predict, ['C2','D2','E2','F2'])

Now let us generate a multistage Excel computation, putting the first intermediate result into the cell H2, the second into I2, etc along the row until whatever cell will take the final prediction output:

from skompiler.fromskast.excel import excel_row_generator
import sys

code ='excel', multistage=True, assign_to=excel_row_generator('H', 2))

Note that we need to increase the system recursion limit in order to process large expressions. The resulting code object is a dictionary, mapping cell names to the corresponding expressions:

for k, v in code.items():
    print(f'{k}={v[0:20]}... ({len(v)} bytes)')

outputs a long-ish sequence of cells that we need to fill now in the second row:

H2=((((((((((((((((((((... (7965 bytes)
I2=((((((((((((((((((((... (7965 bytes)
AZ2=((((((((((((((((((((... (6139 bytes)
BA2=MAX(AX2,AY2,AZ2)... (16 bytes)
BB2=IF(AX2=BA2,0,IF(AY2=... (29 bytes)

We can use the clipboard to help us paste the formulas:

formulas = pd.DataFrame({k: [f'={v}'] for k, v in code.items()})

Now select the cell G1 in the worksheet (right next to the data) and use the following keyboard incantation to paste and "drag" the formulas:

Ctrl+V, Left, Ctrl+Down, Right, F, Ctrl+Up, Ctrl+Shift+Down, Ctrl+Shift+Right, Ctrl+D

(of course, you may also fill the cells in any other way, but this one is amusing, isn't it?). And here you are - the column BB now contains the predictions of the random forest model, all computed in pure Excel.

Other formats

By changing the first parameter of the .to() call you may produce output in a variety of other formats besides SQLite:

  • sqlalchemy: raw SQLAlchemy expression (which is a dialect-independent way of representing SQL). Jokes aside, SQL is sometimes a totally valid choice for deploying models into production.

    Note that generated SQL may (depending on the chosen method) include functions exp and log. If you work with SQLite, bear in mind that these functions are not supported out of the box and need to be added separately via create_function. You can find an example of how this can be done in tests/ in the package source code.

  • sqlalchemy/<dialect>: SQL string in any of the SQLAlchemy-supported dialects (firebird, mssql, mysql, oracle, postgresql, sqlite, sybase). This is a convenience feature for those who are lazy to figure out how to compile raw SQLAlchemy to actual SQL.

  • excel: Excel formula. Ever tried dragging a random forest equation down along the table? Fun! Due to its 8196-character limit on the formula length, however, Excel will not handle forests larger than n_estimators=30 with max_depth=5 or so, unfortunately.

  • sympy: A SymPy expression. Ever wanted to take a derivative of your model symbolically?

  • sympy/<lang>: Code in the language <lang>, generated via SymPy. Supported values for <lang> are c, cxx, rust, fortran, js, r, julia, mathematica, octave. Note that the quality of the generated code varies depending on the model, language and the value of the assign_to parameter. Again, this is just a convenience feature, you will get more control by dealing with sympy oode printers manually.

  • python: Python syntax tree (the same you'd get via ast.parse). This (and the following three options) are mostly useful for debugging and testing.

  • python/code: Python source code. The generated code will contain references to custom functions, such as __argmax__, __sigmoid__, etc. To execute the code you will need to provide these in the locals dictionary. See skompiler.fromskast.python._eval_vars.

  • python/lambda: Python callable function (primarily useful for debugging and testing). Equivalent to calling expr.lambdify().

  • string: string, equivalent to str(expr).

How it works

The skompile procedure translates a given method into an intermediate syntactic representation (called SKompiler AST or SKAST). This representation uses a limited number of operations so it is reasonably simple to translate it into other forms.

It is important to understand the following:

  • So far this has been a fun mostly single-weekend project, hence the "compilation" of models into SKAST was only implemented for linear models, decision trees, random forest and gradient boosting.

  • In principle, SKAST's utility is not limited to sklearn models. Anything you translate into SKAST becomes automatically compileable to whatever output backends are implemented in SKompiler. Generating SKAST is rather straightforward:

    from skompiler import ast
    expr = ast.BinOp(ast.Add(), ast.Identifier('x'), ast.NumberConstant(1))'sqlalchemy/sqlite', 'result')
    > x + 1 as result

    Simpler expressions can be generated from strings:

    from skompiler.toskast.string import translate as fromstring
    fromstring('10 * (x + 1)')

    Conversely, you can use repr(expr) on any SKAST expression to dump its unformatted internal representation.

  • As noted above, skompiler transforms models into expressions, and this may result in fairly lengthy outputs with repeated subexpressions, unless the translation is performed in a "multistage" manner. The multistage translation is currently only implemented for SQL and Excel, however.

  • For larger models (say, a random forest or a gradient boosted model with 500+ trees) the resulting SKAST expression tree may become deeper than Python's default recursion limit of 1000. As a result you will get a RecursionError when trying to traslate the model. To alleviate this, raise the system recursion limit to sufficiently high value:

    import sys


If you plan to develop or debug the package, consider installing it by running:

$ pip install -e .[dev]

from within the source distribution. This will install the package in "development mode" and include extra dependencies, useful for development.

You can then run the tests by typing

$ py.test

at the root of the source distribution.


Feel free to contribute or report issues via Github:

Copyright & License

Copyright: 2018, Konstantin Tretyakov. License: MIT

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