Creating a new FExpr

The majority of functions available from datatable module are implemented via the FExpr mechanism. These functions have the same common API: they accept one or more FExprs (or fexpr-like objects) as arguments and produce an FExpr as the output. The resulting FExprs can then be used inside the DT[...] call to apply these expressions to a particular frame.

In this document we describe how to create such FExpr-based function. In particular, we describe adding the gcd(a, b) function for computing the greatest common divisor of two integers.

C++ “backend” class

The core of the functionality will reside within a class derived from the class dt::expr::FExpr. So let’s create the file expr/ and declare the skeleton of our class:

#include "expr/fexpr_func.h" #include "expr/eval_context.h" #include "expr/workframe.h" namespace dt { namespace expr { class FExpr_Gcd : public FExpr_Func { private: ptrExpr a_; ptrExpr b_; public: FExpr_Gcd(ptrExpr&& a, ptrExpr&& b) : a_(std::move(a)), b_(std::move(b)) {} std::string repr() const override; Workframe evaluate_n(EvalContext& ctx) const override; }; }}

In this example we are inheriting from FExpr_Func, which is a slightly more specialized version of FExpr.

You can also see that the two arguments in gcd(a, b) are stored within the class as ptrExpr a_, b_. This ptrExpr is actually a typedef for std::shared_ptr<FExpr>, which means that arguments to our FExpr are also FExprs.

The first method that needs to be implemented is repr(), which is more-or-less equivalent to python’s __repr__. The returned string should not have the name of the class in it, instead it must be ready to be combined with reprs of other expressions:

std::string repr() const override { std::string out = "gcd("; out += a_->repr(); out += ", "; out += b_->repr(); out += ')'; return out; }

We construct our repr out of reprs of a_ and b_. They are joined with a comma, which has the lowest precedence in python. For some other FExprs we may need to take into account the precedence of the arguments as well, in order to properly set up parentheses around subexpressions.

The second method to implement is evaluate_n(). The _n suffix here stands for “normal”. If you look into the source of FExpr class, you’ll see that there are other evaluation methods too: evaluate_i(), evaluate_j(), etc. However all of those are not needed when implementing a simple function.

The method evaluate_n() takes an EvalContext object as the argument. This object contains information about the current evaluation environment. The output from evaluate_n() should be a Workframe object. A workframe can be thought of as a “work-in-progress” frame. In our case it is sufficient to treat it as a simple vector of columns.

We begin implementing evaluate_n() by evaluating the arguments a_ and b_ and then making sure that those frames are compatible with each other (i.e. have the same number of columns and rows). After that we compute the result by iterating through the columns of both frames and calling a simple method evaluate1(Column&&, Column&&) (that we still need to implement):

Workframe evaluate_n(EvalContext& ctx) const override { Workframe awf = a_->evaluate_n(ctx); Workframe bwf = b_->evaluate_n(ctx); if (awf.ncols() == 1) awf.repeat_column(bwf.ncols()); if (bwf.ncols() == 1) bwf.repeat_column(awf.ncols()); if (awf.ncols() != bwf.ncols()) { throw TypeError() << "Incompatible number of columns in " << repr() << ": the first argument has " << awf.ncols() << ", while the " << "second has " << bwf.ncols(); } awf.sync_grouping_mode(bwf); auto gmode = awf.get_grouping_mode(); Workframe outputs(ctx); for (size_t i = 0; i < awf.ncols(); ++i) { Column rescol = evaluate1(awf.retrieve_column(i), bwf.retrieve_column(i)); outputs.add_column(std::move(rescol), std::string(), gmode); } return outputs; }

The method evaluate1() will take a pair of two columns and produce the output column containing the result of gcd(a, b) calculation. We must take into account the stypes of both columns, and decide which stypes are acceptable for our function:

Column evaluate1(Column&& a, Column&& b) const { SType stype1 = a.stype(); SType stype2 = b.stype(); SType stype0 = common_stype(stype1, stype2); switch (stype0) { case SType::BOOL: case SType::INT8: case SType::INT16: case SType::INT32: return make<int32_t>(std::move(a), std::move(b), SType::INT32); case SType::INT64: return make<int64_t>(std::move(a), std::move(b), SType::INT64); default: throw TypeError() << "Invalid columns of types " << stype1 << " and " << stype2 << " in " << repr(); } } template <typename T> Column make(Column&& a, Column&& b, SType stype0) const { a.cast_inplace(stype0); b.cast_inplace(stype0); return Column(new Column_Gcd<T>(std::move(a), std::move(b))); }

As you can see, the job of the FExpr_Gcd class is to produce a workframe containing one or more Column_Gcd virtual columns. This is where the actual calculation of GCD values will take place, and we shall declare this class too. It can be done either in a separate file in the core/column/ folder, or inside the current file expr/

#include "column/virtual.h" template <typename T> class Column_Gcd : public Virtual_ColumnImpl { private: Column acol_; Column bcol_; public: Column_Gcd(Column&& a, Column&& b) : Virtual_ColumnImpl(a.nrows(), a.stype()), acol_(std::move(a)), bcol_(std::move(b)) { xassert(acol_.nrows() == bcol_.nrows()); xassert(acol_.stype() == bcol_.stype()); xassert(compatible_type<T>(acol_.stype())); } ColumnImpl* clone() const override { return new Column_Gcd(Column(acol_), Column(bcol_)); } size_t n_children() const noexcept { return 2; } const Column& child(size_t i) { return i==0? acol_ : bcol_; } bool get_element(size_t i, T* out) { T a, b; bool avalid = acol_.get_element(i, &a); bool bvalid = bcol_.get_element(i, &b); if (avalid && bvalid) { while (b) { T tmp = b; b = a % b; a = tmp; } *out = a; return true; } return false; } };

Python-facing gcd() function

Now that we have created the FExpr_Gcd class, we also need to have a python function responsible for creating these objects. This is done in 4 steps:

First, declare a function with signature py::oobj(const py::PKArgs&). The py::PKArgs object here encapsulates all parameters that were passed to the function, and it returns a py::oobj, which is a simple wrapper around python’s PyObject*.

static py::oobj py_gcd(const py::XArgs& args) { auto a = args[0].to_oobj(); auto b = args[1].to_oobj(); return PyFExpr::make(new FExpr_Gcd(as_fexpr(a), as_fexpr(b))); }

This function takes the python arguments, if necessary validates and converts them into C++ objects, then creates a new FExpr_Gcd object, and then returns it wrapped into a PyFExpr (which is a python equivalent of the generic FExpr class).

In the second step, we declare the signature and the docstring of this python function:

static const char* doc_gcd = R"(gcd(a, b) -- Compute the greatest common divisor of `a` and `b`. Parameters ---------- a, b: FExpr Only integer columns are supported. return: FExpr The returned column will have stype int64 if either `a` or `b` are of type int64, or otherwise it will be int32. )"; DECLARE_PYFN(&py_gcd) ->name("gcd") ->docs(doc_gcd) ->arg_names({"a", "b"}) ->n_positional_args(2) ->n_required_args(2);

At this point the method will be visible from python in the _datatable module. So the next step is to import it into the main datatable module. To do this, go to src/datatable/ and write

from .lib._datatable import ( ... gcd, ... ) ... __all__ = ( ... "gcd", ... )


Any functionality must be properly tested. We recommend creating a dedicated test file for each new function. Thus, create file tests/expr/ and add some tests in it. We use the pytest framework for testing. In this framework, each test is a single function (whose name starts with test_) which performs some actions and then asserts the validity of results.

import pytest import random from datatable import dt, f, gcd from tests import assert_equals # checks equality of Frames from math import gcd as math_gcd def test_equal_columns(): DT = dt.Frame(A=[1, 2, 3, 4, 5]) RES = DT[:, gcd(f.A, f.A)] assert_equals(RES, dt.Frame([1, 1, 1, 1, 1]/dt.int32)) @pytest.mark.parametrize("seed", [random.getrandbits(63)]) def test_random(seed): random.seed(seed) n = 100 src1 = [random.randint(1, 1000) for i in range(n)] src2 = [random.randint(1, 100) for i in range(n)] DT = dt.Frame(A=src1, B=src2) RES = DT[:, gcd(f.A, f.B)] assert_equals(RES, dt.Frame([math_gcd(src1[i], src2[i]) for i in range(n)]))

When writing tests try to test any corner cases that you can think of. For example, what if one of the numbers is 0? Negative? Add tests for various column types, including invalid ones.


The final piece of the puzzle is the documentation. We’ve already written the documentation for our function: the doc_gcd variable declared earlier. However, for now this is only visible from python when you run help(gcd). We also want the documentation to be visible on our official readthedocs website, which requires a few more steps. So:

First, create file docs/api/dt/gcd.rst. The content of the file should contain just few lines:

.. xfunction:: datatable.gcd :doc: src/core/fexpr/ doc_gcd :src: src/core/fexpr/ py_gcd :tests: tests/expr/

In these lines we declare: in which source file the docstring can be found, and what is the name of its variable. The documentation generator will be looking for a static const char* doc_gcd variable in the source. Then we also declare the name of the function which provides the gcd functionality. The generator will look for a function with that name in the specified source file and create a link to that source in the output doc file. Lastly, the :tests: parameter says which file contains tests dedicated to this function, this will also become a link in the generated documentation.

This RST file now needs to be added to the toctree: open the file docs/api/index-api.rst and add it into the .. toctree:: list at the bottom, and also add it to the table of all functions.

Lastly, open docs/releases/v{LATEST}.rst (this is our changelog) and write a brief paragraph about the new function:

Frame ----- ... -[new] Added new function :func:`gcd()` to compute the greatest common divisor of two columns. [#NNNN]

The [#NNNN] is a link to the GitHub issue where the gcd() function was requested.


Some functions are declared within submodules of the datatable module. For example, math-related functions can be found in dt.math, string functions in dt.str, etc. Declaring such functions is not much different from what is described above. For example, if we wanted our gcd() function to be in the dt.math submodule, we’d made the following changes:

  • Create file expr/math/ instead of expr/;

  • Instead of importing the function in src/datatable/ we’d have imported it from src/datatable/;

  • The test file name can be tests/math/ instead of tests/expr/;

  • The doc file name can be docs/api/math/gcd.rst instead of docs/api/dt/gcd.rst, and it should be added to the toctree in docs/api/math.rst.