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A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

Features

Installation

The recommended way to install the library is through Composer:

composer require rybakit/msgpack

Usage

Packing

To pack values you can either use an instance of a Packer:

$packer = new Packer();
$packed = $packer->pack($value);

or call a static method on the MessagePack class:

$packed = MessagePack::pack($value);

In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:

$mpArr1 = $packer->pack([1, 2]);               // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]);     // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]);     // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]);     // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}

However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:

$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}

Here is a list of type-specific packing methods:

$packer->packNil();           // MP nil
$packer->packBool(true);      // MP bool
$packer->packInt(42);         // MP int
$packer->packFloat(M_PI);     // MP float (32 or 64)
$packer->packFloat32(M_PI);   // MP float 32
$packer->packFloat64(M_PI);   // MP float 64
$packer->packStr('foo');      // MP str
$packer->packBin("\x80");     // MP bin
$packer->packArray([1, 2]);   // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa");  // MP ext

Check the "Custom types" section below on how to pack custom types.

Packing options

The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):

NameDescription
FORCE_STRForces PHP strings to be packed as MessagePack UTF-8 strings
FORCE_BINForces PHP strings to be packed as MessagePack binary data
DETECT_STR_BINDetects MessagePack str/bin type automatically
  
FORCE_ARRForces PHP arrays to be packed as MessagePack arrays
FORCE_MAPForces PHP arrays to be packed as MessagePack maps
DETECT_ARR_MAPDetects MessagePack array/map type automatically
  
FORCE_FLOAT32Forces PHP floats to be packed as 32-bits MessagePack floats
FORCE_FLOAT64Forces PHP floats to be packed as 64-bits MessagePack floats

The type detection mode (DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this, Map and Bin. Check the "Custom types" section below for details.

Examples:

// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);

// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);

Unpacking

To unpack data you can either use an instance of a BufferUnpacker:

$unpacker = new BufferUnpacker();

$unpacker->reset($packed);
$value = $unpacker->unpack();

or call a static method on the MessagePack class:

$value = MessagePack::unpack($packed);

If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:

while ($chunk = ...) {
    $unpacker->append($chunk);
    if ($messages = $unpacker->tryUnpack()) {
        return $messages;
    }
}

If you want to unpack from a specific position in a buffer, use seek:

$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer

To skip bytes from the current position, use skip:

$unpacker->skip(10); // set position to 10 bytes ahead of the current position

To get the number of remaining (unread) bytes in the buffer:

$unreadBytesCount = $unpacker->getRemainingCount();

To check whether the buffer has unread data:

$hasUnreadBytes = $unpacker->hasRemaining();

If needed, you can remove already read data from the buffer by calling:

$releasedBytesCount = $unpacker->release();

With the read method you can read raw (packed) data:

$packedData = $unpacker->read(2); // read 2 bytes

Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:

$unpacker->unpackNil();   // PHP null
$unpacker->unpackBool();  // PHP bool
$unpacker->unpackInt();   // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr();   // PHP UTF-8 string
$unpacker->unpackBin();   // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap();   // PHP associative array
$unpacker->unpackExt();   // PHP MessagePack\Type\Ext object

Unpacking options

The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):

NameDescription
BIGINT_AS_STRConverts overflowed integers to strings [1]
BIGINT_AS_GMPConverts overflowed integers to GMP objects [2]
BIGINT_AS_DECConverts overflowed integers to Decimal\Decimal objects [3]

1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.

2. Make sure the GMP extension is enabled.

3. Make sure the Decimal extension is enabled.

Examples:

$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";

$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}

Custom types

In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.

Type objects

If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:

$packer = new Packer();

$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);

More type examples can be found in the src/Type directory.

Type transformers

As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.

A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:

$packer = new Packer(null, [new StreamTransformer()]);

$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));

More type transformer examples can be found in the src/TypeTransformer directory.

Extensions

In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).

An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.

The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.

Timestamp

The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:

$timestampExtension = new TimestampExtension();

$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);

$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);

$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();

$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();

When using the MessagePack class, the Timestamp extension is already registered:

$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);

Application-specific extensions

In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).

More extension examples can be found in the examples/MessagePack directory.

To learn more about how extension types can be useful, check out this article.

Exceptions

If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.

An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.

Tests

Run tests as follows:

vendor/bin/phpunit

Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:

./dockerfile.sh | docker build -t msgpack -

The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:

PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -

See a list of various images here.

Then run the unit tests:

docker run --rm -v $PWD:/msgpack -w /msgpack msgpack

Fuzzing

To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:

php-fuzzer fuzz tests/fuzz_buffer_unpacker.php

Performance

To check performance, run:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total                  2.7618          4.0820
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

With JIT:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total                  1.6432          1.9674
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

You may change default benchmark settings by defining the following environment variables:

NameDefault
MP_BENCH_TARGETSpure_p,pure_u, see a list of available targets
MP_BENCH_ITERATIONS100_000
MP_BENCH_DURATIONnot set
MP_BENCH_ROUNDS3
MP_BENCH_TESTS-@slow, see a list of available tests

For example:

export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'

Another example, benchmarking both the library and the PECL extension:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  1.5625          2.3866        0.7735          0.7243
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

With JIT:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  0.9642          1.0909        0.8224          0.7213
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.

License

The library is released under the MIT License. See the bundled LICENSE file for details.

Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License

#php 

Treebender: A Symbolic Natural Language Parsing Library for Rust

Treebender

A symbolic natural language parsing library for Rust, inspired by HDPSG.

What is this?

This is a library for parsing natural or constructed languages into syntax trees and feature structures. There's no machine learning or probabilistic models, everything is hand-crafted and deterministic.

You can find out more about the motivations of this project in this blog post.

But what are you using it for?

I'm using this to parse a constructed language for my upcoming xenolinguistics game, Themengi.

Motivation

Using a simple 80-line grammar, introduced in the tutorial below, we can parse a simple subset of English, checking reflexive pronoun binding, case, and number agreement.

$ cargo run --bin cli examples/reflexives.fgr
> she likes himself
Parsed 0 trees

> her likes herself
Parsed 0 trees

> she like herself
Parsed 0 trees

> she likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-2: [
    case: acc
    pron: ref
    needs_pron: #0 she
    num: sg
    child-0: [ word: herself ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1 sg
  ]
  child-0: [
    child-0: [ word: she ]
    case: nom
    pron: #0
    num: #1
  ]
]

Low resource language? Low problem! No need to train on gigabytes of text, just write a grammar using your brain. Let's hypothesize that in American Sign Language, topicalized nouns (expressed with raised eyebrows) must appear first in the sentence. We can write a small grammar (18 lines), and plug in some sentences:

$ cargo run --bin cli examples/asl-wordorder.fgr -n
> boy sit
Parsed 1 tree
(0..2: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: IV (1..2: sit)))

> boy throw ball
Parsed 1 tree
(0..3: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: TV (1..2: throw))
  (2..3: NP ((2..3: N (2..3: ball)))))

> ball nm-raised-eyebrows boy throw
Parsed 1 tree
(0..4: S
  (0..2: NP
    (0..1: N (0..1: ball))
    (1..2: Topic (1..2: nm-raised-eyebrows)))
  (2..3: NP ((2..3: N (2..3: boy))))
  (3..4: TV (3..4: throw)))

> boy throw ball nm-raised-eyebrows
Parsed 0 trees

Tutorial

As an example, let's say we want to build a parser for English reflexive pronouns (himself, herself, themselves, themself, itself). We'll also support number ("He likes X" v.s. "They like X") and simple embedded clauses ("He said that they like X").

Grammar files are written in a custom language, similar to BNF, called Feature GRammar (.fgr). There's a VSCode syntax highlighting extension for these files available as fgr-syntax.

We'll start by defining our lexicon. The lexicon is the set of terminal symbols (symbols in the actual input) that the grammar will match. Terminal symbols must start with a lowercase letter, and non-terminal symbols must start with an uppercase letter.

// pronouns
N -> he
N -> him
N -> himself
N -> she
N -> her
N -> herself
N -> they
N -> them
N -> themselves
N -> themself

// names, lowercase as they are terminals
N -> mary
N -> sue
N -> takeshi
N -> robert

// complementizer
Comp -> that

// verbs -- intransitive, transitive, and clausal
IV -> falls
IV -> fall
IV -> fell

TV -> likes
TV -> like
TV -> liked

CV -> says
CV -> say
CV -> said

Next, we can add our sentence rules (they must be added at the top, as the first rule in the file is assumed to be the top-level rule):

// sentence rules
S -> N IV
S -> N TV N
S -> N CV Comp S

// ... previous lexicon ...

Assuming this file is saved as examples/no-features.fgr (which it is :wink:), we can test this file with the built-in CLI:

$ cargo run --bin cli examples/no-features.fgr
> he falls
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: falls)))
[
  child-1: [ child-0: [ word: falls ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he falls her
Parsed 0 trees

> he likes her
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: likes ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he likes
Parsed 0 trees

> he said that he likes her
Parsed 1 tree
(0..6: S
  (0..1: N (0..1: he))
  (1..2: CV (1..2: said))
  (2..3: Comp (2..3: that))
  (3..6: S
    (3..4: N (3..4: he))
    (4..5: TV (4..5: likes))
    (5..6: N (5..6: her))))
[
  child-0: [ child-0: [ word: he ] ]
  child-2: [ child-0: [ word: that ] ]
  child-1: [ child-0: [ word: said ] ]
  child-3: [
    child-2: [ child-0: [ word: her ] ]
    child-1: [ child-0: [ word: likes ] ]
    child-0: [ child-0: [ word: he ] ]
  ]
]

> he said that he
Parsed 0 trees

This grammar already parses some correct sentences, and blocks some trivially incorrect ones. However, it doesn't care about number, case, or reflexives right now:

> she likes himself  // unbound reflexive pronoun
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-0: [ child-0: [ word: she ] ]
  child-2: [ child-0: [ word: himself ] ]
  child-1: [ child-0: [ word: likes ] ]
]

> him like her  // incorrect case on the subject pronoun, should be nominative
                // (he) instead of accusative (him)
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: him))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-0: [ child-0: [ word: him ] ]
  child-1: [ child-0: [ word: like ] ]
  child-2: [ child-0: [ word: her ] ]
]

> he like her  // incorrect verb number agreement
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: like ] ]
  child-0: [ child-0: [ word: he ] ]
]

To fix this, we need to add features to our lexicon, and restrict the sentence rules based on features.

Features are added with square brackets, and are key: value pairs separated by commas. **top** is a special feature value, which basically means "unspecified" -- we'll come back to it later. Features that are unspecified are also assumed to have a **top** value, but sometimes explicitly stating top is more clear.

/// Pronouns
// The added features are:
// * num: sg or pl, whether this noun wants a singular verb (likes) or
//   a plural verb (like). note this is grammatical number, so for example
//   singular they takes plural agreement ("they like X", not *"they likes X")
// * case: nom or acc, whether this noun is nominative or accusative case.
//   nominative case goes in the subject, and accusative in the object.
//   e.g., "he fell" and "she likes him", not *"him fell" and *"her likes he"
// * pron: he, she, they, or ref -- what type of pronoun this is
// * needs_pron: whether this is a reflexive that needs to bind to another
//   pronoun.
N[ num: sg, case: nom, pron: he ]                    -> he
N[ num: sg, case: acc, pron: he ]                    -> him
N[ num: sg, case: acc, pron: ref, needs_pron: he ]   -> himself
N[ num: sg, case: nom, pron: she ]                   -> she
N[ num: sg, case: acc, pron: she ]                   -> her
N[ num: sg, case: acc, pron: ref, needs_pron: she]   -> herself
N[ num: pl, case: nom, pron: they ]                  -> they
N[ num: pl, case: acc, pron: they ]                  -> them
N[ num: pl, case: acc, pron: ref, needs_pron: they ] -> themselves
N[ num: sg, case: acc, pron: ref, needs_pron: they ] -> themself

// Names
// The added features are:
// * num: sg, as people are singular ("mary likes her" / *"mary like her")
// * case: **top**, as names can be both subjects and objects
//   ("mary likes her" / "she likes mary")
// * pron: whichever pronoun the person uses for reflexive agreement
//   mary    pron: she  => mary likes herself
//   sue     pron: they => sue likes themself
//   takeshi pron: he   => takeshi likes himself
N[ num: sg, case: **top**, pron: she ]  -> mary
N[ num: sg, case: **top**, pron: they ] -> sue
N[ num: sg, case: **top**, pron: he ]   -> takeshi
N[ num: sg, case: **top**, pron: he ]   -> robert

// Complementizer doesn't need features
Comp -> that

// Verbs -- intransitive, transitive, and clausal
// The added features are:
// * num: sg, pl, or **top** -- to match the noun numbers.
//   **top** will match either sg or pl, as past-tense verbs in English
//   don't agree in number: "he fell" and "they fell" are both fine
// * tense: past or nonpast -- this won't be used for agreement, but will be
//   copied into the final feature structure, and the client code could do
//   something with it
IV[ num:      sg, tense: nonpast ] -> falls
IV[ num:      pl, tense: nonpast ] -> fall
IV[ num: **top**, tense: past ]    -> fell

TV[ num:      sg, tense: nonpast ] -> likes
TV[ num:      pl, tense: nonpast ] -> like
TV[ num: **top**, tense: past ]    -> liked

CV[ num:      sg, tense: nonpast ] -> says
CV[ num:      pl, tense: nonpast ] -> say
CV[ num: **top**, tense: past ]    -> said

Now that our lexicon is updated with features, we can update our sentence rules to constrain parsing based on those features. This uses two new features, tags and unification. Tags allow features to be associated between nodes in a rule, and unification controls how those features are compatible. The rules for unification are:

  1. A string feature can unify with a string feature with the same value
  2. A top feature can unify with anything, and the nodes are merged
  3. A complex feature ([ ... ] structure) is recursively unified with another complex feature.

If unification fails anywhere, the parse is aborted and the tree is discarded. This allows the programmer to discard trees if features don't match.

// Sentence rules
// Intransitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
S -> N[ case: nom, num: #1 ] IV[ num: #1 ]
// Transitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #2)
// * If there's a reflexive in the object position, make sure its `needs_pron`
//   feature matches the subject's `pron` feature. If the object isn't a
//   reflexive, then its `needs_pron` feature will implicitly be `**top**`, so
//   will unify with anything.
S -> N[ case: nom, pron: #1, num: #2 ] TV[ num: #2 ] N[ case: acc, needs_pron: #1 ]
// Clausal verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
// * Reflexives can't cross clause boundaries (*"He said that she likes himself"),
//   so we can ignore reflexives and delegate to inner clause rule
S -> N[ case: nom, num: #1 ] CV[ num: #1 ] Comp S

Now that we have this augmented grammar (available as examples/reflexives.fgr), we can try it out and see that it rejects illicit sentences that were previously accepted, while still accepting valid ones:

> he fell
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: fell)))
[
  child-1: [
    child-0: [ word: fell ]
    num: #0 sg
    tense: past
  ]
  child-0: [
    pron: he
    case: nom
    num: #0
    child-0: [ word: he ]
  ]
]

> he like him
Parsed 0 trees

> he likes himself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-1: [
    num: #0 sg
    child-0: [ word: likes ]
    tense: nonpast
  ]
  child-2: [
    needs_pron: #1 he
    num: sg
    child-0: [ word: himself ]
    pron: ref
    case: acc
  ]
  child-0: [
    child-0: [ word: he ]
    pron: #1
    num: #0
    case: nom
  ]
]

> he likes herself
Parsed 0 trees

> mary likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: mary))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-0: [
    pron: #0 she
    num: #1 sg
    case: nom
    child-0: [ word: mary ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1
  ]
  child-2: [
    child-0: [ word: herself ]
    num: sg
    pron: ref
    case: acc
    needs_pron: #0
  ]
]

> mary likes themself
Parsed 0 trees

> sue likes themself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: sue))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: themself)))
[
  child-0: [
    pron: #0 they
    child-0: [ word: sue ]
    case: nom
    num: #1 sg
  ]
  child-1: [
    tense: nonpast
    num: #1
    child-0: [ word: likes ]
  ]
  child-2: [
    needs_pron: #0
    case: acc
    pron: ref
    child-0: [ word: themself ]
    num: sg
  ]
]

> sue likes himself
Parsed 0 trees

If this is interesting to you and you want to learn more, you can check out my blog series, the excellent textbook Syntactic Theory: A Formal Introduction (2nd ed.), and the DELPH-IN project, whose work on the LKB inspired this simplified version.

Using from code

I need to write this section in more detail, but if you're comfortable with Rust, I suggest looking through the codebase. It's not perfect, it started as one of my first Rust projects (after migrating through F# -> TypeScript -> C in search of the right performance/ergonomics tradeoff), and it could use more tests, but overall it's not too bad.

Basically, the processing pipeline is:

  1. Make a Grammar struct
  • Grammar is defined in rules.rs.
  • The easiest way to make a Grammar is Grammar::parse_from_file, which is mostly a hand-written recusive descent parser in parse_grammar.rs. Yes, I recognize the irony here.
  1. It takes input (in Grammar::parse, which does everything for you, or Grammar::parse_chart, which just does the chart)
  2. The input is first chart-parsed in earley.rs
  3. Then, a forest is built from the chart, in forest.rs, using an algorithm I found in a very useful blog series I forget the URL for, because the algorithms in the academic literature for this are... weird.
  4. Finally, the feature unification is used to prune the forest down to only valid trees. It would be more efficient to do this during parsing, but meh.

The most interesting thing you can do via code and not via the CLI is probably getting at the raw feature DAG, as that would let you do things like pronoun coreference. The DAG code is in featurestructure.rs, and should be fairly approachable -- there's a lot of Rust ceremony around Rc<RefCell<...>> because using an arena allocation crate seemed too harlike overkill, but that is somewhat mitigated by the NodeRef type alias. Hit me up at https://vgel.me/contact if you need help with anything here!

Download Details:
Author: vgel
Source Code: https://github.com/vgel/treebender
License: MIT License

#rust  #machinelearning 

Py Rouge: A Full Python Implementation Of The ROUGE Metric

Py-rouge

A full Python implementation of the ROUGE metric, producing same results as in the official perl implementation.

Important remarks

  • The original Porter stemmer in NLTK is slightly different than the one use in the official ROUGE perl script as it has been written by end. Therefore, there might be slightly different stems for certain words. For DUC2004 dataset, I have identified these words and this script produces same stems.
  • The official ROUGE perl script use resampling strategy to compute the average with confidence intervals. Therefore, we might have a difference <3e-5 for ROUGE-L as well as ROUGE-W and <4e-5 for ROUGE-N.
  • Finally, ROUGE-1.5.5. has a bug: should have $tmpTextLen += $sLen at line 2101. Here, the last sentence, $limitBytes is taken instead of $limitBytes-$tmpTextLen (as $tmpTextLen is never updated with bytes length limit). It has been fixed in this code. This bug does not have a consequence for the default evaluation -b 665.

In case of doubts, please see all the implemented tests to compare outputs between the official ROUGE-1.5.5 and this script.

Installation

Package is uploaded on PyPI <https://pypi.org/project/py-rouge>_.

You can install it with pip:

pip install py-rouge

or do it manually:

git clone https://github.com/Diego999/py-rouge
cd py-rouge
python setup.py install

Issues/Pull Requests/Feedbacks

Don't hesitate to contact for any feedback or create issues/pull requests (especially if you want to rewrite the stemmer implemented in ROUGE-1.5.5 in python ;)).

Example

import rouge


def prepare_results(m, p, r, f):
    return '\t{}:\t{}: {:5.2f}\t{}: {:5.2f}\t{}: {:5.2f}'.format(m, 'P', 100.0 * p, 'R', 100.0 * r, 'F1', 100.0 * f)


for aggregator in ['Avg', 'Best', 'Individual']:
    print('Evaluation with {}'.format(aggregator))
    apply_avg = aggregator == 'Avg'
    apply_best = aggregator == 'Best'

    evaluator = rouge.Rouge(metrics=['rouge-n', 'rouge-l', 'rouge-w'],
                           max_n=4,
                           limit_length=True,
                           length_limit=100,
                           length_limit_type='words',
                           apply_avg=apply_avg,
                           apply_best=apply_best,
                           alpha=0.5, # Default F1_score
                           weight_factor=1.2,
                           stemming=True)


    hypothesis_1 = "King Norodom Sihanouk has declined requests to chair a summit of Cambodia 's top political leaders , saying the meeting would not bring any progress in deadlocked negotiations to form a government .\nGovernment and opposition parties have asked King Norodom Sihanouk to host a summit meeting after a series of post-election negotiations between the two opposition groups and Hun Sen 's party to form a new government failed .\nHun Sen 's ruling party narrowly won a majority in elections in July , but the opposition _ claiming widespread intimidation and fraud _ has denied Hun Sen the two-thirds vote in parliament required to approve the next government .\n"
    references_1 = ["Prospects were dim for resolution of the political crisis in Cambodia in October 1998.\nPrime Minister Hun Sen insisted that talks take place in Cambodia while opposition leaders Ranariddh and Sam Rainsy, fearing arrest at home, wanted them abroad.\nKing Sihanouk declined to chair talks in either place.\nA U.S. House resolution criticized Hun Sen's regime while the opposition tried to cut off his access to loans.\nBut in November the King announced a coalition government with Hun Sen heading the executive and Ranariddh leading the parliament.\nLeft out, Sam Rainsy sought the King's assurance of Hun Sen's promise of safety and freedom for all politicians.",
                    "Cambodian prime minister Hun Sen rejects demands of 2 opposition parties for talks in Beijing after failing to win a 2/3 majority in recent elections.\nSihanouk refuses to host talks in Beijing.\nOpposition parties ask the Asian Development Bank to stop loans to Hun Sen's government.\nCCP defends Hun Sen to the US Senate.\nFUNCINPEC refuses to share the presidency.\nHun Sen and Ranariddh eventually form a coalition at summit convened by Sihanouk.\nHun Sen remains prime minister, Ranariddh is president of the national assembly, and a new senate will be formed.\nOpposition leader Rainsy left out.\nHe seeks strong assurance of safety should he return to Cambodia.\n",
                    ]

    hypothesis_2 = "China 's government said Thursday that two prominent dissidents arrested this week are suspected of endangering national security _ the clearest sign yet Chinese leaders plan to quash a would-be opposition party .\nOne leader of a suppressed new political party will be tried on Dec. 17 on a charge of colluding with foreign enemies of China '' to incite the subversion of state power , '' according to court documents given to his wife on Monday .\nWith attorneys locked up , harassed or plain scared , two prominent dissidents will defend themselves against charges of subversion Thursday in China 's highest-profile dissident trials in two years .\n"
    references_2 = "Hurricane Mitch, category 5 hurricane, brought widespread death and destruction to Central American.\nEspecially hard hit was Honduras where an estimated 6,076 people lost their lives.\nThe hurricane, which lingered off the coast of Honduras for 3 days before moving off, flooded large areas, destroying crops and property.\nThe U.S. and European Union were joined by Pope John Paul II in a call for money and workers to help the stricken area.\nPresident Clinton sent Tipper Gore, wife of Vice President Gore to the area to deliver much needed supplies to the area, demonstrating U.S. commitment to the recovery of the region.\n"

    all_hypothesis = [hypothesis_1, hypothesis_2]
    all_references = [references_1, references_2]

    scores = evaluator.get_scores(all_hypothesis, all_references)

    for metric, results in sorted(scores.items(), key=lambda x: x[0]):
        if not apply_avg and not apply_best: # value is a type of list as we evaluate each summary vs each reference
            for hypothesis_id, results_per_ref in enumerate(results):
                nb_references = len(results_per_ref['p'])
                for reference_id in range(nb_references):
                    print('\tHypothesis #{} & Reference #{}: '.format(hypothesis_id, reference_id))
                    print('\t' + prepare_results(metric,results_per_ref['p'][reference_id], results_per_ref['r'][reference_id], results_per_ref['f'][reference_id]))
            print()
        else:
            print(prepare_results(metric, results['p'], results['r'], results['f']))
    print()

It produces the following output:

Evaluation with Avg
    rouge-1:    P: 28.62    R: 26.46    F1: 27.49
    rouge-2:    P:  4.21    R:  3.92    F1:  4.06
    rouge-3:    P:  0.80    R:  0.74    F1:  0.77
    rouge-4:    P:  0.00    R:  0.00    F1:  0.00
    rouge-l:    P: 30.52    R: 28.57    F1: 29.51
    rouge-w:    P: 15.85    R:  8.28    F1: 10.87

Evaluation with Best
    rouge-1:    P: 30.44    R: 28.36    F1: 29.37
    rouge-2:    P:  4.74    R:  4.46    F1:  4.59
    rouge-3:    P:  1.06    R:  0.98    F1:  1.02
    rouge-4:    P:  0.00    R:  0.00    F1:  0.00
    rouge-l:    P: 31.54    R: 29.71    F1: 30.60
    rouge-w:    P: 16.42    R:  8.82    F1: 11.47

Evaluation with Individual
    Hypothesis #0 & Reference #0: 
        rouge-1:    P: 38.54    R: 35.58    F1: 37.00
    Hypothesis #0 & Reference #1: 
        rouge-1:    P: 45.83    R: 43.14    F1: 44.44
    Hypothesis #1 & Reference #0: 
        rouge-1:    P: 15.05    R: 13.59    F1: 14.29

    Hypothesis #0 & Reference #0: 
        rouge-2:    P:  7.37    R:  6.80    F1:  7.07
    Hypothesis #0 & Reference #1: 
        rouge-2:    P:  9.47    R:  8.91    F1:  9.18
    Hypothesis #1 & Reference #0: 
        rouge-2:    P:  0.00    R:  0.00    F1:  0.00

    Hypothesis #0 & Reference #0: 
        rouge-3:    P:  2.13    R:  1.96    F1:  2.04
    Hypothesis #0 & Reference #1: 
        rouge-3:    P:  1.06    R:  1.00    F1:  1.03
    Hypothesis #1 & Reference #0: 
        rouge-3:    P:  0.00    R:  0.00    F1:  0.00

    Hypothesis #0 & Reference #0: 
        rouge-4:    P:  0.00    R:  0.00    F1:  0.00
    Hypothesis #0 & Reference #1: 
        rouge-4:    P:  0.00    R:  0.00    F1:  0.00
    Hypothesis #1 & Reference #0: 
        rouge-4:    P:  0.00    R:  0.00    F1:  0.00

    Hypothesis #0 & Reference #0: 
        rouge-l:    P: 42.11    R: 39.39    F1: 40.70
    Hypothesis #0 & Reference #1: 
        rouge-l:    P: 46.19    R: 43.92    F1: 45.03
    Hypothesis #1 & Reference #0: 
        rouge-l:    P: 16.88    R: 15.50    F1: 16.16

    Hypothesis #0 & Reference #0: 
        rouge-w:    P: 22.27    R: 11.49    F1: 15.16
    Hypothesis #0 & Reference #1: 
        rouge-w:    P: 24.56    R: 13.60    F1: 17.51
    Hypothesis #1 & Reference #0: 
        rouge-w:    P:  8.29    R:  4.04    F1:  5.43

Download Details:

Author: Diego999
Source Code: https://github.com/Diego999/py-rouge

License: Apache-2.0 license

#perl #python 

Ananya Gupta

Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

  1. **Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

  1. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

  1. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

  1. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

  1. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

  1. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

  1. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

  1. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

#machine learning online training #machine learning online course #machine learning course #machine learning certification course #machine learning training

Ananya Gupta

Ananya Gupta

1601875752

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Artificial intelligence has been around since a minimum of the 1950s, but it’s only within the past few years that it’s become ubiquitous. Companies we interact with every day— Amazon, Facebook, and Google—have fully embraced AI. It powers product recommendations, maps, and social media feeds.

But it’s not only the tech giants that will employ AI in their products. AI solutions are now accessible to several businesses and individuals. And it’s becoming clear that understanding and employing AI is critical for the companies of tomorrow.

What Is AI?
In the last 20 years, there are major changes in technology—notably the arrival of the mobile. But the innovation that’s on par with inventing electricity is AI.

Machine Learning
Machine learning may be a subset of AI and maybe a set of techniques that give computers the power to find out without being explicitly programmed to try to so. One example is classification, like classifying images: during a very simplistic interpretation, for instance, a computer could automatically classify pictures of apples and oranges to travel in several folders. And with more data over time, the machine will become better future scope and career oppertunity for students who want to make career in Machine Learning.

Deep Learning and Neural Networks
Deep learning may be a further subset of machine learning that permits computers to find out more complex patterns and solve more complex problems. one among the clearest applications of deep learning is in tongue processing, which powers chatbots and voice assistants like Siri. It’s the recent advent of deep learning that has particularly been driving the AI boom.

And all of those are supported neural networks, which is that the concept machines could mimic the human brain, with many layers of artificial neurons. Neural networks are powerful once they are multi-layered, with more neurons and interconnectivity. Neural networks are researched for years, but only recently has the research been pushed to the subsequent level and commercialized.

AI Business Benefits
Now that you simply have a conceptual understanding of AI and its subsets, let’s get to the guts of it: what can AI do for you and your business? We’ll explore highlights within five areas: human resources, accounting, legal, marketing and sales, and customer support.

Human Resources
Artificial intelligence poses a big opportunity in process automation. One example would be recruitment and human resources. As an example, tasks like onboarding and administration of advantages are often automated.If you want to learn deep about AI then join Artificial Intellegence class in Noida and get offer to work on live projects.

Accounting
The dutiful accountant, languishing over the bookkeeping—it’s a classic image. But now many of their services might not be needed. Many traditional bookkeeping tasks are already being performed by AI. Areas like accounts payable and receivable are taking advantage of automated data entry and categorization.

Legal
Some of the foremost fascinating advancements in AI are associated with law and legal technology. Specifically, AI can now read “legal and contractual documents to extract provisions using tongue processing.” Blue J Legal’s website touts the platform’s ability to help with employment law. The Foresight technology “analyzes data drawn from common law cases, using deep learning to get hidden patterns in previous rulings.” briefly, cases can now be analyzed much faster, insights are often drawn from across a good array of legal knowledge, and thus business decisions are often more accurate and assured.

Sales and Marketing Analytics
Analytics can now be done much more rapidly with much larger data sets because of AI. This has profound impacts on all kinds of data analysis, including business and financial decisions.

One of the quickly changing areas is marketing and sales applications. AI makes it easier to predict what a customer is probably going to shop for by learning and understanding their purchasing patterns.

Customer Support
You’ve been there. Waiting forever on a customer support line. Perhaps with a cable company or an enormous bank. Luckily, AI is close to making your life easier, if it hasn’t already.

According to the Harvard Business Review, one of the most benefits of AI is that “intelligent agents offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions—all within the customer’s tongue .” For customer support, a mixture of machine and deep learning can allow queries to be analyzed quicker.

Conclusion
With AI becoming ever more pervasive, having a fundamental understanding of it’s a requirement for continued business success. Whatever role you hold in your business, understanding AI may assist you to solve problems in new and innovative ways, saving time and money. Further, it’s going to assist you to build and style the products and services of the longer term.

#machine learning online training #machine learning online course #machine learning course #machine learning training in noida #artificial intelligence training in noida #artificial intelligence online training