module nlp.completion_simple

Short summary

module mlstatpy.nlp.completion_simple

About completion, simple algorithm

source on GitHub

Classes

class	truncated documentation
CompletionElement	Definition of an element in a completion system, it contains the following members:
CompletionSystem	define a completion system

Static Methods

staticmethod	truncated documentation
empty_prefix	return an instance filled with an empty prefix

Methods

method	truncated documentation
__getitem__	Returns elements[i].
__init__	constructor
__init__	fill the completion system
__iter__	Iterates over elements.
__len__	Number of elements.
__repr__	usual
compare_with_trie	Compares the results with the other implementation.
compute_metrics	Computes the metric for the completion itself.
enumerate_test_metric	Evaluates the completion set on a set of queries, the function returns a list of CompletionElement …
find	Not very efficient, finds an item in a the list.
init_metrics	initiate the metrics
items	Iterates on (e.value, e).
sort_values	sort the elements by value
sort_weight	Sorts the elements by value.
str_all_completions	builds a string with all completions for all prefixes along the paths, this is only available if parameter …
str_mks	return a string with metric information
str_mks0	return a string with metric information
test_metric	Evaluates the completion set on a set of queries, the function returns a dictionary with the aggregated metrics …
to_dict	Returns a dictionary.
tuples	Iterates on (e.weight, e.value).
update_metrics	update the metrics

Documentation

About completion, simple algorithm

source on GitHub

class mlstatpy.nlp.completion_simple.CompletionElement(value: str, weight=1.0, disp=None)

Bases : object

Definition of an element in a completion system, it contains the following members:

• value: the completion

• weight: a weight or a position, we assume a completion with a lower weight is shown at a lower position

• disp: display string (no impact on the algorithm)

• mks0*: value of minimum keystroke

• mks0_*: length of the prefix to obtain mks0

• mks1: value of dynamic minimum keystroke

• mks1_: length of the prefix to obtain mks1

• mks2: value of modified dynamic minimum keystroke

• mks2_: length of the prefix to obtain mks2

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constructor

Paramètres

• value – value (a character)

• weight – ordering (the lower, the first)

• disp – original string, use this to identify the node

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__init__(value: str, weight=1.0, disp=None)

constructor

Paramètres

• value – value (a character)

• weight – ordering (the lower, the first)

• disp – original string, use this to identify the node

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__repr__()

usual

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__slots__ = ('value', 'weight', 'disp', 'mks0', 'mks0_', 'mks1', 'mks1_', 'mks2', 'mks2_', 'prefix', '_info')

_info

static empty_prefix()

return an instance filled with an empty prefix

source on GitHub

init_metrics(position: int, completions: List[CompletionElement] = None)

initiate the metrics

Paramètres

• position – position in the completion system when prefix is null, position starting from 0

• completions – displayed completions, if not None, the method will store them in member _completions

Renvoie

boolean which indicates there was an update

source on GitHub

str_all_completions(maxn=10, use_precompute=True) → str

builds a string with all completions for all prefixes along the paths, this is only available if parameter completions was used when calling method update_metrics.

Paramètres

• maxn – maximum number of completions to show

• use_precompute – use intermediate results built by precompute_stat

Renvoie

str

source on GitHub

str_mks() → str

return a string with metric information

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str_mks0() → str

return a string with metric information

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update_metrics(prefix: str, position: int, improved: dict, delta: float, completions: List[CompletionElement] = None, iteration=- 1)

update the metrics

Paramètres

• prefix – prefix

• position – position in the completion system when prefix has length k, position starting from 0

• improved – if one metrics is < to the completion length, it means it can be used to improve others queries

• delta – delta in the dynamic modified mks

• completions – displayed completions, if not None, the method will store them in member _completions

• iteration – for debugging purpose, indicates when this improvment was detected

Renvoie

boolean which indicates there was an update

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class mlstatpy.nlp.completion_simple.CompletionSystem(elements: List[mlstatpy.nlp.completion_simple.CompletionElement])

Bases : object

define a completion system

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fill the completion system

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__getitem__(i)

Returns elements[i].

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__init__(elements: List[mlstatpy.nlp.completion_simple.CompletionElement])

fill the completion system

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__iter__() → Iterator[mlstatpy.nlp.completion_simple.CompletionElement]

Iterates over elements.

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__len__() → int

Number of elements.

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compare_with_trie(delta=0.8, fLOG=<function noLOG>)

Compares the results with the other implementation.

Paramètres

• delta – parameter delta in the dynamic modified mks

• fLOG – logging function

Renvoie

None or differences

source on GitHub

compute_metrics(ffilter=None, delta=0.8, details=False, fLOG=<function noLOG>) → int

Computes the metric for the completion itself.

Paramètres

• ffilter – filter function

• delta – parameter delta in the dynamic modified mks

• details – log more details about displayed completions

• fLOG – logging function

Renvoie

number of iterations

The function ends by sorting the set of completion by alphabetical order.

source on GitHub

enumerate_test_metric(qset: Iterator[Tuple[str, float]]) → Iterator[Tuple[mlstatpy.nlp.completion_simple.CompletionElement, mlstatpy.nlp.completion_simple.CompletionElement]]

Evaluates the completion set on a set of queries, the function returns a list of CompletionElement with the three metrics , , for these particular queries.

Paramètres

qset – list of tuple(str, float) = (query, weight)

Renvoie

list of tuple of CompletionElement, the first one is the query, the second one is the None or the matching completion

The method compute_metric() needs to be called first.

source on GitHub

find(value: str, is_sorted=False) → mlstatpy.nlp.completion_simple.CompletionElement

Not very efficient, finds an item in a the list.

Paramètres

• value – string to find

• is_sorted – the function will assume the elements are sorted by alphabetical order

Renvoie

element or None

source on GitHub

items() → Iterator[Tuple[str, mlstatpy.nlp.completion_simple.CompletionElement]]

Iterates on (e.value, e).

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sort_values()

sort the elements by value

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sort_weight()

Sorts the elements by value.

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test_metric(qset: Iterator[Tuple[str, float]]) → Dict[str, float]

Evaluates the completion set on a set of queries, the function returns a dictionary with the aggregated metrics and some statistics about them.

Paramètres

qset – list of tuple(str, float) = (query, weight)

Renvoie

list of CompletionElement

The method compute_metric() needs to be called first. It then calls enumerate_metric().

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to_dict() → Dict[str, mlstatpy.nlp.completion_simple.CompletionElement]

Returns a dictionary.

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tuples() → Iterator[Tuple[float, str]]

Iterates on (e.weight, e.value).

source on GitHub