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Sachin's batting average in matches that India won
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navigation: next | index
With every line of code that we write, we make assumptions about them.
With every piece of data that we receive, we likewise make assumptions about them.
This is a "book" dedicated to helping you uncover those assumptions, express them as code, and automatically execute them to make your data analysis workflow and pipelines more robust.
Things to check in doing data science
Like any good scientist, before you go on to analyze the data you have on hand, you should probably check for a few basic things:
- Data types. Are your data categorical, ordinal, or continuous? If they are categorical or ordinal, are they encoded as strings or as integers? If they are continuous, is it continuous in terms of having a long list of integers, or continuous floating point numbers?
- Data structure. Do you have a set of definitions for what each column means?
- Data completeness. Are you expecting that every column of data that you have will have a value from each row? Are there particular data points (rows) that should be present?
- Data integrity. Do you have a stray comma present somewhere in a CSV file? Do you have a string present in a column that should only be comprised of floats?
Things to check when writing code
Like any good software engineer, any code that you write, particularly those encapsulated in functions, should be accompanied by a suite of tests written for them. This will help you check for:
- Function correctness. Does the function transform the input into the output in ways that are expected?
- Boundary cases. Are there edge cases that cause your functions to fail?
- Counter-examples. Are there logically incoherent combinations of parameter values that may cause downstream problems?
- Code coverage. Have you written at least one test for every single line call in a function, done for every function?


predict Predicts response(s) for the provided sample(s)
[results,f] = model.predict(samples)
[...] = model.predict(..., 'OptionName', optionValue, ...)
Input
- samples Input row vectors (one or more) stored as rows of
a floating-point matrix.
Output
- results Output labels or regression values.
- f The same as the response of the first sample.
Options
- Flags The optional predict flags, model-dependent. For
convenience, you can set the individual flag options
below, instead of directly setting bits here. default 0
- RawOutput makes the method return the raw results (the
sum), not the class label. default false
- CompressedInput compressed data, containing only the
active samples/variables. default false
- PreprocessedInput This parameter is normally set to false,
implying a regular input. If it is true, the method
assumes that all the values of the discrete input
variables have been already normalized to 0..NCategories
ranges since the decision tree uses such normalized
representation internally. It is useful for faster
prediction with tree ensembles. For ordered input
variables, the flag is not used. Default false
- PredictAuto Setting this to true, overrides all of the
other
Predict*
flags. It automatically chooses between
PredictSum
and PredictMaxVote
(if the model is a
regressor or the number of classes are 2 with RawOutput
set then it picks PredictSum
, otherwise it picks
PredictMaxVote
by default). default true
- PredictSum If true then return sum of votes instead of the
class label. default false
- PredictMaxVote If true then return the class label with
the max vote. default false
The method traverses the decision tree and returns rhe
prediction result from the reached leaf node, either the class
label or the estimated function value.


- Order date
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Shipping address
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Billing address
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Please leave packages in shed by driveway.
- Lawnmower
- 1
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- Confirm this is electric
- Baby Monitor
- 2
- 39.98
- 1999-05-21


Trader Viktor:
Did you forget my request already? I asked you to find
10 scales of Medusa
for me. I shall begin my research on the bezoars, trusting you to do your part.


{% if datasource.description %}
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{% if request.user != datasource.owner and not request.user.is_superuser %}
Manage My Access
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You do not have permission to access this resource.
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{% else %}
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{% endif %}


This week we're reading chapter 5 of Search Inside Yourself, which talks about triggers - seemingly small things that cause a disproportionally large emotional response in us (e.g. when a casual comment touches a nerve and causes someone to blow up). Some things about triggers I found interesting:
The Siberian North Railroad, or SBNRR, is an exercise that gets you to Stop, Breathe, Notice, Reflect and Respond. E.g. if someone says something critical about your work, and your immediate reaction is to think of a defensive statement, then putting the SBNRR exercise into action, you'd first have to pause, breathe and notice what you are feeling. You'd then have to reflect on why you're feeling that way and try to put yourself in the other person's shoes. Finally, you'd have to imagine a more positive response (whether you actually go through with that positive response is up to you).
I like the way this exercise forces you to pause, and recognise that you're about to react in a certain (probably unwise) way. In theory it's possible to whip through all the SBNRR steps in just a few seconds, which I think is very cool and a bit of a sci-fi (like you're somehow slowing down time to have an in-depth heart-to-heart with yourself without the other person being any wiser).


function ColorMax(C1, C2: TColor32): TColor32;
Resulting color components are maximums of corresponding components in C1 and C2.
[See Also] ColorMin


To get started with a new device, download Raspbian Lite and burn it on a SD card.
You can use ssh
to input commands and remotely control the device, follow these steps to get started.
touch /Volumes/boot/ssh# Enable SSH touch /Volumes/boot/wpa_supplicant.conf# Auto-connect to wifi
Input your wireless network details to enable the device to automatically connect to the network when available.
# nano /Volumes/boot/wpa_supplicant.conf country=CA ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev update_config=1 network={ ssid=\"network_name\" psk=\"network_password\" }
When ssh
has been activated and the wifi details updated, the device should become discoverable.
ping raspberrypi.local# Find its IP address ssh<EMAIL_ADDRESS>Use password \"raspberry\" sudo raspi-config# Interface/SSH & Advanced/Expand FS sudo nano /etc/hostname# Rename sudo nano /etc/hosts# Rename passwd# Change Password sudo reboot
Gadget mode allows to SSH into a " SENDNAME("raspberry", "Raspberry Pi Zero") " device via USB. Begin by enabling ssh, by adding /Volumes/boot/ssh
to the SD Card. Activate gadget mode , allowing the device to be discovered and controlled via USB.
dtoverlay=dwc2# Append to config.txt modules-load=dwc2,g_ether# Append to cmdline.txt, or after rootwait
Make sure everything is up-to-date and install git
.
sudo apt-get update sudo apt-get dist-upgrade sudo apt-get install git


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|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface ExecutionContext
Represents context of current execution. Provides hook to query the current flow state and to schedule/submit tasks.
| Method Summary | |
|---|---|
void |
addFault(Fault fault)
Adds fault to the current execution. |
Fault |
getFault()
Fetchs the first fault in current execution |
java.util.Collection<Fault> |
getFaults()
Fetches all the faults in current execution |
Marker |
getMarker()
|
boolean |
isErrorFlow()
Checks if the current flow is in error path |
boolean |
isRequestFlow()
Checks if the current flow is in request path |
void |
resume()
Resumes the current execution |
void |
resume(Fault fault)
Adds a fault and resumes the current execution |
void |
scheduleTask(java.lang.Runnable task,
long delay,
java.util.concurrent.TimeUnit timeUnit)
Schedules a task which is run by messageprocessor's theadpool. |
void |
submitTask(java.lang.Runnable task)
Submits a task which is run by messageprocessor's theadpool. |
void |
submitTask(java.lang.Runnable task,
Callback callback,
java.lang.Object handback)
Submits a task with a callback which is run by messageprocessor's theadpool. |
| Method Detail |
|---|
Marker getMarker()
boolean isRequestFlow()
boolean isErrorFlow()
void submitTask(java.lang.Runnable task)
task
- Runnable that is executed in a different threadvoid scheduleTask(java.lang.Runnable task, long delay, java.util.concurrent.TimeUnit timeUnit)
task
- Runnable that is executed in a different threaddelay
- time to delay the executiontimeUnit
- timeunit for delayvoid submitTask(java.lang.Runnable task, Callback callback, java.lang.Object handback)
task
- Runnable that is executed in a different threadcallback
- Post the execution of the task, the flow resumes and invokes the callback method on this interface.handback
- Context exchange b/w the task and the callback.void resume()
void resume(Fault fault)
java.util.Collection<Fault> getFaults()
Fault getFault()
void addFault(Fault fault)
fault
- - fault to be set in current execution
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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Use modern tools to turn your design into a web site


AMERICAN OUTLAWS (2001) 2 stars out of 4. Starring Colin Farrell, Scott Caan, Ali Larter, Gabriel Macht, Gregory Smith, Harris Yulin, Will McCormack, Ronny Cox, Terry O'Quinn, Nathaniel Arcand, Kathy Bates and Timothy Dalton. Story by Roderick Taylor. Screenplay by Taylor and John Rogers. Directed by Les Mayfield. About 95 minutes. Rated PG-13 for violence. American Outlaws is a pretty, but disposable movie.
This feature is just another retelling of the Jesse James legend. It adds nothing to the canon. Its only reason for being is to show off a few studly young actors shooting pistols and riding horses.
The story of Jesse James has been told and retold so many times that it is almost unnecessary to discuss the plot. After all, actors from Tyrone Power to Roy Rogers to Clayton ("The Lone Ranger") Moore to James Keach have portrayed the famed (or infamous, depending on your point of view) Missouri outlaw.
Under the direction of Les Mayfield (who is comfortable with remakes, having helmed Flubber as well as Miracle on 34th Street), we get the same, tired retread: brothers Jesse and Frank James, along with their cousins, the Younger brothers, return home after the Civil War only to find that the evil railroad interests are trying to buy up their land cheaply, and, when failing, are fomenting a reign of terror to drive the farmers out.
Jesse, Frank and the Youngers declare war on the railroad, holding up their banks and trains, giving some of the loot to their poor neighbors and acting like 19th-century Robin Hoods.
The scenery is lovely, the action is quick-paced, the bullets fly and the explosions are massive and loud. But, what gnaws at you as you watch is, Why another Jesse James movie? The only notable divergence is a tip of the hat to contemporary gender equality as Jesse's bride leads a daring rescue of her imprisoned husband. Big deal.
That is not say the film is not fun. It is very likable with a personable cast headed by Colin Farrell as Jesse; Scott Caan (looking and sounding a lot like his old man, James) as Cole Younger; Ali Larter as Jesse's woman, Zee; and Gabriel Macht as the wise and careful Frank James.
Also in the cast is ex-007 Timothy Dalton as Allan Pinkerton, hired to capture Jesse; and Harris Yulin and Terry O'Quinn as the hissable railroad baddies.
Farrell is a very pleasant Jesse, a soft-spoken young man, sure of himself and of his mission. The film's only tension is between him and Caan's Cole Younger as to who is the leader of their outlaw band.
Larter is most photogenic and she is given plenty of opportunity to wave around her golden locks, which has a nice clean, sheen, considering she lives on a farm in 19th century Missouri and is without any hair products.
American Outlaws is a nice diversion. It's one of those movies you watch, enjoy for the moment then quickly forget. It won't bring about a resurgence in the sadly neglected Western genre, but it should make enough money at the box office for other filmmakers wanting to saddle up to consider looking west.
Bob Bloom is the film critic at the Journal and Courier in Lafayette, IN. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>Other reviews by Bloom can be found at www.jconline.com by clicking on golafayette. Bloom's reviews also can be found on the Web at the Internet Movie Database: http://www.imdb.com/M/reviews_by?Bob+Bloom
========== X-RAMR-ID: 29223 X-Language: en X-RT-ReviewID: 245232 X-RT-TitleID: 1109343 X-RT-SourceID: 872 X-RT-AuthorID: 1363 X-RT-RatingText: 2/4
The review above was posted to the
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NCS - Natural Color System - Modern
Colorcharts.org certification: *** Fandecks scanned on a X-Rite CFS-57 Spectrophotometer


About Me
I have always been fascinated with technology. When I was a child, I would always be on the computer playing educational video games, practicing spelling, math, and typing. As I grew older, so did my appreciation for new technology. I would save up whatever money I would earn so I could buy myself the latest PlayStation, GameBoy, or iPod Photo (revolutionary at the time). Admittedly, my fondness for technology was mostly attributed to the entertainment value. It wasn’t until I got my first job that I realized the true value and potential.
Right after I graduated high school, I knew that I wanted to work while I was enrolled at UCI. I submitted an application to Apple and was fortunate enough to be offered a job at one the retail locations. It was there, while working with many different people, from many different walks of life, that I developed a true understanding that technology can enrich life in a variety of ways. My role at Apple is called a “Creative,” which is essentially one of the trainers that help people use their products to foster their passions such as photography, videography, music, and coding! While immersed in this role, I see, on a daily basis, all of the different ways that people are using their technology to make their life more efficient and/or more fun (as indoctrinated as that makes me sound). Most importantly, it helped me decide which career path I want to forge forward with and pushed me to sign up for the Coding Bootcamp at UCI!
My Favorite Things
- • LA Angels Baseball
- • LA Rams Football
- • Star Wars
- • Game of Thrones
- • Apple
- • PS4
- • bootcampspot-v2.com
Guilty Pleasures
- • Miley Cyrus
- • Frozen
- • "Sorry" by Justin Bieber (not pround of this one)


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class OOOptimizer(object):
Known subclasses: cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract base class for an Object Oriented Optimizer interface.
Relevant methods are __init__
, ask
, tell
, optimize
and stop
,
and property result
. Only optimize
is fully implemented in this
base class.
Examples
All examples minimize the function elli
, the output is not shown.
(A preferred environment to execute all examples is ipython.)
First we need:
# CMAEvolutionStrategy derives from the OOOptimizer class from cma import CMAEvolutionStrategy from cma.fitness_functions import elli
The shortest example uses the inherited method
OOOptimizer.optimize
:
es = CMAEvolutionStrategy(8 * [0.1], 0.5).optimize(elli)
The input parameters to CMAEvolutionStrategy
are specific to this
inherited class. The remaining functionality is based on interface
defined by OOOptimizer
. We might have a look at the result:
print(es.result[0]) # best solution and print(es.result[1]) # its function value
Virtually the same example can be written with an explicit loop
instead of using optimize
. This gives the necessary insight into
the OOOptimizer
class interface and entire control over the
iteration loop:
# a new CMAEvolutionStrategy instance optim = CMAEvolutionStrategy(9 * [0.5], 0.3) # this loop resembles optimize() while not optim.stop(): # iterate X = optim.ask() # get candidate solutions f = [elli(x) for x in X] # evaluate solutions # in case do something else that needs to be done optim.tell(X, f) # do all the real "update" work optim.disp(20) # display info every 20th iteration optim.logger.add() # log another "data line", non-standard # final output print('termination by', optim.stop()) print('best f-value =', optim.result[1]) print('best solution =', optim.result[0]) optim.logger.plot() # if matplotlib is available
| Method | __init__ |
xstart is a mandatory argument |
| Method | ask |
abstract method, AKA "get" or "sample_distribution", deliver new candidate solution(s), a list of "vectors" |
| Method | disp |
abstract method, display some iteration info when self.iteration_counter % modulo < 1, using a reasonable default for modulo if modulo is None. |
| Method | initialize |
(re-)set to the initial state |
| Method | optimize |
find minimizer of objective_fct. |
| Method | stop |
abstract method, return satisfied termination conditions in a dictionary like {'termination reason': value, ...} or {}. |
| Method | tell |
abstract method, AKA "update", pass f-values and prepare for next iteration |
| Instance Variable | countiter |
Undocumented |
| Instance Variable | more |
Undocumented |
| Instance Variable | optional |
Undocumented |
| Instance Variable | xcurrent |
Undocumented |
| Instance Variable | xstart |
Undocumented |
| Property | result |
abstract property, contain (x, f(x), ...), that is, the minimizer, its function value, ... |
| Method | _force |
try force the logger to log NOW |
| Method | _prepare |
return a list of callbacks including self.logger.add. |
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
xstart is a mandatory argument
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract method, AKA "get" or "sample_distribution", deliver new candidate solution(s), a list of "vectors"
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract method, display some iteration info when
self.iteration_counter % modulo < 1, using a reasonable
default for modulo
if modulo is None.
find minimizer of objective_fct.
CAVEAT: the return value for optimize
has changed to self,
allowing for a call like:
solver = OOOptimizer(x0).optimize(f)
and investigate the state of the solver.
Arguments
- objective_fct: f(x: array_like) -> float
- function be to minimized
- maxfun: number
- maximal number of function evaluations
- iterations: number
- number of (maximal) iterations, while not self.stop(), it can be useful to conduct only one iteration at a time.
- min_iterations: number
- minimal number of iterations, even if not self.stop()
- args: sequence_like
- arguments passed to objective_fct
- verb_disp: number
- print to screen every verb_disp iteration, if
None
the value from self.logger is "inherited", if available. - callback: callable or list of callables
- callback function called like callback(self) or
a list of call back functions called in the same way. If
available, self.logger.add is added to this list.
TODO: currently there is no way to prevent this other than
changing the code of
_prepare_callback_list
. - n_jobs=0: number of processes to be acquired for
- multiprocessing to parallelize calls to
objective_fct
. Must be >1 to expect any speed-up orNone
or-1
, which both default to the number of available CPUs. The default n_jobs=0 avoids the use of multiprocessing altogether.
return self, that is, the OOOptimizer
instance.
Example
>>> import cma >>> es = cma.CMAEvolutionStrategy(7 * [0.1], 0.1 ... ).optimize(cma.ff.rosen, verb_disp=100) ... #doctest: +ELLIPSIS (4_w,9)-aCMA-ES (mu_w=2.8,w_1=49%) in dimension 7 (seed=...) Iterat #Fevals function value axis ratio sigma ... 1 9 ... 2 18 ... 3 27 ... 100 900 ... >>> cma.s.Mh.vequals_approximately(es.result[0], 7 * [1], 1e-5) True
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract method, return satisfied termination conditions in a dictionary like {'termination reason': value, ...} or {}.
For example {'tolfun': 1e-12}, or the empty dictionary {}.
TODO: this should rather be a property!? Unfortunately, a change would break backwards compatibility.
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract method, AKA "update", pass f-values and prepare for next iteration
cma.evolution_strategy.CMAEvolutionStrategy
, cma.purecma.CMAES
abstract property, contain (x, f(x), ...), that is, the minimizer, its function value, ...


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Comments
-
Greg Hurrell
Just did a quick test of the possible gains here:
1.6 release (ie. standard recursion)
user system total real pathological 0.010000 0.000000 0.010000 ( 0.005239) command-t 0.430000 0.000000 0.430000 ( 0.433271) chromium (subset) 1.440000 0.010000 1.450000 ( 0.576339) chromium (whole) 4.990000 0.020000 5.010000 ( 1.943098)
With recursion disabled
user system total real pathological 0.000000 0.000000 0.000000 ( 0.000615) command-t 0.340000 0.010000 0.350000 ( 0.336757) chromium (subset) 0.470000 0.010000 0.480000 ( 0.292511) chromium (whole) 1.610000 0.010000 1.620000 ( 0.961244)
Analysis
Oddly, the numbers with recursion disabled were wildly variable, suggesting perhaps some additional, unpredictable cache pressure when running without recursion.
Obviously, the pathological case is much, much faster in this way. I'm surprised that the "chromium (whole)" case didn't see a bigger benefit, but perhaps I shouldn't be so surprised; the prevalence of matches that can be scored in multiple ways is probably pretty low (and the cases that are common, like matching against "e", are probably pretty cheap to recurse on).
So, based on these numbers, I am not sure whether this angle is worth pursuing, although I might try a simple safety valve threshold of, say, 3, and run the numbers again to see if I can get scores of comparable quality at a reduced efficiency cost.
-
Greg Hurrell
Numbers with a depth limit of 3 (ie. recurse 3 times):
user system total real pathological 0.000000 0.000000 0.000000 ( 0.000906) command-t 0.440000 0.000000 0.440000 ( 0.439680) chromium (subset) 1.080000 0.020000 1.100000 ( 0.465615) chromium (whole) 3.820000 0.010000 3.830000 ( 1.590747)
Depth limit of 2 (ie. recurse 2 times):
user system total real pathological 0.000000 0.000000 0.000000 ( 0.000687) command-t 0.420000 0.000000 0.420000 ( 0.428034) chromium (subset) 0.860000 0.020000 0.880000 ( 0.404738) chromium (whole) 3.110000 0.010000 3.120000 ( 1.396657)
Depth limit of 1 (ie. recurse once only):
user system total real pathological 0.000000 0.000000 0.000000 ( 0.000465) command-t 0.390000 0.000000 0.390000 ( 0.389400) chromium (subset) 0.670000 0.020000 0.690000 ( 0.366292) chromium (whole) 2.320000 0.010000 2.330000 ( 1.141482)
Depth limit of 0 (no recursion):
user system total real pathological 0.000000 0.000000 0.000000 ( 0.000466) command-t 0.330000 0.000000 0.330000 ( 0.329658) chromium (subset) 0.480000 0.020000 0.500000 ( 0.289310) chromium (whole) 1.620000 0.010000 1.630000 ( 0.958717)
For the first three, the specs pass; with recursion disabled, we have one failing spec. Alas, I don't think I really have enough spec coverage around the ranking to get a truly realistic sense of how this is affecting match quality (and the benchmarks, too, might be a little sparse in terms of exposing the cases where these changes would really make a difference).
I'm going to stash this change on a branch of now and leave it sitting there.
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claf.model.token_classification package¶
Submodules¶
-
class
claf.model.token_classification.mixin.
TokenClassification
[source]¶ Bases:
object
Token Classification Mixin Class
-
make_metrics
(predictions)[source]¶ Make metrics with prediction dictionary
- Args:
- predictions: prediction dictionary consisting of
key: ‘id’ (sequence id)
- value: dictionary consisting of
tag_idxs
- Returns:
- metrics: metric dictionary consisting of
‘accuracy’: sequence level accuracy
‘tag_accuracy’: tag level accuracy
‘macro_f1’: tag prediction macro(unweighted mean) f1
‘macro_precision’: tag prediction macro(unweighted mean) precision
‘macro_recall’: tag prediction macro(unweighted mean) recall
-
make_predictions
(output_dict)[source]¶ Make predictions with model’s output_dict
- Args:
- output_dict: model’s output dictionary consisting of
sequence_embed: embedding vector of the sequence
tag_logits: representing unnormalized log probabilities of the tag
tag_idxs: target tag idxs
data_idx: data idx
loss: a scalar loss to be optimized
- Returns:
- predictions: prediction dictionary consisting of
key: ‘id’ (sequence id)
- value: dictionary consisting of
tag_idxs
-
predict
(**kwargs)¶
-
print_examples
(index, inputs, predictions)[source]¶ Print evaluation examples
- Args:
index: data index inputs: mini-batch inputs predictions: prediction dictionary consisting of
key: ‘id’ (sequence id)
- value: dictionary consisting of
class_idx
- Returns:
print(Sequence, Target Tags, Target Slots, Predicted Tags, Predicted Slots)
-
Module contents¶
-
class
claf.model.token_classification.
BertForTokCls
(token_makers, num_tags, ignore_tag_idx, pretrained_model_name=None, dropout=0.2)[source]¶ Bases:
claf.model.token_classification.mixin.TokenClassification
,claf.model.base.ModelWithoutTokenEmbedder
Implementation of Single Sentence Tagging model presented in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (https://arxiv.org/abs/1810.04805)
- Args:
token_embedder: used to embed the sequence num_tags: number of classified tags ignore_tag_idx: index of the tag to ignore when calculating loss (tag pad value)
- Kwargs:
pretrained_model_name: the name of a pre-trained model dropout: classification layer dropout
-
forward
(features, labels=None)[source]¶ - Args:
features: feature dictionary like below. {
- “bert_input”: {
- “feature”: [
[100, 576, 21, 45, 7, 91, 101, 0, 0, …], …,
]
} “token_type”: {
- “feature”: [
[0, 0, 0, 0, 0, 0, 0, 0, 0, …], …,
]
}, “tagged_sub_token_idxs”: {
- [
[1, 3, 4, 0, 0, 0, 0, 0, 0, …], …,
]
}
}
- Kwargs:
label: label dictionary like below. {
“class_idx”: [2, 1, 0, 4, 5, …] “data_idx”: [2, 4, 5, 7, 2, 1, …]
} Do not calculate loss when there is no label. (inference/predict mode)
- Returns: output_dict (dict) consisting of
sequence_embed: embedding vector of the sequence
tag_logits: representing unnormalized log probabilities of the tags.
tag_idxs: target class idx
data_idx: data idx
loss: a scalar loss to be optimized
-
print_examples
(index, inputs, predictions)[source]¶ Print evaluation examples
- Args:
index: data index inputs: mini-batch inputs predictions: prediction dictionary consisting of
key: ‘id’ (sequence id)
- value: dictionary consisting of
class_idx
- Returns:
print(Sequence, Sequence Tokens, Target Tags, Target Slots, Predicted Tags, Predicted Slots)


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