File Coverage

blib/lib/Paws/MachineLearning/MLModel.pm
Criterion Covered Total %
statement 6 6 100.0
branch n/a
condition n/a
subroutine 2 2 100.0
pod n/a
total 8 8 100.0


line stmt bran cond sub pod time code
1             package Paws::MachineLearning::MLModel;
2 1     1   433 use Moose;
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  1         8  
  1         361  
  1         2  
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3             has Algorithm => (is => 'ro', isa => 'Str');
4             has ComputeTime => (is => 'ro', isa => 'Int');
5             has CreatedAt => (is => 'ro', isa => 'Str');
6             has CreatedByIamUser => (is => 'ro', isa => 'Str');
7             has EndpointInfo => (is => 'ro', isa => 'Paws::MachineLearning::RealtimeEndpointInfo');
8             has FinishedAt => (is => 'ro', isa => 'Str');
9             has InputDataLocationS3 => (is => 'ro', isa => 'Str');
10             has LastUpdatedAt => (is => 'ro', isa => 'Str');
11             has Message => (is => 'ro', isa => 'Str');
12             has MLModelId => (is => 'ro', isa => 'Str');
13             has MLModelType => (is => 'ro', isa => 'Str');
14             has Name => (is => 'ro', isa => 'Str');
15             has ScoreThreshold => (is => 'ro', isa => 'Num');
16             has ScoreThresholdLastUpdatedAt => (is => 'ro', isa => 'Str');
17             has SizeInBytes => (is => 'ro', isa => 'Int');
18             has StartedAt => (is => 'ro', isa => 'Str');
19             has Status => (is => 'ro', isa => 'Str');
20             has TrainingDataSourceId => (is => 'ro', isa => 'Str');
21             has TrainingParameters => (is => 'ro', isa => 'Paws::MachineLearning::TrainingParameters');
22             1;
23              
24             ### main pod documentation begin ###
25              
26             =head1 NAME
27              
28             Paws::MachineLearning::MLModel
29              
30             =head1 USAGE
31              
32             This class represents one of two things:
33              
34             =head3 Arguments in a call to a service
35              
36             Use the attributes of this class as arguments to methods. You shouldn't make instances of this class.
37             Each attribute should be used as a named argument in the calls that expect this type of object.
38              
39             As an example, if Att1 is expected to be a Paws::MachineLearning::MLModel object:
40              
41             $service_obj->Method(Att1 => { Algorithm => $value, ..., TrainingParameters => $value });
42              
43             =head3 Results returned from an API call
44              
45             Use accessors for each attribute. If Att1 is expected to be an Paws::MachineLearning::MLModel object:
46              
47             $result = $service_obj->Method(...);
48             $result->Att1->Algorithm
49              
50             =head1 DESCRIPTION
51              
52             Represents the output of a C<GetMLModel> operation.
53              
54             The content consists of the detailed metadata and the current status of
55             the C<MLModel>.
56              
57             =head1 ATTRIBUTES
58              
59              
60             =head2 Algorithm => Str
61              
62             The algorithm used to train the C<MLModel>. The following algorithm is
63             supported:
64              
65             =over
66              
67             =item * C<SGD> -- Stochastic gradient descent. The goal of C<SGD> is to
68             minimize the gradient of the loss function.
69              
70             =back
71              
72              
73              
74             =head2 ComputeTime => Int
75              
76            
77              
78              
79             =head2 CreatedAt => Str
80              
81             The time that the C<MLModel> was created. The time is expressed in
82             epoch time.
83              
84              
85             =head2 CreatedByIamUser => Str
86              
87             The AWS user account from which the C<MLModel> was created. The account
88             type can be either an AWS root account or an AWS Identity and Access
89             Management (IAM) user account.
90              
91              
92             =head2 EndpointInfo => L<Paws::MachineLearning::RealtimeEndpointInfo>
93              
94             The current endpoint of the C<MLModel>.
95              
96              
97             =head2 FinishedAt => Str
98              
99            
100              
101              
102             =head2 InputDataLocationS3 => Str
103              
104             The location of the data file or directory in Amazon Simple Storage
105             Service (Amazon S3).
106              
107              
108             =head2 LastUpdatedAt => Str
109              
110             The time of the most recent edit to the C<MLModel>. The time is
111             expressed in epoch time.
112              
113              
114             =head2 Message => Str
115              
116             A description of the most recent details about accessing the
117             C<MLModel>.
118              
119              
120             =head2 MLModelId => Str
121              
122             The ID assigned to the C<MLModel> at creation.
123              
124              
125             =head2 MLModelType => Str
126              
127             Identifies the C<MLModel> category. The following are the available
128             types:
129              
130             =over
131              
132             =item * C<REGRESSION> - Produces a numeric result. For example, "What
133             price should a house be listed at?"
134              
135             =item * C<BINARY> - Produces one of two possible results. For example,
136             "Is this a child-friendly web site?".
137              
138             =item * C<MULTICLASS> - Produces one of several possible results. For
139             example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
140              
141             =back
142              
143              
144              
145             =head2 Name => Str
146              
147             A user-supplied name or description of the C<MLModel>.
148              
149              
150             =head2 ScoreThreshold => Num
151              
152            
153              
154              
155             =head2 ScoreThresholdLastUpdatedAt => Str
156              
157             The time of the most recent edit to the C<ScoreThreshold>. The time is
158             expressed in epoch time.
159              
160              
161             =head2 SizeInBytes => Int
162              
163            
164              
165              
166             =head2 StartedAt => Str
167              
168            
169              
170              
171             =head2 Status => Str
172              
173             The current status of an C<MLModel>. This element can have one of the
174             following values:
175              
176             =over
177              
178             =item * C<PENDING> - Amazon Machine Learning (Amazon ML) submitted a
179             request to create an C<MLModel>.
180              
181             =item * C<INPROGRESS> - The creation process is underway.
182              
183             =item * C<FAILED> - The request to create an C<MLModel> didn't run to
184             completion. The model isn't usable.
185              
186             =item * C<COMPLETED> - The creation process completed successfully.
187              
188             =item * C<DELETED> - The C<MLModel> is marked as deleted. It isn't
189             usable.
190              
191             =back
192              
193              
194              
195             =head2 TrainingDataSourceId => Str
196              
197             The ID of the training C<DataSource>. The C<CreateMLModel> operation
198             uses the C<TrainingDataSourceId>.
199              
200              
201             =head2 TrainingParameters => L<Paws::MachineLearning::TrainingParameters>
202              
203             A list of the training parameters in the C<MLModel>. The list is
204             implemented as a map of key-value pairs.
205              
206             The following is the current set of training parameters:
207              
208             =over
209              
210             =item *
211              
212             C<sgd.maxMLModelSizeInBytes> - The maximum allowed size of the model.
213             Depending on the input data, the size of the model might affect its
214             performance.
215              
216             The value is an integer that ranges from C<100000> to C<2147483648>.
217             The default value is C<33554432>.
218              
219             =item *
220              
221             C<sgd.maxPasses> - The number of times that the training process
222             traverses the observations to build the C<MLModel>. The value is an
223             integer that ranges from C<1> to C<10000>. The default value is C<10>.
224              
225             =item *
226              
227             C<sgd.shuffleType> - Whether Amazon ML shuffles the training data.
228             Shuffling the data improves a model's ability to find the optimal
229             solution for a variety of data types. The valid values are C<auto> and
230             C<none>. The default value is C<none>.
231              
232             =item *
233              
234             C<sgd.l1RegularizationAmount> - The coefficient regularization L1 norm,
235             which controls overfitting the data by penalizing large coefficients.
236             This parameter tends to drive coefficients to zero, resulting in sparse
237             feature set. If you use this parameter, start by specifying a small
238             value, such as C<1.0E-08>.
239              
240             The value is a double that ranges from C<0> to C<MAX_DOUBLE>. The
241             default is to not use L1 normalization. This parameter can't be used
242             when C<L2> is specified. Use this parameter sparingly.
243              
244             =item *
245              
246             C<sgd.l2RegularizationAmount> - The coefficient regularization L2 norm,
247             which controls overfitting the data by penalizing large coefficients.
248             This tends to drive coefficients to small, nonzero values. If you use
249             this parameter, start by specifying a small value, such as C<1.0E-08>.
250              
251             The value is a double that ranges from C<0> to C<MAX_DOUBLE>. The
252             default is to not use L2 normalization. This parameter can't be used
253             when C<L1> is specified. Use this parameter sparingly.
254              
255             =back
256              
257              
258              
259              
260             =head1 SEE ALSO
261              
262             This class forms part of L<Paws>, describing an object used in L<Paws::MachineLearning>
263              
264             =head1 BUGS and CONTRIBUTIONS
265              
266             The source code is located here: https://github.com/pplu/aws-sdk-perl
267              
268             Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues
269              
270             =cut
271