File Coverage

blib/lib/Paws/MachineLearning/GetMLModelOutput.pm
Criterion Covered Total %
statement 3 3 100.0
branch n/a
condition n/a
subroutine 1 1 100.0
pod n/a
total 4 4 100.0


line stmt bran cond sub pod time code
1              
2             package Paws::MachineLearning::GetMLModelOutput;
3 1     1   553 use Moose;
  1         3  
  1         10  
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 LogUri => (is => 'ro', isa => 'Str');
12             has Message => (is => 'ro', isa => 'Str');
13             has MLModelId => (is => 'ro', isa => 'Str');
14             has MLModelType => (is => 'ro', isa => 'Str');
15             has Name => (is => 'ro', isa => 'Str');
16             has Recipe => (is => 'ro', isa => 'Str');
17             has Schema => (is => 'ro', isa => 'Str');
18             has ScoreThreshold => (is => 'ro', isa => 'Num');
19             has ScoreThresholdLastUpdatedAt => (is => 'ro', isa => 'Str');
20             has SizeInBytes => (is => 'ro', isa => 'Int');
21             has StartedAt => (is => 'ro', isa => 'Str');
22             has Status => (is => 'ro', isa => 'Str');
23             has TrainingDataSourceId => (is => 'ro', isa => 'Str');
24             has TrainingParameters => (is => 'ro', isa => 'Paws::MachineLearning::TrainingParameters');
25              
26             has _request_id => (is => 'ro', isa => 'Str');
27              
28             ### main pod documentation begin ###
29              
30             =head1 NAME
31              
32             Paws::MachineLearning::GetMLModelOutput
33              
34             =head1 ATTRIBUTES
35              
36              
37             =head2 ComputeTime => Int
38              
39             The approximate CPU time in milliseconds that Amazon Machine Learning
40             spent processing the C<MLModel>, normalized and scaled on computation
41             resources. C<ComputeTime> is only available if the C<MLModel> is in the
42             C<COMPLETED> state.
43              
44              
45             =head2 CreatedAt => Str
46              
47             The time that the C<MLModel> was created. The time is expressed in
48             epoch time.
49              
50              
51             =head2 CreatedByIamUser => Str
52              
53             The AWS user account from which the C<MLModel> was created. The account
54             type can be either an AWS root account or an AWS Identity and Access
55             Management (IAM) user account.
56              
57              
58             =head2 EndpointInfo => L<Paws::MachineLearning::RealtimeEndpointInfo>
59              
60             The current endpoint of the C<MLModel>
61              
62              
63             =head2 FinishedAt => Str
64              
65             The epoch time when Amazon Machine Learning marked the C<MLModel> as
66             C<COMPLETED> or C<FAILED>. C<FinishedAt> is only available when the
67             C<MLModel> is in the C<COMPLETED> or C<FAILED> state.
68              
69              
70             =head2 InputDataLocationS3 => Str
71              
72             The location of the data file or directory in Amazon Simple Storage
73             Service (Amazon S3).
74              
75              
76             =head2 LastUpdatedAt => Str
77              
78             The time of the most recent edit to the C<MLModel>. The time is
79             expressed in epoch time.
80              
81              
82             =head2 LogUri => Str
83              
84             A link to the file that contains logs of the C<CreateMLModel>
85             operation.
86              
87              
88             =head2 Message => Str
89              
90             A description of the most recent details about accessing the
91             C<MLModel>.
92              
93              
94             =head2 MLModelId => Str
95              
96             The MLModel ID, which is same as the C<MLModelId> in the request.
97              
98              
99             =head2 MLModelType => Str
100              
101             Identifies the C<MLModel> category. The following are the available
102             types:
103              
104             =over
105              
106             =item * REGRESSION -- Produces a numeric result. For example, "What
107             price should a house be listed at?"
108              
109             =item * BINARY -- Produces one of two possible results. For example,
110             "Is this an e-commerce website?"
111              
112             =item * MULTICLASS -- Produces one of several possible results. For
113             example, "Is this a HIGH, LOW or MEDIUM risk trade?"
114              
115             =back
116              
117              
118             Valid values are: C<"REGRESSION">, C<"BINARY">, C<"MULTICLASS">
119             =head2 Name => Str
120              
121             A user-supplied name or description of the C<MLModel>.
122              
123              
124             =head2 Recipe => Str
125              
126             The recipe to use when training the C<MLModel>. The C<Recipe> provides
127             detailed information about the observation data to use during training,
128             and manipulations to perform on the observation data during training.
129              
130             This parameter is provided as part of the verbose format.
131              
132              
133             =head2 Schema => Str
134              
135             The schema used by all of the data files referenced by the
136             C<DataSource>.
137              
138             This parameter is provided as part of the verbose format.
139              
140              
141             =head2 ScoreThreshold => Num
142              
143             The scoring threshold is used in binary classification C<MLModel>
144             models. It marks the boundary between a positive prediction and a
145             negative prediction.
146              
147             Output values greater than or equal to the threshold receive a positive
148             result from the MLModel, such as C<true>. Output values less than the
149             threshold receive a negative response from the MLModel, such as
150             C<false>.
151              
152              
153             =head2 ScoreThresholdLastUpdatedAt => Str
154              
155             The time of the most recent edit to the C<ScoreThreshold>. The time is
156             expressed in epoch time.
157              
158              
159             =head2 SizeInBytes => Int
160              
161              
162              
163              
164             =head2 StartedAt => Str
165              
166             The epoch time when Amazon Machine Learning marked the C<MLModel> as
167             C<INPROGRESS>. C<StartedAt> isn't available if the C<MLModel> is in the
168             C<PENDING> state.
169              
170              
171             =head2 Status => Str
172              
173             The current status of the 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 describe a C<MLModel>.
180              
181             =item * C<INPROGRESS> - The request is processing.
182              
183             =item * C<FAILED> - The request did not run to completion. The ML model
184             isn't usable.
185              
186             =item * C<COMPLETED> - The request 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             Valid values are: C<"PENDING">, C<"INPROGRESS">, C<"FAILED">, C<"COMPLETED">, C<"DELETED">
195             =head2 TrainingDataSourceId => Str
196              
197             The ID of the training C<DataSource>.
198              
199              
200             =head2 TrainingParameters => L<Paws::MachineLearning::TrainingParameters>
201              
202             A list of the training parameters in the C<MLModel>. The list is
203             implemented as a map of key-value pairs.
204              
205             The following is the current set of training parameters:
206              
207             =over
208              
209             =item *
210              
211             C<sgd.maxMLModelSizeInBytes> - The maximum allowed size of the model.
212             Depending on the input data, the size of the model might affect its
213             performance.
214              
215             The value is an integer that ranges from C<100000> to C<2147483648>.
216             The default value is C<33554432>.
217              
218             =item *
219              
220             C<sgd.maxPasses> - The number of times that the training process
221             traverses the observations to build the C<MLModel>. The value is an
222             integer that ranges from C<1> to C<10000>. The default value is C<10>.
223              
224             =item *
225              
226             C<sgd.shuffleType> - Whether Amazon ML shuffles the training data.
227             Shuffling data improves a model's ability to find the optimal solution
228             for a variety of data types. The valid values are C<auto> and C<none>.
229             The default value is C<none>. We strongly recommend that you shuffle
230             your data.
231              
232             =item *
233              
234             C<sgd.l1RegularizationAmount> - The coefficient regularization L1 norm.
235             It controls overfitting the data by penalizing large coefficients. This
236             tends to drive coefficients to zero, resulting in a sparse feature set.
237             If you use this parameter, start by specifying a small value, such as
238             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             It controls overfitting the data by penalizing large coefficients. This
248             tends to drive coefficients to small, nonzero values. If you use this
249             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             =head2 _request_id => Str
260              
261              
262             =cut
263              
264             1;