File Coverage

blib/lib/Paws/MachineLearning/Evaluation.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	sub	time	code
1				package Paws::MachineLearning::Evaluation;
2	1	1	493	use Moose;
	1		3
	1		6
3				has ComputeTime => (is => 'ro', isa => 'Int');
4				has CreatedAt => (is => 'ro', isa => 'Str');
5				has CreatedByIamUser => (is => 'ro', isa => 'Str');
6				has EvaluationDataSourceId => (is => 'ro', isa => 'Str');
7				has EvaluationId => (is => 'ro', isa => 'Str');
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 Name => (is => 'ro', isa => 'Str');
14				has PerformanceMetrics => (is => 'ro', isa => 'Paws::MachineLearning::PerformanceMetrics');
15				has StartedAt => (is => 'ro', isa => 'Str');
16				has Status => (is => 'ro', isa => 'Str');
17				1;
18
19				### main pod documentation begin ###
20
21				=head1 NAME
22
23				Paws::MachineLearning::Evaluation
24
25				=head1 USAGE
26
27				This class represents one of two things:
28
29				=head3 Arguments in a call to a service
30
31				Use the attributes of this class as arguments to methods. You shouldn't make instances of this class.
32				Each attribute should be used as a named argument in the calls that expect this type of object.
33
34				As an example, if Att1 is expected to be a Paws::MachineLearning::Evaluation object:
35
36				$service_obj->Method(Att1 => { ComputeTime => $value, ..., Status => $value });
37
38				=head3 Results returned from an API call
39
40				Use accessors for each attribute. If Att1 is expected to be an Paws::MachineLearning::Evaluation object:
41
42				$result = $service_obj->Method(...);
43				$result->Att1->ComputeTime
44
45				=head1 DESCRIPTION
46
47				Represents the output of C<GetEvaluation> operation.
48
49				The content consists of the detailed metadata and data file information
50				and the current status of the C<Evaluation>.
51
52				=head1 ATTRIBUTES
53
54
55				=head2 ComputeTime => Int
56
57
58
59
60				=head2 CreatedAt => Str
61
62				The time that the C<Evaluation> was created. The time is expressed in
63				epoch time.
64
65
66				=head2 CreatedByIamUser => Str
67
68				The AWS user account that invoked the evaluation. The account type can
69				be either an AWS root account or an AWS Identity and Access Management
70				(IAM) user account.
71
72
73				=head2 EvaluationDataSourceId => Str
74
75				The ID of the C<DataSource> that is used to evaluate the C<MLModel>.
76
77
78				=head2 EvaluationId => Str
79
80				The ID that is assigned to the C<Evaluation> at creation.
81
82
83				=head2 FinishedAt => Str
84
85
86
87
88				=head2 InputDataLocationS3 => Str
89
90				The location and name of the data in Amazon Simple Storage Server
91				(Amazon S3) that is used in the evaluation.
92
93
94				=head2 LastUpdatedAt => Str
95
96				The time of the most recent edit to the C<Evaluation>. The time is
97				expressed in epoch time.
98
99
100				=head2 Message => Str
101
102				A description of the most recent details about evaluating the
103				C<MLModel>.
104
105
106				=head2 MLModelId => Str
107
108				The ID of the C<MLModel> that is the focus of the evaluation.
109
110
111				=head2 Name => Str
112
113				A user-supplied name or description of the C<Evaluation>.
114
115
116				=head2 PerformanceMetrics => L<Paws::MachineLearning::PerformanceMetrics>
117
118				Measurements of how well the C<MLModel> performed, using observations
119				referenced by the C<DataSource>. One of the following metrics is
120				returned, based on the type of the C<MLModel>:
121
122				=over
123
124				=item *
125
126				BinaryAUC: A binary C<MLModel> uses the Area Under the Curve (AUC)
127				technique to measure performance.
128
129				=item *
130
131				RegressionRMSE: A regression C<MLModel> uses the Root Mean Square Error
132				(RMSE) technique to measure performance. RMSE measures the difference
133				between predicted and actual values for a single variable.
134
135				=item *
136
137				MulticlassAvgFScore: A multiclass C<MLModel> uses the F1 score
138				technique to measure performance.
139
140				=back
141
142				For more information about performance metrics, please see the Amazon
143				Machine Learning Developer Guide.
144
145
146				=head2 StartedAt => Str
147
148
149
150
151				=head2 Status => Str
152
153				The status of the evaluation. This element can have one of the
154				following values:
155
156				=over
157
158				=item * C<PENDING> - Amazon Machine Learning (Amazon ML) submitted a
159				request to evaluate an C<MLModel>.
160
161				=item * C<INPROGRESS> - The evaluation is underway.
162
163				=item * C<FAILED> - The request to evaluate an C<MLModel> did not run
164				to completion. It is not usable.
165
166				=item * C<COMPLETED> - The evaluation process completed successfully.
167
168				=item * C<DELETED> - The C<Evaluation> is marked as deleted. It is not
169				usable.
170
171				=back
172
173
174
175
176				=head1 SEE ALSO
177
178				This class forms part of L<Paws>, describing an object used in L<Paws::MachineLearning>
179
180				=head1 BUGS and CONTRIBUTIONS
181
182				The source code is located here: https://github.com/pplu/aws-sdk-perl
183
184				Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues
185
186				=cut
187