1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct CreateSolutionInput {
    /// <p>The name for the solution.</p>
    #[doc(hidden)]
    pub name: std::option::Option<std::string::String>,
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    #[doc(hidden)]
    pub perform_hpo: std::option::Option<bool>,
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    #[doc(hidden)]
    pub perform_auto_ml: bool,
    /// <p>The ARN of the recipe to use for model training. Only specified when <code>performAutoML</code> is false.</p>
    #[doc(hidden)]
    pub recipe_arn: std::option::Option<std::string::String>,
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    #[doc(hidden)]
    pub dataset_group_arn: std::option::Option<std::string::String>,
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    #[doc(hidden)]
    pub event_type: std::option::Option<std::string::String>,
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    #[doc(hidden)]
    pub solution_config: std::option::Option<crate::types::SolutionConfig>,
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dev/tagging-resources.html">tags</a> to apply to the solution.</p>
    #[doc(hidden)]
    pub tags: std::option::Option<std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionInput {
    /// <p>The name for the solution.</p>
    pub fn name(&self) -> std::option::Option<&str> {
        self.name.as_deref()
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn perform_hpo(&self) -> std::option::Option<bool> {
        self.perform_hpo
    }
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn perform_auto_ml(&self) -> bool {
        self.perform_auto_ml
    }
    /// <p>The ARN of the recipe to use for model training. Only specified when <code>performAutoML</code> is false.</p>
    pub fn recipe_arn(&self) -> std::option::Option<&str> {
        self.recipe_arn.as_deref()
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(&self) -> std::option::Option<&str> {
        self.dataset_group_arn.as_deref()
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(&self) -> std::option::Option<&str> {
        self.event_type.as_deref()
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn solution_config(&self) -> std::option::Option<&crate::types::SolutionConfig> {
        self.solution_config.as_ref()
    }
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dev/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn tags(&self) -> std::option::Option<&[crate::types::Tag]> {
        self.tags.as_deref()
    }
}
impl CreateSolutionInput {
    /// Creates a new builder-style object to manufacture [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
    pub fn builder() -> crate::operation::create_solution::builders::CreateSolutionInputBuilder {
        crate::operation::create_solution::builders::CreateSolutionInputBuilder::default()
    }
}

/// A builder for [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct CreateSolutionInputBuilder {
    pub(crate) name: std::option::Option<std::string::String>,
    pub(crate) perform_hpo: std::option::Option<bool>,
    pub(crate) perform_auto_ml: std::option::Option<bool>,
    pub(crate) recipe_arn: std::option::Option<std::string::String>,
    pub(crate) dataset_group_arn: std::option::Option<std::string::String>,
    pub(crate) event_type: std::option::Option<std::string::String>,
    pub(crate) solution_config: std::option::Option<crate::types::SolutionConfig>,
    pub(crate) tags: std::option::Option<std::vec::Vec<crate::types::Tag>>,
}
impl CreateSolutionInputBuilder {
    /// <p>The name for the solution.</p>
    pub fn name(mut self, input: impl Into<std::string::String>) -> Self {
        self.name = Some(input.into());
        self
    }
    /// <p>The name for the solution.</p>
    pub fn set_name(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.name = input;
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.perform_hpo = Some(input);
        self
    }
    /// <p>Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is <code>false</code>.</p>
    /// <p>When performing AutoML, this parameter is always <code>true</code> and you should not set it to <code>false</code>.</p>
    pub fn set_perform_hpo(mut self, input: std::option::Option<bool>) -> Self {
        self.perform_hpo = input;
        self
    }
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.perform_auto_ml = Some(input);
        self
    }
    /// <p>Whether to perform automated machine learning (AutoML). The default is <code>false</code>. For this case, you must specify <code>recipeArn</code>.</p>
    /// <p>When set to <code>true</code>, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit <code>recipeArn</code>. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.</p>
    pub fn set_perform_auto_ml(mut self, input: std::option::Option<bool>) -> Self {
        self.perform_auto_ml = input;
        self
    }
    /// <p>The ARN of the recipe to use for model training. Only specified when <code>performAutoML</code> is false.</p>
    pub fn recipe_arn(mut self, input: impl Into<std::string::String>) -> Self {
        self.recipe_arn = Some(input.into());
        self
    }
    /// <p>The ARN of the recipe to use for model training. Only specified when <code>performAutoML</code> is false.</p>
    pub fn set_recipe_arn(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.recipe_arn = input;
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn dataset_group_arn(mut self, input: impl Into<std::string::String>) -> Self {
        self.dataset_group_arn = Some(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the dataset group that provides the training data.</p>
    pub fn set_dataset_group_arn(
        mut self,
        input: std::option::Option<std::string::String>,
    ) -> Self {
        self.dataset_group_arn = input;
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn event_type(mut self, input: impl Into<std::string::String>) -> Self {
        self.event_type = Some(input.into());
        self
    }
    /// <p>When your have multiple event types (using an <code>EVENT_TYPE</code> schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.</p>
    /// <p>If you do not provide an <code>eventType</code>, Amazon Personalize will use all interactions for training with equal weight regardless of type.</p>
    pub fn set_event_type(mut self, input: std::option::Option<std::string::String>) -> Self {
        self.event_type = input;
        self
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn solution_config(mut self, input: crate::types::SolutionConfig) -> Self {
        self.solution_config = Some(input);
        self
    }
    /// <p>The configuration to use with the solution. When <code>performAutoML</code> is set to true, Amazon Personalize only evaluates the <code>autoMLConfig</code> section of the solution configuration.</p> <note>
    /// <p>Amazon Personalize doesn't support configuring the <code>hpoObjective</code> at this time.</p>
    /// </note>
    pub fn set_solution_config(
        mut self,
        input: std::option::Option<crate::types::SolutionConfig>,
    ) -> Self {
        self.solution_config = input;
        self
    }
    /// Appends an item to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dev/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        let mut v = self.tags.unwrap_or_default();
        v.push(input);
        self.tags = Some(v);
        self
    }
    /// <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dev/tagging-resources.html">tags</a> to apply to the solution.</p>
    pub fn set_tags(
        mut self,
        input: std::option::Option<std::vec::Vec<crate::types::Tag>>,
    ) -> Self {
        self.tags = input;
        self
    }
    /// Consumes the builder and constructs a [`CreateSolutionInput`](crate::operation::create_solution::CreateSolutionInput).
    pub fn build(
        self,
    ) -> Result<
        crate::operation::create_solution::CreateSolutionInput,
        aws_smithy_http::operation::error::BuildError,
    > {
        Ok(crate::operation::create_solution::CreateSolutionInput {
            name: self.name,
            perform_hpo: self.perform_hpo,
            perform_auto_ml: self.perform_auto_ml.unwrap_or_default(),
            recipe_arn: self.recipe_arn,
            dataset_group_arn: self.dataset_group_arn,
            event_type: self.event_type,
            solution_config: self.solution_config,
            tags: self.tags,
        })
    }
}