Meteos/ExampleDecisionTree
< Meteos
Revision as of 23:21, 15 December 2016 by HiroyukiEguchi (talk | contribs) (→Make a Decision to buy a stock using Meteos)
Contents
Make a Decision to buy a stock using Meteos
In this example, you creates a prediction model which make a decision to buy a stock by using Decision Tree Model.
1. Create a experiment template
Create template of experiment. Experiment is a workspace of Machine Learning.
You have to confirm a glance image id of meteos image, and a neutron network id before creating a template.
You can use a format located in python-meteosclient/sample/json/template.json
$ glance image-list | grep meteos | a6b7de0b-10ff-462c-9c86-25c8a5622a57 | meteos |
$ neutron net-list | grep public | 4222b557-6d9f-405c-b1ff-0f454d2f35bf | public | 1f979ae3-d6b7-4d03-ba0f-9d9112581783 |
$ vim sample/json/template.json
$ cat sample/json/template.json
{
"display_name": "example-template",
"display_description": "This is a sample template of experiment",
"image_id" : "a6b7de0b-10ff-462c-9c86-25c8a5622a57",
"master_nodes_num": 1,
"master_flavor_id": "4",
"worker_nodes_num": 2,
"worker_flavor_id": "2",
"spark_version": "1.6.0",
"floating_ip_pool": "4222b557-6d9f-405c-b1ff-0f454d2f35bf"
}
$ meteos template-create --json sample/json/template.json +---------------+-----------------------------------------+ | Property | Value | +---------------+-----------------------------------------+ | cluster_id | None | | created_at | 2016-12-15T22:55:03.000000 | | description | This is a sample template of experiment | | id | d3180a78-74cc-432d-9e9d-68640b18abae | | master_flavor | 4 | | master_nodes | 1 | | name | example-template | | project_id | 4034bb3cd9324776a68c140fdd18baa4 | | spark_version | 1.6.0 | | status | available | | user_id | 64ad04e101df40b4b51e5f370a83412c | | worker_flavor | 2 | | worker_nodes | 2 | +---------------+-----------------------------------------+
2. Create a experiment from template
Create a experiment by using template created in the above step. You have to confirm a neutron private network id and create keypair before creating a template.
You can use a format located in python-meteosclient/sample/json/experiment.json
$ nova keypair-add key1 > ~/key1.pem && chmod 600 ~/key1.pem
$ neutron net-list | grep private | e4fcc49c-48e5-48f8-9599-bb5eba1339c9 | private | e15c24a5-dfdd-4428-b27d-9827b35600c0 10.0.0.0/26 |
$ vim sample/json/experiment.json
$ cat sample/json/experiment.json
{
"display_name": "example-experiment",
"display_description": "This is a sample experiment",
"key_name": "key1",
"neutron_management_network": "e4fcc49c-48e5-48f8-9599-bb5eba1339c9",
"template_id": "d3180a78-74cc-432d-9e9d-68640b18abae"
}
$ meteos experiment-create --json sample/json/experiment.json +--------------------+--------------------------------------+ | Property | Value | +--------------------+--------------------------------------+ | created_at | 2016-12-15T22:56:51.000000 | | description | This is a sample experiment | | id | fcc8d055-e801-4652-af8c-5aabedbf0286 | | key_name | key1 | | management_network | e4fcc49c-48e5-48f8-9599-bb5eba1339c9 | | name | example-experiment | | project_id | 4034bb3cd9324776a68c140fdd18baa4 | | status | creating | | user_id | 64ad04e101df40b4b51e5f370a83412c | +--------------------+--------------------------------------+
Meteos creates a experiment using OpenStack Sahara spark plugin.
You can see a sahara cluster and nova VMs created by Meteos as below.
$ openstack dataprocessing cluster list (or sahara cluster-list) +------------------+--------------------------------------+-------------+----------------+----------+ | Name | Id | Plugin name | Plugin version | Status | +------------------+--------------------------------------+-------------+----------------+----------+ | cluster-fcc8d055 | 5736d157-ac7c-41de-8aca-78f7afa7e99c | spark | 1.6.0 | Spawning | +------------------+--------------------------------------+-------------+----------------+----------+
$ openstack server list (or nova list) +--------------------------------------+----------------------------+--------+------------+-------------+------------------+ | ID | Name | Status | Task State | Power State | Networks | +--------------------------------------+----------------------------+--------+------------+-------------+------------------+ | 48a9f429-7756-4bed-8dd6-6dc6140ef897 | cluster-fcc8d055-master-0 | ACTIVE | - | Running | private=10.0.0.5 | | 88ff2070-dfe8-45da-aa5c-02ac3e9de3b8 | cluster-fcc8d055-workers-0 | ACTIVE | - | Running | private=10.0.0.7 | | a57dfa5d-8b55-47c7-aae7-d5b3c8779787 | cluster-fcc8d055-workers-1 | ACTIVE | - | Running | private=10.0.0.4 | +--------------------------------------+----------------------------+--------+------------+-------------+------------------+
3. Upload a raw data
Upload a raw data (in this example past stock market data) to OpenStack Swift.
You can use a sample data located in python-meteosclient/sample/data/decision_tree_data.txt
Raw data shows "Flag which indicate buy or not buy", "Related Stock Code : Price Change",... from left.
$ cd sample/data/
$ head decision_tree_data.txt 1 1:-5 2:3 3:1 4:-5 5:-4 6:3 0 1:2 2:-5 3:0 4:0 5:1 6:-4 1 1:-3 2:1 3:4 4:-5 5:-3 6:4 0 1:4 2:-4 3:4 4:4 5:0 6:-1 1 1:-4 2:4 3:0 4:-5 5:-4 6:0 0 1:1 2:-4 3:3 4:3 5:0 6:-4 1 1:-3 2:4 3:0 4:-4 5:-3 6:2 0 1:2 2:-5 3:2 4:2 5:4 6:-3 1 1:-1 2:0 3:1 4:-2 5:-5 6:4 0 1:0 2:-4 3:1 4:1 5:4 6:-4
/sample/data$ swift upload meteos decision_tree_data.txt decision_tree_data.txt
4. Create a prediction model
In this example, you create a Decision Tree Model from dataset in swift directly.
$ vim sample/json/model_decision_tree.json
$ cat sample/json/model_decision_tree.json
{
"display_name": "sample-tree-model",
"display_description": "Decision Tree Model",
"source_dataset_url": "swift://meteos/decision_tree_data.txt",
"model_type": "DecisionTreeRegression",
"model_params": "{'numIterations': 100}",
"dataset_format": "libsvm",
"experiment_id": "fad1b912-8f7d-4600-b048-7db27aae1af3",
"swift_tenant": "demo",
"swift_username": "demo",
"swift_password": "nova"
}
$ meteos model-create --json sample/json/model_decision_tree.json +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | created_at | 2016-12-08T03:18:47.000000 | | description | Decision Tree Model | | id | 5c16c3f7-0093-4f82-a4f9-2ae421fc901f | | name | sample-tree-model | | params | eydudW1JdGVyYXRpb25zJzogMTAwfQ== | | project_id | 0e3e0f01952948848d8ae438279122fe | | status | creating | | stderr | None | | stdout | None | | type | DecisionTreeRegression | | user_id | 2c3120e8228c4d0e9768f09346fe842d | +-------------+--------------------------------------+
$ meteos model-list +--------------------------------------+-------------------+------------------------+-----------+------------------------+-----------------------------------------+----------------------------+ | id | name | description | status | type | source_dataset_url | created_at | +--------------------------------------+-------------------+------------------------+-----------+------------------------+-----------------------------------------+----------------------------+ | 5c16c3f7-0093-4f82-a4f9-2ae421fc901f | sample-tree-model | Decision Tree Model | available | DecisionTreeRegression | swift://meteos/decision_tree_data.txt | 2016-12-08T03:18:47.000000 | +--------------------------------------+-------------------+------------------------+-----------+------------------------+-----------------------------------------+----------------------------+
5. Predict
Create a learning job making a decision to buy or not to buy.
Specify the input value as "args" parameter.
$ vim sample/json/learning.json
$ cat sample/json/learning.json
{
"display_name": "example-learning-job",
"display_description": "This is a sample job",
"model_id": "5c16c3f7-0093-4f82-a4f9-2ae421fc901f",
"method": "predict",
"args": "1:1 2:-4 3:1 4:2 5:2 6:-2"
}
$ meteos learning-create --json sample/json/learning.json +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | args | MToxIDI6LTQgMzoxIDQ6MiA1OjIgNjotMg== | | created_at | 2016-12-08T03:26:25.000000 | | description | This is a sample job | | id | bbfaa00b-0857-441a-ac12-ef766a419ae0 | | method | predict | | name | example-learning-job | | project_id | 0e3e0f01952948848d8ae438279122fe | | status | creating | | stderr | None | | stdout | None | | user_id | 2c3120e8228c4d0e9768f09346fe842d | +-------------+--------------------------------------+
Retrieve a predicted data as a stdout of job execution.
$ meteos learning-list +--------------------------------------+----------------------+----------------------+-----------+----------------------------+--------+----------------------------+ | id | name | description | status | args | stdout | created_at | +--------------------------------------+----------------------+----------------------+-----------+----------------------------+--------+----------------------------+ | 429dc79d-bac0-4ea8-aa7d-6853066a7b26 | example-learning-job | This is a sample job | available | 1:-3 2:3 3:0 4:-3 5:-2 6:4 | 1.0 | 2016-12-08T03:30:08.000000 | | bbfaa00b-0857-441a-ac12-ef766a419ae0 | example-learning-job | This is a sample job | available | 1:1 2:-4 3:1 4:2 5:2 6:-2 | 0.0 | 2016-12-08T03:26:25.000000 | | cff6fa79-9a8a-4452-8afa-b6b284393a8c | example-learning-job | This is a sample job | available | 1:-4 2:2 3:2 4:-1 5:-2 6:1 | 1.0 | 2016-12-08T03:29:42.000000 | | e6802bb1-24ee-4a6f-b7ff-a45e61b7e8c2 | example-learning-job | This is a sample job | available | 1:3 2:-2 3:-1 4:3 5:2 6:-4 | 0.0 | 2016-12-08T03:31:01.000000 | +--------------------------------------+----------------------+----------------------+-----------+----------------------------+--------+----------------------------+
