Meteos/ExampleDecisionTree

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 $ neutron net-list | grep public $ 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 +---+-+ +---+-+ +---+-+
 * a6b7de0b-10ff-462c-9c86-25c8a5622a57 | meteos                         |
 * 4222b557-6d9f-405c-b1ff-0f454d2f35bf | public | 1f979ae3-d6b7-4d03-ba0f-9d9112581783             |
 * 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 $ 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 ++--+ ++--+ ++--+
 * e4fcc49c-48e5-48f8-9599-bb5eba1339c9 | private | e15c24a5-dfdd-4428-b27d-9827b35600c0 10.0.0.0/26 |
 * 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) +--+--+-++--+ +--+--+-++--+ +--+--+-++--+ $ openstack server list (or nova list) +--++++-+--+ +--++++-+--+ +--++++-+--+
 * Name            | Id                                   | Plugin name | Plugin version | Status   |
 * cluster-fcc8d055 | 5736d157-ac7c-41de-8aca-78f7afa7e99c | spark      | 1.6.0          | Spawning |
 * 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": "tree-model", "display_description": "Sample Decision Tree Model", "source_dataset_url": "swift://meteos/decision_tree_data.txt", "model_type": "DecisionTreeRegression", "model_params": "{'numIterations': 100}", "dataset_format": "libsvm", "experiment_id": "fcc8d055-e801-4652-af8c-5aabedbf0286", "swift_tenant": "demo", "swift_username": "demo", "swift_password": "nova" } $ meteos model-create --json sample/json/model_decision_tree.json +-+--+ +-+--+ +-+--+ $ meteos model-list +--++---++---+ +--++---++---+ +--++---++---+
 * Property   | Value                                |
 * created_at | 2016-12-15T23:23:04.000000           |
 * description | Sample Decision Tree Model          |
 * id         | 1e3547ff-294d-4a9c-8e94-9cc31c861288 |
 * name       | tree-model                           |
 * params     | eydudW1JdGVyYXRpb25zJzogMTAwfQ==     |
 * project_id | 4034bb3cd9324776a68c140fdd18baa4     |
 * status     | creating                             |
 * stderr     | None                                 |
 * stdout     | None                                 |
 * type       | DecisionTreeRegression               |
 * user_id    | 64ad04e101df40b4b51e5f370a83412c     |
 * id                                  | name       | status    | type                   | source_dataset_url                    |
 * 1e3547ff-294d-4a9c-8e94-9cc31c861288 | tree-model | available | DecisionTreeRegression | swift://meteos/decision_tree_data.txt |

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": "1e3547ff-294d-4a9c-8e94-9cc31c861288", "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 | 2016-12-15T23:27:08.000000           |
 * description | This is a sample job                |
 * id         | cf2a6b1f-bb78-419e-a2fc-608fa0df52fc |
 * method     | predict                              |
 * name       | example-learning-job                 |
 * project_id | 4034bb3cd9324776a68c140fdd18baa4     |
 * status     | creating                             |
 * stderr     | None                                 |
 * stdout     | None                                 |
 * user_id    | 64ad04e101df40b4b51e5f370a83412c     |

Retrieve a predicted data as a stdout of job execution.

$ meteos learning-list +--+--+---+---+---+ +--+--+---+---+---+ +--+--+---+---+---+
 * id                                  | name                 | status    | args                      | stdout        |
 * cf2a6b1f-bb78-419e-a2fc-608fa0df52fc | example-learning-job | available | 1:1 2:-4 3:1 4:2 5:2 6:-2 | 0.0          |

6. Online Prediction
You can load a Prediction Model in advance for online prediction by using "meteos-load" command.

In online prediction, user can retrieve a predicted data immediately.

For more details, please see here.