Meteos/ExampleLinear
Contents
Predict a Sales Figures using Meteos
In this example, you create a prediction model which predict sales by using Linear Regression.
Linear Regression is one of the algorithms in supervised learning.
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 sales figures data) to OpenStack Swift.
You can use a sample data located in python-meteosclient/sample/data/linear_data.txt
Raw data shows "sales figures", "day", "month", "year", "day of week", "parameter which indicates weather", "degree", "humidity" from left.
$ cd sample/data/
/sample/data$ head linear_data.txt 4500,1,1,2016,5,0,40,50 8000,2,1,2016,6,1,60,80 9500,3,1,2016,0,2,88,92 5000,4,1,2016,1,3,90,90 0,5,1,2016,2,2,90,80 4500,6,1,2016,3,3,80,90 4000,7,1,2016,4,1,60,80 4500,8,1,2016,5,0,40,50 8000,9,1,2016,6,0,30,50 9500,10,1,2016,0,0,40,50
/sample/data$ swift upload meteos linear_data.txt linear_data.txt
4. Parse a raw data
Parse a raw data to eliminate exception data.
In this case, you have to eliminate holiday sales figures because exception data affect the accuracy of prediction model.
You can use a sample format located in python-meteosclient/sample/json/dataset_parse.json
You can see the head data of parsed dataset by executing "meteos dataset-show <dataset-uuid>" command.
$ vim ../python-meteosclient/sample/json/dataset_parse.json
$ cat sample/json/dataset_parse.json { "source_dataset_url": "swift://meteos/linear_data.txt", "display_name": "sample-data", "display_description": "This is a sample dataset", "method": "parse", "params": [{"method": "filter", "args": "lambda l: l.split(',')[0] != '0'"}], "experiment_id": "fcc8d055-e801-4652-af8c-5aabedbf0286", "swift_tenant": "demo", "swift_username": "demo", "swift_password": "nova" }
$ meteos dataset-create --json sample/json/dataset_parse.json +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | created_at | 2016-12-15T23:04:11.000000 | | description | This is a sample dataset | | head | None | | id | 91d98f6d-a065-431b-b8e0-1b996ac85cec | | name | sample-data | | project_id | 4034bb3cd9324776a68c140fdd18baa4 | | status | creating | | stderr | None | | user_id | 64ad04e101df40b4b51e5f370a83412c | +-------------+--------------------------------------+
$ meteos dataset-list +--------------------------------------+-------------+-----------+--------------------------------+ | id | name | status | source_dataset_url | +--------------------------------------+-------------+-----------+--------------------------------+ | 91d98f6d-a065-431b-b8e0-1b996ac85cec | sample-data | available | swift://meteos/linear_data.txt | +--------------------------------------+-------------+-----------+--------------------------------+
$ meteos dataset-show 91d98f6d-a065-431b-b8e0-1b996ac85cec +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | created_at | 2016-12-15T23:04:11.000000 | | description | This is a sample dataset | | head | [u'4500,1,1,2016,5,0,40,50', | | | u'8000,2,1,2016,6,1,60,80', | | | u'9500,3,1,2016,0,2,88,92', | | | u'5000,4,1,2016,1,3,90,90', | | | u'4500,6,1,2016,3,3,80,90', | | | u'4000,7,1,2016,4,1,60,80', | | | u'4500,8,1,2016,5,0,40,50', | | | u'8000,9,1,2016,6,0,30,50', | | | u'9500,10,1,2016,0,0,40,50', | | | u'5000,11,1,2016,1,1,60,80'] | | id | 91d98f6d-a065-431b-b8e0-1b996ac85cec | | name | sample-data | | project_id | 4034bb3cd9324776a68c140fdd18baa4 | | status | available | | stderr | | | user_id | 64ad04e101df40b4b51e5f370a83412c | +-------------+--------------------------------------+
5. Create a prediction model
In this example, User creates a Linear Regression Model from parsed dataset.
Parsed dataset has been already distributed in hdfs of experiment environment.
So, you spefity the internal url (internal://<dataset-id>) in source_dataset_url parameter.
$ vim sample/json/model_linear.json
$ cat sample/json/model_linear.json { "display_name": "sample-linear-model", "display_description": "Sample LinearRegression Model", "source_dataset_url": "internal://91d98f6d-a065-431b-b8e0-1b996ac85cec", "model_type": "LinearRegression", "model_params": "{'numIterations': 200}", "experiment_id": "fcc8d055-e801-4652-af8c-5aabedbf0286" }
$ meteos model-create --json sample/json/model_linear.json +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | created_at | 2016-12-15T23:09:36.000000 | | description | Sample LinearRegression Model | | id | 3cf02c2f-f043-49e5-a0df-0dc782868312 | | name | sample-linear-model | | params | eydudW1JdGVyYXRpb25zJzogMjAwfQ== | | project_id | 4034bb3cd9324776a68c140fdd18baa4 | | status | creating | | stderr | None | | stdout | None | | type | LinearRegression | | user_id | 64ad04e101df40b4b51e5f370a83412c | +-------------+--------------------------------------+
$ meteos model-list +--------------------------------------+---------------------+-----------+------------------+-------------------------------------------------+ | id | name | status | type | source_dataset_url | +--------------------------------------+---------------------+-----------+------------------+-------------------------------------------------+ | 3cf02c2f-f043-49e5-a0df-0dc782868312 | sample-linear-model | available | LinearRegression | internal://91d98f6d-a065-431b-b8e0-1b996ac85cec | +--------------------------------------+---------------------+-----------+------------------+-------------------------------------------------+
6. Predict
Create a learning job predicting a Sales Figures.
Specify the input value as "args" parameter.
Retrieve a predicted data as a stdout of job execution.
$ vim sample/json/learning.json
$ cat sample/json/learning.json { "display_name": "example-learning-job", "display_description": "This is a sample job", "model_id": "8a4e0313-3fc1-4467-8117-198320c05493", "method": "predict", "args": "1,7,2016,5,0,60,80" }
$ meteos learning-create --json sample/json/learning.json +-------------+--------------------------------------+ | Property | Value | +-------------+--------------------------------------+ | args | MSw3LDIwMTYsNSwwLDYwLDgw | | created_at | 2016-12-08T02:42:35.000000 | | description | This is a sample job | | id | 38fdc2e1-85f8-4f4d-89d3-e896e8511c89 | | method | predict | | name | example-learning-job | | project_id | 0e3e0f01952948848d8ae438279122fe | | status | creating | | stderr | None | | stdout | None | | user_id | 2c3120e8228c4d0e9768f09346fe842d | +-------------+--------------------------------------+
$ meteos learning-list +--------------------------------------+----------------------+----------------------+-----------+--------------------+---------------+----------------------------+ | id | name | description | status | args | stdout | created_at | +--------------------------------------+----------------------+----------------------+-----------+--------------------+---------------+----------------------------+ | 38fdc2e1-85f8-4f4d-89d3-e896e8511c89 | example-learning-job | This is a sample job | available | 1,7,2016,5,0,60,80 | 4044.73614882 | 2016-12-08T02:42:35.000000 | +--------------------------------------+----------------------+----------------------+-----------+--------------------+---------------+----------------------------+