GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cloud/notebooks/python_sdk/experiments/autoai/Use AutoAI and timeseries data with supporting features to predict PM2.5.ipynb
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Kernel: Python 3.9

Use AutoAI and timeseries data with supporting features to predict PM2.5 by `ibm-watsonx-ai`

This notebook contains the steps and code to demonstrate support of AutoAI experiments for timeseries data sets in watsonx.ai Runtime service. It introduces commands for data retrieval, training experiments, persisting pipelines, testing pipelines, deploying pipelines, and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.11.

Learning goals

The learning goals of this notebook are:

Define watsonx.ai Runtime experiment for timeseries data sets with supporting features.
Work with experiments to train AutoAI models.
Compare trained models quality and select the best one for further deployment.
Online deployment and score the trained model.

This notebook contains the following parts:

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Create a watsonx.ai Runtime Service instance (a free plan is offered and information about how to create the instance can be found here).
Create a Cloud Object Storage (COS) instance (a lite plan is offered and information about how to order storage can be found here).
Note: When using Watson Studio, you already have a COS instance associated with the project you are running the notebook in.

Install and import the `ibm-watsonx-ai` and dependecies

Note: ibm-watsonx-ai documentation can be found here.

In [ ]:

!pip install wget | tail -n 1
!pip install plotly | tail -n 1
!pip install -U ibm-watsonx-ai | tail -n 1

Connection to watsonx.ai Runtime

Authenticate the watsonx.ai Runtime service on IBM Cloud. You need to provide Cloud API key and location.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the watsonx.ai Runtime docs. You can check your instance location in your watsonx.ai Runtime Service instance details.

You can use IBM Cloud CLI to retrieve the instance location.

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance INSTANCE_NAME

NOTE: You can also get a service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, and then copy the created key and paste it in the following cell.

Action: Enter your api_key and location in the following cell.

In [1]:

api_key = 'PUT_YOUR_KEY_HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

In [1]:

from ibm_watsonx_ai import Credentials

credentials = Credentials(
    api_key=api_key,
    url='https://' + location + '.ml.cloud.ibm.com'
)

In [4]:

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)

Working with spaces

You need to create a space that will be used for your work. If you do not have a space, you can use Deployment Spaces Dashboard to create one.

Click New Deployment Space
Create an empty space
Select Cloud Object Storage
Select watsonx.ai Runtime instance and press Create
Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: assign space ID below

In [7]:

space_id = 'PASTE YOUR SPACE ID HERE'

You can use the list method to print all existing spaces.

In [ ]:

client.spaces.list(limit=10)

To be able to interact with all resources available in watsonx.ai Runtime, you need to set the space which you will be using.

In [8]:

client.set.default_space(space_id)

Out[8]:

'SUCCESS'

2. Timeseries data set

Connections to COS

In next cell we read the COS credentials from the space.

In [9]:

cos_credentials = client.spaces.get_details(space_id=space_id)['entity']['storage']['properties']

Training data sets

Download training data from git repository and upload it to a COS. This example uses the China daily PM2.5.

In [10]:

datasource_name = 'bluemixcloudobjectstorage'
bucketname = cos_credentials['bucket_name']

In [11]:

import os, wget

filename = 'PM25.csv'
base_url = 'https://raw.githubusercontent.com/wjcougar/air_pollution/master/'

if not os.path.isfile(filename): wget.download(base_url + filename)

Visualize the data

In [12]:

import plotly.express as px
import pandas as pd

df = pd.read_csv(filename)
fig = px.line(df, x='date', y=df.columns)
fig.show()

Out[12]:

MIME type unknown not supported

Create connection

In [13]:

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {datasource_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_id_by_name(datasource_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucketname,
        'access_key': cos_credentials['credentials']['editor']['access_key_id'],
        'secret_key': cos_credentials['credentials']['editor']['secret_access_key'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': cos_credentials['endpoint_url']
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

Out[13]:

Creating connections...
SUCCESS

Note: The above connection can be initialized alternatively with api_key and resource_instance_id. The above cell can be replaced with:

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_id_by_name(db_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucket_name,
        'api_key': cos_credentials['apikey'],
        'resource_instance_id': cos_credentials['resource_instance_id'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': 'https://s3.us.cloud-object-storage.appdomain.cloud'
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

In [14]:

connection_id = client.connections.get_id(conn_details)

Training data connection

The code in next cell defines connections to created assets.

In [15]:

from ibm_watsonx_ai.helpers import DataConnection, S3Location

data_connections = []
data_connection = DataConnection(
        connection_asset_id=connection_id,
        location=S3Location(bucket=cos_credentials['bucket_name'],
                            path=filename)
    )

data_connection.set_client(client)
data_connection.write(data=filename, remote_name=filename)
data_connections.append(data_connection)

3. Optimizer definition

Optimizer configuration

Provide the input information for AutoAI optimizer:

name - experiment name
prediction_type - type of the problem
prediction_columns - target columns names
timestamp_column_name — date&time column name/index
feature_columns – names/indices of supporting feature columns
forecast_window — future date/time range to be predicted
holdout_size - number of holdout records
lookback_window – past date/time range used for model training, -1 means auto-determined
backtest_num – number of backtests
supporting_features_at_forecast – whether leveraging future values of supporting features
pipeline_types – specify an indiviual or a group of pipelins by type

In [16]:

from ibm_watsonx_ai.experiment import AutoAI
from ibm_watsonx_ai.utils.autoai.enums import ForecastingPipelineTypes

experiment = AutoAI(credentials, space_id=space_id)
forecast_window = 7
backtest_num = 4

pipeline_optimizer = experiment.optimizer(
            name="PM25 prediction",
            prediction_type=AutoAI.PredictionType.FORECASTING,
            prediction_columns=['pollution'],
            timestamp_column_name='date',
            feature_columns=['pollution','dew','temp','press','wnd_spd','snow','rain'],
            lookback_window=-1,
            backtest_num=backtest_num,
            holdout_size=14,
            forecast_window=forecast_window,
            supporting_features_at_forecast=True,
            pipeline_types=[ForecastingPipelineTypes.Bats] + ForecastingPipelineTypes.get_exogenous()
)

Configuration parameters can be retrieved via pipeline_optimizer.get_params().

4. Experiment run

Call the fit() method to trigger the AutoAI experiment. You can either use interactive mode (synchronous job) or background mode (asychronous job) by specifying background_model=True.

In [17]:

fit_details = pipeline_optimizer.fit(training_data_reference=data_connections, background_mode=False)

Out[17]:

Training job d39b6d6b-c11f-4a06-943e-f89107e503f9 completed: 100%|████████| [05:37<00:00,  3.38s/it]

You can use the get_run_status() method to monitor AutoAI jobs in background mode.

In [18]:

pipeline_optimizer.get_run_status()

Out[18]:

'completed'

5. Pipelines comparison

You can list trained pipelines and evaluation metrics information in the form of a Pandas DataFrame by calling the summary() method. You can use the DataFrame to compare all discovered pipelines and select the one you like for further deployment.

In [19]:

summary = pipeline_optimizer.summary()
summary

Out[19]:

Check pipeline details by calling get_pipeline_details(). By default details of best pipeline are returned.

In [20]:

best_pipeline_name = summary[summary.Winner==True].index.values[0]
print('Best pipeline is:', best_pipeline_name)

pipeline_details = pipeline_optimizer.get_pipeline_details()

Out[20]:

Best pipeline is: Pipeline_4

Holdout data visualization

In [21]:

visualization = pipeline_details['visualization']['holdout']
holdout_dates = visualization['time']
holdout_predictions = visualization['predicted_values'][0]
holdout_observed_values = visualization['observed_values'][0]
holdout_df = pd.DataFrame({'time':holdout_dates, 'observed_values':holdout_observed_values, 'predicted_values': holdout_predictions})
fig = px.line(holdout_df, x="time", y=holdout_df.columns, hover_data={"time": "|%B %d, %Y"}, title='Holdout data')
fig.update_xaxes(dtick="M1", tickformat="%b\n%Y")
fig.show()

Out[21]:

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Backtest data visualization

In [22]:

from datetime import datetime, timedelta
import numpy as np

backtest_dfs = []
for i in range(backtest_num):
    visualization = pipeline_details['visualization']['backtest']['iterations'][i]
    backtest_dates = visualization['time']
    backtest_predictions = visualization['predicted_values'][0]
    observed_values = visualization['observed_values'][0]
    backtest_dfs.append(pd.DataFrame({'time':backtest_dates, 'observed_values':observed_values, 'predicted_values': backtest_predictions}))
backtest_df = pd.concat(backtest_dfs)
fig = px.line(backtest_df, x="time", y=backtest_df.columns, hover_data={"time": "|%B %d, %Y"}, title='Backtest data')
fig.update_xaxes(dtick="M1", tickformat="%b\n%Y")
fig.show()

Out[22]:

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6. Deploy and Score

In this section you will learn how to deploy and score pipeline model as online deployment using watsonx.ai Runtime instance.

Online deployment creation

In [23]:

from ibm_watsonx_ai.deployment import WebService

service = WebService(credentials, source_space_id=space_id)

service.create(
    experiment_run_id=pipeline_optimizer.get_run_details()['metadata']['id'],
    model=best_pipeline_name, 
    deployment_name="PM2.5 Forecasting"
    )

Out[23]:

Preparing an AutoAI Deployment...
Published model uid: 798cf352-2812-4f12-892b-28f95e852922
Deploying model 798cf352-2812-4f12-892b-28f95e852922 using V4 client.


#######################################################################################

Synchronous deployment creation for uid: '798cf352-2812-4f12-892b-28f95e852922' started

#######################################################################################


initializing
Note: online_url and serving_urls are deprecated and will be removed in a future release. Use inference instead.
..
ready


------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='b58df0bc-880f-425e-8baf-830d70224f66'
------------------------------------------------------------------------------------------------

To show all available information about the deployment use the .get_params() method:

In [ ]:

service.get_params()

Scoring

You can use the score method to get predictions for defined forecasting window. You can either send payload records or use empty list.

In [25]:

predictions = service.score(payload=pd.DataFrame({'daily_cases' : []}))
print('predictions for next 7 days:\n')
predictions

Out[25]:

predictions for next 7 days:

{'predictions': [{'fields': ['prediction'],
   'values': [[[129.5439294077758]],
    [[129.81703947189507]],
    [[126.70353558876782]],
    [[123.59116561586549]],
    [[126.16670897809692]],
    [[130.53538843411673]],
    [[134.82070412310827]]]}]}

Or you could send payload with new obeservations:

filename = 'PM25_NewObservations.csv'

if not os.path.isfile(filename): wget.download(base_url + filename)
predictions = service.score(pd.read_csv(filename).drop("date", axis=1))
predictions

Visualization of predictions

In [26]:

last_date = datetime.strptime(holdout_df.tail(1).time.values.tolist()[0],'%Y-%m-%d')
prediction_dates = [(last_date + timedelta(days=1 + i)).date() for i in range(forecast_window)]
prediction_values = [pred[0][0] for pred in  predictions['predictions'][0]['values']]
pred_df = pd.DataFrame({'time' : holdout_dates + prediction_dates,
                        'observed_values' : holdout_observed_values + [np.NAN for i in range(forecast_window)],
                        'predicted_values' : holdout_predictions + prediction_values})

fig = px.line(pred_df, x="time", y=pred_df.columns, hover_data={"time": "|%B %d, %Y"}, title='Forecast data')
fig.update_xaxes(dtick="M1", tickformat="%b\n%Y")
fig.show()

Out[26]:

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7. Clean up

If you want to clean up all created assets:

experiments
trainings
pipelines
model definitions
models
functions
deployments

please follow up this sample notebook.

8. Summary and next steps

You successfully completed this notebook!

You learned how to use ibm-watsonx-ai to run AutoAI experiments.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Jun Wang, is a Software Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Mateusz Szewczyk, Software Engineer at watsonx.ai.

Use AutoAI and timeseries data with supporting features to predict PM2.5 by `ibm-watsonx-ai`

Learning goals

Contents

1. Set up the environment

Install and import the `ibm-watsonx-ai` and dependecies

Connection to watsonx.ai Runtime

Working with spaces

2. Timeseries data set

Connections to COS

Training data sets

Visualize the data

Create connection

Training data connection

3. Optimizer definition

Optimizer configuration

4. Experiment run

5. Pipelines comparison

Holdout data visualization

Backtest data visualization

6. Deploy and Score

Online deployment creation

Scoring

Visualization of predictions

7. Clean up

8. Summary and next steps

Authors

Product

Resources

Company

Use AutoAI and timeseries data with supporting features to predict PM2.5 by ibm-watsonx-ai

Learning goals

Contents

1. Set up the environment

Install and import the ibm-watsonx-ai and dependecies

Connection to watsonx.ai Runtime

Working with spaces

2. Timeseries data set

Connections to COS

Training data sets

Visualize the data

Create connection

Training data connection

3. Optimizer definition

Optimizer configuration

4. Experiment run

5. Pipelines comparison

Holdout data visualization

Backtest data visualization

6. Deploy and Score

Online deployment creation

Scoring

Visualization of predictions

7. Clean up

8. Summary and next steps

Authors

Use AutoAI and timeseries data with supporting features to predict PM2.5 by `ibm-watsonx-ai`

Install and import the `ibm-watsonx-ai` and dependecies