fasttsfeatures 0.0.4

Scalable time series features computation

FastTSFeatures

Compute static or temporal time-series features at scale.

Install

pip install fasttsfeatures

How to use

1. Request free trial

Request a free trial sending an email to: [e-mail].

2. Required information

To use fasttsfeatures you need:

• An AWS url provided by Nixtla. You'll upload your dataset here.
• An user and a password to enter the previous url.
• An API Key to interact with the scalable API.
• An API ID to interact with the scalable API.
• A pair of AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

3.1 Case 1: Upload to S3 from python

• Import the library.

from fasttsfeatures.core import TSFeatures

• Instantiate TSFeatures introduce your api_id and api_key, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY.

tsfeatures = TSFeatures(api_id=os.environ['API_ID'], 
                        api_key=os.environ['API_KEY'],
                        aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], 
                        aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])

• Upload your local file introducing its name and the bucket's name (provided by Nixtla).

s3_uri = tsfeatures.upload_to_s3('../train.csv', 'nixtla-user-test')

• Run the process introducing the S3 uri.

response = tsfeatures.calculate_features_from_s3_uri(s3_uri=s3_uri,
                                                     freq=7)

display_df(response)

	status	body	id	message
0	200	"s3://nixtla-user-test/features/features.csv"	f7bdb6dc-dcdb-4d87-87e8-b5428e4c98db	Check job status at GET /tsfeatures/jobs/{job_id}

• Monitor the process with the following code. Once it's done, access to your bucket to download the generated features.

job_id = response['id'].item()

display(tsfeatures.get_status(job_id))

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

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    vertical-align: top;
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.dataframe thead th {
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</style>

	status	processing_time_seconds
0	InProgress	3

3.2 Case 2: Upload to S3 Manually

A. Upload your dataset

• Access the url provided by Nixtla. You'll see a login page like the following. Just enter your user and paswsword.

• Next you'll see the bucket where you can upload your dataset:

• Upload your dataset and copy its S3 URI.

B. Run the process

• Import the library.

from fasttsfeatures.core import TSFeatures

• Instantiate TSFeatures introduce your api_id and api_key.

tsfeatures = TSFeatures(api_id=os.environ['API_ID'], 
                        api_key=os.environ['API_KEY'])

• Run the process introducing your previous copied S3 uri.

response = tsfeatures.calculate_features_from_s3_uri(s3_uri='s3://tsfeatures-api-public/train.csv',
                                                     freq=7)

display_df(response)

	status	body	id	message
0	200	"s3://tsfeatures-api-public/features/features.csv"	740a410a-d138-41b4-8373-581710f020f8	Check job status at GET /tsfeatures/jobs/{job_id}

• Monitor the process with the following code. Once it's done, access to your bucket to download the generated features.

job_id = response['id'].item()

display(tsfeatures.get_status(job_id))

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	status	processing_time_seconds
0	InProgress	20