Scalable time series features computation
Project description
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: fede.garza.ramirez@gmail.com.
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 yourapi_id
andapi_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;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</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 yourapi_id
andapi_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 |
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