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Weather Forecast Dataset - Usage Example

This an example demonstrating the usage of the Weather Forecast Dataset.

For more information about the dataset itself, check the documentation on : https://whylogs.readthedocs.io/en/latest/datasets/weather.html

Installing the datasets module

Uncomment the cell below if you don't have the datasets module installed:

# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[datasets]'

Loading the Dataset

You can load the dataset of your choice by calling it from the datasets module:

from whylogs.datasets import Weather

dataset = Weather(version="in_domain")

This will create a folder in the current directory named whylogs_data with the csv files for the Weather Dataset. If the files already exist, the module will not redownload the files.

Notice we're specifying the version of the dataset. A dataset can have multiple versions that can be used for differente purposes. In this case, the version "in_domain" has data from the same domain between baseline and inference subsets (data from the same set of regions - tropical, dry, polar, etc.).

If we're interested in assessing drift issues, the version "out_domain" could be used, in which we have out-of-domain data in the inference subset, when compare to the baseline.

Similarly, datasets could have other versions for other purposes, such as assessing data quality or outlier detection strategies.

Discovering Information

To know what are the available versions for a given dataset, you can call:

Weather.describe_versions()

To get access to overall description of the dataset:

print(Weather.describe()[:1000])

note: the output was truncated to first 1000 characters as describe() will print a rather lengthy description.

Getting Baseline Data

You can access data from two different partitions: the baseline dataset and inference dataset.

The baseline can be accessed as a whole, whereas the inference dataset can be accessed in periodic batches, defined by the user.

To get a baseline object, just call dataset.get_baseline():

from whylogs.datasets import Weather

dataset = Weather(version="out_domain")

baseline = dataset.get_baseline()

baseline will contain different attributes - one timestamp and five dataframes.

• timestamp: the batch's timestamp (at the start)
• data: the complete dataframe
• features: input features
• target: output feature(s)
• prediction: output prediction and, possibly, features such as uncertainty, confidence, probability
• misc: metadata features that are not of any of the previous categories, but still contain relevant information about the data.

baseline.timestamp

baseline.extra.head()

Setting Parameters

With set_parameters, you can specify the timestamps for both baseline and inference datasets, as well as the inference interval.

By default, the timestamp is set as:

• Current date for baseline dataset
• Tomorrow's date for inference dataset

These timestamps can be defined by the user to any given day, including the dataset's original date.

The inference_interval defines the interval for each batch: '1d' means that we will have daily batches, while '7d' would mean weekly batches.

To set the timestamps to the original dataset's date, set original to true, like below:

# Currently, the inference interval takes a str in the format "Xd", where X is an integer between 1-30
dataset.set_parameters(inference_interval="1d", original=True)

baseline = dataset.get_baseline()
baseline.timestamp

You can set timestamp by using the baseline_timestamp and inference_start_timestamp, and the inference interval like below:

from datetime import datetime, timezone
now = datetime.now(timezone.utc)
dataset.set_parameters(baseline_timestamp=now, inference_start_timestamp=now, inference_interval="1d")

Note that we are passing the datetime converted to the UTC timezone. If a naive datetime is passed (no information on timezones), local time zone will be assumed. The local timestamp, however, will be converted to the proper datetime in UTC timezone. Passing a naive datetime will trigger a warning, letting you know of this behavior.

Note that if both original and a timestamp (baseline or inference) is passed simultaneously, the defined timestamp will be overwritten by the original dataset timestamp.

Getting Inference Data #1 - By Date

You can get inference data in two different ways. The first is to specify the exact date you want, which will return a single batch:

batch = dataset.get_inference_data(target_date=now)

You can access the attributes just as showed before:

batch.timestamp

batch.data

batch.prediction.head()

Getting Inference Data #2 - By Number of Batches

The second way is to specify the number of batches you want and also the date for the first batch.

You can then iterate over the returned object to get the batches. You can then use the batch any way you want. Here's an example that retrieves daily batches for a period of 5 days and logs each one with whylogs, saving the binary profiles to disk:

import whylogs as why
batches = dataset.get_inference_data(number_batches=5)

for batch in batches:
  print("logging batch of size {} for {}".format(len(batch.data),batch.timestamp))
  profile = why.log(batch.data).profile()
  profile.set_dataset_timestamp(batch.timestamp)
  profile.view().write("batch_{}".format(batch.timestamp))