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Weather Forecast Dataset - Usage Example#

Open in Colab

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:

[1]:
# 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:

[2]:
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:

[3]:
Weather.describe_versions()
[3]:
('in_domain', 'out_domain')

To get access to overall description of the dataset:

[4]:
print(Weather.describe()[:1000])
Weather Forecast Dataset
========================

The Weather Forecast Dataset contains meteorological features at a particular place (defined by latitude and longitude features) and time. This dataset can present data distribution shifts over both time and space.

The original data was sourced from the `Weather Prediction Dataset <https://github.com/Shifts-Project/shifts>`_. From the source data additional transformations were made, such as: feature renaming, feature selection and subsampling.
The original dataset is described in `Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks <https://arxiv.org/pdf/2107.07455.pdf>`_, by **Malinin, Andrey, et al.**

Usage
-----

You can follow this guide to see how to use the weather dataset:

.. toctree::
    :maxdepth: 1

    ../examples/datasets/weather


Versions and Data Partitions
----------------------------

Currently the dataset contains two versions: **in_domain** and **out_domain**. The task is the same fo

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():

[5]:
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.

[6]:
baseline.timestamp
[6]:
datetime.datetime(2022, 9, 12, 0, 0, tzinfo=datetime.timezone.utc)
[7]:
baseline.extra.head()
[7]:
meta_latitude meta_longitude meta_climate
date
2022-09-12 00:00:00+00:00 28.702900 -105.964996 dry
2022-09-12 00:00:00+00:00 -35.165298 147.466003 mild temperate
2022-09-12 00:00:00+00:00 29.607300 -95.158798 mild temperate
2022-09-12 00:00:00+00:00 39.077999 -77.557503 mild temperate
2022-09-12 00:00:00+00:00 26.152599 -81.775299 mild temperate

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:

[8]:
# 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)
[9]:
baseline = dataset.get_baseline()
baseline.timestamp
[9]:
datetime.datetime(2018, 9, 1, 0, 0, tzinfo=datetime.timezone.utc)

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

[10]:
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:

[11]:
batch = dataset.get_inference_data(target_date=now)

You can access the attributes just as showed before:

[12]:
batch.timestamp
[12]:
datetime.datetime(2022, 9, 12, 0, 0, tzinfo=datetime.timezone.utc)
[13]:
batch.data
[13]:
height_sea_level sun_elevation pressure cmc_temperature_grad cmc_temperature dew_point_temperature absolute_humidity snow_depth rain_accumulated snow_accumulated ... snow_accumulated_grad ice_rain_grad iced_graupel_grad cloud_coverage_grad meta_latitude meta_longitude meta_climate prediction_temperature temperature uncertainty
date
2022-09-12 00:00:00+00:00 166.0 24.134473 749.287193 -0.670923 289.282080 285.220886 0.0090 0.000000 2.641950 0.00000 ... 0.0 0.0 0.0 -2.0 46.516667 29.483333 snow 17.459501 19.0 5.046475
2022-09-12 00:00:00+00:00 180.0 36.168942 738.731879 -3.726770 290.226721 284.868256 0.0095 0.000000 0.149825 0.00000 ... 0.0 0.0 0.0 -23.0 46.521900 26.910299 snow 15.650873 15.0 10.590467
2022-09-12 00:00:00+00:00 25.0 4.931765 753.034922 7.741565 280.471216 279.016144 0.0054 0.000000 3.536025 0.00000 ... 0.0 0.0 0.0 0.0 39.033333 125.783333 snow 9.651232 11.0 5.512176
2022-09-12 00:00:00+00:00 -11.0 22.337882 754.533835 2.323230 277.726721 274.868256 0.0043 0.181638 1.822488 0.00000 ... 0.0 0.0 0.0 -70.0 55.281898 -77.765297 snow 7.948395 8.0 3.395677
2022-09-12 00:00:00+00:00 119.0 58.290232 767.426533 0.235266 290.554565 283.905655 0.0116 0.000000 2.715500 0.00000 ... 0.0 0.0 0.0 -1.0 38.519901 -28.715900 polar 18.248093 18.0 2.023753
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2022-09-12 00:00:00+00:00 260.0 15.023227 741.406826 8.416382 279.342383 274.999222 0.0048 0.000000 14.099825 0.00000 ... 0.0 0.0 0.0 0.0 39.843498 -85.897102 snow 8.233539 7.0 5.549324
2022-09-12 00:00:00+00:00 48.0 30.655498 758.121661 -2.092969 303.854272 293.944244 0.0194 0.000000 1.182225 0.00000 ... 0.0 0.0 0.0 -9.0 -5.911420 -35.247700 polar 30.618527 30.0 2.319395
2022-09-12 00:00:00+00:00 99.0 19.245194 752.505533 -2.072693 290.389832 282.104599 0.0067 0.000000 0.088375 0.00000 ... 0.0 0.0 0.0 30.0 58.100000 38.683333 snow 16.601422 17.0 4.060273
2022-09-12 00:00:00+00:00 296.0 38.102269 734.381076 2.616138 268.365942 267.126648 0.0024 1.002448 0.000000 0.00366 ... 0.0 0.0 0.0 -1.0 66.580000 -61.620000 polar 1.004967 -1.0 3.510967
2022-09-12 00:00:00+00:00 48.0 -10.442588 755.390211 -0.808435 281.321216 277.766144 0.0060 0.000000 0.158425 0.00000 ... 0.0 0.0 0.0 -26.0 60.289167 5.226389 snow 7.546170 7.0 2.232020

100 rows × 54 columns

[14]:
batch.prediction.head()
[14]:
prediction_temperature uncertainty
date
2022-09-12 00:00:00+00:00 17.459501 5.046475
2022-09-12 00:00:00+00:00 15.650873 10.590467
2022-09-12 00:00:00+00:00 9.651232 5.512176
2022-09-12 00:00:00+00:00 7.948395 3.395677
2022-09-12 00:00:00+00:00 18.248093 2.023753

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:

[15]:
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))
logging batch of size 100 for 2022-09-12 00:00:00+00:00
logging batch of size 227 for 2022-09-13 00:00:00+00:00
logging batch of size 186 for 2022-09-14 00:00:00+00:00
logging batch of size 197 for 2022-09-15 00:00:00+00:00
logging batch of size 194 for 2022-09-16 00:00:00+00:00