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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 donât 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 weâre specifying the version of the dataset. A dataset can have multiple versions that can be used for differente purposes. In this case, the version âin_domainâ has data from the same domain between baseline and inference subsets (data from the same set of regions - tropical, dry, polar, etc.).
If weâre interested in assessing drift issues, the version âout_domainâ 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 batchâs 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 - Tomorrowâs date for inference dataset
These timestamps can be defined by the user to any given day, including the datasetâs original date.
The inference_interval
defines the interval for each batch: â1dâ means that we will have daily batches, while â7dâ would mean weekly batches.
To set the timestamps to the original datasetâs 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 Ă 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. Hereâs 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