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Writing Profiles to WhyLabs¶

In this example, we'll show how to send your logged profiles to your monitoring dashboard at WhyLabs Platform. We will:

Define environment variables with the appropriate Credentials and IDs
Log data into a profile
Use the WhyLabs Writer to send the profile to your Project at WhyLabs

Installing whylogs¶

First, let's install whylogs. Since we want to write to WhyLabs, we'll also install the whylabs extra.

If you don't have it installed already, uncomment the line below:

In [1]:

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

✔️ Setting the Environment Variables¶

In order to send our profile to WhyLabs, let's first set up an account. You can skip this if you already have an account and a model set up.

We will need three pieces of information:

API token
Organization ID
Dataset ID (or model-id)

Go to https://whylabs.ai/free and grab a free account. You can follow along with the examples if you wish, but if you’re interested in only following this demonstration, you can go ahead and skip the quick start instructions.

After that, you’ll be prompted to create an API token. Once you create it, copy and store it locally. The second important information here is your org ID. Take note of it as well. After you get your API Token and Org ID, you can go to https://hub.whylabsapp.com/models to see your projects dashboard. You can create a new project and take note of it's ID (if it's a model project it will look like model-xxxx).

We'll now set the credentials as environment variables. The WhyLabs Writer will check for the existence of these variables in order to send the profiles to your dashboard.

In [1]:

import getpass
import os

# set your org-id here - should be something like "org-xxxx"
print("Enter your WhyLabs Org ID") 
os.environ["WHYLABS_DEFAULT_ORG_ID"] = input()

# set your datased_id (or model_id) here - should be something like "model-xxxx"
print("Enter your WhyLabs Dataset ID")
os.environ["WHYLABS_DEFAULT_DATASET_ID"] = input()


# set your API key here
print("Enter your WhyLabs API key")
os.environ["WHYLABS_API_KEY"] = getpass.getpass()
print("Using API Key ID: ", os.environ["WHYLABS_API_KEY"][0:10])

Enter your WhyLabs Org ID
Enter your WhyLabs Dataset ID
Enter your WhyLabs API key
Using API Key ID:  ygG04qE3gQ

Fetching the Data¶

For demonstration, let's use data for transactions from a small retail business:

In [2]:

import pandas as pd

csv_url = "https://whylabs-public.s3.us-west-2.amazonaws.com/datasets/tour/current.csv"
df = pd.read_csv(csv_url)

df.head()

Out[2]:

	Transaction ID	Customer ID	Quantity	Item Price	Total Tax	Total Amount	Store Type	Product Category	Product Subcategory	Gender	Transaction Type	Age
0	T14259136777	C274477	1	148.9	15.6345	164.5345	TeleShop	Electronics	Audio and video	F	Purchase	37.0
1	T7313351894	C267568	4	48.1	20.2020	212.6020	Flagship store	Home and kitchen	Furnishing	M	Purchase	25.0
2	T37745642681	C267098	1	10.9	1.1445	12.0445	Flagship store	Footwear	Mens	F	Purchase	42.0
3	T13861409908	C271608	2	135.2	28.3920	298.7920	MBR	Footwear	Mens	F	Purchase	43.0
4	T58956348529	C272484	4	144.3	60.6060	637.8060	TeleShop	Clothing	Mens	F	Purchase	39.0

📊 Profiling the Data¶

Let's profile the data with whylogs:

In [3]:

import whylogs as why
from datetime import datetime

current_date = datetime.now()
profile = why.log(df).profile()
profile.set_dataset_timestamp(current_date)

No timezone set in the datetime_timestamp object. Default to local timezone

We're also setting the profile's dataset timestamp as the current datetime. If this is not set, the Writer would simply assign the current datetime automatically to the profile.

You can set the timestamp to past dates, if you want to backfill data into your dashboard. You should see data for the last 14 days at your dashboard within seconds or minutes once you send it.

✍️ The WhyLabs Writer¶

Now, you can simply create a WhyLabsWriter object and use it to send your profiles, like this:

In [7]:

from whylogs.api.writer.whylabs import WhyLabsWriter

writer = WhyLabsWriter()
writer.write(file=profile.view())

A 200 response should mean that it went through successfully.

The writer expects a Profile View as parameter.

Option #2: Profile Result writer¶

A second way to write to WhyLabs is by directly using the writer method of a Profile Result set, like this:

In [8]:

profile_results = why.log(df)
profile_results.writer("whylabs").write()

🔍 A Look on the Other Side¶

Now, check your dashboard to verify everything went ok. At the Profile tab, you should see something like this:

alt text

Going from One to Many Profiles¶

This was a simple example on how you can send a single profile to WhyLabs.

To better experience WhyLab's features, try sending multiple profiles for a number of days to visualize how the feature's metrics change over time! To do that, follow the same steps shown here, but setting the dataset timestamp to past days for backfill.

The following code snippet is a very simple way to do that. We're just tweaking the code in the previous sessions in order to split the dataframe into seven chunks, and logging each chunk for a given day:

In [ ]:

import numpy as np
from datetime import datetime, timedelta
from whylogs.api.writer.whylabs import WhyLabsWriter

writer = WhyLabsWriter()

df_splits = np.array_split(df, 7)

def log_data(df,timestamp):
    profile = why.log(df).profile()
    profile.set_dataset_timestamp(timestamp)
    writer.write(profile=profile.view())
    print("Logged profile for {}".format(timestamp))

# This will log each data split for the last 7 days, starting with the current date and finishing 7 days in the past
for day, df_split in enumerate(df_splits):
    dataset_timestamp = datetime.now() - timedelta(days=day)
    log_data(df_split,dataset_timestamp)

This should give you a quick way to look at how your features can be monitored over time!

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