#!/usr/bin/env python
# coding: utf-8

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# # Ecommerce Dataset - Usage Example

# [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/datasets/ecommerce.ipynb)

# This an example demonstrating the usage of the Ecommerce Dataset.
# 
# For more information about the dataset itself, check the documentation on :
# https://whylogs.readthedocs.io/en/latest/datasets/ecommerce.html

# ## Installing the datasets module

# In[1]:


# Note: you may need to restart the kernel to use updated packages.
get_ipython().run_line_magic('pip', "install 'whylogs[datasets]'")


# ## Loading the Dataset
# 
# You can load the dataset of your choice by calling it from the `datasets` module:

# In[2]:


from whylogs.datasets import Ecommerce

dataset = Ecommerce(version="base")


# If no `version` parameter is passed, the default version is `base`.

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

# ## Discovering Information

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

# In[3]:


Ecommerce.describe_versions()


# To get access to overall description of the dataset:

# In[4]:


print(Ecommerce.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()`:

# In[5]:


from whylogs.datasets import Weather

dataset = Ecommerce()

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
# - extra: metadata features that are not of any of the previous categories, but still contain relevant information about the data.

# In[6]:


baseline.timestamp


# In[7]:


baseline.features.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:

# In[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)


# In[9]:


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:

# In[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:

# In[11]:


batch = dataset.get_inference_data(target_date=now)


# You can access the attributes just as showed before:

# In[12]:


batch.timestamp


# In[13]:


batch.data


# In[14]:


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:

# In[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))