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

# # Example of usage model from sklift.models in sklearn.pipeline
# 
# <br>
# <center>
#     <a href="https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb">
#         <img src="https://colab.research.google.com/assets/colab-badge.svg">
#     </a>
#     <br>
#     <b><a href="https://github.com/maks-sh/scikit-uplift/">SCIKIT-UPLIFT REPO</a> | </b>
#     <b><a href="https://scikit-uplift.readthedocs.io/en/latest/">SCIKIT-UPLIFT DOCS</a></b>
#     <br>
#     <b><a href="https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb">RUSSIAN VERSION</a></b>
# 
# </center>

# This is a simple example on how to use [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html) with [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline).
# 
# The data is taken from [MineThatData E-Mail Analytics And Data Mining Challenge dataset by Kevin Hillstrom](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).
# 
# This dataset contains 64,000 customers who last purchased within twelve months. The customers were involved in an e-mail test:
# * 1/3 were randomly chosen to receive an e-mail campaign featuring Mens merchandise.
# * 1/3 were randomly chosen to receive an e-mail campaign featuring Womens merchandise.
# * 1/3 were randomly chosen to not receive an e-mail campaign.
# 
# During a period of two weeks following the e-mail campaign, results were tracked. The task is to tell the world if the Mens or Womens e-mail campaign was successful.
# 
# The full description of the dataset can be found at the [link](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).
# 
# Firstly, install the necessary libraries:

# In[1]:


get_ipython().system('pip install scikit-uplift==0.1.1 xgboost==1.0.2 category_encoders==2.1.0')


# Secondly, load the data:

# In[2]:


import urllib.request
import pandas as pd


csv_path = '/content/Hilstorm.csv'
url = 'http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv'
urllib.request.urlretrieve(url, csv_path)


# For simplicity of the example, we will leave only two user segments:
# * those who were sent an e-mail advertising campaign with women's products;
# * those who were not sent out the ad campaign.
# 
# We will use the `visit` variable as the target variable.

# In[3]:


import pandas as pd


get_ipython().run_line_magic('matplotlib', 'inline')

dataset = pd.read_csv(csv_path)
print(f'Shape of the dataset before processing: {dataset.shape}')
dataset = dataset[dataset['segment']!='Mens E-Mail']
dataset.loc[:, 'treatment'] = dataset['segment'].map({
    'Womens E-Mail': 1,
    'No E-Mail': 0
})

dataset = dataset.drop(['segment', 'conversion', 'spend'], axis=1)
print(f'Shape of the dataset after processing: {dataset.shape}')
dataset.head()


# Divide all the data into a training and validation sample:

# In[4]:


from sklearn.model_selection import train_test_split


Xyt_tr, Xyt_val = train_test_split(dataset, test_size=0.5, random_state=42)

X_tr = Xyt_tr.drop(['visit', 'treatment'], axis=1)
y_tr = Xyt_tr['visit']
treat_tr = Xyt_tr['treatment']

X_val = Xyt_val.drop(['visit', 'treatment'], axis=1)
y_val = Xyt_val['visit']
treat_val = Xyt_val['treatment']


# Select categorical features:

# In[5]:


cat_cols = X_tr.select_dtypes(include='object').columns.tolist()
print(cat_cols)


# Create the necessary objects and combining them into a pipieline:

# In[6]:


from sklearn.pipeline import Pipeline
from category_encoders import CatBoostEncoder
from sklift.models import ClassTransformation
from xgboost import XGBClassifier


encoder = CatBoostEncoder(cols=cat_cols)
estimator = XGBClassifier(max_depth=2, random_state=42)
ct = ClassTransformation(estimator=estimator)

my_pipeline = Pipeline([
    ('encoder', encoder),
    ('model', ct)
])


# <b>Train pipeline as usual, but adding the treatment column in the step model as a parameter `model__treatment`.</b>

# In[7]:


my_pipeline = my_pipeline.fit(
    X=X_tr,
    y=y_tr,
    model__treatment=treat_tr
)


# Predict the uplift and calculate the uplift@30%

# In[8]:


from sklift.metrics import uplift_at_k


uplift_predictions = my_pipeline.predict(X_val)

uplift_30 = uplift_at_k(y_val, uplift_predictions, treat_val, strategy='overall')
print(f'uplift@30%: {uplift_30:.4f}')