.. Fuzzy Causal documentation master file, created by
   sphinx-quickstart on Fri Mar 26 10:04:52 2021.
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Welcome to Fuzzy Causal's documentation!
========================================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

Causal Estimation Example on IHDP :
==================================
.. code-block:: python



   import numpy as np
   import pandas as pd
   from source.utils import Score
   from source.Fuzzy import Fuzzy
   from sklearn.model_selection import train_test_split



   df= pd.read_csv('https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/master/datasets/IHDP/csv/ihdp_npci_1.csv', header = None)

   df.dataframeName = 'data'

   cols =  ["treatment", "y_factual", "y_cfactual", "mu0", "mu1"] + [i for i in range(25)]

   df.columns = cols
   print(df.head())


   #precising variables type
   binfeats = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]
   contfeats = [i for i in range(25) if i not in binfeats]
   perm = binfeats + contfeats
   df = df.reset_index(drop=True)
   df.head()

   X = df[perm].values
   treatment = df['treatment'].values
   y = df['y_factual'].values
   y_cf = df['y_cfactual'].values
   tau = df.apply(lambda y: y['y_factual'] - y['y_cfactual'] if y['treatment']==1
                  else y['y_cfactual'] - y['y_factual'],
                  axis=1)
   mu_0 = df['mu0'].values
   mu_1 = df['mu1'].values

   # train and test
   itr, ite = train_test_split(np.arange(X.shape[0]), test_size=0.2, random_state=1)
   X_train, treatment_train, y_train, y_cf_train, tau_train, mu_0_train, mu_1_train = X[itr], treatment[itr], y[itr], y_cf[itr], tau[itr], mu_0[itr], mu_1[itr]
   X_val, treatment_val, y_val, y_cf_val, tau_val, mu_0_val, mu_1_val = X[ite], treatment[ite], y[ite], y_cf[ite], tau[ite], mu_0[ite], mu_1[ite]

   m=min(len(y_val),len(y_train))

   test=Fuzzy()
   new=test.fuzzify(df,[1,2],0,1)
   eval=Score(y_train[:m],treatment_train[:m],y_cf_train[:m],mu_0_train[:m],mu_1_train[:m])
   print('ATE ',eval.evaluate(y_val,y_cf_val)[1])
   y = new['y_factual_low'].values
   y_cf = new['y_cfactual_low'].values
   y_val, y_cf_val=y[ite], y_cf[ite]
   low=eval.evaluate(y_val,y_cf_val)[1]
   print('ATE low ',low)
   y = new['y_factual_average'].values
   y_cf = new['y_cfactual_average'].values
   y_val, y_cf_val=y[ite], y_cf[ite]
   medium=eval.evaluate(y_val,y_cf_val)[1]
   print('ATE medium ',medium)
   y = new['y_factual_high'].values
   y_cf = new['y_cfactual_high'].values
   y_val, y_cf_val=y[ite], y_cf[ite]
   high=eval.evaluate(y_val,y_cf_val)[1]
   print('ATE high',high)
   print('The average value of low, medium and high :', np.mean([low,medium,high]))




| ATE low  3.8494642946001822
| ATE medium  4.091268219603135
| ATE high 4.255198638764754
| The average value of low, medium and high : 4.06531038432269



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`


Fuzzy Input
=====================
.. automodule:: source.Input
   :members:

Fuzzy Fuzzification
=================
.. automodule:: source.Fuzzy
   :members:

Fuzzy Score
===================
.. automodule:: source.utils
   :members: