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

# # Comparing the result with Shap
# 
# Shap is a method developed by Scott Lundberg et al that also tries to estimate Shapley value.  
# The estimated Shapley values agree on linear systems. 

# In[1]:


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

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

from keras.models import Sequential
from keras.layers import Flatten, Dense, Dropout
from keras.layers.core import Activation

import sys
sys.path.append("../")
from IntegratedGradients import *

from shap import KernelExplainer, DenseData, visualize, initjs


# Loading data

# In[2]:


X = np.array([[float(j) for j in i.rstrip().split(",")[:-1]] for i in open("iris.data").readlines()][:-1])
Y = np.array([0 for i in range(100)] + [1 for i in range(50)])


# Training models

# In[3]:


model = Sequential([
    Dense(1, input_dim=4),
    #Activation('sigmoid'),
])
model.compile(optimizer='sgd', loss='mean_squared_error')


# In[4]:


history = model.fit(X, Y,
          epochs=300, batch_size=10,
          validation_split=0.1, verbose=0)


# Predict

# In[5]:


predictions = model.predict(X)


# In[6]:


plt.figure(figsize=(10,0.25))

ax = sns.heatmap(np.transpose(predictions), cbar=False)
plt.xticks([],[])
plt.yticks([],[])
plt.xlabel("samples (150)")
plt.title("Predictions")


# Explaning with integrated gradients

# In[7]:


ig = integrated_gradients(model)


# In[11]:


exp1 = ig.explain(X[0], num_steps=10000)


# In[12]:


f = lambda x: model.predict(x)[:,0]


# In[13]:


# use Shap to explain a single prediction
x = X[0:1,:]
background = DenseData(np.zeros((1,4)), range(4)) 
explainer = KernelExplainer(f, background, nsamples=10000)
exp2=explainer.explain(x).effects


# In[14]:


print "Integrated Gradients:", exp1
print "Shap:", exp2