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

# ## <center> Neural Networks in scikit-learn </center>
# 
# #### Linear models:
# 
# <center> ŷ = w[0] \* x[0] + w[1] \* x[1] + ... + w[p] \* x[p] + b </center>

# In[8]:


import mglearn
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

mglearn.plots.plot_logistic_regression_graph()


# In[2]:


mglearn.plots.plot_single_hidden_layer_graph()


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mglearn.plots.plot_two_hidden_layer_graph()


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from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

cancer = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=0)

mlp = MLPClassifier(random_state=42)
mlp.fit(X_train, y_train)

print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train, y_train)))
print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test, y_test)))


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print('The maximum per each feature:\n{}'.format(cancer.data.max(axis=0)))


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from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit(X_train).transform(X_train)
X_test_scaled = scaler.fit(X_test).transform(X_test)

mlp = MLPClassifier(max_iter=1000, random_state=42)   

mlp.fit(X_train_scaled, y_train)

print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train_scaled, y_train)))
print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test_scaled, y_test)))


# In[7]:


mlp = MLPClassifier(max_iter=1000, alpha=1, random_state=42)
mlp.fit(X_train_scaled, y_train)

print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train_scaled, y_train)))
print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test_scaled, y_test)))


# In[9]:


plt.figure(figsize=(20,5))
plt.imshow(mlp.coefs_[0], interpolation='None', cmap='GnBu')
plt.yticks(range(30), cancer.feature_names)
plt.xlabel('Columns in weight matrix')
plt.ylabel('Input feature')
plt.colorbar()


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