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

# In[7]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')

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

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams['font.size'] = 12
plt.rcParams['figure.figsize'] = [12, 8]
plt.rcParams['lines.linewidth'] = 2.5


# In[6]:


from sklearn.datasets import load_boston
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeRegressor
from sklearn.inspection import plot_partial_dependence


##############################################################################
# Train models on the boston housing price dataset
# ================================================
#
# First, we load the boston housing price dataset and split the the dataset
# into a training and test set. Then, we train a histogram gradient boosting
# decision tree and a multi-layer perceptron on the training set.

from sklearn.preprocessing import scale
boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(scale(X), y, test_size=0.1,
                                                    random_state=0)

hgbr = DecisionTreeRegressor()
mlp = MLPRegressor(hidden_layer_sizes=(100, 100),
                    tol=1e-2, max_iter=500, random_state=0)
hgbr.fit(X_train, y_train)
mlp.fit(X_train, y_train)


# ## Bounds are weird

# In[8]:


mlp_disp = plot_partial_dependence(mlp, X_test, ["LSTAT", "RM"],
                                   feature_names=boston.feature_names,
                                   n_cols=3, line_kw={"c": "red"})


# In[18]:


hgbr_disp = plot_partial_dependence(hgbr, X_test, ["LSTAT", "RM"],
                                    feature_names=boston.feature_names,
                                    n_cols=3, line_kw={"c": "blue"})


# In[20]:


plot_partial_dependence(hgbr, X_test, ["LSTAT", "RM"],
                        feature_names=boston.feature_names, ax=mlp_disp.axes_,
                        n_cols=3, line_kw={"c": "blue"})


# In[14]:


h = plot_partial_dependence(hgbr, X_test, ["LSTAT", "RM"],
                            feature_names=boston.feature_names, ax=mlp_disp.axes_,
                            n_cols=3, line_kw={"c": "blue"})
h.figure_