"""
A simple regression analysis on the California housing data
===========================================================

Here we perform a simple regression analysis on the California housing
data, exploring two types of regressors.

"""

from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True)

##############################################################################
# Print a histogram of the quantity to predict: price
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 3))
plt.hist(data.target)
plt.xlabel('price ($100k)')
plt.ylabel('count')
plt.tight_layout()

##############################################################################
# Print the join histogram for each feature

for index, feature_name in enumerate(data.feature_names):
    plt.figure(figsize=(4, 3))
    plt.scatter(data.data[feature_name], data.target)
    plt.ylabel('Price', size=15)
    plt.xlabel(feature_name, size=15)
    plt.tight_layout()


##############################################################################
# Simple prediction

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predicted = clf.predict(X_test)
expected = y_test

plt.figure(figsize=(4, 3))
plt.scatter(expected, predicted)
plt.plot([0, 8], [0, 8], '--k')
plt.axis('tight')
plt.xlabel('True price ($100k)')
plt.ylabel('Predicted price ($100k)')
plt.tight_layout()


##############################################################################
# Prediction with gradient boosted tree

from sklearn.ensemble import GradientBoostingRegressor

clf = GradientBoostingRegressor()
clf.fit(X_train, y_train)

predicted = clf.predict(X_test)
expected = y_test

plt.figure(figsize=(4, 3))
plt.scatter(expected, predicted)
plt.plot([0, 5], [0, 5], '--k')
plt.axis('tight')
plt.xlabel('True price ($100k)')
plt.ylabel('Predicted price ($100k)')
plt.tight_layout()

##############################################################################
# Print the error rate
import numpy as np
print(f"RMS: {np.sqrt(np.mean((predicted - expected) ** 2))!r} ")

plt.show()