import graphlab

"""
Explanation: Fire up graphlab create
End of explanation
"""


sales = graphlab.SFrame('home_data.gl/')

sales

"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""


graphlab.canvas.set_target('ipynb')
sales.show(view="Scatter Plot", x="sqft_living", y="price")

"""
Explanation: Exploring the data for housing sales
The house price is correlated with the number of square feet of living space.
End of explanation
"""


train_data,test_data = sales.random_split(.8,seed=0)

"""
Explanation: Create a simple regression model of sqft_living to price
Split data into training and testing.
We use seed=0 so that everyone running this notebook gets the same results.  In practice, you may set a random seed (or let GraphLab Create pick a random seed for you).
End of explanation
"""


sqft_model = graphlab.linear_regression.create(train_data, target='price', features=['sqft_living'])

"""
Explanation: Build the regression model using only sqft_living as a feature
End of explanation
"""


print test_data['price'].mean()

print sqft_model.evaluate(test_data)

"""
Explanation: Evaluate the simple model
End of explanation
"""


import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(test_data['sqft_living'],test_data['price'],'.',
        test_data['sqft_living'],sqft_model.predict(test_data),'-')

"""
Explanation: RMSE of about \$255,170!
Let's show what our predictions look like
Matplotlib is a Python plotting library that is also useful for plotting.  You can install it with:
'pip install matplotlib'
End of explanation
"""


sqft_model.get('coefficients')

"""
Explanation: Above:  blue dots are original data, green line is the prediction from the simple regression.
Below: we can view the learned regression coefficients.
End of explanation
"""


my_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']

sales[my_features].show()

sales.show(view='BoxWhisker Plot', x='zipcode', y='price')

"""
Explanation: Explore other features in the data
To build a more elaborate model, we will explore using more features.
End of explanation
"""


my_features_model = graphlab.linear_regression.create(train_data,target='price',features=my_features)

print my_features

"""
Explanation: Pull the bar at the bottom to view more of the data.  
98039 is the most expensive zip code.
Build a regression model with more features
End of explanation
"""


print sqft_model.evaluate(test_data)
print my_features_model.evaluate(test_data)

"""
Explanation: Comparing the results of the simple model with adding more features
End of explanation
"""


house1 = sales[sales['id']=='5309101200']

house1

"""
Explanation: The RMSE goes down from \$255,170 to \$179,508 with more features.
Apply learned models to predict prices of 3 houses
The first house we will use is considered an "average" house in Seattle.
End of explanation
"""


print house1['price']

print sqft_model.predict(house1)

print my_features_model.predict(house1)

"""
Explanation: <img src="house-5309101200.jpg">
End of explanation
"""


house2 = sales[sales['id']=='1925069082']

house2

"""
Explanation: In this case, the model with more features provides a worse prediction than the simpler model with only 1 feature.  However, on average, the model with more features is better.
Prediction for a second, fancier house
We will now examine the predictions for a fancier house.
End of explanation
"""


print sqft_model.predict(house2)

print my_features_model.predict(house2)

"""
Explanation: <img src="house-1925069082.jpg">
End of explanation
"""


bill_gates = {'bedrooms':[8], 
              'bathrooms':[25], 
              'sqft_living':[50000], 
              'sqft_lot':[225000],
              'floors':[4], 
              'zipcode':['98039'], 
              'condition':[10], 
              'grade':[10],
              'waterfront':[1],
              'view':[4],
              'sqft_above':[37500],
              'sqft_basement':[12500],
              'yr_built':[1994],
              'yr_renovated':[2010],
              'lat':[47.627606],
              'long':[-122.242054],
              'sqft_living15':[5000],
              'sqft_lot15':[40000]}

"""
Explanation: In this case, the model with more features provides a better prediction.  This behavior is expected here, because this house is more differentiated by features that go beyond its square feet of living space, especially the fact that it's a waterfront house. 
Last house, super fancy
Our last house is a very large one owned by a famous Seattleite.
End of explanation
"""


print my_features_model.predict(graphlab.SFrame(bill_gates))

"""
Explanation: <img src="house-bill-gates.jpg">
End of explanation
"""


