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@philoniare
Created August 9, 2017 10:22
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Boston House Pricing
###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
###########################################
import matplotlib.pyplot as pl
import numpy as np
import sklearn.learning_curve as curves
from sklearn.tree import DecisionTreeRegressor
from sklearn.cross_validation import ShuffleSplit, train_test_split
def ModelLearning(X, y):
""" Calculates the performance of several models with varying sizes of training data.
The learning and testing scores for each model are then plotted. """
# Create 10 cross-validation sets for training and testing
cv = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.2, random_state = 0)
# Generate the training set sizes increasing by 50
train_sizes = np.rint(np.linspace(1, X.shape[0]*0.8 - 1, 9)).astype(int)
# Create the figure window
fig = pl.figure(figsize=(10,7))
# Create three different models based on max_depth
for k, depth in enumerate([1,3,6,10]):
# Create a Decision tree regressor at max_depth = depth
regressor = DecisionTreeRegressor(max_depth = depth)
# Calculate the training and testing scores
sizes, train_scores, test_scores = curves.learning_curve(regressor, X, y, \
cv = cv, train_sizes = train_sizes, scoring = 'r2')
# Find the mean and standard deviation for smoothing
train_std = np.std(train_scores, axis = 1)
train_mean = np.mean(train_scores, axis = 1)
test_std = np.std(test_scores, axis = 1)
test_mean = np.mean(test_scores, axis = 1)
# Subplot the learning curve
ax = fig.add_subplot(2, 2, k+1)
ax.plot(sizes, train_mean, 'o-', color = 'r', label = 'Training Score')
ax.plot(sizes, test_mean, 'o-', color = 'g', label = 'Testing Score')
ax.fill_between(sizes, train_mean - train_std, \
train_mean + train_std, alpha = 0.15, color = 'r')
ax.fill_between(sizes, test_mean - test_std, \
test_mean + test_std, alpha = 0.15, color = 'g')
# Labels
ax.set_title('max_depth = %s'%(depth))
ax.set_xlabel('Number of Training Points')
ax.set_ylabel('Score')
ax.set_xlim([0, X.shape[0]*0.8])
ax.set_ylim([-0.05, 1.05])
# Visual aesthetics
ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad = 0.)
fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 16, y = 1.03)
fig.tight_layout()
fig.show()
def ModelComplexity(X, y):
""" Calculates the performance of the model as model complexity increases.
The learning and testing errors rates are then plotted. """
# Create 10 cross-validation sets for training and testing
cv = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.2, random_state = 0)
# Vary the max_depth parameter from 1 to 10
max_depth = np.arange(1,11)
# Calculate the training and testing scores
train_scores, test_scores = curves.validation_curve(DecisionTreeRegressor(), X, y, \
param_name = "max_depth", param_range = max_depth, cv = cv, scoring = 'r2')
# Find the mean and standard deviation for smoothing
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
# Plot the validation curve
pl.figure(figsize=(7, 5))
pl.title('Decision Tree Regressor Complexity Performance')
pl.plot(max_depth, train_mean, 'o-', color = 'r', label = 'Training Score')
pl.plot(max_depth, test_mean, 'o-', color = 'g', label = 'Validation Score')
pl.fill_between(max_depth, train_mean - train_std, \
train_mean + train_std, alpha = 0.15, color = 'r')
pl.fill_between(max_depth, test_mean - test_std, \
test_mean + test_std, alpha = 0.15, color = 'g')
# Visual aesthetics
pl.legend(loc = 'lower right')
pl.xlabel('Maximum Depth')
pl.ylabel('Score')
pl.ylim([-0.05,1.05])
pl.show()
def PredictTrials(X, y, fitter, data):
""" Performs trials of fitting and predicting data. """
# Store the predicted prices
prices = []
for k in range(10):
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, \
test_size = 0.2, random_state = k)
# Fit the data
reg = fitter(X_train, y_train)
# Make a prediction
pred = reg.predict([data[0]])[0]
prices.append(pred)
# Result
print("Trial {}: ${:,.2f}".format(k+1, pred))
# Display price range
print("\nRange in prices: ${:,.2f}".format(max(prices) - min(prices)))
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