Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn,seaborn
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# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
In [3]:
from IPython.display import Image
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%matplotlib inline
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Image(filename='./images/10_01.png', width=500)
Out[5]:
Source: https://archive.ics.uci.edu/ml/datasets/Housing
Attributes:
1. CRIM per capita crime rate by town
2. ZN proportion of residential land zoned for lots over
25,000 sq.ft.
3. INDUS proportion of non-retail business acres per town
4. CHAS Charles River dummy variable (= 1 if tract bounds
river; 0 otherwise)
5. NOX nitric oxides concentration (parts per 10 million)
6. RM average number of rooms per dwelling
7. AGE proportion of owner-occupied units built prior to 1940
8. DIS weighted distances to five Boston employment centres
9. RAD index of accessibility to radial highways
10. TAX full-value property-tax rate per $10,000
11. PTRATIO pupil-teacher ratio by town
12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks
by town
13. LSTAT % lower status of the population
14. MEDV Median value of owner-occupied homes in $1000's
In [7]:
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/'
'housing/housing.data',
header=None,
sep='\s+')
df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',
'NOX', 'RM', 'AGE', 'DIS', 'RAD',
'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
df.head()
Out[7]:
If the link to the Housing dataset provided above does not work for you, you can find a local copy in this repository at ./../datasets/housing/housing.data.
Or you could fetch it via
In [6]:
df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python-machine-learning-book/master/code/datasets/housing/housing.data',
header=None, sep='\s+')
df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',
'NOX', 'RM', 'AGE', 'DIS', 'RAD',
'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
df.head()
Out[6]:
In [8]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid', context='notebook')
cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']
sns.pairplot(df[cols], size=2.5)
plt.tight_layout()
# plt.savefig('./figures/scatter.png', dpi=300)
plt.show()
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import numpy as np
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.5)
hm = sns.heatmap(cm,
cbar=True,
annot=True,
square=True,
fmt='.2f',
annot_kws={'size': 15},
yticklabels=cols,
xticklabels=cols)
# plt.tight_layout()
# plt.savefig('./figures/corr_mat.png', dpi=300)
plt.show()
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sns.reset_orig()
%matplotlib inline
...
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class LinearRegressionGD(object):
def __init__(self, eta=0.001, n_iter=20):
self.eta = eta
self.n_iter = n_iter
def fit(self, X, y):
self.w_ = np.zeros(1 + X.shape[1])
self.cost_ = []
for i in range(self.n_iter):
output = self.net_input(X)
errors = (y - output)
self.w_[1:] += self.eta * X.T.dot(errors)
self.w_[0] += self.eta * errors.sum()
cost = (errors**2).sum() / 2.0
self.cost_.append(cost)
return self
def net_input(self, X):
return np.dot(X, self.w_[1:]) + self.w_[0]
def predict(self, X):
return self.net_input(X)
In [12]:
X = df[['RM']].values
y = df['MEDV'].values
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from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
sc_y = StandardScaler()
X_std = sc_x.fit_transform(X)
y_std = sc_y.fit_transform(y)
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lr = LinearRegressionGD()
lr.fit(X_std, y_std)
Out[14]:
In [15]:
plt.plot(range(1, lr.n_iter+1), lr.cost_)
plt.ylabel('SSE')
plt.xlabel('Epoch')
plt.tight_layout()
# plt.savefig('./figures/cost.png', dpi=300)
plt.show()
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def lin_regplot(X, y, model):
plt.scatter(X, y, c='lightblue')
plt.plot(X, model.predict(X), color='red', linewidth=2)
return
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lin_regplot(X_std, y_std, lr)
plt.xlabel('Average number of rooms [RM] (standardized)')
plt.ylabel('Price in $1000\'s [MEDV] (standardized)')
plt.tight_layout()
# plt.savefig('./figures/gradient_fit.png', dpi=300)
plt.show()
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print('Slope: %.3f' % lr.w_[1])
print('Intercept: %.3f' % lr.w_[0])
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num_rooms_std = sc_x.transform([5.0])
price_std = lr.predict(num_rooms_std)
print("Price in $1000's: %.3f" % sc_y.inverse_transform(price_std))
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from sklearn.linear_model import LinearRegression
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slr = LinearRegression()
slr.fit(X, y)
y_pred = slr.predict(X)
print('Slope: %.3f' % slr.coef_[0])
print('Intercept: %.3f' % slr.intercept_)
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lin_regplot(X, y, slr)
plt.xlabel('Average number of rooms [RM]')
plt.ylabel('Price in $1000\'s [MEDV]')
plt.tight_layout()
# plt.savefig('./figures/scikit_lr_fit.png', dpi=300)
plt.show()
Normal Equations alternative:
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# adding a column vector of "ones"
Xb = np.hstack((np.ones((X.shape[0], 1)), X))
w = np.zeros(X.shape[1])
z = np.linalg.inv(np.dot(Xb.T, Xb))
w = np.dot(z, np.dot(Xb.T, y))
print('Slope: %.3f' % w[1])
print('Intercept: %.3f' % w[0])
In [24]:
from sklearn.linear_model import RANSACRegressor
ransac = RANSACRegressor(LinearRegression(),
max_trials=100,
min_samples=50,
residual_metric=lambda x: np.sum(np.abs(x), axis=1),
residual_threshold=5.0,
random_state=0)
ransac.fit(X, y)
inlier_mask = ransac.inlier_mask_
outlier_mask = np.logical_not(inlier_mask)
line_X = np.arange(3, 10, 1)
line_y_ransac = ransac.predict(line_X[:, np.newaxis])
plt.scatter(X[inlier_mask], y[inlier_mask],
c='blue', marker='o', label='Inliers')
plt.scatter(X[outlier_mask], y[outlier_mask],
c='lightgreen', marker='s', label='Outliers')
plt.plot(line_X, line_y_ransac, color='red')
plt.xlabel('Average number of rooms [RM]')
plt.ylabel('Price in $1000\'s [MEDV]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/ransac_fit.png', dpi=300)
plt.show()
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print('Slope: %.3f' % ransac.estimator_.coef_[0])
print('Intercept: %.3f' % ransac.estimator_.intercept_)
In [26]:
from sklearn.cross_validation import train_test_split
X = df.iloc[:, :-1].values
y = df['MEDV'].values
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=0)
In [27]:
slr = LinearRegression()
slr.fit(X_train, y_train)
y_train_pred = slr.predict(X_train)
y_test_pred = slr.predict(X_test)
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plt.scatter(y_train_pred, y_train_pred - y_train,
c='blue', marker='o', label='Training data')
plt.scatter(y_test_pred, y_test_pred - y_test,
c='lightgreen', marker='s', label='Test data')
plt.xlabel('Predicted values')
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')
plt.xlim([-10, 50])
plt.tight_layout()
# plt.savefig('./figures/slr_residuals.png', dpi=300)
plt.show()
In [29]:
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)))
In [30]:
from sklearn.linear_model import Lasso
lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)
y_train_pred = lasso.predict(X_train)
y_test_pred = lasso.predict(X_test)
print(lasso.coef_)
In [31]:
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)))
In [32]:
X = np.array([258.0, 270.0, 294.0,
320.0, 342.0, 368.0,
396.0, 446.0, 480.0, 586.0])[:, np.newaxis]
y = np.array([236.4, 234.4, 252.8,
298.6, 314.2, 342.2,
360.8, 368.0, 391.2,
390.8])
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from sklearn.preprocessing import PolynomialFeatures
lr = LinearRegression()
pr = LinearRegression()
quadratic = PolynomialFeatures(degree=2)
X_quad = quadratic.fit_transform(X)
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# fit linear features
lr.fit(X, y)
X_fit = np.arange(250, 600, 10)[:, np.newaxis]
y_lin_fit = lr.predict(X_fit)
# fit quadratic features
pr.fit(X_quad, y)
y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))
# plot results
plt.scatter(X, y, label='training points')
plt.plot(X_fit, y_lin_fit, label='linear fit', linestyle='--')
plt.plot(X_fit, y_quad_fit, label='quadratic fit')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/poly_example.png', dpi=300)
plt.show()
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y_lin_pred = lr.predict(X)
y_quad_pred = pr.predict(X_quad)
In [36]:
print('Training MSE linear: %.3f, quadratic: %.3f' % (
mean_squared_error(y, y_lin_pred),
mean_squared_error(y, y_quad_pred)))
print('Training R^2 linear: %.3f, quadratic: %.3f' % (
r2_score(y, y_lin_pred),
r2_score(y, y_quad_pred)))
In [37]:
X = df[['LSTAT']].values
y = df['MEDV'].values
regr = LinearRegression()
# create quadratic features
quadratic = PolynomialFeatures(degree=2)
cubic = PolynomialFeatures(degree=3)
X_quad = quadratic.fit_transform(X)
X_cubic = cubic.fit_transform(X)
# fit features
X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]
regr = regr.fit(X, y)
y_lin_fit = regr.predict(X_fit)
linear_r2 = r2_score(y, regr.predict(X))
regr = regr.fit(X_quad, y)
y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))
quadratic_r2 = r2_score(y, regr.predict(X_quad))
regr = regr.fit(X_cubic, y)
y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))
cubic_r2 = r2_score(y, regr.predict(X_cubic))
# plot results
plt.scatter(X, y, label='training points', color='lightgray')
plt.plot(X_fit, y_lin_fit,
label='linear (d=1), $R^2=%.2f$' % linear_r2,
color='blue',
lw=2,
linestyle=':')
plt.plot(X_fit, y_quad_fit,
label='quadratic (d=2), $R^2=%.2f$' % quadratic_r2,
color='red',
lw=2,
linestyle='-')
plt.plot(X_fit, y_cubic_fit,
label='cubic (d=3), $R^2=%.2f$' % cubic_r2,
color='green',
lw=2,
linestyle='--')
plt.xlabel('% lower status of the population [LSTAT]')
plt.ylabel('Price in $1000\'s [MEDV]')
plt.legend(loc='upper right')
plt.tight_layout()
# plt.savefig('./figures/polyhouse_example.png', dpi=300)
plt.show()
Transforming the dataset:
In [38]:
X = df[['LSTAT']].values
y = df['MEDV'].values
# transform features
X_log = np.log(X)
y_sqrt = np.sqrt(y)
# fit features
X_fit = np.arange(X_log.min()-1, X_log.max()+1, 1)[:, np.newaxis]
regr = regr.fit(X_log, y_sqrt)
y_lin_fit = regr.predict(X_fit)
linear_r2 = r2_score(y_sqrt, regr.predict(X_log))
# plot results
plt.scatter(X_log, y_sqrt, label='training points', color='lightgray')
plt.plot(X_fit, y_lin_fit,
label='linear (d=1), $R^2=%.2f$' % linear_r2,
color='blue',
lw=2)
plt.xlabel('log(% lower status of the population [LSTAT])')
plt.ylabel('$\sqrt{Price \; in \; \$1000\'s [MEDV]}$')
plt.legend(loc='lower left')
plt.tight_layout()
# plt.savefig('./figures/transform_example.png', dpi=300)
plt.show()
...
In [39]:
from sklearn.tree import DecisionTreeRegressor
X = df[['LSTAT']].values
y = df['MEDV'].values
tree = DecisionTreeRegressor(max_depth=3)
tree.fit(X, y)
sort_idx = X.flatten().argsort()
lin_regplot(X[sort_idx], y[sort_idx], tree)
plt.xlabel('% lower status of the population [LSTAT]')
plt.ylabel('Price in $1000\'s [MEDV]')
# plt.savefig('./figures/tree_regression.png', dpi=300)
plt.show()
In [40]:
X = df.iloc[:, :-1].values
y = df['MEDV'].values
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=1)
In [41]:
from sklearn.ensemble import RandomForestRegressor
forest = RandomForestRegressor(n_estimators=1000,
criterion='mse',
random_state=1,
n_jobs=-1)
forest.fit(X_train, y_train)
y_train_pred = forest.predict(X_train)
y_test_pred = forest.predict(X_test)
print('MSE train: %.3f, test: %.3f' % (
mean_squared_error(y_train, y_train_pred),
mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' % (
r2_score(y_train, y_train_pred),
r2_score(y_test, y_test_pred)))
In [42]:
plt.scatter(y_train_pred,
y_train_pred - y_train,
c='black',
marker='o',
s=35,
alpha=0.5,
label='Training data')
plt.scatter(y_test_pred,
y_test_pred - y_test,
c='lightgreen',
marker='s',
s=35,
alpha=0.7,
label='Test data')
plt.xlabel('Predicted values')
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')
plt.xlim([-10, 50])
plt.tight_layout()
# plt.savefig('./figures/slr_residuals.png', dpi=300)
plt.show()
...