RFCT Update figure generation code

Author: luispedro

ch07/boston1.py

@@ -27,11 +27,13 @@
 rmse = np.sqrt(lr.residues_/len(x))
 print('RMSE: {}'.format(rmse))
 
+fig, ax = plt.subplots()
+# Plot a diagonal (for reference):
+ax.plot([0, 50], [0, 50], '-', color=(.9,.3,.3), lw=4)
+
 # Plot the prediction versus real:
-plt.scatter(lr.predict(x), boston.target)
+ax.scatter(lr.predict(x), boston.target)
 
-# Plot a diagonal (for reference):
-plt.plot([0, 50], [0, 50], '-', color=(.9,.3,.3), lw=4)
-plt.xlabel('predicted')
-plt.ylabel('real')
-plt.savefig('Figure_07_08.png')
+ax.set_xlabel('predicted')
+ax.set_ylabel('real')
+fig.savefig('Figure_07_08.png')

ch07/figure1_2.py

@@ -14,9 +14,10 @@
 boston = load_boston()
 
 # Index number five in the number of rooms
-plt.scatter(boston.data[:, 5], boston.target)
-plt.xlabel("Number of rooms (RM)")
-plt.ylabel("House Price")
+fig,ax = plt.subplots()
+ax.scatter(boston.data[:, 5], boston.target)
+ax.set_xlabel("Number of rooms (RM)")
+ax.set_ylabel("House Price")
 
 x = boston.data[:, 5]
 # fit (used below) takes a two-dimensional array as input. We use np.atleast_2d
@@ -29,9 +30,9 @@
 lr = LinearRegression(fit_intercept=False)
 lr.fit(x, y)
 
-plt.plot([0, boston.data[:, 5].max() + 1],
+ax.plot([0, boston.data[:, 5].max() + 1],
          [0, lr.predict(boston.data[:, 5].max() + 1)], '-', lw=4)
-plt.savefig('Figure1.png', dpi=150)
+fig.savefig('Figure1.png')
 
 mse = mean_squared_error(y, lr.predict(x))
 rmse = np.sqrt(mse)
@@ -42,14 +43,14 @@
 
 lr.fit(x, y)
 
-plt.clf()
-plt.xlabel("Number of rooms (RM)")
-plt.ylabel("House Price")
-plt.scatter(boston.data[:, 5], boston.target)
+fig,ax = plt.subplots()
+ax.set_xlabel("Number of rooms (RM)")
+ax.set_ylabel("House Price")
+ax.scatter(boston.data[:, 5], boston.target)
 xmin = x.min()
 xmax = x.max()
-plt.plot([xmin, xmax], lr.predict([[xmin], [xmax]]) , '-', lw=4)
-plt.savefig('Figure2.png', dpi=150)
+ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]) , '-', lw=4)
+fig.savefig('Figure2.png')
 
 mse = mean_squared_error(y, lr.predict(x))
 print("Mean squared error (of training data): {:.3}".format(mse))

ch07/figure3.py

@@ -8,12 +8,13 @@
 from sklearn.linear_model import LinearRegression, Lasso
 import numpy as np
 from sklearn.datasets import load_boston
-import pylab as plt
+from matplotlib import pyplot as plt
 
 boston = load_boston()
-plt.scatter(boston.data[:, 5], boston.target)
-plt.xlabel("Number of rooms (RM)")
-plt.ylabel("House Price")
+fig, ax = plt.subplots()
+ax.scatter(boston.data[:, 5], boston.target)
+ax.set_xlabel("Number of rooms (RM)")
+ax.set_ylabel("House Price")
 
 
 x = boston.data[:, 5]
@@ -24,9 +25,9 @@
 
 lr = LinearRegression()
 lr.fit(x, y)
-plt.plot([xmin, xmax], lr.predict([[xmin], [xmax]]), ':', lw=4, label='OLS model')
+ax.plot([xmin, xmax], lr.predict([[xmin], [xmax]]), ':', lw=4, label='OLS model')
 
 las = Lasso()
 las.fit(x, y)
-plt.plot([xmin, xmax], las.predict([ [xmin], [xmax] ]), '-', lw=4, label='Lasso model')
-plt.savefig('Figure3.png', dpi=150)
+ax.plot([xmin, xmax], las.predict([ [xmin], [xmax] ]), '-', lw=4, label='Lasso model')
+fig.savefig('Figure3.png')

ch07/figure4.py

@@ -12,7 +12,7 @@
 from sklearn.linear_model import LinearRegression
 from sklearn.datasets import load_boston
 from sklearn.metrics import mean_squared_error
-import pylab as plt
+from matplotlib import pyplot as plt
 
 boston = load_boston()
 
@@ -24,9 +24,10 @@
 p = lr.predict(x)
 print("RMSE: {:.2}.".format(np.sqrt(mean_squared_error(y, p))))
 print("R2: {:.2}.".format(lr.score(x, y)))
-plt.scatter(p, y)
-plt.xlabel('Predicted price')
-plt.ylabel('Actual price')
-plt.plot([y.min(), y.max()], [y.min(), y.max()], lw=4)
+fig,ax = plt.subplots()
+ax.scatter(p, y)
+ax.set_xlabel('Predicted price')
+ax.set_ylabel('Actual price')
+ax.plot([y.min(), y.max()], [y.min(), y.max()], lw=4)
 
-plt.savefig('Figure4.png', dpi=150)
+fig.savefig('Figure4.png')

ch07/lasso_path_plot.py

@@ -25,5 +25,5 @@
 ax.set_xlabel('Lasso coefficient path as a function of alpha')
 ax.set_xlabel('Alpha')
 ax.set_ylabel('Coefficient weight')
-fig.savefig('Figure_LassoPath.png', dpi=150)
+fig.savefig('Figure_LassoPath.png')
 

ch07/predict10k_en.py

@@ -69,4 +69,5 @@
 ax.plot([-5,-1], [-5,-1], 'r-', lw=2)
 ax.set_xlabel('Actual value')
 ax.set_ylabel('Predicted value')
-fig.savefig('Figure_10k_scatter_EN_l1_ratio.png', dpi=150)
+fig.savefig('Figure_10k_scatter_EN_l1_ratio.png')
+