1.3 MiB
1.3 MiB
In [1]:
from numpy import *
from numpy.random import randint, randn
from time import time
def jackknife(data, stat):
n = len(data);t = zeros(n); inds = arange(n); t0 = time()
## 'jackknifing' by leaving out an observation for each i
for i in range(n):
t[i] = stat(delete(data,i) )
# analysis
print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :")
print("original bias std. error")
print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5))
return t
# Returns mean of data samples
def stat(data):
return mean(data)
mu, sigma = 100, 15
datapoints = 10000
x = mu + sigma*random.randn(datapoints)
# jackknife returns the data sample
t = jackknife(x, stat)Runtime: 0.0903549 sec Jackknife Statistics : original bias std. error 100.107 100.097 0.150184
In [2]:
%matplotlib inline
import numpy as np
from time import time
from scipy.stats import norm
import matplotlib.pyplot as plt
# Returns mean of bootstrap samples
# Bootstrap algorithm
def bootstrap(data, datapoints):
t = np.zeros(datapoints)
n = len(data)
# non-parametric bootstrap
for i in range(datapoints):
t[i] = np.mean(data[np.random.randint(0,n,n)])
# analysis
print("Bootstrap Statistics :")
print("original bias std. error")
print("%8g %8g %14g %15g" % (np.mean(data), np.std(data),np.mean(t),np.std(t)))
return t
# We set the mean value to 100 and the standard deviation to 15
mu, sigma = 100, 15
datapoints = 10000
# We generate random numbers according to the normal distribution
x = mu + sigma*np.random.randn(datapoints)
# bootstrap returns the data sample
t = bootstrap(x, datapoints)Bootstrap Statistics : original bias std. error 99.9033 14.9678 99.904 0.151348
In [3]:
# the histogram of the bootstrapped data (normalized data if density = True)
n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)
# add a 'best fit' line
y = norm.pdf(binsboot, np.mean(t), np.std(t))
lt = plt.plot(binsboot, y, 'b', linewidth=1)
plt.xlabel('x')
plt.ylabel('Probability')
plt.grid(True)
plt.show()
In [4]:
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.utils import resample
np.random.seed(2018)
n = 500
n_boostraps = 100
degree = 18 # A quite high value, just to show.
noise = 0.1
# Make data set.
x = np.linspace(-1, 3, n).reshape(-1, 1)
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2) + np.random.normal(0, 0.1, x.shape)
# Hold out some test data that is never used in training.
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
# Combine x transformation and model into one operation.
# Not neccesary, but convenient.
model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))
# The following (m x n_bootstraps) matrix holds the column vectors y_pred
# for each bootstrap iteration.
y_pred = np.empty((y_test.shape[0], n_boostraps))
for i in range(n_boostraps):
x_, y_ = resample(x_train, y_train)
# Evaluate the new model on the same test data each time.
y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()
# Note: Expectations and variances taken w.r.t. different training
# data sets, hence the axis=1. Subsequent means are taken across the test data
# set in order to obtain a total value, but before this we have error/bias/variance
# calculated per data point in the test set.
# Note 2: The use of keepdims=True is important in the calculation of bias as this
# maintains the column vector form. Dropping this yields very unexpected results.
error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
variance = np.mean( np.var(y_pred, axis=1, keepdims=True) )
print('Error:', error)
print('Bias^2:', bias)
print('Var:', variance)
print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance))
plt.plot(x[::5, :], y[::5, :], label='f(x)')
plt.scatter(x_test, y_test, label='Data points')
plt.scatter(x_test, np.mean(y_pred, axis=1), label='Pred')
plt.legend()
plt.show()Error: 0.013121574062587286 Bias^2: 0.012073649469946107 Var: 0.0010479245926411787 0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286
In [5]:
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.utils import resample
np.random.seed(2018)
n = 40
n_boostraps = 100
maxdegree = 14
# Make data set.
x = np.linspace(-3, 3, n).reshape(-1, 1)
y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
error = np.zeros(maxdegree)
bias = np.zeros(maxdegree)
variance = np.zeros(maxdegree)
polydegree = np.zeros(maxdegree)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
for degree in range(maxdegree):
model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))
y_pred = np.empty((y_test.shape[0], n_boostraps))
for i in range(n_boostraps):
x_, y_ = resample(x_train, y_train)
y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()
polydegree[degree] = degree
error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )
bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )
variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )
print('Polynomial degree:', degree)
print('Error:', error[degree])
print('Bias^2:', bias[degree])
print('Var:', variance[degree])
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
plt.plot(polydegree, error, label='Error')
plt.plot(polydegree, bias, label='bias')
plt.plot(polydegree, variance, label='Variance')
plt.legend()
plt.show()Polynomial degree: 0 Error: 0.32149601703519115 Bias^2: 0.3123314713548606 Var: 0.009164545680330616 0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912 Polynomial degree: 1 Error: 0.08426840630693412 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 Polynomial degree: 2 Error: 0.10398646080125037 Bias^2: 0.10077114273548984 Var: 0.0032153180657605116 0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036 Polynomial degree: 3 Error: 0.06547790180152352 Bias^2: 0.062082386342319454 Var: 0.0033955154592040923 0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355 Polynomial degree: 4 Error: 0.06844519414009445 Bias^2: 0.06453579006728322 Var: 0.003909404072811221 0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444
Polynomial degree: 5 Error: 0.05227921801205679 Bias^2: 0.04818727730430286 Var: 0.004091940707753925 0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679 Polynomial degree: 6 Error: 0.03781367141738902 Bias^2: 0.03365768507152769 Var: 0.0041559863458613296 0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902 Polynomial degree: 7 Error: 0.027609773491022394 Bias^2: 0.022999498260366198 Var: 0.004610275230656182 0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238 Polynomial degree: 8 Error: 0.017355848195593312 Bias^2: 0.010331721306655165 Var: 0.007024126888938144 0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331 Polynomial degree: 9 Error: 0.026605727637184558 Bias^2: 0.010018312644139219 Var: 0.016587414993045335 0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554 Polynomial degree: 10 Error: 0.021592704588021178 Bias^2: 0.010516485576646504 Var: 0.01107621901137467 0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174 Polynomial degree: 11 Error: 0.07160048164232538 Bias^2: 0.014436800088896381 Var: 0.05716368155342902 0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254 Polynomial degree: 12 Error: 0.11547777218876518 Bias^2: 0.016285782696017142 Var: 0.09919198949274803 0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518 Polynomial degree: 13 Error: 0.2284246870217162 Bias^2: 0.01975416527168255 Var: 0.20867052175003364 0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162
In [6]:
"""
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
which is a part of the cosine function. In addition, the samples from the
real function and the approximations of different models are displayed. The
models have polynomial features of different degrees. We can see that a
linear function (polynomial with degree 1) is not sufficient to fit the
training samples. This is called **underfitting**. A polynomial of degree 4
approximates the true function almost perfectly. However, for higher degrees
the model will **overfit** the training data, i.e. it learns the noise of the
training data.
We evaluate quantitatively **overfitting** / **underfitting** by using
cross-validation. We calculate the mean squared error (MSE) on the validation
set, the higher, the less likely the model generalizes correctly from the
training data.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
def true_fun(X):
return np.cos(1.5 * np.pi * X)
np.random.seed(0)
n_samples = 30
degrees = [1, 4, 15]
X = np.sort(np.random.rand(n_samples))
y = true_fun(X) + np.random.randn(n_samples) * 0.1
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
plt.setp(ax, xticks=(), yticks=())
polynomial_features = PolynomialFeatures(degree=degrees[i],
include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("polynomial_features", polynomial_features),
("linear_regression", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
# Evaluate the models using crossvalidation
scores = cross_val_score(pipeline, X[:, np.newaxis], y,
scoring="neg_mean_squared_error", cv=10)
X_test = np.linspace(0, 1, 100)
plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
plt.plot(X_test, true_fun(X_test), label="True function")
plt.scatter(X, y, edgecolor='b', s=20, label="Samples")
plt.xlabel("x")
plt.ylabel("y")
plt.xlim((0, 1))
plt.ylim((-2, 2))
plt.legend(loc="best")
plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format(
degrees[i], -scores.mean(), scores.std()))
plt.show()============================ Underfitting vs. Overfitting ============================ This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions. The plot shows the function that we want to approximate, which is a part of the cosine function. In addition, the samples from the real function and the approximations of different models are displayed. The models have polynomial features of different degrees. We can see that a linear function (polynomial with degree 1) is not sufficient to fit the training samples. This is called **underfitting**. A polynomial of degree 4 approximates the true function almost perfectly. However, for higher degrees the model will **overfit** the training data, i.e. it learns the noise of the training data. We evaluate quantitatively **overfitting** / **underfitting** by using cross-validation. We calculate the mean squared error (MSE) on the validation set, the higher, the less likely the model generalizes correctly from the training data.
In [7]:
# Common imports
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
from sklearn.metrics import mean_squared_error
# Where to save the figures and data files
PROJECT_ROOT_DIR = "Results"
FIGURE_ID = "Results/FigureFiles"
DATA_ID = "DataFiles/"
if not os.path.exists(PROJECT_ROOT_DIR):
os.mkdir(PROJECT_ROOT_DIR)
if not os.path.exists(FIGURE_ID):
os.makedirs(FIGURE_ID)
if not os.path.exists(DATA_ID):
os.makedirs(DATA_ID)
def image_path(fig_id):
return os.path.join(FIGURE_ID, fig_id)
def data_path(dat_id):
return os.path.join(DATA_ID, dat_id)
def save_fig(fig_id):
plt.savefig(image_path(fig_id) + ".png", format='png')
infile = open(data_path("EoS.csv"),'r')
# Read the EoS data as csv file and organize the data into two arrays with density and energies
EoS = pd.read_csv(infile, names=('Density', 'Energy'))
EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')
EoS = EoS.dropna()
Energies = EoS['Energy']
Density = EoS['Density']
# The design matrix now as function of various polytrops
Maxpolydegree = 30
X = np.zeros((len(Density),Maxpolydegree))
X[:,0] = 1.0
testerror = np.zeros(Maxpolydegree)
trainingerror = np.zeros(Maxpolydegree)
polynomial = np.zeros(Maxpolydegree)
trials = 100
for polydegree in range(1, Maxpolydegree):
polynomial[polydegree] = polydegree
for degree in range(polydegree):
X[:,degree] = Density**(degree/3.0)
# loop over trials in order to estimate the expectation value of the MSE
testerror[polydegree] = 0.0
trainingerror[polydegree] = 0.0
for samples in range(trials):
x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)
model = LinearRegression(fit_intercept=False).fit(x_train, y_train)
ypred = model.predict(x_train)
ytilde = model.predict(x_test)
testerror[polydegree] += mean_squared_error(y_test, ytilde)
trainingerror[polydegree] += mean_squared_error(y_train, ypred)
testerror[polydegree] /= trials
trainingerror[polydegree] /= trials
print("Degree of polynomial: %3d"% polynomial[polydegree])
print("Mean squared error on training data: %.8f" % trainingerror[polydegree])
print("Mean squared error on test data: %.8f" % testerror[polydegree])
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
plt.plot(polynomial, np.log10(testerror), label='Test Error')
plt.xlabel('Polynomial degree')
plt.ylabel('log10[MSE]')
plt.legend()
plt.show()Degree of polynomial: 1
Mean squared error on training data: 439230.69504801 Mean squared error on test data: 481979.17861098 Degree of polynomial: 2 Mean squared error on training data: 115822.95008046 Mean squared error on test data: 123711.53703498 Degree of polynomial: 3 Mean squared error on training data: 9011.85263220 Mean squared error on test data: 10913.84780262 Degree of polynomial: 4 Mean squared error on training data: 303.47610036 Mean squared error on test data: 426.30787294 Degree of polynomial: 5 Mean squared error on training data: 3.80354994 Mean squared error on test data: 5.98822371 Degree of polynomial: 6 Mean squared error on training data: 3.66204648 Mean squared error on test data: 8.14812206 Degree of polynomial: 7 Mean squared error on training data: 0.47075725 Mean squared error on test data: 2.00607783
Degree of polynomial: 8 Mean squared error on training data: 0.04912436 Mean squared error on test data: 0.21596432 Degree of polynomial: 9 Mean squared error on training data: 0.02522069 Mean squared error on test data: 0.08576932 Degree of polynomial: 10 Mean squared error on training data: 0.02511518 Mean squared error on test data: 1.20015436 Degree of polynomial: 11 Mean squared error on training data: 0.01640891 Mean squared error on test data: 1.35533773 Degree of polynomial: 12 Mean squared error on training data: 0.00813803 Mean squared error on test data: 0.17446471 Degree of polynomial: 13 Mean squared error on training data: 0.00759119 Mean squared error on test data: 1.08131003 Degree of polynomial: 14 Mean squared error on training data: 0.00472199 Mean squared error on test data: 0.81333804
Degree of polynomial: 15 Mean squared error on training data: 0.00410478 Mean squared error on test data: 92.09172409 Degree of polynomial: 16 Mean squared error on training data: 0.00315593 Mean squared error on test data: 234.38533185 Degree of polynomial: 17 Mean squared error on training data: 0.00242999 Mean squared error on test data: 1271.35771826 Degree of polynomial: 18 Mean squared error on training data: 0.00228742 Mean squared error on test data: 108.27092910 Degree of polynomial: 19 Mean squared error on training data: 0.00156376 Mean squared error on test data: 1371.99051150 Degree of polynomial: 20 Mean squared error on training data: 0.00137818 Mean squared error on test data: 1887.86252988
Degree of polynomial: 21 Mean squared error on training data: 0.00118508 Mean squared error on test data: 14859.69908626 Degree of polynomial: 22 Mean squared error on training data: 0.00092647 Mean squared error on test data: 876.51191552 Degree of polynomial: 23 Mean squared error on training data: 0.00085889 Mean squared error on test data: 5594.60815105 Degree of polynomial: 24 Mean squared error on training data: 0.00084705 Mean squared error on test data: 1277.61702282 Degree of polynomial: 25 Mean squared error on training data: 0.00079129 Mean squared error on test data: 128664.31650694 Degree of polynomial: 26 Mean squared error on training data: 0.00076905 Mean squared error on test data: 19003.94822514
Degree of polynomial: 27 Mean squared error on training data: 0.00068946 Mean squared error on test data: 2379.66219404 Degree of polynomial: 28 Mean squared error on training data: 0.00062595 Mean squared error on test data: 4082.19983530 Degree of polynomial: 29 Mean squared error on training data: 0.00060705 Mean squared error on test data: 3250.17647619
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/626635268.py:73: RuntimeWarning: divide by zero encountered in log10 plt.plot(polynomial, np.log10(trainingerror), label='Training Error') /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/626635268.py:74: RuntimeWarning: divide by zero encountered in log10 plt.plot(polynomial, np.log10(testerror), label='Test Error')
In [8]:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import PolynomialFeatures
# A seed just to ensure that the random numbers are the same for every run.
# Useful for eventual debugging.
np.random.seed(3155)
# Generate the data.
nsamples = 100
x = np.random.randn(nsamples)
y = 3*x**2 + np.random.randn(nsamples)
## Cross-validation on Ridge regression using KFold only
# Decide degree on polynomial to fit
poly = PolynomialFeatures(degree = 6)
# Decide which values of lambda to use
nlambdas = 500
lambdas = np.logspace(-3, 5, nlambdas)
# Initialize a KFold instance
k = 5
kfold = KFold(n_splits = k)
# Perform the cross-validation to estimate MSE
scores_KFold = np.zeros((nlambdas, k))
i = 0
for lmb in lambdas:
ridge = Ridge(alpha = lmb)
j = 0
for train_inds, test_inds in kfold.split(x):
xtrain = x[train_inds]
ytrain = y[train_inds]
xtest = x[test_inds]
ytest = y[test_inds]
Xtrain = poly.fit_transform(xtrain[:, np.newaxis])
ridge.fit(Xtrain, ytrain[:, np.newaxis])
Xtest = poly.fit_transform(xtest[:, np.newaxis])
ypred = ridge.predict(Xtest)
scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)
j += 1
i += 1
estimated_mse_KFold = np.mean(scores_KFold, axis = 1)
## Cross-validation using cross_val_score from sklearn along with KFold
# kfold is an instance initialized above as:
# kfold = KFold(n_splits = k)
estimated_mse_sklearn = np.zeros(nlambdas)
i = 0
for lmb in lambdas:
ridge = Ridge(alpha = lmb)
X = poly.fit_transform(x[:, np.newaxis])
estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)
# cross_val_score return an array containing the estimated negative mse for every fold.
# we have to the the mean of every array in order to get an estimate of the mse of the model
estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
i += 1
## Plot and compare the slightly different ways to perform cross-validation
plt.figure()
plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
plt.xlabel('log10(lambda)')
plt.ylabel('mse')
plt.legend()
plt.show()
In [9]:
# Common imports
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
# Where to save the figures and data files
PROJECT_ROOT_DIR = "Results"
FIGURE_ID = "Results/FigureFiles"
DATA_ID = "DataFiles/"
if not os.path.exists(PROJECT_ROOT_DIR):
os.mkdir(PROJECT_ROOT_DIR)
if not os.path.exists(FIGURE_ID):
os.makedirs(FIGURE_ID)
if not os.path.exists(DATA_ID):
os.makedirs(DATA_ID)
def image_path(fig_id):
return os.path.join(FIGURE_ID, fig_id)
def data_path(dat_id):
return os.path.join(DATA_ID, dat_id)
def save_fig(fig_id):
plt.savefig(image_path(fig_id) + ".png", format='png')
infile = open(data_path("EoS.csv"),'r')
# Read the EoS data as csv file and organize the data into two arrays with density and energies
EoS = pd.read_csv(infile, names=('Density', 'Energy'))
EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')
EoS = EoS.dropna()
Energies = EoS['Energy']
Density = EoS['Density']
# The design matrix now as function of various polytrops
Maxpolydegree = 30
X = np.zeros((len(Density),Maxpolydegree))
X[:,0] = 1.0
estimated_mse_sklearn = np.zeros(Maxpolydegree)
polynomial = np.zeros(Maxpolydegree)
k =5
kfold = KFold(n_splits = k)
for polydegree in range(1, Maxpolydegree):
polynomial[polydegree] = polydegree
for degree in range(polydegree):
X[:,degree] = Density**(degree/3.0)
OLS = LinearRegression(fit_intercept=False)
# loop over trials in order to estimate the expectation value of the MSE
estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)
#[:, np.newaxis]
estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
plt.xlabel('Polynomial degree')
plt.ylabel('log10[MSE]')
plt.legend()
plt.show()/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10 plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
In [10]:
"""
#Model training, we compute the mean value of y and X
y_train_mean = np.mean(y_train)
X_train_mean = np.mean(X_train,axis=0)
X_train = X_train - X_train_mean
y_train = y_train - y_train_mean
# The we fit our model with the training data
trained_model = some_model.fit(X_train,y_train)
#Model prediction, we need also to transform our data set used for the prediction.
X_test = X_test - X_train_mean #Use mean from training data
y_pred = trained_model(X_test)
y_pred = y_pred + y_train_mean
"""Out [10]:
'\n#Model training, we compute the mean value of y and X\ny_train_mean = np.mean(y_train)\nX_train_mean = np.mean(X_train,axis=0)\nX_train = X_train - X_train_mean\ny_train = y_train - y_train_mean\n\n# The we fit our model with the training data\ntrained_model = some_model.fit(X_train,y_train)\n\n\n#Model prediction, we need also to transform our data set used for the prediction.\nX_test = X_test - X_train_mean #Use mean from training data\ny_pred = trained_model(X_test)\ny_pred = y_pred + y_train_mean\n'
Warning:
Output truncated. This notebook contains too many cells to display efficiently.