diff --git a/doc/BookChapters/chapter3.do.txt b/doc/BookChapters/chapter3.do.txt
index bc4fc936d..f1f70e6ee 100644
--- a/doc/BookChapters/chapter3.do.txt
+++ b/doc/BookChapters/chapter3.do.txt
@@ -919,7 +919,7 @@ for polydegree in range(1, Maxpolydegree):
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=True).fit(x_train, y_train)
+ 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)
@@ -1156,7 +1156,7 @@ for polydegree in range(1, Maxpolydegree):
polynomial[polydegree] = polydegree
for degree in range(polydegree):
X[:,degree] = Density**(degree/3.0)
- OLS = LinearRegression()
+ 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]
@@ -1170,46 +1170,7 @@ plt.show()
!ec
-
-!bc pycod
-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.
-np.random.seed(3155)
-# Generate the data.
-n = 100
-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)
-# Decide degree on polynomial to fit
-poly = PolynomialFeatures(degree = 10)
-
-# 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)
-estimated_mse_sklearn = np.zeros(nlambdas)
-i = 0
-for lmb in lambdas:
- ridge = Ridge(alpha = lmb)
- estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)
- estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
- i += 1
-plt.figure()
-plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
-plt.xlabel('log10(lambda)')
-plt.ylabel('MSE')
-plt.legend()
-plt.show()
-
-
-!ec
+Note that we have kept the intercept in the first column of design matrix $\bm{X}$. When we call the corresponding _Scikit-Learn_ function we need thus to set the intercept to _False_. Libraries like _Scikit-Learn_ normally scale the design matrix and does not fit intercept. See the discussions below.
===== More on Rescaling data =====
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index df911b5f7..d1b31b1aa 100644
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diff --git a/doc/LectureNotes/_build/html/_images/chapter3_73_1.png b/doc/LectureNotes/_build/html/_images/chapter3_73_1.png
index 6dff76ded..30a446da4 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/chapter3.ipynb b/doc/LectureNotes/_build/html/_sources/chapter3.ipynb
index 18cd038f8..ad19ad998 100644
--- a/doc/LectureNotes/_build/html/_sources/chapter3.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/chapter3.ipynb
@@ -1253,7 +1253,7 @@
" trainingerror[polydegree] = 0.0\n",
" for samples in range(trials):\n",
" x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
- " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n",
+ " model = LinearRegression(fit_intercept=False).fit(x_train, y_train)\n",
" ypred = model.predict(x_train)\n",
" ytilde = model.predict(x_test)\n",
" testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
@@ -1535,7 +1535,7 @@
" polynomial[polydegree] = polydegree\n",
" for degree in range(polydegree):\n",
" X[:,degree] = Density**(degree/3.0)\n",
- " OLS = LinearRegression()\n",
+ " OLS = LinearRegression(fit_intercept=False)\n",
"# loop over trials in order to estimate the expectation value of the MSE\n",
" estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
"#[:, np.newaxis]\n",
@@ -1548,56 +1548,12 @@
"plt.show()"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from sklearn.model_selection import KFold\n",
- "from sklearn.linear_model import Ridge\n",
- "from sklearn.model_selection import cross_val_score\n",
- "from sklearn.preprocessing import PolynomialFeatures\n",
- "\n",
- "# A seed just to ensure that the random numbers are the same for every run.\n",
- "np.random.seed(3155)\n",
- "# Generate the data.\n",
- "n = 100\n",
- "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
- "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
- "# Decide degree on polynomial to fit\n",
- "poly = PolynomialFeatures(degree = 10)\n",
- "\n",
- "# Decide which values of lambda to use\n",
- "nlambdas = 500\n",
- "lambdas = np.logspace(-3, 5, nlambdas)\n",
- "# Initialize a KFold instance\n",
- "k = 5\n",
- "kfold = KFold(n_splits = k)\n",
- "estimated_mse_sklearn = np.zeros(nlambdas)\n",
- "i = 0\n",
- "for lmb in lambdas:\n",
- " ridge = Ridge(alpha = lmb)\n",
- " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n",
- " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
- " i += 1\n",
- "plt.figure()\n",
- "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
- "plt.xlabel('log10(lambda)')\n",
- "plt.ylabel('MSE')\n",
- "plt.legend()\n",
- "plt.show()"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "Note that we have kept the intercept in the first column of design matrix $\\boldsymbol{X}$. When we call the corresponding **Scikit-Learn** function we need thus to set the intercept to **False**. Libraries like **Scikit-Learn** normally scale the design matrix and does not fit intercept. See the discussions below.\n",
+ "\n",
"## More on Rescaling data\n",
"\n",
"We end this chapter by adding some words on scaling and how to deal with the intercept for regression cases.\n",
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html
index 43a2d40ef..b80787259 100644
--- a/doc/LectureNotes/_build/html/chapter3.html
+++ b/doc/LectureNotes/_build/html/chapter3.html
@@ -640,10 +640,10 @@ number \(i\) is left out. Usin
The bias-variance tradeoff summarizes the fundamental tension in
@@ -1329,7 +1332,7 @@ training data.
trainingerror[polydegree]=0.0forsamplesinrange(trials):x_train,x_test,y_train,y_test=train_test_split(X,Energies,test_size=0.2)
- model=LinearRegression(fit_intercept=True).fit(x_train,y_train)
+ 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)
@@ -1360,12 +1363,12 @@ 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
+Degree of polynomial: 4
Mean squared error on training data: 303.47610036
Mean squared error on test data: 426.30787294
-Degree of polynomial: 5
+
+
+
Degree of polynomial: 5
Mean squared error on training data: 3.80354994
Mean squared error on test data: 5.98822371
Degree of polynomial: 6
@@ -1385,80 +1388,80 @@ 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
+
Degree of polynomial: 11
+Mean squared error on training data: 0.01640891
+Mean squared error on test data: 1.35533774
+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
+Mean squared error on test data: 1.08131001
Degree of polynomial: 14
Mean squared error on training data: 0.00472199
-Mean squared error on test data: 0.81333793
+Mean squared error on test data: 0.81333802
Degree of polynomial: 15
Mean squared error on training data: 0.00410478
-Mean squared error on test data: 92.09145189
+Mean squared error on test data: 92.09160813
Degree of polynomial: 16
Mean squared error on training data: 0.00315593
-Mean squared error on test data: 234.39716546
+Mean squared error on test data: 234.40530431
Degree of polynomial: 17
-Mean squared error on training data: 0.00242998
-Mean squared error on test data: 1271.05295709
+Mean squared error on training data: 0.00242999
+Mean squared error on test data: 1270.94936405
Degree of polynomial: 18
-Mean squared error on training data: 0.00228740
-Mean squared error on test data: 108.42208194
+Mean squared error on training data: 0.00228741
+Mean squared error on test data: 108.11945731
Degree of polynomial: 19
Mean squared error on training data: 0.00156372
-Mean squared error on test data: 1388.41078073
+Mean squared error on test data: 1376.61081005
Degree of polynomial: 20
-Mean squared error on training data: 0.00137982
-Mean squared error on test data: 1761.43341615
+Mean squared error on training data: 0.00137945
+Mean squared error on test data: 1931.97211078
+Degree of polynomial: 21
+Mean squared error on training data: 0.00118678
+Mean squared error on test data: 14496.70992192
-
Degree of polynomial: 21
-Mean squared error on training data: 0.00118170
-Mean squared error on test data: 15061.31603087
-Degree of polynomial: 22
-Mean squared error on training data: 0.00092354
-Mean squared error on test data: 890.63488525
+
Degree of polynomial: 22
+Mean squared error on training data: 0.00092686
+Mean squared error on test data: 873.95463048
Degree of polynomial: 23
-Mean squared error on training data: 0.00085887
-Mean squared error on test data: 5483.16796929
+Mean squared error on training data: 0.00085890
+Mean squared error on test data: 5535.20053452
+Degree of polynomial: 24
+Mean squared error on training data: 0.00084714
+Mean squared error on test data: 1289.22422186
-
Degree of polynomial: 24
-Mean squared error on training data: 0.00084589
-Mean squared error on test data: 1695.57143061
-Degree of polynomial: 25
-Mean squared error on training data: 0.00078806
-Mean squared error on test data: 131343.30655001
+
Degree of polynomial: 25
+Mean squared error on training data: 0.00079022
+Mean squared error on test data: 136582.88824397
Degree of polynomial: 26
-Mean squared error on training data: 0.00076916
-Mean squared error on test data: 17709.14370264
+Mean squared error on training data: 0.00076923
+Mean squared error on test data: 18194.23521766
+Degree of polynomial: 27
+Mean squared error on training data: 0.00069302
+Mean squared error on test data: 2579.13493762
-
Degree of polynomial: 27
-Mean squared error on training data: 0.00068970
-Mean squared error on test data: 2975.38903780
-Degree of polynomial: 28
-Mean squared error on training data: 0.00062588
-Mean squared error on test data: 3848.64522721
+
Degree of polynomial: 28
+Mean squared error on training data: 0.00062728
+Mean squared error on test data: 3984.82493809
Degree of polynomial: 29
-Mean squared error on training data: 0.00060728
-Mean squared error on test data: 2988.64001211
+Mean squared error on training data: 0.00060724
+Mean squared error on test data: 3204.07047448
-
<ipython-input-7-8dc29df57a8c>:73: RuntimeWarning: divide by zero encountered in log10
+
<ipython-input-7-40a38ad763f1>:73: RuntimeWarning: divide by zero encountered in log10
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-<ipython-input-7-8dc29df57a8c>:74: RuntimeWarning: divide by zero encountered in log10
+<ipython-input-7-40a38ad763f1>:74: RuntimeWarning: divide by zero encountered in log10
plt.plot(polynomial, np.log10(testerror), label='Test Error')
@@ -1677,7 +1680,7 @@ cross-validation (LOOCV).
polynomial[polydegree]=polydegreefordegreeinrange(polydegree):X[:,degree]=Density**(degree/3.0)
- OLS=LinearRegression()
+ OLS=LinearRegression(fit_intercept=False)# loop over trials in order to estimate the expectation value of the MSEestimated_mse_folds=cross_val_score(OLS,X,Energies,scoring='neg_mean_squared_error',cv=kfold)#[:, np.newaxis]
@@ -1692,57 +1695,14 @@ cross-validation (LOOCV).
-
<ipython-input-9-49b0ef2e51e2>:63: RuntimeWarning: divide by zero encountered in log10
+
<ipython-input-9-6e75736fdab1>:63: RuntimeWarning: divide by zero encountered in log10
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
-
-
-
importnumpyasnp
-importmatplotlib.pyplotasplt
-fromsklearn.model_selectionimportKFold
-fromsklearn.linear_modelimportRidge
-fromsklearn.model_selectionimportcross_val_score
-fromsklearn.preprocessingimportPolynomialFeatures
-
-# A seed just to ensure that the random numbers are the same for every run.
-np.random.seed(3155)
-# Generate the data.
-n=100
-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)
-# Decide degree on polynomial to fit
-poly=PolynomialFeatures(degree=10)
-
-# 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)
-estimated_mse_sklearn=np.zeros(nlambdas)
-i=0
-forlmbinlambdas:
- ridge=Ridge(alpha=lmb)
- estimated_mse_folds=cross_val_score(ridge,x,y,scoring='neg_mean_squared_error',cv=kfold)
- estimated_mse_sklearn[i]=np.mean(-estimated_mse_folds)
- i+=1
-plt.figure()
-plt.plot(np.log10(lambdas),estimated_mse_sklearn,label='cross_val_score')
-plt.xlabel('log10(lambda)')
-plt.ylabel('MSE')
-plt.legend()
-plt.show()
-
-
-
-
-
-
-
+
Note that we have kept the intercept in the first column of design matrix \(\boldsymbol{X}\). When we call the corresponding Scikit-Learn function we need thus to set the intercept to False. Libraries like Scikit-Learn normally scale the design matrix and does not fit intercept. See the discussions below.
@@ -2020,7 +1980,7 @@ MSE with Sklearn intercept
0.004113634617443135
-
+
The intercept is the value of our output/target variable
@@ -2215,7 +2175,7 @@ MSE values for Scikit-Learn Ridge implementation
0.26409315307910036
-
+
The results here agree when we force Scikit-Learn’s Ridge function to include the first column in our design matrix.
@@ -2420,7 +2380,7 @@ MSE values for Scikit-Learn Ridge implementation
0.002381316302584886
-
+
We see here, when compared to the code which includes explicitely the
@@ -2634,11 +2594,11 @@ linear system as an equation would reduce this down to
-
<ipython-input-22-6f7a6bd7d79f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-21-6f7a6bd7d79f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
+
It is interesting to note that OLS
@@ -2778,11 +2738,11 @@ with the form utilized in linear regression, viz.
-
<ipython-input-27-5dd54edf2138>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-26-5dd54edf2138>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
+
The results agree perfectly with our previous discussion where we used our own code.
@@ -2826,11 +2786,11 @@ K
-
<ipython-input-28-fe5b9d300cc0>:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-27-fe5b9d300cc0>:10: UserWarning: FixedFormatter should only be used together with FixedLocator
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
+
In the Least Absolute Shrinkage and Selection Operator (LASSO)-method we get a third cost function.
@@ -2861,11 +2821,11 @@ K
-
<ipython-input-29-25845e8df859>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-28-25845e8df859>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
+
It is quite striking how LASSO breaks the symmetry of the coupling
@@ -2920,43 +2880,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.924197515789051, tolerance: 1.796796
model = cd_fast.enet_coordinate_descent(
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70%|███████ | 7/10 [00:01<00:00, 5.94it/s]
+
70%|███████ | 7/10 [00:01<00:00, 5.36it/s]
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80%|████████ | 8/10 [00:01<00:00, 6.59it/s]
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80%|████████ | 8/10 [00:01<00:00, 6.06it/s]
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90%|█████████ | 9/10 [00:01<00:00, 7.10it/s]
+
90%|█████████ | 9/10 [00:01<00:00, 6.05it/s]
-
100%|██████████| 10/10 [00:01<00:00, 7.07it/s]
+
100%|██████████| 10/10 [00:02<00:00, 4.64it/s]
-
100%|██████████| 10/10 [00:01<00:00, 5.83it/s]
+
100%|██████████| 10/10 [00:02<00:00, 4.68it/s]
-
+
We see that LASSO reaches a good solution for low
@@ -3005,7 +2965,7 @@ testing set that is close to the accuracy of the training set.
-
+
From the above figure we can see that LASSO with \(\lambda = 10^{-2}\)
@@ -3097,7 +3057,7 @@ which polynomial fits the data best.
-
+
@@ -3254,7 +3214,7 @@ Python program using
---------------------------------------------------------------------------NameErrorTraceback (most recent call last)
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+<ipython-input-32-d985fb40c43d>in<module>----> 1scipy.misc.imreadNameError: name 'scipy' is not defined
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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index a5fdd1e14..c6ea7ef02 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
@@ -397,10 +397,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Runtime: 0.139992 sec\n",
+ "Runtime: 0.14109 sec\n",
"Jackknife Statistics :\n",
"original bias std. error\n",
- " 99.9142 99.9042 0.148517\n"
+ " 100.203 100.193 0.149917\n"
]
}
],
@@ -774,7 +774,7 @@
"text": [
"Bootstrap Statistics :\n",
"original bias std. error\n",
- " 100.028 14.8941 100.026 0.148225\n"
+ " 99.9348 15.1379 99.9341 0.151076\n"
]
}
],
@@ -828,7 +828,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1088,17 +1088,18 @@
"Bias^2: 0.3123314713548606\n",
"Var: 0.009164545680330616\n",
"0.32149601703519126 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n",
- "Polynomial degree: 1\n",
- "Error: 0.08426840630693411\n",
- "Bias^2: 0.07968918676726028\n",
- "Var: 0.004579219539673833\n",
- "0.08426840630693411 >= 0.07968918676726028 + 0.004579219539673833 = 0.08426840630693411\n"
+ "Polynomial degree:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ " 1\n",
+ "Error: 0.08426840630693411\n",
+ "Bias^2: 0.07968918676726028\n",
+ "Var: 0.004579219539673833\n",
+ "0.08426840630693411 >= 0.07968918676726028 + 0.004579219539673833 = 0.08426840630693411\n",
"Polynomial degree: 2\n",
"Error: 0.10398646080125035\n",
"Bias^2: 0.10077114273548986\n",
@@ -1155,23 +1156,29 @@
"Error: 0.021592704588043153\n",
"Bias^2: 0.010516485576652981\n",
"Var: 0.011076219011390184\n",
- "0.021592704588043153 >= 0.010516485576652981 + 0.011076219011390184 = 0.021592704588043167\n",
- "Polynomial degree: 11\n",
- "Error: 0.07160048164228314\n",
- "Bias^2: 0.01443680008897583\n",
- "Var: 0.0571636815533073\n",
- "0.07160048164228314 >= 0.01443680008897583 + 0.0571636815533073 = 0.07160048164228312\n"
+ "0.021592704588043153 >= 0.010516485576652981 + 0.011076219011390184 = 0.021592704588043167\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Polynomial degree: 11\n",
+ "Error: 0.07160048164228314\n",
+ "Bias^2: 0.01443680008897583\n",
+ "Var: 0.0571636815533073\n",
+ "0.07160048164228314 >= 0.01443680008897583 + 0.0571636815533073 = 0.07160048164228312\n",
"Polynomial degree: 12\n",
"Error: 0.1154777721897675\n",
"Bias^2: 0.01628578269590588\n",
"Var: 0.09919198949386163\n",
- "0.1154777721897675 >= 0.01628578269590588 + 0.09919198949386163 = 0.11547777218976751\n",
+ "0.1154777721897675 >= 0.01628578269590588 + 0.09919198949386163 = 0.11547777218976751\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Polynomial degree: 13\n",
"Error: 0.22842468702166951\n",
"Bias^2: 0.01975416527163567\n",
@@ -1188,7 +1195,7 @@
},
"metadata": {
"filenames": {
- "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_62_5.png"
+ "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_62_6.png"
},
"needs_background": "light"
},
@@ -1425,16 +1432,17 @@
"Mean squared error on test data: 123711.53703498\n",
"Degree of polynomial: 3\n",
"Mean squared error on training data: 9011.85263220\n",
- "Mean squared error on test data: 10913.84780262\n"
+ "Mean squared error on test data: 10913.84780262\n",
+ "Degree of polynomial: 4\n",
+ "Mean squared error on training data: 303.47610036\n",
+ "Mean squared error on test data: 426.30787294"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Degree of polynomial: 4\n",
- "Mean squared error on training data: 303.47610036\n",
- "Mean squared error on test data: 426.30787294\n",
+ "\n",
"Degree of polynomial: 5\n",
"Mean squared error on training data: 3.80354994\n",
"Mean squared error on test data: 5.98822371\n",
@@ -1458,25 +1466,25 @@
"Mean squared error on test data: 0.08576932\n",
"Degree of polynomial: 10\n",
"Mean squared error on training data: 0.02511518\n",
- "Mean squared error on test data: 1.20015436\n",
- "Degree of polynomial: 11\n",
- "Mean squared error on training data: 0.01640891\n",
- "Mean squared error on test data: 1.35533773\n"
+ "Mean squared error on test data: 1.20015436\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Degree of polynomial: 11\n",
+ "Mean squared error on training data: 0.01640891\n",
+ "Mean squared error on test data: 1.35533774\n",
"Degree of polynomial: 12\n",
"Mean squared error on training data: 0.00813803\n",
"Mean squared error on test data: 0.17446471\n",
"Degree of polynomial: 13\n",
"Mean squared error on training data: 0.00759119\n",
- "Mean squared error on test data: 1.08131003\n",
+ "Mean squared error on test data: 1.08131001\n",
"Degree of polynomial: 14\n",
"Mean squared error on training data: 0.00472199\n",
- "Mean squared error on test data: 0.81333793\n"
+ "Mean squared error on test data: 0.81333802\n"
]
},
{
@@ -1485,13 +1493,13 @@
"text": [
"Degree of polynomial: 15\n",
"Mean squared error on training data: 0.00410478\n",
- "Mean squared error on test data: 92.09145189\n",
+ "Mean squared error on test data: 92.09160813\n",
"Degree of polynomial: 16\n",
"Mean squared error on training data: 0.00315593\n",
- "Mean squared error on test data: 234.39716546\n",
+ "Mean squared error on test data: 234.40530431\n",
"Degree of polynomial: 17\n",
- "Mean squared error on training data: 0.00242998\n",
- "Mean squared error on test data: 1271.05295709\n"
+ "Mean squared error on training data: 0.00242999\n",
+ "Mean squared error on test data: 1270.94936405\n"
]
},
{
@@ -1499,74 +1507,74 @@
"output_type": "stream",
"text": [
"Degree of polynomial: 18\n",
- "Mean squared error on training data: 0.00228740\n",
- "Mean squared error on test data: 108.42208194\n",
+ "Mean squared error on training data: 0.00228741\n",
+ "Mean squared error on test data: 108.11945731\n",
"Degree of polynomial: 19\n",
"Mean squared error on training data: 0.00156372\n",
- "Mean squared error on test data: 1388.41078073\n",
+ "Mean squared error on test data: 1376.61081005\n",
"Degree of polynomial: 20\n",
- "Mean squared error on training data: 0.00137982\n",
- "Mean squared error on test data: 1761.43341615\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Mean squared error on training data: 0.00137945\n",
+ "Mean squared error on test data: 1931.97211078\n",
"Degree of polynomial: 21\n",
- "Mean squared error on training data: 0.00118170\n",
- "Mean squared error on test data: 15061.31603087\n",
+ "Mean squared error on training data: 0.00118678\n",
+ "Mean squared error on test data: 14496.70992192\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Degree of polynomial: 22\n",
- "Mean squared error on training data: 0.00092354\n",
- "Mean squared error on test data: 890.63488525\n",
+ "Mean squared error on training data: 0.00092686\n",
+ "Mean squared error on test data: 873.95463048\n",
"Degree of polynomial: 23\n",
- "Mean squared error on training data: 0.00085887\n",
- "Mean squared error on test data: 5483.16796929\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Mean squared error on training data: 0.00085890\n",
+ "Mean squared error on test data: 5535.20053452\n",
"Degree of polynomial: 24\n",
- "Mean squared error on training data: 0.00084589\n",
- "Mean squared error on test data: 1695.57143061\n",
- "Degree of polynomial: 25\n",
- "Mean squared error on training data: 0.00078806\n",
- "Mean squared error on test data: 131343.30655001\n",
- "Degree of polynomial: 26\n",
- "Mean squared error on training data: 0.00076916\n",
- "Mean squared error on test data: 17709.14370264\n"
+ "Mean squared error on training data: 0.00084714\n",
+ "Mean squared error on test data: 1289.22422186\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Degree of polynomial: 25\n",
+ "Mean squared error on training data: 0.00079022\n",
+ "Mean squared error on test data: 136582.88824397\n",
+ "Degree of polynomial: 26\n",
+ "Mean squared error on training data: 0.00076923\n",
+ "Mean squared error on test data: 18194.23521766\n",
"Degree of polynomial: 27\n",
- "Mean squared error on training data: 0.00068970\n",
- "Mean squared error on test data: 2975.38903780\n",
+ "Mean squared error on training data: 0.00069302\n",
+ "Mean squared error on test data: 2579.13493762\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Degree of polynomial: 28\n",
- "Mean squared error on training data: 0.00062588\n",
- "Mean squared error on test data: 3848.64522721\n",
+ "Mean squared error on training data: 0.00062728\n",
+ "Mean squared error on test data: 3984.82493809\n",
"Degree of polynomial: 29\n",
- "Mean squared error on training data: 0.00060728\n",
- "Mean squared error on test data: 2988.64001211\n"
+ "Mean squared error on training data: 0.00060724\n",
+ "Mean squared error on test data: 3204.07047448\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- ":73: RuntimeWarning: divide by zero encountered in log10\n",
+ ":73: RuntimeWarning: divide by zero encountered in log10\n",
" plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
- ":74: RuntimeWarning: divide by zero encountered in log10\n",
+ ":74: RuntimeWarning: divide by zero encountered in log10\n",
" plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
]
},
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1641,7 +1649,7 @@
" trainingerror[polydegree] = 0.0\n",
" for samples in range(trials):\n",
" x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
- " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n",
+ " model = LinearRegression(fit_intercept=False).fit(x_train, y_train)\n",
" ypred = model.predict(x_train)\n",
" ytilde = model.predict(x_test)\n",
" testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
@@ -1886,13 +1894,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- ":63: RuntimeWarning: divide by zero encountered in log10\n",
+ ":63: RuntimeWarning: divide by zero encountered in log10\n",
" plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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HM/umOPp1dDaxlDkMs1ce4Pmle2gVGsQzVw/inHqOJcg7VcKSzam8u/4g+7MKiAgN5Oph0dwwqgtd24T9YtvDuae4eOYq2rcI4eO7x9a5zMOKPc6keemgjsycNqRWfRKzvv+J577dyxVDOvKfawbrY521pAlAKTe6/e2NJGefZNkDE9z6udtScpnxTjzHT5Xy/LWDGdCpJQ8s3MrG5GNcPLA9T04ZSKtK7g7qyhjDugPZvLf+EN/sTKfUYRjfsw03nNWF8/q2xWFg6ux1/JRxgs/uHfer5FBbr6zYxzNf7+GhC3tz/cjOiAh+ws9/I4iACPiJIMCs5ft4cdlP/GZoJ569ZnCTrqvkKZoAlHKjkU8uY1yPNjw/dYjbPzvzeCF3vruJzYdyCQn0I9DPjyem9GfKkE4eHWmaebyQBRtTeH/DIY7kFdK+RQjd24axZl82r9wwjIsHdqj3MYwx3PP+Fr5IOFKr/a4ZHs1TVw3Si38duUoAOnpCqRrKOF5IZn6RyxHAddW2RQjzZ4zin5/v4nDuKZ6YMoBOEc08cqyKx713Uk/uOqc73+/O5N0fD7FybxbTx8a65eIPzl/6z10zmAm9ojhRVIrD+uHpMAZjwGHA4HxtjMFhoF2LYK4ZHoOfXvw9RhOAUjW03aoAOshDCQAgOMCff0wZ4LHPr06Avx/n92/P+f3bc6ygmIhQ91YxbRbkz7Ujav4UkfI8TQBK1VBCai5+Av06eC4BNBbu7G9QjZd2qStVQ9vT8ujVLlxLD6gmQxOAUjVgjCEhNe8XE8Ao5e00AShVA4fzCskuKPZo+79SDU0TgFI1sD0lF/h5DmClmgJNAErVwPa0PAL9hb4dwu0ORSm30QSgVA0kpObRu324VqBUTYomAKVcMMawPTWXgdXMAayUN9IEoJQLh3JOcrywVDuAVZOjCUApF06PANZHQFVTowlAKRcS0vIICvCjVzvtAFZNiyYApVzYnppL3w4tdBYq1eTof9FKVcPhMOxIO84gbf5RTZAmAKWqkZRdwImiUo+VgFbKTpoAlKpGQgOUgFbKLpoAlKrG9tQ8QgL96BHV3O5QlHI7TQBKVSMhLZf+HVvqZOSqSdL/qpWqQpnVAazP/6umShOAUlXYn3WCUyVl2v6vmiyPJQARmSMimSKyo9y6Z0Vkt4hsF5GPRCTCU8dXqr4aYg5gpezkyTuAucCFFdYtBQYYYwYBe4FHPHh8peolITWXsCB/urbRDmDVNHksARhjVgI5FdZ9a4wptRbXA9GeOr5S9bU9LY/+nVri7yd2h6KUR9jZB3Ar8FVVb4rIDBGJF5H4rKysBgxLKSgpc5B4WEcAq6bNlgQgIo8CpcB7VW1jjJltjIkzxsRFRUU1XHBKAXsz8ikqdTAoJsLuUJTymICGPqCI3AJcCkwyxpiGPr5SNXFmBLDeAagmrEETgIhcCDwETDDGnGzIYytVG9vT8ggPCaBL61C7Q1HKYzz5GOh8YB3QW0RSReQ2YBYQDiwVka0i8pqnjq9UfSSk5jEouiUi2gGsmi6P3QEYY66rZPWbnjqeUtXZl5lPTGRojSZ1LyotY3f6cW4b160BIlPKPg3eB6BUQ9ty6BhXvrKW9i1CuHNCN6aN7ExIYNWJYE96PiVlRgeAqSZPS0GoJm/OmmTCgwPo3DqUxz9L5OxnlvPm6iROFZdVur3OAax8hSYA1aSl5xXyVcIRpo6IYeEdo/lgxii6RzXnH58nMv6Z75m9cj8ni0t/sU9Cah6tQgOJbtXMpqiVahiaAFST9u76g5QZw81jYgEY1a0182eMYuEdo+nboQX/+nI3455ezqsr9nOiyJkItqflMTA6QjuAVZOnfQCqySosKeP9DYc4r287YiJ/+TjnyK6RvHPbWWw6eIyXvv+Jp7/ezf9W7mf6mK7szchnUp+2NkWtVMPRBKCarE+3HianoJjpY2Or3GZ4l1bMnT6SbSm5zPzuJ15YtheAAdr+r3yAJgDVJBljmLMmiT7twxndrbXL7QfHRPDmLSPYkZbH8t2ZTOyj5UdU01dtAhCRyBp8hsMYk+uecJRyj/UHctidns/TVw2sVVv+gE4t9de/8hmu7gAOW3+q+z/IH+jstoiUcoO5a5NoFRrIFUM62R2KUo2WqwSwyxgztLoNRGSLG+NRqt5Sck6yNDGDOyd0r3bAl1K+ztVjoKNr8Bk12UapBjNvXTIiwk2ju9gdilKNmqsEMOb0CxHpWv4NEfkNgDGm0ANxKVUnBUWlfLAxhYsGtKdDSx3IpVR1XCWA58q9/rDCe4+5ORal6m3JljTyC0uZPrar642V8nGuEoBU8bqyZaVs5XAY5q5JYlB0S4Z1jrA7HKUaPVcJwFTxurJlpWy1at9R9mcVMH1srJZxUKoGXD0F1E1EPsX5a//0a6xlvcdWjcpba5KICg/mkoEd7Q5FKa/gKgFcUe71cxXeq7islG32Z51gxZ4s7j+vF0EBWuNQqZqoNgEYY34ovywigcAAIM0Yk+nJwJSqjXlrkwny9+P6s3RMolI1Ve1PJRF5TUT6W69bAtuAecAWEalsykelGtzxwhIWb0rl0sEdiAoPtjscpbyGq3vl8caYndbr6cBeY8xAYDjwkEcjU6qGFm5MoaC4jFv10U+lasVVAigu93oy8DGAMSbdUwEpVRtlDsPb65IZEdtKi7gpVUuuEkCuiFwqIkOBscDXACISAOgwS2W773ZlkJJzSgd+KVUHrp4CugOYCbQH/ljul/8k4AtPBqZUTcxdm0zHliGc36+d3aEo5XVcPQW0F7iwkvXfAN94KiilamJ3+nHW7s/mLxf2IcBfH/1UqrZcTQgzs7r3jTH3uTccpWpu7ppkQgL9uG5kjN2hKOWVXDUB3QnsABbiemIYpRpM3qkSPtqSxm+GdSIiNMjucJTySq4SQAfgGmAqUAosABbrFJDKbp9tO0xRqYPrR2rNf6XqqtqGU2NMtjHmNWPMRJzjACKARBG5ydUHi8gcEckUkR3l1kWKyFIR+cn6u1V9T0D5psWbUundLpwBnVrYHYpSXqtGPWciMgz4A3Aj8BWwqQa7zeXXHcgPA98ZY3oC31nLStXKvsx8tqbkck1ctFb9VKoeXHUCPwFcAuwCPgAeMcaU1uSDjTErRSS2wuorgHOs128DK4C/1DxcpWBRfCoBfsKUoTrhu1L14aoP4DEgCRhs/fmX9YtLAGOMGVTL47UzxhyxXqcDVT68LSIzgBkAnTtrgS/lVFrmYMmWNM7p3ZY2zbXuj1L14SoBeGx4pTHGiEiVk8oYY2YDswHi4uJ08hkFwMqfssjKL+KauGi7Q1HK67kaCHbQzcfLEJEOxpgjItIB0JLSqlYWb0olMiyIib3b2h2KUl7PVTnoz119QE22KedT4Gbr9c3AJ7XYV/m4YwXFLEvMZMqQTjrpi1Ju4KoJaFy5aSArI0C/St8QmY+zw7eNiKQCfwOeAhaKyG3AQeDaWkesfNan2w5TXObQ5h+l3KQ2U0JWpbiylcaYqiaMmVSDz1TqVxZtSqF/xxb07aDP/ivlDjWaElJE2gGnn7lLM8ZkeDowpcrbdeQ4O9KO8/hlld5wKqXqwNU4gCHAa0BLIM1aHS0iucBdxpgtHo1OKcviTakE+guXD9Fn/5VyF1dNQHOBO4wxP5ZfKSKjrPcGeyYspX5WUubg4y1pnNe3HZFhWvhNKXdx9ShFWMWLP4AxZj0Q5pmQlPql5bszyS4o1s5fpdzM1R3AVyLyBTAPSLHWxQC/xZoeUilPW7QplajwYM7uGWV3KEo1Ka46ge8TkYtwPg10phMYeNkY86Wng1Pq6Ikilu/O5LZxXXXWL6XczNUdAMaYr3BWAFWqwX28JY1Sh+Hq4dr8o5S71fknlYjMdmcgSlVkjGHxplQGx0TQs1243eEo1eS4egw0sqq3gIvdH45SP9t5+Di70/P555QBdoeiVJPkqgkoC2fJhvKzbhhrWatxKY9aFJ9CUIAflw3qaHcoSjVJrhLAAWCSMeZQxTdEJKWS7ZVyi6LSMj7ZdpgL+renZWig3eEo1SS56gN4Eahq3t5n3BuKUj/7blcmuSdLtPNXKQ9y9Rjoy9W895L7w1HKaVF8Ch1ahjCuRxu7Q1GqyXL5GCiAiPymktV5QIIxRid1UW6VebyQH/Zmcdc53fH300nflfKUGiUA4DZgNLDcWj4H2AR0FZEnjDHveCA25aOWbEnDYeCqYdr8o5Qn1TQBBAB9T5eBtspDzwPOAlYCmgCUWxhjWBSfQlyXVnSLam53OEo1aTUdCBZTYQ6ATGtdDlDi/rCUr9qaksv+rALt/FWqAdT0DmCFNffvImv5amtdGJDricCUb1q0KZWQQD8uGdTB7lCUavJqmgDuBn4DjLOW3wY+NMYYYKInAlO+xeEwvLJiH/M3HOLqYdGEh+iz/0p5Wo0SgDHGiMhqnPP/GmCDdfFXqt7yTpXwp4VbWbYrk8sHd+TvV/S3OySlfEJNHwO9FngWWIGzDMRLIvKgMWaxB2NTPmDXkePc+e4m0o6d4vHL+nHzmFhE9NFPpRpCTZuAHgVGnH7mX0SigGWAJgBVZx9tSeWRJQm0bBbIBzNGERdbVe1BpZQn1DQB+FUY8JVNPUpJK99WXOrgH58n8s76g5zVNZKXrh9K2/AQu8NSyufUNAF8LSLfAPOt5amAzgimau1I3il+/95mthzKZcbZ3Xjogt4605dSNqlpJ/CDInIVMNZaNdsY85HnwlJN0dr9R7n3/S0UlpTxyg3DuHigPuqplJ1qegeAMeZD4EMPxqKaKGMM/1t5gGe+3k23qOa8duNwerTVUb5K2c3VjGD5OB/7/NVbOJ8ObVGXg4rI/cDt1mcnANONMYV1+SzV+P3j813MWZPEJQM78PTVg2geXOPfHUopD3JVDtrtE7GKSCfgPqCfMeaUiCwEpgFz3X0sZb/CkjI+2HiIywd35L/Thugjnko1Inb1vgUAzUQkAAgFDtsUh/KwdQeyOVlcxpVDO+nFX6lGpsETgDEmDXgOOAQcAfKMMd9W3E5EZohIvIjEZ2VlNXSYyk2WJmYQGuTP6O6t7Q5FKVVBgycAEWkFXAF0BToCYSJyY8XtjDGzjTFxxpi4qKiohg5TuYHDYViWmMGEXlGEBPrbHY5SqgI7moDOA5KMMVnGmBJgCTDGhjiUhyWk5ZGZX8R5fdvZHYpSqhJ2JIBDwCgRCRVno/AkYJcNcSgPW5qYgb+fcG6ftnaHopSqhB19AD/irCG0GecjoH7A7IaOQ3ne0sQM4rq0olVYkN2hKKUqYctTQMaYvxlj+hhjBhhjbjLGFNkRh/KcQ9kn2ZORz+R+2vyjVGOlRViURyzd5ZxBVBOAUo2XJgDlEUsT0+nVrjldWofZHYpSqgqaAJTb5Z4sZmPyMf31r1QjpwlAud3yPZmUOYw+/qlUI6cJQLnd0sQM2oYHMzg6wu5QlFLV0ASg3KqotIwf9mQxqW87/Py09o9SjZkmAOVW6/ZnU1BcxuR+OvhLqcZOE4Byq9PF38Z0b2N3KEopFzQBKLdxOAzLdmVwdk8t/qaUN9AEoNxmx+E8Mo4XcZ4+/qmUV9AEoNxmaWIGfoIWf1PKS2gCUG6zNDGDuNhIIrX4m1JeQROAcouUnJPsTs/nfG3+UcpraAJQbrE00Vn8TUf/KuU9NAEot1iamEHPts2JbaPF35TyFpoAVL3lnSxhQ3KOFn9TystoAlD1dqb4myYApbyKJgBVb0sTM4gKD2aIFn9TyqtoAlD1UlRaxoo9mZzXt60Wf1PKy2gCUPWy/kAOBcVl+vSPUl5IE4Cql6WJ6TQL9GdsDy3+ppS30QSg6swYw7LETM7u1UaLvynlhTQBqDrbkXac9OOF2vyjlJfSBKDqbGliOn4CkzQBKOWVNAGoOvs2MYO4Llr8TSlvpQlA1cnp4m86+lcp7xVgdwCq8UhIzWP2qgMIEOAn+PkJAX6Cf7k/p9cfyCoA0NG/SnkxWxKAiEQAbwADAAPcaoxZZ0cs6mdPfpnItpQ82rcModThoKzMUGYMZQ5DqcP59+nXDofhrK6RdNXib0p5LbvuAP4LfG2MuVpEgoBQm+JQlp2H81h/IIdHLurDHRO62x2OUqoBNHgCEJGWwNnALQDGmGKguKHjUL80Z3UyoUH+TBvZ2e5QlFINxI5O4K5AFvCWiGwRkTdE5FftCCIyQ0TiRSQ+Kyur4aP0IZn5hXy27TDXDI+mZbNAu8NRSjUQOxJAADAMeNUYMxQoAB6uuJExZrYxJs4YExcVFdXQMfqUd9cdpMTh4JaxXe0ORSnVgOzoA0gFUo0xP1rLi6kkAbjD+gPZHMo5SWzrMGJbhxIVHoyIVqwsr7CkjHd/PMSkPm21Q1cpH9PgCcAYky4iKSLS2xizB5gEJHriWEs2p7IwPvXMcmiQP12sZBDbxvl3l9ZhdG0TRlsfTQ6fbE0jp6CYW8fpr3+lfI1dTwHdC7xnPQF0AJjuiYM8eeVA7p7Yg6SjBRzMPklydgHJRwvYk57P0sQMSh3mzLbNAv0Z1S2Shy/qS+/24Z4Ip9ExxvDm6iT6dmjB6G6t7Q5HKdXAbEkAxpitQJynjxPo70eX1mF0af3rpo3SMgdH8gqt5FDA/qwClmxO5eKZq7jhrM7cf14vWjXxEger9x1lb8YJnrtmsE/e/Sjl63x2JHCAvx8xkaHERIYCzk7m+yb15IWle3l3/UE+2XqYP57XkxtHdSHQv2lWzHhzdRJtmgdz2eAOdoeilLJB07yy1VFkWBD/mDKAr/5wNgM7teTvnyVy0X9X8cPepvcY6r7MfFbsyeKmUV0IDtBa/kr5Ik0AlejdPpx3bhvJ67+No7TMwc1zNnDr3I3szzphd2hu89aaZIIC/LhhlA78UspXaQKogogwuV87vrn/bB65qA8bknK44IWV/OPzRPJOldgdXr0cKyjmw82pXDmkE22aB9sdjlLKJpoAXAgO8OeOCd1Z/udzuHp4NHPWJDHxuRV8uzPd7tDq7P0Nhygsceijn0r5OE0ANRQVHsxTVw3is3vG0TEihPs+2MJPGfl2h1VrxaUO5q1LZlyPNj7zuKtSqnKaAGppQKeWzLl5BGFBAdzz/hZOFZfZHVKtfJlwhIzjRdymv/6V8nmaAOqgbYsQnp86hD0Z+Tzx+U67w6kxYwxz1iTRLSqMCb20vpJSvk4TQB1N6BXFXed0Z/6GFD7ddtjucGok/uAxtqfmcevYrvj56cAvpXydJoB6eGByL4Z1juCvSxJIPlpgdzguvbkqiZbNArlqWLTdoSilGgFNAPUQ6O/HzOuG4u8n3Dt/C0WlDdMfYIxhR1perY6XknOSbxPTuf6szjQL0oFfSilNAPUW3SqUZ64eREJaHk99tbtBjvnaDwe49KXVjP739/zz80T2ZboeoPbWmmT8RLh5dKznA1RKeQWfrQXkThf0b88tY2J5a00yY7q3YXK/dh471vI9mTzzzW7O7dOW4AA/5q5N5o3VSYyIbcW0EZ25eGCHX/3Czy8sYWF8CpcM6kD7liEei00p5V00AbjJIxf3If5gDn9etI0v/zCeThHN3H6MA1knuG/+Fvq2b8HL1w+jWZA/WflFfLg5lQUbU/jTom08/tlOrhzaiWkjOtOvYwsAFsancqKoVB/9VEr9ghhjXG9ls7i4OBMfH293GC4lHy3g0pdW07t9OB/MGOXWKqL5hSVMeXkNx06W8Ok9Y4luFfqL940xrD+QwwcbD/HVjnSKSx0Mjm7JtJGdeWXFPtqFh7D4rjFui0cp1fiJyCZjTJWl97UPwI1i24Tx5JUD2HTwGC8s3eu2z3U4DPcv2Epy9klevn7Yry7+4KxdNLp7a/47bSgb/jqJ/7u0H6dKynhkSQIpOaf0179S6le0CcjNrhjSiXX7s3n1h/2M6taas90w4OqFZXtZtiuTJ67oz+jurmfuiggN4tZxXZk+NpYtKbkkHj7O+f3b1zsOpVTToncAHvC3y/rTs21zHli4lcz8wnp91lcJR3jp+31MjYvhplFdarWviDCscytuHNUFfx34pZSqQBOABzQL8mfW9cM4UVTKHz/YSpmjbv0su9OP86dF2xjWOYInpvTXaRuVUm6lCcBDerUL5++X92ft/mxuf3sjW1Nya7X/sYJifjcvnvCQAF67cbjO2qWUcjvtA/Cga+NiOHayhFdX7GfKy2sY3a01d53TnfE921T7a760zME98zeTkVfEgjtG0baFPruvlHI/vQPwIBHhzgndWfvwuTx2SV+Sjhbw2zkbuGzWar7YfqTKpqF/f7WbNfuyefLKAQzt3KqBo1ZK+QpNAA0gLDiA28d344eHzuGZqwZxsqiMu9/fzHnP/8AHGw79oqbPh5tSeXN1EreMieWauBgbo1ZKNXU6EMwGZQ7DtzvTeWXFfhLS8mgbHszt47syoGNLbpm7keGdWzHvtpFuHUimlPI9rgaCaR+ADfz9hIsGduDCAe1Zsy+bV3/Yx7++dBaSi27VjJdvGKYXf6WUx2kCsJGIMK5nG8b1bMO2lFwWb0rlptFdiAwLsjs0pZQP0ATQSAyOiWBwTITdYSilfIht7Qwi4i8iW0Tkc7tiUEopX2ZnQ/MfgF02Hl8ppXyaLQlARKKBS4A37Di+Ukop++4AXgQeAhxVbSAiM0QkXkTis7KyGiwwpZTyFQ2eAETkUiDTGLOpuu2MMbONMXHGmLioqPqXVFZKKfVLdtwBjAUuF5Fk4APgXBF514Y4lFLKpzV4AjDGPGKMiTbGxALTgO+NMTc2dBxKKeXrdLipUkr5KK+oBSQiWcDBcqvaAEdtCsfTmuq56Xl5n6Z6br50Xl2MMVV2onpFAqhIROKrK3DkzZrquel5eZ+mem56Xj/TJiCllPJRmgCUUspHeWsCmG13AB7UVM9Nz8v7NNVz0/OyeGUfgFJKqfrz1jsApZRS9aQJQCmlfJTXJQARuVBE9ojIPhF52O543EVEkkUkQUS2iohXT4AsInNEJFNEdpRbFykiS0XkJ+vvVnbGWBdVnNfjIpJmfW9bReRiO2OsCxGJEZHlIpIoIjtF5A/Weq/+zqo5r6bwnYWIyAYR2Wad29+t9V1F5Efr+rhARKqdXtCr+gBExB/YC0wGUoGNwHXGmERbA3MDqzZSnDHG6weoiMjZwAlgnjFmgLXuGSDHGPOUlbhbGWP+YmectVXFeT0OnDDGPGdnbPUhIh2ADsaYzSISDmwCpgC34MXfWTXndS3e/50JEGaMOSEigcBqnHOsPAAsMcZ8ICKvAduMMa9W9TnedgcwEthnjDlgjCnGWUzuCptjUhUYY1YCORVWXwG8bb1+G+f/iF6livPyesaYI8aYzdbrfJwTNXXCy7+zas7L6xmnE9ZioPXHAOcCi631Lr8zb0sAnYCUcsupNJEvFOeX962IbBKRGXYH4wHtjDFHrNfpQDs7g3Gze0Rku9VE5FXNJBWJSCwwFPiRJvSdVTgvaALfmTWt7lYgE1gK7AdyjTGl1iYur4/elgCasnHGmGHARcDdVnNDk2Sc7Y7e0/ZYvVeB7sAQ4AjwH1ujqQcRaQ58CPzRGHO8/Hve/J1Vcl5N4jszxpQZY4YA0ThbR/rU9jO8LQGkATHllqOtdV7PGJNm/Z0JfITzC21KMqw22dNts5k2x+MWxpgM639EB/A6Xvq9We3IHwLvGWOWWKu9/jur7Lyaynd2mjEmF1gOjAYiRCTAesvl9dHbEsBGoKfV0x2Ecz6BT22Oqd5EJMzqpEJEwoDzgR3V7+V1PgVutl7fDHxiYyxuc/oCabkSL/zerA7FN4Fdxpjny73l1d9ZVefVRL6zKBGJsF43w/lgzC6cieBqazOX35lXPQUEYD2y9SLgD8wxxjxpb0T1JyLdcP7qBwgA3vfm8xKR+cA5OMvTZgB/Az4GFgKdcZb2vtYY41UdqlWc1zk4mxIMkAzcUa7d3CuIyDhgFZDAz/N0/xVne7nXfmfVnNd1eP93NghnJ68/zh/yC40xT1jXkg+ASGALcKMxpqjKz/G2BKCUUso9vK0JSCmllJtoAlBKKR+lCUAppXyUJgCllPJRmgCUUspHaQJQjYaIlFnVGXeIyCIRCa1m21tEZFZDxlfu2E+IyHkutpkrIle72Ca2fGVRpRqaJgDVmJwyxgyxKm0WA3faHVBljDH/Z4xZZncc5VmVcpWqFU0AqrFaBfSwatJ/bBXuWm8NgDlDRMJFJMka8o+ItDi9LCIrRORpq276XhEZb20TIiJviXP+hS0iMtFaf4t1rKXinJ/hHhF5wNpmvYhEWtud+XUvIv8nIhutu5bZ1ujTKonIcKuG+zbg7nLr/UXkWeuztovIHdZ6PxF5RUR2W3F9We7Yydb5bQauEZHzRWSdiGy27qCalzvmD1ahwW8qjIRVPkwTgGp0rFomF+Ecwfl3YIsxZhDOUZzzym9rlfldAVxirZqGsx56ibUcYIwZCfwR58hdcF54jTFmIM5RoW+LSIj13gDgN8AI4EngpDFmKLAO+G0l4c4yxoyw7lqaAZe6OL23gHuNMYMrrL8NyDPGjLCO/TsR6WrFEgv0A27CWe+lvGyriOAy4DHgPGs5HnjASowvAVcbY4YDc6zzUooA15so1WCaWeVtwXkH8CbOcgRXARhjvheR1iLSosJ+bwAP4Sw3MR34Xbn3Thc224TzQgowDudFEWPMbhE5CPSy3ltuJZV8EckDPrPWJwC/uPuwTBSRh4BQnMPvd5bb5xes2i0R1rwCAO/gTHTgrP80qFy/QUugpxXrIqtwWbqILK/wsQusv0fhTBJrrJuQIJxJqzfOpLbUWu+PswKmUpoAVKNyyipve4aLFhUAjDFrrA7VcwB/Y0z5jtXTdVDKqNl/7+XrpjjKLTsq7m/dNbyCcya3FHHODhZC3QjOO4NvKhzD1XSFBeX2X2qMua7C/gOBncaYincOSmkTkGr0VgE3AFgX+KMVa9Vb5gHv42xiqc1n9sJZ7GxPHWI7fbE/arW3V/vUj1W2N9cqUsbpGCzfAHeV68voJc7KsGuAq6y+gHY4i89VZj0wVkR6WPuHWee2B4gSkdHW+kAR6V/L81RNlN4BqMbucWCOiGwHTvJzeeKK3gP+CcyvwWe+ArwqIglAKXCLMaaoJncb5RljckXkdZzlhNNxlit3ZTrO8zHAt+XWv4GziWqz1ZGchXM6vw+BSUAiztnwNgN5lcSSJSK3APNFJNha/ZgxZq/VrDRTRFri/H/+RZxNVcrHaTVQ1SRYF7krjDE32R2Lu4lIc2vy79bABmCsMSbd7riU99M7AOX1ROQlnJ2prtrLvdXnVgdyEPAPvfgrd9E7AKWU8lHaCayUUj5KE4BSSvkoTQBKKeWjNAEopZSP0gSglFI+6v8BGJYmwBdsB44AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
@@ -1963,7 +1971,7 @@
" polynomial[polydegree] = polydegree\n",
" for degree in range(polydegree):\n",
" X[:,degree] = Density**(degree/3.0)\n",
- " OLS = LinearRegression()\n",
+ " OLS = LinearRegression(fit_intercept=False)\n",
"# loop over trials in order to estimate the expectation value of the MSE\n",
" estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
"#[:, np.newaxis]\n",
@@ -1976,72 +1984,12 @@
"plt.show()"
]
},
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "filenames": {
- "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_74_0.png"
- },
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from sklearn.model_selection import KFold\n",
- "from sklearn.linear_model import Ridge\n",
- "from sklearn.model_selection import cross_val_score\n",
- "from sklearn.preprocessing import PolynomialFeatures\n",
- "\n",
- "# A seed just to ensure that the random numbers are the same for every run.\n",
- "np.random.seed(3155)\n",
- "# Generate the data.\n",
- "n = 100\n",
- "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
- "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
- "# Decide degree on polynomial to fit\n",
- "poly = PolynomialFeatures(degree = 10)\n",
- "\n",
- "# Decide which values of lambda to use\n",
- "nlambdas = 500\n",
- "lambdas = np.logspace(-3, 5, nlambdas)\n",
- "# Initialize a KFold instance\n",
- "k = 5\n",
- "kfold = KFold(n_splits = k)\n",
- "estimated_mse_sklearn = np.zeros(nlambdas)\n",
- "i = 0\n",
- "for lmb in lambdas:\n",
- " ridge = Ridge(alpha = lmb)\n",
- " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n",
- " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
- " i += 1\n",
- "plt.figure()\n",
- "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
- "plt.xlabel('log10(lambda)')\n",
- "plt.ylabel('MSE')\n",
- "plt.legend()\n",
- "plt.show()"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "Note that we have kept the intercept in the first column of design matrix $\\boldsymbol{X}$. When we call the corresponding **Scikit-Learn** function we need thus to set the intercept to **False**. Libraries like **Scikit-Learn** normally scale the design matrix and does not fit intercept. See the discussions below.\n",
+ "\n",
"## More on Rescaling data\n",
"\n",
"We end this chapter by adding some words on scaling and how to deal with the intercept for regression cases.\n",
@@ -2098,7 +2046,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {
"collapsed": false,
"editable": true
@@ -2110,7 +2058,7 @@
"'\\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'"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -2217,7 +2165,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {
"collapsed": false,
"editable": true
@@ -2423,7 +2371,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {
"collapsed": false,
"editable": true
@@ -2459,7 +2407,7 @@
},
"metadata": {
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@@ -2740,7 +2688,7 @@
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@@ -2958,7 +2906,7 @@
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@@ -3213,7 +3161,7 @@
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@@ -3278,7 +3226,7 @@
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@@ -3369,7 +3317,7 @@
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@@ -3421,7 +3369,7 @@
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@@ -3431,7 +3379,7 @@
"name": "stderr",
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"text": [
- ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
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]
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@@ -3444,7 +3392,7 @@
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@@ -3691,7 +3639,7 @@
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@@ -3701,7 +3649,7 @@
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"text": [
- ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
" cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
]
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@@ -3714,7 +3662,7 @@
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@@ -3781,7 +3729,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- ":10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ ":10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
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]
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@@ -3794,7 +3742,7 @@
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@@ -3856,7 +3804,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- ":9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+ ":9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
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+ "execution_count": 29,
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@@ -3926,7 +3874,7 @@
"/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.924197515789051, tolerance: 1.796796\n",
" model = cd_fast.enet_coordinate_descent(\n",
"\r",
- " 10%|█ | 1/10 [00:00<00:04, 2.06it/s]"
+ " 10%|█ | 1/10 [00:00<00:04, 2.01it/s]"
]
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@@ -3934,7 +3882,7 @@
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"\r",
- " 20%|██ | 2/10 [00:00<00:03, 2.39it/s]"
+ " 20%|██ | 2/10 [00:00<00:03, 2.26it/s]"
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@@ -3942,7 +3890,7 @@
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+ " 30%|███ | 3/10 [00:00<00:02, 2.75it/s]"
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@@ -3950,7 +3898,7 @@
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- " 40%|████ | 4/10 [00:00<00:01, 3.75it/s]"
+ " 40%|████ | 4/10 [00:01<00:01, 3.39it/s]"
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@@ -3958,7 +3906,7 @@
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- " 50%|█████ | 5/10 [00:01<00:01, 4.51it/s]"
+ " 50%|█████ | 5/10 [00:01<00:01, 4.00it/s]"
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@@ -3966,7 +3914,7 @@
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"\r",
- " 60%|██████ | 6/10 [00:01<00:00, 5.17it/s]"
+ " 60%|██████ | 6/10 [00:01<00:00, 4.67it/s]"
]
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@@ -3974,7 +3922,7 @@
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- " 70%|███████ | 7/10 [00:01<00:00, 5.94it/s]"
+ " 70%|███████ | 7/10 [00:01<00:00, 5.36it/s]"
]
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@@ -3982,7 +3930,7 @@
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"\r",
- " 80%|████████ | 8/10 [00:01<00:00, 6.59it/s]"
+ " 80%|████████ | 8/10 [00:01<00:00, 6.06it/s]"
]
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@@ -3990,7 +3938,7 @@
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"text": [
"\r",
- " 90%|█████████ | 9/10 [00:01<00:00, 7.10it/s]"
+ " 90%|█████████ | 9/10 [00:01<00:00, 6.05it/s]"
]
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@@ -3998,7 +3946,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 10/10 [00:01<00:00, 7.07it/s]"
+ "100%|██████████| 10/10 [00:02<00:00, 4.64it/s]"
]
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@@ -4006,7 +3954,7 @@
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"\r",
- "100%|██████████| 10/10 [00:01<00:00, 5.83it/s]"
+ "100%|██████████| 10/10 [00:02<00:00, 4.68it/s]"
]
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@@ -4025,7 +3973,7 @@
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@@ -4105,7 +4053,7 @@
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"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined"
]
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.py b/doc/LectureNotes/_build/jupyter_execute/chapter3.py
index 1b4dd4f8f..53e60a46a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.py
@@ -887,7 +887,7 @@ for polydegree in range(1, Maxpolydegree):
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=True).fit(x_train, y_train)
+ 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)
@@ -1119,7 +1119,7 @@ for polydegree in range(1, Maxpolydegree):
polynomial[polydegree] = polydegree
for degree in range(polydegree):
X[:,degree] = Density**(degree/3.0)
- OLS = LinearRegression()
+ 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]
@@ -1131,41 +1131,7 @@ plt.ylabel('log10[MSE]')
plt.legend()
plt.show()
-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.
-np.random.seed(3155)
-# Generate the data.
-n = 100
-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)
-# Decide degree on polynomial to fit
-poly = PolynomialFeatures(degree = 10)
-
-# 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)
-estimated_mse_sklearn = np.zeros(nlambdas)
-i = 0
-for lmb in lambdas:
- ridge = Ridge(alpha = lmb)
- estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)
- estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
- i += 1
-plt.figure()
-plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
-plt.xlabel('log10(lambda)')
-plt.ylabel('MSE')
-plt.legend()
-plt.show()
+Note that we have kept the intercept in the first column of design matrix $\boldsymbol{X}$. When we call the corresponding **Scikit-Learn** function we need thus to set the intercept to **False**. Libraries like **Scikit-Learn** normally scale the design matrix and does not fit intercept. See the discussions below.
## More on Rescaling data
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index df911b5f7..d1b31b1aa 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3_73_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter3_73_1.png
index 6dff76ded..30a446da4 100644
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diff --git a/doc/LectureNotes/chapter3.ipynb b/doc/LectureNotes/chapter3.ipynb
index 18cd038f8..ad19ad998 100644
--- a/doc/LectureNotes/chapter3.ipynb
+++ b/doc/LectureNotes/chapter3.ipynb
@@ -1253,7 +1253,7 @@
" trainingerror[polydegree] = 0.0\n",
" for samples in range(trials):\n",
" x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
- " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n",
+ " model = LinearRegression(fit_intercept=False).fit(x_train, y_train)\n",
" ypred = model.predict(x_train)\n",
" ytilde = model.predict(x_test)\n",
" testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
@@ -1535,7 +1535,7 @@
" polynomial[polydegree] = polydegree\n",
" for degree in range(polydegree):\n",
" X[:,degree] = Density**(degree/3.0)\n",
- " OLS = LinearRegression()\n",
+ " OLS = LinearRegression(fit_intercept=False)\n",
"# loop over trials in order to estimate the expectation value of the MSE\n",
" estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
"#[:, np.newaxis]\n",
@@ -1548,56 +1548,12 @@
"plt.show()"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from sklearn.model_selection import KFold\n",
- "from sklearn.linear_model import Ridge\n",
- "from sklearn.model_selection import cross_val_score\n",
- "from sklearn.preprocessing import PolynomialFeatures\n",
- "\n",
- "# A seed just to ensure that the random numbers are the same for every run.\n",
- "np.random.seed(3155)\n",
- "# Generate the data.\n",
- "n = 100\n",
- "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
- "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
- "# Decide degree on polynomial to fit\n",
- "poly = PolynomialFeatures(degree = 10)\n",
- "\n",
- "# Decide which values of lambda to use\n",
- "nlambdas = 500\n",
- "lambdas = np.logspace(-3, 5, nlambdas)\n",
- "# Initialize a KFold instance\n",
- "k = 5\n",
- "kfold = KFold(n_splits = k)\n",
- "estimated_mse_sklearn = np.zeros(nlambdas)\n",
- "i = 0\n",
- "for lmb in lambdas:\n",
- " ridge = Ridge(alpha = lmb)\n",
- " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n",
- " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
- " i += 1\n",
- "plt.figure()\n",
- "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
- "plt.xlabel('log10(lambda)')\n",
- "plt.ylabel('MSE')\n",
- "plt.legend()\n",
- "plt.show()"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "Note that we have kept the intercept in the first column of design matrix $\\boldsymbol{X}$. When we call the corresponding **Scikit-Learn** function we need thus to set the intercept to **False**. Libraries like **Scikit-Learn** normally scale the design matrix and does not fit intercept. See the discussions below.\n",
+ "\n",
"## More on Rescaling data\n",
"\n",
"We end this chapter by adding some words on scaling and how to deal with the intercept for regression cases.\n",
diff --git a/doc/pub/week37/ipynb/week37.ipynb b/doc/pub/week37/ipynb/week37.ipynb
index 15af1b5f5..248b41046 100644
--- a/doc/pub/week37/ipynb/week37.ipynb
+++ b/doc/pub/week37/ipynb/week37.ipynb
@@ -294,10 +294,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -399,10 +396,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -829,10 +823,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from numpy import *\n",
@@ -1255,10 +1246,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1301,10 +1289,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# the histogram of the bootstrapped data (normalized data if density = True)\n",
@@ -1458,10 +1443,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -1530,10 +1512,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -1632,10 +1611,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
@@ -1810,12 +1786,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -1917,12 +1903,125 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Degree of polynomial: 1\n",
+ "Mean squared error on training data: 446033.51374050\n",
+ "Mean squared error on test data: 455173.80460179\n",
+ "Degree of polynomial: 2\n",
+ "Mean squared error on training data: 114550.54637219\n",
+ "Mean squared error on test data: 129963.83146596\n",
+ "Degree of polynomial: 3\n",
+ "Mean squared error on training data: 9054.61775176\n",
+ "Mean squared error on test data: 10572.87627342\n",
+ "Degree of polynomial: 4\n",
+ "Mean squared error on training data: 302.15313054\n",
+ "Mean squared error on test data: 433.26292364\n",
+ "Degree of polynomial: 5\n",
+ "Mean squared error on training data: 3.64316192\n",
+ "Mean squared error on test data: 7.23528337\n",
+ "Degree of polynomial: 6\n",
+ "Mean squared error on training data: 3.56589683\n",
+ "Mean squared error on test data: 10.50427787\n",
+ "Degree of polynomial: 7\n",
+ "Mean squared error on training data: 0.47313680\n",
+ "Mean squared error on test data: 1.53738247\n",
+ "Degree of polynomial: 8\n",
+ "Mean squared error on training data: 0.04926746\n",
+ "Mean squared error on test data: 0.14629156\n",
+ "Degree of polynomial: 9\n",
+ "Mean squared error on training data: 0.02546675\n",
+ "Mean squared error on test data: 0.11202337\n",
+ "Degree of polynomial: 10\n",
+ "Mean squared error on training data: 0.02424794\n",
+ "Mean squared error on test data: 0.22467274\n",
+ "Degree of polynomial: 11\n",
+ "Mean squared error on training data: 0.01594452\n",
+ "Mean squared error on test data: 1.07641937\n",
+ "Degree of polynomial: 12\n",
+ "Mean squared error on training data: 0.00805074\n",
+ "Mean squared error on test data: 0.04295757\n",
+ "Degree of polynomial: 13\n",
+ "Mean squared error on training data: 0.00781918\n",
+ "Mean squared error on test data: 0.56965674\n",
+ "Degree of polynomial: 14\n",
+ "Mean squared error on training data: 0.00465099\n",
+ "Mean squared error on test data: 0.28443037\n",
+ "Degree of polynomial: 15\n",
+ "Mean squared error on training data: 0.00420072\n",
+ "Mean squared error on test data: 568.46764611\n",
+ "Degree of polynomial: 16\n",
+ "Mean squared error on training data: 0.00325450\n",
+ "Mean squared error on test data: 48.97816846\n",
+ "Degree of polynomial: 17\n",
+ "Mean squared error on training data: 0.00242954\n",
+ "Mean squared error on test data: 2.52781930\n",
+ "Degree of polynomial: 18\n",
+ "Mean squared error on training data: 0.00219194\n",
+ "Mean squared error on test data: 429.08472112\n",
+ "Degree of polynomial: 19\n",
+ "Mean squared error on training data: 0.00154858\n",
+ "Mean squared error on test data: 243.07381539\n",
+ "Degree of polynomial: 20\n",
+ "Mean squared error on training data: 0.00140907\n",
+ "Mean squared error on test data: 1345.82751541\n",
+ "Degree of polynomial: 21\n",
+ "Mean squared error on training data: 0.00119942\n",
+ "Mean squared error on test data: 1836.90791879\n",
+ "Degree of polynomial: 22\n",
+ "Mean squared error on training data: 0.00092941\n",
+ "Mean squared error on test data: 1186.40097146\n",
+ "Degree of polynomial: 23\n",
+ "Mean squared error on training data: 0.00089187\n",
+ "Mean squared error on test data: 3897.46661311\n",
+ "Degree of polynomial: 24\n",
+ "Mean squared error on training data: 0.00083294\n",
+ "Mean squared error on test data: 1373.69301339\n",
+ "Degree of polynomial: 25\n",
+ "Mean squared error on training data: 0.00079928\n",
+ "Mean squared error on test data: 7803.21490115\n",
+ "Degree of polynomial: 26\n",
+ "Mean squared error on training data: 0.00075608\n",
+ "Mean squared error on test data: 1099.96635131\n",
+ "Degree of polynomial: 27\n",
+ "Mean squared error on training data: 0.00068344\n",
+ "Mean squared error on test data: 3165.69836349\n",
+ "Degree of polynomial: 28\n",
+ "Mean squared error on training data: 0.00063301\n",
+ "Mean squared error on test data: 545.87095029\n",
+ "Degree of polynomial: 29\n",
+ "Mean squared error on training data: 0.00063870\n",
+ "Mean squared error on test data: 3115.85424414\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ ":73: RuntimeWarning: divide by zero encountered in log10\n",
+ " plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
+ ":74: RuntimeWarning: divide by zero encountered in log10\n",
+ " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -2018,12 +2117,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "editable": true
- },
- "outputs": [],
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ ":63: RuntimeWarning: divide by zero encountered in log10\n",
+ " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -2093,9 +2210,34 @@
"plt.legend()\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
- "metadata": {},
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
"nbformat": 4,
"nbformat_minor": 4
}
+
-
-
+
+
+
+
+
+
+
+
+
+
+