-
diff --git a/doc/pub/week38/html/._week38-bs007.html b/doc/pub/week38/html/._week38-bs007.html index 7cdbb1c01..08007745f 100644 --- a/doc/pub/week38/html/._week38-bs007.html +++ b/doc/pub/week38/html/._week38-bs007.html @@ -238,7 +238,7 @@ MathJax.Hub.Config({
When you are comparing your own code with for example Scikit-Learn's library, there are some minor things to keep in mind. -The example here shows how one can leave out or keep the intercept. +The example here shows how one can keep the intercept in order to compare own code.
@@ -319,10 +319,10 @@ lambdas = np.print(RegRidge.coef_) # Now plot the results plt.figure() -plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test') -plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test') plt.xlabel('log10(lambda)') plt.ylabel('MSE') diff --git a/doc/pub/week38/html/week38-bs.html b/doc/pub/week38/html/week38-bs.html index f40e5396c..ea3f3b366 100644 --- a/doc/pub/week38/html/week38-bs.html +++ b/doc/pub/week38/html/week38-bs.html @@ -253,7 +253,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html index e822d9758..261aba841 100644 --- a/doc/pub/week38/html/week38-reveal.html +++ b/doc/pub/week38/html/week38-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
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@@ -423,7 +423,7 @@ plt.show()
When you are comparing your own code with for example Scikit-Learn's library, there are some minor things to keep in mind. -The example here shows how one can leave out or keep the intercept. +The example here shows how one can keep the intercept in order to compare own code.
@@ -504,10 +504,10 @@ lambdas = np.logspace(-4, print(RegRidge.coef_) # Now plot the results plt.figure() -plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test') -plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test') plt.xlabel('log10(lambda)') plt.ylabel('MSE') diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html index 10c67f7b6..0c7d4a431 100644 --- a/doc/pub/week38/html/week38-solarized.html +++ b/doc/pub/week38/html/week38-solarized.html @@ -179,7 +179,7 @@ MathJax.Hub.Config({
-
@@ -430,7 +430,7 @@ plt.show()
When you are comparing your own code with for example Scikit-Learn's library, there are some minor things to keep in mind. -The example here shows how one can leave out or keep the intercept. +The example here shows how one can keep the intercept in order to compare own code.
@@ -511,10 +511,10 @@ lambdas = np.logspace(-4, print(RegRidge.coef_) # Now plot the results plt.figure() -plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test') -plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test') plt.xlabel('log10(lambda)') plt.ylabel('MSE') diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html index 90b0585ae..dfcb42880 100644 --- a/doc/pub/week38/html/week38.html +++ b/doc/pub/week38/html/week38.html @@ -184,7 +184,7 @@ MathJax.Hub.Config({
-
@@ -435,7 +435,7 @@ plt.show()
When you are comparing your own code with for example Scikit-Learn's library, there are some minor things to keep in mind. -The example here shows how one can leave out or keep the intercept. +The example here shows how one can keep the intercept in order to compare own code.
@@ -516,10 +516,10 @@ lambdas = np.print(RegRidge.coef_) # Now plot the results plt.figure() -plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test') -plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test') plt.xlabel('log10(lambda)') plt.ylabel('MSE') diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz index 9ae162997..f5c6a0d61 100644 Binary files a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz and b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz differ diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb index d6dd2c4ec..142984b8c 100644 --- a/doc/pub/week38/ipynb/week38.ipynb +++ b/doc/pub/week38/ipynb/week38.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Sep 20, 2021**\n", + "Date: **Sep 21, 2021**\n", "\n", "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -350,7 +350,7 @@ "## To think about\n", "\n", "When you are comparing your own code with for example **Scikit-Learn**'s library, there are some minor things to keep in mind.\n", - "The example here shows how one can leave out or keep the intercept." + "The example here shows how one can keep the intercept in order to compare own code." ] }, { @@ -438,10 +438,10 @@ " print(RegRidge.coef_)\n", "# Now plot the results\n", "plt.figure()\n", - "plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train')\n", - "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test')\n", - "plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train')\n", - "plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test')\n", + "plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train')\n", + "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test')\n", + "plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train')\n", + "plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')\n", "\n", "plt.xlabel('log10(lambda)')\n", "plt.ylabel('MSE')\n", diff --git a/doc/src/week36/scale.py b/doc/src/week36/scale.py new file mode 100644 index 000000000..0acdb1a2b --- /dev/null +++ b/doc/src/week36/scale.py @@ -0,0 +1,85 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn import linear_model +from sklearn.preprocessing import StandardScaler + +def R2(y_data, y_model): + return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2) +def MSE(y_data,y_model): + n = np.size(y_model) + return np.sum((y_data-y_model)**2)/n + + +# A seed just to ensure that the random numbers are the same for every run. +# Useful for eventual debugging. +np.random.seed(3155) + +n = 10 +x = np.random.rand(n) +y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2) + +Maxpolydegree = 5 +X = np.zeros((n,Maxpolydegree)) +X[:,0] = 1.0 + +for polydegree in range(1, Maxpolydegree): + for degree in range(polydegree): + X[:,degree] = x**(degree) + + + + +# We split the data in test and training data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + + +# Do not scale by std +scaler = StandardScaler(with_std=False) +scaler.fit(X_train) +X_train_scaled = scaler.transform(X_train) +X_test_scaled = scaler.transform(X_test) + +#X_train_scaled = X_train +#X_test_scaled = X_test + + + +p = Maxpolydegree +I = np.eye(p,p) +# Decide which values of lambda to use +nlambdas = 2 +MSEOwnRidgePredict = np.zeros(nlambdas) +MSERidgePredict = np.zeros(nlambdas) + +lambdas = np.logspace(-4, 1, nlambdas) +for i in range(nlambdas): + lmb = lambdas[i] + OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ y_train + RegRidge = linear_model.Ridge(lmb,fit_intercept=False)#True, normalize=False) + RegRidge.fit(X_train_scaled,y_train) + ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + print("Values for own Ridge prediction") + print(ypredictOwnRidge) + ypredictRidge = RegRidge.predict(X_test_scaled) + print("Values for SL Ridge prediction") + print(ypredictRidge) + MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge) + MSERidgePredict[i] = MSE(y_test,ypredictRidge) + print("Beta values for own Ridge implementation") + print(OwnRidgeBeta) + print("Beta values for Scikit-Learn Ridge implementation") + print(RegRidge.coef_) +# Now plot the results +""" +plt.figure() +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test') + +plt.xlabel('log10(lambda)') +plt.ylabel('MSE') +plt.legend() +plt.show() +""" diff --git a/doc/src/week36/scaler.py b/doc/src/week36/scaler.py new file mode 100644 index 000000000..4a123a221 --- /dev/null +++ b/doc/src/week36/scaler.py @@ -0,0 +1,8 @@ +from sklearn.preprocessing import StandardScaler +data = [[0, 0], [0, 0], [1, 1], [1, 1]] +scaler = StandardScaler() +print(scaler.fit(data)) +StandardScaler() +print(scaler.mean_) +print(scaler.transform(data)) +print(scaler.transform([[2, 2]])) diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt index 0b6311775..c51b19c4b 100644 --- a/doc/src/week38/week38.do.txt +++ b/doc/src/week38/week38.do.txt @@ -237,7 +237,7 @@ plt.show() ===== To think about ===== When you are comparing your own code with for example _Scikit-Learn_'s library, there are some minor things to keep in mind. -The example here shows how one can leave out or keep the intercept. +The example here shows how one can keep the intercept in order to compare own code. !bc pycod import numpy as np @@ -316,10 +316,10 @@ for i in range(nlambdas): print(RegRidge.coef_) # Now plot the results plt.figure() -plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r--', label = 'MSE Ridge Test') -plt.plot(np.log10(lambdas), MSERidgeTrain, label = 'MSE Ridge train') -plt.plot(np.log10(lambdas), MSERidgePredict, 'r--', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSEOwnRidgeTrain, 'b', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE Ridge Test') +plt.plot(np.log10(lambdas), MSERidgeTrain, 'y', label = 'MSE Ridge train') +plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test') plt.xlabel('log10(lambda)') plt.ylabel('MSE')