moving files
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mhjensen
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_breast_cancer
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from sklearn.linear_model import LogisticRegression
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cancer = load_breast_cancer()
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import pandas as pd
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cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)
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import seaborn as sns
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correlation_matrix = cancerpd.corr().round(1)
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# use the heatmap function from seaborn to plot the correlation matrix
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# annot = True to print the values inside the square
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sns.heatmap(data=correlation_matrix, annot=True)
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EigValues, EigVectors = np.linalg.eig(correlation_matrix)
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print(EigValues)
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X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
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print(X_train.shape)
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print(X_test.shape)
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logreg = LogisticRegression()
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logreg.fit(X_train, y_train)
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print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train)))
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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logreg.fit(X_train_scaled, y_train)
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print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train)))
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#thereafter we do a PCA with Scikit-learn
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from sklearn.decomposition import PCA
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pca = PCA(n_components = 2)
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X2D_train = pca.fit_transform(X_train_scaled)
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logreg.fit(X2D_train,y_train)
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print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train)))
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@@ -1,31 +0,0 @@
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import numpy as np
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import pandas as pd
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from IPython.display import display
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np.random.seed(100)
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# setting up a 10 x 5 matrix
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rows = 10
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cols = 5
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X = np.random.randn(rows,cols)
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df = pd.DataFrame(X)
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# Pandas does the centering for us
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df = df -df.mean()
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display(df)
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# we center it ourselves
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X_centered = X - X.mean(axis=0)
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print(X_centered-df)
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#Now we do an SVD
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U, s, V = np.linalg.svd(X_centered)
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c1 = V.T[:, 0]
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c2 = V.T[:, 1]
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W2 = V.T[:, :2]
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X2D = X_centered.dot(W2)
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print(X2D)
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#thereafter we do a PCA with Scikit-learn
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from sklearn.decomposition import PCA
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pca = PCA(n_components = 2)
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X2D = pca.fit_transform(X)
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print(X2D)
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print(pca.components_.T[:, 0])
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_breast_cancer
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from sklearn.svm import SVC
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from sklearn.linear_model import LogisticRegression
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cancer = load_breast_cancer()
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X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
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print(X_train.shape)
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print(X_test.shape)
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logreg = LogisticRegression()
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logreg.fit(X_train, y_train)
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#svm = SVC(C=100)
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#svm.fit(X_train, y_train)
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print("Test set accuracy: {:.2f}".format(logreg.score(X_test,y_test)))
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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print("Feature min values before scaling:\n {}".format(X_train.min(axis=0)))
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print("Feature max values before scaling:\n {}".format(X_train.max(axis=0)))
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print("Feature min values before scaling:\n {}".format(X_train_scaled.min(axis=0)))
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print("Feature max values before scaling:\n {}".format(X_train_scaled.max(axis=0)))
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logreg.fit(X_train_scaled, y_train)
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#svm.fit(X_train_scaled, y_train)
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print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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logreg.fit(X_train_scaled, y_train)
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#svm.fit(X_train_scaled, y_train)
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print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_breast_cancer
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from sklearn.linear_model import LogisticRegression
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cancer = load_breast_cancer()
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fig, axes = plt.subplots(15,2,figsize=(10,20))
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malignant = cancer.data[cancer.target == 0]
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benign = cancer.data[cancer.target == 1]
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ax = axes.ravel()
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for i in range(30):
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_, bins = np.histogram(cancer.data[:,i], bins =50)
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ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)
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ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)
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ax[i].set_title(cancer.feature_names[i])
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ax[i].set_yticks(())
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ax[0].set_xlabel("Feature magnitude")
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ax[0].set_ylabel("Frequency")
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ax[0].legend(["Malignant", "Benign"], loc ="best")
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fig.tight_layout()
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plt.show()
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X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
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print(X_train.shape)
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print(X_test.shape)
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logreg = LogisticRegression()
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logreg.fit(X_train, y_train)
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print("Test set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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scaler = StandardScaler()
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scaler.fit(X_train)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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logreg.fit(X_train_scaled, y_train)
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print("Test set accuracy scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
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@@ -1,44 +0,0 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_breast_cancer
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from sklearn.linear_model import LogisticRegression
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cancer = load_breast_cancer()
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fig, axes = plt.subplots(15,2,figsize=(10,20))
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malignant = cancer.data[cancer.target == 0]
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benign = cancer.data[cancer.target == 1]
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ax = axes.ravel()
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for i in range(30):
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_, bins = np.histogram(cancer.data[:,i], bins =50)
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ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)
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ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)
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ax[i].set_title(cancer.feature_names[i])
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ax[i].set_yticks(())
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ax[0].set_xlabel("Feature magnitude")
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ax[0].set_ylabel("Frequency")
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ax[0].legend(["Malignant", "Benign"], loc ="best")
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fig.tight_layout()
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plt.show()
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X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
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logreg = LogisticRegression()
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logreg.fit(X_train, y_train)
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print("Test set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
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beta = np.random.randn(2,1)
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eta = 0.1
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Niterations = 100
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for iter in range(Niterations):
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gradients = 2.0/m*xb.T @ (xb @ (beta)-y)+2*lmbda*beta
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beta -= eta*gradients
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print(beta)
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ypredict = xb @ beta
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ypredict2 = xb @ beta_linreg
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# Importing various packages
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import numpy as np
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n = 100
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# define two vectors
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x = np.random.random(size=n)
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y = 4+3*x+np.random.normal(size=n)
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#scaling the x and y vectors
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x = x - np.mean(x)
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y = y - np.mean(y)
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variance_x = np.sum(x@x)/n
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variance_y = np.sum(y@y)/n
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print(variance_x)
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print(variance_y)
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cov_xy = np.sum(x@y)/n
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cov_xx = np.sum(x@x)/n
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cov_yy = np.sum(y@y)/n
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C = np.zeros((2,2))
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C[0,0]= cov_xx/variance_x
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C[1,1]= cov_yy/variance_y
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C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
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C[1,0]= C[0,1]
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print(C)
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Eigvals, Eigvecs = np.linalg.eig(C)
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print(Eigvals)
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# Common imports
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import numpy as np
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import pandas as pd
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def FrankeFunction(x,y):
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term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
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term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
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term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
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term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
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return term1 + term2 + term3 + term4
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def create_X(x, y, n ):
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if len(x.shape) > 1:
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x = np.ravel(x)
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y = np.ravel(y)
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N = len(x)
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l = int((n+1)*(n+2)/2) # Number of elements in beta
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X = np.ones((N,l))
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for i in range(1,n+1):
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q = int((i)*(i+1)/2)
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for k in range(i+1):
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X[:,q+k] = (x**(i-k))*(y**k)
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return X
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# Making meshgrid of datapoints and compute Franke's function
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n = 4
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N = 1000
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x = np.sort(np.random.uniform(0, 1, N))
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y = np.sort(np.random.uniform(0, 1, N))
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z = FrankeFunction(x, y)
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X = create_X(x, y, n=n)
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Xpd = pd.DataFrame(X)
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Xpd = Xpd - Xpd.mean()
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correlation_matrix = Xpd.cov()
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print(correlation_matrix)
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# Common imports
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import numpy as np
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from sklearn.neural_network import MLPRegressor
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from sklearn.metrics import accuracy_score
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import seaborn as sns
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import matplotlib.pyplot as plt
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def FrankeFunction(x,y):
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term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
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term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
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term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
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term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
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return term1 + term2 + term3 + term4
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def create_X(x, y, n ):
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if len(x.shape) > 1:
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x = np.ravel(x)
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y = np.ravel(y)
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N = len(x)
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l = int((n+1)*(n+2)/2) # Number of elements in beta
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X = np.ones((N,l))
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for i in range(1,n+1):
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q = int((i)*(i+1)/2)
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for k in range(i+1):
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X[:,q+k] = (x**(i-k))*(y**k)
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return X
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# Making meshgrid of datapoints and compute Franke's function
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n = 4
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N = 100
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x = np.sort(np.random.uniform(0, 1, N))
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y = np.sort(np.random.uniform(0, 1, N))
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z = FrankeFunction(x, y)
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X = create_X(x, y, n=n)
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# only training data, no advanced splitting
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X_train = X
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Y_train = z
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# only one simple layer with 100 neurons
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n_hidden_neurons = 100
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epochs = 100
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# store models for later use
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eta_vals = np.logspace(-5, 1, 7)
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lmbd_vals = np.logspace(-5, 1, 7)
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# store the models for later use
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DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
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train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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sns.set()
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for i, eta in enumerate(eta_vals):
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for j, lmbd in enumerate(lmbd_vals):
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dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
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alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
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dnn.fit(X_train, Y_train)
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DNN_scikit[i][j] = dnn
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train_accuracy[i][j] = dnn.score(X_train, Y_train)
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Training Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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@@ -1,28 +0,0 @@
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# Importing various packages
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import numpy as np
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n = 10
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x = np.random.normal(size=n)
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x = x - np.mean(x)
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y = 4+3*x+np.random.normal(size=n)
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y = y - np.mean(y)
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X = (np.vstack((x, y))).T
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print(X)
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import pandas as pd
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Xpd = pd.DataFrame(X)
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print(Xpd)
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correlation_matrix = Xpd.corr()
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print(correlation_matrix)
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variance_x = np.sum(x@x)/n
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variance_y = np.sum(y@y)/n
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cov_xy = np.sum(x@y)/n
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cov_xx = np.sum(x@x)/n
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cov_yy = np.sum(y@y)/n
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C = np.zeros((2,2))
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C[0,0]= cov_xx/variance_x
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C[1,1]= cov_yy/variance_y
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C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
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C[1,0]= C[0,1]
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print(C)
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