From dbf463f3c58bfd19b076d0926ae9f5b384f944c5 Mon Sep 17 00:00:00 2001
From: mhjensen <morten.hjorth-jensen@fys.uio.no>
Date: Fri, 8 Nov 2019 06:42:37 +0100
Subject: [PATCH] added codes

---
 doc/src/DecisionTrees/Programs/adaboost.py~ | 65 +++++++++++++++++++++
 doc/src/DecisionTrees/Programs/gdreg.py~    | 48 +++++++++++++++
 2 files changed, 113 insertions(+)
 create mode 100644 doc/src/DecisionTrees/Programs/adaboost.py~
 create mode 100644 doc/src/DecisionTrees/Programs/gdreg.py~

diff --git a/doc/src/DecisionTrees/Programs/adaboost.py~ b/doc/src/DecisionTrees/Programs/adaboost.py~
new file mode 100644
index 000000000..09eaec98c
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/adaboost.py~
@@ -0,0 +1,65 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import AdaBoostClassifier
+
+def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):
+    x1s = np.linspace(axes[0], axes[1], 100)
+    x2s = np.linspace(axes[2], axes[3], 100)
+    x1, x2 = np.meshgrid(x1s, x2s)
+    X_new = np.c_[x1.ravel(), x2.ravel()]
+    y_pred = clf.predict(X_new).reshape(x1.shape)
+    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
+    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
+    if contour:
+        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
+        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
+    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", alpha=alpha)
+    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", alpha=alpha)
+    plt.axis(axes)
+    plt.xlabel(r"$x_1$", fontsize=18)
+    plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
+
+# Load the data
+cancer = load_breast_cancer()
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+ada_clf = AdaBoostClassifier(
+    DecisionTreeClassifier(max_depth=1), n_estimators=200,
+    algorithm="SAMME.R", learning_rate=0.5, random_state=42)
+
+ada_clf.fit(X_train_scaled, y_train)
+plot_decision_boundary(ada_clf, cancer.data,cancer.target)
+
+m = len(X_train_scaled)
+
+plt.figure(figsize=(11, 4))
+for subplot, learning_rate in ((121, 1), (122, 0.5)):
+    sample_weights = np.ones(m)
+    plt.subplot(subplot)
+    for i in range(5):
+        svm_clf = SVC(kernel="rbf", C=0.05, gamma="auto", random_state=42)
+        svm_clf.fit(X_train_scaled, y_train, sample_weight=sample_weights)
+        y_pred = svm_clf.predict(X_train_scaled)
+        sample_weights[y_pred != y_train] *= (1 + learning_rate)
+        plot_decision_boundary(svm_clf, cancer.data,cancer.target, alpha=0.2)
+        plt.title("learning_rate = {}".format(learning_rate), fontsize=16)
+    if subplot == 121:
+        plt.text(-0.7, -0.65, "1", fontsize=14)
+        plt.text(-0.6, -0.10, "2", fontsize=14)
+        plt.text(-0.5,  0.10, "3", fontsize=14)
+        plt.text(-0.4,  0.55, "4", fontsize=14)
+        plt.text(-0.3,  0.90, "5", fontsize=14)
+
+plt.show()
+
diff --git a/doc/src/DecisionTrees/Programs/gdreg.py~ b/doc/src/DecisionTrees/Programs/gdreg.py~
new file mode 100644
index 000000000..1fc003877
--- /dev/null
+++ b/doc/src/DecisionTrees/Programs/gdreg.py~
@@ -0,0 +1,48 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 1000
+maxdegree = 6
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(1,maxdegree):
+    model = GradientBoostingRegressor(max_depth=degree, n_estimators=3, learning_rate=1.0)  
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdegree-1)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+
+