Merge branch 'TP_python' into 'master'

TP Python

See merge request !1

TP/TP1_lundi/machine learning/helpers_05_08.py

+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeClassifier
+from ipywidgets import interact
+
+
+def visualize_tree(estimator, X, y, boundaries=True,
+                   xlim=None, ylim=None, ax=None):
+    ax = ax or plt.gca()
+    
+    # Plot the training points
+    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',
+               clim=(y.min(), y.max()), zorder=3)
+    ax.axis('tight')
+    ax.axis('off')
+    if xlim is None:
+        xlim = ax.get_xlim()
+    if ylim is None:
+        ylim = ax.get_ylim()
+    
+    # fit the estimator
+    estimator.fit(X, y)
+    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),
+                         np.linspace(*ylim, num=200))
+    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
+
+    # Put the result into a color plot
+    n_classes = len(np.unique(y))
+    Z = Z.reshape(xx.shape)
+    contours = ax.contourf(xx, yy, Z, alpha=0.3,
+                           levels=np.arange(n_classes + 1) - 0.5,
+                           cmap='viridis', clim=(y.min(), y.max()),
+                           zorder=1)
+
+    ax.set(xlim=xlim, ylim=ylim)
+    
+    # Plot the decision boundaries
+    def plot_boundaries(i, xlim, ylim):
+        if i >= 0:
+            tree = estimator.tree_
+        
+            if tree.feature[i] == 0:
+                ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)
+                plot_boundaries(tree.children_left[i],
+                                [xlim[0], tree.threshold[i]], ylim)
+                plot_boundaries(tree.children_right[i],
+                                [tree.threshold[i], xlim[1]], ylim)
+        
+            elif tree.feature[i] == 1:
+                ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)
+                plot_boundaries(tree.children_left[i], xlim,
+                                [ylim[0], tree.threshold[i]])
+                plot_boundaries(tree.children_right[i], xlim,
+                                [tree.threshold[i], ylim[1]])
+            
+    if boundaries:
+        plot_boundaries(0, xlim, ylim)
+
+
+def plot_tree_interactive(X, y):
+    def interactive_tree(depth=5):
+        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
+        visualize_tree(clf, X, y)
+
+    return interact(interactive_tree, depth=[1, 5])
+
+
+def randomized_tree_interactive(X, y):
+    N = int(0.75 * X.shape[0])
+    
+    xlim = (X[:, 0].min(), X[:, 0].max())
+    ylim = (X[:, 1].min(), X[:, 1].max())
+    
+    def fit_randomized_tree(random_state=0):
+        clf = DecisionTreeClassifier(max_depth=15)
+        i = np.arange(len(y))
+        rng = np.random.RandomState(random_state)
+        rng.shuffle(i)
+        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,
+                       xlim=xlim, ylim=ylim)
+    
+    interact(fit_randomized_tree, random_state=[0, 100]);
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