Add ml1

ml1/util_ds.py

@@ -0,0 +1,117 @@
+import numpy as np
+
+# Taken from http://chrisstrelioff.ws/sandbox/2015/06/25/decision_trees_in_python_again_cross_validation.html
+
+def get_code(tree, feature_names, target_names,
+             spacer_base="    "):
+    """Produce psuedo-code for decision tree.
+
+    Args
+    ----
+    tree -- scikit-leant DescisionTree.
+    feature_names -- list of feature names.
+    target_names -- list of target (class) names.
+    spacer_base -- used for spacing code (default: "    ").
+
+    Notes
+    -----
+    based on http://stackoverflow.com/a/30104792.
+    """
+    left      = tree.tree_.children_left
+    right     = tree.tree_.children_right
+    threshold = tree.tree_.threshold
+    features  = [feature_names[i] for i in tree.tree_.feature]
+    value = tree.tree_.value
+
+    def recurse(left, right, threshold, features, node, depth):
+        spacer = spacer_base * depth
+        if (threshold[node] != -2):
+            print(spacer + "if ( " + features[node] + " <= " + \
+                  str(threshold[node]) + " ) {")
+            if left[node] != -1:
+                    recurse(left, right, threshold, features,
+                            left[node], depth+1)
+            print(spacer + "}\n" + spacer +"else {")
+            if right[node] != -1:
+                    recurse(left, right, threshold, features,
+                            right[node], depth+1)
+            print(spacer + "}")
+        else:
+            target = value[node]
+            for i, v in zip(np.nonzero(target)[1],
+                            target[np.nonzero(target)]):
+                target_name = target_names[i]
+                target_count = int(v)
+                print(spacer + "return " + str(target_name) + \
+                      " ( " + str(target_count) + " examples )")
+
+    recurse(left, right, threshold, features, 0, 0)
+
+# Taken from http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+
+def plot_tree_iris():
+    """
+
+    Taken fromhttp://scikit-learn.org/stable/auto_examples/tree/plot_iris.html
+    """
+    # Parameters
+    n_classes = 3
+    plot_colors = "bry"
+    plot_step = 0.02
+
+    # Load data
+    iris = load_iris()
+
+    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], 
+                                [1, 2], [1, 3], [2, 3]]):
+        # We only take the two corresponding features
+        X = iris.data[:, pair]
+        y = iris.target
+
+        # Shuffle
+        idx = np.arange(X.shape[0])
+        np.random.seed(13)
+        np.random.shuffle(idx)
+        X = X[idx]
+        y = y[idx]
+
+        # Standardize
+        mean = X.mean(axis=0)
+        std = X.std(axis=0)
+        X = (X - mean) / std
+
+        # Train
+        model = DecisionTreeClassifier(max_depth=3, random_state=1).fit(X, y)
+
+        # Plot the decision boundary
+        plt.subplot(2, 3, pairidx + 1)
+
+        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
+                 np.arange(y_min, y_max, plot_step))
+
+        Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+        Z = Z.reshape(xx.shape)
+        cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
+
+        plt.xlabel(iris.feature_names[pair[0]])
+        plt.ylabel(iris.feature_names[pair[1]])
+        plt.axis("tight")
+
+        # Plot the training points
+        for i, color in zip(range(n_classes), plot_colors):
+            idx = np.where(y == i)
+            plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
+                        cmap=plt.cm.Paired)
+
+        plt.axis("tight")
+
+    plt.suptitle("Decision surface of a decision tree using paired features")
+    plt.legend()
+    plt.show()