machine_learning.decision_tree

Implementation of a basic regression decision tree. Input data set: The input data set must be 1-dimensional with continuous labels. Output: The decision tree maps a real number input to a real number output.

Classes

DecisionTree
TestDecisionTree	Decision Tres test class

Functions

main(→ None)

In this demonstration we're generating a sample data set from the sin function in

Module Contents

class machine_learning.decision_tree.DecisionTree(depth=5, min_leaf_size=5)

mean_squared_error(labels, prediction)

mean_squared_error: @param labels: a one-dimensional numpy array @param prediction: a floating point value return value: mean_squared_error calculates the error if prediction is used to

estimate the labels

>>> tester = DecisionTree()
>>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
>>> test_prediction = float(6)
>>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
...         test_prediction)))
True
>>> test_labels = np.array([1,2,3])
>>> test_prediction = float(2)
>>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
...         test_prediction)))
True

predict(x)

predict: @param x: a floating point value to predict the label of the prediction function works by recursively calling the predict function of the appropriate subtrees based on the tree’s decision boundary

train(x, y) → None

train: @param x: a one-dimensional numpy array @param y: a one-dimensional numpy array. The contents of y are the labels for the corresponding X values

train() does not have a return value

Examples: 1. Try to train when x & y are of same length & 1 dimensions (No errors) >>> dt = DecisionTree() >>> dt.train(np.array([10,20,30,40,50]),np.array([0,0,0,1,1]))

2. Try to train when x is 2 dimensions >>> dt = DecisionTree() >>> dt.train(np.array([[1,2,3,4,5],[1,2,3,4,5]]),np.array([0,0,0,1,1])) Traceback (most recent call last):

…

ValueError: Input data set must be one-dimensional

3. Try to train when x and y are not of the same length >>> dt = DecisionTree() >>> dt.train(np.array([1,2,3,4,5]),np.array([[0,0,0,1,1],[0,0,0,1,1]])) Traceback (most recent call last):

…

ValueError: x and y have different lengths

4. Try to train when x & y are of the same length but different dimensions >>> dt = DecisionTree() >>> dt.train(np.array([1,2,3,4,5]),np.array([[1],[2],[3],[4],[5]])) Traceback (most recent call last):

…

ValueError: Data set labels must be one-dimensional

This section is to check that the inputs conform to our dimensionality constraints

decision_boundary = 0

depth = 5

left = None

min_leaf_size = 5

prediction = None

right = None

class machine_learning.decision_tree.TestDecisionTree

Decision Tres test class

static helper_mean_squared_error_test(labels, prediction) → float

helper_mean_squared_error_test: @param labels: a one dimensional numpy array @param prediction: a floating point value return value: helper_mean_squared_error_test calculates the mean squared error

machine_learning.decision_tree.main() → None

In this demonstration we’re generating a sample data set from the sin function in numpy. We then train a decision tree on the data set and use the decision tree to predict the label of 10 different test values. Then the mean squared error over this test is displayed.