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@@ -1,140 +0,0 @@
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import sys
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sys.path.insert(1, '../')
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from utils.utils import *
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class DecisionFork:
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"""
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A fork of a decision tree holds an attribute to test, and a dict
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of branches, one for each of the attribute's values.
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"""
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def __init__(self, attr, attr_name=None, default_child=None, branches=None):
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"""Initialize by saying what attribute this node tests."""
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self.attr = attr
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self.attr_name = attr_name or attr
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self.default_child = default_child
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self.branches = branches or {}
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def __call__(self, example):
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"""Given an example, classify it using the attribute and the branches."""
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attr_val = example[self.attr]
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if attr_val in self.branches:
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return self.branches[attr_val](example)
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else:
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# return default class when attribute is unknown
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return self.default_child(example)
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def add(self, val, subtree):
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"""Add a branch. If self.attr = val, go to the given subtree."""
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self.branches[val] = subtree
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def display(self, indent=0):
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name = self.attr_name
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print('Test', name)
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for (val, subtree) in self.branches.items():
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print(' ' * 4 * indent, name, '=', val, '==>', end=' ')
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subtree.display(indent + 1)
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def __repr__(self):
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return 'DecisionFork({0!r}, {1!r}, {2!r})'.format(self.attr, self.attr_name, self.branches)
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class DecisionLeaf:
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"""A leaf of a decision tree holds just a result."""
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def __init__(self, result):
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self.result = result
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def __call__(self, example):
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return self.result
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def display(self):
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print('RESULT =', self.result)
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def __repr__(self):
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return repr(self.result)
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class DecisionTreeLearner:
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"""DecisionTreeLearner: based on information gain"""
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def __init__(self, dataset):
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self.dataset = dataset
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self.tree = self.decision_tree_learning(dataset.examples, dataset.inputs)
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def decision_tree_learning(self, examples, attrs, parent_examples=()):
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if len(examples) == 0:
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return self.plurality_value(parent_examples)
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if self.all_same_class(examples):
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return DecisionLeaf(examples[0][self.dataset.target])
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if len(attrs) == 0:
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return self.plurality_value(examples)
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A = self.choose_attribute(attrs, examples)
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tree = DecisionFork(A, self.dataset.attr_names[A], self.plurality_value(examples))
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for (v_k, exs) in self.split_by(A, examples):
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subtree = self.decision_tree_learning(exs, remove_all(A, attrs), examples)
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tree.add(v_k, subtree)
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return tree
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def plurality_value(self, examples):
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"""
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Return the most popular target value for this set of examples.
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(If target is binary, this is the majority; otherwise plurality).
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"""
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popular = argmax_random_tie(self.dataset.values[self.dataset.target],
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key=lambda v: self.count(self.dataset.target, v, examples))
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return DecisionLeaf(popular)
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def count(self, attr, val, examples):
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"""Count the number of examples that have example[attr] = val."""
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return sum(e[attr] == val for e in examples)
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def all_same_class(self, examples):
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"""Are all these examples in the same target class?"""
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class0 = examples[0][self.dataset.target]
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return all(e[self.dataset.target] == class0 for e in examples)
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def choose_attribute(self, attrs, examples):
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"""Choose the attribute with the highest information gain."""
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return argmax_random_tie(attrs, key=lambda a: self.information_gain(a, examples))
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def information_gain(self, attr, examples):
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"""Return the expected reduction in entropy from splitting by attr."""
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def I(examples):
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return information_content([self.count(self.dataset.target, v, examples)
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for v in self.dataset.values[self.dataset.target]])
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n = len(examples)
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remainder = sum((len(examples_i) / n) * I(examples_i)
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for (v, examples_i) in self.split_by(attr, examples))
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return I(examples) - remainder
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def split_by(self, attr, examples):
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"""Return a list of (val, examples) pairs for each val of attr."""
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return [(v, [e for e in examples if e[attr] == v]) for v in self.dataset.values[attr]]
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def predict(self, x):
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return self.tree(x)
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def __call__(self, x):
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return self.predict(x)
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def information_content(values):
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"""Number of bits to represent the probability distribution in values."""
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raise NotImplementedError
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if __name__ == "__main__":
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from utils.dataset4learners import *
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iris = DataSet(name="iris")
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DTL = DecisionTreeLearner(iris)
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print(f'DTL.predict([5, 3, 1, 0.1]): {DTL.predict([5, 3, 1, 0.1])}')
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assert DTL.predict([5, 3, 1, 0.1]) == 'setosa'
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print(f'DTL.predict([6, 5, 3, 1.5]): {DTL.predict([6, 5, 3, 1.5])}')
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assert DTL.predict([6, 5, 3, 1.5]) == 'versicolor'
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print(f'DTL.predict([7.5, 4, 6, 2]): {DTL.predict([7.5, 4, 6, 2])}')
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assert DTL.predict([7.5, 4, 6, 2]) == 'virginica'
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@@ -1,132 +0,0 @@
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import sys
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sys.path.insert(1, '../')
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from utils.utils import *
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class DecisionFork:
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"""
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A fork of a decision tree holds an attribute to test, and a dict
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of branches, one for each of the attribute's values.
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"""
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def __init__(self, attr, attr_name=None, default_child=None, branches=None):
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"""Initialize by saying what attribute this node tests."""
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self.attr = attr
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self.attr_name = attr_name or attr
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self.default_child = default_child
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self.branches = branches or {}
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def __call__(self, example):
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"""Given an example, classify it using the attribute and the branches."""
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attr_val = example[self.attr]
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if attr_val in self.branches:
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return self.branches[attr_val](example)
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else:
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# return default class when attribute is unknown
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return self.default_child(example)
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def add(self, val, subtree):
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"""Add a branch. If self.attr = val, go to the given subtree."""
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self.branches[val] = subtree
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def display(self, indent=0):
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name = self.attr_name
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print('Test', name)
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for (val, subtree) in self.branches.items():
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print(' ' * 4 * indent, name, '=', val, '==>', end=' ')
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subtree.display(indent + 1)
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def __repr__(self):
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return 'DecisionFork({0!r}, {1!r}, {2!r})'.format(self.attr, self.attr_name, self.branches)
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class DecisionLeaf:
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"""A leaf of a decision tree holds just a result."""
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def __init__(self, result):
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self.result = result
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def __call__(self, example):
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return self.result
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def display(self):
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print('RESULT =', self.result)
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def __repr__(self):
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return repr(self.result)
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class DecisionTreeLearner:
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"""DecisionTreeLearner: based on information gain"""
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def __init__(self, dataset):
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self.dataset = dataset
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self.tree = self.decision_tree_learning(dataset.examples, dataset.inputs)
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def decision_tree_learning(self, examples, attrs, parent_examples=()):
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if len(examples) == 0:
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return self.plurality_value(parent_examples)
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if self.all_same_class(examples):
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return DecisionLeaf(examples[0][self.dataset.target])
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if len(attrs) == 0:
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return self.plurality_value(examples)
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A = self.choose_attribute(attrs, examples)
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tree = DecisionFork(A, self.dataset.attr_names[A], self.plurality_value(examples))
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for (v_k, exs) in self.split_by(A, examples):
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subtree = self.decision_tree_learning(exs, remove_all(A, attrs), examples)
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tree.add(v_k, subtree)
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return tree
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def plurality_value(self, examples):
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"""
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Return the most popular target value for this set of examples.
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(If target is binary, this is the majority; otherwise plurality).
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"""
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popular = argmax_random_tie(self.dataset.values[self.dataset.target],
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key=lambda v: self.count(self.dataset.target, v, examples))
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return DecisionLeaf(popular)
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def count(self, attr, val, examples):
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"""Count the number of examples that have example[attr] = val."""
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return sum(e[attr] == val for e in examples)
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def all_same_class(self, examples):
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"""Are all these examples in the same target class?"""
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class0 = examples[0][self.dataset.target]
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return all(e[self.dataset.target] == class0 for e in examples)
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def choose_attribute(self, attrs, examples):
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"""Choose the attribute with the highest information gain."""
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return argmax_random_tie(attrs, key=lambda a: self.information_gain(a, examples))
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def information_gain(self, attr, examples):
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"""Return the expected reduction in entropy from splitting by attr."""
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raise NotImplementedError
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def split_by(self, attr, examples):
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"""Return a list of (val, examples) pairs for each val of attr."""
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return [(v, [e for e in examples if e[attr] == v]) for v in self.dataset.values[attr]]
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def predict(self, x):
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return self.tree(x)
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def __call__(self, x):
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return self.predict(x)
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def information_content(values):
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"""Number of bits to represent the probability distribution in values."""
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raise NotImplementedError
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if __name__ == "__main__":
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from utils.dataset4learners import *
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iris = DataSet(name="iris")
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DTL = DecisionTreeLearner(iris)
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print(f'DTL.predict([5, 3, 1, 0.1]): {DTL.predict([5, 3, 1, 0.1])}')
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assert DTL.predict([5, 3, 1, 0.1]) == 'setosa'
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print(f'DTL.predict([6, 5, 3, 1.5]): {DTL.predict([6, 5, 3, 1.5])}')
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assert DTL.predict([6, 5, 3, 1.5]) == 'versicolor'
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print(f'DTL.predict([7.5, 4, 6, 2]): {DTL.predict([7.5, 4, 6, 2])}')
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assert DTL.predict([7.5, 4, 6, 2]) == 'virginica'
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@@ -1,121 +0,0 @@
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import sys
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sys.path.insert(1, '../')
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from utils.utils import *
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class DecisionFork:
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"""
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A fork of a decision tree holds an attribute to test, and a dict
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of branches, one for each of the attribute's values.
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"""
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def __init__(self, attr, attr_name=None, default_child=None, branches=None):
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"""Initialize by saying what attribute this node tests."""
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self.attr = attr
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self.attr_name = attr_name or attr
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self.default_child = default_child
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self.branches = branches or {}
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def __call__(self, example):
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"""Given an example, classify it using the attribute and the branches."""
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attr_val = example[self.attr]
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if attr_val in self.branches:
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return self.branches[attr_val](example)
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else:
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# return default class when attribute is unknown
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return self.default_child(example)
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def add(self, val, subtree):
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"""Add a branch. If self.attr = val, go to the given subtree."""
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self.branches[val] = subtree
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def display(self, indent=0):
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name = self.attr_name
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print('Test', name)
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for (val, subtree) in self.branches.items():
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print(' ' * 4 * indent, name, '=', val, '==>', end=' ')
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subtree.display(indent + 1)
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def __repr__(self):
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return 'DecisionFork({0!r}, {1!r}, {2!r})'.format(self.attr, self.attr_name, self.branches)
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class DecisionLeaf:
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"""A leaf of a decision tree holds just a result."""
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def __init__(self, result):
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self.result = result
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def __call__(self, example):
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return self.result
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def display(self):
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print('RESULT =', self.result)
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def __repr__(self):
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return repr(self.result)
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class DecisionTreeLearner:
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"""DecisionTreeLearner: based on information gain"""
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def __init__(self, dataset):
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self.dataset = dataset
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self.tree = self.decision_tree_learning(dataset.examples, dataset.inputs)
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def decision_tree_learning(self, examples, attrs, parent_examples=()):
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raise NotImplementedError
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def plurality_value(self, examples):
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"""
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Return the most popular target value for this set of examples.
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(If target is binary, this is the majority; otherwise plurality).
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"""
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popular = argmax_random_tie(self.dataset.values[self.dataset.target],
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key=lambda v: self.count(self.dataset.target, v, examples))
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return DecisionLeaf(popular)
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def count(self, attr, val, examples):
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"""Count the number of examples that have example[attr] = val."""
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return sum(e[attr] == val for e in examples)
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def all_same_class(self, examples):
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"""Are all these examples in the same target class?"""
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class0 = examples[0][self.dataset.target]
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return all(e[self.dataset.target] == class0 for e in examples)
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def choose_attribute(self, attrs, examples):
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"""Choose the attribute with the highest information gain."""
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return argmax_random_tie(attrs, key=lambda a: self.information_gain(a, examples))
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def information_gain(self, attr, examples):
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"""Return the expected reduction in entropy from splitting by attr."""
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raise NotImplementedError
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def split_by(self, attr, examples):
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"""Return a list of (val, examples) pairs for each val of attr."""
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return [(v, [e for e in examples if e[attr] == v]) for v in self.dataset.values[attr]]
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def predict(self, x):
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return self.tree(x)
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def __call__(self, x):
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return self.predict(x)
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def information_content(values):
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"""Number of bits to represent the probability distribution in values."""
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raise NotImplementedError
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|
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if __name__ == "__main__":
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from utils.dataset4learners import *
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iris = DataSet(name="iris")
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DTL = DecisionTreeLearner(iris)
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print(f'DTL.predict([5, 3, 1, 0.1]): {DTL.predict([5, 3, 1, 0.1])}')
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assert DTL.predict([5, 3, 1, 0.1]) == 'setosa'
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print(f'DTL.predict([6, 5, 3, 1.5]): {DTL.predict([6, 5, 3, 1.5])}')
|
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assert DTL.predict([6, 5, 3, 1.5]) == 'versicolor'
|
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print(f'DTL.predict([7.5, 4, 6, 2]): {DTL.predict([7.5, 4, 6, 2])}')
|
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assert DTL.predict([7.5, 4, 6, 2]) == 'virginica'
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@@ -1,34 +0,0 @@
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import sys
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sys.path.insert(1, '../')
|
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from utils.utils import *
|
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|
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|
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class DecisionFork:
|
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"""
|
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A fork of a decision tree holds an attribute to test, and a dict
|
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of branches, one for each of the attribute's values.
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"""
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raise NotImplementedError
|
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|
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|
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class DecisionLeaf:
|
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"""A leaf of a decision tree holds just a result."""
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raise NotImplementedError
|
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|
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|
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class DecisionTreeLearner:
|
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"""DecisionTreeLearner: based on information gain"""
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raise NotImplementedError
|
||||
|
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|
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if __name__ == "__main__":
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from utils.dataset4learners import *
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|
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iris = DataSet(name="iris")
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DTL = DecisionTreeLearner(iris)
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print(f'DTL.predict([5, 3, 1, 0.1]): {DTL.predict([5, 3, 1, 0.1])}')
|
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assert DTL.predict([5, 3, 1, 0.1]) == 'setosa'
|
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print(f'DTL.predict([6, 5, 3, 1.5]): {DTL.predict([6, 5, 3, 1.5])}')
|
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assert DTL.predict([6, 5, 3, 1.5]) == 'versicolor'
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print(f'DTL.predict([7.5, 4, 6, 2]): {DTL.predict([7.5, 4, 6, 2])}')
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assert DTL.predict([7.5, 4, 6, 2]) == 'virginica'
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File renamed without changes.
@@ -1,131 +0,0 @@
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import numpy as np
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|
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|
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class LinearRegression:
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def solve(self, lr, nepoch):
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raise NotImplementedError
|
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|
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class LinearRegressionLS(LinearRegression):
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"""
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solve linear regression problem via least squares
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"""
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X: np.array
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Y: np.array
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w: np.array
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def __init__(self, ylist):
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num_data = len(ylist)
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self.X = self.homogeneous([x for x in range(num_data)])
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self.Y = np.array(ylist).reshape(num_data, 1)
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self.w = np.random.rand(2)
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def homogeneous(self, xlist):
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||||
""" build homogeneous coordinates """
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raise NotImplementedError
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||||
|
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def linout(self, xlist):
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||||
""" linear output for given data """
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||||
raise NotImplementedError
|
||||
|
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def loss_sq(self, X, Y):
|
||||
""" loss function: (half) sum of square errors """
|
||||
raise NotImplementedError
|
||||
|
||||
def solve(self, lr, nepoch):
|
||||
""" form normal equation """
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class LinearRegressionGD1(LinearRegression):
|
||||
"""
|
||||
solve linear regression problem via gradient descent,
|
||||
using single weight vector: homogeneous coordinates
|
||||
"""
|
||||
X: np.array
|
||||
Y: np.array
|
||||
w: np.array
|
||||
|
||||
def __init__(self, ylist):
|
||||
num_data = len(ylist)
|
||||
self.X = self.homogeneous([x for x in range(num_data)])
|
||||
self.Y = np.array(ylist).reshape(num_data, 1)
|
||||
self.w = np.random.rand(2)
|
||||
|
||||
def homogeneous(self, xlist):
|
||||
""" build homogeneous coordinates """
|
||||
raise NotImplementedError
|
||||
|
||||
def linout(self, xlist):
|
||||
""" linear output for given data """
|
||||
raise NotImplementedError
|
||||
|
||||
def loss_sq(self, X, Y):
|
||||
""" loss function: (half) sum of square errors """
|
||||
raise NotImplementedError
|
||||
|
||||
def gd(self, lr):
|
||||
""" gradient descent update """
|
||||
raise NotImplementedError
|
||||
|
||||
def solve(self, lr, nepoch):
|
||||
""" iterative solver """
|
||||
for epoch in range(num_epochs):
|
||||
self.gd(lr)
|
||||
print(f'epoch {epoch + 1}, loss {self.loss_sq(self.X, self.Y)}')
|
||||
|
||||
|
||||
class LinearRegressionGD2(LinearRegression):
|
||||
"""
|
||||
solve linear regression problem via gradient descent,
|
||||
using two weights: w, b
|
||||
"""
|
||||
X: np.array
|
||||
Y: np.array
|
||||
w: np.array
|
||||
b: np.array
|
||||
|
||||
def __init__(self, ylist):
|
||||
num_data = len(ylist)
|
||||
self.X = np.array([x for x in range(num_data)]).reshape(-1, 1)
|
||||
self.Y = np.array(ylist).reshape(num_data, 1)
|
||||
self.w = np.random.rand(1)
|
||||
self.b = np.min(ylist)
|
||||
|
||||
def linout(self, xlist):
|
||||
""" linear output for given data """
|
||||
raise NotImplementedError
|
||||
|
||||
def loss_sq(self, X, Y):
|
||||
""" loss function: (half) sum of square errors """
|
||||
raise NotImplementedError
|
||||
|
||||
def gd(self, lr):
|
||||
""" gradient descent update """
|
||||
raise NotImplementedError
|
||||
|
||||
def solve(self, lr, nepoch):
|
||||
""" iterative solver """
|
||||
for epoch in range(num_epochs):
|
||||
self.gd(lr)
|
||||
print(f'epoch {epoch + 1}, loss {self.loss_sq(self.X, self.Y)}')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
hp = [14213, 13448, 13870, 16192, 16415, 21501, 25910, 24866, 28981, 32926, 36741, 40974]
|
||||
hp = [x / 10000. for x in hp]
|
||||
|
||||
lr = 0.001
|
||||
num_epochs = 10
|
||||
|
||||
ls = LinearRegressionLS(hp)
|
||||
ls.solve(lr, num_epochs)
|
||||
print(f'next prediction (LS): {ls.linout([len(hp)])}')
|
||||
|
||||
gd1 = LinearRegressionGD1(hp)
|
||||
gd1.solve(lr, num_epochs)
|
||||
print(f'next prediction year (GD1): {gd1.linout([len(hp)])}')
|
||||
|
||||
gd2 = LinearRegressionGD1(hp)
|
||||
gd2.solve(lr, num_epochs)
|
||||
print(f'next prediction year (GD2): {gd2.linout([len(hp)])}')
|
||||
Reference in new issue
Block a user