"""Learning from examples (Chapters 18)"""
import copy
from collections import defaultdict
from statistics import stdev
from qpsolvers import solve_qp
from probabilistic_learning import NaiveBayesLearner
from utils4e import *
class DataSet:
"""
A data set for a machine learning problem. It has the following fields:
d.examples A list of examples. Each one is a list of attribute values.
d.attrs A list of integers to index into an example, so example[attr]
gives a value. Normally the same as range(len(d.examples[0])).
d.attr_names Optional list of mnemonic names for corresponding attrs.
d.target The attribute that a learning algorithm will try to predict.
By default the final attribute.
d.inputs The list of attrs without the target.
d.values A list of lists: each sublist is the set of possible
values for the corresponding attribute. If initially None,
it is computed from the known examples by self.set_problem.
If not None, an erroneous value raises ValueError.
d.distance A function from a pair of examples to a non-negative number.
Should be symmetric, etc. Defaults to mean_boolean_error
since that can handle any field types.
d.name Name of the data set (for output display only).
d.source URL or other source where the data came from.
d.exclude A list of attribute indexes to exclude from d.inputs. Elements
of this list can either be integers (attrs) or attr_names.
Normally, you call the constructor and you're done; then you just
access fields like d.examples and d.target and d.inputs.
"""
def __init__(self, examples=None, attrs=None, attr_names=None, target=-1, inputs=None,
values=None, distance=mean_boolean_error, name='', source='', exclude=()):
"""
Accepts any of DataSet's fields. Examples can also be a
string or file from which to parse examples using parse_csv.
Optional parameter: exclude, as documented in .set_problem().
>>> DataSet(examples='1, 2, 3')
"""
self.name = name
self.source = source
self.values = values
self.distance = distance
self.got_values_flag = bool(values)
# initialize .examples from string or list or data directory
if isinstance(examples, str):
self.examples = parse_csv(examples)
elif examples is None:
self.examples = parse_csv(open_data(name + '.csv').read())
else:
self.examples = examples
# attrs are the indices of examples, unless otherwise stated.
if self.examples is not None and attrs is None:
attrs = list(range(len(self.examples[0])))
self.attrs = attrs
# initialize .attr_names from string, list, or by default
if isinstance(attr_names, str):
self.attr_names = attr_names.split()
else:
self.attr_names = attr_names or attrs
self.set_problem(target, inputs=inputs, exclude=exclude)
def set_problem(self, target, inputs=None, exclude=()):
"""
Set (or change) the target and/or inputs.
This way, one DataSet can be used multiple ways. inputs, if specified,
is a list of attributes, or specify exclude as a list of attributes
to not use in inputs. Attributes can be -n .. n, or an attr_name.
Also computes the list of possible values, if that wasn't done yet.
"""
self.target = self.attr_num(target)
exclude = list(map(self.attr_num, exclude))
if inputs:
self.inputs = remove_all(self.target, inputs)
else:
self.inputs = [a for a in self.attrs if a != self.target and a not in exclude]
if not self.values:
self.update_values()
self.check_me()
def check_me(self):
"""Check that my fields make sense."""
assert len(self.attr_names) == len(self.attrs)
assert self.target in self.attrs
assert self.target not in self.inputs
assert set(self.inputs).issubset(set(self.attrs))
if self.got_values_flag:
# only check if values are provided while initializing DataSet
list(map(self.check_example, self.examples))
def add_example(self, example):
"""Add an example to the list of examples, checking it first."""
self.check_example(example)
self.examples.append(example)
def check_example(self, example):
"""Raise ValueError if example has any invalid values."""
if self.values:
for a in self.attrs:
if example[a] not in self.values[a]:
raise ValueError('Bad value {} for attribute {} in {}'
.format(example[a], self.attr_names[a], example))
def attr_num(self, attr):
"""Returns the number used for attr, which can be a name, or -n .. n-1."""
if isinstance(attr, str):
return self.attr_names.index(attr)
elif attr < 0:
return len(self.attrs) + attr
else:
return attr
def update_values(self):
self.values = list(map(unique, zip(*self.examples)))
def sanitize(self, example):
"""Return a copy of example, with non-input attributes replaced by None."""
return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)]
def classes_to_numbers(self, classes=None):
"""Converts class names to numbers."""
if not classes:
# if classes were not given, extract them from values
classes = sorted(self.values[self.target])
for item in self.examples:
item[self.target] = classes.index(item[self.target])
def remove_examples(self, value=''):
"""Remove examples that contain given value."""
self.examples = [x for x in self.examples if value not in x]
self.update_values()
def split_values_by_classes(self):
"""Split values into buckets according to their class."""
buckets = defaultdict(lambda: [])
target_names = self.values[self.target]
for v in self.examples:
item = [a for a in v if a not in target_names] # remove target from item
buckets[v[self.target]].append(item) # add item to bucket of its class
return buckets
def find_means_and_deviations(self):
"""
Finds the means and standard deviations of self.dataset.
means : a dictionary for each class/target. Holds a list of the means
of the features for the class.
deviations: a dictionary for each class/target. Holds a list of the sample
standard deviations of the features for the class.
"""
target_names = self.values[self.target]
feature_numbers = len(self.inputs)
item_buckets = self.split_values_by_classes()
means = defaultdict(lambda: [0] * feature_numbers)
deviations = defaultdict(lambda: [0] * feature_numbers)
for t in target_names:
# find all the item feature values for item in class t
features = [[] for _ in range(feature_numbers)]
for item in item_buckets[t]:
for i in range(feature_numbers):
features[i].append(item[i])
# calculate means and deviations fo the class
for i in range(feature_numbers):
means[t][i] = mean(features[i])
deviations[t][i] = stdev(features[i])
return means, deviations
def __repr__(self):
return ''.format(self.name, len(self.examples), len(self.attrs))
def parse_csv(input, delim=','):
r"""
Input is a string consisting of lines, each line has comma-delimited
fields. Convert this into a list of lists. Blank lines are skipped.
Fields that look like numbers are converted to numbers.
The delim defaults to ',' but '\t' and None are also reasonable values.
>>> parse_csv('1, 2, 3 \n 0, 2, na')
[[1, 2, 3], [0, 2, 'na']]
"""
lines = [line for line in input.splitlines() if line.strip()]
return [list(map(num_or_str, line.split(delim))) for line in lines]
def err_ratio(predict, dataset, examples=None, verbose=0):
"""
Return the proportion of the examples that are NOT correctly predicted.
verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct
"""
examples = examples or dataset.examples
if len(examples) == 0:
return 0.0
right = 0
for example in examples:
desired = example[dataset.target]
output = predict(dataset.sanitize(example))
if output == desired:
right += 1
if verbose >= 2:
print(' OK: got {} for {}'.format(desired, example))
elif verbose:
print('WRONG: got {}, expected {} for {}'.format(output, desired, example))
return 1 - (right / len(examples))
def grade_learner(predict, tests):
"""
Grades the given learner based on how many tests it passes.
tests is a list with each element in the form: (values, output).
"""
return mean(int(predict(X) == y) for X, y in tests)
def train_test_split(dataset, start=None, end=None, test_split=None):
"""
If you are giving 'start' and 'end' as parameters,
then it will return the testing set from index 'start' to 'end'
and the rest for training.
If you give 'test_split' as a parameter then it will return
test_split * 100% as the testing set and the rest as
training set.
"""
examples = dataset.examples
if test_split is None:
train = examples[:start] + examples[end:]
val = examples[start:end]
else:
total_size = len(examples)
val_size = int(total_size * test_split)
train_size = total_size - val_size
train = examples[:train_size]
val = examples[train_size:total_size]
return train, val
def model_selection(learner, dataset, k=10, trials=1):
"""
[Figure 18.8]
Return the optimal value of size having minimum error on validation set.
err: a validation error array, indexed by size
"""
errs = []
size = 1
while True:
err = cross_validation(learner, dataset, size, k, trials)
# check for convergence provided err_val is not empty
if err and not np.isclose(err[-1], err, rtol=1e-6):
best_size = 0
min_val = np.inf
i = 0
while i < size:
if errs[i] < min_val:
min_val = errs[i]
best_size = i
i += 1
return learner(dataset, best_size)
errs.append(err)
size += 1
def cross_validation(learner, dataset, size=None, k=10, trials=1):
"""
Do k-fold cross_validate and return their mean.
That is, keep out 1/k of the examples for testing on each of k runs.
Shuffle the examples first; if trials > 1, average over several shuffles.
Returns Training error
"""
k = k or len(dataset.examples)
if trials > 1:
trial_errs = 0
for t in range(trials):
errs = cross_validation(learner, dataset, size, k, trials)
trial_errs += errs
return trial_errs / trials
else:
fold_errs = 0
n = len(dataset.examples)
examples = dataset.examples
random.shuffle(dataset.examples)
for fold in range(k):
train_data, val_data = train_test_split(dataset, fold * (n // k), (fold + 1) * (n // k))
dataset.examples = train_data
h = learner(dataset, size)
fold_errs += err_ratio(h, dataset, train_data)
# reverting back to original once test is completed
dataset.examples = examples
return fold_errs / k
def leave_one_out(learner, dataset, size=None):
"""Leave one out cross-validation over the dataset."""
return cross_validation(learner, dataset, size, len(dataset.examples))
def learning_curve(learner, dataset, trials=10, sizes=None):
if sizes is None:
sizes = list(range(2, len(dataset.examples) - trials, 2))
def score(learner, size):
random.shuffle(dataset.examples)
return cross_validation(learner, dataset, size, trials)
return [(size, mean([score(learner, size) for _ in range(trials)])) for size in sizes]
def PluralityLearner(dataset):
"""
A very dumb algorithm: always pick the result that was most popular
in the training data. Makes a baseline for comparison.
"""
most_popular = mode([e[dataset.target] for e in dataset.examples])
def predict(example):
"""Always return same result: the most popular from the training set."""
return most_popular
return predict
class DecisionFork:
"""
A fork of a decision tree holds an attribute to test, and a dict
of branches, one for each of the attribute's values.
"""
def __init__(self, attr, attr_name=None, default_child=None, branches=None):
"""Initialize by saying what attribute this node tests."""
self.attr = attr
self.attr_name = attr_name or attr
self.default_child = default_child
self.branches = branches or {}
def __call__(self, example):
"""Given an example, classify it using the attribute and the branches."""
attr_val = example[self.attr]
if attr_val in self.branches:
return self.branches[attr_val](example)
else:
# return default class when attribute is unknown
return self.default_child(example)
def add(self, val, subtree):
"""Add a branch. If self.attr = val, go to the given subtree."""
self.branches[val] = subtree
def display(self, indent=0):
name = self.attr_name
print('Test', name)
for (val, subtree) in self.branches.items():
print(' ' * 4 * indent, name, '=', val, '==>', end=' ')
subtree.display(indent + 1)
def __repr__(self):
return 'DecisionFork({0!r}, {1!r}, {2!r})'.format(self.attr, self.attr_name, self.branches)
class DecisionLeaf:
"""A leaf of a decision tree holds just a result."""
def __init__(self, result):
self.result = result
def __call__(self, example):
return self.result
def display(self):
print('RESULT =', self.result)
def __repr__(self):
return repr(self.result)
def DecisionTreeLearner(dataset):
"""[Figure 18.5]"""
target, values = dataset.target, dataset.values
def decision_tree_learning(examples, attrs, parent_examples=()):
if len(examples) == 0:
return plurality_value(parent_examples)
if all_same_class(examples):
return DecisionLeaf(examples[0][target])
if len(attrs) == 0:
return plurality_value(examples)
A = choose_attribute(attrs, examples)
tree = DecisionFork(A, dataset.attr_names[A], plurality_value(examples))
for (v_k, exs) in split_by(A, examples):
subtree = decision_tree_learning(exs, remove_all(A, attrs), examples)
tree.add(v_k, subtree)
return tree
def plurality_value(examples):
"""
Return the most popular target value for this set of examples.
(If target is binary, this is the majority; otherwise plurality).
"""
popular = argmax_random_tie(values[target], key=lambda v: count(target, v, examples))
return DecisionLeaf(popular)
def count(attr, val, examples):
"""Count the number of examples that have example[attr] = val."""
return sum(e[attr] == val for e in examples)
def all_same_class(examples):
"""Are all these examples in the same target class?"""
class0 = examples[0][target]
return all(e[target] == class0 for e in examples)
def choose_attribute(attrs, examples):
"""Choose the attribute with the highest information gain."""
return argmax_random_tie(attrs, key=lambda a: information_gain(a, examples))
def information_gain(attr, examples):
"""Return the expected reduction in entropy from splitting by attr."""
def I(examples):
return information_content([count(target, v, examples) for v in values[target]])
n = len(examples)
remainder = sum((len(examples_i) / n) * I(examples_i) for (v, examples_i) in split_by(attr, examples))
return I(examples) - remainder
def split_by(attr, examples):
"""Return a list of (val, examples) pairs for each val of attr."""
return [(v, [e for e in examples if e[attr] == v]) for v in values[attr]]
return decision_tree_learning(dataset.examples, dataset.inputs)
def information_content(values):
"""Number of bits to represent the probability distribution in values."""
probabilities = normalize(remove_all(0, values))
return sum(-p * np.log2(p) for p in probabilities)
def DecisionListLearner(dataset):
"""
[Figure 18.11]
A decision list implemented as a list of (test, value) pairs.
"""
def decision_list_learning(examples):
if not examples:
return [(True, False)]
t, o, examples_t = find_examples(examples)
if not t:
raise Exception
return [(t, o)] + decision_list_learning(examples - examples_t)
def find_examples(examples):
"""
Find a set of examples that all have the same outcome under
some test. Return a tuple of the test, outcome, and examples.
"""
raise NotImplementedError
def passes(example, test):
"""Does the example pass the test?"""
raise NotImplementedError
def predict(example):
"""Predict the outcome for the first passing test."""
for test, outcome in predict.decision_list:
if passes(example, test):
return outcome
predict.decision_list = decision_list_learning(set(dataset.examples))
return predict
def NearestNeighborLearner(dataset, k=1):
"""k-NearestNeighbor: the k nearest neighbors vote."""
def predict(example):
"""Find the k closest items, and have them vote for the best."""
best = heapq.nsmallest(k, ((dataset.distance(e, example), e) for e in dataset.examples))
return mode(e[dataset.target] for (d, e) in best)
return predict
def LinearLearner(dataset, learning_rate=0.01, epochs=100):
"""
[Section 18.6.4]
Linear classifier with hard threshold.
"""
idx_i = dataset.inputs
idx_t = dataset.target
examples = dataset.examples
num_examples = len(examples)
# X transpose
X_col = [dataset.values[i] for i in idx_i] # vertical columns of X
# add dummy
ones = [1 for _ in range(len(examples))]
X_col = [ones] + X_col
# initialize random weights
num_weights = len(idx_i) + 1
w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights)
for epoch in range(epochs):
err = []
# pass over all examples
for example in examples:
x = [1] + example
y = dot_product(w, x)
t = example[idx_t]
err.append(t - y)
# update weights
for i in range(len(w)):
w[i] = w[i] + learning_rate * (dot_product(err, X_col[i]) / num_examples)
def predict(example):
x = [1] + example
return dot_product(w, x)
return predict
def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100):
"""
[Section 18.6.5]
Linear classifier with logistic regression.
"""
idx_i = dataset.inputs
idx_t = dataset.target
examples = dataset.examples
num_examples = len(examples)
# X transpose
X_col = [dataset.values[i] for i in idx_i] # vertical columns of X
# add dummy
ones = [1 for _ in range(len(examples))]
X_col = [ones] + X_col
# initialize random weights
num_weights = len(idx_i) + 1
w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights)
for epoch in range(epochs):
err = []
h = []
# pass over all examples
for example in examples:
x = [1] + example
y = Sigmoid().function(dot_product(w, x))
h.append(Sigmoid().derivative(y))
t = example[idx_t]
err.append(t - y)
# update weights
for i in range(len(w)):
buffer = [x * y for x, y in zip(err, h)]
w[i] = w[i] + learning_rate * (dot_product(buffer, X_col[i]) / num_examples)
def predict(example):
x = [1] + example
return Sigmoid().function(dot_product(w, x))
return predict
class BinarySVM:
def __init__(self, kernel=linear_kernel, C=1.0):
self.kernel = kernel
self.C = C # hyper-parameter
self.eps = 1e-6
self.n_sv = -1
self.sv_x, self.sv_y, = np.zeros(0), np.zeros(0)
self.alphas = np.zeros(0)
self.w = None
self.b = 0.0 # intercept
def fit(self, X, y):
"""
Trains the model by solving a quadratic programming problem.
:param X: array of size [n_samples, n_features] holding the training samples
:param y: array of size [n_samples] holding the class labels
"""
# In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations)
self.QP(X, y)
sv_indices = list(filter(lambda i: self.alphas[i] > self.eps, range(len(y))))
self.sv_x, self.sv_y, self.alphas = X[sv_indices], y[sv_indices], self.alphas[sv_indices]
self.n_sv = len(sv_indices)
if self.kernel == linear_kernel:
self.w = np.dot(self.alphas * self.sv_y, self.sv_x)
# calculate b: average over all support vectors
sv_boundary = self.alphas < self.C - self.eps
self.b = np.mean(self.sv_y[sv_boundary] - np.dot(self.alphas * self.sv_y,
self.kernel(self.sv_x, self.sv_x[sv_boundary])))
def QP(self, X, y):
"""
Solves a quadratic programming problem. In QP formulation (dual):
m variables, 2m+1 constraints (1 equation, 2m inequations).
:param X: array of size [n_samples, n_features] holding the training samples
:param y: array of size [n_samples] holding the class labels
"""
#
m = len(y) # m = n_samples
K = self.kernel(X) # gram matrix
P = K * np.outer(y, y)
q = -np.ones(m)
G = np.vstack((-np.identity(m), np.identity(m)))
h = np.hstack((np.zeros(m), np.ones(m) * self.C))
A = y.reshape((1, -1))
b = np.zeros(1)
# make sure P is positive definite
P += np.eye(P.shape[0]).__mul__(1e-3)
self.alphas = solve_qp(P, q, G, h, A, b, sym_proj=True)
def predict_score(self, x):
"""
Predicts the score for a given example.
"""
if self.w is None:
return np.dot(self.alphas * self.sv_y, self.kernel(self.sv_x, x)) + self.b
return np.dot(x, self.w) + self.b
def predict(self, x):
"""
Predicts the class of a given example.
"""
return np.sign(self.predict_score(x))
class MultiSVM:
def __init__(self, kernel=linear_kernel, decision_function='ovr', C=1.0):
self.kernel = kernel
self.decision_function = decision_function
self.C = C # hyper-parameter
self.n_class, self.classifiers = 0, []
def fit(self, X, y):
"""
Trains n_class or n_class * (n_class - 1) / 2 classifiers
according to the training method, ovr or ovo respectively.
:param X: array of size [n_samples, n_features] holding the training samples
:param y: array of size [n_samples] holding the class labels
:return: array of classifiers
"""
labels = np.unique(y)
self.n_class = len(labels)
if self.decision_function == 'ovr': # one-vs-rest method
for label in labels:
y1 = np.array(y)
y1[y1 != label] = -1.0
y1[y1 == label] = 1.0
clf = BinarySVM(self.kernel, self.C)
clf.fit(X, y1)
self.classifiers.append(copy.deepcopy(clf))
elif self.decision_function == 'ovo': # use one-vs-one method
n_labels = len(labels)
for i in range(n_labels):
for j in range(i + 1, n_labels):
neg_id, pos_id = y == labels[i], y == labels[j]
x1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]]
y1[y1 == labels[i]] = -1.0
y1[y1 == labels[j]] = 1.0
clf = BinarySVM(self.kernel, self.C)
clf.fit(x1, y1)
self.classifiers.append(copy.deepcopy(clf))
else:
return ValueError("Decision function must be either 'ovr' or 'ovo'.")
def predict(self, x):
"""
Predicts the class of a given example according to the training method.
"""
n_samples = len(x)
if self.decision_function == 'ovr': # one-vs-rest method
assert len(self.classifiers) == self.n_class
score = np.zeros((n_samples, self.n_class))
for i in range(self.n_class):
clf = self.classifiers[i]
score[:, i] = clf.predict_score(x)
return np.argmax(score, axis=1)
elif self.decision_function == 'ovo': # use one-vs-one method
assert len(self.classifiers) == self.n_class * (self.n_class - 1) / 2
vote = np.zeros((n_samples, self.n_class))
clf_id = 0
for i in range(self.n_class):
for j in range(i + 1, self.n_class):
res = self.classifiers[clf_id].predict(x)
vote[res < 0, i] += 1.0 # negative sample: class i
vote[res > 0, j] += 1.0 # positive sample: class j
clf_id += 1
return np.argmax(vote, axis=1)
else:
return ValueError("Decision function must be either 'ovr' or 'ovo'.")
def EnsembleLearner(learners):
"""Given a list of learning algorithms, have them vote."""
def train(dataset):
predictors = [learner(dataset) for learner in learners]
def predict(example):
return mode(predictor(example) for predictor in predictors)
return predict
return train
def ada_boost(dataset, L, K):
"""[Figure 18.34]"""
examples, target = dataset.examples, dataset.target
n = len(examples)
eps = 1 / (2 * n)
w = [1 / n] * n
h, z = [], []
for k in range(K):
h_k = L(dataset, w)
h.append(h_k)
error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k(example))
# avoid divide-by-0 from either 0% or 100% error rates
error = np.clip(error, eps, 1 - eps)
for j, example in enumerate(examples):
if example[target] == h_k(example):
w[j] *= error / (1 - error)
w = normalize(w)
z.append(np.log((1 - error) / error))
return weighted_majority(h, z)
def weighted_majority(predictors, weights):
"""Return a predictor that takes a weighted vote."""
def predict(example):
return weighted_mode((predictor(example) for predictor in predictors), weights)
return predict
def weighted_mode(values, weights):
"""
Return the value with the greatest total weight.
>>> weighted_mode('abbaa', [1, 2, 3, 1, 2])
'b'
"""
totals = defaultdict(int)
for v, w in zip(values, weights):
totals[v] += w
return max(totals, key=totals.__getitem__)
def RandomForest(dataset, n=5):
"""An ensemble of Decision Trees trained using bagging and feature bagging."""
def data_bagging(dataset, m=0):
"""Sample m examples with replacement"""
n = len(dataset.examples)
return weighted_sample_with_replacement(m or n, dataset.examples, [1] * n)
def feature_bagging(dataset, p=0.7):
"""Feature bagging with probability p to retain an attribute"""
inputs = [i for i in dataset.inputs if probability(p)]
return inputs or dataset.inputs
def predict(example):
print([predictor(example) for predictor in predictors])
return mode(predictor(example) for predictor in predictors)
predictors = [DecisionTreeLearner(DataSet(examples=data_bagging(dataset), attrs=dataset.attrs,
attr_names=dataset.attr_names, target=dataset.target,
inputs=feature_bagging(dataset))) for _ in range(n)]
return predict
def WeightedLearner(unweighted_learner):
"""
[Page 749 footnote 14]
Given a learner that takes just an unweighted dataset, return
one that takes also a weight for each example.
"""
def train(dataset, weights):
return unweighted_learner(replicated_dataset(dataset, weights))
return train
def replicated_dataset(dataset, weights, n=None):
"""Copy dataset, replicating each example in proportion to its weight."""
n = n or len(dataset.examples)
result = copy.copy(dataset)
result.examples = weighted_replicate(dataset.examples, weights, n)
return result
def weighted_replicate(seq, weights, n):
"""
Return n selections from seq, with the count of each element of
seq proportional to the corresponding weight (filling in fractions
randomly).
>>> weighted_replicate('ABC', [1, 2, 1], 4)
['A', 'B', 'B', 'C']
"""
assert len(seq) == len(weights)
weights = normalize(weights)
wholes = [int(w * n) for w in weights]
fractions = [(w * n) % 1 for w in weights]
return (flatten([x] * nx for x, nx in zip(seq, wholes)) +
weighted_sample_with_replacement(n - sum(wholes), seq, fractions))
def flatten(seqs):
return sum(seqs, [])
orings = DataSet(name='orings', target='Distressed', attr_names='Rings Distressed Temp Pressure Flightnum')
zoo = DataSet(name='zoo', target='type', exclude=['name'],
attr_names='name hair feathers eggs milk airborne aquatic predator toothed backbone '
'breathes venomous fins legs tail domestic catsize type')
iris = DataSet(name='iris', target='class', attr_names='sepal-len sepal-width petal-len petal-width class')
def RestaurantDataSet(examples=None):
"""
[Figure 18.3]
Build a DataSet of Restaurant waiting examples.
"""
return DataSet(name='restaurant', target='Wait', examples=examples,
attr_names='Alternate Bar Fri/Sat Hungry Patrons Price Raining Reservation Type WaitEstimate Wait')
restaurant = RestaurantDataSet()
def T(attr_name, branches):
branches = {value: (child if isinstance(child, DecisionFork) else DecisionLeaf(child))
for value, child in branches.items()}
return DecisionFork(restaurant.attr_num(attr_name), attr_name, print, branches)
"""
[Figure 18.2]
A decision tree for deciding whether to wait for a table at a hotel.
"""
waiting_decision_tree = T('Patrons',
{'None': 'No', 'Some': 'Yes',
'Full': T('WaitEstimate',
{'>60': 'No', '0-10': 'Yes',
'30-60': T('Alternate',
{'No': T('Reservation',
{'Yes': 'Yes',
'No': T('Bar', {'No': 'No',
'Yes': 'Yes'})}),
'Yes': T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})}),
'10-30': T('Hungry',
{'No': 'Yes',
'Yes': T('Alternate',
{'No': 'Yes',
'Yes': T('Raining',
{'No': 'No',
'Yes': 'Yes'})})})})})
def SyntheticRestaurant(n=20):
"""Generate a DataSet with n examples."""
def gen():
example = list(map(random.choice, restaurant.values))
example[restaurant.target] = waiting_decision_tree(example)
return example
return RestaurantDataSet([gen() for _ in range(n)])
def Majority(k, n):
"""
Return a DataSet with n k-bit examples of the majority problem:
k random bits followed by a 1 if more than half the bits are 1, else 0.
"""
examples = []
for i in range(n):
bits = [random.choice([0, 1]) for _ in range(k)]
bits.append(int(sum(bits) > k / 2))
examples.append(bits)
return DataSet(name='majority', examples=examples)
def Parity(k, n, name='parity'):
"""
Return a DataSet with n k-bit examples of the parity problem:
k random bits followed by a 1 if an odd number of bits are 1, else 0.
"""
examples = []
for i in range(n):
bits = [random.choice([0, 1]) for _ in range(k)]
bits.append(sum(bits) % 2)
examples.append(bits)
return DataSet(name=name, examples=examples)
def Xor(n):
"""Return a DataSet with n examples of 2-input xor."""
return Parity(2, n, name='xor')
def ContinuousXor(n):
"""2 inputs are chosen uniformly from (0.0 .. 2.0]; output is xor of ints."""
examples = []
for i in range(n):
x, y = [random.uniform(0.0, 2.0) for _ in '12']
examples.append([x, y, x != y])
return DataSet(name='continuous xor', examples=examples)
def compare(algorithms=None, datasets=None, k=10, trials=1):
"""
Compare various learners on various datasets using cross-validation.
Print results as a table.
"""
# default list of algorithms
algorithms = algorithms or [PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, DecisionTreeLearner]
# default list of datasets
datasets = datasets or [iris, orings, zoo, restaurant, SyntheticRestaurant(20),
Majority(7, 100), Parity(7, 100), Xor(100)]
print_table([[a.__name__.replace('Learner', '')] + [cross_validation(a, d, k=k, trials=trials) for d in datasets]
for a in algorithms], header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f')