"""

def __init__(self, examples=None, attrs=None, attr_names=None, target=-1, inputs=None,

values=None, distance=mean_boolean_error, name='', source='', exclude=()):

"""

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=()):

"""

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)][:-1]

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):

"""

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):

r"""

lines = [line for line in input.splitlines() if line.strip()]

"""

examples = examples or dataset.examples

if len(examples) == 0:

return 0.0

right = 0

for example in examples:

desired = example[dataset.target]

output = learner.predict(dataset.sanitize(example))

if np.allclose(output, desired):

right += 1

"""

"""

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]

"""

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)

"""

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

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)

"""

def __init__(self, dataset):

self.most_popular = mode([e[dataset.target] for e in dataset.examples])

def predict(self, example):

"""Always return same result: the most popular from the training set."""

"""

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):

"""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):

"""[Figure 18.5]"""

def __init__(self, dataset):

self.dataset = dataset

self.tree = self.decision_tree_learning(dataset.examples, dataset.inputs)

def decision_tree_learning(self, examples, attrs, parent_examples=()):

if len(examples) == 0:

return self.plurality_value(parent_examples)

if self.all_same_class(examples):

return DecisionLeaf(examples[0][self.dataset.target])

if len(attrs) == 0:

return self.plurality_value(examples)

A = self.choose_attribute(attrs, examples)

tree = DecisionFork(A, self.dataset.attr_names[A], self.plurality_value(examples))

for (v_k, exs) in self.split_by(A, examples):

subtree = self.decision_tree_learning(exs, remove_all(A, attrs), examples)

tree.add(v_k, subtree)

return tree

def plurality_value(self, examples):

"""

popular = argmax_random_tie(self.dataset.values[self.dataset.target],

key=lambda v: self.count(self.dataset.target, v, examples))

return DecisionLeaf(popular)

def count(self, 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(self, examples):

"""Are all these examples in the same target class?"""

class0 = examples[0][self.dataset.target]

return all(e[self.dataset.target] == class0 for e in examples)

def choose_attribute(self, attrs, examples):

"""Choose the attribute with the highest information gain."""

return argmax_random_tie(attrs, key=lambda a: self.information_gain(a, examples))

def information_gain(self, attr, examples):

"""Return the expected reduction in entropy from splitting by attr."""

def I(examples):

return information_content([self.count(self.dataset.target, v, examples)

for v in self.dataset.values[self.dataset.target]])

n = len(examples)

remainder = sum((len(examples_i) / n) * I(examples_i)

for (v, examples_i) in self.split_by(attr, examples))

return I(examples) - remainder

def split_by(self, 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 self.dataset.values[attr]]

def predict(self, x):

"""

def __init__(self, dataset):

self.predict.decision_list = self.decision_list_learning(set(dataset.examples))

def decision_list_learning(self, examples):

if not examples:

return [(True, False)]

t, o, examples_t = self.find_examples(examples)

if not t:

raise Exception

return [(t, o)] + self.decision_list_learning(examples - examples_t)

def find_examples(self, examples):

"""

raise NotImplementedError

def passes(self, example, test):

"""Does the example pass the test?"""

raise NotImplementedError

def predict(self, example):

"""Predict the outcome for the first passing test."""

for test, outcome in self.predict.decision_list:

if self.passes(example, test):

"""k-NearestNeighbor: the k nearest neighbors vote."""

def __init__(self, dataset, k=1):

self.dataset = dataset

self.k = k

def predict(self, example):

"""Find the k closest items, and have them vote for the best."""

best = heapq.nsmallest(self.k, ((self.dataset.distance(e, example), e) for e in self.dataset.examples))

def __init__(self, kernel=linear_kernel, C=1.0, verbose=False):

self.kernel = kernel

self.C = C # hyper-parameter

self.sv_idx, self.sv, self.sv_y = np.zeros(0), np.zeros(0), np.zeros(0)

self.alphas = np.zeros(0)

self.w = None

self.b = 0.0 # intercept

self.verbose = verbose

def fit(self, X, y):

"""

# In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations)

self.solve_qp(X, y)

sv = self.alphas > 1e-5

self.sv_idx = np.arange(len(self.alphas))[sv]

self.sv, self.sv_y, self.alphas = X[sv], y[sv], self.alphas[sv]

if self.kernel == linear_kernel:

self.w = np.dot(self.alphas * self.sv_y, self.sv)

for n in range(len(self.alphas)):

self.b += self.sv_y[n]

self.b -= np.sum(self.alphas * self.sv_y * self.K[self.sv_idx[n], sv])

self.b /= len(self.alphas)

return self

def solve_qp(self, X, y):

"""

m = len(y) # m = n_samples

self.K = self.kernel(X) # gram matrix

P = self.K * np.outer(y, y)

q = -np.ones(m)

lb = np.zeros(m) # lower bounds

ub = np.ones(m) * self.C # upper bounds

A = y.astype(np.float64) # equality matrix

b = np.zeros(1) # equality vector

self.alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt',

sym_proj=True, verbose=self.verbose)

def predict_score(self, X):

"""

if self.w is None:

return np.dot(self.alphas * self.sv_y, self.kernel(self.sv, X)) + self.b

return np.dot(X, self.w) + self.b

def predict(self, X):

"""

def __init__(self, kernel=linear_kernel, C=1.0, epsilon=0.1, verbose=False):

self.kernel = kernel

self.C = C # hyper-parameter

self.epsilon = epsilon # epsilon insensitive loss value

self.sv_idx, self.sv = np.zeros(0), np.zeros(0)

self.alphas_p, self.alphas_n = np.zeros(0), np.zeros(0)

self.w = None

self.b = 0.0 # intercept

self.verbose = verbose

def fit(self, X, y):

"""

# In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations)

self.solve_qp(X, y)

sv = np.logical_or(self.alphas_p > 1e-5, self.alphas_n > 1e-5)

self.sv_idx = np.arange(len(self.alphas_p))[sv]

self.sv, sv_y = X[sv], y[sv]

self.alphas_p, self.alphas_n = self.alphas_p[sv], self.alphas_n[sv]

if self.kernel == linear_kernel:

self.w = np.dot(self.alphas_p - self.alphas_n, self.sv)

for n in range(len(self.alphas_p)):

self.b += sv_y[n]

self.b -= np.sum((self.alphas_p - self.alphas_n) * self.K[self.sv_idx[n], sv])

self.b -= self.epsilon

self.b /= len(self.alphas_p)

return self

def solve_qp(self, X, y):

"""

m = len(y) # m = n_samples

self.K = self.kernel(X) # gram matrix

P = np.vstack((np.hstack((self.K, -self.K)), # alphas_p, alphas_n

np.hstack((-self.K, self.K)))) # alphas_n, alphas_p

q = np.hstack((-y, y)) + self.epsilon

lb = np.zeros(2 * m) # lower bounds

ub = np.ones(2 * m) * self.C # upper bounds

A = np.hstack((np.ones(m), -np.ones(m))) # equality matrix

b = np.zeros(1) # equality vector

alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt',

sym_proj=True, verbose=self.verbose)

self.alphas_p = alphas[:m]

self.alphas_n = alphas[m:]

def predict(self, X):

if self.kernel != linear_kernel:

return np.dot(self.alphas_p - self.alphas_n, self.kernel(self.sv, X)) + self.b

def __init__(self, clf, decision_function='ovr'):

self.clf = clf

self.decision_function = decision_function

self.n_class, self.classifiers = 0, []

def fit(self, X, y):

"""

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

self.clf.fit(X, y1)

self.classifiers.append(copy.deepcopy(self.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

self.clf.fit(X1, y1)

self.classifiers.append(copy.deepcopy(self.clf))

else:

return ValueError("Decision function must be either 'ovr' or 'ovo'.")

return self

def predict(self, X):

"""

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:

"""

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 = np.dot(w, x)

t = example[idx_t]

err.append(t - y)

# update weights

for i in range(len(w)):

w[i] = w[i] + learning_rate * (np.dot(err, X_col[i]) / num_examples)

def predict(example):

x = [1] + example

return np.dot(w, x)

"""

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()(np.dot(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 * (np.dot(buffer, X_col[i]) / num_examples)

def predict(example):

x = [1] + example

return Sigmoid()(np.dot(w, x))

"""Given a list of learning algorithms, have them vote."""

def __init__(self, learners):

self.learners = learners

def train(self, dataset):

self.predictors = [learner(dataset) for learner in self.learners]

def predict(self, example):

"""[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.predict(example[:-1]))

# 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.predict(example[:-1]):

w[j] *= error / (1 - error)

w = normalize(w)

z.append(np.log((1 - error) / error))

"""Return a predictor that takes a weighted vote."""

def __init__(self, predictors, weights):

self.predictors = predictors

self.weights = weights

def predict(self, example):

"""

totals = defaultdict(int)

for v, w in zip(values, weights):

totals[v] += w

"""An ensemble of Decision Trees trained using bagging and feature bagging."""

def __init__(self, dataset, n=5):

self.dataset = dataset

self.n = n

self.predictors = [DecisionTreeLearner(DataSet(examples=self.data_bagging(), attrs=self.dataset.attrs,

attr_names=self.dataset.attr_names, target=self.dataset.target,

inputs=self.feature_bagging())) for _ in range(self.n)]

def data_bagging(self, m=0):

"""Sample m examples with replacement"""

n = len(self.dataset.examples)

return weighted_sample_with_replacement(m or n, self.dataset.examples, [1] * n)

def feature_bagging(self, p=0.7):

"""Feature bagging with probability p to retain an attribute"""

inputs = [i for i in self.dataset.inputs if probability(p)]

return inputs or self.dataset.inputs

def predict(self, example):

"""

def train(dataset, weights):

dataset = replicated_dataset(dataset, weights)

n_samples, n_features = len(dataset.examples), dataset.target

X, y = (np.array([x[:n_features] for x in dataset.examples]),

np.array([x[n_features] for x in dataset.examples]))

return unweighted_learner.fit(X, y)

"""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)

"""

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)) +

"""

return DataSet(name='restaurant', target='Wait', examples=examples,

{'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',

"""Generate a DataSet with n examples."""

def gen():

example = list(map(random.choice, restaurant.values))

example[restaurant.target] = waiting_decision_tree(example)

return example

"""

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)