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"""Learn to estimate functions from examples. (Chapters 18-20)"""

from utils import *

import copy, heapq, math, random

from collections import defaultdict

#______________________________________________________________________________

def rms_error(predictions, targets):

return math.sqrt(ms_error(predictions, targets))

def ms_error(predictions, targets):

return mean([(p - t)**2 for p, t in zip(predictions, targets)])

def mean_error(predictions, targets):

return mean([abs(p - t) for p, t in zip(predictions, targets)])

def mean_boolean_error(predictions, targets):

return mean([(p != t) for p, t in zip(predictions, targets)])

#______________________________________________________________________________

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.attrnames 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.setproblem.

If not None, an erroneous value raises ValueError.

d.distance A function from a pair of examples to a nonnegative 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.

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, attrnames=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 .setproblem().

>>> DataSet(examples='1, 2, 3')

<DataSet(): 1 examples, 3 attributes>

"""

update(self, name=name, source=source, values=values, distance=distance)

# 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(DataFile(name+'.csv').read())

else:

self.examples = examples

# Attrs are the indices of examples, unless otherwise stated.

if not attrs and self.examples:

attrs = range(len(self.examples[0]))

self.attrs = attrs

# Initialize .attrnames from string, list, or by default

if isinstance(attrnames, str):

self.attrnames = attrnames.split()

else:

self.attrnames = attrnames or attrs

self.setproblem(target, inputs=inputs, exclude=exclude)

def setproblem(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 attrname.

Also computes the list of possible values, if that wasn't done yet."""

self.target = self.attrnum(target)

exclude = map(self.attrnum, exclude)

if inputs:

self.inputs = removeall(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.values = map(unique, zip(*self.examples))

self.check_me()

def check_me(self):

"Check that my fields make sense."

assert len(self.attrnames) == len(self.attrs)

assert self.target in self.attrs

assert self.target not in self.inputs

assert set(self.inputs).issubset(set(self.attrs))

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 %s for attribute %s in %s' %

(example[a], self.attrnames[a], example))

def attrnum(self, attr):

"Returns the number used for attr, which can be a name, or -n .. n-1."

if attr < 0:

return len(self.attrs) + attr

elif isinstance(attr, str):

return self.attrnames.index(attr)

else:

return attr

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 __repr__(self):

return '<DataSet(%s): %d examples, %d attributes>' % (

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 [map(num_or_str, line.split(delim)) for line in lines]

#______________________________________________________________________________

class CountingProbDist:

"""A probability distribution formed by observing and counting examples.

If p is an instance of this class and o is an observed value, then

there are 3 main operations:

p.add(o) increments the count for observation o by 1.

p.sample() returns a random element from the distribution.

p[o] returns the probability for o (as in a regular ProbDist)."""

def __init__(self, observations=[], default=0):

"""Create a distribution, and optionally add in some observations.

By default this is an unsmoothed distribution, but saying default=1,

for example, gives you add-one smoothing."""

update(self, dictionary={}, n_obs=0.0, default=default, sampler=None)

for o in observations:

self.add(o)

def add(self, o):

"Add an observation o to the distribution."

self.smooth_for(o)

self.dictionary[o] += 1

self.n_obs += 1

self.sampler = None

def smooth_for(self, o):

"""Include o among the possible observations, whether or not

it's been observed yet."""

if o not in self.dictionary:

self.dictionary[o] = self.default

self.n_obs += self.default

self.sampler = None

def __getitem__(self, item):

"Return an estimate of the probability of item."

self.smooth_for(item)

return self.dictionary[item] / self.n_obs

# (top() and sample() are not used in this module, but elsewhere.)

def top(self, n):

"Return (count, obs) tuples for the n most frequent observations."

return heapq.nlargest(n, [(v, k) for (k, v) in self.dictionary.items()])

def sample(self):

"Return a random sample from the distribution."

if self.sampler is None:

self.sampler = weighted_sampler(self.dictionary.keys(),

self.dictionary.values())

return self.sampler()

#______________________________________________________________________________

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

#______________________________________________________________________________

def NaiveBayesLearner(dataset):

"""Just count how many times each value of each input attribute

occurs, conditional on the target value. Count the different

target values too."""

targetvals = dataset.values[dataset.target]

target_dist = CountingProbDist(targetvals)

attr_dists = dict(((gv, attr), CountingProbDist(dataset.values[attr]))

for gv in targetvals

for attr in dataset.inputs)

for example in dataset.examples:

targetval = example[dataset.target]

target_dist.add(targetval)

for attr in dataset.inputs:

attr_dists[targetval, attr].add(example[attr])

def predict(example):

"""Predict the target value for example. Consider each possible value,

and pick the most likely by looking at each attribute independently."""

def class_probability(targetval):

return (target_dist[targetval]

* product(attr_dists[targetval, attr][example[attr]]

for attr in dataset.inputs))

return argmax(targetvals, class_probability)

return predict

#______________________________________________________________________________

def NearestNeighborLearner(dataset, k=1):

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

def predict(example):

"Find the k closest, 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

#______________________________________________________________________________

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, attrname=None, branches=None):

"Initialize by saying what attribute this node tests."

update(self, attr=attr, attrname=attrname or attr,

branches=branches or {})

def __call__(self, example):

"Given an example, classify it using the attribute and the branches."

attrvalue = example[self.attr]

return self.branches[attrvalue](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.attrname

print 'Test', name

for (val, subtree) in self.branches.items():

print ' '*4*indent, name, '=', val, '==>',

subtree.display(indent+1)

def __repr__(self):

return ('DecisionFork(%r, %r, %r)'

% (self.attr, self.attrname, 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, indent=0):

print 'RESULT =', self.result

def __repr__(self):

return repr(self.result)

#______________________________________________________________________________

def DecisionTreeLearner(dataset):

"[Fig. 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)

elif all_same_class(examples):

return DecisionLeaf(examples[0][target])

elif len(attrs) == 0:

return plurality_value(examples)

else:

A = choose_attribute(attrs, examples)

tree = DecisionFork(A, dataset.attrnames[A])

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

subtree = decision_tree_learning(

exs, removeall(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],

lambda v: count(target, v, examples))

return DecisionLeaf(popular)

def count(attr, val, examples):

return count_if(lambda e: e[attr] == val, 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,

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 = float(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(removeall(0, values))

return sum(-p * log2(p) for p in probabilities)

#______________________________________________________________________________

### A decision list is implemented as a list of (test, value) pairs.

def DecisionListLearner(dataset):

"""[Fig. 18.11]"""

def decision_list_learning(examples):

if not examples:

return [(True, False)]

t, o, examples_t = find_examples(examples)

if not t:

raise Failure

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."""

unimplemented()

def passes(example, test):

"Does the example pass the test?"

unimplemented()

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 NeuralNetLearner(dataset, sizes):

"""Layered feed-forward network."""

activations = map(lambda n: [0.0 for i in range(n)], sizes)

weights = []

def predict(example):

unimplemented()

return predict

class NNUnit:

"""Unit of a neural net."""

def __init__(self):

unimplemented()

def PerceptronLearner(dataset, sizes):

def predict(example):

return sum([])

unimplemented()

#______________________________________________________________________________

def Linearlearner(dataset):

"""Fit a linear model to the data."""

unimplemented()

#______________________________________________________________________________

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 AdaBoost(L, K):

"""[Fig. 18.34]"""

def train(dataset):

examples, target = dataset.examples, dataset.target

N = len(examples)

epsilon = 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 = clip(error, epsilon, 1-epsilon)

for j, example in enumerate(examples):

if example[target] == h_k(example):

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

w = normalize(w)

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

return WeightedMajority(h, z)

return train

def WeightedMajority(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.keys(), key=totals.get)

#_____________________________________________________________________________

# Adapting an unweighted learner for AdaBoost

def WeightedLearner(unweighted_learner):

"""Given a learner that takes just an unweighted dataset, return

one that takes also a weight for each example. [p. 749 footnote 14]"""

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(seq, fractions, n - sum(wholes)))

def flatten(seqs): return sum(seqs, [])

#_____________________________________________________________________________

# Functions for testing learners on examples

def test(predict, dataset, examples=None, verbose=0):

"Return the proportion of the examples that are correctly predicted."

if examples is None: examples = dataset.examples

if len(examples) == 0: return 0.0

right = 0.0

for example in examples:

desired = example[dataset.target]

output = predict(dataset.sanitize(example))

if output == desired:

right += 1

if verbose >= 2:

print ' OK: got %s for %s' % (desired, example)

elif verbose:

print 'WRONG: got %s, expected %s for %s' % (

output, desired, example)

return right / len(examples)

def train_and_test(learner, dataset, start, end):

"""Reserve dataset.examples[start:end] for test; train on the remainder.

Return the proportion of examples correct on the test examples."""

examples = dataset.examples

try:

dataset.examples = examples[:start] + examples[end:]

return test(learner(dataset), dataset, examples[start:end])

finally:

dataset.examples = examples

def cross_validation(learner, dataset, 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."""

if k is None:

k = len(dataset.examples)

if trials > 1:

return mean([cross_validation(learner, dataset, k, trials=1)

for t in range(trials)])

else:

n = len(dataset.examples)

random.shuffle(dataset.examples)

return mean([train_and_test(learner, dataset, i*(n/k), (i+1)*(n/k))

for i in range(k)])

def leave1out(learner, dataset):

"Leave one out cross-validation over the dataset."

return cross_validation(learner, dataset, k=len(dataset.examples))

def learningcurve(learner, dataset, trials=10, sizes=None):

if sizes is None:

sizes = range(2, len(dataset.examples)-10, 2)

def score(learner, size):

random.shuffle(dataset.examples)

return train_and_test(learner, dataset, 0, size)

return [(size, mean([score(learner, size) for t in range(trials)]))

for size in sizes]

#______________________________________________________________________________

# The rest of this file gives datasets for machine learning problems.

orings = DataSet(name='orings', target='Distressed',

attrnames="Rings Distressed Temp Pressure Flightnum")

zoo = DataSet(name='zoo', target='type', exclude=['name'],

attrnames="name hair feathers eggs milk airborne aquatic " +

"predator toothed backbone breathes venomous fins legs tail " +

"domestic catsize type")

iris = DataSet(name="iris", target="class",

attrnames="sepal-len sepal-width petal-len petal-width class")

#______________________________________________________________________________

# The Restaurant example from Fig. 18.2

def RestaurantDataSet(examples=None):

"Build a DataSet of Restaurant waiting examples. [Fig. 18.3]"

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

attrnames='Alternate Bar Fri/Sat Hungry Patrons Price '

+ 'Raining Reservation Type WaitEstimate Wait')

restaurant = RestaurantDataSet()

def T(attrname, branches):

branches = dict((value, (child if isinstance(child, DecisionFork)

else DecisionLeaf(child)))

for value, child in branches.items())

return DecisionFork(restaurant.attrnum(attrname), attrname, branches)

Fig[18,2] = 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'})})})})})

__doc__ += """

[Fig. 18.6]

>>> random.seed(437)

>>> restaurant_tree = DecisionTreeLearner(restaurant)

>>> restaurant_tree.display()

Test Patrons

Patrons = None ==> RESULT = No

Patrons = Full ==> Test Hungry

Hungry = Yes ==> Test Type

Type = Burger ==> RESULT = Yes

Type = Thai ==> Test Fri/Sat

Fri/Sat = Yes ==> RESULT = Yes

Fri/Sat = No ==> RESULT = No

Type = French ==> RESULT = Yes

Type = Italian ==> RESULT = No

Hungry = No ==> RESULT = No

Patrons = Some ==> RESULT = Yes

"""

def SyntheticRestaurant(n=20):

"Generate a DataSet with n examples."

def gen():

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

example[restaurant.target] = Fig[18,2](example)

return example

return RestaurantDataSet([gen() for i in range(n)])

#______________________________________________________________________________

# Artificial, generated datasets.

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 i 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 i 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 i in '12']

examples.append([x, y, int(x) != int(y)])

return DataSet(name="continuous xor", examples=examples)

#______________________________________________________________________________

def compare(algorithms=[PluralityLearner, NaiveBayesLearner,

NearestNeighborLearner, DecisionTreeLearner],

datasets=[iris, orings, zoo, restaurant, SyntheticRestaurant(20),

Majority(7, 100), Parity(7, 100), Xor(100)],

k=10, trials=1):

"""Compare various learners on various datasets using cross-validation.

Print results as a table."""

print_table([[a.__name__.replace('Learner','')] +

[cross_validation(a, d, k, trials) for d in datasets]

for a in algorithms],

header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f')

aima-python/learning.py at master · theaverageguy/aima-python · GitHub

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