learning4e.py

from utils4e import (
    removeall, unique, mode, argmax_random_tie, isclose, dotproduct, weighted_sample_with_replacement,
    num_or_str, normalize, clip, print_table, open_data, probability, random_weights, euclidean_distance
)

import copy
import heapq
import math
import random

from statistics import mean, stdev
from collections import defaultdict

# Learn to estimate functions from examples. (Chapters 18)
# ______________________________________________________________________________
# 18.2 Supervised learning.
# define supervised learning dataset and utility functions/


def mean_boolean_error(X, Y):
    return mean(int(x != y) for x, y in zip(X, Y))


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.
    d.exclude    A list of attribute indexes to exclude from d.inputs. Elements
                 of this list can either be integers (attrs) or attrnames.

    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>
        """
        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 .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 = list(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.update_values()
        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))
        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.attrnames[a], example))

    def attrnum(self, attr):
        """Returns the number used for attr, which can be a name, or -n .. n-1."""
        if isinstance(attr, str):
            return self.attrnames.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 i 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 '<DataSet({}): {:d} examples, {:d} attributes>'.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]

# ______________________________________________________________________________
# 18.3 Learning decision trees


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, default_child=None, branches=None):
        """Initialize by saying what attribute this node tests."""
        self.attr = attr
        self.attrname = attrname 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."""
        attrvalue = example[self.attr]
        if attrvalue in self.branches:
            return self.branches[attrvalue](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.attrname
        print('Test', name)
        for (val, subtree) in self.branches.items():
            print(' ' * 4 * indent, name, '=', val, '==>', end=' ')
            subtree.display(indent + 1)
        print()   # newline

    def __repr__(self):
        return ('DecisionFork({0!r}, {1!r}, {2!r})'
                .format(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)

# decision tree learning in Figure 18.5


def DecisionTreeLearner(dataset):

    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], plurality_value(examples))
            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],
                                    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(removeall(0, values))
    return sum(-p * math.log2(p) for p in probabilities)

# ______________________________________________________________________________
# 18.4 Model selection and optimization


def model_selection(learner, dataset, k=10, trials=1):
    """[Fig 18.8]
    Return the optimal value of size having minimum error
    on validation set.
    err_train: A training error array, indexed by size
    err_val: A validation error array, indexed by size
    """
    errs = []
    size = 1

    while True:
        err = cross_validation(learner, size, dataset, k, trials)
        # Check for convergence provided err_val is not empty
        if err and not isclose(err[-1], err, rel_tol=1e-6):
            best_size = 0
            min_val = math.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, size, 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.
    Returns Training error, Validataion error"""
    k = k or len(dataset.examples)
    if trials > 1:
        trial_errs = 0
        for t in range(trials):
            errs = cross_validation(learner, size, dataset,
                                          k=10, trials=1)
            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 cross_validation_nosize(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.
    Returns Training error, Validataion error"""
    k = k or len(dataset.examples)
    if trials > 1:
        trial_errs = 0
        for t in range(trials):
            errs = cross_validation(learner, dataset,
                                          k=10, trials=1)
            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)
            fold_errs += err_ratio(h, dataset, train_data)

            # Reverting back to original once test is completed
            dataset.examples = examples
        return fold_errs/k



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 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 == 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 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 leave_one_out(learner, dataset, size=None):
    """Leave one out cross-validation over the dataset."""
    return cross_validation(learner, size, dataset, k=len(dataset.examples))


# TODO learningcurve needs to fixed
def learningcurve(learner, dataset, trials=10, sizes=None):
    if sizes is None:
        sizes = list(range(2, len(dataset.examples) - 10, 2))

    def score(learner, size):
        random.shuffle(dataset.examples)
        return train_test_split(learner, dataset, 0, size)
    return [(size, mean([score(learner, size) for t in range(trials)]))
            for size in sizes]

# ______________________________________________________________________________
# 18.5 The theory Of learning


def DecisionListLearner(dataset):
    """A decision list is implemented as a list of (test, value) pairs.[Figure 18.11]"""

    # TODO: where are the tests from?
    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?"""
        return test.test(example)
        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

# ______________________________________________________________________________
# 18.6 Linear regression and classification


def LinearLearner(dataset, learning_rate=0.01, epochs=100):
    """Define with learner = LinearLearner(data); infer with learner(x)."""
    idx_i = dataset.inputs
    idx_t = dataset.target  # As of now, dataset.target gives only one index.
    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 weigts
    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 = dotproduct(w, x)
            # if threshold:
            #     y = threshold(y)
            t = example[idx_t]
            err.append(t - y)

        # update weights
        for i in range(len(w)):
            w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples)

    def predict(example):
        x = [1] + example
        return dotproduct(w, x)
    return predict


def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100):
    """Define logistic regression classifier in 18.6.5"""
    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 weigts
    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 = 1/(1 + math.exp(-dotproduct(w, x)))
            h.append(y * (1-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 * (dotproduct(err, X_col[i]) / num_examples)
            w[i] = w[i] + learning_rate * (dotproduct(buffer, X_col[i]) / num_examples)

    def predict(example):
        x = [1] + example
        return 1/(1 + math.exp(-dotproduct(w, x)))

    return predict

# ______________________________________________________________________________
# 18.7 Nonparametric models


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."""
        example.pop(dataset.target)
        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


# ______________________________________________________________________________
# 18.8 Ensemble learning


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 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,
                                              attrnames=dataset.attrnames,
                                              target=dataset.target,
                                              inputs=feature_bagging(dataset))) for _ in range(n)]

    return predict


def AdaBoost(L, K):
    """[Figure 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, key=totals.__getitem__)

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


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

# _____________________________________________________________________________
# Functions for testing learners on examples
# 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 [Figure 18.2]


def RestaurantDataSet(examples=None):
    """Build a DataSet of Restaurant waiting examples. [Figure 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 = {value: (child if isinstance(child, DecisionFork)
                        else DecisionLeaf(child))
                for value, child in branches.items()}
    return DecisionFork(restaurant.attrnum(attrname), attrname, 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 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=None, datasets=None, k=10, trials=1):
    """Compare various learners on various datasets using cross-validation.
    Print results as a table."""
    algorithms = algorithms or [                # default list
                                NearestNeighborLearner, DecisionTreeLearner]         # of algorithms

    datasets = datasets or [iris, orings, zoo, restaurant, SyntheticRestaurant(20),  # default list
                            Majority(7, 100), Parity(7, 100), Xor(100)]              # of datasets

    print_table([[a.__name__.replace('Learner', '')] +
                 [cross_validation_nosize(a, d, k, trials) for d in datasets]
                 for a in algorithms],
                header=[''] + [d.name[0:7] for d in datasets], numfmt='{0:.2f}')