'''
This script randomly generates earth-style models, then randomly generates data
from those models and fits earth models to those data using both the python and
R implementations.  It records the sample size, m, the number of input
dimensions, n, the number of forward pass iterations, the runtime, and the
r^2 statistic for each fit and writes the result to a CSV file.
'''

import numpy
import pandas.rpy.common as com
import rpy2.robjects as robjects
import time
import pandas
from pyearth import Earth


class DataGenerator(object):

    def __init__(self):
        pass

    def generate(self, m):
        pass


class NoiseGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        y = numpy.random.normal(size=m)
        return X, y


class LinearGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        beta = numpy.random.normal(size=self.n)
        y = numpy.dot(X, beta) + numpy.random.normal(m)
        return X, y


class VFunctionGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        var = numpy.random.randint(self.n)
        y = 10 * abs(X[:, var]) + numpy.random.normal(m)
        return X, y


class UFunctionGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        var = numpy.random.randint(self.n)
        y = 10 * (X[:, var] ** 2) + numpy.random.normal(m)
        return X, y


class RandomComplexityGenerator(DataGenerator):

    def __init__(self, n, max_terms=10, max_degree=2):
        self.n = n
        self.max_terms = max_terms
        self.max_degree = max_degree

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        # Including the intercept
        num_terms = numpy.random.randint(2, self.max_terms)
        coef = 10 * numpy.random.normal(size=num_terms)
        B = numpy.ones(shape=(m, num_terms))
        B[:, 0] += coef[0]
        for i in range(1, num_terms):
            degree = numpy.random.randint(1, self.max_degree)
            for bf in range(degree):
                knot = numpy.random.normal()
                dir = 1 - 2 * numpy.random.binomial(1, .5)
                var = numpy.random.randint(0, self.n)
                B[:, i] *= (dir * (X[:, var] - knot)) * \
                    (dir * (X[:, var] - knot) > 0)
        y = numpy.dot(B, coef) + numpy.random.normal(size=m)
        return X, y


def run_earth(X, y, **kwargs):
    '''
    Run with the R package earth.
    Return prediction value, training time, and number
    of forward pass iterations.
    '''
    r = robjects.r
    m, n = X.shape
    data = pandas.DataFrame(X)
    data['y'] = y
    r_data = com.convert_to_r_dataframe(data)
    r('library(earth)')
    r_func = '''
    run <-  function(data, degree=1, fast.k=0, penalty=3.0){
       time = system.time(model <- earth(y~.,data=data,degree=degree,penalty=penalty))[3]
       forward_terms = dim(summary(model)$prune.terms)[1]
       y_pred = predict(model,data)
       return(list(y_pred, time, forward_terms, model))
     }
     '''
    r(r_func)
    run = r('run')
    r_list = run(
        **{'data': r_data,
           'degree': kwargs['max_degree'],
           'fast.k': 0,
           'penalty': kwargs['penalty']})
    y_pred = numpy.array(r_list[0]).reshape(m)
    time = r_list[1][0]
    forward_terms = r_list[2][0]
    return y_pred, time, (forward_terms - 1) / 2


def run_pyearth(X, y, **kwargs):
    '''
    Run with pyearth.
    Return prediction value, training time, and number of
    forward pass iterations.
    '''
    model = Earth(**kwargs)
    t0 = time.time()
    model.fit(X, y)
    t1 = time.time()
    y_pred = model.predict(X)
    forward_iterations = len(model.forward_trace()) - 1
    return y_pred, t1 - t0, forward_iterations


def compare(generator_class, sample_sizes, dimensions, repetitions, **kwargs):
    '''
    Return a data table that includes m, n, pyearth or earth, training time,
    and number of forward pass iterations.
    '''
    header = [
        'm',
        'n',
        'pyearth',
        'earth',
        'time',
        'forward_iterations',
        'rsq']
    data = []
    for n in dimensions:
        generator = generator_class(n=n)
        for m in sample_sizes:
            for rep in range(repetitions):
                print n, m, rep
                X, y = generator.generate(m=m)
                y_pred_r, time_r, iter_r = run_earth(X, y, **kwargs)
                rsq_r = 1 - (numpy.sum((y - y_pred_r) ** 2)) / (
                    numpy.sum((y - numpy.mean(y)) ** 2))
                data.append([m, n, 0, 1, time_r, iter_r, rsq_r])
                y_pred_py, time_py, iter_py = run_pyearth(X, y, **kwargs)
                rsq_py = 1 - \
                    (numpy.sum((y - y_pred_py) ** 2)) / (
                        numpy.sum((y - numpy.mean(y)) ** 2))
                data.append([m, n, 1, 0, time_py, iter_py, rsq_py])
    return pandas.DataFrame(data, columns=header)

if __name__ == '__main__':
    sample_sizes = [100, 200, 300, 500]
    dimensions = [10, 20, 30]
    rep = 5
    numpy.random.seed(1)
    data = compare(
        RandomComplexityGenerator,
        sample_sizes,
        dimensions,
        rep,
        max_degree=2,
        penalty=3.0)
    print data
    data.to_csv('comparison.csv')

'''
This script randomly generates earth-style models, then randomly generates data
from those models and fits earth models to those data using both the python and
R implementations.  It records the sample size, m, the number of input
dimensions, n, the number of forward pass iterations, the runtime, and the
r^2 statistic for each fit and writes the result to a CSV file.
'''

import numpy
import pandas.rpy.common as com
import rpy2.robjects as robjects
import time
import pandas
from pyearth import Earth


class DataGenerator(object):

    def __init__(self):
        pass

    def generate(self, m):
        pass


class NoiseGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        y = numpy.random.normal(size=m)
        return X, y


class LinearGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        beta = numpy.random.normal(size=self.n)
        y = numpy.dot(X, beta) + numpy.random.normal(m)
        return X, y


class VFunctionGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        var = numpy.random.randint(self.n)
        y = 10 * abs(X[:, var]) + numpy.random.normal(m)
        return X, y


class UFunctionGenerator(DataGenerator):

    def __init__(self, n):
        self.n = n

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        var = numpy.random.randint(self.n)
        y = 10 * (X[:, var] ** 2) + numpy.random.normal(m)
        return X, y


class RandomComplexityGenerator(DataGenerator):

    def __init__(self, n, max_terms=10, max_degree=2):
        self.n = n
        self.max_terms = max_terms
        self.max_degree = max_degree

    def generate(self, m):
        X = numpy.random.normal(size=(m, self.n))
        # Including the intercept
        num_terms = numpy.random.randint(2, self.max_terms)
        coef = 10 * numpy.random.normal(size=num_terms)
        B = numpy.ones(shape=(m, num_terms))
        B[:, 0] += coef[0]
        for i in range(1, num_terms):
            degree = numpy.random.randint(1, self.max_degree)
            for bf in range(degree):
                knot = numpy.random.normal()
                dir = 1 - 2 * numpy.random.binomial(1, .5)
                var = numpy.random.randint(0, self.n)
                B[:, i] *= (dir * (X[:, var] - knot)) * \
                    (dir * (X[:, var] - knot) > 0)
        y = numpy.dot(B, coef) + numpy.random.normal(size=m)
        return X, y


def run_earth(X, y, **kwargs):
    '''
    Run with the R package earth.
    Return prediction value, training time, and number
    of forward pass iterations.
    '''
    r = robjects.r
    m, n = X.shape
    data = pandas.DataFrame(X)
    data['y'] = y
    r_data = com.convert_to_r_dataframe(data)
    r('library(earth)')
    r_func = '''
    run <-  function(data, degree=1, fast.k=0, penalty=3.0){
       time = system.time(model <- earth(y~.,data=data,degree=degree,penalty=penalty))[3]
       forward_terms = dim(summary(model)$prune.terms)[1]
       y_pred = predict(model,data)
       return(list(y_pred, time, forward_terms, model))
     }
     '''
    r(r_func)
    run = r('run')
    r_list = run(
        **{'data': r_data,
           'degree': kwargs['max_degree'],
           'fast.k': 0,
           'penalty': kwargs['penalty']})
    y_pred = numpy.array(r_list[0]).reshape(m)
    time = r_list[1][0]
    forward_terms = r_list[2][0]
    return y_pred, time, (forward_terms - 1) / 2


def run_pyearth(X, y, **kwargs):
    '''
    Run with pyearth.
    Return prediction value, training time, and number of
    forward pass iterations.
    '''
    model = Earth(**kwargs)
    t0 = time.time()
    model.fit(X, y)
    t1 = time.time()
    y_pred = model.predict(X)
    forward_iterations = len(model.forward_trace()) - 1
    return y_pred, t1 - t0, forward_iterations


def compare(generator_class, sample_sizes, dimensions, repetitions, **kwargs):
    '''
    Return a data table that includes m, n, pyearth or earth, training time,
    and number of forward pass iterations.
    '''
    header = [
        'm',
        'n',
        'pyearth',
        'earth',
        'time',
        'forward_iterations',
        'rsq']
    data = []
    for n in dimensions:
        generator = generator_class(n=n)
        for m in sample_sizes:
            for rep in range(repetitions):
                print n, m, rep
                X, y = generator.generate(m=m)
                y_pred_r, time_r, iter_r = run_earth(X, y, **kwargs)
                rsq_r = 1 - (numpy.sum((y - y_pred_r) ** 2)) / (
                    numpy.sum((y - numpy.mean(y)) ** 2))
                data.append([m, n, 0, 1, time_r, iter_r, rsq_r])
                y_pred_py, time_py, iter_py = run_pyearth(X, y, **kwargs)
                rsq_py = 1 - \
                    (numpy.sum((y - y_pred_py) ** 2)) / (
                        numpy.sum((y - numpy.mean(y)) ** 2))
                data.append([m, n, 1, 0, time_py, iter_py, rsq_py])
    return pandas.DataFrame(data, columns=header)

if __name__ == '__main__':
    sample_sizes = [100, 200, 300, 500]
    dimensions = [10, 20, 30]
    rep = 5
    numpy.random.seed(1)
    data = compare(
        RandomComplexityGenerator,
        sample_sizes,
        dimensions,
        rep,
        max_degree=2,
        penalty=3.0)
    print data
    data.to_csv('comparison.csv')

This script randomly generates earth-style models, then randomly generates data from those models and fits earth models to those data using both the python and R implementations.  It records the sample size, m, the number of input dimensions, n, the number of forward pass iterations, the runtime, and the r^2 statistic for each fit and writes the result to a CSV file. # Including the intercept Run with the R package earth.     Return prediction value, training time, and number     of forward pass iterations. run <-  function(data, degree=1, fast.k=0, penalty=3.0){        time = system.time(model <- earth(y~.,data=data,degree=degree,penalty=penalty))[3]        forward_terms = dim(summary(model)$prune.terms)[1]        y_pred = predict(model,data)        return(list(y_pred, time, forward_terms, model))      } Run with pyearth.     Return prediction value, training time, and number of     forward pass iterations. Return a data table that includes m, n, pyearth or earth, training time,     and number of forward pass iterations.