import numpy as np
from scipy.special import expit
from scipy.optimize import minimize
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import KFold
from sklearn import preprocessing
import seaborn
import matplotlib.pyplot as plt
from math import exp
import sys

seaborn.set_style("white")


class LogReg(BaseEstimator, ClassifierMixin):

    def __init__(self, sigmas=1.):
        self.sigmas = sigmas

    def fit(self, x, y):
        w = np.zeros(x.shape[1])

        def aux(w):
            return l_gradient(w, x, y, self.sigmas)

        self.w = minimize(aux, w, jac=True, method='L-BFGS-B',
                          options={'disp': False, 'gtol': 1e-10}).x
        return self

    def predict(self, x):
        pred = np.dot(x, self.w)
        return (pred > 0).astype(int)


def l(w, x, y, sigmas):
    x2 = np.dot(x, w)
    l = (-np.log(expit(x2)).sum() + np.inner(1-y, x2)
         + 1 / (2 * sigmas) * np.inner(w, w))
    return l


def l_gradient(w, x, y, sigmas):
    x2 = np.dot(x, w)
    l = (-np.log(expit(x2)).sum() + np.inner(x2, (1 - y))
         + 1 / (2 * sigmas) * np.inner(w, w))
    grad = (np.sum(-x * expit(-x2)[:, None], axis=0)
            + np.sum(x * (1 - y)[:, None], axis=0) + 1 / sigmas * w)
    return l, grad


def test_gradient(w, x, y, sigmas):
    eps = 1e-10
    _, g = l_gradient(w, x, y, sigmas)
    for i in xrange(len(g)):
        z = np.zeros(len(g))
        z[i] = eps
        l1 = l(w + z, x, y, sigmas)
        z = np.zeros(len(g))
        z[i] = -eps
        l2 = l(w + z, x, y, sigmas)
        print (l1 - l2) / (2 * eps), g[i]


def cross_validation(x, y):
    for sigmas in [10 ** i for i in xrange(-8, 9)]:
        train_scores = []
        test_scores = []
        for train, test in KFold(n=x.shape[0], n_folds=10, shuffle=True):
            mod = LogReg(sigmas=sigmas)
            x_train_f = x[train]
            y_train_f = y[train]
            x_test_f = x[test]
            y_test_f = y[test]
            mod = mod.fit(x_train_f, y_train_f)
            train_scores.append(accuracy_score(y_test_f,
                                               mod.predict(x_test_f)))
            test_scores.append(accuracy_score(y_test, mod.predict(x_test)))
        print np.mean(train_scores), np.mean(test_scores)


def plot_scores():
    plt.figure(figsize=(9, 6))
    values = [map(float, line.strip().split())
              for line in open(sys.argv[1])]
    y1, y2 = zip(*values)
    x = xrange(-8, 9)
    y1 = 1 - np.array(y1)
    y2 = 1 - np.array(y2)
    plt.plot(x, y1, label='CV error')
    plt.plot(x, y2, label='Test error')
    plt.legend()
    plt.xlabel("log(sigma^2)")
    plt.ylabel("Error")
    plt.savefig(sys.argv[1] + ".pdf")


def three(x, y):
    sigmas = exp(2)
    mod = LogReg(sigmas=sigmas)
    mod.fit(x, y)
    print 1 - accuracy_score(y, mod.predict(x))
    print 1 - accuracy_score(y_test, mod.predict(x_test))
    print mod.w


def norm(x):
    for i in [54, 55, 56]:
        x[:, i] = np.log(1 + x[:, i])
    return x

if __name__ == "__main__":
    data_train = np.loadtxt("spam.train.dat")
    x_train = data_train[:, :-1]
    y_train = data_train[:, -1].astype(int)
    data_test = np.loadtxt("spam.test.dat")
    x_test = data_test[:, :-1]
    y_test = data_test[:, -1].astype(int)
    #x_train = preprocessing.scale(x_train)
    #x_test = preprocessing.scale(x_test)
    #x_train = norm(x_train)
    #x_test = norm(x_test)
    cross_validation(x_train, y_train)
    #plot_scores()
    #three(x_train, y_train)