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psf/black code formatting (#1277)
This commit is contained in:
William Zhang
committed by
Christian Clauss
Christian Clauss
parent
07f04a2e55
commit
9eac17a408
@ -41,11 +41,20 @@ import pandas as pd
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from sklearn.datasets import make_blobs, make_circles
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from sklearn.preprocessing import StandardScaler
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CANCER_DATASET_URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'
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CANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"
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class SmoSVM(object):
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def __init__(self, train, kernel_func, alpha_list=None, cost=0.4, b=0.0, tolerance=0.001, auto_norm=True):
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def __init__(
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self,
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train,
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kernel_func,
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alpha_list=None,
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cost=0.4,
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b=0.0,
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tolerance=0.001,
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auto_norm=True,
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):
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self._init = True
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self._auto_norm = auto_norm
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self._c = np.float64(cost)
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@ -91,13 +100,25 @@ class SmoSVM(object):
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self.alphas[i1], self.alphas[i2] = a1_new, a2_new
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# 3: update threshold(b)
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b1_new = np.float64(-e1 - y1 * K(i1, i1) * (a1_new - a1) - y2 * K(i2, i1) * (a2_new - a2) + self._b)
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b2_new = np.float64(-e2 - y2 * K(i2, i2) * (a2_new - a2) - y1 * K(i1, i2) * (a1_new - a1) + self._b)
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b1_new = np.float64(
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-e1
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- y1 * K(i1, i1) * (a1_new - a1)
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- y2 * K(i2, i1) * (a2_new - a2)
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+ self._b
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)
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b2_new = np.float64(
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-e2
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- y2 * K(i2, i2) * (a2_new - a2)
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- y1 * K(i1, i2) * (a1_new - a1)
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+ self._b
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)
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if 0.0 < a1_new < self._c:
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b = b1_new
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if 0.0 < a2_new < self._c:
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b = b2_new
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if not (np.float64(0) < a2_new < self._c) and not (np.float64(0) < a1_new < self._c):
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if not (np.float64(0) < a2_new < self._c) and not (
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np.float64(0) < a1_new < self._c
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):
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b = (b1_new + b2_new) / 2.0
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b_old = self._b
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self._b = b
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@ -107,7 +128,11 @@ class SmoSVM(object):
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for s in self.unbound:
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if s == i1 or s == i2:
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continue
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self._error[s] += y1 * (a1_new - a1) * K(i1, s) + y2 * (a2_new - a2) * K(i2, s) + (self._b - b_old)
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self._error[s] += (
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y1 * (a1_new - a1) * K(i1, s)
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+ y2 * (a2_new - a2) * K(i2, s)
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+ (self._b - b_old)
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)
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# if i1 or i2 is non-bound,update there error value to zero
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if self._is_unbound(i1):
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@ -119,7 +144,9 @@ class SmoSVM(object):
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def predict(self, test_samples, classify=True):
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if test_samples.shape[1] > self.samples.shape[1]:
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raise ValueError("Test samples' feature length does not equal to that of train samples")
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raise ValueError(
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"Test samples' feature length does not equal to that of train samples"
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)
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if self._auto_norm:
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test_samples = self._norm(test_samples)
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@ -173,14 +200,23 @@ class SmoSVM(object):
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k_matrix = np.zeros([self.length, self.length])
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for i in self._all_samples:
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for j in self._all_samples:
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k_matrix[i, j] = np.float64(self.Kernel(self.samples[i, :], self.samples[j, :]))
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k_matrix[i, j] = np.float64(
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self.Kernel(self.samples[i, :], self.samples[j, :])
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)
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return k_matrix
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# Predict test sample's tag
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def _predict(self, sample):
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k = self._k
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predicted_value = np.sum(
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[self.alphas[i1] * self.tags[i1] * k(i1, sample) for i1 in self._all_samples]) + self._b
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predicted_value = (
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np.sum(
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[
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self.alphas[i1] * self.tags[i1] * k(i1, sample)
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for i1 in self._all_samples
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]
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)
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+ self._b
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)
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return predicted_value
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# Choose alpha1 and alpha2
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@ -200,23 +236,27 @@ class SmoSVM(object):
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while True:
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all_not_obey = True
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# all sample
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print('scanning all sample!')
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print("scanning all sample!")
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for i1 in [i for i in self._all_samples if self._check_obey_kkt(i)]:
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all_not_obey = False
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yield from self._choose_a2(i1)
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# non-bound sample
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print('scanning non-bound sample!')
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print("scanning non-bound sample!")
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while True:
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not_obey = True
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for i1 in [i for i in self._all_samples if self._check_obey_kkt(i) and self._is_unbound(i)]:
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for i1 in [
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i
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for i in self._all_samples
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if self._check_obey_kkt(i) and self._is_unbound(i)
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]:
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not_obey = False
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yield from self._choose_a2(i1)
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if not_obey:
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print('all non-bound samples fit the KKT condition!')
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print("all non-bound samples fit the KKT condition!")
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break
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if all_not_obey:
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print('all samples fit the KKT condition! Optimization done!')
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print("all samples fit the KKT condition! Optimization done!")
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break
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return False
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@ -231,7 +271,11 @@ class SmoSVM(object):
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if len(self.unbound) > 0:
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tmp_error = self._error.copy().tolist()
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tmp_error_dict = {index: value for index, value in enumerate(tmp_error) if self._is_unbound(index)}
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tmp_error_dict = {
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index: value
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for index, value in enumerate(tmp_error)
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if self._is_unbound(index)
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}
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if self._e(i1) >= 0:
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i2 = min(tmp_error_dict, key=lambda index: tmp_error_dict[index])
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else:
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@ -289,8 +333,20 @@ class SmoSVM(object):
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# way 1
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f1 = y1 * (e1 + b) - a1 * K(i1, i1) - s * a2 * K(i1, i2)
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f2 = y2 * (e2 + b) - a2 * K(i2, i2) - s * a1 * K(i1, i2)
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ol = l1 * f1 + L * f2 + 1 / 2 * l1 ** 2 * K(i1, i1) + 1 / 2 * L ** 2 * K(i2, i2) + s * L * l1 * K(i1, i2)
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oh = h1 * f1 + H * f2 + 1 / 2 * h1 ** 2 * K(i1, i1) + 1 / 2 * H ** 2 * K(i2, i2) + s * H * h1 * K(i1, i2)
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ol = (
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l1 * f1
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+ L * f2
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+ 1 / 2 * l1 ** 2 * K(i1, i1)
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+ 1 / 2 * L ** 2 * K(i2, i2)
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+ s * L * l1 * K(i1, i2)
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)
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oh = (
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h1 * f1
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+ H * f2
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+ 1 / 2 * h1 ** 2 * K(i1, i1)
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+ 1 / 2 * H ** 2 * K(i2, i2)
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+ s * H * h1 * K(i1, i2)
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)
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"""
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# way 2
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Use objective function check which alpha2 new could get the minimal objectives
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@ -370,14 +426,10 @@ class Kernel(object):
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def _check(self):
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if self._kernel == self._rbf:
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if self.gamma < 0:
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raise ValueError('gamma value must greater than 0')
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raise ValueError("gamma value must greater than 0")
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def _get_kernel(self, kernel_name):
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maps = {
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'linear': self._linear,
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'poly': self._polynomial,
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'rbf': self._rbf
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}
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maps = {"linear": self._linear, "poly": self._polynomial, "rbf": self._rbf}
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return maps[kernel_name]
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def __call__(self, v1, v2):
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@ -390,34 +442,35 @@ class Kernel(object):
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def count_time(func):
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def call_func(*args, **kwargs):
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import time
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start_time = time.time()
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func(*args, **kwargs)
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end_time = time.time()
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print('smo algorithm cost {} seconds'.format(end_time - start_time))
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print("smo algorithm cost {} seconds".format(end_time - start_time))
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return call_func
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@count_time
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def test_cancel_data():
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print('Hello!\r\nStart test svm by smo algorithm!')
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print("Hello!\r\nStart test svm by smo algorithm!")
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# 0: download dataset and load into pandas' dataframe
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if not os.path.exists(r'cancel_data.csv'):
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if not os.path.exists(r"cancel_data.csv"):
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request = urllib.request.Request(
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CANCER_DATASET_URL,
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headers={'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'}
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headers={"User-Agent": "Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)"},
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)
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response = urllib.request.urlopen(request)
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content = response.read().decode('utf-8')
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with open(r'cancel_data.csv', 'w') as f:
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content = response.read().decode("utf-8")
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with open(r"cancel_data.csv", "w") as f:
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f.write(content)
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data = pd.read_csv(r'cancel_data.csv', header=None)
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data = pd.read_csv(r"cancel_data.csv", header=None)
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# 1: pre-processing data
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del data[data.columns.tolist()[0]]
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data = data.dropna(axis=0)
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data = data.replace({'M': np.float64(1), 'B': np.float64(-1)})
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data = data.replace({"M": np.float64(1), "B": np.float64(-1)})
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samples = np.array(data)[:, :]
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# 2: deviding data into train_data data and test_data data
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@ -425,11 +478,18 @@ def test_cancel_data():
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test_tags, test_samples = test_data[:, 0], test_data[:, 1:]
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# 3: choose kernel function,and set initial alphas to zero(optional)
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mykernel = Kernel(kernel='rbf', degree=5, coef0=1, gamma=0.5)
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mykernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5)
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al = np.zeros(train_data.shape[0])
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# 4: calculating best alphas using SMO algorithm and predict test_data samples
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mysvm = SmoSVM(train=train_data, kernel_func=mykernel, alpha_list=al, cost=0.4, b=0.0, tolerance=0.001)
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mysvm = SmoSVM(
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train=train_data,
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kernel_func=mykernel,
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alpha_list=al,
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cost=0.4,
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b=0.0,
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tolerance=0.001,
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)
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mysvm.fit()
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predict = mysvm.predict(test_samples)
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@ -439,14 +499,18 @@ def test_cancel_data():
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for i in range(test_tags.shape[0]):
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if test_tags[i] == predict[i]:
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score += 1
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print('\r\nall: {}\r\nright: {}\r\nfalse: {}'.format(test_num, score, test_num - score))
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print(
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"\r\nall: {}\r\nright: {}\r\nfalse: {}".format(
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test_num, score, test_num - score
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)
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)
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print("Rough Accuracy: {}".format(score / test_tags.shape[0]))
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def test_demonstration():
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# change stdout
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print('\r\nStart plot,please wait!!!')
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sys.stdout = open(os.devnull, 'w')
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print("\r\nStart plot,please wait!!!")
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sys.stdout = open(os.devnull, "w")
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ax1 = plt.subplot2grid((2, 2), (0, 0))
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ax2 = plt.subplot2grid((2, 2), (0, 1))
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@ -464,32 +528,50 @@ def test_demonstration():
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sys.stdout = sys.__stdout__
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print("Plot done!!!")
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def test_linear_kernel(ax, cost):
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train_x, train_y = make_blobs(n_samples=500, centers=2,
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n_features=2, random_state=1)
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train_x, train_y = make_blobs(
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n_samples=500, centers=2, n_features=2, random_state=1
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)
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train_y[train_y == 0] = -1
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scaler = StandardScaler()
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train_x_scaled = scaler.fit_transform(train_x, train_y)
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train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
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mykernel = Kernel(kernel='linear', degree=5, coef0=1, gamma=0.5)
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mysvm = SmoSVM(train=train_data, kernel_func=mykernel, cost=cost, tolerance=0.001, auto_norm=False)
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mykernel = Kernel(kernel="linear", degree=5, coef0=1, gamma=0.5)
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mysvm = SmoSVM(
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train=train_data,
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kernel_func=mykernel,
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cost=cost,
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tolerance=0.001,
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auto_norm=False,
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)
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mysvm.fit()
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plot_partition_boundary(mysvm, train_data, ax=ax)
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def test_rbf_kernel(ax, cost):
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train_x, train_y = make_circles(n_samples=500, noise=0.1, factor=0.1, random_state=1)
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train_x, train_y = make_circles(
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n_samples=500, noise=0.1, factor=0.1, random_state=1
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)
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train_y[train_y == 0] = -1
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scaler = StandardScaler()
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train_x_scaled = scaler.fit_transform(train_x, train_y)
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train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
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mykernel = Kernel(kernel='rbf', degree=5, coef0=1, gamma=0.5)
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mysvm = SmoSVM(train=train_data, kernel_func=mykernel, cost=cost, tolerance=0.001, auto_norm=False)
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mykernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5)
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mysvm = SmoSVM(
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train=train_data,
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kernel_func=mykernel,
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cost=cost,
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tolerance=0.001,
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auto_norm=False,
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)
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mysvm.fit()
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plot_partition_boundary(mysvm, train_data, ax=ax)
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def plot_partition_boundary(model, train_data, ax, resolution=100, colors=('b', 'k', 'r')):
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def plot_partition_boundary(
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model, train_data, ax, resolution=100, colors=("b", "k", "r")
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):
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"""
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We can not get the optimum w of our kernel svm model which is different from linear svm.
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For this reason, we generate randomly destributed points with high desity and prediced values of these points are
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@ -502,25 +584,44 @@ def plot_partition_boundary(model, train_data, ax, resolution=100, colors=('b',
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train_data_tags = train_data[:, 0]
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xrange = np.linspace(train_data_x.min(), train_data_x.max(), resolution)
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yrange = np.linspace(train_data_y.min(), train_data_y.max(), resolution)
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test_samples = np.array([(x, y) for x in xrange for y in yrange]).reshape(resolution * resolution, 2)
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test_samples = np.array([(x, y) for x in xrange for y in yrange]).reshape(
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resolution * resolution, 2
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)
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test_tags = model.predict(test_samples, classify=False)
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grid = test_tags.reshape((len(xrange), len(yrange)))
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# Plot contour map which represents the partition boundary
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ax.contour(xrange, yrange, np.mat(grid).T, levels=(-1, 0, 1), linestyles=('--', '-', '--'),
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linewidths=(1, 1, 1),
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colors=colors)
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ax.contour(
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xrange,
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yrange,
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np.mat(grid).T,
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levels=(-1, 0, 1),
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linestyles=("--", "-", "--"),
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linewidths=(1, 1, 1),
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colors=colors,
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)
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# Plot all train samples
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ax.scatter(train_data_x, train_data_y, c=train_data_tags, cmap=plt.cm.Dark2, lw=0, alpha=0.5)
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ax.scatter(
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train_data_x,
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train_data_y,
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c=train_data_tags,
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cmap=plt.cm.Dark2,
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lw=0,
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alpha=0.5,
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)
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# Plot support vectors
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support = model.support
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ax.scatter(train_data_x[support], train_data_y[support], c=train_data_tags[support], cmap=plt.cm.Dark2)
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ax.scatter(
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train_data_x[support],
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train_data_y[support],
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c=train_data_tags[support],
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cmap=plt.cm.Dark2,
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)
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if __name__ == '__main__':
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if __name__ == "__main__":
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test_cancel_data()
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test_demonstration()
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plt.show()
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