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libsvm-3.36/python/libsvm/commonutil.py
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189
libsvm-3.36/python/libsvm/commonutil.py
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from __future__ import print_function
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from array import array
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import sys
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try:
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import numpy as np
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import scipy
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from scipy import sparse
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except:
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scipy = None
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__all__ = ['svm_read_problem', 'evaluations', 'csr_find_scale_param', 'csr_scale']
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def svm_read_problem(data_source, return_scipy=False):
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"""
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svm_read_problem(data_source, return_scipy=False) -> [y, x], y: list, x: list of dictionary
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svm_read_problem(data_source, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix
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Read LIBSVM-format data from data_source and return labels y
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and data instances x.
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"""
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if scipy != None and return_scipy:
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prob_y = array('d')
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prob_x = array('d')
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row_ptr = array('l', [0])
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col_idx = array('l')
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else:
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prob_y = []
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prob_x = []
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row_ptr = [0]
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col_idx = []
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indx_start = 1
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if hasattr(data_source, "read"):
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file = data_source
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else:
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file = open(data_source)
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try:
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for line in file:
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line = line.split(None, 1)
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# In case an instance with all zero features
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if len(line) == 1: line += ['']
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label, features = line
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prob_y.append(float(label))
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if scipy != None and return_scipy:
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nz = 0
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for e in features.split():
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ind, val = e.split(":")
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if ind == '0':
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indx_start = 0
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val = float(val)
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if val != 0:
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col_idx.append(int(ind)-indx_start)
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prob_x.append(val)
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nz += 1
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row_ptr.append(row_ptr[-1]+nz)
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else:
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xi = {}
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for e in features.split():
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ind, val = e.split(":")
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xi[int(ind)] = float(val)
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prob_x += [xi]
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except Exception as err_msg:
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raise err_msg
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finally:
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if not hasattr(data_source, "read"):
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# close file only if it was created by us
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file.close()
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if scipy != None and return_scipy:
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prob_y = np.frombuffer(prob_y, dtype='d')
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prob_x = np.frombuffer(prob_x, dtype='d')
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col_idx = np.frombuffer(col_idx, dtype='l')
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row_ptr = np.frombuffer(row_ptr, dtype='l')
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prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
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return (prob_y, prob_x)
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def evaluations_scipy(ty, pv):
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"""
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evaluations_scipy(ty, pv) -> (ACC, MSE, SCC)
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ty, pv: ndarray
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Calculate accuracy, mean squared error and squared correlation coefficient
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using the true values (ty) and predicted values (pv).
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"""
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if not (scipy != None and isinstance(ty, np.ndarray) and isinstance(pv, np.ndarray)):
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raise TypeError("type of ty and pv must be ndarray")
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if len(ty) != len(pv):
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raise ValueError("len(ty) must be equal to len(pv)")
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ACC = 100.0*(ty == pv).mean()
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MSE = ((ty - pv)**2).mean()
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l = len(ty)
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sumv = pv.sum()
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sumy = ty.sum()
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sumvy = (pv*ty).sum()
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sumvv = (pv*pv).sum()
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sumyy = (ty*ty).sum()
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with np.errstate(all = 'raise'):
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try:
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SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
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except:
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SCC = float('nan')
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return (float(ACC), float(MSE), float(SCC))
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def evaluations(ty, pv, useScipy = True):
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"""
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evaluations(ty, pv, useScipy) -> (ACC, MSE, SCC)
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ty, pv: list, tuple or ndarray
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useScipy: convert ty, pv to ndarray, and use scipy functions for the evaluation
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Calculate accuracy, mean squared error and squared correlation coefficient
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using the true values (ty) and predicted values (pv).
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"""
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if scipy != None and useScipy:
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return evaluations_scipy(np.asarray(ty), np.asarray(pv))
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if len(ty) != len(pv):
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raise ValueError("len(ty) must be equal to len(pv)")
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total_correct = total_error = 0
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sumv = sumy = sumvv = sumyy = sumvy = 0
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for v, y in zip(pv, ty):
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if y == v:
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total_correct += 1
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total_error += (v-y)*(v-y)
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sumv += v
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sumy += y
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sumvv += v*v
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sumyy += y*y
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sumvy += v*y
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l = len(ty)
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ACC = 100.0*total_correct/l
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MSE = total_error/l
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try:
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SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
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except:
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SCC = float('nan')
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return (float(ACC), float(MSE), float(SCC))
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def csr_find_scale_param(x, lower=-1, upper=1):
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assert isinstance(x, sparse.csr_matrix)
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assert lower < upper
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l, n = x.shape
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feat_min = x.min(axis=0).toarray().flatten()
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feat_max = x.max(axis=0).toarray().flatten()
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coef = (feat_max - feat_min) / (upper - lower)
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coef[coef != 0] = 1.0 / coef[coef != 0]
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# (x - ones(l,1) * feat_min') * diag(coef) + lower
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# = x * diag(coef) - ones(l, 1) * (feat_min' * diag(coef)) + lower
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# = x * diag(coef) + ones(l, 1) * (-feat_min' * diag(coef) + lower)
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# = x * diag(coef) + ones(l, 1) * offset'
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offset = -feat_min * coef + lower
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offset[coef == 0] = 0
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if sum(offset != 0) * l > 3 * x.getnnz():
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print(
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"WARNING: The #nonzeros of the scaled data is at least 2 times larger than the original one.\n"
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"If feature values are non-negative and sparse, set lower=0 rather than the default lower=-1.",
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file=sys.stderr)
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return {'coef':coef, 'offset':offset}
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def csr_scale(x, scale_param):
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assert isinstance(x, sparse.csr_matrix)
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offset = scale_param['offset']
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coef = scale_param['coef']
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assert len(coef) == len(offset)
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l, n = x.shape
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if not n == len(coef):
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print("WARNING: The dimension of scaling parameters and feature number do not match.", file=sys.stderr)
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coef = coef.resize(n) # zeros padded if n > len(coef)
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offset = offset.resize(n)
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# scaled_x = x * diag(coef) + ones(l, 1) * offset'
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offset = sparse.csr_matrix(offset.reshape(1, n))
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offset = sparse.vstack([offset] * l, format='csr', dtype=x.dtype)
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scaled_x = x.dot(sparse.diags(coef, 0, shape=(n, n))) + offset
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if scaled_x.getnnz() > x.getnnz():
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print(
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"WARNING: original #nonzeros %d\n" % x.getnnz() +
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" > new #nonzeros %d\n" % scaled_x.getnnz() +
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"If feature values are non-negative and sparse, get scale_param by setting lower=0 rather than the default lower=-1.",
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file=sys.stderr)
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return scaled_x
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