286 lines
11 KiB
Python
286 lines
11 KiB
Python
import os, sys
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from .liblinear import *
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from .liblinear import __all__ as liblinear_all
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from .commonutil import *
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from .commonutil import __all__ as common_all
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from ctypes import c_double
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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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if sys.version_info[0] < 3:
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range = xrange
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from itertools import izip as zip
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_cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s)
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else:
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_cstr = lambda s: bytes(s, "utf-8")
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__all__ = ['load_model', 'save_model', 'train', 'predict'] + liblinear_all + common_all
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def load_model(model_file_name):
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"""
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load_model(model_file_name) -> model
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Load a LIBLINEAR model from model_file_name and return.
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"""
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model = liblinear.load_model(_cstr(model_file_name))
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if not model:
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print("can't open model file %s" % model_file_name)
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return None
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model = toPyModel(model)
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return model
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def save_model(model_file_name, model):
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"""
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save_model(model_file_name, model) -> None
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Save a LIBLINEAR model to the file model_file_name.
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"""
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liblinear.save_model(_cstr(model_file_name), model)
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def train(arg1, arg2=None, arg3=None):
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"""
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train(y, x [, options]) -> model | ACC
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y: a list/tuple/ndarray of l true labels (type must be int/double).
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x: 1. a list/tuple of l training instances. Feature vector of
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each training instance is a list/tuple or dictionary.
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2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
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train(prob [, options]) -> model | ACC
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train(prob, param) -> model | ACC
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Train a model from data (y, x) or a problem prob using
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'options' or a parameter param.
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If '-v' is specified in 'options' (i.e., cross validation)
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either accuracy (ACC) or mean-squared error (MSE) is returned.
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options:
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-s type : set type of solver (default 1)
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for multi-class classification
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0 -- L2-regularized logistic regression (primal)
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1 -- L2-regularized L2-loss support vector classification (dual)
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2 -- L2-regularized L2-loss support vector classification (primal)
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3 -- L2-regularized L1-loss support vector classification (dual)
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4 -- support vector classification by Crammer and Singer
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5 -- L1-regularized L2-loss support vector classification
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6 -- L1-regularized logistic regression
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7 -- L2-regularized logistic regression (dual)
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for regression
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11 -- L2-regularized L2-loss support vector regression (primal)
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12 -- L2-regularized L2-loss support vector regression (dual)
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13 -- L2-regularized L1-loss support vector regression (dual)
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for outlier detection
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21 -- one-class support vector machine (dual)
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-c cost : set the parameter C (default 1)
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-p epsilon : set the epsilon in loss function of SVR (default 0.1)
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-e epsilon : set tolerance of termination criterion
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-s 0 and 2
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|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
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where f is the primal function, (default 0.01)
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-s 11
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|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)
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-s 1, 3, 4, 7, and 21
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Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)
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-s 5 and 6
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|f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf,
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where f is the primal function (default 0.01)
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-s 12 and 13
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|f'(alpha)|_1 <= eps |f'(alpha0)|,
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where f is the dual function (default 0.1)
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-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
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-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is
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(for -s 0, 2, 5, 6, 11)"
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-wi weight: weights adjust the parameter C of different classes (see README for details)
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-v n: n-fold cross validation mode
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-C : find parameters (C for -s 0, 2 and C, p for -s 11)
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-q : quiet mode (no outputs)
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"""
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prob, param = None, None
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if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, np.ndarray)):
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assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (np.ndarray, sparse.spmatrix)))
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y, x, options = arg1, arg2, arg3
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prob = problem(y, x)
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param = parameter(options)
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elif isinstance(arg1, problem):
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prob = arg1
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if isinstance(arg2, parameter):
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param = arg2
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else:
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param = parameter(arg2)
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if prob == None or param == None :
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raise TypeError("Wrong types for the arguments")
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prob.set_bias(param.bias)
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liblinear.set_print_string_function(param.print_func)
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err_msg = liblinear.check_parameter(prob, param)
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if err_msg :
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raise ValueError('Error: %s' % err_msg)
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if param.flag_find_parameters:
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nr_fold = param.nr_fold
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best_C = c_double()
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best_p = c_double()
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best_score = c_double()
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if param.flag_C_specified:
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start_C = param.C
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else:
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start_C = -1.0
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if param.flag_p_specified:
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start_p = param.p
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else:
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start_p = -1.0
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liblinear.find_parameters(prob, param, nr_fold, start_C, start_p, best_C, best_p, best_score)
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if param.solver_type in [solver_names.L2R_LR, solver_names.L2R_L2LOSS_SVC]:
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print("Best C = %g CV accuracy = %g%%\n"% (best_C.value, 100.0*best_score.value))
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elif param.solver_type in [solver_names.L2R_L2LOSS_SVR]:
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print("Best C = %g Best p = %g CV MSE = %g\n"% (best_C.value, best_p.value, best_score.value))
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return best_C.value,best_p.value,best_score.value
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elif param.flag_cross_validation:
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l, nr_fold = prob.l, param.nr_fold
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target = (c_double * l)()
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liblinear.cross_validation(prob, param, nr_fold, target)
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ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
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if param.solver_type in [solver_names.L2R_L2LOSS_SVR, solver_names.L2R_L2LOSS_SVR_DUAL, solver_names.L2R_L1LOSS_SVR_DUAL]:
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print("Cross Validation Mean squared error = %g" % MSE)
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print("Cross Validation Squared correlation coefficient = %g" % SCC)
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return MSE
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else:
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print("Cross Validation Accuracy = %g%%" % ACC)
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return ACC
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else:
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m = liblinear.train(prob, param)
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m = toPyModel(m)
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return m
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def predict(y, x, m, options=""):
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"""
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predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)
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y: a list/tuple/ndarray of l true labels (type must be int/double).
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It is used for calculating the accuracy. Use [] if true labels are
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unavailable.
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x: 1. a list/tuple of l training instances. Feature vector of
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each training instance is a list/tuple or dictionary.
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2. an l * n numpy ndarray or scipy spmatrix (n: number of features).
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Predict data (y, x) with the SVM model m.
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options:
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-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
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-q quiet mode (no outputs)
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The return tuple contains
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p_labels: a list of predicted labels
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p_acc: a tuple including accuracy (for classification), mean-squared
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error, and squared correlation coefficient (for regression).
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p_vals: a list of decision values or probability estimates (if '-b 1'
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is specified). If k is the number of classes, for decision values,
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each element includes results of predicting k binary-class
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SVMs. if k = 2 and solver is not MCSVM_CS, only one decision value
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is returned. For probabilities, each element contains k values
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indicating the probability that the testing instance is in each class.
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Note that the order of classes here is the same as 'model.label'
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field in the model structure.
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"""
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def info(s):
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print(s)
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if scipy and isinstance(x, np.ndarray):
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x = np.ascontiguousarray(x) # enforce row-major
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elif scipy and isinstance(x, sparse.spmatrix):
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x = x.tocsr()
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elif not isinstance(x, (list, tuple)):
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raise TypeError("type of x: {0} is not supported!".format(type(x)))
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if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, np.ndarray))):
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raise TypeError("type of y: {0} is not supported!".format(type(y)))
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predict_probability = 0
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argv = options.split()
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i = 0
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while i < len(argv):
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if argv[i] == '-b':
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i += 1
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predict_probability = int(argv[i])
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elif argv[i] == '-q':
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info = print_null
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else:
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raise ValueError("Wrong options")
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i+=1
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solver_type = m.param.solver_type
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nr_class = m.get_nr_class()
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nr_feature = m.get_nr_feature()
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is_prob_model = m.is_probability_model()
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bias = m.bias
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if bias >= 0:
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biasterm = feature_node(nr_feature+1, bias)
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else:
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biasterm = feature_node(-1, bias)
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pred_labels = []
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pred_values = []
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if scipy and isinstance(x, sparse.spmatrix):
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nr_instance = x.shape[0]
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else:
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nr_instance = len(x)
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if predict_probability:
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if not is_prob_model:
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raise TypeError('probability output is only supported for logistic regression')
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prob_estimates = (c_double * nr_class)()
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for i in range(nr_instance):
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if scipy and isinstance(x, sparse.spmatrix):
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indslice = slice(x.indptr[i], x.indptr[i+1])
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xi, idx = gen_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
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else:
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xi, idx = gen_feature_nodearray(x[i], feature_max=nr_feature)
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xi[-2] = biasterm
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label = liblinear.predict_probability(m, xi, prob_estimates)
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values = prob_estimates[:nr_class]
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pred_labels += [label]
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pred_values += [values]
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else:
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if nr_class <= 2:
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nr_classifier = 1
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else:
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nr_classifier = nr_class
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dec_values = (c_double * nr_classifier)()
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for i in range(nr_instance):
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if scipy and isinstance(x, sparse.spmatrix):
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indslice = slice(x.indptr[i], x.indptr[i+1])
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xi, idx = gen_feature_nodearray((x.indices[indslice], x.data[indslice]), feature_max=nr_feature)
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else:
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xi, idx = gen_feature_nodearray(x[i], feature_max=nr_feature)
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xi[-2] = biasterm
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label = liblinear.predict_values(m, xi, dec_values)
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values = dec_values[:nr_classifier]
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pred_labels += [label]
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pred_values += [values]
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if len(y) == 0:
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y = [0] * nr_instance
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ACC, MSE, SCC = evaluations(y, pred_labels)
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if m.is_regression_model():
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info("Mean squared error = %g (regression)" % MSE)
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info("Squared correlation coefficient = %g (regression)" % SCC)
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else:
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info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance))
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return pred_labels, (ACC, MSE, SCC), pred_values
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