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#!/usr/local/bin/python3
# avenir-python: Machine Learning
# Author: Pranab Ghosh
#
# Licensed under the Apache License, Version 2.0 (the "License"); you
# may not use this file except in compliance with the License. You may
# obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.
# Package imports
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import sklearn as sk
import sklearn.linear_model
import matplotlib
import random
import jprops
from random import randint
sys.path.append(os.path.abspath("../lib"))
from util import *
from mlutil import *
from pasearch import *
from bacl import *
# gradient boosting classification
class SupportVectorMachine(BaseClassifier):
def __init__(self, configFile):
defValues = {}
defValues["common.mode"] = ("train", None)
defValues["common.model.directory"] = ("model", None)
defValues["common.model.file"] = (None, None)
defValues["common.scale.file.path"] = (None, "missing scale file path")
defValues["common.preprocessing"] = (None, None)
defValues["common.verbose"] = (False, None)
defValues["train.data.file"] = (None, "missing training data file")
defValues["train.data.fields"] = (None, "missing training data field ordinals")
defValues["train.data.feature.fields"] = (None, "missing training data feature field ordinals")
defValues["train.data.class.field"] = (None, "missing class field ordinal")
defValues["train.validation"] = ("kfold", None)
defValues["train.num.folds"] = (5, None)
defValues["train.algorithm"] = ("svc", None)
defValues["train.kernel.function"] = ("rbf", None)
defValues["train.poly.degree"] = (3, None)
defValues["train.penalty"] = (1.0, None)
defValues["train.gamma"] = ("scale", None)
defValues["train.penalty.norm"] = ("l2", None)
defValues["train.loss"] = ("squared_hinge", None)
defValues["train.dual"] = (True, None)
defValues["train.shrinking"] = (True, None)
defValues["train.nu"] = (0.5, None)
defValues["train.predict.probability"] = (False, None)
defValues["train.print.sup.vectors"] = (False, None)
defValues["train.success.criterion"] = ("error", None)
defValues["train.model.save"] = (False, None)
defValues["train.score.method"] = ("accuracy", None)
defValues["train.search.param.strategy"] = (None, None)
defValues["train.search.params"] = (None, None)
defValues["predict.data.file"] = (None, None)
defValues["predict.data.fields"] = (None, "missing data field ordinals")
defValues["predict.data.feature.fields"] = (None, "missing data feature field ordinals")
defValues["predict.use.saved.model"] = (False, None)
defValues["validate.data.file"] = (None, "missing validation data file")
defValues["validate.data.fields"] = (None, "missing validation data field ordinals")
defValues["validate.data.feature.fields"] = (None, "missing validation data feature field ordinals")
defValues["validate.data.class.field"] = (None, "missing class field ordinal")
defValues["validate.use.saved.model"] = (False, None)
defValues["validate.score.method"] = ("accuracy", None)
super(SupportVectorMachine, self).__init__(configFile, defValues, __name__)
# builds model object
def buildModel(self):
self.logger.info("...building svm model")
algo = self.config.getStringConfig("train.algorithm")[0]
kernelFun = self.config.getStringConfig("train.kernel.function")[0]
penalty = self.config.getFloatConfig("train.penalty")[0]
polyDegree = self.config.getIntConfig("train.poly.degree")[0]
kernelCoeff = self.config.getStringConfig("train.gamma")[0]
kernelCoeff = typedValue(kernelCoeff)
penaltyNorm = self.config.getStringConfig("train.penalty.norm")[0]
trainLoss = self.config.getStringConfig("train.loss")[0]
dualOpt = self.config.getBooleanConfig("train.dual")[0]
shrinkHeuristic = self.config.getBooleanConfig("train.shrinking")[0]
predictProb = self.config.getBooleanConfig("train.predict.probability")[0]
supVecBound = self.config.getFloatConfig("train.nu")[0]
if (algo == "svc"):
if kernelFun == "poly":
model = sk.svm.SVC(C=penalty,kernel=kernelFun,degree=polyDegree,gamma=kernelCoeff, shrinking=shrinkHeuristic, \
probability=predictProb)
elif kernelFun == "rbf" or kernelFun == "sigmoid":
model = sk.svm.SVC(C=penalty,kernel=kernelFun,gamma=kernelCoeff, shrinking=shrinkHeuristic, probability=predictProb)
else:
model = sk.svm.SVC(C=penalty, kernel=kernelFun, shrinking=shrinkHeuristic, probability=predictProb)
elif (algo == "nusvc"):
if kernelFun == "poly":
model = sk.svm.NuSVC(nu=supVecBound, kernel=kernelFun,degree=polyDegree,gamma=kernelCoeff, shrinking=shrinkHeuristic, \
probability=predictProb)
elif kernelFun == "rbf" or kernelFun == "sigmoid":
model = sk.svm.NuSVC(nu=supVecBound, kernel=kernelFun,gamma=kernelCoeff, shrinking=shrinkHeuristic, probability=predictProb)
else:
model = sk.svm.NuSVC(nu=supVecBound, kernel=kernelFun, shrinking=shrinkHeuristic, probability=predictProb)
elif (algo == "linearsvc"):
model = sk.svm.LinearSVC(penalty=penaltyNorm, loss=trainLoss, dual=dualOpt)
else:
self.logger.info("invalid svm algorithm")
sys.exit()
self.classifier = model
return self.classifier
#predict probability with in memory data
def predictProb(self, recs):
# create model
self.prepModel()
#input record
if type(recs) is str:
featData = self.prepStringPredictData(recs)
else:
featData = recs
if (featData.ndim == 1):
featData = featData.reshape(1, -1)
#predict
self.logger.info("...predicting class probability")
clsData = self.classifier.predict_proba(featData)
return clsData
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