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#!/usr/local/bin/python3
# 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 random
import jprops
from random import randint
from matumizi.util import *
from matumizi.mlutil import *
"""
Markov chain classifier
"""
class MarkovChainClassifier():
def __init__(self, configFile):
"""
constructor
Parameters
configFile: config file path
"""
defValues = {}
defValues["common.model.directory"] = ("model", None)
defValues["common.model.file"] = (None, None)
defValues["common.verbose"] = (False, None)
defValues["common.states"] = (None, "missing state list")
defValues["train.data.file"] = (None, "missing training data file")
defValues["train.data.class.labels"] = (["F", "T"], None)
defValues["train.data.key.len"] = (1, None)
defValues["train.model.save"] = (False, None)
defValues["train.score.method"] = ("accuracy", None)
defValues["predict.data.file"] = (None, None)
defValues["predict.use.saved.model"] = (True, None)
defValues["predict.log.odds.threshold"] = (0, None)
defValues["validate.data.file"] = (None, "missing validation data file")
defValues["validate.use.saved.model"] = (False, None)
defValues["valid.accuracy.metric"] = ("acc", None)
self.config = Configuration(configFile, defValues)
self.stTranPr = dict()
self.clabels = self.config.getStringListConfig("train.data.class.labels")[0]
self.states = self.config.getStringListConfig("common.states")[0]
self.nstates = len(self.states)
for cl in self.clabels:
stp = np.ones((self.nstates,self.nstates))
self.stTranPr[cl] = stp
def train(self):
"""
train model
"""
#state transition matrix
tdfPath = self.config.getStringConfig("train.data.file")[0]
klen = self.config.getIntConfig("train.data.key.len")[0]
for rec in fileRecGen(tdfPath):
cl = rec[klen]
rlen = len(rec)
for i in range(klen+1, rlen-1, 1):
fst = self.states.index(rec[i])
tst = self.states.index(rec[i+1])
self.stTranPr[cl][fst][tst] += 1
#normalize to probability
for cl in self.clabels:
stp = self.stTranPr[cl]
for i in range(self.nstates):
s = stp[i].sum()
r = stp[i] / s
stp[i] = r
#save
if self.config.getBooleanConfig("train.model.save")[0]:
mdPath = self.config.getStringConfig("common.model.directory")[0]
assert os.path.exists(mdPath), "model save directory does not exist"
mfPath = self.config.getStringConfig("common.model.file")[0]
mfPath = os.path.join(mdPath, mfPath)
with open(mfPath, "w") as fh:
for cl in self.clabels:
fh.write("label:" + cl +"\n")
stp = self.stTranPr[cl]
for r in stp:
rs = ",".join(toStrList(r, 6)) + "\n"
fh.write(rs)
def validate(self):
"""
validate using model
"""
useSavedModel = self.config.getBooleanConfig("predict.use.saved.model")[0]
if useSavedModel:
self.__restoreModel()
else:
self.train()
vdfPath = self.config.getStringConfig("validate.data.file")[0]
accMetric = self.config.getStringConfig("valid.accuracy.metric")[0]
yac, ypr = self.__getPrediction(vdfPath, True)
if type(self.clabels[0]) == str:
yac = self.__toIntClabel(yac)
ypr = self.__toIntClabel(ypr)
score = perfMetric(accMetric, yac, ypr)
print(formatFloat(3, score, "perf score"))
def predict(self):
"""
predict using model
"""
useSavedModel = self.config.getBooleanConfig("predict.use.saved.model")[0]
if useSavedModel:
self.__restoreModel()
else:
self.train()
#predict
pdfPath = self.config.getStringConfig("predict.data.file")[0]
_ , ypr = self.__getPrediction(pdfPath)
return ypr
def __restoreModel(self):
"""
restore model
"""
mdPath = self.config.getStringConfig("common.model.directory")[0]
assert os.path.exists(mdPath), "model save directory does not exist"
mfPath = self.config.getStringConfig("common.model.file")[0]
mfPath = os.path.join(mdPath, mfPath)
stp = None
cl = None
for rec in fileRecGen(mfPath):
if len(rec) == 1:
if stp is not None:
stp = np.array(stp)
self.stTranPr[cl] = stp
cl = rec[0].split(":")[1]
stp = list()
else:
frec = asFloatList(rec)
stp.append(frec)
stp = np.array(stp)
self.stTranPr[cl] = stp
def __getPrediction(self, fpath, validate=False):
"""
get predictions
Parameters
fpath : data file path
validate: True if validation
"""
nc = self.clabels[0]
pc = self.clabels[1]
thold = self.config.getFloatConfig("predict.log.odds.threshold")[0]
klen = self.config.getIntConfig("train.data.key.len")[0]
offset = klen+1 if validate else klen
ypr = list()
yac = list()
for rec in fileRecGen(fpath):
lodds = 0
rlen = len(rec)
for i in range(offset, rlen-1, 1):
fst = self.states.index(rec[i])
tst = self.states.index(rec[i+1])
odds = self.stTranPr[pc][fst][tst] / self.stTranPr[nc][fst][tst]
lodds += math.log(odds)
prc = pc if lodds > thold else nc
ypr.append(prc)
if validate:
yac.append(rec[klen])
else:
recp = prc + "\t" + ",".join(rec)
print(recp)
re = (yac, ypr)
return re
def __toIntClabel(self, labels):
"""
convert string class label to int
Parameters
labels : class label values
"""
return list(map(lambda l : self.clabels.index(l), labels))
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