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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 sklearn.linear_model import LogisticRegression | |
| from random import randint | |
| sys.path.append(os.path.abspath("../lib")) | |
| from util import * | |
| from mlutil import * | |
| from pasearch import * | |
| from bacl import * | |
| # logistic regression classification | |
| class LogisticRegressionDiscriminant(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.penalty"] = ("l2", None) | |
| defValues["train.dual"] = (False, None) | |
| defValues["train.tolerance"] = (0.0001, None) | |
| defValues["train.regularization"] = (1.0, None) | |
| defValues["train.fit.intercept"] = (True, None) | |
| defValues["train.intercept.scaling"] = (1.0, None) | |
| defValues["train.class.weight"] = (None, None) | |
| defValues["train.random.state"] = (None, None) | |
| defValues["train.solver"] = ("liblinear", None) | |
| defValues["train.max.iter"] = (100, None) | |
| defValues["train.multi.class"] = ("ovr", None) | |
| defValues["train.verbose"] = (0, None) | |
| defValues["train.warm.start"] = (False, None) | |
| defValues["train.num.jobs"] = (None, None) | |
| defValues["train.l1.ratio"] = (None, 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(LogisticRegressionDiscriminant, self).__init__(configFile, defValues, __name__) | |
| # builds model object | |
| def buildModel(self): | |
| print ("...building logistic regression model") | |
| penalty = self.config.getStringConfig("train.penalty")[0] | |
| dual = self.config.getBooleanConfig("train.dual")[0] | |
| tol = self.config.getFloatConfig("train.tolerance")[0] | |
| c = self.config.getFloatConfig("train.regularization")[0] | |
| fitIntercept = self.config.getBooleanConfig("train.fit.intercept")[0] | |
| interceptScaling = self.config.getFloatConfig("train.intercept.scaling")[0] | |
| classWeight = self.config.getStringConfig("train.class.weight")[0] | |
| randomState = self.config.getIntConfig("train.random.state")[0] | |
| solver = self.config.getStringConfig("train.solver")[0] | |
| maxIter = self.config.getIntConfig("train.max.iter")[0] | |
| multiClass = self.config.getStringConfig("train.multi.class")[0] | |
| verbos = self.config.getIntConfig("train.verbose")[0] | |
| warmStart = self.config.getBooleanConfig("train.warm.start")[0] | |
| nJobs = self.config.getIntConfig("train.num.jobs")[0] | |
| l1Ratio = self.config.getFloatConfig("train.l1.ratio")[0] | |
| self.classifier = LogisticRegression(penalty=penalty, dual=dual, tol=tol, C=c, fit_intercept=fitIntercept,\ | |
| intercept_scaling=interceptScaling, class_weight=classWeight, random_state=randomState, solver=solver,\ | |
| max_iter=maxIter, multi_class=multiClass, verbose=verbos, warm_start=warmStart, n_jobs=nJobs, l1_ratio=l1Ratio) | |
| return self.classifier | |