def main():
print("#--- zerorok -----------------------")
print("weathernon")
f = open("weather.csv", "r")
# ZeroR(f,3)
abcd = Abcd()
abcd.Abcds(f, 3, ZeroR(Tbl()))
f.close()
print("diabetes")
f = open("diabetes.csv", "r")
# ZeroR(f,3)
abcd = Abcd()
abcd.Abcds(f, 3, ZeroR(Tbl()))
f.close()
print("#--- nbok -----------------------")
print("weathernon")
f = open("weather.csv", "r")
abcd2 = Abcd()
abcd2.Abcds(f, 4, NB(Tbl()))
f.close()
print("diabetes")
f = open("diabetes.csv", "r")
abcd2 = Abcd()
abcd2.Abcds(f, 5, NB(Tbl()))
f.close()
def buildNBInput(documents, allTokens):
nbInput = []
for doc in documents:
input = {}
input[0] = int( float(doc.readability) + 0.5)
input[1] = doc.name
for i in range(len(allTokens)):
word = allTokens[i]
if word in doc.tokens:
input[i+2] = int(doc.tokens[word])
nbInput.append(input)
nbTest = []
nbTraining = []
random.shuffle(nbInput)
for t in nbInput:
if random.random() >= 0.8:
nbTest.append(t)
else:
nbTraining.append(t)
print "Using %d intances: %d for training and %d for test" %(len(nbInput), len(nbTraining), len(nbTest))
#nbInput is read!
nb = NB()
nb.trainClassifier(nbTraining)
nb.testInBatch(nbTest)
def main():
train_filename = sys.argv[1]
test_filename = sys.argv[2]
if sys.argv[3][1] == 'N':
k_value = int(sys.argv[3][0])
knn = KNN()
train_data, label_data = knn.getTrainData(train_filename)
test_data = knn.getData(test_filename)
for i in range(len(test_data)):
neighbors = knn.get_neighbors(train_data, label_data, test_data[i],
k_value)
print(knn.vote(neighbors))
else:
nb = NB()
train_data = nb.getData(train_filename)
test__data = nb.getData(test_filename)
result = nb.getPredictions(test__data, train_data)
# separated = nb.separateByClass(train_data)
# print(separated[1][separated[1]=='yes'].count())
# print(result[20][8],result[21][8],result[22][8])
for i in range(len(result)):
if result[i][8] == 1:
print('yes')
else:
print('no')
def main():
#production
#logging.basicConfig(filename='getSimpleAndRegularEnglish.log', filemode='w', level=logging.INFO, format='%(asctime)s-%(levelname)s: %(message)s', datefmt='%d/%m/%Y-%H:%M:%S')
#testing
logging.basicConfig(filename='getSimpleAndRegularEnglish.log', filemode='w', level=logging.DEBUG, format='%(asctime)s-%(levelname)s: %(message)s', datefmt='%d/%m/%Y-%H:%M:%S')
simpleApi = myWikiApi("http://simple.wikipedia.org/w/api.php?")
enApi = myWikiApi("http://en.wikipedia.org/w/api.php?")
maxDepth = 15
dataDir = "data"
#Create or load training set
arffh = ArffHandler()
arffFileCreated = generateTraining(enApi, "training", arffh)
[featureNames, featureVector] = arffh.readArffFile(arffFileCreated)
nb = NB()
nb.trainClassifier(featureVector)
initialCategories = [["Category:Medicine"]]
for category in initialCategories:
simpleApi.getAllSubCategories(category, maxDepth, nb, arffh)
simpleCategories = simpleApi.getVisitedCategories()
simplePageTitles = []
logging.info("Total number of simple categories used: %d", len(simpleCategories))
for category in simpleCategories:
simplePageTitles += simpleApi.getAllPagesBelongingToACategory(category)
equals = 0
total = 0
for page in simplePageTitles:
print page
pageFileName = re.sub(" ","_",page)
simpleFile = open(dataDir + "/" + pageFileName + ".simple", "w")
enFile = open(dataDir + "/" + pageFileName + ".en", "w")
simpleContent = simpleApi.getPageContent(page, bagOfWords=False)
enContent = enApi.getPageContent(page, bagOfWords=False)
if simpleContent and enContent:
for w in enContent:
enFile.write("%s" % w)
for w in simpleContent:
simpleFile.write("%s" % w)
#print "simpleContent:", simpleContent
#print "enContent:", enContent
print "Equal?", simpleContent == enContent
if simpleContent == enContent:
equals += 1
total += 1
print ("Number of equals = %d . Total number = %d" % (equals, total))
def main():
#Simple argument consistency check
#Exit if arguments not specified
if len(sys.argv) != 3:
print("usage: python3 nbclassify.py MODELFILE TESTFILE")
sys.exit(0)
#Create an instance of Naive Bayes class in classification mode
naive_bayes = NB('CLASSIFY')
#Load the model file into the class
naive_bayes.load(sys.argv[1])
#Get the predictions from the classifier
predictions = naive_bayes.classify(sys.argv[2])
#print out the predictions to stdout
for prediction in predictions:
print(prediction)
def create_architecture(self, offsets=None, mode='add', in_proba=False):
## generate abiotic and biotic covariates
self.gen_covs()
## Abiotic response
x_abio = self.fe_env_net(self.env_feat)
## Here, replace following dense layer by another network that yields regression parameters given traits
if in_proba:
print('HSM proba given')
abio_resp = x_abio
else:
abio_resp = tfkl.Dense(
self.m, use_bias=self.hsm_config['archi']['fit_bias'])(x_abio)
## Generate biotic response
if self.var_assoc:
x_bio = tf.expand_dims(self.fe_bio_net(self.bio_feat), axis=1)
else:
x_bio = tfkl.Lambda(lambda x: tf.ones((tf.shape(x)[0], 1, self.d)),
name=self.model_name + '_loadings')(abio_resp)
assoc = self.association(x_bio)
bio_resp = tfkl.Dot(axes=1)([self.counts, assoc])
### Aggregate abiotic and biotic effects
drivers = [abio_resp, bio_resp]
# if self.im_config['archi']['fit_bias']=='offset':
# drivers+=[tf.expand_dims(tf.constant(offsets,dtype=tf.float32),0)]
### aggregation is done using addition here, could be extended to more complex differentiable functions
if (self.dist[0] == 'binomial') & (mode == 'bam'):
## Add an intercept (useful for basal species)
off_bio_resp = BiasLayer(name=self.model_name +
'_indep_offset')(bio_resp)
pred = tfkl.Activation('sigmoid')(abio_resp) * tfkl.Activation(
'sigmoid')(off_bio_resp)
self.eta_model = tfk.Model(self.inputs, [abio_resp, off_bio_resp])
else:
logits = tfkl.Add(name=self.model_name + '_out')(drivers)
if self.dist[0] in ['negbin']:
self.disp = GlobalParam(self.m, 'disp')
logits = self.disp(logits)
self.nbr = NB(theta_var=self.disp.kernel)
pred = tfkl.Activation(act_fn.get(self.dist[0]))(logits)
self.eta_model = tfk.Model(self.inputs, logits)
self.pred_model = tfk.Model(self.inputs, pred)
self.hsm_model = tfk.Model(self.env_in, abio_resp)
if self.var_assoc:
self.assoc_model = tfk.Model(self.bio_in, assoc)
def main():
#Simple argument consistency check
#Exit if arguments not specified
if len(sys.argv) != 3:
print("usage: python3 nblearn.py TRAININGFILE MODELFILE")
sys.exit(0)
#Create an instance of Naive Bayes class in training mode
naive_bayes = NB('TRAIN')
#Fit the data with the training file
naive_bayes.fit(sys.argv[1])
#Train the classifier
naive_bayes.train()
#Output a model file to the file specified
naive_bayes.generate_model(sys.argv[2])
def main():
train_filename = sys.argv[1]
# This command is to cross validate the given training data set
if sys.argv[2] == 'cross_validate':
# cross validate the given training data set with kNN algorithm
if sys.argv[3][1] == 'N':
k_value = int(sys.argv[3][0])
knn = KNN()
print(knn.cross_validation(k_value, train_filename))
# cross validate the given training data set with NB algorithm
else:
nb = NB()
print(nb.cross_validation(train_filename))
# This command will generate a 10‐fold stratified data with given train dataset in required format
# The required format is described in assignment documentation
elif sys.argv[2] == 'write_stratification':
knn = KNN()
knn.writeStratification(train_filename, "10_folds_stratified.csv")
else:
test_filename = sys.argv[2]
if sys.argv[3][1] == 'N':
k_value = int(sys.argv[3][0])
knn = KNN()
train_dataset = knn.getData(train_filename)
test_data = knn.getData(test_filename)
train_data, label_data = knn.splitAttributeLabels(train_dataset)
results = knn.knn_predict(train_data, label_data, test_data,
k_value)
for i in results:
print(i)
elif sys.argv[3] == 'NB':
nb = NB()
train_data = nb.getTrainData(train_filename)
test__data = nb.getTestData(test_filename)
result = nb.getPredictions(test__data, train_data)
for i in range(len(result)):
if result[i][-1] == 1:
print('yes')
else:
print('no')
zr = ZeroR()
zr.train(t, rows)
print("\ndiabetes")
zr.dump()
# NB
print("\n\n#--- Nbok ---------------------")
tbl = Tbl("weathernon.csv")
rows = []
for lst in tbl.fromString(False, "file"):
rows.append(lst)
c = Col()
tbl.cols = c.colNum(tbl.rows)
nb = NB(tbl, 3)
nb.train(tbl, rows)
print("\nweathernon")
nb.dump()
print()
tbl = Tbl("diabetes.csv")
rows = []
for lst in tbl.fromString(False, "file"):
rows.append(lst)
c = Col()
tbl.cols = c.colNum(tbl.rows)
nb = NB(tbl, 19)
nb.train(tbl, rows)
print("\ndiabetes")
from NB import NB
from scipy.io import arff
import pandas as pd
import sys
import random
import numpy as np
# read input
def readInput(file):
dataset, meta = arff.loadarff(file)
data = pd.DataFrame(dataset)
# decode all attributes in dataframe
for att in meta._attrnames:
data[att] = data[att].str.decode('utf-8')
return data, meta
# define cases
trainFile = sys.argv[1]
testFile = sys.argv[2]
model = sys.argv[3]
trainData, meta = readInput(trainFile)
testData, meta = readInput(testFile)
if model == "n":
accuracy = NB(trainData, testData, meta)
elif model == "t":
accuracy = TAN(trainData, testData, meta)
def __init__(self):
NB.__init__(self)
def __init__(self): NB.__init__(self)
'sci.med', 'sci.space', 'talk.politics.misc'
],
columns=[
'comp.graphics', 'rec.sport.hockey',
'sci.med', 'sci.space', 'talk.politics.misc'
])
plt.figure(figsize=(10, 7))
ax = seaborn.heatmap(df_cm, annot=True)
ax.set(xlabel='Ground Truth', ylabel='Predicted')
plt.title("TF-IDF/" + str(j) + "/" + str(i))
plt.savefig("plots/TF_IDF_" + str(j) + "_" + str(i) + ".png")
# plt.show()
#
#
model = NB()
model.fit(trainx, trainy)
result = model.predict(testx)
acr = accuracy(result, testy)
#
f = open('accuracy.txt', mode='a')
#
f.write("NA_NA_" + str(j) + "_" + str(i) + " " + str(acr) + "\n")
f.close()
conf = confusion_matrix(testy,
result,
labels=[
'comp.graphics', 'rec.sport.hockey',
'sci.med', 'sci.space',
'talk.politics.misc'
])
print('parents of each nodes(attributes):\n', TAN_data.parents, '\n')
print('\n所有平均交叉结果:\n', round(sum(loss01)/len(loss01), 3))
if Global_V.SCHEME.upper() == 'KDB':
pass
if Global_V.SCHEME.upper() == 'AODE':
pass
if Global_V.SCHEME.upper() == 'NB':
result = []
fold_count = 0
for CrossTest_count in range(0, data_len, data_len // 10): # 交叉验证
p_c_fold = []
loss01_fold = 0
NB_data = NB(data_initial, 0.9, CrossTest_count)
NB_data.train()
p_c_fold, result_fold = NB_data.classify()
result.append(result_fold)
# print('\n第', fold_count, '次交叉结果：\n')
print('\n第', fold_count, '次交叉')
loss01_fold = estimate_Output(NB_data.testData, p_c_fold, result_fold, 1)
loss01.append(loss01_fold)
fold_count += 1
# 每个分类的过程，必须保存：1. 每一个testdata里的instance分类到每个Ci的概率p_y 2. 分类结果：result
# output(result, TAN_car.testData,1) # output 函数的输入为：
# output的输出，应该要有：1.每个testdata分到每个类的概率 2.分类结果result 3.testData，(以及参数1,2,3;不同的参数，输出不同)
# compare the prediction results with the real results
if Global_V.PRINTPAR == 3:
print('testset:\n', NB_data.testData, '\n')
print('p_ci= \n', p_c_fold, '\n', 'result:\n', result)
