from sklearn.datasets import fetch_20newsgroups
import helper as hlp
import taskd as td
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import sklearn.metrics as smet
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
categories = hlp.fetch_categories()
twenty_train, twenty_test = hlp.fetch_data()
hlp.classify_into_two_class(twenty_train)
hlp.classify_into_two_class(twenty_test)
svdListTrain = td.getsvdListTrain()
nmfListTrain = td.getnmfListTrain()
svdListTest = td.getsvdListTest()
nmfListTest = td.getnmfListTest()
classifier = LogisticRegression(C=10000)
def classifyLR(train, test):
classifier.fit(train, twenty_train.target)
predicted = classifier.predict(test)
predicted_probs = classifier.predict_proba(test)
hlp.getStats(twenty_test.target, predicted)
hlp.plot_roc(twenty_test.target, predicted_probs[:, 1],
'Logistic Regression')
for min_df in [2, 5]:
import helper as hlp import task1 as t1 dataset = hlp.fetch_data() hlp.classify_into_two_class(dataset) labels = hlp.fetch_labels(dataset) tfidf_matrix = t1.getTFIDF_matrix(dataset, 3) km = hlp.getKmeans(2) km.fit(tfidf_matrix) hlp.getStats(labels, km.labels_)
