# Beginner - How can I use ranked values in my Logistic Regression?

I am running a Logistic Regression on some data to predict if a webpage is "good" or "bad". I got the dataset from a finished Kaggle competiton here (train.tsv). I extract the second column of this dataset, the boilerplate text, run TF-IDF, then Logistic Regression as follows :

  import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics,preprocessing,cross_validation
from sklearn.feature_extraction.text import TfidfVectorizer
import sklearn.linear_model as lm
import pandas as p
loadData = lambda f: np.genfromtxt(open(f,'r'), delimiter=' ')

tfv = TfidfVectorizer(min_df=3,  max_features=None, strip_accents='unicode',
analyzer='word',token_pattern=r'\w{1,}',ngram_range=(1, 2), use_idf=1,smooth_idf=1,sublinear_tf=1)

rd = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001,
C=1, fit_intercept=True, intercept_scaling=1.0,
class_weight=None, random_state=None)

X_all = traindata + testdata
lentrain = len(traindata)

print "fitting pipeline"
tfv.fit(X_all)
print "transforming data"
X_all = tfv.transform(X_all)

X = X_all[:lentrain]
X_test = X_all[lentrain:]

print "20 Fold CV Score: ", np.mean(cross_validation.cross_val_score(rd, X, y, cv=20, scoring='roc_auc'))

print "training on full data"
rd.fit(X,y)
pred = rd.predict_proba(X_test)[:,1]
testfile = p.read_csv('test.tsv', sep="\t", na_values=['?'], index_col=1)
pred_df = p.DataFrame(pred, index=testfile.index, columns=['label'])
pred_df.to_csv('benchmark.csv')
print "submission file created.."


Now, I have three simple beginner questions I was hoping to get some opinions on please.

Firstly, as you can see, I am only using the second column in my Logistic Regression, combined with TF-IDF. How can I edit my code to also use all integer columns?

Secondly, I understand TF-IDF, and I understand Logistic Regression; but how in this code are they combined? I don't understand how the output of a TF-IDF can work with a Logistic Regression - isn't it just a list of the most common words?

Finally , each row in the training data corresponds to some information about a webpage. I have wrote a program to get the Google PageRank of a URL and spit this into a CSV for me. As a Google PageRank is a rank figure how can I use it in my Logistic Regression? Do I need to visualise it on a graph and then select a average value for a "bad" page, an average value for a "good" page and see which is closer, or is there a smarter way to do this?