Commit: 2c007f732b7bab7aa98c765d88647b0014c2bdcf - myprojects-hg-reborn (hg)

Commit MetaInfo

• Revision: 2c007f732b7bab7aa98c765d88647b0014c2bdcf (tree)
• Zeit: 2015-03-26 01:36:49
• Autor: Lorenzo Isella <lorenzo.isella@gmai...>
• Commiter: Lorenzo Isella

Log Message

A simple script to convert the test and train datasets (without the target values!) to a numerical matrix based on the term frequency–inverse document frequency.

Ändern Zusammenfassung

• add: Python-codes/convert-to-tf-idf.py

Diff

diff -r 7eded1ec4fda -r 2c007f732b7b Python-codes/convert-to-tf-idf.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Python-codes/convert-to-tf-idf.py	Wed Mar 25 17:36:49 2015 +0100
@@ -0,0 +1,43 @@
+#! /usr/bin/env python
+
+
+
+import pandas as pd
+import numpy as np
+from sklearn import ensemble, feature_extraction, preprocessing
+
+
+# import data
+train = pd.read_csv('train.csv')
+test = pd.read_csv('test.csv')
+#sample = pd.read_csv('sampleSubmission.csv')
+
+# drop ids and get labels
+labels = train.target.values
+#labels2=np.copy(labels)
+train = train.drop('id', axis=1)
+train = train.drop('target', axis=1)
+test = test.drop('id', axis=1)
+
+# transform counts to TFIDF features
+tfidf = feature_extraction.text.TfidfTransformer()
+train = tfidf.fit_transform(train).toarray()
+test = tfidf.transform(test).toarray()
+
+#labels=labels.reshape(-1,1)
+
+# train=np.hstack((train,labels))
+
+# train=pd.DataFrame(train)
+# test=pd.DataFrame(test)
+
+
+np.savetxt("train-tfidf.csv", train, delimiter=",")
+np.savetxt("test-tfidf.csv", test, delimiter=",")
+
+# train.to_csv("train-tfidf.csv", train)
+# test.to_csv("test-tfidf.csv", test)
+
+
+
+print "So far so good"