{"id":2628,"date":"2015-03-10T13:15:32","date_gmt":"2015-03-10T11:15:32","guid":{"rendered":"http:\/\/www.webcodegeeks.com\/?p=2628"},"modified":"2015-03-08T11:41:11","modified_gmt":"2015-03-08T09:41:11","slug":"python-scikit-learn-training-classifier-non-numeric-features","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/python-scikit-learn-training-classifier-non-numeric-features\/","title":{"rendered":"Python: scikit-learn \u2013 Training a classifier with non numeric features"},"content":{"rendered":"

Following on from my previous<\/a> posts<\/a> on training a classifier<\/a> to pick out the speaker in sentences of HIMYM<\/a> transcripts the next thing to do was train a random forest of decision trees to see how that fared.<\/p>\n

I\u2019ve used scikit-learn for this before<\/a> so I decided to use that. However, before building a random forest I wanted to check that I could build an equivalent decision tree<\/a>.<\/p>\n

I initially thought that scikit-learn\u2019s DecisionTree classifier would take in data in the same format as nltk\u2019s so I started out with the following code:
\n 
\n <\/p>\n

import json\r\nimport nltk\r\nimport collections\r\n \r\nfrom himymutil.ml import pos_features\r\nfrom sklearn import tree\r\nfrom sklearn.cross_validation import train_test_split\r\n \r\nwith open('data\/import\/trained_sentences.json', 'r') as json_file:\r\n    json_data = json.load(json_file)\r\n \r\ntagged_sents = []\r\nfor sentence in json_data:\r\n    tagged_sents.append([(word['word'], word['speaker']) for word in sentence['words']])\r\n \r\nfeaturesets = []\r\nfor tagged_sent in tagged_sents:\r\n    untagged_sent = nltk.tag.untag(tagged_sent)\r\n    sentence_pos = nltk.pos_tag(untagged_sent)\r\n    for i, (word, tag) in enumerate(tagged_sent):\r\n        featuresets.append((pos_features(untagged_sent, sentence_pos, i), tag) )\r\n \r\nclf = tree.DecisionTreeClassifier()\r\n \r\ntrain_data, test_data = train_test_split(featuresets, test_size=0.20, train_size=0.80)\r\n \r\n>>> train_data[1]\r\n({'word': u'your', 'word-pos': 'PRP$', 'next-word-pos': 'NN', 'prev-word-pos': 'VB', 'prev-word': u'throw', 'next-word': u'body'}, False)\r\n \r\n>>> clf.fit([item[0] for item in train_data], [item[1] for item in train_data])\r\nTraceback (most recent call last):\r\n  File '<stdin>', line 1, in <module>\r\n  File '\/Users\/markneedham\/projects\/neo4j-himym\/himym\/lib\/python2.7\/site-packages\/sklearn\/tree\/tree.py', line 137, in fit\r\n    X, = check_arrays(X, dtype=DTYPE, sparse_format='dense')\r\n  File '\/Users\/markneedham\/projects\/neo4j-himym\/himym\/lib\/python2.7\/site-packages\/sklearn\/utils\/validation.py', line 281, in check_arrays\r\n    array = np.asarray(array, dtype=dtype)\r\n  File '\/Users\/markneedham\/projects\/neo4j-himym\/himym\/lib\/python2.7\/site-packages\/numpy\/core\/numeric.py', line 460, in asarray\r\n    return array(a, dtype, copy=False, order=order)\r\nTypeError: float() argument must be a string or a number<\/pre>\n
In fact, the classifier can only deal with numeric features so we need to translate our features into that format using DictVectorizer<\/a>.<\/p>\n
from sklearn.feature_extraction import DictVectorizer\r\n \r\nvec = DictVectorizer()\r\nX = vec.fit_transform([item[0] for item in featuresets]).toarray()\r\n \r\n>>> len(X)\r\n13016\r\n \r\n>>> len(X[0])\r\n7302\r\n \r\n>>> vec.get_feature_names()[10:15]\r\n['next-word-pos=EX', 'next-word-pos=IN', 'next-word-pos=JJ', 'next-word-pos=JJR', 'next-word-pos=JJS']<\/pre>\n
We end up with one feature for every key\/value combination that exists in featuresets<\/cite>.<\/p>\n
I was initially confused about how to split up training and test data sets<\/a> but it\u2019s actually fairly easy \u2013 train_test_split<\/cite> allows us to pass in multiple lists which it splits along the same seam:<\/p>\n
vec = DictVectorizer()\r\nX = vec.fit_transform([item[0] for item in featuresets]).toarray()\r\nY = [item[1] for item in featuresets]\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, train_size=0.80)<\/pre>\n
Next we want to train the classifier which is a couple of lines of code:<\/p>\n
clf = tree.DecisionTreeClassifier()\r\nclf = clf.fit(X_train, Y_train)<\/pre>\n
I wrote the following function to assess the classifier:<\/p>\n
import collections\r\nimport nltk\r\n \r\ndef assess(text, predictions_actual):\r\n    refsets = collections.defaultdict(set)\r\n    testsets = collections.defaultdict(set)\r\n    for i, (prediction, actual) in enumerate(predictions_actual):\r\n        refsets[actual].add(i)\r\n        testsets[prediction].add(i)\r\n    speaker_precision = nltk.metrics.precision(refsets[True], testsets[True])\r\n    speaker_recall = nltk.metrics.recall(refsets[True], testsets[True])\r\n    non_speaker_precision = nltk.metrics.precision(refsets[False], testsets[False])\r\n    non_speaker_recall = nltk.metrics.recall(refsets[False], testsets[False])\r\n    return [text, speaker_precision, speaker_recall, non_speaker_precision, non_speaker_recall]<\/pre>\n
We can call it like so:<\/p>\n
predictions = clf.predict(X_test)\r\nassessment = assess('Decision Tree', zip(predictions, Y_test))\r\n \r\n>>> assessment\r\n['Decision Tree', 0.9459459459459459, 0.9210526315789473, 0.9970134395221503, 0.9980069755854509]<\/pre>\n
Those values are in the same ball park as we\u2019ve seen with the nltk classifier so I\u2019m happy it\u2019s all wired up correctly.<\/p>\n
The last thing I wanted to do was visualise the decision tree<\/a> that had been created and the easiest way to do that is export the classifier to DOT format<\/a> and then use graphviz to create an image:<\/p>\n
with open('\/tmp\/decisionTree.dot', 'w') as file:\r\n    tree.export_graphviz(clf, out_file = file, feature_names = vec.get_feature_names())<\/pre>\n
dot -Tpng \/tmp\/decisionTree.dot -o \/tmp\/decisionTree.png<\/pre>\n
The decision tree is quite a few levels deep so here\u2019s part of it:<\/p>\n
\"decisionTreeSection\"<\/a><\/p>\n
The full script is on github<\/a> if you want to play around with it.<\/p>\n
\n\n\n\n
Reference: <\/span><\/td>\nPython: scikit-learn \u2013 Training a classifier with non numeric features<\/a> from our WCG partner<\/a> Mark Needham at the Mark Needham Blog<\/a> blog.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
Following on from my previous posts on training a classifier to pick out the speaker in sentences of HIMYM transcripts the next thing to do was train a random forest of decision trees to see how that fared. I\u2019ve used scikit-learn for this before so I decided to use that. However, before building a random …<\/p>\n","protected":false},"author":48,"featured_media":1651,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[],"class_list":["post-2628","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python"],"yoast_head":"\nPython: scikit-learn \u2013 Training a classifier with non numeric features - Web Code Geeks - 2026<\/title>\n<meta name=\"description\" content=\"Following on from my previous posts on training a classifier to pick out the speaker in sentences of HIMYM transcripts the next thing to do was train a\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.webcodegeeks.com\/python\/python-scikit-learn-training-classifier-non-numeric-features\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python: scikit-learn \u2013 Training a classifier with non numeric features - 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