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benchmark.py
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benchmark.py
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__author__ = 'dowling'
logFormat = '%(asctime)s %(levelname)-8s %(name)-18s: %(message)s'
import logging
logging.basicConfig(format=logFormat, level=logging.DEBUG)
ln = logging.getLogger(__name__)
from reuters import stream_reuters_documents
from collections import defaultdict
from linear_msda import mSDA
from gensim.models import LsiModel
from gensim.utils import simple_preprocess
from gensim.corpora import Dictionary
from gensim.matutils import sparse2full, full2sparse
from sklearn.linear_model import Perceptron, PassiveAggressiveClassifier
import numpy as np
dictionary = None
settings = {
"models": {"lsi": False, "msda": True, "noise": True, "bow": True},
"dimensionalities": {
"lsi": 200,
"msda": 1000
},
"held_out_docs": 2000,
#"classifier": "PassiveAggressive"
"classifier": "Perceptron"
}
class ReutersCorpus(object):
def get_documents(self):
for document in stream_reuters_documents():
yield simple_preprocess(document["content"])
def __iter__(self):
return self.get_documents()
class BOWmodel(object):
def __getitem__(self, item):
return item
class NoiseModel(object):
def __init__(self, dims):
self.dims = dims
def __getitem__(self, item):
return full2sparse(np.random.randn(1, self.dims))
class bow_corpus():
def __iter__(self):
for doc in ReutersCorpus():
yield dictionary.doc2bow(doc)
def train_models():
models = dict()
if settings["models"]["msda"]:
dims = settings["dimensionalities"]["msda"]
try:
msda = mSDA.load("reuters_msda_%sdims" % dims)
# the line below is for testing a model I have locally on my machine
#msda = mSDA.load("persist/mSDA/mSDA_wiki_dim-1000_stem-False_tfidf-False_noise-0.5_num_layers-3")
except:
ln.info("Training mSDA...")
prototype_ids = [id_ for id_, freq in sorted(dictionary.dfs.items(), key=lambda (k, v): v, reverse=True)[:dims]]
msda = mSDA(0.5, 5, len(dictionary), dims, prototype_ids=prototype_ids)
msda.train(bow_corpus())
msda.save("reuters_msda_%sdims" % dims)
msda.__out_size = dims
models["msda"] = msda
if settings["models"]["lsi"]:
dims = settings["dimensionalities"]["lsi"]
try:
lsi = LsiModel.load("reuters_lsi_%sdims" % dims)
except:
ln.info("Training LSI...")
lsi = LsiModel(corpus=bow_corpus(), num_topics=dims, id2word=dictionary)
lsi.save("reuters_lsi_%sdims" % dims)
lsi.__out_size = dims
models["lsi"] = lsi
return models
def train_classifiers(models, train_data):
classifiers = dict()
for modelname, model in models.items():
if settings["classifier"] == "Perceptron":
classifier = Perceptron()
if settings["classifier"] == "PassiveAggressive":
classifier = PassiveAggressiveClassifier()
for sample_no, (text, is_acq) in enumerate(train_data):
bow = dictionary.doc2bow(simple_preprocess(text))
model_features = sparse2full(model[bow], model.__out_size)
label = np.array([is_acq])
#ln.debug("%s, %s "% (model_features, label.shape))
classifier.partial_fit(model_features, label, classes=np.array([True, False]))
if sample_no % 500 == 0:
ln.debug("Classifier for %s trained %s samples so far." % (modelname, sample_no))
classifiers[modelname] = classifier
ln.info("Finished training classifier for %s" % modelname)
return classifiers
def run_evaluation(classifiers, models, eval_samples):
ln.info("Beginning evaluation")
classifications = dict()
for modelname, classifier in classifiers.items():
model = models[modelname]
model_classifications = defaultdict(int)
for sample_no, (eval_sample_text, actual_label) in enumerate(eval_samples):
bow = dictionary.doc2bow(simple_preprocess(eval_sample_text))
model_features = sparse2full(model[bow], model.__out_size)
predicted_label = classifier.predict(model_features)[0]
model_classifications[(actual_label, predicted_label)] += 1
if sample_no % 500 == 0:
ln.debug("Classifier for %s evaluated %s samples so far." % (modelname, sample_no))
classifications[modelname] = model_classifications
ln.info("Finished evaluation")
return classifications
def output_results(classifications):
ln.debug("settings: %s" % settings)
ln.info("")
ln.info("### EVALUATION RESULTS ###")
for modelname in classifications:
tp = classifications[modelname][(True, True)]
fp = classifications[modelname][(False, True)]
fn = classifications[modelname][(True, False)]
tn = classifications[modelname][(False, False)]
total = tp + fp + fn + tn
accuracy = float((tp + tn)) / float(total)
P = float(tp) / float(tp + fp)
R = float(tp) / float(tp + fn)
F1 = 2 * (P * R) / (P + R)
ln.info("%s:" % modelname)
ln.info("TP: %s\t FP: %s" % (tp, fp))
ln.info("FN: %s\t TN: %s" % (fn, tn))
ln.info("Total test samples: %s" % total)
ln.info("Accuracy: %s" % accuracy)
ln.info("P:%s \t R: %s" % (P, R))
ln.info("F1: %s" % F1)
ln.info("")
ln.info("")
def main():
global dictionary
try:
dictionary = Dictionary.load_from_text("persist/reuters_dictionary.txt")
#dictionary = Dictionary.load_from_text("persist/wiki_stem-False_keep-100000_nobelow-20_noabove-0.1_wordids.txt.bz2")
except:
dictionary = Dictionary(ReutersCorpus())
dictionary.filter_extremes()
dictionary.save_as_text("persist/reuters_dictionary.txt")
models = train_models()
if settings["models"]["bow"]:
bowmodel = BOWmodel()
bowmodel.__out_size = len(dictionary)
models["bow"] = bowmodel
if settings["models"]["noise"]:
noisemodel = NoiseModel(1000)
noisemodel.__out_size = 1000
models["noise"] = noisemodel
num_train_samples = 21578 - settings["held_out_docs"]
test_samples = []
class generate_train_samples(object):
first_iteration = True
def __iter__(self):
count = 0
for document in stream_reuters_documents():
sample = document["content"], "acq" in document["topics"] # todo: maybe try "usa" or "earn"
if count > num_train_samples:
if self.first_iteration:
test_samples.append(sample)
else:
yield sample
count += 1
self.first_iteration = False
classifiers = train_classifiers(models, generate_train_samples())
classifications = run_evaluation(classifiers, models, test_samples)
#output_results(classifications)
return classifications
if __name__ == "__main__":
main()
### MISCELLANEOUS STUFF, not important. ###
def get_topic_counts():
labels = defaultdict(int)
for document in stream_reuters_documents():
for topic in document["topics"]:
labels[topic] += 1
return labels