def test_simple_duplicates(self):
dupe = Datapoint(phraseid="a", sentenceid="b", phrase="b a", sentiment="1")
# Train has a lot of "2" sentiments
train = [Datapoint(phraseid=str(i),
sentenceid=str(i),
phrase="a b",
sentiment="2") for i in range(10)]
train.append(dupe)
test = [Datapoint(*dupe)]
predictor = PhraseSentimentPredictor(duplicates=True)
predictor.fit(train)
predicted = predictor.predict(test)[0]
self.assertEqual(predicted, "1")
def test_simple_predict(self):
train, test = corpus.make_train_test_split("inhaler")
predictor = PhraseSentimentPredictor()
predictor.fit(train)
predictions = predictor.predict(test)
# Same amount of predictions than input values
self.assertEqual(len(predictions), len(test))
# Predicted labels where seen during training
train_labels = set(x.sentiment for x in train)
predicted_labels = set(predictions)
self.assertEqual(predicted_labels - train_labels, set())
def test_simple_predict(self):
train, test = corpus.make_train_test_split("inhaler")
predictor = PhraseSentimentPredictor()
predictor.fit(train)
predictions = predictor.predict(test)
# Same amount of predictions than input values
self.assertEqual(len(predictions), len(test))
# Predicted labels where seen during training
train_labels = set(x.sentiment for x in train)
predicted_labels = set(predictions)
self.assertEqual(predicted_labels - train_labels, set())
def test_simple_duplicates(self):
dupe = Datapoint(phraseid="a",
sentenceid="b",
phrase="b a",
sentiment="1")
# Train has a lot of "2" sentiments
train = [
Datapoint(phraseid=str(i),
sentenceid=str(i),
phrase="a b",
sentiment="2") for i in range(10)
]
train.append(dupe)
test = [Datapoint(*dupe)]
predictor = PhraseSentimentPredictor(duplicates=True)
predictor.fit(train)
predicted = predictor.predict(test)[0]
self.assertEqual(predicted, "1")
value = float(value)
except ValueError:
pass
new[key] = value
return new
if __name__ == "__main__":
import argparse
import json
import csv
import sys
from samr.corpus import iter_corpus, iter_test_corpus
from samr.predictor import PhraseSentimentPredictor
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
config = parser.parse_args()
config = json.load(open(config.filename))
predictor = PhraseSentimentPredictor(**config)
predictor.fit(list(iter_corpus()))
test = list(iter_test_corpus())
prediction = predictor.predict(test)
writer = csv.writer(sys.stdout)
writer.writerow(("PhraseId", "Sentiment"))
for datapoint, sentiment in zip(test, prediction):
writer.writerow((datapoint.phraseid, sentiment))
# print(list(iter_corpus())[:10])
predictor.fit(list(iter_corpus()))
# print("prediction Fitting Done")
test = list(iter_test_corpus())
# print("Testing Fitting Done")
# print("Testing done")
dataF = open("../data/outfinal2.csv")
header_csv = dataF.readline()
data = dataF.read().splitlines()
test = [Datapoint(d.split(",")[0],None) for d in data]
prediction = predictor.predict(test)
print(header_csv[:-1]+",Sentiment")
i=0
for datapoint, sentiment in zip(test, prediction):
data[i]+=","+str(sentiment)
print(data[i])
i+=1
