def main():
feature_functions = [get_character_count, get_word_count]
predictions = {}
for essay_set in cio.get_essay_sets():
print("Making Predictions for Essay Set %s" % essay_set)
train = list(cio.essays_by_set(essay_set))
features = extract_features([x["EssayText"] for x in train],
feature_functions)
rf = RandomForestRegressor(n_estimators=50)
rf.fit(features, [float(x["Score1"]) for x in train])
test = list(
cio.essays_by_set(essay_set,
"../Data/public_leaderboard_rel_2.tsv"))
features = extract_features([x["EssayText"] for x in test],
feature_functions)
predicted_scores = rf.predict(features)
for essay_id, pred_score in zip([x["Id"] for x in test],
predicted_scores):
predictions[essay_id] = round(pred_score)
output_file = "../Submissions/length_benchmark.csv"
print("Writing submission to %s" % output_file)
f = open(output_file, "w")
f.write("id,essay_score\n")
for key in sorted(predictions.keys()):
f.write("%d,%d\n" % (key, predictions[key]))
f.close()
def main():
predictions = {}
for essay_set in cio.get_essay_sets():
print("Making Predictions for Essay Set %s" % essay_set)
train = list(cio.essays_by_set(essay_set))
bag = features.train_bag(" ".join(x["EssayText"] for x in train), 500)
fea = [features.bag_representation(bag, x["EssayText"]) for x in train]
rf = RandomForestRegressor(n_estimators=50)
rf.fit(fea, [float(x["Score1"]) for x in train])
test = list(
cio.essays_by_set(essay_set,
"../Data/public_leaderboard_rel_2.tsv"))
fea = [features.bag_representation(bag, x["EssayText"]) for x in test]
predicted_scores = rf.predict(fea)
for essay_id, pred_score in zip([x["Id"] for x in test],
predicted_scores):
predictions[essay_id] = round(pred_score)
output_file = "../Submissions/bag_of_words_benchmark.csv"
print("Writing submission to %s" % output_file)
f = open(output_file, "w")
f.write("id,essay_score\n")
for key in sorted(predictions.keys()):
f.write("%d,%d\n" % (key, predictions[key]))
f.close()
def main():
feature_functions = [get_character_count, get_word_count]
predictions = {}
for essay_set in cio.get_essay_sets():
print("Making Predictions for Essay Set %s" % essay_set)
train = list(cio.essays_by_set(essay_set))
features = extract_features([x["EssayText"] for x in train], feature_functions)
rf = RandomForestRegressor(n_estimators = 50)
rf.fit(features,[float(x["Score1"]) for x in train])
test = list(cio.essays_by_set(essay_set, "../Data/public_leaderboard_rel_2.tsv"))
features = extract_features([x["EssayText"] for x in test], feature_functions)
predicted_scores = rf.predict(features)
for essay_id, pred_score in zip([x["Id"] for x in test], predicted_scores):
predictions[essay_id] = round(pred_score)
output_file = "../Submissions/length_benchmark.csv"
print("Writing submission to %s" % output_file)
f = open(output_file, "w")
f.write("id,essay_score\n")
for key in sorted(predictions.keys()):
f.write("%d,%d\n" % (key,predictions[key]))
f.close()
def main():
predictions = {}
for essay_set in cio.get_essay_sets():
print("Making Predictions for Essay Set %s" % essay_set)
train = list(cio.essays_by_set(essay_set))
bag = features.train_bag(" ".join(x["EssayText"] for x in train), 500)
fea = [features.bag_representation(bag, x["EssayText"]) for x in train]
rf = RandomForestRegressor(n_estimators = 50)
rf.fit(fea,[float(x["Score1"]) for x in train])
test = list(cio.essays_by_set(essay_set, "../Data/public_leaderboard_rel_2.tsv"))
fea = [features.bag_representation(bag, x["EssayText"]) for x in test]
predicted_scores = rf.predict(fea)
for essay_id, pred_score in zip([x["Id"] for x in test], predicted_scores):
predictions[essay_id] = round(pred_score)
output_file = "../Submissions/bag_of_words_benchmark.csv"
print("Writing submission to %s" % output_file)
f = open(output_file, "w")
f.write("id,essay_score\n")
for key in sorted(predictions.keys()):
f.write("%d,%d\n" % (key,predictions[key]))
f.close()
