def main():
print("Reading the test data")
test = data_io.read_test()
print("Reading in the meta data")
paper_author, paper_author_indexed = f.get_paper_author()
print("Computing Relational Information")
computed_features = f.get_all_computed_features(paper_author)
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = []
for author_id, row in test.iterrows():
features = []
paper_ids = []
for paper_id in row["PaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" % (author_id, paper_id))
else:
features.append(s)
paper_ids.append(paper_id)
feature_matrix = pd.DataFrame(features)
preds = classifier.predict_proba(feature_matrix)[:,1]
paper_ids_sorted = sorted(zip(preds,row["PaperIds"]), reverse=True)
print(paper_ids_sorted)
predictions.append([x[1] for x in paper_ids_sorted])
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Reading the test data")
test = data_io.read_test()
print("Reading in the meta data")
paper_author, paper_author_indexed = f.get_paper_author()
print("Computing Relational Information")
computed_features = f.get_all_computed_features(paper_author)
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = []
for author_id, row in test.iterrows():
features = []
paper_ids = []
for paper_id in row["PaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,
computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" %
(author_id, paper_id))
else:
features.append(s)
paper_ids.append(paper_id)
feature_matrix = pd.DataFrame(features)
preds = classifier.predict_proba(feature_matrix)[:, 1]
paper_ids_sorted = sorted(zip(preds, row["PaperIds"]), reverse=True)
print(paper_ids_sorted)
predictions.append([x[1] for x in paper_ids_sorted])
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Reading in the training data")
train = data_io.read_train()
print("Reading in the meta data")
paper_author, paper_author_indexed = f.get_paper_author()
print("Computing Relational Information")
computed_features = f.get_all_computed_features(paper_author)
print("Extracting features")
features = []
target = []
for author_id, row in train.iterrows():
for paper_id in row["DeletedPaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,
computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" %
(author_id, paper_id))
else:
target.append(1)
features.append(s)
for paper_id in row["ConfirmedPaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,
computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" %
(author_id, paper_id))
else:
target.append(0)
features.append(s)
print("Target Length: %d" % len(target))
print("Feature Length: %d" % len(features))
feature_matrix = pd.DataFrame(features)
print("Training the Classifier")
classifier = RandomForestClassifier(n_estimators=50,
verbose=2,
n_jobs=1,
min_samples_split=10,
random_state=1)
try:
classifier.fit(feature_matrix, target)
except:
import pdb
pdb.set_trace()
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading in the training data")
train = data_io.read_train()
print("Reading in the meta data")
paper_author, paper_author_indexed = f.get_paper_author()
print("Computing Relational Information")
computed_features = f.get_all_computed_features(paper_author)
print("Extracting features")
features = []
target = []
for author_id, row in train.iterrows():
for paper_id in row["DeletedPaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" % (author_id, paper_id))
else:
target.append(1)
features.append(s)
for paper_id in row["ConfirmedPaperIds"]:
s = f.get_features(paper_id, author_id, paper_author_indexed,computed_features)
if s is None:
print("Error at Author Id %d And Paper Id %d" % (author_id, paper_id))
else:
target.append(0)
features.append(s)
print("Target Length: %d" % len(target))
print("Feature Length: %d" % len(features))
feature_matrix = pd.DataFrame(features)
print("Training the Classifier")
classifier = RandomForestClassifier(n_estimators=50,
verbose=2,
n_jobs=1,
min_samples_split=10,
random_state=1)
try:
classifier.fit(feature_matrix, target)
except:
import pdb;pdb.set_trace()
print("Saving the classifier")
data_io.save_model(classifier)
