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("Getting features for valid papers from the database")
if(os.path.exists("features_valid.obj")):
with open("features_valid.obj", 'r') as loadfile:
data = cPickle.load(loadfile)
else:
data = data_io.get_features_db("ValidPaper")
with open("features_valid.obj", 'w') as dumpfile:
cPickle.dump(data, dumpfile, protocol=cPickle.HIGHEST_PROTOCOL)
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model(prefix="forest_")
print("Making predictions")
predictions = classifier.predict_proba(features)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions, prefix="forest_")
def main():
print("Getting features for valid papers from the database")
data = data_io.get_features_db("ValidPaper")
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
featuresfloat = []
for tup in features:
a, b, c, d, e = tup
featuresfloat.append((float(a), float(b), float(c), float(d), float(e)))
print("Totoal number of samples: ", len(featuresfloat))
print("Loading the logistic regression model")
logistic = data_io.load_model()
print("Making predictions")
predictions = logistic.predict_proba(featuresfloat)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions)
def main():
testset = pd.read_csv(path + "test_x.csv", index_col=0)
## deal with the NAs, and add features
#train.feature_eng(test)
## predict
print "Loading the predict_model classifier.."
tstart = datetime.now()
classifier = data_io.load_model("predict_model")
print "Time used", datetime.now() - tstart
print "Making predictions on the predict_model"
tstart = datetime.now()
fnames = ['year', 'month', 'trade_no', 'sigungu_no', 'price', 'monthly_expense']
test_f = testset[fnames].values
predic_proba = classifier.predict_proba(test_f)[:,1]
print "Time used", datetime.now() - tstart
## Making prediction
prediction = zip(testset['year'],
testset['month'],
testset['trade_no'],
testset['sigungu_no'],
testset['price'],
testset['monthly_expense'],
predic_proba)
print "Writing predictions to file.."
tstart = datetime.now()
data_io.write_submission(prediction)
print "Time used,", datetime.now() - tstart
def main():
print("Reading test data")
test_chunks = data_io.read_test_features()
test = pandas.concat([chunk for chunk in test_chunks], ignore_index=True)
feature_names = list(test.columns)
#feature_names.remove("date_time")
features = test[feature_names].values
print("Loading the classifier")
classifiers = data_io.load_model()
print("Making predictions")
#orig_predictions = classifier.predict_proba(features)
#multiplier = 2 ** classifier.classes_
#predictions = orig_predictions * multiplier
#predictions = predictions.sum(axis=1)
predictions = class_probabilities(features, classifiers)
print predictions
predictions = list(-1.0*predictions)
recommendations = zip(test["srch_id"], test["prop_id"], predictions)
print("Writing predictions to file")
data_io.write_submission(recommendations)
def main():
print "sklearn version", pkg_resources.get_distribution("scikit-learn").version
print "numpy version", pkg_resources.get_distribution("numpy").version
print "pandas version", pkg_resources.get_distribution("pandas").version
print("Loading the classifier")
clf = data_io.load_model()
X = data_io.load_matlab_valid_features()
X = delete_unused_columns(X)
X = X.fillna(0)
if(X is None):
print("No feature file found!")
exit(1)
print_importances(X,clf, 0.0)
print("Predictions outcomes with shape: " + str(X.shape))
print clf
predictions = clf.predict(X)
#predictions = clf.predict_pruned(X,3000)
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Getting features for valid papers from the database")
data = data_io.get_features_db("ValidPaper")
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model()
print classifier.feature_importances_
print("Making predictions")
predictions = classifier.predict_proba(features)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions)
def main():
print("Reading test data")
test_chunks = data_io.read_test_features()
test = pandas.concat([chunk for chunk in test_chunks], ignore_index=True)
feature_names = list(test.columns)
#feature_names.remove("date_time")
features = test[feature_names].values
print("Loading the classifier")
classifiers = data_io.load_model()
print("Making predictions")
#orig_predictions = classifier.predict_proba(features)
#multiplier = 2 ** classifier.classes_
#predictions = orig_predictions * multiplier
#predictions = predictions.sum(axis=1)
predictions = class_probabilities(features, classifiers)
print predictions
predictions = list(-1.0 * predictions)
recommendations = zip(test["srch_id"], test["prop_id"], predictions)
print("Writing predictions to file")
data_io.write_submission(recommendations)
def main():
print "sklearn version", pkg_resources.get_distribution(
"scikit-learn").version
print "numpy version", pkg_resources.get_distribution("numpy").version
print "pandas version", pkg_resources.get_distribution("pandas").version
print("Loading the classifier")
clf = data_io.load_model()
X = data_io.load_matlab_valid_features()
X = delete_unused_columns(X)
X = X.fillna(0)
if (X is None):
print("No feature file found!")
exit(1)
print_importances(X, clf, 0.0)
print("Predictions outcomes with shape: " + str(X.shape))
print clf
predictions = clf.predict(X)
#predictions = clf.predict_pruned(X,3000)
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Getting features for valid papers from the database")
if (os.path.exists("features_valid.obj")):
with open("features_valid.obj", 'r') as loadfile:
data = cPickle.load(loadfile)
else:
data = data_io.get_features_db("ValidPaper")
with open("features_valid.obj", 'w') as dumpfile:
cPickle.dump(data, dumpfile, protocol=cPickle.HIGHEST_PROTOCOL)
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model(prefix="forest_")
print("Making predictions")
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions, prefix="forest_")
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 "Getting features for valid papers from the database"
data = data_io.get_features_db("ValidPaper")
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print "Loading the classifier"
classifier = data_io.load_model()
print "Making predictions"
predictions = classifier.predict_proba(features)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print "Writing predictions to file"
data_io.write_submission(paper_predictions)
def main():
print("Getting features for valid papers from the database")
data = data_io.get_features_db("ValidPaper")
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
featuresfloat = []
for tup in features:
a, b, c, d, e = tup
featuresfloat.append(
(float(a), float(b), float(c), float(d), float(e)))
print("Totoal number of samples: ", len(featuresfloat))
print("Loading the logistic regression model")
logistic = data_io.load_model()
print("Making predictions")
predictions = logistic.predict_proba(featuresfloat)[:, 1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions)
def get_cv_score():
classifier = data_io.load_model()
train = data_io.get_train_df()
scores = cv.cross_val_score(classifier, train[[x for x in train.columns if x != 'label']], train['label'])
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
def runWithoutWndchrm(self):
print "Loading the classifier"
classifier = data_io.load_model()
imageCollections = data_io.get_valid_df()
featureGetter = FeatureGetter()
print "Getting the features"
fileName = data_io.get_savez_name_test()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates,
valid) = Utils.calculateFeatures(fileName, featureGetter,
imageCollections)
else:
(namesObservations, coordinates,
valid) = Utils.loadFeatures(fileName)
print "Making predictions"
#valid = normalize(valid, axis=0) #askdfhashdf
predictions = classifier.predict(valid)
predictions = predictions.reshape(len(predictions), 1)
print "Writing predictions to file"
data_io.write_submission(namesObservations, coordinates, predictions)
data_io.write_submission_nice(namesObservations, coordinates,
predictions)
print "Calculating final results"
return Predictor.finalResults(namesObservations, predictions,
coordinates)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-tv',
type=float,
action='store',
dest='threshold_val',
help='specify how to generate recommendation result.')
parser.add_argument('-t',
type=int,
action='store',
dest='target',
help='for validation or test dataset')
if len(sys.argv) != 5:
print 'Command e.g.: python predict.py -tv 0.8 -t 0(1)'
sys.exit(1)
para = parser.parse_args()
if para.target == 0:
file_name = settings["MTLR_TEST_FILE"]
gbt_feature_file = settings["MTLR_FEATURE_FILE"]
elif para.target == 1:
file_name = settings["MTLR_TEST_FILE_FOR_SUBMIT"]
gbt_feature_file = settings["MTLR_FEATURE_FILE_FOR_SUBMIT"]
writer = csv.writer(open(gbt_feature_file, "w"), lineterminator="\n")
classifier = data_io.load_model(settings["MTLR_MODEL_FILE"])
#print classifier.coef_
#raw_input()
user_recommend_result = defaultdict(list)
finished_num = 0
features = []
user_product_ids = []
cache_uid = -1
for i, entry in enumerate(csv.reader(open(file_name))):
feature = map(float, entry[2:])
uid, pid = map(int, entry[:2])
if i == 0:
cache_uid = uid
if uid != cache_uid:
predictions = classifier.predict_proba(user_product_ids, features)
#predictions = classifier.predict(features)
for (t_uid, t_pid), pred in zip(user_product_ids, predictions):
writer.writerow([t_uid, t_pid, pred])
if pred > para.threshold_val:
user_recommend_result[t_uid].append(t_pid)
features = [feature]
user_product_ids = [[uid, pid]]
cache_uid = uid
finished_num += 1
#print("FINISHED UID NUM: %d. " % (finished_num))
#sys.stderr.write("\rFINISHED UID NUM: %d. " % (finished_num))
#sys.stderr.flush()
else:
features.append(feature)
user_product_ids.append([uid, pid])
data_io.write_submission(user_recommend_result)
def estimate(features, target):
print("[INFO] Loading the classifier")
classifier = data_io.load_model()
print("[INFO] Making predictions")
predictions = classifier.predict_proba(features)
return predictions
def get_cv_score():
classifier = data_io.load_model()
train = data_io.get_train_df()
scores = cv.cross_val_score(
classifier, train[[x for x in train.columns if x != 'label']],
train['label'])
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
def main():
test = data_io.read_test()
## deal with the NAs, and add features
train.feature_eng(test)
## predict the booking_bool
print("Loading the Booking classifier..")
tstart = datetime.now()
classifier = data_io.load_model(True)
print("Time used,")
print(datetime.now() - tstart)
print("Making predictions on the booking_bool..")
tstart = datetime.now()
b_fnames = train.get_features(test, True)
b_test_f = test[b_fnames].values
b_prob = classifier.predict_proba(b_test_f)[:, 1]
b_prob = list(-1.0 * b_prob)
print("Time used,")
print(datetime.now() - tstart)
## predict the click_bool
print("Loading the Click classifier..")
tstart = datetime.now()
classifier = data_io.load_model(False)
print("Time used,")
print(datetime.now() - tstart)
print("Making predictions on the click_bool..")
tstart = datetime.now()
c_fnames = train.get_features(test, False)
c_test_f = test[c_fnames].values
c_prob = classifier.predict_proba(c_test_f)[:, 1]
c_prob = list(-1.0 * c_prob)
print("Time used,")
print(datetime.now() - tstart)
## Making Recommendations
recommendations = zip(test["srch_id"], test["prop_id"],
4 * b_prob + c_prob)
print("Writing predictions to file..")
tstart = datetime.now()
data_io.write_submission(recommendations)
print("Time used,")
print(datetime.now() - tstart)
def main():
print("Loading the model")
model = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = model * np.ones(len(valid))
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Loading the model")
model = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = model * np.ones(len(valid))
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
test = data_io.get_test()
predictions = classifier.predict(test)
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-t', type=int, action='store',
dest='target', help='for validation or test dataset')
parser.add_argument('-c1', type=int, action='store',
dest='ucluster_num', help='cluster number of users')
parser.add_argument('-c2', type=int, action='store',
dest='icluster_num', help='cluster number of items')
if len(sys.argv) != 7:
print 'Command e.g.: python cluster.py -t 0(1) -c1 20 -c2 50'
sys.exit(1)
para = parser.parse_args()
if para.target == 0:
user_features = [map(int, entry) for entry in csv.reader(open(settings["USER_CLUSTER_TRAIN_FILE"]))]
item_features = [map(int, entry) for entry in csv.reader(open(settings["ITEM_CLUSTER_TRAIN_FILE"]))]
user_cluster_file = settings["USER_CLUSTER_TEST_FILE"]
item_cluster_file = settings["ITEM_CLUSTER_TEST_FILE"]
elif para.target == 1:
user_features = [map(int, entry) for entry in csv.reader(open(settings["USER_CLUSTER_TRAIN_FILE_FOR_SUBMIT"]))]
item_features = [map(int, entry) for entry in csv.reader(open(settings["ITEM_CLUSTER_TRAIN_FILE_FOR_SUBMIT"]))]
user_cluster_file = settings["USER_CLUSTER_TEST_FILE_FOR_SUBMIT"]
item_cluster_file = settings["ITEM_CLUSTER_TEST_FILE_FOR_SUBMIT"]
else:
print 'Invalid train data target choice...'
sys.exit(1)
writer = csv.writer(open(user_cluster_file, "w"), lineterminator="\n")
cluster = data_io.load_model(settings["USER_CLUSTER_MODEL_FILE"])
uids = [entry[0] for entry in user_features]
features = [entry[1:] for entry in user_features]
labels = cluster.predict(features)
for uid, label in zip(uids, labels):
writer.writerow([uid, label])
writer = csv.writer(open(item_cluster_file, "w"), lineterminator="\n")
cluster = data_io.load_model(settings["ITEM_CLUSTER_MODEL_FILE"])
pids = [entry[0] for entry in item_features]
features = [entry[1:] for entry in item_features]
labels = cluster.predict(features)
for pid, label in zip(pids, labels):
writer.writerow([pid, label])
def main():
test = data_io.read_test()
## deal with the NAs, and add features
train.feature_eng(test)
## predict the booking_bool
print("Loading the Booking classifier..")
tstart = datetime.now()
classifier = data_io.load_model(True)
print("Time used,")
print datetime.now() - tstart
print("Making predictions on the booking_bool..")
tstart = datetime.now()
b_fnames = train.get_features(test, True)
b_test_f = test[b_fnames].values
b_prob = classifier.predict_proba(b_test_f)[:,1]
b_prob = list(-1.0*b_prob)
print("Time used,")
print datetime.now() - tstart
## predict the click_bool
print("Loading the Click classifier..")
tstart = datetime.now()
classifier = data_io.load_model(False)
print("Time used,")
print datetime.now() - tstart
print("Making predictions on the click_bool..")
tstart = datetime.now()
c_fnames = train.get_features(test, False)
c_test_f = test[c_fnames].values
c_prob = classifier.predict_proba(c_test_f)[:,1]
c_prob = list(-1.0*c_prob)
print("Time used,")
print datetime.now() - tstart
## Making Recommendations
recommendations = zip(test["srch_id"], test["prop_id"], 4*b_prob+c_prob)
print("Writing predictions to file..")
tstart = datetime.now()
data_io.write_submission(recommendations)
print("Time used,")
print datetime.now() - tstart
def test_trainingData():
print("Loading processed data ...")
features, targets = data_io.load_processed_data()
print("Loading Regressor ... ")
classifier = data_io.load_model()
print("Doing prediction ...")
predictions = classifier.predict(features)
results = zip(targets[:100], predictions[:100])
results = sorted(map(lambda x: abs(x[0] - x[1]), results))
return sum(results) / len(results)
def main():
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = classifier.predict(valid)
predictions = np.rint(predictions) # Round predictions to nearest integer.
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = classifier.predict(valid)
predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = classifier.predict(valid)
predictions = np.rint(predictions) # Round predictions to nearest integer.
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
valid = data_io.get_valid_df()
predictions = classifier.predict(valid)
predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
valid = data_io.get_valid_df()
P={}
for key in valid:
print("Loading the classifier for %s" %key)
classifier = data_io.load_model(key)
print("Making predictions")
P[key] = classifier.predict(valid[key])
P[key] = P[key].reshape(len(P[key]), 1)
print("Writing predictions to file")
data_io.write_submission(P)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-tv', type=float, action='store',
dest='threshold_val', help='specify how to generate recommendation result.')
parser.add_argument('-t', type=int, action='store',
dest='target', help='for validation or test dataset')
if len(sys.argv) != 5:
print 'Command e.g.: python predict.py -tv 0.8 -t 0(1)'
sys.exit(1)
para = parser.parse_args()
if para.target == 0:
file_name = settings["MTLR_TEST_FILE"]
gbt_feature_file = settings["MTLR_FEATURE_FILE"]
elif para.target == 1:
file_name = settings["MTLR_TEST_FILE_FOR_SUBMIT"]
gbt_feature_file = settings["MTLR_FEATURE_FILE_FOR_SUBMIT"]
writer = csv.writer(open(gbt_feature_file, "w"), lineterminator="\n")
classifier = data_io.load_model(settings["MTLR_MODEL_FILE"])
#print classifier.coef_
#raw_input()
user_recommend_result = defaultdict(list)
finished_num = 0
features = []
user_product_ids = []
cache_uid = -1
for i, entry in enumerate(csv.reader(open(file_name))):
feature = map(float, entry[2:])
uid, pid = map(int, entry[:2])
if i == 0:
cache_uid = uid
if uid != cache_uid:
predictions = classifier.predict_proba(user_product_ids, features)
#predictions = classifier.predict(features)
for (t_uid, t_pid), pred in zip(user_product_ids, predictions):
writer.writerow([t_uid, t_pid, pred])
if pred > para.threshold_val:
user_recommend_result[t_uid].append(t_pid)
features = [feature]
user_product_ids = [[uid, pid]]
cache_uid = uid
finished_num += 1
#print("FINISHED UID NUM: %d. " % (finished_num))
#sys.stderr.write("\rFINISHED UID NUM: %d. " % (finished_num))
#sys.stderr.flush()
else:
features.append(feature)
user_product_ids.append([uid, pid])
data_io.write_submission(user_recommend_result)
def estimate():
features, target = load_svmlight_file(data_io.read_test_svm())
features = features.todense()
print("[INFO] Loading the classifier")
classifier = data_io.load_model()
print("[INFO] Making predictions")
predictions = classifier.predict_proba(features)
return predictions
def estimate():
features, target = load_svmlight_file(data_io.read_test_svm())
features = features.todense()
print("[INFO] Loading the classifier")
classifier = data_io.load_model()
print("[INFO] Making predictions")
predictions = classifier.predict_proba(features)
return predictions
def main():
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
conn = data_io.get_db_conn()
feature_name = open("feature_list.txt").read().split()
# if size < len(feature_name): # to be done!
for table_name in ["ValidPaper"]:
if rank > 0:
# getting features by parallel computing
print "getting features at node " + str(rank)
feature = data_io_parallel.get_features_db_parallel(conn, rank, table_name, feature_name[rank - 1])
else:
feature = data_io_parallel.get_trained_validation_data(conn, table_name)
# sending features to rank 0
print "sending features to node " + str(rank)
features = comm.gather(feature, root = 0)
#print features
if rank == 0:
temp = []
for f in features:
temp.extend(f)
print "Successfully got the features from " + table_name
data = map(list, np.array(temp).T)
if rank == 0:
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model()
print classifier.feature_importances_
print("Making predictions")
predictions = classifier.predict_proba(features)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions)
print "Prediction completed, exit..."
comm.Abort()
def run(self):
valid = self.getValidationDataset()
if f.preprocessedFeatures != []:
intermediate = data_io.read_intermediate_valid()
for i in f.preprocessedFeatures:
valid[i] = intermediate[i]
print "Loading the classifier"
classifier = data_io.load_model()
print "Making predictions"
predictions = classifier.predict(valid)
predictions = predictions.flatten()
print "Writing predictions to file"
data_io.write_submission(predictions)
def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = classifier.predict(valid)
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = classifier.predict(valid)
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
cf = ClassifierFactory()
filename = None
modelnames = ["basic_python_benchmark"]
numRows = None
try:
opts, args = getopt.getopt(sys.argv[1:], "f:m:n:h")
except getopt.GetoptError as err:
print str(err)
sys.exit(2)
for o, a in opts:
if o == "-f":
filename = a
elif o == "-n":
numRows = int(a)
elif o == "-m":
if a == "all":
modelnames = []
for clf_key in cf.get_all_keys():
modelnames.append(clf_key)
elif cf.is_valid_key(a):
modelnames = [a]
elif o == "-h":
print 'options:'
print "\t -m [classifier key | all]"
print "\t -f [filename]"
sys.exit(0)
else:
print "try help: python predict.py -h"
sys.exit(1)
print "Reading the test pairs"
test = data_io.read_test_pairs(numRows)
testInfo = data_io.read_test_info(numRows)
test['A type'] = testInfo['A type']
test['B type'] = testInfo['B type']
for modelname in modelnames:
print "Loading the classifier:", cf.get_classifier_name(modelname)
classifier = data_io.load_model(modelname)
print "Making predictions"
predictions = classifier.predict(test)
predictions = predictions.flatten()
filename = modelname + '.csv'
data_io.write_submission(predictions, filename)
def reclassify():
print("Getting the questions in the database")
questions = get_questions_from_postgres()
print("%d questions retrieved" % len(questions))
print("Loading the trained model")
classifier = data_io.load_model("model.pickle")
print("Making predictions")
probs = classifier.predict_proba(questions)
prob_closed = 1-probs[:,1]
update_postgres_close_likelihood(questions["PostId"], prob_closed)
def prediction(n_train_samples):
proc_test_samples_file = get_paths()["proc_test_samples_path"]
if os.path.exists(proc_test_samples_file):
print "Loading processed test data..."
new_test_samples = pd.read_csv(proc_test_samples_file)
else:
print "Reading test data..."
test_samples = data_io.read_test()
test_samples = test_samples.fillna(value=0)
print "Porcessing test samples"
new_test_samples = process_test_samples(test_samples)
new_test_samples.to_csv(proc_test_samples_file, index=None)
test_feature = new_test_samples.values
print "Loading the Random Forest Classifier"
rf_classifier = data_io.load_model(model_name="rf_classifier.pkl")
print "Random Forest Predicting"
rf_predictions = rf_classifier.predict_proba(test_feature)[:, 1]
print "Loading the Gradient Boosting Classifier"
gb_classifier = data_io.load_model(model_name="gb_classifier.pkl")
print "Gradient Boosting Predicting"
gb_predictions = gb_classifier.predict_proba(test_feature)[:, 1]
print "Loading the SGD Classifier"
sgd_classifier = data_io.load_model(model_name="sgd_classifier.pkl")
print "SGD Predicting"
sgd_predictions = sgd_classifier.predict_proba(test_feature)[:, 1]
prob_arr = np.vstack((rf_predictions, gb_predictions, sgd_predictions))
mean_score = np.mean(prob_arr, axis=0)
mean_score = -1.0 * mean_score
mean_recommendations = zip(new_test_samples["srch_id"], new_test_samples["prod_id"], mean_score)
print "Writing predictions to file"
data_io.write_submission(mean_recommendations, submission_file="mean_result_%i.csv" % n_train_samples)
def main():
markdown = PagedownToHtml()
print("Reading in the training data")
train = data_io.get_train_df()
for i in train.index:
train["BodyMarkdown"][i] = markdown.convert(train["BodyMarkdown"][i])
print("Extracting features and training")
classifier = get_pipeline()
classifier.fit(train, train["OpenStatus"])
print("Saving the classifier")
data_io.save_model(classifier, "model.pickle")
model = data_io.load_model("model.pickle")
def main():
markdown = PagedownToHtml()
print("Reading the private leaderboard file")
test = data_io.get_test_df()
for i in test.index:
test["BodyMarkdown"][i] = markdown.convert(test["BodyMarkdown"][i])
print("Loading the trained model")
classifier = data_io.load_model("model.pickle")
print("Making predictions")
probs = classifier.predict_proba(test)
solution = data_io.get_private_leaderboard_solution_df()
print("Open AUC: %0.6f" % metrics.auc(solution["open"], probs[:,1]))
def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
valid_info = data_io.read_valid_info()
valid = pd.concat([valid, valid_info],axis =1)
valid = train.get_types(valid)
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = classifier.predict(valid)
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions, fn)
def main():
print("Loading the test data")
classifier = data_io.load_model()
print ("Load test data. And Clean..")
test = data_io.get_test_df()
test = FeatureConverter().clean_data(test)
passengerIds = test['Id']
test.drop(['Id'], axis = 1, inplace = True)
test = test.values
print("Making predictions")
predictions = classifier.predict(test).astype(int)
#predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions, passengerIds, ['Id', 'Cover_Type'])
def main():
print("Getting features for valid papers from the database")
if(os.path.exists("features_valid.obj")):
with open("features_valid.obj", 'r') as loadfile:
data = cPickle.load(loadfile)
else:
data = data_io.get_features_db("ValidPaper")
with open("features_valid.obj", 'w') as dumpfile:
cPickle.dump(data, dumpfile, protocol=cPickle.HIGHEST_PROTOCOL)
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
predictInts = []
for tup in features:
a, b, c, d, e = tup
predictInts.append((int(a), int(b), int(c), int(d), int(e)))
print("Loading the classifier")
mlp = data_io.load_model(prefix="mlp_")
print("Making predictions")
predictions = []
for x in predictInts :
#Propagate the inputs forward to compute the outputs
outp = list(x) #output of input layer i.e. output of previous layer to be used as input for next layer
for layer in mlp.layers[1:] : #for all layers starting from the second layer
for i in range(layer.nNeurons):
layer.net[i] = weightedSum(outp, layer.W[1:,i]) + layer.W[0,i]
layer.out[i] = g(layer.net[i], layer.transferF) #pass this weighted sum through the transfer function of this layer
outp = layer.out
predictions.append(mlp.layers[-1].out[0])
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions, prefix="mlp_")
def main():
print("Loading the test data")
classifier = data_io.load_model()
print("Load test data. And Clean..")
test = data_io.get_test_df()
test = FeatureConverter().clean_data(test)
passengerIds = test['Id']
test.drop(['Id'], axis=1, inplace=True)
test = test.values
print("Making predictions")
predictions = classifier.predict(test).astype(int)
#predictions = predictions.reshape(len(predictions), 1)
print("Writing predictions to file")
data_io.write_submission(predictions, passengerIds, ['Id', 'Cover_Type'])
def main():
print("Getting features for valid papers from the database")
if(os.path.exists("features_valid.obj")):
with open("features_valid.obj", 'r') as loadfile:
data = cPickle.load(loadfile)
else:
data = data_io.get_features_db("ValidPaper")
with open("features_valid.obj", 'w') as dumpfile:
cPickle.dump(data, dumpfile, protocol=cPickle.HIGHEST_PROTOCOL)
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
#code for including keywords match feature
print "adding addtional features..."
import additional_features as af
all_features = af.get_additional_features()
_, _, kw_features = all_features
for i in range(len(features)):
features[i]+= tuple(kw_features[i][2:])
featuresnp = np.array(features, dtype='int32')
# featuresnp -= np.mean(featuresnp, axis=0)
# featuresnp /= np.std(featuresnp, axis=0)
print("Loading the classifier")
classifier = data_io.load_model(prefix="forest_")
print("Making predictions")
predictions = classifier.predict_proba(featuresnp)[:,1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
print("Writing predictions to file")
data_io.write_submission(paper_predictions, prefix="forest_")
def predict_write(data, predict_type):
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
features = np.array(features) # This line is for xgboost
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(predictions)
author_predictions = defaultdict(list)
paper_predictions = {}
if (predict_type == "valid"):
targetset = pd.read_csv('dataRev2/Valid.csv')
else:
targetset = pd.read_csv('dataRev2/Test.csv')
parsed_counter = parse_targetset_maintain_duplicate(targetset)
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
author_predictions[a_id].append((pred, p_id))
for author_id in sorted(author_predictions):
paper_ids_sorted = sorted(author_predictions[author_id], reverse=True)
new_result = []
for x in paper_ids_sorted:
pid = x[1]
for i in range(parsed_counter[author_id, pid]):
new_result.append(pid)
paper_predictions[author_id] = new_result
#paper_predictions[author_id] = [x[1] for x in paper_ids_sorted]
paper_predictions[author_id] = processDuplicates(
paper_predictions[author_id])
print("Writing predictions to file")
data_io.write_submission(paper_predictions, predict_type)
def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
features = fe.feature_extractor()
print("Transforming features")
trans_valid = features.fit_transform(valid)
trans_valid = np.nan_to_num(trans_valid)
print("Saving Valid Features")
data_io.save_valid_features(trans_valid)
print("Loading the classifier")
#(both_classifier, A_classifier, B_classifier, none_classifier) = data_io.load_model()
classifier = data_io.load_model()
print("Making predictions")
valid_info = data_io.read_valid_info()
predictions = list()
curr_pred = None
"""
for i in range(len(trans_valid)):
if valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = both_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] != "Numerical":
curr_pred = A_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] != "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = B_classifier.predict_proba(trans_valid[i, :])
else:
curr_pred = none_classifier.predict_proba(trans_valid[i, :])
predictions.append(curr_pred[0][2] - curr_pred[0][0])
"""
orig_predictions = classifier.predict_proba(trans_valid)
predictions = orig_predictions[:, 2] - orig_predictions[:, 0]
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def predict_feature_from_aid(data):
author_paper_ids = [x[:2] for x in data]
features = [x[2:] for x in data]
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions\n")
features = np.array(features) # This line is for xgboost
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(predictions)
result = []
for (a_id, p_id), pred in zip(author_paper_ids, predictions):
result.append((pred, p_id))
#print(author_predictions)
paper_ids_sorted = sorted(result, reverse=True)
result = paper_ids_sorted[0:25]
return result
def main():
print("Reading test data")
test = data_io.read_test()
test.fillna(0, inplace=True)
feature_names = list(test.columns)
feature_names.remove("date_time")
features = test[feature_names].values
print("Loading the classifier")
classifier = data_io.load_model()
print("Making predictions")
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(-1.0 * predictions)
recommendations = zip(test["srch_id"], test["prop_id"], predictions)
print("Writing predictions to file")
data_io.write_submission(recommendations)
def run(self):
print "Preparing the environment"
self.prepareEnvironment()
print "Loading the classifier"
classifier = data_io.load_model()
imageCollections = data_io.get_valid_df()
featureGetter = FeatureGetter()
wndchrmWorker = WndchrmWorkerPredict()
print "Getting the features"
if not self.loadWndchrm: #Last wndchrm set of features
fileName = data_io.get_savez_name_test()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates,
_) = Utils.calculateFeatures(fileName, featureGetter,
imageCollections)
else:
(namesObservations, coordinates,
_) = Utils.loadFeatures(fileName)
print "Saving images"
imageSaver = ImageSaver(coordinates, namesObservations,
imageCollections, featureGetter.patchSize)
imageSaver.saveImages()
print "Executing wndchrm algorithm"
valid = wndchrmWorker.executeWndchrm(namesObservations)
else:
(valid, namesObservations) = wndchrmWorker.loadWndchrmFeatures()
print "Making predictions"
predictions = classifier.predict(valid)
predictions = predictions.reshape(len(predictions), 1)
print "Writing predictions to file"
data_io.write_submission(namesObservations, coordinates, predictions)
data_io.write_submission_nice(namesObservations, coordinates,
predictions)
print "Calculating final results"
return Predictor.finalResults(namesObservations, predictions,
coordinates)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-mPred',
type=int,
action='store',
dest='rec_num',
help='specify how to generate recommendation result.')
if len(sys.argv) != 3:
print 'Command e.g.: python predict.py -mPred(0 or >0)'
sys.exit(1)
classifier = data_io.load_model()
user_recommend_result = defaultdict(list)
para = parser.parse_args()
if para.rec_num > 0:
features = []
user_product_ids = []
cache_uid = -1
finished_num = 0
for i, entry in enumerate(csv.reader(open(settings["GBT_TEST_FILE"]))):
pair = map(float, entry[:2])
uid, pid = map(int, pair)
if i == 0:
cache_uid = uid
if uid != cache_uid:
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(predictions)
user_predictions = []
for (t_uid, t_pid), pred in zip(user_product_ids, predictions):
user_predictions.append((pred, t_pid))
sorted_result = sorted(user_predictions, reverse=True)
pid_sorted = [x[1] for x in sorted_result]
user_recommend_result[cache_uid] = pid_sorted[:para.rec_num]
features = [map(float, entry[2:])]
user_product_ids = [[uid, pid]]
cache_uid = uid
finished_num += 1
print finished_num
else:
features.append(map(float, entry[2:]))
user_product_ids.append([uid, pid])
else:
product_sellnum = getProductSellNum()
product_predictions = defaultdict(list)
features = []
user_product_ids = []
cache_uid = -1
finished_num = 0
for i, entry in enumerate(csv.reader(open(settings["GBT_TEST_FILE"]))):
pair = map(float, entry[:2])
uid, pid = map(int, pair)
if i == 0:
cache_uid = uid
if uid != cache_uid:
predictions = classifier.predict_proba(features)[:, 1]
predictions = list(predictions)
for (t_uid, t_pid), pred in zip(user_product_ids, predictions):
product_predictions[t_pid].append((pred, t_uid))
features = [map(float, entry[2:])]
user_product_ids = [[uid, pid]]
cache_uid = uid
finished_num += 1
print finished_num
else:
features.append(map(float, entry[2:]))
user_product_ids.append([uid, pid])
recommend_pairs = []
for pid in product_predictions:
if pid not in product_sellnum:
continue
sorted_results = sorted(product_predictions[pid], reverse=True)
uid_sorted = [x[1] for x in sorted_results]
for uid in uid_sorted[:product_sellnum[pid]]:
recommend_pairs.append([uid, pid])
for pair in recommend_pairs:
user_recommend_result[pair[0]].append(pair[1])
data_io.write_submission(user_recommend_result)