def main():
sample_size = int(sys.argv[1])
train = data_io.read_train()
print("Data Size:")
print(train.shape)
feature_eng(train)
## originally sample size = 100000
train_sample = train[:sample_size]
## Train the booking model
for i in range(0,2):
if i==0:
model_name = "Booking"
response_name = "booking_bool"
isBook = True
else:
model_name = "Click"
response_name = "click_bool"
isBook = False
print("Training the "+model_name+" Classifier...")
tstart = datetime.now()
feature_names = get_features(train_sample, isBook)
print("Using "+str(len(feature_names))+" features...")
features = train_sample[feature_names].values
target = train_sample[response_name].values
classifier = model.model()
classifier.fit(features, target)
# print the time interval
print("Time used,")
print datetime.now() - tstart
print("Saving the classifier...")
tstart = datetime.now()
data_io.save_model(classifier, isBook)
print("Time used,")
print datetime.now() - tstart
def main():
print("Reading training data")
train = data_io.read_train()
train.fillna(-1, inplace=True)
#train_sample = train.fillna(value=-2)
#train_sample = train[:2500000].fillna(value=0)
train_sample = train[:100000]
#train_sample = train.fillna(value=0)
feature_names = list(train_sample.columns)
feature_names.remove("click_bool")
feature_names.remove("booking_bool")
feature_names.remove("gross_bookings_usd")
feature_names.remove("date_time")
feature_names.remove("position")
features = train_sample[feature_names].values
#train_sample["position"] *= -1.0
#target = train_sample["position"].values
#target = train_sample["booking_bool"].values
target = train_sample["booking_bool"].values
print("Training the Classifier")
classifier = LambdaMART(n_estimators=50,
verbose=2,
min_samples_split=10,
random_state=1)
classifier.fit(features, target)
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading training data")
train = data_io.read_train()
train.fillna(0, inplace=True)
train_sample = train[:100000].fillna(value=0)
feature_names = list(train_sample.columns)
feature_names.remove("click_bool")
feature_names.remove("booking_bool")
feature_names.remove("gross_bookings_usd")
feature_names.remove("date_time")
feature_names.remove("position")
features = train_sample[feature_names].values
target = train_sample["booking_bool"].values
print("Training the Classifier")
classifier = RandomForestClassifier(n_estimators=50,
verbose=2,
n_jobs=1,
min_samples_split=10,
random_state=1)
classifier.fit(features, target)
print("Saving the classifier")
data_io.save_model(classifier)
def main():
sample_size = int(sys.argv[1])
train = data_io.read_train()
print("Data Size:")
print(train.shape)
feature_eng(train)
## originally sample size = 100000
train_sample = train[:sample_size]
## Train the booking model
for i in range(0, 2):
if i == 0:
model_name = "Booking"
response_name = "booking_bool"
isBook = True
else:
model_name = "Click"
response_name = "click_bool"
isBook = False
print("Training the " + model_name + " Classifier...")
tstart = datetime.now()
feature_names = get_features(train_sample, isBook)
print("Using " + str(len(feature_names)) + " features...")
features = train_sample[feature_names].values
target = train_sample[response_name].values
classifier = model.model()
classifier.fit(features, target)
# print the time interval
print("Time used,")
print datetime.now() - tstart
print("Saving the classifier...")
tstart = datetime.now()
data_io.save_model(classifier, isBook)
print("Time used,")
print datetime.now() - tstart
def main():
print("Reading training data ...")
train = data_io.read_train()
train.fillna(0, inplace=True)
train_sample = train.fillna(value=0)
features = ut.preprocess(train_sample)
target = ut.construct_target(train_sample)
# target = train_sample["booking_bool"].values
# save the processed data, which may be useful
# to test the performance of our model
print("Saving processed training data ...")
data_io.save_processed_data([features, target])
print("Training the Regressor ...")
regressor = RandomForestRegressor(n_estimators=10, #RandomForestClassifier
verbose=2,
n_jobs=-1,
max_features = "sqrt",
min_samples_split=10,
random_state=1)
regressor.fit(features, target)
print("Saving the Regressor ...")
data_io.save_model(regressor)
def __init__(self):
self.train = data_io.read_train()
self.test = data_io.read_test()
self.destin = data_io.read_desin()
# pca analysis on the destination
pca = PCA(n_components=3)
self.dest_pca = pca.fit_transform(
self.destin[["d{0}".format(i + 1) for i in range(149)]])
self.dest_pca = pd.DataFrame(self.dest_pca)
self.dest_pca["srch_destination_id"] = self.destin[
"srch_destination_id"]
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():
#sample_size = int(sys.argv[1])
sample_size = int(1000000)
train = data_io.read_train()
print("Data Size:")
print(train.shape)
feature_eng(train)
# originally sample size = 100000
train_set = train #[:sample_size]
book_trainset = train_set[train_set['booking_bool'] == 1]
book_rows = book_trainset.index.tolist()
bsize = len(book_trainset.index)
click_trainset = train_set[train_set['click_bool'] == 1]
click_rows = click_trainset.index.tolist()
csize = len(click_trainset.index)
print('bsize ' + str(bsize))
print('csize ' + str(csize))
book_trainset = book_trainset.append(train_set.iloc[random.sample(
list(train_set.drop(book_rows).index), bsize)])
click_trainset = click_trainset.append(train_set.iloc[random.sample(
list(train_set.drop(click_rows).index), csize)])
# Train the booking model
for i in range(0, 2):
if i == 0:
model_name = "Booking"
response_name = "booking_bool"
train_sample = book_trainset
isBook = True
else:
model_name = "Click"
response_name = "click_bool"
train_sample = click_trainset
isBook = False
print("Training the " + model_name + " Classifier...")
tstart = datetime.now()
feature_names = get_features(train_sample, isBook)
print("Using " + str(len(feature_names)) + " features...")
features = train_sample[feature_names].values
target = train_sample[response_name].values
classifier = model.model()
classifier.fit(features, target)
# print the time interval
print("Time used,")
print(datetime.now() - tstart)
print("Saving the classifier...")
tstart = datetime.now()
data_io.save_model(classifier, isBook)
print("Time used,")
print(datetime.now() - tstart)
def do_train_samples_processing():
## step1 read training data
print "reading training data..."
# train_samples = data_io.read_train(nrows= 100000)
train_samples = data_io.read_train()
print "Done"
## step2 Data preprocessing
print "Processing training data..."
# replace NAN with 0 fillna函数
train_samples = train_samples.fillna(value=0)
# processing training samples
process_train_samples(train_samples)
print "Processing training data done"
def main():
sample_size = int(sys.argv[1])
## sample_size = int(1000)
train = data_io.read_train()
print("Data Size:")
print(train.shape)
feature_eng(train)
## originally sample size = 100000
train_set = train[:sample_size]
book_trainset = train_set[train_set['booking_bool']==1]
book_rows = book_trainset.index.tolist()
bsize = len(book_trainset.index)
click_trainset = train_set[train_set['click_bool']==1]
click_rows = click_trainset.index.tolist()
csize = len(click_trainset.index)
print 'bsize ' + str(bsize)
print 'csize ' + str(csize)
book_trainset = book_trainset.append(train_set.ix[random.sample(train_set.drop(book_rows).index, bsize)])
click_trainset =click_trainset.append(train_set.ix[random.sample(train_set.drop(click_rows).index, csize)])
## Train the booking model
for i in range(0,2):
if i==0:
model_name = "Booking"
response_name = "booking_bool"
train_sample = book_trainset
isBook = True
else:
model_name = "Click"
response_name = "click_bool"
train_sample = click_trainset
isBook = False
print("Training the "+model_name+" Classifier...")
tstart = datetime.now()
feature_names = get_features(train_sample, isBook)
print("Using "+str(len(feature_names))+" features...")
features = train_sample[feature_names].values
target = train_sample[response_name].values
classifier = model.model()
classifier.fit(features, target)
# print the time interval
print("Time used,")
print datetime.now() - tstart
print("Saving the classifier...")
tstart = datetime.now()
data_io.save_model(classifier, isBook)
print("Time used,")
print datetime.now() - tstart
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 do_train_samples_processing():
print "Reading training data..."
train_samples = data_io.read_train()
print "Processing training data..."
train_samples = train_samples.fillna(value=0)
process_train_samples(train_samples)
srch_length_of_stay_features.SrchLengthOfStayFeatures(self.X),
srch_booking_window_features.SrchBookingWindowFeatures(self.X),
]
return map(self.transformer, feature_list)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate features using train/test data")
parser.add_argument("--test", action="store_true", default=False, help="Weather to use test data", required=False)
result = parser.parse_args()
if result.test:
print("Reading test data")
data = data_io.read_test()
else:
print("Reading training data")
data = data_io.read_train()
fm = FeatureExtractor(data)
derived_features = fm.feature_extractor()
data.fillna(0, inplace=True)
data = pandas.concat([data] + derived_features, axis=1)
if result.test:
data_io.save_test_features(data)
else:
data_io.save_train_features(data)
def main():
print("Reading training data")
train = data_io.read_train()
train.fillna(-2, inplace=True)
#train_sample = train.fillna(value=-2)
train_sample = train
#train_sample = train[:100000]
#train_sample = train.fillna(value=0)
#feature_names = [
#'srch_id',
#'price_usd',
#'price_person',
#'price_usd',
#'prop_location_score2',
#'prop_log_historical_price',
#'srch_children_count',
#'srch_query_affinity_score',
#'prop_starrating',
#'visitor_hist_starrating',
#'promotion_flag',
#'prop_review_score',
#'srch_destination_id',
#'prop_id',
#'visitor_hist_adr_usd',
#'prop_brand_bool',
#]
feature_names = list(train_sample.columns)
feature_names.remove("click_bool")
feature_names.remove("booking_bool")
feature_names.remove("gross_bookings_usd")
feature_names.remove("date_time")
feature_names.remove("position")
#feature_names.remove('price_diff')
#feature_names.remove('price_person')
feature_names.remove('star_diff')
#feature_names.remove('pay_diff')
feature_names.remove('price_night')
feature_names.remove('loc_desire')
feature_names.remove('no_kids')
feature_names.remove('couple')
feature_names.remove('price_down')
feature_names.remove('same_country')
#feature_names.remove('prop_location_score1')
features = train_sample[feature_names].values
#train_sample["position"] *= -1.0
#target = train_sample["position"].values
#target = train_sample["booking_bool"].values
target = train_sample["click_bool"].values
print("Training the Classifier")
classifier = GradientBoostingClassifier(n_estimators=80,
verbose=2,
min_samples_split=10,
random_state=1)
classifier.fit(features, target)
print("Saving the classifier")
data_io.save_model(classifier, 'click')
def main():
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
print bcolors.HEADER + "Start Training" + bcolors.HEADER
print bcolors.OKBLUE + "Reading and making Trainingset" + bcolors.OKBLUE
train = data_io.read_train()
train.fillna(0, inplace=True)
train_sample = train[:1250000].fillna(value=0) # change the samplesize over here
# list of features that can be removed if you want
feature_names = list(train_sample.columns)
feature_names.remove("click_bool")
feature_names.remove("booking_bool")
feature_names.remove("gross_bookings_usd")
feature_names.remove("date_time")
feature_names.remove("position")
features = train_sample[feature_names].values
target = train_sample["booking_bool"].values
print bcolors.OKGREEN + "Training Dataset" + bcolors.OKGREEN
# check over here , you can find the algorithms at http://scikit-learn.org/stable/modules/ensemble.html
# random forest
classifier = RandomForestClassifier(n_estimators=3200, verbose=2,n_jobs=-1,min_samples_split=10,random_state=1)
# extra Trees (better then random forest) (best till now!)
#classifier = ExtraTreesClassifier(n_estimators=300, verbose=2, n_jobs=-1, min_samples_split=10,random_state=1)
# Adaboost
#classifier = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2),n_estimators=600,learning_rate=1)
# Knearest neighbour with bagging
#classifier = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)
# Gradient Boosting BEST SOLUTION (i suppose,will try tomorrow)
# classifier = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=2, min_samples_leaf=1, max_depth=3, init=None, random_state=None, max_features=None, verbose=0)
classifier.fit(features, target)
print bcolors.OKBLUE + "Saving Classifier" + bcolors.OKBLUE
data_io.save_model(classifier)
print bcolors.OKGREEN + "Start Making Predictions On Testset" + bcolors.OKGREEN
print bcolors.OKBLUE + "Reading Testset" + bcolors.OKBLUE
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
classifier = data_io.load_model()
print bcolors.OKGREEN + "Make Predictions" + bcolors.OKGREEN
predictions = classifier.predict_proba(features)[:,1]
print bcolors.OKBLUE + "Calculate NDcg" + bcolors.OKBLUE
predictions = list(-1.0*predictions)
print bcolors.OKBLUE + "Sort Predictions" + bcolors.OKBLUE
recommendations = zip(test["srch_id"], test["prop_id"], predictions)
print bcolors.OKGREEN + "Writing Predictions To Outputfile" + bcolors.OKGREEN
data_io.write_submission(recommendations)
print ""
print bcolors.ENDC + "Thats all folks,goodbye!" + bcolors.ENDC
