def main():
Use_scale = True
Usecv = True
SEED = 15
config = libavito.get_config()
cache_loc = config.cache_loc
nthreads = config.nthreads
Usecv = True # true will split the training data 66-33 and do cv
SEED = 15
threads = nthreads # number of workers for parallelism
######### Load files ############
print("Loading input data")
train = feather.read_dataframe(cache_loc + 'final_featureSet_train.fthr')
y = train['isDuplicate'].values
X = train.drop(['itemID_1', 'itemID_2', 'isDuplicate'], 1).values
del train
print(X.shape)
test = feather.read_dataframe(cache_loc + 'final_featureSet_test.fthr')
ids = test['id'].values
X_test = test.drop(['itemID_1', 'itemID_2', 'id'], 1).values
del test
print(X_test.shape)
metafolder = cache_loc + "meta_folder/" # folder to use to store for meta predictions
#remove begatives
X[X < 0] = 0
X_test[X_test < 0] = 0
#transform the data with log1p
X = np.log1p(X)
X_test = np.log1p(X_test)
#create meta folder to drop predictions for train and test
if not os.path.exists(metafolder): #if it does not exists, we create it
os.makedirs(metafolder)
outset = "marios_nnnew_v3" # predic of all files
#model to use
idex1 = [k for k in range(0, (X.shape[0] * 2) / 3)]
idex2 = [k for k in range((X.shape[0] * 2) / 3, X.shape[0])]
kfolder = [[idex1, idex2]]
#Create Arrays for meta
train_stacker = [0.0 for k in range(0, len(idex2))]
test_stacker = [0.0 for k in range(0, (X_test.shape[0]))]
# CHECK EVerything in five..it could be more efficient
#create target variable
mean_kapa = 0.0
#kfolder=StratifiedKFold(y, n_folds=number_of_folds,shuffle=True, random_state=SEED)
#number_of_folds=0
#X,y=shuffle(X,y, random_state=SEED) # Shuffle since the data is ordered by time
i = 0 # iterator counter
if Usecv:
print("starting cross validation")
for train_index, test_index in kfolder:
# creaning and validation sets
X_train, X_cv = X[train_index], X[test_index]
y_train, y_cv = np.array(y)[train_index], np.array(y)[test_index]
print(" train size: %d. test size: %d, cols: %d " %
((X_train.shape[0]), (X_cv.shape[0]), (X_train.shape[1])))
if Use_scale:
stda = StandardScaler()
X_train = stda.fit_transform(X_train)
X_cv = stda.transform(X_cv)
preds = bagged_set(X_train,
y_train,
SEED,
10,
X_cv,
nval=0.0,
verbos=0)
# compute Loglikelihood metric for this CV fold
#scalepreds(preds)
kapa = roc_auc_score(y_cv, preds)
print "size train: %d size cv: %d AUC (fold %d/%d): %f" % (
(X_train.shape[0]), (X_cv.shape[0]), i + 1, 1, kapa)
mean_kapa += kapa
#save the results
no = 0
for real_index in test_index:
train_stacker[no] = (preds[no])
no += 1
i += 1
if Usecv:
print(" Average AUC: %f" % (mean_kapa))
print(" printing train datasets ")
printfilcsve(np.array(train_stacker),
metafolder + outset + "train.csv")
if Use_scale:
stda = StandardScaler()
X = stda.fit_transform(X)
X_test = stda.transform(X_test)
#preds=bagged_set(X, y,model, SEED, 1, X_test, update_seed=True)
preds = bagged_set(X, y, SEED, 10, X_test, nval=0.0, verbos=0)
for pr in range(0, len(preds)):
test_stacker[pr] = (preds[pr])
preds = np.array(preds)
printfilcsve(np.array(test_stacker), metafolder + outset + "test.csv")
print("Write results...")
output_file = "submission_" + outset + str((mean_kapa)) + ".csv"
print("Writing submission to %s" % output_file)
f = open(config.output_loc + output_file, "w")
f.write("id,probability\n") # the header
for g in range(0, len(preds)):
pr = preds[g]
f.write("%d,%f\n" % (((ids[g]), pr)))
f.close()
print("Done.")
def main():
config = libavito.get_config()
nthreads = config.nthreads
cache_loc = config.cache_loc
output_loc = config.output_loc
load_data = True
SEED = 15
Usecv = True
meta_folder = cache_loc + "meta_folder/" # this is how you name the foler that keeps the held-out and test predictions to be used later for meta modelling
# least of meta models. All the train held out predictions end with a 'train.csv' notation while all test set predictions with a 'test.csv'
meta = [
'marios_xg_v1', 'marios_nn_v1', 'marios_nnnew_v2', 'marios_sgd_v2',
'marios_logit_v2', 'marios_ridge_v2', 'marios_xgson_v2',
'marios_xgrank_v2', 'marios_xgson_v3', 'marios_nnnew_v3',
'marios_xgrank_v3', 'marios_xgreg_v3', 'marios_nnnew_v4',
'marios_xgson_v4', 'marios_xgsonv2_v5'
] #,'marios_xgsonv2_v5'
bags = 5 # helps to avoid overfitting. Istead of 1, we ran 10 models with differnt seed and different shuffling
######### Load files (...or not!) ############
#y = np.loadtxt(meta_folder+"meta_pairs_and_labels.csv", delimiter=',',usecols=[2], skiprows=1)
#ids=np.loadtxt("Random_submission.csv", delimiter=',',usecols=[0], skiprows=1)
print("Loading input data")
train = feather.read_dataframe(cache_loc + 'final_featureSet_train.fthr')
y = train['isDuplicate'].values
del train
test = feather.read_dataframe(cache_loc + 'final_featureSet_test.fthr')
ids = test['id'].values
del test
# the trainstacked features is a dataset provided from Henk and Mathes that contains:
#a couple of FTRL models, non alphanumeric , sentiment scores and some additional models
if load_data:
Xmetatrain = None
Xmetatest = None
for modelname in meta:
mini_xtrain = np.loadtxt(
meta_folder + modelname + 'train.csv'
) # we load the held out prediction of the int'train.csv' model
mini_xtest = np.loadtxt(
meta_folder + modelname + 'test.csv'
) # we load the test set prediction of the int'test.csv' model
mean_train = np.mean(
mini_xtrain
) # we calclaute the mean of the train set held out predictions for reconciliation purposes
mean_test = np.mean(
mini_xtest
) # we calclaute the mean of the test set predictions
# we print the AUC and the means and we still hope that everything makes sense. Eg. the mean of the train set preds is 1232314.34 and the test is 0.7, there is something wrong...
print("model %s auc %f mean train/test %f/%f " %
(modelname, roc_auc_score(
np.array(y), mini_xtrain), mean_train, mean_test))
if Xmetatrain == None:
Xmetatrain = mini_xtrain
Xmetatest = mini_xtest
else:
Xmetatrain = np.column_stack((Xmetatrain, mini_xtrain))
Xmetatest = np.column_stack((Xmetatest, mini_xtest))
# we combine with the stacked features
X = Xmetatrain
X_test = Xmetatest
# we print the pickles
printfile(X, meta_folder + "xmetahome.pkl")
printfile(X_test, meta_folder + "xtmetahome.pkl")
X = load_datas(meta_folder + "xmetahome.pkl")
print("rows %d columns %d " % (X.shape[0], X.shape[1]))
#X_test=load_datas("onegramtest.pkl")
#print("rows %d columns %d " % (X_test.shape[0],X_test.shape[1] ))
else:
X = load_datas(meta_folder + "xmetahome.pkl")
print("rows %d columns %d " % (X.shape[0], X.shape[1]))
X_test = load_datas(meta_folder + "xtmetahome.pkl")
print("rows %d columns %d " % (X_test.shape[0], X_test.shape[1]))
outset = "marios_rf_meta_v1" # Name of the model (quite catchy admitedly)
print("len of target=%d" % (len(y))
) # print the length of the target variable because we can
#model we are going to use
#ExtraTreesClassifier
model = RandomForestClassifier(n_estimators=500,
criterion='entropy',
max_depth=9,
min_samples_leaf=2,
max_features=8,
n_jobs=nthreads,
random_state=1,
verbose=1)
#model=LogisticRegression(C=0.01)
train_stacker = [0.0 for k in range(0, (X.shape[0]))
] # the object to hold teh held-out preds
idex1 = [k for k in range(0, (X.shape[0] * 2) / 3)] # indices for trai
idex2 = [k for k in range((X.shape[0] * 2) / 3, X.shape[0])
] #indices for test
kfolder = [[idex1, idex2]] # create an object to put indices in
#arrays to save predictions for validation and test for meta modelling (stacking)
train_stacker = [0.0 for k in range(0, len(idex2))]
test_stacker = [0.0 for k in range(0, (X_test.shape[0]))]
#create target variable
mean_kapa = 0.0
#X,y=shuffle(X,y, random_state=SEED) # Shuffle since the data is ordered by time
i = 0 # iterator counter
if Usecv:
print("starting cross validation")
for train_index, test_index in kfolder:
# creaning and validation sets
X_train, X_cv = X[train_index], X[test_index]
y_train, y_cv = np.array(y)[train_index], np.array(y)[test_index]
print(" train size: %d. test size: %d, cols: %d " %
((X_train.shape[0]), (X_cv.shape[0]), (X_train.shape[1])))
#use xgboost bagger
preds = bagged_set(X_train,
y_train,
model,
SEED,
bags,
X_cv,
update_seed=True)
# compute Loglikelihood metric for this CV fold
#scalepreds(preds)
kapa = roc_auc_score(y_cv, preds)
print "size train: %d size cv: %d AUC (fold %d/%d): %f" % (
(X_train.shape[0]), (X_cv.shape[0]), i + 1, 1, kapa)
mean_kapa += kapa
#save the results
no = 0
for real_index in test_index:
train_stacker[no] = (preds[no])
no += 1
i += 1
if (Usecv):
#print the array of validation predictions for stacking later on inside the 'meta_folder'
print(" Average AUC: %f" % (mean_kapa))
print(" printing train datasets ")
printfilcsve(np.array(train_stacker),
meta_folder + outset + "train.csv")
preds = bagged_set(X, y, model, SEED, bags, X_test, update_seed=True)
for pr in range(0, len(preds)):
test_stacker[pr] = (preds[pr])
#print prediction as numpy array for stacking later on
preds = np.array(preds)
printfilcsve(np.array(test_stacker), meta_folder + outset + "test.csv")
#create submission file
print("Write results...")
output_file = "submission_" + outset + str((mean_kapa)) + ".csv"
print("Writing submission to %s" % output_file)
f = open(output_loc + output_file, "w")
f.write("id,probability\n") # the header
for g in range(0, len(preds)):
pr = preds[g]
f.write("%d,%f\n" % (((ids[g]), pr)))
f.close()
print("Done.")
def main():
config = libavito.get_config()
cache_loc = config.cache_loc
nthreads = config.nthreads
Usecv=True # true will split the training data 66-33 and do cv
SEED=15
threads=nthreads # number of workers for parallelism
######### Load files ############
print("Loading input data")
train = feather.read_dataframe(cache_loc + 'final_featureSet_train.fthr')
y = train['isDuplicate'].values
X = train.drop(['itemID_1', 'itemID_2', 'isDuplicate'], 1).values
del train
print(X.shape)
test = feather.read_dataframe(cache_loc + 'final_featureSet_test.fthr')
ids = test['id'].values
X_test = test.drop(['itemID_1', 'itemID_2', 'id'], 1).values
del test
print(X_test.shape)
metafolder=cache_loc + "meta_folder/" # folder to use to store for meta predictions
if not os.path.exists(metafolder): #if it does not exists, we create it
os.makedirs(metafolder)
outset="marios_xgson_v2" # predic of all files
#model to use
model=xg.XGBoostClassifier(num_round=1000 ,nthread=threads, eta=0.02, gamma=7.0,max_depth=20, min_child_weight=20, subsample=0.9,
colsample_bytree=0.4,objective='binary:logistic',seed=1)
#Create Arrays for meta
idex1=[k for k in range( 0,(X.shape[0] * 2)/ 3)] # indices for trai
idex2=[k for k in range( (X.shape[0] * 2)/ 3,X.shape[0] )] #indices for test
kfolder=[[idex1,idex2]] # create an object to put indices in
#arrays to save predictions for validation and test for meta modelling (stacking)
train_stacker=[ 0.0 for k in range (0,(X.shape[0])) ]
test_stacker=[0.0 for k in range (0,(X_test.shape[0]))]
#create target variable
mean_kapa = 0.0
#X,y=shuffle(X,y, random_state=SEED) # Shuffle since the data is ordered by time
i=0 # iterator counter
if Usecv:
print ("starting cross validation" )
for train_index, test_index in kfolder:
# creaning and validation sets
X_train, X_cv = X[train_index], X[test_index]
y_train, y_cv = np.array(y)[train_index], np.array(y)[test_index]
print (" train size: %d. test size: %d, cols: %d " % ((X_train.shape[0]) ,(X_cv.shape[0]) ,(X_train.shape[1]) ))
#use xgboost bagger
preds=bagged_set(X_train,y_train,model, SEED, 5, X_cv, update_seed=True)
# compute Loglikelihood metric for this CV fold
#scalepreds(preds)
kapa = roc_auc_score(y_cv,preds)
print "size train: %d size cv: %d AUC (fold %d/%d): %f" % ((X_train.shape[0]), (X_cv.shape[0]), i + 1, 1, kapa)
mean_kapa += kapa
#save the results
no=0
for real_index in test_index:
train_stacker[no]=(preds[no])
no+=1
i+=1
if (Usecv):
#print the array of validation predictions for stacking later on inside the 'meta_folder'
print (" Average AUC: %f" % (mean_kapa) )
print (" printing train datasets ")
printfilcsve(np.array(train_stacker), metafolder+ outset + "train.csv")
preds=bagged_set(X, y,model, SEED ,5, X_test, update_seed=True)
for pr in range (0,len(preds)):
test_stacker[pr]=(preds[pr])
#print prediction as numpy array for stacking later on
preds=np.array(preds)
printfilcsve(np.array(test_stacker), metafolder+ outset + "test.csv")
#create submission file
print("Write results...")
output_file = "submission_"+ outset +str( (mean_kapa ))+".csv"
print("Writing submission to %s" % output_file)
f = open(config.output_loc + output_file, "w")
f.write("id,probability\n")# the header
for g in range(0, len(preds)) :
pr=preds[g]
f.write("%d,%f\n" % (((ids[g]),pr ) ) )
f.close()
print("Done.")
import pandas as pd
import numpy as np
import libavito as a
import feather as f
import time
cache_loc = a.get_config().cache_loc
start = time.time()
print('Transforming training data ... ', end='', flush=True)
df = f.read_dataframe(cache_loc + 'final_featureSet_train.fthr')
df.replace([np.nan, None], -1, inplace=True)
df.replace([np.inf, -np.inf], 9999.99, inplace=True)
f.write_dataframe(df, cache_loc + 'final_featureSet_train.fthr')
del df
a.print_elapsed(start)
start = time.time()
print('Transforming testing data ... ', end='', flush=True)
df = f.read_dataframe(cache_loc + 'final_featureSet_test.fthr')
df.replace([np.nan, None], -1, inplace=True)
df.replace([np.inf, -np.inf], 9999.99, inplace=True)
f.write_dataframe(df, cache_loc + 'final_featureSet_test.fthr')
a.print_elapsed(start)
