def make_predictions(load_list=[]):
train, test, target, test_index = io.load_data()
result = np.zeros(len(test))
seed_list = [1234, 2345, 3456, 4567, 5678, 6789, 7890, 8901, 9012, 123]
mcw_list = [1, 1, 1, 1, 1, 8, 8, 8, 8, 8]
seed_list2 = [1242, 5432]
mcw_list2 = [1, 8]
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
train_meta, test_meta = train_level1(load_list=["xgb1", "xgb2", "knn", "ext1", "ext2", "rf1", "rf2"])
train = np.column_stack((train, train_meta))
test = np.column_stack((test, test_meta))
print "Training model1..."
pred1 = np.zeros(len(test))
for i in range(10):
if "xgb1" not in load_list:
model1 = xgb.XGBClassifier(n_estimators=550, learning_rate=0.01, max_depth=6, colsample_bytree=0.95,
subsample=1, min_child_weight=mcw_list[i], seed=seed_list[i])
model1.fit(train, target)
pickle.dump(model1, open("final_models/xgb/1/xgb_n_"+str(i)+".pkl", "wb"))
else:
model1 = pickle.load(open("final_models/xgb/1/xgb_n_"+str(i)+".pkl", "rb"))
pred1 += model1.predict_proba(test)[:, 1]
pred1 /= len(seed_list)
print "Training model2..."
pred2 = np.zeros(len(test))
for i in range(2):
if "xgb2" not in load_list:
model2 = xgb.XGBClassifier(n_estimators=550, learning_rate=0.01, max_depth=6, colsample_bytree=0.95,
subsample=1, min_child_weight=mcw_list2[i], seed=seed_list2[i])
model2 = sklearn.calibration.CalibratedClassifierCV(model2, method="isotonic", cv=10)
model2.fit(train, target)
pickle.dump(model2, open("final_models/xgb/2/xgb_n_"+str(i)+".pkl", "wb"))
else:
model2 = pickle.load(open("final_models/xgb/2/xgb_n_"+str(i)+".pkl", "rb"))
pred2 += model2.predict_proba(test)[:, 1]
pred2 /= len(seed_list2)
print "Training model4..."
pred4 = np.zeros(len(test))
if "ext1" not in load_list:
model4 = sklearn.ensemble.ExtraTreesClassifier(n_estimators=1000,max_features=50,criterion='entropy',min_samples_split=4,
max_depth=35, min_samples_leaf=2, n_jobs =-1, random_state=1234)
model4.fit(train, target)
pred4 = model4.predict_proba(test)[:, 1]
pred4.dump("final_models/ext/ext1_pred")
else:
pred4 = np.load("final_models/ext/ext1_pred")
result = 0.7*np.sqrt(pred1*pred2) + 0.3*pred4
return result
def main():
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
#tuning.parametr_tuning(train, target, param_grid={})
#tuning.ensemble_tuning(train, test, target, load_list=["xgb1"])
result = make_predictions(load_list=["xgb1", "xgb2", "ext1"])
io.save_result(test_index, result)
def main():
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target)
#tuning.tune_xgboost(train, target, load_list=[])
#tuning.parametr_tuning(train, target, param_grid={})
#tuning.ensemble_tuning(train, target, load_list=[])
model = sklearn.ensemble.RandomForestClassifier(n_estimators=2000, max_depth=8, criterion="entropy", bootstrap=False,
min_samples_leaf=4, min_samples_split=2, random_state=1234)
model.fit(train, target)
result = model.predict_proba(test)[:, 1]
"""
result = make_predictions(train, target, test, load_list=["rf_entropy", "xgb"])
"""
io.save_result(test_index, result)
update_momentum=0.9, eval_size=0.01, verbose=0,
max_epochs=100, use_label_encoder=True)
model3.fit(train, target)
pickle.dump(model3, open("final_models/nn/nn_n_"+str(i)+".pkl", "wb"))
else:
model3 = pickle.load(open("final_models/nn/nn_n_"+str(i)+".pkl", "rb"))
pred3 += model3.predict_proba(test)[:, 1]
pred3 /= 10
if ranking:
pred1 = scipy.stats.rankdata(pred1)
pred2 = scipy.stats.rankdata(pred2)
pred3 = scipy.stats.rankdata(pred3)
result = 0.21*pred1 + 0.47*pred2 + 0.32*pred3
return result
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target)
tuning.parametr_tuning(train, target, param_grid={"alpha": [0.01]})
#tuning.ensemble_tuning(train, target, ranking=True, load_list=["linear", "xgb"])
"""
result = make_predictions(train, target, test, ranking=True, load_list=["linear", "xgb", "nn"])
io.save_result(test_index, result)
"""
def train_level1(load_list=[]):
print "Training xgb1..."
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_xgb1 = np.zeros(train.shape[0])
test_xgb1 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
if "xgb1" not in load_list:
model = xgb.XGBClassifier(n_estimators=180, learning_rate=0.05, max_depth=11, colsample_bytree=0.8,
subsample=0.96, min_child_weight=4, seed=1234)
model.fit(train[train_index], target[train_index])
pickle.dump(model, open("level1/xgb/1/xgb_1_fold_"+str(fold)+".pkl", "wb"))
else:
model = pickle.load(open("level1/xgb/1/xgb_1_fold_"+str(fold)+".pkl", "rb"))
train_xgb1[test_index] = model.predict_proba(train[test_index])[:, 1]
test_xgb1 += model.predict_proba(test)[:, 1]/N_FOLDS
train_meta = train_xgb1.reshape((train_xgb1.shape[0], 1))
test_meta = test_xgb1.reshape((test_xgb1.shape[0], 1))
print "Training xgb2..."
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_xgb2 = np.zeros(train.shape[0])
test_xgb2 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
if "xgb2" not in load_list:
model = xgb.XGBRegressor(n_estimators=600, learning_rate=0.02, max_depth=9, colsample_bytree=1,
subsample=1, min_child_weight=1, seed=1234)
model.fit(train[train_index], target[train_index])
pickle.dump(model, open("level1/xgb/2/xgb_1_fold_"+str(fold)+".pkl", "wb"))
else:
model = pickle.load(open("level1/xgb/2/xgb_1_fold_"+str(fold)+".pkl", "rb"))
train_xgb2[test_index] = model.predict(train[test_index])
test_xgb2 += model.predict(test)/N_FOLDS
train_meta = np.column_stack((train_meta, train_xgb2))
test_meta = np.column_stack((test_meta, test_xgb2))
print "Training knn..."
if "knn" not in load_list:
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=4)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_knn = np.zeros(train.shape[0])
test_knn = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=128, metric="minkowski", weights="distance", n_jobs=-1)
model.fit(train[train_index], target[train_index])
train_knn[test_index] = model.predict_proba(train[test_index])[:, 1]
model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=128, metric="minkowski", weights="distance", n_jobs=-1)
model.fit(train, target)
test_knn = model.predict_proba(test)[:, 1]
train_knn.dump("level1/knn/train_knn")
test_knn.dump("level1/knn/test_knn")
else:
train_knn = np.load("level1/knn/train_knn")
test_knn = np.load("level1/knn/test_knn")
train_meta = np.column_stack((train_meta, train_knn))
test_meta = np.column_stack((test_meta, test_knn))
print "Training ext1..."
if "ext1" not in load_list:
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=3)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_ext1 = np.zeros(train.shape[0])
test_ext1 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
model = sklearn.ensemble.ExtraTreesClassifier(n_estimators=1000,max_features=50,criterion='entropy',min_samples_split=4,
max_depth=35, min_samples_leaf=2, n_jobs =-1, random_state=1234)
model.fit(train[train_index], target[train_index])
train_ext1[test_index] = model.predict_proba(train[test_index])[:, 1]
test_ext1 += model.predict_proba(test)[:, 1]/N_FOLDS
train_ext1.dump("level1/ext/1/train_ext")
test_ext1.dump("level1/ext/1/test_ext")
else:
train_ext1 = np.load("level1/ext/1/train_ext")
test_ext1 = np.load("level1/ext/1/test_ext")
train_meta = np.column_stack((train_meta, train_ext1))
test_meta = np.column_stack((test_meta, test_ext1))
print "Training ext2..."
if "ext2" not in load_list:
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=5)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_ext2 = np.zeros(train.shape[0])
test_ext2 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
model = sklearn.ensemble.ExtraTreesRegressor(n_estimators=1000,max_features=50,min_samples_split=4,
max_depth=35, min_samples_leaf=2, n_jobs =-1, random_state=1234)
model.fit(train[train_index], target[train_index])
train_ext2[test_index] = model.predict(train[test_index])
test_ext2 += model.predict(test)/N_FOLDS
train_ext2.dump("level1/ext/2/train_ext")
test_ext2.dump("level1/ext/2/test_ext")
else:
train_ext2 = np.load("level1/ext/2/train_ext")
test_ext2 = np.load("level1/ext/2/test_ext")
train_meta = np.column_stack((train_meta, train_ext2))
test_meta = np.column_stack((test_meta, test_ext2))
print "Training rf1..."
if "rf1" not in load_list:
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_rf1 = np.zeros(train.shape[0])
test_rf1 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
model = sklearn.ensemble.RandomForestClassifier(n_estimators=500, criterion="entropy", max_depth=32, min_samples_leaf=4, n_jobs=-1, random_state=1234)
model.fit(train[train_index], target[train_index])
train_rf1[test_index] = model.predict_proba(train[test_index])[:, 1]
test_rf1 += model.predict_proba(test)[:, 1]/N_FOLDS
train_rf1.dump("level1/rf/1/train_rf")
test_rf1.dump("level1/rf/1/test_rf")
else:
train_rf1 = np.load("level1/rf/1/train_rf")
test_rf1 = np.load("level1/rf/1/test_rf")
train_meta = np.column_stack((train_meta, train_rf1))
test_meta = np.column_stack((test_meta, test_rf1))
print "Training rf2..."
if "rf2" not in load_list:
train, test, target, test_index = io.load_data()
train, test, target = fe.preprocess_data(train, test, target, preprocess_type=1)
N_FOLDS = 10
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
train_rf2 = np.zeros(train.shape[0])
test_rf2 = np.zeros(test.shape[0])
for fold, (train_index, test_index) in enumerate(cv):
model = sklearn.ensemble.RandomForestRegressor(n_estimators=500, max_depth=32, min_samples_leaf=4, n_jobs=-1, random_state=1234)
model.fit(train[train_index], target[train_index])
train_rf2[test_index] = model.predict(train[test_index])
test_rf2 += model.predict(test)/N_FOLDS
train_rf2.dump("level1/rf/2/train_rf")
test_rf2.dump("level1/rf/2/test_rf")
else:
train_rf2 = np.load("level1/rf/2/train_rf")
test_rf2 = np.load("level1/rf/2/test_rf")
train_meta = np.column_stack((train_meta, train_rf2))
test_meta = np.column_stack((test_meta, test_rf2))
return train_meta, test_meta
def ensemble_tuning(train, test, target, load_list=[]):
N_FOLDS = 10
seed_list = [1234, 2345, 6789, 7890]
mcw_list = [1, 1, 8, 8]
cv = sklearn.cross_validation.StratifiedKFold(target, n_folds=N_FOLDS, shuffle=True, random_state=1234)
scores = np.zeros((N_FOLDS, 101), dtype=np.float32)
train, test, target, test_index = io.load_data()
X1, _, target = fe.preprocess_data(train, test, target, preprocess_type=1)
train_meta, test_meta = main.train_level1(load_list=["xgb1", "xgb2", "knn", "ext1", "ext2", "rf1", "rf2"])
X1 = np.column_stack((X1, train_meta))
for fold, (train_index, test_index) in enumerate(cv):
print("Training model1...")
pred1 = np.zeros(len(X1[test_index]))
for i in range(4):
if "xgb1" not in load_list:
model1 = xgb.XGBClassifier(n_estimators=550, learning_rate=0.01, max_depth=6, colsample_bytree=0.95,
subsample=1, min_child_weight=mcw_list[i], seed=seed_list[i])
model1.fit(X1[train_index], target[train_index])
pickle.dump(model1, open("cv/xgb/1/xgb_1_fold_"+str(fold)+"_n_"+str(i)+".pkl", "wb"))
else:
model1 = pickle.load(open("cv/xgb/1/xgb_1_fold_"+str(fold)+"_n_"+str(i)+".pkl", "rb"))
pred1 += model1.predict_proba(X1[test_index])[:, 1]
pred1 /= len(seed_list)
#print("Training model2...")
#pred2 = np.zeros(len(X1[test_index]))
"""
for i in range(6):
if "xgb2" not in load_list:
model2 = xgb.XGBRegressor(n_estimators=550, learning_rate=0.01, max_depth=6, colsample_bytree=0.95,
subsample=1, min_child_weight=mcw_list[i], seed=seed_list[i])
model2.fit(X1[train_index], target[train_index])
pickle.dump(model1, open("cv/xgb/2/xgb_1_fold_"+str(fold)+"_n_"+str(i)+".pkl", "wb"))
else:
model2 = pickle.load(open("cv/xgb/2/xgb_1_fold_"+str(fold)+"_n_"+str(i)+".pkl", "rb"))
pred2 += model1.predict(X1[test_index])
pred2 /= len(seed_list)
pred2[pred2 >= 0.99] = 0.99
pred2[pred2 <= 0.01] = 0.01
"""
print("Training model3...")
if "ext1" not in load_list:
model3 = sklearn.ensemble.ExtraTreesClassifier(n_estimators=1000,max_features=50,criterion='entropy',min_samples_split=4,
max_depth=35, min_samples_leaf=2, n_jobs =-1, random_state=1234)
model3.fit(X1[train_index], target[train_index])
pred3 = model3.predict_proba(X1[test_index])[:, 1]
pred3.dump("cv/rf/pred_fold_"+str(fold))
else:
pred3 = np.load("cv/rf/pred_fold_"+str(fold))
print("Calculating scores...")
for alpha in np.ndindex(101):
scores[fold][alpha] = sklearn.metrics.log_loss(target[test_index], 0.01*alpha[0]*pred1 + np.max(1 - 0.01*alpha[0], 0)*pred3)#np.power(pred1**(0.01*alpha[0])*pred2**(0.01*alpha[1]), 1/(0.01*(alpha[0] + alpha[1] + 1))))
# 0.01*alpha[0]*pred1 + np.max(1 - 0.01*alpha[0], 0)*pred2)
print("Current fold:", np.min(scores[fold]), np.unravel_index(scores[fold].argmin(), scores[fold].shape), scores[fold][100], scores[fold][0])
sc1 = np.mean(scores, axis=0) * 1.0 / (fold+1) * N_FOLDS
print("Accumulated:", np.min(sc1), np.unravel_index(sc1.argmin(), sc1.shape), sc1[100], sc1[0])
scores1 = np.mean(scores, axis=0)
print(np.min(scores1), np.unravel_index(scores1.argmin(), scores1.shape), scores1[100], scores1[0])
return scores
