清华镜像源安装 NGboost XGboost Catboost
清华镜像源安装 NGboost XGboost Catboost
pip install catboost -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install ngboost -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install xgboost -i https://pypi.tuna.tsinghua.edu.cn/simple
数据比赛常用预测模型：LGB、XGB与ANN
LGB
lightgbm：由于现在的比赛数据越来越大，想要获得一个比较高的预测精度，同时又要减少内存占用以及提升训练速度，lightgbm是一个非常不错的选择，其可达到与xgboost相似的预测效果。
def LGB_predict(train_x,train_y,test_x,res,index):
print("LGB test")
clf = lgb.LGBMClassifier(
boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,
max_depth=-1, n_estimators=5000, objective='binary',
subsample=0.7, colsample_bytree=0.7, subsample_freq=1,
learning_rate=0.05, min_child_weight=50, random_state=2018, n_jobs=-1
)
clf.fit(train_x, train_y, eval_set=[(train_x, train_y)], eval_metric='auc',early_stopping_rounds=100)
res['score'+str(index)] = clf.predict_proba(test_x)[:,1]
res['score'+str(index)] = res['score'+str(index)].apply(lambda x: float('%.6f' % x))
print(str(index)+' predict finish!')
gc.collect()
res=res.reset_index(drop=True)
return res['score'+str(index)]
XGB
xgboost：在lightgbm出来之前，是打比赛的不二之选，现在由于需要做模型融合以提高预测精度，所以也需要使用到xgboost。
def XGB_predict(train_x,train_y,val_X,val_Y,test_x,res):
print("XGB test")
# create dataset for lightgbm
xgb_val = xgb.DMatrix(val_X, label=val_Y)
xgb_train = xgb.DMatrix(X_train, label=y_train)
xgb_test = xgb.DMatrix(test_x)
# specify your configurations as a dict
params = {
'booster': 'gbtree',
# 'objective': 'multi:softmax', # 多分类的问题、
# 'objective': 'multi:softprob', # 多分类概率
'objective': 'binary:logistic',
'eval_metric': 'auc',
# 'num_class': 9, # 类别数，与 multisoftmax 并用
'gamma': 0.1, # 用于控制是否后剪枝的参数,越大越保守，一般0.1、0.2这样子。
'max_depth': 8, # 构建树的深度，越大越容易过拟合
'alpha': 0, # L1正则化系数
'lambda': 10, # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。
'subsample': 0.7, # 随机采样训练样本
'colsample_bytree': 0.5, # 生成树时进行的列采样
'min_child_weight': 3,
# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言
# ，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。
# 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。
'silent': 0, # 设置成1则没有运行信息输出，最好是设置为0.
'eta': 0.03, # 如同学习率
'seed': 1000,
'nthread': -1, # cpu 线程数
'missing': 1,
'scale_pos_weight': (np.sum(y==0)/np.sum(y==1)) # 用来处理正负样本不均衡的问题,通常取：sum(negative cases) / sum(positive cases)
# 'eval_metric': 'auc'
}
plst = list(params.items())
num_rounds = 5000 # 迭代次数
watchlist = [(xgb_train, 'train'), (xgb_val, 'val')]
# 交叉验证
# result = xgb.cv(plst, xgb_train, num_boost_round=200, nfold=4, early_stopping_rounds=200, verbose_eval=True, folds=StratifiedKFold(n_splits=4).split(X, y))
# 训练模型并保存
# early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
model = xgb.train(plst, xgb_train, num_rounds, watchlist, early_stopping_rounds=200)
res['score'] = model.predict(xgb_test)
res['score'] = res['score'].apply(lambda x: float('%.6f' % x))
return resANN
ANN：得益于现在的计算机技术的高度发展，以及GPU性能的提高，还有Keras，tensorflow，pytorch等多重工具的使用，人工神经网络也可以作为最后模型融合的子模型之一，可以有效地提升最终的预测结果。
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
X_train = imp.fit_transform(X_train)
sc = StandardScaler(with_mean=False)
sc.fit(X_train)
X_train = sc.transform(X_train)
val_X = sc.transform(val_X)
X_test = sc.transform(X_test)
ann_scale = 1
from keras.layers import Embedding
model = Sequential()
model.add(Embedding(X_train.shape[1] + 1,
EMBEDDING_DIM,
input_length=MAX_SEQUENCE_LENGTH))
#model.add(Dense(int(256 / ann_scale), input_shape=(X_train.shape[1],)))
model.add(Dense(int(256 / ann_scale)))
model.add(Activation('tanh'))
model.add(Dropout(0.3))
model.add(Dense(int(512 / ann_scale)))
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(int(512 / ann_scale)))
model.add(Activation('tanh'))
model.add(Dropout(0.3))
model.add(Dense(int(256 / ann_scale)))
model.add(Activation('linear'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
# For a multi-class classification problem
model.summary()
class_weight1 = class_weight.compute_class_weight('balanced',
np.unique(y),
y)
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# AUC for a binary classifier
def auc(y_true, y_pred):
ptas = tf.stack([binary_PTA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)
pfas = tf.stack([binary_PFA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)
pfas = tf.concat([tf.ones((1,)) ,pfas],axis=0)
binSizes = -(pfas[1:]-pfas[:-1])
s = ptas*binSizes
return K.sum(s, axis=0)
# PFA, prob false alert for binary classifier
def binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)):
y_pred = K.cast(y_pred >= threshold, 'float32')
# N = total number of negative labels
N = K.sum(1 - y_true)
# FP = total number of false alerts, alerts from the negative class labels
FP = K.sum(y_pred - y_pred * y_true)
return FP/N
# P_TA prob true alerts for binary classifier
def binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)):
y_pred = K.cast(y_pred >= threshold, 'float32')
# P = total number of positive labels
P = K.sum(y_true)
# TP = total number of correct alerts, alerts from the positive class labels
TP = K.sum(y_pred * y_true)
return TP/P
#---------------------------------------------------------------------------------------------------------------------------------------------------
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
# metrics=['accuracy'],
metrics=[auc])
epochs = 100
model.fit(X_train, y, epochs=epochs, batch_size=2000,
validation_data=(val_X, val_y), shuffle=True,
class_weight = class_weight1)