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大数据挖掘DT数据分析  公众号： datadw

最近打各种比赛，在这里分享一些General Model，稍微改改就能用的

XGBoost调参大全： http://blog.csdn.net/han_xiaoyang/article/details/52665396 
XGBoost 官方API： 
http://xgboost.readthedocs.io/en/latest//python/python_api.html

Preprocess

# 通用的预处理框架

import pandas as pd

import numpy as np

import scipy as sp

# 文件读取

def read_csv_file(f, logging=False):

print("==========读取数据=========")

data =  pd.read_csv(f)

if logging:

print(data.head(5))

print(f, "包含以下列")

print(data.columns.values)

print(data.describe())

print(data.info())

return data

Logistic Regression

# 通用的LogisticRegression框架

import pandas as pd

import numpy as np

from scipy import sparse

from sklearn.preprocessing import OneHotEncoder

from sklearn.linear_model import LogisticRegression

from sklearn.preprocessing import StandardScaler

# 1. load data

df_train = pd.DataFrame()

df_test  = pd.DataFrame()

y_train = df_train['label'].values

# 2. process data

ss = StandardScaler()

# 3. feature engineering/encoding

# 3.1 For Labeled Feature

enc = OneHotEncoder()

feats = ["creativeID", "adID", "campaignID"]

for i, feat in enumerate(feats):

x_train = enc.fit_transform(df_train[feat].values.reshape(-1, 1))

x_test = enc.fit_transform(df_test[feat].values.reshape(-1, 1))

if i == 0:

X_train, X_test = x_train, x_test

else:

X_train, X_test = sparse.hstack((X_train, x_train)), sparse.hstack((X_test, x_test))

# 3.2 For Numerical Feature

# It must be a 2-D Data for StandardScalar, otherwise reshape(-1, len(feats)) is required

feats = ["price", "age"]

x_train = ss.fit_transform(df_train[feats].values)

x_test  = ss.fit_transform(df_test[feats].values)

X_train, X_test = sparse.hstack((X_train, x_train)), sparse.hstack((X_test, x_test))

# model training

lr = LogisticRegression()

lr.fit(X_train, y_train)

proba_test = lr.predict_proba(X_test)[:, 1]

LightGBM

1. 二分类

import lightgbm as lgb

import pandas as pd

import numpy as np

import pickle

from sklearn.metrics import roc_auc_score

from sklearn.model_selection import train_test_split

print("Loading Data ... ")

# 导入数据

train_x, train_y, test_x = load_data()

# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置

X, val_X, y, val_y = train_test_split(

train_x,

train_y,

test_size=0.05,

random_state=1,

stratify=train_y ## 这里保证分割后y的比例分布与原数据一致

)

X_train = X

y_train = y

X_test = val_X

y_test = val_y

# create dataset for lightgbm

lgb_train = lgb.Dataset(X_train, y_train)

lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# specify your configurations as a dict

params = {

'boosting_type': 'gbdt',

'objective': 'binary',

'metric': {'binary_logloss', 'auc'},

'num_leaves': 5,

'max_depth': 6,

'min_data_in_leaf': 450,

'learning_rate': 0.1,

'feature_fraction': 0.9,

'bagging_fraction': 0.95,

'bagging_freq': 5,

'lambda_l1': 1,

'lambda_l2': 0.001,  # 越小l2正则程度越高

'min_gain_to_split': 0.2,

'verbose': 5,

'is_unbalance': True

}

# train

print('Start training...')

gbm = lgb.train(params,

lgb_train,

num_boost_round=10000,

valid_sets=lgb_eval,

early_stopping_rounds=500)

print('Start predicting...')

preds = gbm.predict(test_x, num_iteration=gbm.best_iteration)  # 输出的是概率结果

# 导出结果

threshold = 0.5

for pred in preds:

result = 1 if pred > threshold else 0

# 导出特征重要性

importance = gbm.feature_importance()

names = gbm.feature_name()

with open('./feature_importance.txt', 'w+') as file:

for index, im in enumerate(importance):

string = names[index] + ', ' + str(im) + '\n'

file.write(string)

2. 多分类

import lightgbm as lgb

import pandas as pd

import numpy as np

import pickle

from sklearn.metrics import roc_auc_score

from sklearn.model_selection import train_test_split

print("Loading Data ... ")

# 导入数据

train_x, train_y, test_x = load_data()

# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置

X, val_X, y, val_y = train_test_split(

train_x,

train_y,

test_size=0.05,

random_state=1,

stratify=train_y ## 这里保证分割后y的比例分布与原数据一致

)

X_train = X

y_train = y

X_test = val_X

y_test = val_y

# create dataset for lightgbm

lgb_train = lgb.Dataset(X_train, y_train)

lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# specify your configurations as a dict

params = {

'boosting_type': 'gbdt',

'objective': 'multiclass',

'num_class': 9,

'metric': 'multi_error',

'num_leaves': 300,

'min_data_in_leaf': 100,

'learning_rate': 0.01,

'feature_fraction': 0.8,

'bagging_fraction': 0.8,

'bagging_freq': 5,

'lambda_l1': 0.4,

'lambda_l2': 0.5,

'min_gain_to_split': 0.2,

'verbose': 5,

'is_unbalance': True

}

# train

print('Start training...')

gbm = lgb.train(params,

lgb_train,

num_boost_round=10000,

valid_sets=lgb_eval,

early_stopping_rounds=500)

print('Start predicting...')

preds = gbm.predict(test_x, num_iteration=gbm.best_iteration)  # 输出的是概率结果

# 导出结果

for pred in preds:

result = prediction = int(np.argmax(pred))

# 导出特征重要性

importance = gbm.feature_importance()

names = gbm.feature_name()

with open('./feature_importance.txt', 'w+') as file:

for index, im in enumerate(importance):

string = names[index] + ', ' + str(im) + '\n'

file.write(string)

XGBoost

1. 二分类

import numpy as np

import pandas as pd

import xgboost as xgb

import time

from sklearn.model_selection import StratifiedKFold

from sklearn.model_selection import train_test_split

train_x, train_y, test_x = load_data()

# 构建特征

# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置

X, val_X, y, val_y = train_test_split(

train_x,

train_y,

test_size=0.01,

random_state=1,

stratify=train_y

)

# xgb矩阵赋值

xgb_val = xgb.DMatrix(val_X, label=val_y)

xgb_train = xgb.DMatrix(X, label=y)

xgb_test = xgb.DMatrix(test_x)

# xgboost模型 #####################

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': 'logloss',

# '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 = 2000  # 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)

model.save_model('../data/model/xgb.model')  # 用于存储训练出的模型

preds = model.predict(xgb_test)

# 导出结果

threshold = 0.5

for pred in preds:

result = 1 if pred > threshold else 0

CatBoost

没用过，听老铁说还行

Keras

1. 二分类

import numpy as np

import pandas as pd

import time

from sklearn.model_selection import train_test_split

from matplotlib import pyplot as plt

from keras.models import Sequential

from keras.layers import Dropout

from keras.layers import Dense, Activation

from keras.utils.np_utils import to_categorical

# coding=utf-8

from model.util import load_data as load_data_1

from model.util_combine_train_test import load_data as load_data_2

from sklearn.preprocessing import StandardScaler # 用于特征的标准化

from sklearn.preprocessing import Imputer

print("Loading Data ... ")

# 导入数据

train_x, train_y, test_x = load_data()

# 构建特征

X_train = train_x.values

X_test  = test_x.values

y = train_y

imp = Imputer(missing_values='NaN', strategy='mean', axis=0)

X_train = imp.fit_transform(X_train)

sc = StandardScaler()

sc.fit(X_train)

X_train = sc.transform(X_train)

X_test  = sc.transform(X_test)

model = Sequential()

model.add(Dense(256, input_shape=(X_train.shape[1],)))

model.add(Activation('tanh'))

model.add(Dropout(0.3))

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.3))

model.add(Dense(512))

model.add(Activation('tanh'))

model.add(Dropout(0.3))

model.add(Dense(256))

model.add(Activation('linear'))

model.add(Dense(1)) # 这里需要和输出的维度一致

model.add(Activation('sigmoid'))

# For a multi-class classification problem

model.compile(loss='binary_crossentropy',

optimizer='rmsprop',

metrics=['accuracy'])

epochs = 100

model.fit(X_train, y, epochs=epochs, batch_size=2000, validation_split=0.1, shuffle=True)

# 导出结果

threshold = 0.5

for index, case in enumerate(X_test):

case =np.array([case])

prediction_prob = model.predict(case)

prediction = 1 if prediction_prob[0][0] > threshold else 0

2. 多分类

import numpy as np

import pandas as pd

import time

from sklearn.model_selection import train_test_split

from matplotlib import pyplot as plt

from keras.models import Sequential

from keras.layers import Dropout

from keras.layers import Dense, Activation

from keras.utils.np_utils import to_categorical

# coding=utf-8

from model.util import load_data as load_data_1

from model.util_combine_train_test import load_data as load_data_2

from sklearn.preprocessing import StandardScaler # 用于特征的标准化

from sklearn.preprocessing import Imputer

print("Loading Data ... ")

# 导入数据

train_x, train_y, test_x = load_data()

# 构建特征

X_train = train_x.values

X_test  = test_x.values

y = train_y

# 特征处理

sc = StandardScaler()

sc.fit(X_train)

X_train = sc.transform(X_train)

X_test  = sc.transform(X_test)

y = to_categorical(y) ## 这一步很重要，一定要将多类别的标签进行one-hot编码

model = Sequential()

model.add(Dense(256, input_shape=(X_train.shape[1],)))

model.add(Activation('tanh'))

model.add(Dropout(0.3))

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.3))

model.add(Dense(512))

model.add(Activation('tanh'))

model.add(Dropout(0.3))

model.add(Dense(256))

model.add(Activation('linear'))

model.add(Dense(9)) # 这里需要和输出的维度一致

model.add(Activation('softmax'))

# For a multi-class classification problem

model.compile(optimizer='rmsprop',

loss='categorical_crossentropy',

metrics=['accuracy'])

epochs = 200

model.fit(X_train, y, epochs=epochs, batch_size=200, validation_split=0.1, shuffle=True)

# 导出结果

for index, case in enumerate(X_test):

case = np.array([case])

prediction_prob = model.predict(case)

prediction = np.argmax(prediction_prob)

处理正负样本不均匀的案例

有些案例中，正负样本数量相差非常大，数据严重unbalanced，这里提供几个解决的思路

# 计算正负样本比例

positive_num = df_train[df_train['label']==1].values.shape[0]

negative_num = df_train[df_train['label']==0].values.shape[0]

print(float(positive_num)/float(negative_num))

主要思路

1. 手动调整正负样本比例

2. 过采样 Over-Sampling

对训练集里面样本数量较少的类别（少数类）进行过采样，合成新的样本来缓解类不平衡，比如SMOTE算法

3. 欠采样 Under-Sampling

4. 将样本按比例一一组合进行训练，训练出多个弱分类器，最后进行集成

框架推荐

Github上大神写的相关框架，专门用来处理此类问题：

https://github.com/scikit-learn-contrib/imbalanced-learn

实践永远是检验真理的不二选择。

多打打比赛，对各种业务环境下的任务都能有所了解，也能学习新技术。

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大数据挖掘DT数据分析

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