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 

LR

# 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] 

LightBGM

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) 

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) 

XGB

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 

Keras

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 

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)