0621

README.md

@@ -18,8 +18,8 @@
 | 6月14日 （周日） 10:30AM | （直播-Lecture4)    <br> 凸优化 (2) |Dualtiy, KKT条件，SVM的primal-Dual|[Lecture4](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/0614%E5%87%B8%E4%BC%98%E5%8C%96%EF%BC%882%EF%BC%89.pptx)|||
 | 6月14日 （周日） 8:00PM  |  (直播-Discussion)  <br> Inventory Optimization with Stochastic Programming ||[课件](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/0614%20Inventory%20Optimization%20with%20Stochastic%20Programming%5B%E9%98%BF%E5%8B%87%5D.pptx)|||
 | PART 1  自然语言处理基础 |
-| 6月21日 （周日，父亲节）  10:30AM | (直播-Lecture5)    <br>文本表示|分词<br>拼写纠错<br>停用词过滤<br>词的标准化<br>词袋模型<br>文本相似度计算<br>词向量<br>句子向量<br>语言模型||||[project1](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/project1/Project1%E9%A1%B9%E7%9B%AE.zip) <br><br> 截止日期：7月1日（周三）<br>北京时间 23:59PM, <br>上传到gitlab|
-| 6月21日 （周日） 5:30PM  | (直播-Discussion)  <br>各类文本相似度计算技术的Survey| 短文本<br>长文本 |||||
+| 6月21日 （周日，父亲节）  10:30AM | (直播-Lecture5)    <br>文本表示|分词<br>拼写纠错<br>停用词过滤<br>词的标准化<br>词袋模型<br>文本相似度计算<br>词向量<br>句子向量<br>语言模型|[Lecture5](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/0621%E6%96%87%E6%9C%AC%E8%A1%A8%E7%A4%BA.pptx)|||[project1](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/project1/Project1%E9%A1%B9%E7%9B%AE.zip) <br><br> 截止日期：7月1日（周三）<br>北京时间 23:59PM, <br>上传到gitlab|
+| 6月21日 （周日） 5:30PM  | (直播-Discussion)  <br>各类文本相似度计算技术的Survey| 短文本<br>长文本 |[课件](http://47.94.6.102/NLP7/course-info/blob/master/%E8%AF%BE%E4%BB%B6/0621%E6%96%87%E6%9C%AC%E7%9B%B8%E4%BC%BC%E5%BA%A6%E8%AE%A1%E7%AE%97%E6%8A%80%E6%9C%AF.pptx)||||
 | 6月21日 （周日） 8:00PM  | (直播-Discussion） 搜索引擎技术介绍|Vector-space model, 倒排表索引，PageRank等||||
 | TBD | (直播-Discussion) 词向量实战：如何使用Glove, BERT词向量在自己的项目当中？ ||||||
 | TBD | (直播-Discussion) 问答系统的搭建：完整流程，相似度匹配，排序，文本预处理等||||

课件/0606 Ensemble 模型实战--资料/代码/code/Readme.txt

+需要安装包
+需要安装包
+pip install sklearn
+pip install pandas
+pip install xgboost
+pip install lightgbm
+pip install catboost
+
+预处理参考https://www.jianshu.com/p/3efc09ef4369
+
+回归任务和部分实验尚未针对该数据集做测试，只有未被注释的部分实验可运行成功。
\ No newline at end of file

课件/0606 Ensemble 模型实战--资料/代码/code/ensemble_learning.py

+#!/usr/bin/env python
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+import numpy as np
+import pandas as pd
+import time
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import VotingClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import BaggingClassifier
+from sklearn import tree
+# import matplotlib as plt
+from sklearn.metrics import accuracy_score
+
+
+def load_loan_pred(path_train):
+    # reading the dataset
+    df = pd.read_csv(path_train)
+
+    print(df.head())
+    print(df.describe())
+    print(df.isnull().sum())
+
+    # filling missing values
+    # pre-process according to https://www.jianshu.com/p/3efc09ef4369
+    df['Gender'].fillna(df['Gender'].value_counts().idxmax(), inplace=True)
+    df['Married'].fillna(df['Married'].value_counts().idxmax(), inplace=True)
+    df['Dependents'].fillna(df['Dependents'].value_counts().idxmax(), inplace=True)
+    df['Self_Employed'].fillna(df['Self_Employed'].value_counts().idxmax(), inplace=True)
+    df["LoanAmount"].fillna(df["LoanAmount"].mean(skipna=True), inplace=True)
+    df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].value_counts().idxmax(), inplace=True)
+    df['Credit_History'].fillna(df['Credit_History'].value_counts().idxmax(), inplace=True)
+    df['LoanAmount_log'] = np.log(df['LoanAmount'])
+    df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']
+    df['TotalIncome_log'] = np.log(df['TotalIncome'])
+    # df['LoanAmount_log'].hist(bins=20)
+
+    # print(df.isnull().sum())
+
+    from sklearn.preprocessing import LabelEncoder
+    var_mod = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status']
+    le = LabelEncoder()
+    for i in var_mod:
+        df[i] = le.fit_transform(df[i])
+
+    fields = ['Credit_History', 'Education', 'Married', 'Self_Employed', 'Property_Area','Loan_Status']
+
+    df = df[fields]
+    print(df.head())
+
+    # split dataset into train and test
+    train, test = train_test_split(df, test_size=0.3, random_state=0)
+
+    x_train = train.drop('Loan_Status', axis=1)
+    y_train = train['Loan_Status']
+
+    x_test = test.drop('Loan_Status', axis=1)
+    y_test = test['Loan_Status']
+
+    # create dummies
+    x_train = pd.get_dummies(x_train)
+    x_test = pd.get_dummies(x_test)
+
+    return  x_train, y_train, x_test, y_test
+
+
+def exp_voting(x_train,y_train,x_test,y_test):
+    model1 = LogisticRegression(random_state=1)
+    model2 = DecisionTreeClassifier(random_state=1)
+    model = VotingClassifier(estimators=[('lr', model1), ('dt', model2)], voting='hard')
+    model.fit(x_train,y_train)
+    model.score(x_test,y_test)
+
+
+def exp_avg(x_train,y_train,x_test):
+    model1 = DecisionTreeClassifier()
+    model2 = KNeighborsClassifier()
+    model3= LogisticRegression()
+
+    model1.fit(x_train,y_train)
+    model2.fit(x_train,y_train)
+    model3.fit(x_train,y_train)
+
+    pred1=model1.predict_proba(x_test)
+    pred2=model2.predict_proba(x_test)
+    pred3=model3.predict_proba(x_test)
+
+    finalpred=(pred1+pred2+pred3)/3
+
+
+def exp_avg_weighted(x_train,y_train,x_test):
+    model1 = DecisionTreeClassifier()
+    model2 = KNeighborsClassifier()
+    model3= LogisticRegression()
+
+    model1.fit(x_train,y_train)
+    model2.fit(x_train,y_train)
+    model3.fit(x_train,y_train)
+
+    pred1=model1.predict_proba(x_test)
+    pred2=model2.predict_proba(x_test)
+    pred3=model3.predict_proba(x_test)
+
+    finalpred=(pred1*0.3+pred2*0.3+pred3*0.4)
+
+
+def Stacking(model,train,y,test,n_fold):
+    folds=StratifiedKFold(n_splits=n_fold,random_state=1)
+    test_pred=np.empty((test.shape[0],1),float)
+    train_pred=np.empty((0,1),float)
+
+    for train_indices,val_indices in folds.split(train,y.values):
+       x_train,x_val=train.iloc[train_indices],train.iloc[val_indices]
+       y_train,y_val=y.iloc[train_indices],y.iloc[val_indices]
+       model.fit(X=x_train,y=y_train)
+       train_pred=np.append(train_pred,model.predict(x_val))
+       test_pred=np.append(test_pred,model.predict(test))
+
+    return test_pred.reshape(-1,1),train_pred
+
+
+def exp_stacking(x_train,y_train,x_test,y_test):
+    model1 = DecisionTreeClassifier(random_state=1)
+    test_pred1, train_pred1 = Stacking(model=model1, n_fold=10, train=x_train, test=x_test, y=y_train)
+    train_pred1 = pd.DataFrame(train_pred1)
+    test_pred1 = pd.DataFrame(test_pred1)
+
+    model2 = KNeighborsClassifier()
+    test_pred2, train_pred2 = Stacking(model=model2, n_fold=10, train=x_train, test=x_test, y=y_train)
+    train_pred2 = pd.DataFrame(train_pred2)
+    test_pred2 = pd.DataFrame(test_pred2)
+
+    df = pd.concat([train_pred1, train_pred2], axis=1)
+    df_test = pd.concat([test_pred1, test_pred2], axis=1)
+
+    model = LogisticRegression(random_state=1)
+    model.fit(df, y_train)
+    score = model.score(df_test, y_test)
+    print(score)
+
+
+def exp_bagging(x_train,y_train,x_test,y_test):
+    from sklearn.ensemble import BaggingClassifier
+    from sklearn import tree
+    model = BaggingClassifier(tree.DecisionTreeClassifier(random_state=1))
+    model.fit(x_train, y_train)
+    score = model.score(x_test, y_test)
+    print(score)
+
+
+def exp_bagging_regress(x_train,y_train,x_test,y_test):
+    from sklearn.ensemble import BaggingRegressor
+    model = BaggingRegressor(tree.DecisionTreeRegressor(random_state=1))
+    model.fit(x_train, y_train)
+    model.score(x_test,y_test)
+
+
+def exp_adaboost(x_train,y_train,x_test,y_test):
+    from sklearn.ensemble import AdaBoostClassifier
+    model = AdaBoostClassifier(random_state=1)
+    model.fit(x_train, y_train)
+    score = model.score(x_test, y_test)
+    print(score)
+
+
+def exp_adaboost_regress(x_train, y_train, x_test, y_test):
+    from sklearn.ensemble import AdaBoostRegressor
+    model = AdaBoostRegressor()
+    model.fit(x_train, y_train)
+    model.score(x_test, y_test)
+
+
+def exp_gbm(x_train, y_train, x_test, y_test):
+    from sklearn.ensemble import GradientBoostingClassifier
+    model = GradientBoostingClassifier(learning_rate=0.01, random_state=1)
+    model.fit(x_train, y_train)
+    score = model.score(x_test, y_test)
+    print(score)
+
+
+def exp_gbm_regress(x_train, y_train, x_test, y_test):
+    from sklearn.ensemble import GradientBoostingRegressor
+    model = GradientBoostingRegressor()
+    model.fit(x_train, y_train)
+    model.score(x_test, y_test)
+
+
+def exp_xgboost(x_train, y_train, x_test, y_test):
+    import xgboost as xgb
+    model = xgb.XGBClassifier(random_state=1, learning_rate=0.01)
+    model.fit(x_train, y_train)
+    score = model.score(x_test, y_test)
+    print(score)
+
+
+def exp_xgboost_regress(x_train, y_train, x_test, y_test):
+    import xgboost as xgb
+    model = xgb.XGBRegressor()
+    model.fit(x_train, y_train)
+    model.score(x_test, y_test)
+
+
+def exp_lgb(x_train, y_train, x_test, y_test):
+    import lightgbm as lgb
+    train_data = lgb.Dataset(x_train, label=y_train)
+    # define parameters
+    params = {'learning_rate': 0.001}
+    model = lgb.train(params, train_data, 100)
+    y_pred = model.predict(x_test)
+    for i in range(0, 185):
+        if y_pred[i] >= 0.5:
+            y_pred[i] = 1
+    else:
+        y_pred[i] = 0
+    score = accuracy_score(y_test, y_pred)
+    print(score)
+
+
+def exp_lgb_regress(x_train, y_train, x_test, y_test):
+    import lightgbm as lgb
+    train_data = lgb.Dataset(x_train, label=y_train)
+    params = {'learning_rate': 0.001}
+    model = lgb.train(params, train_data, 100)
+    from sklearn.metrics import mean_squared_error
+    rmse = mean_squared_error(y_pred, y_test) ** 0.5
+
+
+def exp_catboost(x_train, y_train, x_test, y_test):
+    from catboost import CatBoostClassifier
+    model = CatBoostClassifier()
+    # categorical_features_indices = np.where(df.dtypes != np.float)[0]
+    model.fit(x_train, y_train, cat_features=([0, 1, 2, 3, 4, ]), eval_set=(x_test, y_test))
+    score = model.score(x_test, y_test)
+    print(score)
+
+
+def exp_catboost_regress(x_train, y_train, x_test, y_test):
+    from catboost import CatBoostRegressor
+    model = CatBoostRegressor()
+    categorical_features_indices = np.where(df.dtypes != np.float)[0]
+    model.fit(x_train, y_train, cat_features=([0, 1, 2, 3, 4, 10]), eval_set=(x_test, y_test))
+    model.score(x_test, y_test)
+
+
+def main():
+    path_train = 'train_loan_pred.csv'
+    [x_train, y_train, x_test, y_test] = load_loan_pred(path_train)
+    # voting/avging
+    # exp_voting(x_train,y_train,x_test,y_test)
+    # exp_avg(x_train, y_train, x_test)
+    # exp_avg_weighted(x_train, y_train, x_test)
+    # stacking
+    # exp_stacking(x_train, y_train, x_test, y_test)
+    # bagging
+    exp_bagging(x_train, y_train, x_test, y_test)
+    # boosting
+    exp_adaboost(x_train, y_train, x_test, y_test)
+    exp_gbm(x_train, y_train, x_test, y_test)
+    exp_xgboost(x_train, y_train, x_test, y_test)
+    # exp_lgb(x_train, y_train, x_test, y_test)
+    # exp_catboost(x_train, y_train, x_test, y_test)
+
+    # bagging/boosting regression
+    # exp_bagging_regress(x_train, y_train, x_test, y_test)
+    # exp_adaboost_regress(x_train, y_train, x_test, y_test)
+    # exp_gbm_regress(x_train, y_train, x_test, y_test)
+    # exp_xgboost_regress(x_train, y_train, x_test, y_test)
+    # exp_lgb_regress(x_train, y_train, x_test, y_test)
+    # exp_catboost_regress(x_train, y_train, x_test, y_test)
+
+
+if __name__ == '__main__':
+    t_start = time.time()
+    main()
+    t_end = time.time()
+    t_all = t_end - t_start
+    print('whole time: {:.2f} min'.format(t_all / 60.))