集成算法实例：泰坦尼克船员获救

心已赠人 2023-06-22 05:19 21阅读 0赞

import pandas #ipython notebook
    titanic = pandas.read_csv("titanic_train.csv")
    titanic.head(5)
    #print (titanic.describe())

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]

print (titanic.describe())

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 1]  
发现age中发生了数据缺失！！！！

##### 需要进行数据预处理： #####

numpy模块下的median作用为：计算沿指定轴的均值，返回数组元素的均值。

median(a, 
           axis=None, 
           out=None,
           overwrite_input=False, 
           keepdims=False)

其中各参数为：  
a：输入的数组；  
axis：计算哪个轴上的均值，比如输入是二维数组，那么axis=0对应行，axis=1对应列；  
out：用于放置求取中位数后的数组。 它必须具有与预期输出相同的形状和缓冲区长度；  
overwrite\_input :一个bool型的参数，默认为Flase。如果为True那么将直接在数组内存中计算，这意味着计算之后原数组没办法保存，但是好处在于节省内存资源，Flase则相反；  
keepdims：一个bool型的参数，默认为Flase。如果为True那么求取中位数的那个轴将保留在结果中；

具体使用：

titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
    print(titanic.describe())

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 2]  
将字符变成数值：

print (titanic["Sex"].unique())
    
    # Replace all the occurences of male with the number 0.
    titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
    titanic.loc[titanic["Sex"] == "female", "Sex"] = 1

![在这里插入图片描述][20191214104931792.png]

print (titanic["Embarked"].unique())
    # 不能用均值填充了，谁最多就用谁就可以了
    titanic["Embarked"] = titanic["Embarked"].fillna('S')
    titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
    titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
    titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2

![在这里插入图片描述][20191214105146653.png]

##### 使用线性回归算法进行预测： #####

这里我们还是使用sklearn  
K折交叉验证时使用：

KFold（n_split, shuffle, random_state）

参数：  
n\_split:要划分的折数  
shuffle: 每次都进行shuffle，测试集中折数的总和就是训练集的个数  
random\_state:随机状态

# Import the linear regression class
    from sklearn.linear_model import LinearRegression
    # Sklearn also has a helper that makes it easy to do cross validation
    # from sklearn.cross_validation import KFold
    from sklearn.model_selection import KFold
    
    # The columns we'll use to predict the target
    # 指定传给分类器哪些特征
    predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
    
    # Initialize our algorithm class
    # 实例化模型
    alg = LinearRegression()
    # Generate cross validation folds for the titanic dataset. It return the row indices corresponding to train and test.
    # We set random_state to ensure we get the same splits every time we run this.
    # kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
    # 交叉验证
    kf = KFold(n_splits=3, shuffle=False, random_state=1)
    
    predictions = []
    for train, test in kf.split(titanic[predictions]):
        # The predictors we're using the train the algorithm. Note how we only take the rows in the train folds.
        train_predictors = (titanic[predictors].iloc[train,:])
        # The target we're using to train the algorithm.
        train_target = titanic["Survived"].iloc[train]
        # Training the algorithm using the predictors and target.
        alg.fit(train_predictors, train_target)
        # We can now make predictions on the test fold
        test_predictions = alg.predict(titanic[predictors].iloc[test,:])
        predictions.append(test_predictions)

import numpy as np
    
    # The predictions are in three separate numpy arrays. Concatenate them into one. 
    # We concatenate them on axis 0, as they only have one axis.
    # 将多个数组进行拼接
    predictions = np.concatenate(predictions, axis=0)
    
    # Map predictions to outcomes (only possible outcomes are 1 and 0)
    predictions[predictions > .5] = 1
    predictions[predictions <=.5] = 0
    accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
    print (accuracy)

不知道为什么结果不对！！！  
![在这里插入图片描述][20191214133901632.png]

from sklearn import model_selection
    from sklearn.linear_model import LogisticRegression
    # Initialize our algorithm
    alg = LogisticRegression(random_state=1)
    # Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
    scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)
    # Take the mean of the scores (because we have one for each fold)
    print(scores.mean())

![在这里插入图片描述][20191215102430579.png]

#### 使用随机森林改进模型 ####

titanic_test = pandas.read_csv("test.csv")
    titanic_test["Age"] = titanic_test["Age"].fillna(titanic["Age"].median())
    titanic_test["Fare"] = titanic_test["Fare"].fillna(titanic_test["Fare"].median())
    titanic_test.loc[titanic_test["Sex"] == "male", "Sex"] = 0 
    titanic_test.loc[titanic_test["Sex"] == "female", "Sex"] = 1
    titanic_test["Embarked"] = titanic_test["Embarked"].fillna("S")
    
    titanic_test.loc[titanic_test["Embarked"] == "S", "Embarked"] = 0
    titanic_test.loc[titanic_test["Embarked"] == "C", "Embarked"] = 1
    titanic_test.loc[titanic_test["Embarked"] == "Q", "Embarked"] = 2

from sklearn import model_selection
    from sklearn.ensemble import RandomForestClassifier
    
    predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
    
    # Initialize our algorithm with the default paramters
    # n_estimators is the number of trees we want to make
    # min_samples_split is the minimum number of rows we need to make a split
    # min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)
    alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1)
    # Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
    kf = model_selection.KFold(n_splits=3, random_state=1)
    scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
    
    # Take the mean of the scores (because we have one for each fold)
    print(scores.mean())

![在这里插入图片描述][20191215100812565.png]

#### 简单的调参 ####

alg = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
    # Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
    kf = model_selection.KFold(3, random_state=1)
    scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
    
    # Take the mean of the scores (because we have one for each fold)
    print(scores.mean())

![在这里插入图片描述][20191215103043380.png]

#### 模型优化差不多了，但是还想优化应该怎么办呢？ ####

可以，需要增加其他特征

# Generating a familysize column
    titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]
    
    # The .apply method generates a new series
    titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))

import re
    
    # A function to get the title from a name.
    def get_title(name):
        # Use a regular expression to search for a title. Titles always consist of capital and lowercase letters, and end with a period.
        title_search = re.search(' ([A-Za-z]+)\.', name)
        # If the title exists, extract and return it.
        if title_search:
            return title_search.group(1)
        return ""
    
    # Get all the titles and print how often each one occurs.
    titles = titanic["Name"].apply(get_title)
    print(pandas.value_counts(titles))
    
    # Map each title to an integer. Some titles are very rare, and are compressed into the same codes as other titles.
    title_mapping = { "Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
    for k,v in title_mapping.items():
        titles[titles == k] = v
    
    # Verify that we converted everything.
    print(pandas.value_counts(titles))
    
    # Add in the title column.
    titanic["Title"] = titles

import re
    
    # A function to get the title from a name.
    def get_title(name):
        # Use a regular expression to search for a title. Titles always consist of capital and lowercase letters, and end with a period.
        title_search = re.search('([A-Za-z]+)\.', name)
        # If the title exists, extract and return it.
        if title_search:
            return title_search.group(1)
        return ""
    
    # Get all the titles and print how often each one occurs.
    titles = titanic["Name"].apply(get_title)
    print(pandas.value_counts(titles))
    
    # Map each title to an integer. Some titles are very rare, and are compressed into the same codes as other titles.
    title_mapping = { "Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
    for k,v in title_mapping.items():
        titles[titles == k] = v
    
    # Verify that we converted everything.
    print(pandas.value_counts(titles))
    
    # Add in the title column.
    titanic["Title"] = titles

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 3]

import numpy as np
    from sklearn.feature_selection import SelectKBest, f_classif
    import matplotlib.pyplot as plt
    predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "NameLength"]
    
    # Perform feature selection
    selector = SelectKBest(f_classif, k=5)
    selector.fit(titanic[predictors], titanic["Survived"])
    
    # Get the raw p-values for each feature, and transform from p-values into scores
    scores = -np.log10(selector.pvalues_)
    
    # Plot the scores. See how "Pclass", "Sex", "Title", and "Fare" are the best?
    plt.bar(range(len(predictors)), scores)
    plt.xticks(range(len(predictors)), predictors, rotation='vertical')
    plt.show()
    
    # Pick only the four best features.
    predictors = ["Pclass", "Sex", "Fare", "Title"]
    
    alg = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 4]

#### 算法集成 ####

from sklearn.ensemble import GradientBoostingClassifier
    import numpy as np
    
    # The algorithms we want to ensemble.
    # We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.
    algorithms = [
        [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title",]],
        [LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
    ]
    
    # Initialize the cross validation folds
    kf = KFold(3, random_state=1)
    
    predictions = []
    for train, test in kf.split(titanic["Survived"]):
        train_target = titanic["Survived"].iloc[train]
        full_test_predictions = []
        # Make predictions for each algorithm on each fold
        for alg, predictors in algorithms:
            # Fit the algorithm on the training data.
            alg.fit(titanic[predictors].iloc[train,:], train_target)
            # Select and predict on the test fold. 
            # The .astype(float) is necessary to convert the dataframe to all floats and avoid an sklearn error.
            test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]
            full_test_predictions.append(test_predictions)
        # Use a simple ensembling scheme -- just average the predictions to get the final classification.
        test_predictions = (full_test_predictions[0] + full_test_predictions[1]) / 2
        # Any value over .5 is assumed to be a 1 prediction, and below .5 is a 0 prediction.
        test_predictions[test_predictions <= .5] = 0
        test_predictions[test_predictions > .5] = 1
        predictions.append(test_predictions)
    
    # Put all the predictions together into one array.
    predictions = np.concatenate(predictions, axis=0)
    
    # Compute accuracy by comparing to the training data.
    accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
    print(accuracy)

![在这里插入图片描述][2019121511044852.png]

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[20191214104931792.png]: https://img-blog.csdnimg.cn/20191214104931792.png
[20191214105146653.png]: https://img-blog.csdnimg.cn/20191214105146653.png
[20191214133901632.png]: https://img-blog.csdnimg.cn/20191214133901632.png
[20191215102430579.png]: https://img-blog.csdnimg.cn/20191215102430579.png
[20191215100812565.png]: https://img-blog.csdnimg.cn/20191215100812565.png
[20191215103043380.png]: https://img-blog.csdnimg.cn/20191215103043380.png
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[2019121511044852.png]: https://img-blog.csdnimg.cn/2019121511044852.png