集成算法实例：泰坦尼克船员获救_泰坦尼克随机森林误差收敛图算法

import pandas #ipython notebook
titanic = pandas.read_csv("titanic_train.csv")
titanic.head(5)
#print (titanic.describe())
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print (titanic.describe())
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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)
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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())
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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
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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
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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）
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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)
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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)
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不知道为什么结果不对！！！

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())
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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
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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())
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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())
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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))
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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
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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
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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)
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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)
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