sklearn ensemble algorithm to predict titanic survivors

Original:
http://ihoge.cn/2018/sklearn-ensemble.html

Random forest classification predicts Titanic survivors

import pandas as pd
import numpy as np

def read_dataset(fname):
    data = pd.read_csv(fname, index_col=0)
    data.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
    lables = data['Sex'].unique().tolist()
    data['Sex'] = [*map(lambda x: lables.index(x) , data['Sex'])]
    lables = data['Embarked'].unique().tolist()
    data['Embarked'] = data['Embarked'].apply(lambda n: lables.index(n))
    data = data.fillna(0)
    return data
train = read_dataset('code/datasets/titanic/train.csv')

from sklearn.model_selection import train_test_split

y = train['Survived'].values
X = train.drop(['Survived'], axis=1).values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print("X_train_shape:", X_train.shape, " y_train_shape:", y_train.shape)
print("X_test_shape:", X_test.shape,"  y_test_shape:", y_test.shape)

X_train_shape: (712, 7)  y_train_shape: (712,)
X_test_shape: (179, 7)   y_test_shape: (179,)

****

from sklearn.ensemble import RandomForestClassifier
import time

start = time.clock()
entropy_thresholds = np.linspace(0, 1, 50)
gini_thresholds = np.linspace(0, 0.1, 50)
#设置参数矩阵：
param_grid = [{'criterion': ['entropy'], 'min_impurity_decrease': entropy_thresholds},
              {'criterion': ['gini'], 'min_impurity_decrease': gini_thresholds},
              {'max_depth': np.arange(2,10)},
              {'min_samples_split': np.arange(2,20)},
              {'n_estimators':np.arange(2,20)}]
clf = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)
clf.fit(X, y)

print("耗时：",time.clock() - start)
print("best param:{0}\nbest score:{1}".format(clf.best_params_, clf.best_score_))

耗时： 13.397480000000002
best param:{'min_samples_split': 10}
best score:0.8406285072951739

clf = RandomForestClassifier(min_samples_split=10)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print("训练集得分:", clf.score(X_train, y_train))
print("测试集得分:", clf.score(X_test, y_test))
print("查准率:", metrics.precision_score(y_test, y_pred))
print("召回率:", metrics.recall_score(y_test, y_pred))
print("F1_score:", metrics.f1_score(y_test, y_pred))

训练集得分: 0.8974719101123596
测试集得分: 0.7988826815642458
查准率: 0.8082191780821918
召回率: 0.7283950617283951
F1_score: 0.7662337662337663

This time, the four parameters of the model criterion, , max_depth, and min_samples_split, n_estimatorswere compared.

After multiple executions, it is found that the results are still not very stable, and the optimal parameters are concentrated on min_samples_split8, 10, and 12, respectively.

Self-aggregation algorithm predicts Titanic survivors

from sklearn.ensemble import BaggingClassifier

clf = BaggingClassifier(n_estimators=50)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print("训练集得分:", clf.score(X_train, y_train))
print("测试集得分:", clf.score(X_test, y_test))
print("查准率:", metrics.precision_score(y_test, y_pred))
print("召回率:", metrics.recall_score(y_test, y_pred))
print("F1_score:", metrics.f1_score(y_test, y_pred))

训练集得分: 0.9817415730337079
测试集得分: 0.7877094972067039
查准率: 0.7792207792207793
召回率: 0.7407407407407407
F1_score: 0.7594936708860759

Boosting Positive Incentive Algorithm Predicts Titanic Survivors

from sklearn.ensemble import AdaBoostClassifier

clf = AdaBoostClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print("训练集得分:", clf.score(X_train, y_train))
print("测试集得分:", clf.score(X_test, y_test))
print("查准率:", metrics.precision_score(y_test, y_pred))
print("召回率:", metrics.recall_score(y_test, y_pred))
print("F1_score:", metrics.f1_score(y_test, y_pred))

训练集得分: 0.8300561797752809
测试集得分: 0.8156424581005587
查准率: 0.8076923076923077
召回率: 0.7777777777777778
F1_score: 0.7924528301886792

Extra Trees algorithm predicts Titanic survivors

from sklearn.ensemble import RandomForestClassifier
import time

start = time.clock()
entropy_thresholds = np.linspace(0, 1, 50)
gini_thresholds = np.linspace(0, 0.1, 50)
#设置参数矩阵：
param_grid = [{'criterion': ['entropy'], 'min_impurity_decrease': entropy_thresholds},
              {'criterion': ['gini'], 'min_impurity_decrease': gini_thresholds},
              {'max_depth': np.arange(2,10)},
              {'min_samples_split': np.arange(2,20)},
              {'n_estimators':np.arange(2,20)}]
clf = GridSearchCV(ExtraTreesClassifier(), param_grid, cv=5)
clf.fit(X, y)

print("耗时：",time.clock() - start)
print("best param:{0}\nbest score:{1}".format(clf.best_params_, clf.best_score_))

耗时： 16.29516799999999
best param:{'min_samples_split': 12}
best score:0.8226711560044894

from sklearn.ensemble import ExtraTreesClassifier

clf = ExtraTreesClassifier(min_samples_split=12from sklearn.ensemble import RandomForestClassifier
import time

start = time.clock()
entropy_thresholds = np.linspace(0, 1, 50)
gini_thresholds = np.linspace(0, 0.1, 50)
#设置参数矩阵：
param_grid = [{'criterion': ['entropy'], 'min_impurity_decrease': entropy_thresholds},
              {'criterion': ['gini'], 'min_impurity_decrease': gini_thresholds},
              {'max_depth': np.arange(2,10)},
              {'min_samples_split': np.arange(2,20)},
              {'n_estimators':np.arange(2,20)}]
clf = GridSearchCV(ExtraTreesClassifier(), param_grid, cv=5)
clf.fit(X, y)

print("耗时：",time.clock() - start)
print("best param:{0}\nbest score:{1}".format(clf.best_params_, clf.best_score_)))
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print("训练集得分:", clf.score(X_train, y_train))
print("测试集得分:", clf.score(X_test, y_test))
print("查准率:", metrics.precision_score(y_test, y_pred))
print("召回率:", metrics.recall_score(y_test, y_pred))
print("F1_score:", metrics.f1_score(y_test, y_pred))

训练集得分: 0.8932584269662921
测试集得分: 0.8100558659217877
查准率: 0.8405797101449275
召回率: 0.7160493827160493
F1_score: 0.7733333333333333

in conclusion:

In the comparison of the results of predicting the Titanic for this data set, the Boosting forward excitation algorithm has the best and most stable performance, followed by the Extra Trees algorithm after parameter optimization.