泰坦尼克生还预测:完整的机器学习项目(一)

对kaggle入门题titanic的记录:成绩top 10%。

题目分析

通过查看给出的数据集,该问题是一个二分类问题。

准备工作

我的做法是将数据集下载到同目录的dataset文件夹下面,在本地进行特征工程和训练。

准备工作

打开jupyter或者熟悉的ide,推荐jupyter notebook。 首先是常用包的导入:

# 基本包的导入
import numpy as np
import os

# 画图相关
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
import seaborn as sns
from sklearn.preprocessing import LabelBinarizer
# measure
from sklearn.metrics import accuracy_score

# 忽略警告
import warnings

DATASET_DIR = "./dataset"
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最后一行是数据集的目录。

下面是sklearn后续版本将会添加的一个功能:对类别数据进行one-hot编码,这里要求会使用。

# 上述函数,其输入是包含1个多个枚举类别的2D数组,需要reshape成为这种数组
# from sklearn.preprocessing import CategoricalEncoder  #后面会添加这个方法

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array

from scipy import sparse

# 后面再去理解
class CategoricalEncoder(BaseEstimator, TransformerMixin):
    """Encode categorical features as a numeric array.
    The input to this transformer should be a matrix of integers or strings,
    denoting the values taken on by categorical (discrete) features.
    The features can be encoded using a one-hot aka one-of-K scheme
    (``encoding='onehot'``, the default) or converted to ordinal integers
    (``encoding='ordinal'``).
    This encoding is needed for feeding categorical data to many scikit-learn
    estimators, notably linear models and SVMs with the standard kernels.
    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
    Parameters
    ----------
    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'
        The type of encoding to use (default is 'onehot'):
        - 'onehot': encode the features using a one-hot aka one-of-K scheme
          (or also called 'dummy' encoding). This creates a binary column for
          each category and returns a sparse matrix.
        - 'onehot-dense': the same as 'onehot' but returns a dense array
          instead of a sparse matrix.
        - 'ordinal': encode the features as ordinal integers. This results in
          a single column of integers (0 to n_categories - 1) per feature.
    categories : 'auto' or a list of lists/arrays of values.
        Categories (unique values) per feature:
        - 'auto' : Determine categories automatically from the training data.
        - list : ``categories[i]`` holds the categories expected in the ith
          column. The passed categories are sorted before encoding the data
          (used categories can be found in the ``categories_`` attribute).
    dtype : number type, default np.float64
        Desired dtype of output.
    handle_unknown : 'error' (default) or 'ignore'
        Whether to raise an error or ignore if a unknown categorical feature is
        present during transform (default is to raise). When this is parameter
        is set to 'ignore' and an unknown category is encountered during
        transform, the resulting one-hot encoded columns for this feature
        will be all zeros.
        Ignoring unknown categories is not supported for
        ``encoding='ordinal'``.
    Attributes
    ----------
    categories_ : list of arrays
        The categories of each feature determined during fitting. When
        categories were specified manually, this holds the sorted categories
        (in order corresponding with output of `transform`).
    Examples
    --------
    Given a dataset with three features and two samples, we let the encoder
    find the maximum value per feature and transform the data to a binary
    one-hot encoding.
    >>> from sklearn.preprocessing import CategoricalEncoder
    >>> enc = CategoricalEncoder(handle_unknown='ignore')
    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])
    ... # doctest: +ELLIPSIS
    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
              encoding='onehot', handle_unknown='ignore')
    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()
    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],
           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])
    See also
    --------
    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of
      integer ordinal features. The ``OneHotEncoder assumes`` that input
      features take on values in the range ``[0, max(feature)]`` instead of
      using the unique values.
    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
      dictionary items (also handles string-valued features).
    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
      encoding of dictionary items or strings.
    """

    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,
                 handle_unknown='error'):
        self.encoding = encoding
        self.categories = categories
        self.dtype = dtype
        self.handle_unknown = handle_unknown

    def fit(self, X, y=None):
        """Fit the CategoricalEncoder to X.
        Parameters
        ----------
        X : array-like, shape [n_samples, n_feature]
            The data to determine the categories of each feature.
        Returns
        -------
        self
        """

        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:
            template = ("encoding should be either 'onehot', 'onehot-dense' "
                        "or 'ordinal', got %s")
            raise ValueError(template % self.handle_unknown)

        if self.handle_unknown not in ['error', 'ignore']:
            template = ("handle_unknown should be either 'error' or "
                        "'ignore', got %s")
            raise ValueError(template % self.handle_unknown)

        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':
            raise ValueError("handle_unknown='ignore' is not supported for"
                             " encoding='ordinal'")

        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)
        n_samples, n_features = X.shape

        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]

        for i in range(n_features):
            le = self._label_encoders_[i]
            Xi = X[:, i]
            if self.categories == 'auto':
                le.fit(Xi)
            else:
                valid_mask = np.in1d(Xi, self.categories[i])
                if not np.all(valid_mask):
                    if self.handle_unknown == 'error':
                        diff = np.unique(Xi[~valid_mask])
                        msg = ("Found unknown categories {0} in column {1}"
                               " during fit".format(diff, i))
                        raise ValueError(msg)
                le.classes_ = np.array(np.sort(self.categories[i]))

        self.categories_ = [le.classes_ for le in self._label_encoders_]

        return self

    def transform(self, X):
        """Transform X using one-hot encoding.
        Parameters
        ----------
        X : array-like, shape [n_samples, n_features]
            The data to encode.
        Returns
        -------
        X_out : sparse matrix or a 2-d array
            Transformed input.
        """
        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)
        n_samples, n_features = X.shape
        X_int = np.zeros_like(X, dtype=np.int)
        X_mask = np.ones_like(X, dtype=np.bool)

        for i in range(n_features):
            valid_mask = np.in1d(X[:, i], self.categories_[i])

            if not np.all(valid_mask):
                if self.handle_unknown == 'error':
                    diff = np.unique(X[~valid_mask, i])
                    msg = ("Found unknown categories {0} in column {1}"
                           " during transform".format(diff, i))
                    raise ValueError(msg)
                else:
                    # Set the problematic rows to an acceptable value and
                    # continue `The rows are marked `X_mask` and will be
                    # removed later.
                    X_mask[:, i] = valid_mask
                    X[:, i][~valid_mask] = self.categories_[i][0]
            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])

        if self.encoding == 'ordinal':
            return X_int.astype(self.dtype, copy=False)

        mask = X_mask.ravel()
        n_values = [cats.shape[0] for cats in self.categories_]
        n_values = np.array([0] + n_values)
        indices = np.cumsum(n_values)

        column_indices = (X_int + indices[:-1]).ravel()[mask]
        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
                                n_features)[mask]
        data = np.ones(n_samples * n_features)[mask]

        out = sparse.csc_matrix((data, (row_indices, column_indices)),
                                shape=(n_samples, indices[-1]),
                                dtype=self.dtype).tocsr()
        if self.encoding == 'onehot-dense':
            return out.toarray()
        else:
            return out

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加载数据

将题目提供的数据集下载并保存到相应的目录下。

dataset_train_tf = pd.read_csv("./dataset/train.csv")
dataset_test_tf = pd.read_csv("./dataset/test.csv")
combine = [dataset_train_tf, dataset_test_tf]
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查看训练数据的前五条数据 :

dataset_train_tf.head()
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输出如下:

可以看到包含不同的信息,有数值特征和类别特征。其每个特征的含义请参考kaggle题目介绍。

下面查看一下其数据分布状态:

此图有很多重要的信息:

  1. 有接近38%的人存活。(平均值的计算), 由0,1表示。
  2. silsp表示兄弟姐妹,parch表示配偶,一起表示随亲友出行的个数。75%的人都是0,说明都是单人出行。
  3. 将近30%的乘客有兄弟姐妹和/或配偶。
  4. 票价差别很大,几乎没有乘客(<1%)支付高达512美元。
  5. 几乎没有乘客是65-80岁的。

上面是数值特征分布状态,下面查一下类别特征的状态:

上图针对类别信息做的统计,表示总数,类别数,出现最多的类别和其次数。

一些猜测

  1. 是否女性更容易存活
  2. 是否小孩更容易存活
  3. 票的类型跟存活有什么关系。

下面看一下数据的类型和缺失情况情况:

如上,对于缺失较多的数据,比如Cabin,进行删除。少量缺失的数据进行补全。

上面3步基本上是拿到一个数据集应该进行的必备步骤。下面进行简单分析:

简单分析

通过同groupby针对不同属性进行分组,查看与存活之间的关系。

dataset_train_tf[["Pclass", "Survived"]].groupby(['Pclass'],as_index=False).mean().sort_values(by="Survived", ascending=False)
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其结果如下:

Pclass Survived
0 1 0.629630
1 2 0.472826
2 3 0.242363

不同的船票,其生还的可能也不同。

dataset_train_tf[["Sex", "Survived"]].groupby(['Sex'],as_index=False).mean().sort_values(by="Survived", ascending=False)
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Sex Survived
0 female 0.742038
1 male 0.188908

等等,这里不做过多分析。


可视化部分可以参考kaggle的相关入门参考和titanic的文章。

通过corr函数可以计算不同特征之间的关联性。

# 数据间的相互关系
corr_matrix = dataset_train_tf.corr()
corr_matrix["Survived"].sort_values() #数据建的相互相关, 1, -1 最有用
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其结果输出如下:

Pclass        -0.338481
Age           -0.077221
SibSp         -0.035322
PassengerId   -0.005007
Parch          0.081629
Fare           0.257307
Survived       1.000000
Name: Survived, dtype: float64
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后面介绍缺失数据的补全,Pipeline的使用,类别特征的编码,模型的训练与预测。

github链接:titanic