對kaggle入門題titanic的記錄：成績top 10%。
題目分析
通過檢視給出的資料集，該問題是一個二分類問題。
準備工作
我的做法是將資料集下載到同目錄的dataset資料夾下面，在本地進行特徵工程和訓練。
準備工作
開啟jupyter或者熟悉的ide，推薦jupyter notebook。 首先是常用包的匯入：
# 基本包的匯入
import numpy as np
import os
# 畫圖相關
%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"
複製程式碼最後一行是資料集的目錄。
下面是sklearn後續版本將會新增的一個功能：對類別資料進行one-hot編碼，這裡要求會使用。
# 上述函式，其輸入是包含1個多個列舉類別的2D陣列，需要reshape成為這種陣列
# from sklearn.preprocessing import CategoricalEncoder #後面會新增這個方法
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
from scipy import sparse
# 後面再去理解
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
複製程式碼載入資料
將題目提供的資料集下載並儲存到相應的目錄下。
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]
複製程式碼檢視訓練資料的前五條資料 ：
dataset_train_tf.head()
複製程式碼輸出如下：
可以看到包含不同的資訊，有數值特徵和類別特徵。其每個特徵的含義請參考kaggle題目介紹。
下面檢視一下其資料分佈狀態：
此圖有很多重要的資訊：
- 有接近38%的人存活。(平均值的計算)， 由0，1表示。
- silsp表示兄弟姐妹，parch表示配偶，一起表示隨親友出行的個數。75%的人都是0，說明都是單人出行。
- 將近30%的乘客有兄弟姐妹和/或配偶。
- 票價差別很大，幾乎沒有乘客(<1%)支付高達512美元。
- 幾乎沒有乘客是65-80歲的。
上面是數值特徵分佈狀態，下面查一下類別特徵的狀態：
一些猜測
- 是否女性更容易存活
- 是否小孩更容易存活
- 票的型別跟存活有什麼關係。
下面看一下資料的型別和缺失情況情況：
上面3步基本上是拿到一個資料集應該進行的必備步驟。下面進行簡單分析：
簡單分析
通過同groupby針對不同屬性進行分組，檢視與存活之間的關係。
dataset_train_tf[["Pclass", "Survived"]].groupby(['Pclass'],as_index=False).mean().sort_values(by="Survived", ascending=False)
複製程式碼其結果如下：
| Pclass | Survived | |
|---|---|---|
| 0 | 1 | 0.629630 |
| 1 | 2 | 0.472826 |
| 2 | 3 | 0.242363 |
不同的船票，其生還的可能也不同。
dataset_train_tf[["Sex", "Survived"]].groupby(['Sex'],as_index=False).mean().sort_values(by="Survived", ascending=False)
複製程式碼| Sex | Survived | |
|---|---|---|
| 0 | female | 0.742038 |
| 1 | male | 0.188908 |
等等，這裡不做過多分析。
視覺化部分可以參考kaggle的相關入門參考和titanic的文章。
通過corr函式可以計算不同特徵之間的關聯性。
# 資料間的相互關係
corr_matrix = dataset_train_tf.corr()
corr_matrix["Survived"].sort_values() #資料建的相互相關， 1， -1 最有用
複製程式碼其結果輸出如下：
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
複製程式碼後面介紹缺失資料的補全，Pipeline的使用，類別特徵的編碼，模型的訓練與預測。