Googleドライブのマウント

In [1]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

0. データ表示

In [2]:
#from モジュール名 import クラス名（もしくは関数名や変数名）
import pandas as pd
from pandas import DataFrame
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#matplotlibをinlineで表示するためのおまじない (plt.show()しなくていい)
%matplotlib inline

以下では，Googleドライブのマイドライブ直下にstudy_ai_mlフォルダを置くことを仮定しています．必要に応じて，パスを変更してください。

In [90]:
# titanic data csvファイルの読み込み
titanic_df = pd.read_csv('/content/drive/My Drive/study_ai_ml/data/titanic_train.csv')
In [91]:
# ファイルの先頭部を表示し、データセットを確認する
titanic_df.head(5)
Out[91]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

1. ロジスティック回帰

不要なデータの削除・欠損値の補完

In [92]:
#予測に不要と考えるカラムをドロップ (本当はここの情報もしっかり使うべきだと思っています)
titanic_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)

#一部カラムをドロップしたデータを表示
titanic_df.head()
Out[92]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22.0 1 0 7.2500 S
1 1 1 female 38.0 1 0 71.2833 C
2 1 3 female 26.0 0 0 7.9250 S
3 1 1 female 35.0 1 0 53.1000 S
4 0 3 male 35.0 0 0 8.0500 S
In [93]:
#nullを含んでいる行を表示
titanic_df[titanic_df.isnull().any(axis=1)].head(10)
Out[93]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
5 0 3 male NaN 0 0 8.4583 Q
17 1 2 male NaN 0 0 13.0000 S
19 1 3 female NaN 0 0 7.2250 C
26 0 3 male NaN 0 0 7.2250 C
28 1 3 female NaN 0 0 7.8792 Q
29 0 3 male NaN 0 0 7.8958 S
31 1 1 female NaN 1 0 146.5208 C
32 1 3 female NaN 0 0 7.7500 Q
36 1 3 male NaN 0 0 7.2292 C
42 0 3 male NaN 0 0 7.8958 C
In [94]:
#Ageカラムのnullを中央値で補完

titanic_df['AgeFill'] = titanic_df['Age'].fillna(titanic_df['Age'].mean())

#再度nullを含んでいる行を表示 (Ageのnullは補完されている)
titanic_df[titanic_df.isnull().any(1)]

#titanic_df.dtypes
Out[94]:
Survived Pclass Sex Age SibSp Parch Fare Embarked AgeFill
5 0 3 male NaN 0 0 8.4583 Q 29.699118
17 1 2 male NaN 0 0 13.0000 S 29.699118
19 1 3 female NaN 0 0 7.2250 C 29.699118
26 0 3 male NaN 0 0 7.2250 C 29.699118
28 1 3 female NaN 0 0 7.8792 Q 29.699118
... ... ... ... ... ... ... ... ... ...
859 0 3 male NaN 0 0 7.2292 C 29.699118
863 0 3 female NaN 8 2 69.5500 S 29.699118
868 0 3 male NaN 0 0 9.5000 S 29.699118
878 0 3 male NaN 0 0 7.8958 S 29.699118
888 0 3 female NaN 1 2 23.4500 S 29.699118

179 rows × 9 columns

1. ロジスティック回帰

実装(チケット価格から生死を判別)

In [95]:
#運賃だけのリストを作成
data1 = titanic_df.loc[:, ["Fare"]].values
In [96]:
#生死フラグのみのリストを作成
label1 =  titanic_df.loc[:,["Survived"]].values
In [97]:
from sklearn.linear_model import LogisticRegression
In [98]:
model=LogisticRegression()
In [99]:
model.fit(data1, label1)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
Out[99]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
In [100]:
model.predict([[61]])
Out[100]:
array([0])
In [101]:
model.predict_proba([[61]])
Out[101]:
array([[0.50358033, 0.49641967]])
In [102]:
X_test_value = model.decision_function(data1)
In [103]:
# # 決定関数値（絶対値が大きいほど識別境界から離れている）
#X_test_value = model.decision_function(X_test) 
# # 決定関数値をシグモイド関数で確率に変換
#X_test_prob = normal_sigmoid(X_test_value)
In [104]:
print (model.intercept_)

print (model.coef_)

[-0.94131796]
[[0.01519666]]
In [105]:
w_0 = model.intercept_[0]
w_1 = model.coef_[0,0]

# def normal_sigmoid(x):
#     return 1 / (1+np.exp(-x))

def sigmoid(x):
    return 1 / (1+np.exp(-(w_1*x+w_0)))

x_range = np.linspace(-1, 1000, 3000)

plt.figure(figsize=(9,5))
#plt.xkcd()
plt.legend(loc='upper right')


plt.ylim(-0.1, 1.1)
plt.xlim(-15, 1010)

#plt.plot([-10,10],[0,0], "k", lw=1)
plt.plot([0,0],[-1,1.5], "k", lw=1)
#plt.plot(data1,np.zeros(len(data1)), 'o')
plt.plot(data1, model.predict_proba(data1), 'o')
plt.plot(x_range, sigmoid(x_range), '-')
#plt.plot(x_range, normal_sigmoid(x_range), '-')

No handles with labels found to put in legend.
Out[105]:
[<matplotlib.lines.Line2D at 0x7f80ce0db978>]

1. ロジスティック回帰

実装(2変数から生死を判別)

In [138]:
#AgeFillの欠損値を埋めたので
titanic_df = titanic_df.drop(['Age'], axis=1)
In [139]:
titanic_df['Gender'] = titanic_df['Sex'].map({'female': 0, 'male': 1}).astype(int)
In [140]:
titanic_df.head(3)
Out[140]:
Survived Pclass Sex SibSp Parch Fare Embarked AgeFill Gender
0 0 3 male 1 0 7.2500 S 22.0 1
1 1 1 female 1 0 71.2833 C 38.0 0
2 1 3 female 0 0 7.9250 S 26.0 0
In [112]:
data2 = titanic_df.loc[:, ['Gender', 'AgeFill']].values
In [116]:
model3 = LogisticRegression()
In [117]:
model3.fit(data2, label1)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
Out[117]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
In [127]:
model3.predict([[1, 30]])
Out[127]:
array([0])
In [137]:
model3.predict_proba([[1, 30]])
Out[137]:
array([[0.80668102, 0.19331898]])
In [141]:
titanic_df['Pclass_Gender'] = titanic_df['Pclass'] + titanic_df['Gender']
In [142]:
titanic_df.head()
Out[142]:
Survived Pclass Sex SibSp Parch Fare Embarked AgeFill Gender Pclass_Gender
0 0 3 male 1 0 7.2500 S 22.0 1 4
1 1 1 female 1 0 71.2833 C 38.0 0 1
2 1 3 female 0 0 7.9250 S 26.0 0 3
3 1 1 female 1 0 53.1000 S 35.0 0 1
4 0 3 male 0 0 8.0500 S 35.0 1 4
In [144]:
titanic_df = titanic_df.drop(['Pclass', 'Sex', 'Gender'], axis=1)
In [145]:
titanic_df.head()
Out[145]:
Survived SibSp Parch Fare Embarked AgeFill Pclass_Gender
0 0 1 0 7.2500 S 22.0 4
1 1 1 0 71.2833 C 38.0 1
2 1 0 0 7.9250 S 26.0 3
3 1 1 0 53.1000 S 35.0 1
4 0 0 0 8.0500 S 35.0 4
In [146]:
# 重要だよ！！！
# 境界線の式
# w_1・x + w_2・y + w_0 = 0
#   ⇒ y = (-w_1・x - w_0) / w_2
 
# 境界線 プ#plt.plot([-2,2], map(lambda x: (-w_1 * x - w_0)/w_2, [-2,2]))
 
# # データを重ねる
#plt.scatter(X_train_std[y_train==0, 0], X_train_std[y_train==0, 1], c='red', marker='x', label='train 0')
#plt.scatter(X_train_std[y_train==1, 0], X_train_std[y_train==1, 1], c='blue', marker='x', label='train 1')
#plt.scatter(X_test_std[y_test==0, 0], X_test_std[y_test==0, 1], c='red', marker='o', s=60, label='test 0')
#plt.scatter(X_test_std[y_test==1, 0], X_test_std[y_test==1, 1], c='blue', marker='o', s=60, label='test 1')
In [147]:
np.random.seed = 0

xmin, xmax = -5, 85
ymin, ymax = 0.5, 4.5

index_survived = titanic_df[titanic_df["Survived"]==0].index
index_notsurvived = titanic_df[titanic_df["Survived"]==1].index

from matplotlib.colors import ListedColormap
fig, ax = plt.subplots()
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
sc = ax.scatter(titanic_df.loc[index_survived, 'AgeFill'],
                titanic_df.loc[index_survived, 'Pclass_Gender']+(np.random.rand(len(index_survived))-0.5)*0.1,
                color='r', label='Not Survived', alpha=0.3)
sc = ax.scatter(titanic_df.loc[index_notsurvived, 'AgeFill'],
                titanic_df.loc[index_notsurvived, 'Pclass_Gender']+(np.random.rand(len(index_notsurvived))-0.5)*0.1,
                color='b', label='Survived', alpha=0.3)
ax.set_xlabel('AgeFill')
ax.set_ylabel('Pclass_Gender')
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.legend(bbox_to_anchor=(1.4, 1.03))
Out[147]:
<matplotlib.legend.Legend at 0x7f80ce5e3ef0>
In [148]:
#運賃だけのリストを作成
data2 = titanic_df.loc[:, ["AgeFill", "Pclass_Gender"]].values
In [149]:
data2
Out[149]:
array([[22.        ,  4.        ],
       [38.        ,  1.        ],
       [26.        ,  3.        ],
       ...,
       [29.69911765,  3.        ],
       [26.        ,  2.        ],
       [32.        ,  4.        ]])
In [150]:
#生死フラグのみのリストを作成
label2 =  titanic_df.loc[:,["Survived"]].values
In [151]:
model2 = LogisticRegression()
In [152]:
model2.fit(data2, label2)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
Out[152]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
In [157]:
model2.predict([[30,1]])
Out[157]:
array([1])
In [158]:
model2.predict_proba([[30,1]])
Out[158]:
array([[0.08952932, 0.91047068]])
In [159]:
titanic_df.head(3)
Out[159]:
Survived SibSp Parch Fare Embarked AgeFill Pclass_Gender
0 0 1 0 7.2500 S 22.0 4
1 1 1 0 71.2833 C 38.0 1
2 1 0 0 7.9250 S 26.0 3
In [162]:
h = 0.02
xmin, xmax = -5, 85
ymin, ymax = 0.5, 4.5
xx, yy = np.meshgrid(np.arange(xmin, xmax, h), np.arange(ymin, ymax, h))
Z = model2.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
Z = Z.reshape(xx.shape)

fig, ax = plt.subplots()
levels = np.linspace(0, 1.0)
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
#contour = ax.contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.5)

sc = ax.scatter(titanic_df.loc[index_survived, 'AgeFill'],
                titanic_df.loc[index_survived, 'Pclass_Gender']+(np.random.rand(len(index_survived))-0.5)*0.1,
                color='r', label='Not Survived', alpha=0.3)
sc = ax.scatter(titanic_df.loc[index_notsurvived, 'AgeFill'],
                titanic_df.loc[index_notsurvived, 'Pclass_Gender']+(np.random.rand(len(index_notsurvived))-0.5)*0.1,
                color='b', label='Survived', alpha=0.3)

ax.set_xlabel('AgeFill')
ax.set_ylabel('Pclass_Gender')
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
#fig.colorbar(contour)

x1 = xmin
x2 = xmax
y1 = -1*(model2.intercept_[0]+model2.coef_[0][0]*xmin)/model2.coef_[0][1]
y2 = -1*(model2.intercept_[0]+model2.coef_[0][0]*xmax)/model2.coef_[0][1]
ax.plot([x1, x2] ,[y1, y2], 'k--')
Out[162]:
[<matplotlib.lines.Line2D at 0x7f80c39cbf98>]

2. モデル評価

混同行列とクロスバリデーション

In [163]:
from sklearn.model_selection import train_test_split
In [164]:
traindata1, testdata1, trainlabel1, testlabel1 = train_test_split(data1, label1, test_size=0.2)
traindata1.shape
trainlabel1.shape
Out[164]:
(712, 1)
In [165]:
traindata2, testdata2, trainlabel2, testlabel2 = train_test_split(data2, label2, test_size=0.2)
traindata2.shape
trainlabel2.shape
#本来は同じデータセットを分割しなければいけない。(簡易的に別々に分割している。)
Out[165]:
(712, 1)
In [166]:
data = titanic_df.loc[:, ].values
label =  titanic_df.loc[:,["Survived"]].values
traindata, testdata, trainlabel, testlabel = train_test_split(data, label, test_size=0.2)
traindata.shape
trainlabel.shape
Out[166]:
(712, 1)
In [167]:
eval_model1=LogisticRegression()
eval_model2=LogisticRegression()
#eval_model=LogisticRegression()
In [168]:
predictor_eval1=eval_model1.fit(traindata1, trainlabel1).predict(testdata1)
predictor_eval2=eval_model2.fit(traindata2, trainlabel2).predict(testdata2)
#predictor_eval=eval_model.fit(traindata, trainlabel).predict(testdata)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
In [169]:
eval_model1.score(traindata1, trainlabel1)
Out[169]:
0.6615168539325843
In [170]:
eval_model1.score(testdata1,testlabel1)
Out[170]:
0.6703910614525139
In [171]:
eval_model2.score(traindata2, trainlabel2)
Out[171]:
0.7851123595505618
In [172]:
eval_model2.score(testdata2,testlabel2)
Out[172]:
0.7486033519553073
In [173]:
from sklearn import metrics
print(metrics.classification_report(testlabel1, predictor_eval1))
print(metrics.classification_report(testlabel2, predictor_eval2))

              precision    recall  f1-score   support

           0       0.67      0.95      0.78       112
           1       0.70      0.21      0.32        67

    accuracy                           0.67       179
   macro avg       0.68      0.58      0.55       179
weighted avg       0.68      0.67      0.61       179

              precision    recall  f1-score   support

           0       0.80      0.83      0.81       119
           1       0.64      0.58      0.61        60

    accuracy                           0.75       179
   macro avg       0.72      0.71      0.71       179
weighted avg       0.74      0.75      0.75       179
In [174]:
from sklearn.metrics import confusion_matrix
confusion_matrix1=confusion_matrix(testlabel1, predictor_eval1)
confusion_matrix2=confusion_matrix(testlabel2, predictor_eval2)
In [175]:
confusion_matrix1
Out[175]:
array([[106,   6],
       [ 53,  14]])
In [176]:
confusion_matrix2
Out[176]:
array([[99, 20],
       [25, 35]])
In [177]:
fig = plt.figure(figsize = (7,7))
#plt.title(title)
sns.heatmap(
    confusion_matrix1,
    vmin=None,
    vmax=None,
    cmap="Blues",
    center=None,
    robust=False,
    annot=True, fmt='.2g',
    annot_kws=None,
    linewidths=0,
    linecolor='white',
    cbar=True,
    cbar_kws=None,
    cbar_ax=None,
    square=True, ax=None, 
    #xticklabels=columns,
    #yticklabels=columns,
    mask=None)
Out[177]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f80c39e38d0>
In [178]:
fig = plt.figure(figsize = (7,7))
#plt.title(title)
sns.heatmap(
    confusion_matrix2,
    vmin=None,
    vmax=None,
    cmap="Blues",
    center=None,
    robust=False,
    annot=True, fmt='.2g',
    annot_kws=None,
    linewidths=0,
    linecolor='white',
    cbar=True,
    cbar_kws=None,
    cbar_ax=None,
    square=True, ax=None, 
    #xticklabels=columns,
    #yticklabels=columns,
    mask=None)
Out[178]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f80c3917320>
In [179]:
#Paired categorical plots

import seaborn as sns
sns.set(style="whitegrid")

# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")

# Set up a grid to plot survival probability against several variables
g = sns.PairGrid(titanic, y_vars="survived",
                 x_vars=["class", "sex", "who", "alone"],
                 size=5, aspect=.5)

# Draw a seaborn pointplot onto each Axes
g.map(sns.pointplot, color=sns.xkcd_rgb["plum"])
g.set(ylim=(0, 1))
sns.despine(fig=g.fig, left=True)

plt.show()

/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:1150: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(UserWarning(msg))
In [180]:
#Faceted logistic regression

import seaborn as sns
sns.set(style="darkgrid")

# Load the example titanic dataset
df = sns.load_dataset("titanic")

# Make a custom palette with gendered colors
pal = dict(male="#6495ED", female="#F08080")

# Show the survival proability as a function of age and sex
g = sns.lmplot(x="age", y="survived", col="sex", hue="sex", data=df,
               palette=pal, y_jitter=.02, logistic=True)
g.set(xlim=(0, 80), ylim=(-.05, 1.05))
plt.show()

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
In [ ]: