PyDataML¶

PyData Tokyo tutorial Machine Learning¶

Data¶

https://www.kaggle.com/c/titanic-gettingStarted/data

アジェンダ¶

バックグラウンド
ライブラリのインポートとデータの準備
ジェンダーモデルによる生存者推定、推定値の評価
ロジスティック回帰による生存者推定
交差検証（クロスバリデーション）
決定木（Decision Tree）による生存者推定
グリッドサーチ

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

In [2]:

from sklearn.metrics import (
    accuracy_score,
    classification_report,
    confusion_matrix
)
from sklearn.cross_validation import (
    train_test_split,
    cross_val_score,
    KFold
)
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import (
    DecisionTreeClassifier,
    export_graphviz
)
from sklearn.grid_search import GridSearchCV

from matplotlib.colors import ListedColormap
from IPython.display import Image

In [439]:

def estimated_params(estimator):
    return [a for a in dir(estimator) if a.endswith("_") and not a.startswith("_")]

In [3]:

df_train = pd.read_csv('titanic/train.csv')
df_test = pd.read_csv('titanic/test.csv')

In [4]:

df_train.tail(1)

Out[4]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

In [5]:

df_train.describe(include="all", percentiles=[0.05] + list(np.linspace(0.1, 0.9, 9)) + [0.95])

Out[5]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
count	891.000000	891.000000	891.000000	891	891	714.000000	891.000000	891.000000	891	891.000000	204	889
unique	NaN	NaN	NaN	891	2	NaN	NaN	NaN	681	NaN	147	3
top	NaN	NaN	NaN	Kilgannon, Mr. Thomas J	male	NaN	NaN	NaN	CA. 2343	NaN	C23 C25 C27	S
freq	NaN	NaN	NaN	1	577	NaN	NaN	NaN	7	NaN	4	644
mean	446.000000	0.383838	2.308642	NaN	NaN	29.699118	0.523008	0.381594	NaN	32.204208	NaN	NaN
std	257.353842	0.486592	0.836071	NaN	NaN	14.526497	1.102743	0.806057	NaN	49.693429	NaN	NaN
min	1.000000	0.000000	1.000000	NaN	NaN	0.420000	0.000000	0.000000	NaN	0.000000	NaN	NaN
5%	45.500000	0.000000	1.000000	NaN	NaN	4.000000	0.000000	0.000000	NaN	7.225000	NaN	NaN
10%	90.000000	0.000000	1.000000	NaN	NaN	14.000000	0.000000	0.000000	NaN	7.550000	NaN	NaN
20%	179.000000	0.000000	1.000000	NaN	NaN	19.000000	0.000000	0.000000	NaN	7.854200	NaN	NaN
30.0%	268.000000	0.000000	2.000000	NaN	NaN	22.000000	0.000000	0.000000	NaN	8.050000	NaN	NaN
40%	357.000000	0.000000	2.000000	NaN	NaN	25.000000	0.000000	0.000000	NaN	10.500000	NaN	NaN
50%	446.000000	0.000000	3.000000	NaN	NaN	28.000000	0.000000	0.000000	NaN	14.454200	NaN	NaN
60%	535.000000	0.000000	3.000000	NaN	NaN	31.800000	0.000000	0.000000	NaN	21.679200	NaN	NaN
70%	624.000000	1.000000	3.000000	NaN	NaN	36.000000	1.000000	0.000000	NaN	27.000000	NaN	NaN
80%	713.000000	1.000000	3.000000	NaN	NaN	41.000000	1.000000	1.000000	NaN	39.687500	NaN	NaN
90%	802.000000	1.000000	3.000000	NaN	NaN	50.000000	1.000000	2.000000	NaN	77.958300	NaN	NaN
95%	846.500000	1.000000	3.000000	NaN	NaN	56.000000	3.000000	2.000000	NaN	112.079150	NaN	NaN
max	891.000000	1.000000	3.000000	NaN	NaN	80.000000	8.000000	6.000000	NaN	512.329200	NaN	NaN

変数概要¶

PassengerId: 乗客ID
Survived: 1 = 生き残り 0 = 死亡
Pclass: 等級
Name: 名前
Sex: 性別
Age: 年齢
Parch: 子供の数
Ticket: チケット番号
Fare: 運賃
Cabin: 部屋番号
Embarked: 乗船地

わかったこと¶

891レコード
70%が死んだ
半分が３階級
年齢に欠損値多少有り
Cabinに欠損値が多い(そもそも部屋番号は関係なさそう)

In [6]:

df_test.tail(1)

Out[6]:

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
417	1309	3	Peter, Master. Michael J	male	NaN	1	1	2668	22.3583	NaN	C

In [7]:

sex = {val:idx for idx, val in enumerate(df_train["Sex"].unique())}
sex

Out[7]:

{'female': 1, 'male': 0}

In [8]:

s_sex  = df_train["Sex"].map(sex)
s_sex.plot.hist(bins=2)

Out[8]:

<matplotlib.axes._subplots.AxesSubplot at 0x1179ba2e8>

../../../_images/contents_notebooks_kaggle_PyDataML_13_1.png

In [9]:

df_train["Survived"].value_counts().plot.bar()

Out[9]:

<matplotlib.axes._subplots.AxesSubplot at 0x104517828>

../../../_images/contents_notebooks_kaggle_PyDataML_14_1.png

In [10]:

df_train.pivot_table(index="Sex", columns="Survived", values="PassengerId", aggfunc=len).plot.bar()

Out[10]:

<matplotlib.axes._subplots.AxesSubplot at 0x1178ff518>

../../../_images/contents_notebooks_kaggle_PyDataML_15_1.png

In [11]:

fig, axes = plt.subplots(1, 2, figsize=(16, 4))
axes[0].hist([df_train[(df_train["Sex"] == "female") & (df_train["Survived"] == 0)]["Age"],
          df_train[(df_train["Sex"] == "female") & (df_train["Survived"] == 1)]["Age"]],
         bins=10 , stacked=True, range=(1, 80))

axes[1].hist([df_train[(df_train.Survived==0) & (df_train.Sex=='male')]['Age'],
          df_train[(df_train.Survived==1) & (df_train.Sex=='male')]['Age']],
          alpha=0.6, range=(1,80), bins=10, stacked=True,
          label=('Died', 'Survived'))

Out[11]:

([array([ 11.,  18.,  99.,  88.,  60.,  37.,  23.,  14.,   9.,   1.]),
  array([  23.,   23.,  110.,  117.,   73.,   46.,   29.,   16.,    9.,    2.])],
 array([  1. ,   8.9,  16.8,  24.7,  32.6,  40.5,  48.4,  56.3,  64.2,
         72.1,  80. ]),
 <a list of 2 Lists of Patches objects>)

../../../_images/contents_notebooks_kaggle_PyDataML_16_1.png

ジェンダーモデルによる生存者推定、推定値の評価¶

In [12]:

x = df_train['Sex']
y = df_train['Survived']
y_pred = x.map({'female': 1, 'male': 0}).astype(int)

In [13]:

pd.concat([y.head(), y_pred.head()], axis=1)

Out[13]:

	Survived	Sex
0	0	0
1	1	1
2	1	1
3	1	1
4	0	0

In [14]:

accuracy_score(
    [1, 0, 1, 0, 0],
    [0, 0, 1, 1, 1])

Out[14]:

0.40000000000000002

In [15]:

accuracy_score(y, y_pred)

Out[15]:

0.78675645342312006

In [16]:

print(classification_report(y, y_pred))

             precision    recall  f1-score   support

          0       0.81      0.85      0.83       549
          1       0.74      0.68      0.71       342

avg / total       0.78      0.79      0.78       891

In [17]:

cm = confusion_matrix(y, y_pred)
# True, Predict
cm_df = pd.DataFrame(cm, index=["T(0)", "T(1)"], columns=["P(0)", "P(1)"])
cm_df

Out[17]:

	P(0)	P(1)
T(0)	468	81
T(1)	109	233

In [19]:

cm_df.ix["T(0)"]["P(0)"] / cm_df.ix["T(0)"]["P(0)"]

Out[19]:

1.0

In [20]:

def plot_confusion_matrix(cm):
    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    #ax.set_title('Confusion Matrix')
    fig.colorbar(im)

    target_names = ['not survived', 'survived']

    tick_marks = np.arange(len(target_names))
    ax.set_xticks(tick_marks)
    ax.set_xticklabels(target_names, rotation=45)
    ax.set_yticks(tick_marks)
    ax.set_yticklabels(target_names)
    ax.set_ylabel('True label')
    ax.set_xlabel('Predicted label')
    fig.tight_layout()

plot_confusion_matrix(cm)

../../../_images/contents_notebooks_kaggle_PyDataML_25_0.png

作成したkaggle_gendermodel.csvをKaggleに投稿し、スコアと順位を確認してみましょう！これで皆さんもKagglerです！¶

In [21]:

x_test = df_test['Sex']
y_test_pred = x_test.map({'female': 1, 'male': 0}).astype(int)

In [22]:

df_kaggle = pd.DataFrame({'PassengerId': df_test['PassengerId'], 'Survived':np.array(y_test_pred)})
df_kaggle.to_csv('titanic/kaggle_gendermodel.csv', index=False)
df_kaggle.tail()

Out[22]:

	PassengerId	Survived
413	1305	0
414	1306	1
415	1307	0
416	1308	0
417	1309	0

ロジスティック回帰による生存者推定¶

In [23]:

iris = sns.load_dataset("iris")
iris2 = iris[["sepal_length", "sepal_width"]].copy()
iris2 = iris.loc[: , ("sepal_length", "sepal_width")]
iris2["lenght_2x"] = iris2["sepal_length"] * 2

In [24]:

pd.options.mode.chained_assignment

Out[24]:

'warn'

In [25]:

X = df_train[['Age', 'Pclass', 'Sex']].copy()
y = df_train['Survived']

In [26]:

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

In [27]:

X['AgeFill'] = X['Age'].fillna(X['Age'].mean())
X['Gender'] = X['Sex'].map({'female': 0, 'male': 1}).astype(int)
X['Pclass_Gender'] = X['Pclass'] + X['Gender']
X.tail()

Out[27]:

	Age	Pclass	Sex	AgeFill	Gender	Pclass_Gender
886	27.0	2	male	27.000000	1	3
887	19.0	1	female	19.000000	0	1
888	NaN	3	female	29.699118	0	3
889	26.0	1	male	26.000000	1	2
890	32.0	3	male	32.000000	1	4

In [28]:

X = X.drop(['Age'], axis=1)
X = X.drop(['Pclass', 'Sex', 'Gender'], axis=1)
X.tail()

Out[28]:

	AgeFill	Pclass_Gender
886	27.000000	3
887	19.000000	1
888	29.699118	3
889	26.000000	2
890	32.000000	4

In [29]:

np.random.seed(0)

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

index_survived = y[y==0].index
index_notsurvived = y[y==1].index

fig, ax = plt.subplots()
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
sc = ax.scatter(X.loc[index_survived, 'AgeFill'],
                X.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(X.loc[index_notsurvived, 'AgeFill'],
                X.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[29]:

<matplotlib.legend.Legend at 0x11ac4d518>

../../../_images/contents_notebooks_kaggle_PyDataML_36_1.png

In [30]:

_sc_test = X.copy()
_sc_test.Pclass_Gender = _sc_test.Pclass_Gender.astype("category")
_sc_test["y"] = y

sns.swarmplot(x="AgeFill", y="Pclass_Gender", hue="y", data=_sc_test, alpha=0.3, palette=["red", "blue"])
plt.legend(bbox_to_anchor=(1.4, 1.03))
plt.tight_layout()

../../../_images/contents_notebooks_kaggle_PyDataML_37_0.png

In [94]:

titanic = sns.load_dataset("titanic")
_titanic = pd.DataFrame(
    [
        titanic.age.fillna(titanic.age.mean()),
        titanic.pclass + titanic.sex.map({'female': 0, 'male': 1}).astype(int),
        titanic.survived
    ]).T
#_titanic["Unnamed 0"] = _titanic["Unnamed 0"].astype("category")
#_titanic["Unnamed 0"] = _titanic["Unnamed 0"].astype("category", categories=[4,3,2,1], ordered=False)

#sns.swarmplot(
sns.stripplot(jitter=0.1,
    data=_titanic, x="age", y="Unnamed 0", hue="survived",
    alpha=0.3, palette=["red", "blue"])
plt.legend(bbox_to_anchor=(1.4, 1.03))
plt.tight_layout()

../../../_images/contents_notebooks_kaggle_PyDataML_38_0.png

トレーニングデータの分割¶

In [32]:

X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=1)

In [33]:

X_train.tail().T

Out[33]:

	715	767	72	235	37
AgeFill	19.0	30.5	21.0	29.699118	21.0
Pclass_Gender	4.0	3.0	3.0	3.000000	4.0

In [34]:

print('Num of Training Samples: {}'.format(len(X_train)))
print('Num of Validation Samples: {}'.format(len(X_val)))

Num of Training Samples: 712
Num of Validation Samples: 179

ロジスティック回帰による推定¶

In [441]:

# 学習
clf = LogisticRegression()
clf.fit(X_train, y_train)

Out[441]:

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

In [442]:

estimated_params(clf)

Out[442]:

['classes_', 'coef_', 'intercept_', 'n_iter_']

In [443]:

clf.coef_, clf.intercept_

Out[443]:

(array([[-0.03756041, -1.29748813]]), array([ 4.30808898]))

In [444]:

clf.classes_, clf.n_iter_

Out[444]:

(array([0, 1]), array([11], dtype=int32))

In [36]:

y_train_pred = clf.predict(X_train)
y_val_pred = clf.predict(X_val)

print('Accuracy on Training Set: {:.3f}'.format(accuracy_score(y_train, y_train_pred)))
print('Accuracy on Validation Set: {:.3f}'.format(accuracy_score(y_val, y_val_pred)))

Accuracy on Training Set: 0.774
Accuracy on Validation Set: 0.760

In [37]:

cm = confusion_matrix(y_val, y_val_pred)
print(cm)

[[93 13]
 [30 43]]

In [38]:

print(classification_report(y_val, y_val_pred))

             precision    recall  f1-score   support

          0       0.76      0.88      0.81       106
          1       0.77      0.59      0.67        73

avg / total       0.76      0.76      0.75       179

In [39]:

X_val
1

Out[39]:

In [95]:

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 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
Z = Z.reshape(xx.shape)

levels = np.linspace(0, 1.0, 5)
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])

ax = sns.stripplot(jitter=0.1,
    data=_titanic,
    x="age", y="Unnamed 0", hue="survived",
    alpha=0.5, palette=["red", "blue"])

contour = ax.contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)
fig.colorbar(contour)

Out[95]:

<matplotlib.colorbar.Colorbar at 0x11e126ef0>

../../../_images/contents_notebooks_kaggle_PyDataML_52_1.png

In [60]:

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 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
Z = Z.reshape(xx.shape)

fig, ax = plt.subplots(figsize=(14, 6))
levels = np.linspace(0, 1.0, 5)
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
contour = ax.contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)
ax.scatter(X_train.iloc[:, 0], X_train.iloc[:, 1]+(np.random.rand(len(X_train))-0.5)*0.1, c=y_train, cmap=cm_bright)
ax.scatter(X_val.iloc[:, 0], X_val.iloc[:, 1]+(np.random.rand(len(X_val))-0.5)*0.1, c=y_val, cmap=cm_bright, alpha=0.5)
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*(clf.intercept_[0]+clf.coef_[0][0]*xmin)/clf.coef_[0][1]
y2 = -1*(clf.intercept_[0]+clf.coef_[0][0]*xmax)/clf.coef_[0][1]
ax.plot([xmin, xmax] ,[y1, y2], 'k--')

Out[60]:

[<matplotlib.lines.Line2D at 0x123109b70>]

../../../_images/contents_notebooks_kaggle_PyDataML_53_1.png

In [96]:

clf_log = LogisticRegression()
clf_svc_lin = SVC(kernel='linear', probability=True)
clf_svc_rbf = SVC(kernel='rbf', probability=True)
titles = ['Logistic Regression', 'SVC with Linear Kernel', 'SVC with RBF Kernel',]

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))

fig, axes = plt.subplots(1, 3, figsize=(12,4))
levels = np.linspace(0, 1.0, 5)
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
for i, clf in enumerate((clf_log, clf_svc_lin, clf_svc_rbf)):
    clf.fit(X, y)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    axes[i].contourf(xx, yy, Z, cmap=cm, levels=levels, alpha=0.8)
    axes[i].scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, cmap=cm_bright)
    axes[i].set_title(titles[i])
    axes[i].set_xlabel('AgeFill')
    axes[i].set_ylabel('Pclass_Gender')
    axes[i].set_xlim(xmin, xmax)
    axes[i].set_ylim(ymin, ymax)
    fig.tight_layout()

../../../_images/contents_notebooks_kaggle_PyDataML_54_0.png

over fitting¶

In [446]:

clf = SVC(kernel='rbf', probability=True)
clf.fit(X_train, y_train)

y_train_pred = clf.predict(X_train)
y_val_pred = clf.predict(X_val)

accuracy_score(y_train, y_train_pred), accuracy_score(y_val, y_val_pred)

Out[446]:

(0.80758426966292129, 0.7988826815642458)

In [447]:

estimated_params(clf)

Out[447]:

['class_weight_',
 'classes_',
 'coef_',
 'dual_coef_',
 'fit_status_',
 'intercept_',
 'n_support_',
 'probA_',
 'probB_',
 'shape_fit_',
 'support_',
 'support_vectors_']

cross validation¶

In [98]:

X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=33)

clf = LogisticRegression()
clf.fit(X_train, y_train)

y_train_pred = clf.predict(X_train)
y_val_pred = clf.predict(X_val)

accuracy_score(y_train, y_train_pred), accuracy_score(y_val, y_val_pred)

Out[98]:

(0.7837078651685393, 0.74301675977653636)

In [100]:

def cross_val(clf, X, y, K, random_state=0):
    cv = KFold(len(y), K, shuffle=True, random_state=random_state)
    scores = cross_val_score(clf, X, y, cv=cv)
    return scores

In [101]:

clf = LogisticRegression()
scores = cross_val(clf, X, y, 5)
print('Scores:', scores)
print('Mean Score: {0:.3f} (+/-{1:.3f})'.format(scores.mean(), scores.std()*2))

Scores: [ 0.80446927  0.74719101  0.80337079  0.74719101  0.76966292]
Mean Score: 0.774 (+/-0.051)

In [137]:

(train_test_split([1,2,3,4], [5,6,7,8], train_size=0.5))

Out[137]:

[[3, 2], [4, 1], [7, 6], [8, 5]]

In [117]:

(train_test_split([1,2,3,4], [5,6,7,8], train_size=0.5, random_state=33))

Out[117]:

[[4, 1], [2, 3], [8, 5], [6, 7]]

In [198]:

shuffled = np.array(range(9))
np.random.shuffle(shuffled)
shuffled

Out[198]:

array([7, 3, 1, 5, 4, 8, 2, 0, 6])

In [219]:

random_state = np.random.RandomState(seed=0)
random_state.shuffle(shuffled)
shuffled

Out[219]:

array([0, 1, 3, 4, 6, 2, 5, 7, 8])

In [221]:

# http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html
KFold(len(y), 5, shuffle=True, random_state=random_state)

Out[221]:

sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>)

In [223]:

# http://scikit-learn.org/stable/modules/cross_validation.html
# http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html
cross_val_score(LogisticRegression(), X, y, cv=5)

Out[223]:

array([ 0.7150838 ,  0.75418994,  0.82022472,  0.79213483,  0.79661017])

In [238]:

kf = KFold(len(y), 5, shuffle=True, random_state=random_state)
print(kf, "\n")
for i, (kf_train, kf_test) in enumerate(kf):
    print(i)
    print(kf_train)
    print(kf_test)

sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>)

0
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  49  51  52  53  54  55  56  57  58  59  60  62  65  68  69  71  73  74
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In [228]:

from sklearn.cross_validation import StratifiedKFold
StratifiedKFold(y, 5, shuffle=True, random_state=random_state)

Out[228]:

sklearn.cross_validation.StratifiedKFold(labels=[0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 1
0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0
0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0
0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1
1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0
0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1
0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0
0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0
1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1
0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0
0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 0 0 1
1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0
0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0
0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 1
1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1
0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0
1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0
0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0
0 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0
0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0
0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0
0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0
0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1
1 0], n_folds=5, shuffle=True, random_state=<mtrand.RandomState object at 0x1201d8438>)

In [232]:

skf_x = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
skf_y = np.array([0, 0, 1, 1])
skf = StratifiedKFold(skf_y, n_folds=2)
len(skf)

print(skf)


for train_index, test_index in skf:
    print("TRAIN:", train_index, "TEST:", test_index)
    skf_X_train, skf_X_test = skf_x[train_index], skf_x[test_index]
    skf_y_train, skf_y_test = skf_y[train_index], skf_y[test_index]

sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2, shuffle=False, random_state=None)
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]

In [222]:

cross_val_score?

In [454]:

# http://scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html#cross-validated-estimators
from sklearn.linear_model import LogisticRegressionCV
#help(LogisticRegressionCV)

In [ ]:

In [455]:

# http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
le = LabelEncoder()
labels = pd.Series(list("acb") * 5)
#le.fit(labels)
le.fit_transform(labels), le.transform(labels), le.inverse_transform([0,1,2])

Out[455]:

(array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1]),
 array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1]),
 array(['a', 'b', 'c'], dtype=object))

In [261]:

le.fit_transform(labels)

Out[261]:

array([0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1])

In [449]:

estimated_params(le), le.classes_

Out[449]:

(['classes_'], array(['female', 'male'], dtype=object))

In [258]:

classes = {k: v for k, v in enumerate(le.classes_)}
le.classes_, le.classes_.__class__, classes

Out[258]:

(array(['a', 'b', 'c'], dtype=object), numpy.ndarray, {0: 'a', 1: 'b', 2: 'c'})

In [ ]:

In [269]:

X = df_train[['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']].copy()
y = df_train['Survived'].copy()
X_test = df_test[['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']].copy()

X['AgeFill'] = X['Age'].fillna(X['Age'].mean())
X_test['AgeFill'] = X_test['Age'].fillna(X['Age'].mean())
X = X.drop(['Age'], axis=1)
X_test = X_test.drop(['Age'], axis=1)

le = LabelEncoder()
le.fit(X['Sex'])
X['Gender'] = le.transform(X['Sex'])
X_test['Gender'] = le.transform(X_test['Sex'])

X = X.join(pd.get_dummies(X['Embarked'], prefix='Embarked'))
X_test = X_test.join(pd.get_dummies(X['Embarked'], prefix='Embarked'))
X = X.drop(['Sex', 'Embarked'], axis=1)
X_test = X_test.drop(['Sex', 'Embarked'], axis=1)

X.tail()

Out[269]:

	Pclass	SibSp	Parch	AgeFill	Gender	Embarked_C	Embarked_Q	Embarked_S
886	2	0	0	27.000000	1	0.0	0.0	1.0
887	1	0	0	19.000000	0	0.0	0.0	1.0
888	3	1	2	29.699118	0	0.0	0.0	1.0
889	1	0	0	26.000000	1	1.0	0.0	0.0
890	3	0	0	32.000000	1	0.0	1.0	0.0

In [270]:

clf = LogisticRegression()
scores = cross_val(clf, X, y, 5)
scores, scores.mean(), scores.std()*2

Out[270]:

(array([ 0.79329609,  0.78089888,  0.80337079,  0.81460674,  0.80337079]),
 0.79910865607934212,
 0.022661053339677807)

In [450]:

clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)
scores = cross_val(clf, X, y, 5)
print('Scores:', scores)
print('Mean Score: {0:.3f} (+/-{1:.3f})'.format(scores.mean(), scores.std()*2))

Scores: [ 0.78212291  0.76404494  0.79213483  0.78089888  0.7247191 ]
Mean Score: 0.769 (+/-0.048)

In [452]:

estimated_params(clf)

Out[452]:

['classes_',
 'feature_importances_',
 'max_features_',
 'n_classes_',
 'n_features_',
 'n_outputs_',
 'tree_']

In [451]:

tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)
#tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=3)

tree_clf.fit(X, y)

Out[451]:

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=2,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

In [423]:

In [351]:

from sklearn.externals.six import StringIO as SkStringIO
from IPython.display import Image
import graphviz

def tree_plot(decision_tree, width=500, height=500, max_depth=None,
         feature_names=None, class_names=None, label='all',
         filled=False, leaves_parallel=False, impurity=True,
         node_ids=False, proportion=False, rotate=False,
         rounded=False, special_characters=False):
    in_memory_dot_file = SkStringIO()
    export_graphviz(
            decision_tree, out_file=in_memory_dot_file, max_depth=max_depth,
            feature_names=feature_names, class_names=class_names, label=label,
            filled=filled, leaves_parallel=leaves_parallel, impurity=impurity,
            node_ids=node_ids, proportion=proportion, rotate=rotate,
            rounded=rounded, special_characters=special_characters)
    src = graphviz.Source(in_memory_dot_file.getvalue())
    return Image(src.pipe(format='png'), height=height, width=width)

tree_plot(tree_clf,
#tree_plot(clf,
    feature_names=X.columns,
#    class_names=["0", "1"],
    class_names=["d", "s"],
#    class_names={1: "s", 0: "d"},
#    class_names=list(y.apply(str)),
    filled=True,
    rounded=True,
    special_characters=True
)

Out[351]:

../../../_images/contents_notebooks_kaggle_PyDataML_85_0.png

In [334]:

X_y = pd.concat([X, y], axis=1)
t1 = X_y[X_y.Gender <= 0.5]
t1[t1.Pclass <= 2.5].Survived.value_counts()

Out[334]:

1    161
0      9
Name: Survived, dtype: int64

In [335]:

t1[t1.Pclass > 2.5].Survived.value_counts()

Out[335]:

1    72
0    72
Name: Survived, dtype: int64

In [336]:

f1 = X_y[X_y.Gender > 0.5]
f1[f1.Pclass <= 1.5].Survived.value_counts()

Out[336]:

0    77
1    45
Name: Survived, dtype: int64

In [337]:

f1[f1.Pclass > 1.5].Survived.value_counts()

Out[337]:

0    391
1     64
Name: Survived, dtype: int64

In [436]:

estimated_params(tree_clf)

Out[436]:

['classes_',
 'feature_importances_',
 'max_features_',
 'n_classes_',
 'n_features_',
 'n_outputs_',
 'tree_']

In [429]:

tree_clf.classes_, tree_clf.feature_importances_

Out[429]:

(array([0, 1]),
 array([ 0.30417882,  0.        ,  0.        ,  0.        ,  0.69582118,
         0.        ,  0.        ,  0.        ]))

In [430]:

tree_clf.max_features_, tree_clf.n_classes_

Out[430]:

(8, 2)

In [432]:

tree_clf.n_features_, tree_clf.n_outputs_

Out[432]:

(8, 1)

In [431]:

tree_clf.tree_

Out[431]:

<sklearn.tree._tree.Tree at 0x11e626030>

In [ ]:

In [ ]:

In [ ]:

In [311]:

from sklearn.externals.six import StringIO as SkStringIO
from io import StringIO as SkStringIO
import pydot
from IPython.display import Image
dot_data = SkStringIO()
export_graphviz(
    tree_clf, out_file=dot_data,
    feature_names=X.columns,
    class_names=y.unique(),
    filled=True, rounded=True,
    special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
#print(dot_data.getvalue())

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-311-7387233abd8b> in <module>()
     10     filled=True, rounded=True,
     11     special_characters=True)
---> 12 graph = pydot.graph_from_dot_data(dot_data.getvalue())
     13 Image(graph.create_png())
     14 #print(dot_data.getvalue())

/Users/knt/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/pydot.py in graph_from_dot_data(data)
    218     """
    219
--> 220     return dot_parser.parse_dot_data(data)
    221
    222

/Users/knt/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/dot_parser.py in parse_dot_data(data)
    508     top_graphs = list()
    509
--> 510     if data.startswith(codecs.BOM_UTF8):
    511         data = data.decode( 'utf-8' )
    512

TypeError: startswith first arg must be str or a tuple of str, not bytes

グリッドサーチ¶

グリッドサーチは、分類器のパラメータを指定した範囲で変化させ、最もスコアの高いパラメータの組合せを探してくれる便利な機能です。

In [352]:

clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)

param_grid = {'max_depth': [2, 3, 4, 5], 'min_samples_leaf': [2, 3, 4, 5]}
cv = KFold(len(y), 5, shuffle=True, random_state=0)

grid_search = GridSearchCV(clf, param_grid, cv=cv, n_jobs=-1, verbose=1)
grid_search.fit(X, y)

Fitting 5 folds for each of 16 candidates, totalling 80 fits

[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:    0.3s finished

Out[352]:

GridSearchCV(cv=sklearn.cross_validation.KFold(n=891, n_folds=5, shuffle=True, random_state=0),
       error_score='raise',
       estimator=DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=2,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best'),
       fit_params={}, iid=True, n_jobs=-1,
       param_grid={'max_depth': [2, 3, 4, 5], 'min_samples_leaf': [2, 3, 4, 5]},
       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)

In [437]:

estimated_params(grid_search)

Out[437]:

['best_estimator_', 'best_params_', 'best_score_', 'grid_scores_', 'scorer_']

In [420]:

grid_search.best_estimator_

Out[420]:

DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=3,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

In [419]:

grid_search.best_score_, grid_search.best_params_

Out[419]:

[0.81705948372615034, {'max_depth': 4, 'min_samples_leaf': 3}]

In [438]:

grid_search.scorer_

Out[438]:

<function sklearn.metrics.scorer._passthrough_scorer>

In [356]:

grid_search.grid_scores_

Out[356]:

[mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 2},
 mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 3},
 mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 4},
 mean: 0.76880, std: 0.02381, params: {'max_depth': 2, 'min_samples_leaf': 5},
 mean: 0.80471, std: 0.01474, params: {'max_depth': 3, 'min_samples_leaf': 2},
 mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 3},
 mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 4},
 mean: 0.80584, std: 0.01686, params: {'max_depth': 3, 'min_samples_leaf': 5},
 mean: 0.81594, std: 0.01180, params: {'max_depth': 4, 'min_samples_leaf': 2},
 mean: 0.81706, std: 0.01391, params: {'max_depth': 4, 'min_samples_leaf': 3},
 mean: 0.81257, std: 0.01345, params: {'max_depth': 4, 'min_samples_leaf': 4},
 mean: 0.80808, std: 0.01750, params: {'max_depth': 4, 'min_samples_leaf': 5},
 mean: 0.80247, std: 0.01421, params: {'max_depth': 5, 'min_samples_leaf': 2},
 mean: 0.80471, std: 0.01552, params: {'max_depth': 5, 'min_samples_leaf': 3},
 mean: 0.79686, std: 0.02005, params: {'max_depth': 5, 'min_samples_leaf': 4},
 mean: 0.79686, std: 0.01660, params: {'max_depth': 5, 'min_samples_leaf': 5}]

In [382]:

{"a": 1, "b": 2}.values()

Out[382]:

dict_values([1, 2])

In [410]:

gs_df = pd.DataFrame(grid_search.grid_scores_)
params_df = gs_df.parameters.apply(
    lambda p: pd.Series(list(p.values()), p.keys()))
pd.concat([
    gs_df.drop(["parameters", "cv_validation_scores"], axis=1),
    params_df
], axis=1).head(2)

Out[410]:

	mean_validation_score	max_depth	min_samples_leaf
0	0.768799	2	2
1	0.768799	2	3

In [371]:

s = pd.Series(["k:v", "k1:v1"])
s.str.split(":", expand=True)

Out[371]:

	0	1
0	k	v
1	k1	v1

In [418]:

pd.concat([
    s.apply(lambda x: pd.Series(x.split(":"), index=['col1', 'col2'])),
    s.apply(lambda x: (x.split(":")))
], axis=1)

Out[418]:

	col1	col2	0
0	k	v	[k, v]
1	k1	v1	[k1, v1]

In [415]:

series = pd.Series([20, 21, 12], index=['London', 'New York','Helsinki'])
pd.DataFrame([series, series])

Out[415]:

	London	New York	Helsinki
0	20	21	12
1	20	21	12

In [395]:

pd.Series(list("abc")).str.split

Out[395]:

<bound method StringMethods.split of <pandas.core.strings.StringMethods object at 0x11e82b438>>

In [414]:

y_test_pred = grid_search.predict(X_test)
grid_search.predict

Out[414]:

<function sklearn.grid_search.BaseSearchCV.predict>

In [ ]: