import pandas as pd
import numpy as np
data = pd.read_csv('./tt/train.csv')
data.columns
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')
data = data[['Survived', 'Pclass','Sex', 'Age', 'SibSp',
'Parch', 'Fare', 'Embarked']]
data['Age'] = data['Age'].fillna(data['Age'].mean())
data.fillna(0, inplace=True)
data['Sex'] =[1 if x=='male' else 0 for x in data.Sex]
data['p1'] = np.array(data['Pclass'] == 1).astype(np.int32)
data['p2'] = np.array(data['Pclass'] == 2).astype(np.int32)
data['p3'] = np.array(data['Pclass'] == 3).astype(np.int32)
del data['Pclass']
data.Embarked.unique()
array(['S', 'C', 'Q', 0], dtype=object)
data['e1'] = np.array(data['Embarked'] == 'S').astype(np.int32)
data['e2'] = np.array(data['Embarked'] == 'C').astype(np.int32)
data['e3'] = np.array(data['Embarked'] == 'Q').astype(np.int32)
del data['Embarked']
data.values.dtype
dtype('float64')
data.columns
Index(['Survived', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'p1', 'p2', 'p3',
'e1', 'e2', 'e3'],
dtype='object')
data_train = data[['Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'p1', 'p2', 'p3',
'e1', 'e2', 'e3']].values
data_target = data['Survived'].values.reshape(len(data),1)
np.shape(data_train),np.shape(data_target)
((891, 11), (891, 1))
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_train, data_target, test_size = 0.2)
x_train.shape, x_test.shape
((712, 11), (179, 11))
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(x_train, y_train)
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
model.score(x_test, y_test)
0.7932960893854749
model.score(x_train, y_train)
0.9845505617977528
def m_score(depth):
model = DecisionTreeClassifier(max_depth=depth)
model.fit(x_train, y_train)
train_score = model.score(x_train, y_train)
test_score = model.score(x_test, y_test)
return train_score, test_score
depths = range(2, 15)
scores = [m_score(depth) for depth in depths]
scores
[(0.7921348314606742, 0.7932960893854749),
(0.8258426966292135, 0.8547486033519553),
(0.8412921348314607, 0.8491620111731844),
(0.8469101123595506, 0.8435754189944135),
(0.8595505617977528, 0.8435754189944135),
(0.8623595505617978, 0.8435754189944135),
(0.8735955056179775, 0.8435754189944135),
(0.8876404494382022, 0.8435754189944135),
(0.9087078651685393, 0.8603351955307262),
(0.9199438202247191, 0.8491620111731844),
(0.9311797752808989, 0.8268156424581006),
(0.9382022471910112, 0.8156424581005587),
(0.9452247191011236, 0.8379888268156425)]
train_s = [s[0] for s in scores]
test_s = [s[1] for s in scores]
import matplotlib.pyplot as plt
plt.plot(train_s)
plt.plot(test_s)
[<matplotlib.lines.Line2D at 0x1a106577b8>]
def m_score(value):
model = DecisionTreeClassifier(min_impurity_split=value)
model.fit(x_train, y_train)
train_score = model.score(x_train, y_train)
test_scroe = model.score(x_test, y_test)
return train_score, test_scroe
values = np.linspace(0, 0.5, 50)
scores = [m_score(value) for value in values]
train_s = [s[0] for s in scores]
test_s = [s[1] for s in scores]
best_index = np.argmax(test_s)
best_score = test_s[best_index]
best_value = values[best_index]
best_score, best_value
(0.8659217877094972, 0.19387755102040816)
plt.plot(train_s)
plt.plot(test_s)
[<matplotlib.lines.Line2D at 0x1a10657080>]
from sklearn.model_selection import GridSearchCV
values = np.linspace(0, 0.5, 50)
depths = range(2, 15)
param_grid = {'max_depth':depths, 'min_impurity_split':values}
model = GridSearchCV(DecisionTreeClassifier(), param_grid, cv = 5)
model.fit(data_train, data_target)
GridSearchCV(cv=5, error_score='raise',
estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best'),
fit_params=None, iid=True, n_jobs=1,
param_grid={'max_depth': range(2, 15), 'min_impurity_split': array([0. , 0.0102 , 0.02041, 0.03061, 0.04082, 0.05102, 0.06122,
0.07143, 0.08163, 0.09184, 0.10204, 0.11224, 0.12245, 0.13265,
0.14286, 0.15306, 0.16327, 0.17347, 0.18367, 0.19388, 0.20408,
0.21429, 0.22449, 0.23...16, 0.41837,
0.42857, 0.43878, 0.44898, 0.45918, 0.46939, 0.47959, 0.4898 ,
0.5 ])},
pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
scoring=None, verbose=0)
model.best_params_
{'max_depth': 9, 'min_impurity_split': 0.21428571428571427}
model.best_score_
0.8316498316498316
