DummyClassifier
index
DummyClassifier.py
DummyClassifier.py.html

A dummy model, with 3 strategies :
 
 * stratified;
 * most_frequent;
 * uniform.
 
The doc is here : http://scikit-learn.org/dev/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier
 
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Sortie du script
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.. runblock:: console
 
    $ python DummyClassifier.py
 
Résultats
---------
La soumission du résultat à Kaggle donne ??.??% (pas encore fait).
 
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Modules
       
csv
numpy
pylab

 
Data
        Dummy = DummyClassifier(random_state=None, strategy='most_frequent')
Number_try = 100
Output = array([ 0., 0., 0., 0., 0., 0., 0., 0., ..., 0., 0., 0., 0., 0., 0., 0., 0.])
__author__ = 'Lilian BESSON (mailto:lilian.besson[AT]normale.fr)'
age = 4
age_max = 80.0
age_mean = 29.69911764705882
age_min = 0.41999999999999998
attr = 6
best_strategy = 'most_frequent'
cabin = 9
csv_file_object = <_csv.reader object>
data = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82')
data_attributes = ['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', 'embarked']
embarked = 10
fare = 8
fare_max = 512.32920000000001
fare_mean = 32.2042079685746
fare_min = 0.0
from_c_onboard = array([ 1., 1., 1., 0., 0., 1., 0., 1., ...0., 1., 0., 1., 0., 1., 1., 1., 1., 1.])
from_q_onboard = array([ 0., 0., 1., 1., 1., 1., 0., 1., ...0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])
from_s_onboard = array([ 0., 1., 1., 0., 0., 0., 1., 1., ..., 1., 0., 0., 0., 0., 0., 1., 0.])
header = ['pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked']
i = 10
known_ages = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82')
known_ages_died = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82')
known_ages_survived = array([['1', '1', 'Cumings, Mrs. John Bradley (F...', ..., '30', 'C148', 'C']], dtype='|S82')
known_fares = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82')
known_fares_died = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82')
known_fares_survived = array([['1', '1', 'Cumings, Mrs. John Bradley (F...', ..., '30', 'C148', 'C']], dtype='|S82')
list_strategy = ['stratified', 'most_frequent', 'uniform']
men_onboard = array([ 0., 0., 0., 0., 0., 0., 0., 0., ..., 0., 0., 0., 0., 0., 0., 1., 0.])
men_only_stats = array([ True, False, False, False, True, True,..., True, False, False, True, True], dtype=bool)
name = 2
names_columns_test = ['pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked']
names_columns_train = ['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked']
nb_essais = 99
number_dead = 549.0
number_passengers = 891
number_survived = 342.0
open_file_object = <_csv.writer object>
parch = 6
pclass = 1
proportion_c_survived = 0.5535714285714286
proportion_known_ages = 0.8013468013468014
proportion_known_fares = 1.0
proportion_men = 0.6475869809203143
proportion_men_survived = 0.18890814558058924
proportion_q_survived = 0.38961038961038963
proportion_s_survived = 0.33695652173913043
proportion_survivors = 0.38383838383838381
proportion_train = 0.75
proportion_women = 0.35241301907968575
proportion_women_survived = 0.7420382165605095
quality = [50.0, 50.1497005988024, 46.40718562874252, 48.65269461077844, 52.9940119760479, 49.40119760479042, 50.89820359281437, 52.24550898203593, 50.0, 49.10179640718563, 51.047904191616766, 51.047904191616766, 52.69461077844311, 51.34730538922156, 51.796407185628745, 47.75449101796407, 48.952095808383234, 47.604790419161674, 53.74251497005988, 50.449101796407184, ...]
row = [0, '3', 'Peter, Master. Michael J', 'male', '', '1', '1', '2668', '22.3583', '', 'C']
row_dict = {1: 3, 3: 1, 4: 0.37123897058823524, 5: 1, 6: 1, 8: 0.04364049521284361, 10: 0}
score = 61.616161616161612
sex = 3
sibsp = 5
strategy = 'uniform'
strategy_quality = {'most_frequent': 61.976047904191567, 'stratified': 52.438622754491035, 'uniform': 50.414670658682645}
survived = 0
table_embarked = {'C': 0, 'Q': 2, 'S': 1}
table_sex = {'female': 0, 'male': 1}
test_data = array([[ 3. , 1. , 0.43125 , .... 1. , 0.0436405 , 0. ]])
test_file_object = <_csv.reader object>
ticket = 7
train_data = array([[ 1. , 1. , 1. , .... 1. , 0.04113566, 1. ]])
val = [61.976047904191567, 52.438622754491035, 50.414670658682645]
women_onboard = array([ 1., 1., 1., 1., 1., 1., 1., 0., ..., 1., 1., 1., 1., 0., 0., 1., 0.])
women_only_stats = array([False, True, True, True, False, False,..., False, True, True, False, False], dtype=bool)
z = 418

 
Author
        Lilian BESSON (mailto:lilian.besson[AT]normale.fr)