def main():
trainfilename = os.path.join(os.path.dirname(__file__), 'data', 'train.csv')
testfilename = os.path.join(os.path.dirname(__file__), 'data', 'test.csv')
train_X = pd.read_csv(trainfilename)
print "Basic info on training data"
print len(train_X)
print len(train_X.columns)
print train_X.columns
train_Y = train_X.take([1], axis=1)
train_X = train_X.drop('Survived',1)
# print train_X.columns
test_X = pd.read_csv(testfilename)
#Binaries
train_X["has_family"] = train_X.apply(family, axis=1)
train_X["child"] = train_X.apply(child, axis=1)
train_X["smallchild"] = train_X.apply(smallchild, axis=1)
train_X["familysize"] = train_X.apply(familysize, axis=1)
train_X["Sex"] = train_X["Sex"].apply(lambda sex: 0 if sex == "male" else 1)
test_X["has_family"] = test_X.apply(family, axis=1)
test_X["child"] = test_X.apply(child, axis=1)
test_X["smallchild"] = test_X.apply(smallchild, axis=1)
test_X["familysize"] = test_X.apply(familysize, axis=1)
test_X["Sex"] = test_X["Sex"].apply(lambda sex: 0 if sex == "male" else 1)
#Categorical ==> Use one hot necoding
onehot = True
if onehot:
newcolumns= []
train_X["Embarked"] = train_X["Embarked"].apply(lambda port: selectembarkment(port))
test_X["Embarked"] = test_X["Embarked"].apply(lambda port: selectembarkment(port))
newcolumns.append("Embarked")
train_X["fare2"] = train_X.apply(fare2, axis=1)
test_X["fare2"] = test_X.apply(fare2, axis=1)
newcolumns.append("fare2")
train_X["nameinfo"] = train_X["Name"].apply(lambda name: nameinfo(name))
test_X["nameinfo"] = test_X["Name"].apply(lambda name: nameinfo(name))
newcolumns.append("nameinfo")
train_X["Ticket"] = train_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
test_X["Ticket"] = test_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
newcolumns.append("Ticket")
train_X["Name"] = train_X["Name"].apply(lambda name: nameparser(name))
test_X["Name"] = test_X["Name"].apply(lambda name: nameparser(name))
newcolumns.append("Name")
train_X["Cabin"] = train_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
test_X["Cabin"] = test_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
newcolumns.append("Cabin")
train_one_hot_X, test_one_hot_X = one_hot_dataframe(train_X, test_X, newcolumns, replace=True)
train_X = train_one_hot_X
test_X = test_one_hot_X
else:
train_X["Embarked"] = train_X["Embarked"].apply(lambda port: selectembarkment(port))
train_X["fare2"] = train_X["Fare"].apply(fare2, axis=1)
train_X["nameinfo"] = train_X["Name"].apply(lambda name: nameinfo(name), axis=1)
train_X["Ticket"] = train_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
train_X["Name"] = train_X["Name"].apply(lambda name: nameparser(name))
train_X["Cabin"] = train_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
test_X["Embarked"] = test_X["Embarked"].apply(lambda port: selectembarkment(port))
test_X["fare2"] = test_X["Fare"].apply(fare2, axis=1)
test_X["nameinfo"] = test_X["Name"].apply(lambda name: nameinfo(name), axis=1)
test_X["Ticket"] = test_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
test_X["Name"] = test_X["Name"].apply(lambda name: nameparser(name))
test_X["Cabin"] = test_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit_transform(train_X['Age'], test_X['Age'])
print "Finished reading data"
train_X = train_X.fillna(-1)
test_X = test_X.fillna(-1)
# print train_X
if False:
for column in train_X.columns:
print column, train_X[column]
train_X = train_X.values.astype(np.float)
test_X = test_X.values.astype(np.float)
target = np.ravel(train_Y.values.astype(np.float))
trees = ExtraTreesClassifier(n_estimators=100,bootstrap=True,oob_score=True)
trees.fit(train_X, target)
pd.DataFrame(trees.feature_importances_).plot(kind='bar')
selected_features = np.where(trees.feature_importances_ > 0.02)[0] #0.005)[0]
#0.005
#[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 15]
#0.01
#[ 0 1 3 4 5 6 7 8 9 10 11 12 13 15]
#0.02
#[ 0 1 3 4 5 6 7 8 9 10 12 13 15]
#0.05
# [0 1 3 4 8]
print selected_features
train_selected_X = train_X[:, selected_features]
test_selected_X = test_X[:, selected_features]
# random forest code
clf = svm.SVC()
kernels = ['poly'] #, 'rbf', 'sigmoid']
degs=[2,3]
# gammas = [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1., 3., 10.]
gammas = [1e-3]#, 1e-3, 1e-1, 1.]
gs = GridSearchCV(clf, {'kernel': kernels,'degree': degs, 'gamma': gammas}, scoring = 'accuracy', cv = 10)
gs.fit(train_selected_X, target)
print "Score {0} with params {1}".format(gs.best_score_, gs.best_params_)
print('fitting the model')
clf = svm.SVC(**gs.best_params_)
clf.fit(train_selected_X, target)
# run model against test data
predicted_class = clf.predict(test_selected_X)
# print predicted_class[0:9]
# print(len(predicted_class))
predicted_class = ["%d,%d" % (test_selected_X[i,0], predicted_class[i]) for i in range(len(predicted_class))]
current = strftime("%Y%m%d", gmtime())
csv_io.write_delimited_file(os.path.join(os.path.dirname(__file__), 'results', "svm_0.02_solution_est_{0}.csv".format(current)), predicted_class, header=['PassengerId', 'Survived'])
#0.02
#Trying sig, rbg, poly with degree 3 on 1e-3 and 1e-4
# Score 0.760942760943 with params {'kernel': 'poly', 'gamma': 0.001, 'degree': 3} all kernels degree 3
# real 408m13.291s
#Score on kaggle 0.74163
# all polynomial 1-4
# Score 0.772166105499 with params {'kernel': 'poly', 'gamma': 0.001, 'degree': 2}
# real 1077m38.691s
#Score on kaggle 0.76555
#0.05 for comparison
# all polynomial 1-4
# Score 0.763187429854 with params {'kernel': 'poly', 'gamma': 0.001, 'degree': 3}
# real 340m42.558s
# Your submission scored 0.74641,
#Fixed features and implemented one hot coding
# 0.02 polynomial 2 and 3 with 1e-3
# real 283m12.476s
# Score 0.772166105499 with params {'kernel': 'poly', 'gamma': 0.001, 'degree': 2}
# our submission scored 0.75598,
print ('Finished. Exiting.')
def main():
current = strftime("%Y%m%d", gmtime())
trainfilename = os.path.join(os.path.dirname(__file__), 'data', 'train.csv')
testfilename = os.path.join(os.path.dirname(__file__), 'data', 'test.csv')
train_X = pd.read_csv(trainfilename)
print "Basic info on training data"
print len(train_X)
print len(train_X.columns)
print train_X.columns
train_Y = train_X.take([1], axis=1)
# print train_X.columns
orig_test_X = pd.read_csv(testfilename)
test_X = orig_test_X
#Binaries
train_X["has_family"] = train_X.apply(family, axis=1)
train_X["child"] = train_X.apply(child, axis=1)
train_X["smallchild"] = train_X.apply(smallchild, axis=1)
train_X["familysize"] = train_X.apply(familysize, axis=1)
train_X["Sex"] = train_X["Sex"].apply(lambda sex: 0 if sex == "male" else 1)
test_X["has_family"] = test_X.apply(family, axis=1)
test_X["child"] = test_X.apply(child, axis=1)
test_X["smallchild"] = test_X.apply(smallchild, axis=1)
test_X["familysize"] = test_X.apply(familysize, axis=1)
test_X["Sex"] = test_X["Sex"].apply(lambda sex: 0 if sex == "male" else 1)
#Computations
train_X["Embarked"] = train_X["Embarked"].apply(lambda port: selectembarkment(port))
train_X["fare2"] = train_X["Fare"].apply(lambda fare: fare2(fare))
train_X["nameinfo"] = train_X["Name"].apply(lambda name: nameinfo(name))
train_X["Ticket"] = train_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
train_X["Name"] = train_X["Name"].apply(lambda name: nameparser(name))
train_X["Cabin"] = train_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
test_X["Embarked"] = test_X["Embarked"].apply(lambda port: selectembarkment(port))
test_X["fare2"] = test_X["Fare"].apply(lambda fare: fare2(fare))
test_X["nameinfo"] = test_X["Name"].apply(lambda name: nameinfo(name))
test_X["Ticket"] = test_X["Ticket"].apply(lambda ticket: DeptCode(ticket))
test_X["Name"] = test_X["Name"].apply(lambda name: nameparser(name))
test_X["Cabin"] = test_X["Cabin"].apply(lambda cabin: cabinparser(cabin))
for name in train_X['Name'].unique():
print "For name " + str(name)
imp = Imputer(missing_values='NaN', strategy='mean', axis=1)
features = pd.concat([train_X[train_X['Name'] == name]['Age'],test_X[test_X['Name'] == name]['Age']]).values
imp.fit(features)
features = train_X[train_X['Name'] == name]['Age'].values
train_X.loc[train_X.Name == name,'Age'] = list(imp.transform(features)[0])
print np.std(train_X[train_X['Name'] == name]['Age'])
print np.mean(train_X[train_X['Name'] == name]['Age'])
features = test_X[test_X['Name'] == name]['Age'].values
test_X.loc[test_X.Name == name,'Age'] = list(imp.transform(features)[0])
print np.std(test_X[test_X['Name'] == name]['Age'])
print np.mean(test_X[test_X['Name'] == name]['Age'])
train_X["woman_child_man"] = train_X.apply(lambda row: woman_child_or_man(row), axis=1)
test_X["woman_child_man"] = test_X.apply(lambda row: woman_child_or_man(row), axis=1)
newcolumns= ["Embarked", "fare2", "nameinfo", "Ticket", "Name", "Cabin", "BinnedFare", "BinnedAge", "woman_child_man"]
no_bins = 6
#Discretized features
if no_bins > 1:
bins_and_binned_fare = pd.qcut(train_X.Fare, no_bins, retbins=True)
bins=bins_and_binned_fare[1]
train_X["BinnedFare"] = bins_and_binned_fare[0]
test_X["BinnedFare"] = pd.cut(test_X.Fare, bins)
bins_and_binned_age = pd.qcut(train_X.Age, no_bins, retbins=True)
bins=bins_and_binned_age[1]
train_X["BinnedAge"] = bins_and_binned_age[0]
test_X["BinnedAge"] = pd.cut(test_X.Age, bins)
train_one_hot_X, test_one_hot_X = one_hot_dataframe(train_X, test_X, newcolumns, replace=True)
train_X = train_one_hot_X
test_X = test_one_hot_X
print train_X.columns
print test_X.columns
train_X = train_X.drop('PassengerId',1)
test_X = test_X.drop('PassengerId',1)
# http://triangleinequality.wordpress.com/2013/05/19/machine-learning-with-python-first-steps-munging/
#Age through Imputation performed already#
# Fare #
#Fare imputation may not help: see http://nbviewer.ipython.org/gist/mwaskom/8224591
train_X.Fare = train_X.Fare.map(lambda x: np.nan if x==0 else x)
test_X.Fare = test_X.Fare.map(lambda x: np.nan if x==0 else x)
classmeans = pd.concat([train_X, test_X]).pivot_table('Fare', rows='Pclass', aggfunc='median')
# classmeans = test_X.pivot_table('Fare', rows='Pclass', aggfunc='mean')
train_X.Fare = train_X[['Fare', 'Pclass']].apply(lambda x: classmeans[x['Pclass']] if pd.isnull(x['Fare']) else x['Fare'], axis=1 )
test_X.Fare = test_X[['Fare', 'Pclass']].apply(lambda x: classmeans[x['Pclass']] if pd.isnull(x['Fare']) else x['Fare'], axis=1 )
# Cabin #
train_X.Cabin = train_X.Cabin.fillna('Unknown')
test_X.Cabin = test_X.Cabin.fillna('Unknown')
modeEmbarked = mode(pd.concat([train_X, test_X]).Embarked)[0][0]
train_X.Embarked = train_X.Embarked.fillna(modeEmbarked)
test_X.Embarked = test_X.Embarked.fillna(modeEmbarked)
#Farebracket feature array http://www.markhneedham.com/blog/2013/10/30/kaggle-titanic-python-pandas-attempt/
fare_ceiling = 40
fare_bracket_size = 10
number_of_price_brackets = fare_ceiling / fare_bracket_size
for bucket in range(0, number_of_price_brackets):
train_X["fare_bucket_{0}".format(bucket)] = train_X["Fare"].apply(lambda fare: fare_in_bucket(fare, fare_bracket_size, bucket))
test_X["fare_bucket_{0}".format(bucket)] = test_X["Fare"].apply(lambda fare: fare_in_bucket(fare, fare_bracket_size, bucket))
train_X.to_csv(os.path.join(os.path.dirname(__file__), 'data', "processed_train_data_est_{0}.csv".format(current)))
test_X.to_csv(os.path.join(os.path.dirname(__file__), 'data', "processed_test_data_est_{0}.csv".format(current)))
train_X = train_X.drop('Survived',1)
print "Finished reading data"
#Entry 152 has a non-known fare
# test_X['Fare'] = test_X['Fare'].fillna(-1)
# print train_X
if False:
for column in train_X.columns:
print column, train_X[column]
train = train_X.values.astype(np.float)
target = np.ravel(train_Y.values.astype(np.float))
# random forest code
forest = RandomForestClassifier(n_estimators = 100)
forest = forest.fit(train, target)
if True:
from sklearn import cross_validation
scores = cross_validation.cross_val_score(forest, train, target, cv=10)
print scores
if False:
from sklearn.grid_search import GridSearchCV
forest = ExtraTreesClassifier(bootstrap=True,oob_score=True,random_state=42)
max_features_choices = [n * 0.1 for n in range(1,10)]
n_ests=[100, 200, 500, 1000]
gs = GridSearchCV(forest, {'max_features': max_features_choices,'n_estimators': n_ests}, scoring = 'accuracy', cv = 10, n_jobs=-1)
gs.fit(train, target)
print "Score {0} with params {1}".format(gs.best_score_, gs.best_params_)
print('fitting the model')
forest = ExtraTreesClassifier(**gs.best_params_)
forest.fit(train, target)
# run model against test data
predicted_class = forest.predict(test_X.values.astype(np.float))
# print predicted_class[0:9]
# print(len(predicted_class))
predicted_class = ["%d,%d" % (orig_test_X.values[i,0], predicted_class[i]) for i in range(len(predicted_class))]
csv_io.write_delimited_file(os.path.join(os.path.dirname(__file__), 'results', "random_forest_solution_est_{0}.csv".format(current)), predicted_class, header=['PassengerId', 'Survived'])
print ('Finished. Exiting.')