import mord
from mord.datasets.base import load_housing
from sklearn import linear_model, metrics, preprocessing
data = load_housing()
features = data.data
le = preprocessing.LabelEncoder()
le.fit(data.target)
data.target = le.transform(data.target)
features.loc[features.Infl == 'Low', 'Infl'] = 1
features.loc[features.Infl == 'Medium', 'Infl'] = 2
features.loc[features.Infl == 'High', 'Infl'] = 3
features.loc[features.Cont == 'Low', 'Cont'] = 1
features.loc[features.Cont == 'Medium', 'Cont'] = 2
features.loc[features.Cont == 'High', 'Cont'] = 3
le = preprocessing.LabelEncoder()
le.fit(features.loc[:,'Type'])
features.loc[:,'type_encoded'] = le.transform(features.loc[:,'Type'])
X, y = features.loc[:,('Infl', 'Cont', 'type_encoded')], data.target
clf1 = linear_model.LogisticRegression(
solver='lbfgs',
multi_class='multinomial')
clf1.fit(X, y)
print('Mean Absolute Error of LogisticRegression: %s' %
import mord
from mord.datasets.base import load_housing
from sklearn import linear_model, metrics, preprocessing
data = load_housing()
features = data.data
le = preprocessing.LabelEncoder()
le.fit(data.target)
data.target = le.transform(data.target)
features.loc[features.Infl == 'Low', 'Infl'] = 1
features.loc[features.Infl == 'Medium', 'Infl'] = 2
features.loc[features.Infl == 'High', 'Infl'] = 3
features.loc[features.Cont == 'Low', 'Cont'] = 1
features.loc[features.Cont == 'Medium', 'Cont'] = 2
features.loc[features.Cont == 'High', 'Cont'] = 3
le = preprocessing.LabelEncoder()
le.fit(features.loc[:, 'Type'])
features.loc[:, 'type_encoded'] = le.transform(features.loc[:, 'Type'])
X, y = features.loc[:, ('Infl', 'Cont', 'type_encoded')], data.target
clf1 = linear_model.LogisticRegression(solver='lbfgs',
multi_class='multinomial')
clf1.fit(X, y)
print('Mean Absolute Error of LogisticRegression: %s' %
metrics.mean_absolute_error(clf1.predict(X), y))
def test_load_housing():
res = load_housing()
assert_equal(res.data.shape, (1681, 3))
assert_equal(res.target.size, 1681)
assert_equal(len(res.feature_names), 3)
assert_true(res.DESCR)
