def test_get_set_train_test_dataset_property():
X, Y = get_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, Y, test_size=0.6)
train = np.hstack((X_train, y_train[np.newaxis].T))
train = pd.DataFrame(train)
train = copper.Dataset(train)
train.role[4] = train.TARGET
test = np.hstack((X_test, y_test[np.newaxis].T))
test = pd.DataFrame(test)
test = copper.Dataset(test)
test.role[4] = test.TARGET
# --
mc = copper.ModelComparison()
mc.train = train
mc.test = test
eq_(mc.X_train.shape, (150 * 0.4, 4))
eq_(mc.y_train.shape, (150 * 0.4, ))
eq_(mc.X_test.shape, (150 * 0.6, 4))
eq_(mc.y_test.shape, (150 * 0.6, ))
eq_(mc.X_train, X_train)
eq_(mc.y_train, y_train)
eq_(mc.X_test, X_test)
eq_(mc.y_test, y_test)
def get_mc():
ds = get_iris_ds()
mc = copper.ModelComparison()
mc.train_test_split(ds, random_state=0)
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
mc['LR'] = LogisticRegression()
mc['SVM'] = SVC(probability=True)
mc.fit()
return mc
def test_get_set_algorithms():
mc = copper.ModelComparison()
lr = LogisticRegression()
mc['LR'] = lr
eq_(mc['LR'], lr)
lr2 = LogisticRegression(penalty='l1')
mc['LR l1'] = lr2
eq_(mc['LR l1'], lr2)
eq_(len(mc), 2)
def get_mc():
mc = copper.ModelComparison()
mc.train = get_train()
mc.test = get_train()
mc['perf'] = FakePerfect()
mc['f1'] = Fake1()
mc['f2'] = Fake2()
mc.fit()
return mc
def test_deleted_algorithm():
mc = copper.ModelComparison()
lr = LogisticRegression()
mc['LR'] = lr
eq_(mc['LR'], lr)
lr2 = LogisticRegression(penalty='l1')
mc['LR l1'] = lr2
eq_(mc['LR l1'], lr2)
del mc['LR']
eq_(mc['LR l1'], lr2) # Not deleted
mc['LR'] # deleted
def test_fit():
ds = get_iris_ds()
mc = copper.ModelComparison()
mc.train_test_split(ds, test_size=0.4)
lr = LogisticRegression()
lr2 = LogisticRegression(penalty='l1')
mc['LR'] = lr
mc['LR l1'] = lr2
mc.fit()
ok_(mc['LR'].coef_ is not None)
ok_(mc['LR l1'].coef_ is not None)
ok_(mc['LR'] != mc['LR l1'])
def get_mc_string():
ds = get_iris_ds()
ds.type['Target'] = ds.CATEGORY
ds['Target'] = ds['Target'].apply(lambda x: str(x))
ds['Target'][ds['Target'] == '0'] = 'Iris-setosa'
ds['Target'][ds['Target'] == '1'] = 'Iris-versicolor'
ds['Target'][ds['Target'] == '2'] = 'Iris-virginica'
eq_(ds.metadata['dtype']['Target'], object)
mc = copper.ModelComparison()
mc.train_test_split(ds, random_state=0)
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
mc['LR'] = LogisticRegression()
mc['SVM'] = SVC(probability=True)
mc.fit()
return mc
def test_get_set_train_test_directly():
X, Y = get_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, Y, test_size=0.2)
mc = copper.ModelComparison()
mc.X_train = X_train
mc.y_train = y_train
mc.X_test = X_test
mc.y_test = y_test
eq_(mc.X_train.shape, (150 * 0.8, 4))
eq_(mc.y_train.shape, (150 * 0.8, ))
eq_(mc.X_test.shape, (150 * 0.2, 4))
eq_(mc.y_test.shape, (150 * 0.2, ))
eq_(mc.X_train, X_train)
eq_(mc.y_train, y_train)
eq_(mc.X_test, X_test)
eq_(mc.y_test, y_test)
def test_train_test_split():
ds = get_iris_ds()
mc = copper.ModelComparison()
state = int(math.floor(random.random() * 1000))
mc.train_test_split(ds, test_size=0.4, random_state=state)
eq_(mc.X_train.shape, (150 * 0.6, 4))
eq_(mc.y_train.shape, (150 * 0.6, ))
eq_(mc.X_test.shape, (150 * 0.4, 4))
eq_(mc.y_test.shape, (150 * 0.4, ))
eq_((mc.X_train, mc.y_train), mc.train)
eq_((mc.X_test, mc.y_test), mc.test)
eq_(mc.le, None)
# --
X, Y = get_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, Y, test_size=0.4, random_state=state)
eq_(mc.X_train, X_train)
eq_(mc.y_train, y_train)
eq_(mc.X_test, X_test)
eq_(mc.y_test, y_test)
def test_no_auto_fit():
mc = copper.ModelComparison()
lr = LogisticRegression()
mc['LR'] = lr
mc['LR'].coef_ # Doesn't exist yet
