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 test_copy_metadata():
cols = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
df1 = pd.DataFrame(np.random.rand(5, 10), columns=cols)
ds1 = copper.Dataset(df1)
ds1.role[['c', 'd', 'h', 'i']] = ds1.TARGET
ds1.type[['b', 'c', 'g', 'i']] = ds1.CATEGORY
# meta_old = ds1.metadata.copy()
df2 = pd.DataFrame(np.random.rand(5, 10), columns=cols)
ds2 = copper.Dataset(df2)
ds2.copy_metadata(ds1.metadata)
eq_(ds2.metadata, ds1.metadata)
def test_set_column():
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df)
new_col = np.random.rand(5, 1)
eq_(ds[3].values, df[3].values)
ds[3] = new_col
eq_(ds[[3]].values, new_col)
def test_set_metadata_fail_length():
df = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df)
meta = ds.metadata.copy()
meta = meta.drop(0)
ds.metadata = meta
def test_set_metadata_fail_index():
df = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df)
meta = ds.metadata.copy()
meta = meta.reindex([11, 1, 2, 3, 4])
ds.metadata = meta
def test_set_metadata():
df = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df)
rand_col = math.floor(random.random() * 5)
meta = ds.metadata.copy()
meta['Role'][rand_col] = ds.TARGET
eq_(ds.role[rand_col], ds.INPUT) # Not changes until reasigment
ds.metadata = meta
eq_(ds.role[rand_col], ds.TARGET) # Change
for i in range(5):
rand_col = math.floor(random.random() * 5)
meta = ds.metadata.copy()
meta['Role'][rand_col] = ds.TARGET
ds.metadata = meta
eq_(ds.role[rand_col], ds.TARGET)
rand_col = math.floor(random.random() * 5)
meta = ds.metadata.copy()
meta['Type'][rand_col] = ds.CATEGORY
eq_(ds.type[rand_col], ds.NUMBER) # Not changes until reasigment
ds.metadata = meta
eq_(ds.type[rand_col], ds.CATEGORY) # Change
for i in range(5):
rand_col = math.floor(random.random() * 5)
meta = ds.metadata.copy()
meta['Type'][rand_col] = ds.CATEGORY
ds.metadata = meta
eq_(ds.type[rand_col], ds.CATEGORY)
def test_create_empty():
# Checks empty Dataframes
ds = copper.Dataset()
eq_(ds.role, pd.Series())
eq_(ds.type, pd.Series())
eq_(ds.frame.empty, True)
eq_(ds.metadata.empty, True)
def get_iris_ds():
X, Y = get_iris()
df = pd.DataFrame(X)
df['Target'] = pd.Series(Y, name='Target')
ds = copper.Dataset(df)
ds.role['Target'] = ds.TARGET
return ds
def test_update_cat_to_num_float():
sol = np.arange(100) / 100
strings = np.array(['a(%f)' % d for d in sol])
df = pd.DataFrame(strings)
ds = copper.Dataset(df)
ds.type[0] = ds.NUMBER
ds.update()
eq_(sol, ds[0].values)
def get_train():
X = np.ones((12, 3))
y = ['b', 'z', 'b', 'g', 'g', 'z', 'b', 'z', 'g', 'b', 'g', 'z']
# [ 0, 2, 0, 1, 1, 2, 0, 2, 1, 0, 1, 0]
df = pd.DataFrame(X)
df['target'] = y
ds = copper.Dataset(df)
ds.role['target'] = ds.TARGET
return ds
def test_set_frame_different_cols_fail():
# By failing it checks that the metadata is different == was recreated
df1 = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df1)
meta_old = ds.metadata.copy()
df2 = pd.DataFrame(np.random.rand(10, 10))
ds.frame = df2
eq_(ds.metadata, meta_old)
def test_ml_target_number():
df = pd.DataFrame(np.random.rand(8, 6))
ds = copper.Dataset(df)
target_col = math.floor(random.random() * 6)
ds.role[target_col] = ds.TARGET
le, target = copper.t.ml_target(ds)
eq_(target, ds[target_col].values)
eq_(le, None)
def test_save_load_metadata():
tempdir = tempfile.gettempdir()
# Save
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df)
ds.role[2] = ds.TARGET
ds.role[7] = ds.IGNORE
ds.type[1] = ds.CATEGORY
ds.type[5] = ds.CATEGORY
ds.metadata.to_csv(os.path.join(tempdir, 'metadata.csv'))
# Load
ds2 = copper.Dataset(df)
loaded_meta = pd.read_csv(os.path.join(tempdir, 'metadata.csv'))
loaded_meta = loaded_meta.set_index('Columns')
ds2.metadata = loaded_meta
eq_(ds2.role[2], ds.TARGET)
eq_(ds2.role[7], ds.IGNORE)
eq_(ds2.type[1], ds.CATEGORY)
eq_(ds2.type[5], ds.CATEGORY)
def test_set_frame_different_length_same_cols():
# Tests that the metadata is mantained if columns are the same
df1 = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df1.copy())
ds.role[[2, 4]] = ds.TARGET
ds.type[[1, 2]] = ds.CATEGORY
meta_old = ds.metadata.copy()
df2 = pd.DataFrame(np.random.rand(10, 5))
ds.frame = df2
eq_(ds.metadata, meta_old)
def test_set_frame_different_length_same_cols_fail():
# By failing is testing that the default metadata is not in place
df1 = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df1.copy())
default_meta = ds.metadata.copy()
ds.role[[2, 4]] = ds.TARGET
ds.type[[1, 2]] = ds.CATEGORY
df2 = pd.DataFrame(np.random.rand(10, 5))
ds.frame = df2
eq_(ds.metadata, default_meta)
def test_ml_target_string():
df = pd.DataFrame(np.random.rand(6, 6))
strings = ['z', 'h', 'z', 'c', 'h', 'c']
sol = [2, 1, 2, 0, 1, 0]
df['T'] = strings
ds = copper.Dataset(df)
ds.role['T'] = ds.TARGET
le, target = copper.t.ml_target(ds)
eq_(target, np.array(sol))
eq_(le.classes_.tolist(), ['c', 'h', 'z'])
def test_default_type():
df = pd.DataFrame(np.random.rand(5, 20))
rand_col = math.floor(random.random() * 20)
rand_col2 = math.floor(random.random() * 20)
df[rand_col] = df[rand_col].apply(lambda x: str(x))
df[rand_col2] = df[rand_col].apply(lambda x: str(x))
ds = copper.Dataset(df)
eq_(ds.type[rand_col], ds.CATEGORY)
for col in ds.columns:
if col not in (rand_col, rand_col2):
eq_(ds.type[col], ds.NUMBER)
def test_filter_type():
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df)
ds.type[[0, 2, 4, 5, 9]] = ds.CATEGORY
eq_(ds.filter(type=ds.CATEGORY), ds[[0, 2, 4, 5, 9]])
ds.type[:] = ds.CATEGORY
ds.type[[1, 3, 6, 7, 9]] = ds.NUMBER
eq_(ds.filter(type=ds.NUMBER), ds[[1, 3, 6, 7, 9]])
eq_(ds.filter(type=[ds.NUMBER, ds.CATEGORY]), df)
eq_(ds.filter(), df)
def test_set_frame_different_cols():
# Checks default metadata is placed
df1 = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df1)
ds.role[[2, 4]] = ds.TARGET
ds.type[[1, 2]] = ds.CATEGORY
df2 = pd.DataFrame(np.random.rand(10, 10))
ds.frame = df2
eq_(ds.role[2], ds.INPUT)
eq_(ds.role[4], ds.INPUT)
eq_(ds.type[1], ds.NUMBER)
eq_(ds.type[2], ds.NUMBER)
def test_ml_target_more_than_one():
df = pd.DataFrame(np.random.rand(8, 6))
ds = copper.Dataset(df)
ds.role[3] = ds.TARGET
ds.role[5] = ds.TARGET
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
le, target = copper.t.ml_target(ds)
eq_(le, None)
eq_(target, ds[3].values)
def test_create_noempty():
df = pd.DataFrame(np.random.rand(10, 5))
ds = copper.Dataset(df)
eq_(ds.frame, df)
eq_(len(ds), 10)
eq_(len(ds), len(df))
eq_(len(ds.role), 5)
eq_(len(ds.type), 5)
eq_(len(ds.metadata), 5)
eq_(ds.metadata['Role'], ds.role)
eq_(ds.metadata['Type'], ds.type)
eq_(ds.index, df.index)
eq_(ds.columns, df.columns)
eq_(str(ds), str(ds.metadata))
def test_copy_metadata_ignore_false():
cols = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
df1 = pd.DataFrame(np.random.rand(5, 10), columns=cols)
ds1 = copper.Dataset(df1)
ds1.role[['a', 'd', 'h', 'i']] = ds1.TARGET
ds1.type[['b', 'd', 'g', 'i']] = ds1.CATEGORY
cols = ['z', 'y', 'f', 'a', 'b', 'd', 'e']
df2 = pd.DataFrame(np.random.rand(5, 7), columns=cols)
ds2 = copper.Dataset(df2)
ds2.copy_metadata(ds1.metadata, ignoreMissing=False)
eq_(ds2.role['z'], ds1.IGNORE)
eq_(ds2.role['y'], ds1.IGNORE)
eq_(ds2.role['a'], ds1.TARGET)
eq_(ds2.role['b'], ds1.INPUT)
eq_(ds2.role['d'], ds1.TARGET)
eq_(ds2.role['e'], ds1.INPUT)
eq_(ds2.type['z'], ds1.NUMBER)
eq_(ds2.type['y'], ds1.NUMBER)
eq_(ds2.type['a'], ds1.NUMBER)
eq_(ds2.type['b'], ds1.CATEGORY)
eq_(ds2.type['d'], ds1.CATEGORY)
eq_(ds2.type['e'], ds1.NUMBER)
def test_ml_inputs_simple_with_target():
df = pd.DataFrame(np.random.rand(8, 6))
strings = ['1', '2', '1', '3', '5', '2', '1', '5']
df[1] = np.array(strings)
df[3] = np.array(strings)
ds = copper.Dataset(df)
ds.type[[1, 3]] = ds.CATEGORY
ds.role[[2]] = ds.TARGET
ans = copper.t.ml_inputs(ds)
eq_(ans.shape, (8, 5 - 2 + 4 * 2))
eq_(ans[:, 0], df[0].values)
eq_(ans[:, [1, 2, 3, 4]], copper.t.cat_encode(df[1].values))
eq_(ans[:, [5, 6, 7, 8]], copper.t.cat_encode(df[3].values))
eq_(ans[:, 9], df[4].values)
eq_(ans[:, 10], df[5].values)
def test_filter_role():
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df)
ds.role[[0, 2, 4, 5, 9]] = ds.IGNORE
eq_(ds.filter(role=ds.INPUT), ds[[1, 3, 6, 7, 8]])
ds.role[:] = ds.IGNORE
ds.role[[1, 3, 4, 6, 8]] = ds.INPUT
eq_(ds.filter(role=ds.INPUT), ds[[1, 3, 4, 6, 8]])
ds.role[[2, 9]] = ds.TARGET
eq_(ds.filter(role=ds.TARGET), ds[[2, 9]])
eq_(ds.filter(role=[ds.INPUT, ds.TARGET]), ds[[1, 2, 3, 4, 6, 8, 9]])
eq_(ds.filter(), df)
def test_ml_inputs_simple():
df = pd.DataFrame(np.random.rand(8, 6))
strings = ['1', '2', '1', '3', '5', '2', '1', '5']
df[1] = np.array(strings)
df[3] = np.array(strings)
ds = copper.Dataset(df)
ds.type[[1, 3]] = ds.CATEGORY
ans = copper.t.ml_inputs(ds)
eq_(ans.shape, (8, 6 - 2 + 4 * 2))
eq_(ans[:, 0], df[0].values)
eq_(ans[:, [1, 2, 3, 4]], copper.t.cat_encode(df[1].values))
eq_(ans[:, 5], df[2].values)
eq_(ans[:, [6, 7, 8, 9]], copper.t.cat_encode(df[3].values))
eq_(ans[:, 10], df[4].values)
eq_(ans[:, 11], df[5].values)
def test_ml_inputs_big():
abc = 'abcdefghijklmnopqrstuvwxyz'
m, n = 1000, 10
array = np.floor(np.random.rand(m) * 26)
strings = np.array([abc[int(i)] for i in array])
df = pd.DataFrame(np.random.rand(m, 100))
abc_cols = np.arange(n) * 10
for col in abc_cols:
df[col] = strings
ds = copper.Dataset(df)
ds.type[abc_cols.tolist()] = ds.CATEGORY
ans = copper.t.ml_inputs(ds)
eq_(ans.shape, (m, 100 - n + 26 * n))
encoded = copper.t.cat_encode(strings)
for i, abc_col in enumerate(abc_cols):
s = abc_col + 25 * i
f = abc_col + 25 * i + 26
eq_(ans[:, s:f], encoded)
def test_filter_role_and_type():
df = pd.DataFrame(np.random.rand(5, 5))
ds = copper.Dataset(df)
ds.role[:] = ds.IGNORE
ds.role[2] = ds.INPUT
ds.type[2] = ds.CATEGORY
eq_(ds.filter(role=ds.INPUT, type=ds.CATEGORY), df[[2]])
ds.role[4] = ds.INPUT
ds.type[4] = ds.CATEGORY
eq_(ds.filter(role=ds.INPUT, type=ds.CATEGORY), df[[2, 4]])
eq_(ds.filter(role=ds.IGNORE, type=ds.NUMBER), df[[0, 1, 3]])
ds.role[4] = ds.IGNORE
eq_(ds.filter(role=ds.INPUT, type=ds.CATEGORY), df[[2]])
eq_(ds.filter(), df)
def test_create_empty_and_set():
df = pd.DataFrame(np.random.rand(10, 5))
ds = copper.Dataset()
eq_(ds.role, pd.Series())
eq_(ds.type, pd.Series())
eq_(ds.metadata.empty, True)
eq_(ds.frame.empty, True)
ds.frame = df.copy()
eq_(ds.frame, df)
eq_(len(ds), 10)
eq_(len(ds), len(df))
eq_(len(ds.role), 5)
eq_(len(ds.type), 5)
eq_(len(ds.metadata), 5)
eq_(ds.metadata['Role'], ds.role)
eq_(ds.metadata['Type'], ds.type)
eq_(ds.index, df.index)
eq_(ds.columns, df.columns)
eq_(str(ds), str(ds.metadata))
eq_(unicode(ds), unicode(ds.metadata))
def test_tail():
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df.copy())
l = math.floor(random.random() * 10)
eq_(ds.head(l), df.head(l))
def test_get_column():
df = pd.DataFrame(np.random.rand(5, 10))
ds = copper.Dataset(df)
eq_(ds[0], df[0])
eq_(ds[5], df[5])
eq_(ds[9], df[9])
