def test_plot_correlation_scatter_plot(self):
col1 = range(10)
col2 = [cell * 3 + 1 for cell in col1]
col3 = [1, 5, 8, 4, 1, 8, 5, 9, 0, 1]
sa = utils.convert_to_sa(
zip(col1, col2, col3),
col_names=['base', 'linear_trans', 'no_correlation'])
fig = comm.plot_correlation_scatter_plot(sa, verbose=False)
self.add_fig_to_report(fig, 'plot_correlation_scatter_plot')
def test_plot_correlation_scatter_plot(self):
col1 = range(10)
col2 = [cell * 3 + 1 for cell in col1]
col3 = [1, 5, 8, 4, 1, 8, 5, 9, 0, 1]
sa = utils.convert_to_sa(
zip(col1, col2, col3),
col_names=['base', 'linear_trans', 'no_correlation'])
fig = comm.plot_correlation_scatter_plot(sa, verbose=False)
self.add_fig_to_report(fig, 'plot_correlation_scatter_plot')
def test_convert_to_sa(self):
# already a structured array
sa = np.array([(1, 1.0, 'a', datetime(2015, 01, 01)),
(2, 2.0, 'b', datetime(2016, 01, 01))],
dtype=[('int', int), ('float', float), ('str', 'S1'),
('date', 'M8[s]')])
self.assertTrue(np.array_equal(sa, utils.convert_to_sa(sa)))
# homogeneous array no col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('f0', int), ('f1', int), ('f2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(nd)))
# homogeneous array with col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('i0', int), ('i1', int), ('i2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(
nd,
col_names=['i0', 'i1', 'i2'])))
# list of lists no col name provided
lol = [[1, 1, None],
['abc', 2, 3.4]]
ctrl = np.array([('1', 1, np.nan),
('abc', 2, 3.4)],
dtype=[('f0', 'S3'), ('f1', int), ('f2', float)])
res = utils.convert_to_sa(lol)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
# list of lists with col name provided
lol = [['hello', 1.2, datetime(2012, 1, 1), None],
[1.3, np.nan, None, '2013-01-01'],
[1.4, 1.5, '2014-01-01', 'NO_SUCH_RECORD']]
ctrl = np.array([('hello', 1.2, datetime(2012, 1, 1), utils.NOT_A_TIME),
('1.3', np.nan, utils.NOT_A_TIME, datetime(2013, 1, 1)),
('1.4', 1.5, datetime(2014, 1, 1), utils.NOT_A_TIME)],
dtype=[('i0', 'S5'), ('i1', float), ('i2', 'M8[us]'),
('i3', 'M8[us]')])
res = utils.convert_to_sa(lol, col_names = ['i0', 'i1', 'i2', 'i3'])
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
def test_convert_to_sa(self):
# already a structured array
sa = np.array([(1, 1.0, 'a', datetime(2015, 01, 01)),
(2, 2.0, 'b', datetime(2016, 01, 01))],
dtype=[('int', int), ('float', float), ('str', 'S1'),
('date', 'M8[s]')])
self.assertTrue(np.array_equal(sa, utils.convert_to_sa(sa)))
# homogeneous array no col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('f0', int), ('f1', int), ('f2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(nd)))
# homogeneous array with col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('i0', int), ('i1', int), ('i2', int)])
self.assertTrue(
np.array_equal(
ctrl, utils.convert_to_sa(nd, col_names=['i0', 'i1', 'i2'])))
# list of lists no col name provided
lol = [[1, 1, None], ['abc', 2, 3.4]]
ctrl = np.array([('1', 1, np.nan), ('abc', 2, 3.4)],
dtype=[('f0', 'S3'), ('f1', int), ('f2', float)])
res = utils.convert_to_sa(lol)
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
# list of lists with col name provided
lol = [['hello', 1.2, datetime(2012, 1, 1), None],
[1.3, np.nan, None, '2013-01-01'],
[1.4, 1.5, '2014-01-01', 'NO_SUCH_RECORD']]
ctrl = np.array(
[('hello', 1.2, datetime(2012, 1, 1), utils.NOT_A_TIME),
('1.3', np.nan, utils.NOT_A_TIME, datetime(2013, 1, 1)),
('1.4', 1.5, datetime(2014, 1, 1), utils.NOT_A_TIME)],
dtype=[('i0', 'S5'), ('i1', float), ('i2', 'M8[us]'),
('i3', 'M8[us]')])
res = utils.convert_to_sa(lol, col_names=['i0', 'i1', 'i2', 'i3'])
self.assertTrue(utils_for_tests.array_equal(ctrl, res))
def label_encode(M):
"""
Changes string cols to integers so that there is a 1-1 mapping between
strings and ints
"""
M = convert_to_sa(M)
le = preprocessing.LabelEncoder()
new_dtype = []
result_arrays = []
for (col_name, fmt) in M.dtype.descr:
if 'S' in fmt:
result_arrays.append(le.fit_transform(M[col_name]))
new_dtype.append((col_name, int))
else:
result_arrays.append(M[col_name])
new_dtype.append((col_name, fmt))
return np.array(zip(*result_arrays), dtype=new_dtype)
def label_encode(M):
"""
Changes string cols to integers so that there is a 1-1 mapping between
strings and ints
"""
M = convert_to_sa(M)
le = preprocessing.LabelEncoder()
new_dtype = []
result_arrays = []
for (col_name, fmt) in M.dtype.descr:
if 'S' in fmt:
result_arrays.append(le.fit_transform(M[col_name]))
new_dtype.append((col_name, int))
else:
result_arrays.append(M[col_name])
new_dtype.append((col_name, fmt))
return np.array(zip(*result_arrays), dtype=new_dtype)
def test_get_top_features(self):
M, labels = uft.generate_test_matrix(1000, 15, random_state=0)
M = utils.cast_np_sa_to_nd(M)
M_train, M_test, labels_train, labels_test = train_test_split(
M, labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
res = comm.get_top_features(clf, M, verbose=False)
ctrl = utils.convert_to_sa([('f5', 0.0773838526068),
('f13', 0.0769596713039),
('f8', 0.0751584839431),
('f6', 0.0730815879102),
('f11', 0.0684456133071),
('f9', 0.0666747414603),
('f10', 0.0659621889608),
('f7', 0.0657988099065),
('f2', 0.0634000069218),
('f0', 0.0632912268319)],
col_names=('feat_name', 'score'))
self.assertTrue(uft.array_equal(ctrl, res))
def test_get_top_features(self):
M, labels = uft.generate_test_matrix(1000, 15, random_state=0)
M = utils.cast_np_sa_to_nd(M)
M_train, M_test, labels_train, labels_test = train_test_split(
M,
labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
res = comm.get_top_features(clf, M, verbose=False)
ctrl = utils.convert_to_sa(
[('f5', 0.0773838526068),
('f13', 0.0769596713039),
('f8', 0.0751584839431),
('f6', 0.0730815879102),
('f11', 0.0684456133071),
('f9', 0.0666747414603),
('f10', 0.0659621889608),
('f7', 0.0657988099065),
('f2', 0.0634000069218),
('f0', 0.0632912268319)],
col_names=('feat_name', 'score'))
self.assertTrue(uft.array_equal(ctrl, res))
def replace_missing_vals(M, strategy, missing_val=np.nan, constant=0):
# TODO support times, strings
M = convert_to_sa(M)
if strategy not in ['mean', 'median', 'most_frequent', 'constant']:
raise ValueError('Invalid strategy')
M_cp = M.copy()
if strategy == 'constant':
try:
missing_is_nan = np.isnan(missing_val)
except TypeError:
# missing_val is not a float
missing_is_nan = False
if missing_is_nan: # we need to be careful about handling nan
for col_name, col_type in M_cp.dtype.descr:
if 'f' in col_type:
col = M_cp[col_name]
col[np.isnan(col)] = constant
return M_cp
for col_name, col_type in M_cp.dtype.descr:
if 'i' in col_type or 'f' in col_type:
col = M_cp[col_name]
col[col == missing_val] = constant
return M_cp
# we're doing one of the sklearn imputer strategies
imp = Imputer(missing_values=missing_val, strategy=strategy, axis=1)
for col_name, col_type in M_cp.dtype.descr:
if 'f' in col_type or 'i' in col_type:
# The Imputer only works on float and int columns
col = M_cp[col_name]
col[:] = imp.fit_transform(col)
return M_cp
def replace_missing_vals(M, strategy, missing_val=np.nan, constant=0):
# TODO support times, strings
M = convert_to_sa(M)
if strategy not in ['mean', 'median', 'most_frequent', 'constant']:
raise ValueError('Invalid strategy')
M_cp = M.copy()
if strategy == 'constant':
try:
missing_is_nan = np.isnan(missing_val)
except TypeError:
# missing_val is not a float
missing_is_nan = False
if missing_is_nan: # we need to be careful about handling nan
for col_name, col_type in M_cp.dtype.descr:
if 'f' in col_type:
col = M_cp[col_name]
col[np.isnan(col)] = constant
return M_cp
for col_name, col_type in M_cp.dtype.descr:
if 'i' in col_type or 'f' in col_type:
col = M_cp[col_name]
col[col == missing_val] = constant
return M_cp
# we're doing one of the sklearn imputer strategies
imp = Imputer(missing_values=missing_val, strategy=strategy, axis=1)
for col_name, col_type in M_cp.dtype.descr:
if 'f' in col_type or 'i' in col_type:
# The Imputer only works on float and int columns
col = M_cp[col_name]
col[:] = imp.fit_transform(col)
return M_cp