def test_config_rejects_path_and_lv_config_not_matching():
config = c.Config(config_test_path_matrix())
config.add_lv("AGRI", Mode.A, c.MV("gini"))
config.add_lv("IND", Mode.A, c.MV("gnpr"))
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
with pytest.raises(ValueError):
config.filter(russa)
def test_paths():
mobi = pd.read_csv("file:tests/data/mobi.csv", index_col=0)
structure = c.Structure()
structure.add_path(["Expectation", "Quality"], ["Loyalty"])
structure.add_path(["Image"], ["Expectation"])
structure.add_path(["Complaints"], ["Loyalty"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_lv_with_columns_named("Expectation", Mode.A, mobi, "CUEX")
config.add_lv_with_columns_named("Quality", Mode.B, mobi, "PERQ")
config.add_lv_with_columns_named("Loyalty", Mode.A, mobi, "CUSL")
config.add_lv_with_columns_named("Image", Mode.A, mobi, "IMAG")
config.add_lv_with_columns_named("Complaints", Mode.A, mobi, "CUSCO")
mobi_pls = Plspm(mobi, config, Scheme.PATH, 100, 0.00000001)
expected_outer_model = pd.read_csv(
"file:tests/data/seminr-mobi-basic-outer-model.csv", index_col=0)
actual_outer_model = mobi_pls.outer_model().drop(
["communality", "redundancy"], axis=1)
npt.assert_allclose(expected_outer_model.sort_index(),
actual_outer_model.sort_index(),
rtol=1e-5)
expected_paths = pd.read_csv("file:tests/data/seminr-mobi-basic-paths.csv",
index_col=0)
actual_paths = mobi_pls.path_coefficients().transpose()
npt.assert_allclose(expected_paths.sort_index().sort_index(axis=1),
actual_paths.sort_index().sort_index(axis=1),
rtol=1e-6)
def test_cannot_add_mvs_twice():
structure = c.Structure()
structure.add_path(source=["BONOBO"], target=["APE"])
config = c.Config(structure.path())
config.add_lv("BONOBO", Mode.A, c.MV("a"), c.MV("b"))
with pytest.raises(ValueError):
config.add_lv("APE", Mode.A, c.MV("a"), c.MV("b"))
def test_data_should_only_contain_numerical_values():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
russa['gini'] = russa['gini'].astype(str)
config = c.Config(config_test_path_matrix())
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
with pytest.raises(ValueError):
config.filter(russa)
def test_config_filters_mvs():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix())
config.add_lv("POLINS", Mode.A)
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A)
npt.assert_array_equal(list(config.filter(russa)),
["gini", "farm", "rent"])
def test_scaling_should_be_false_if_all_raw():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix(), default_scale=Scale.RAW)
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"),
c.MV("inst"))
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.treat(config.filter(russa))
assert not config.scaled()
def test_all_mvs_should_have_a_scale_if_data_is_nonmetric():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix())
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"),
c.MV("inst"))
config.add_lv("AGRI", Mode.A, c.MV("gini", Scale.NUM), c.MV("farm"),
c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
with pytest.raises(TypeError):
config.treat(config.filter(russa))
def test_config_rejects_bad_path_matrix():
# Takes a matrix
with pytest.raises(TypeError):
c.Config("hello")
# Matrix should be square
with pytest.raises(ValueError):
c.Config(pd.DataFrame([[0, 0, 0]]))
# Matrix should be lower triangular
with pytest.raises(ValueError):
c.Config(pd.DataFrame([[1, 1], [1, 1]]))
# Only 1 and 0 allowed in matrix
with pytest.raises(ValueError):
c.Config(pd.DataFrame([[1, 0], [2, 1]]))
# Indices and columns should have the same names
with pytest.raises(ValueError):
c.Config(
pd.DataFrame([[1, 0], [1, 1]],
index=["A", "B"],
columns=["C", "D"]))
def test_scaling_should_be_true_and_all_scales_set_to_num_if_only_raw_and_num_supplied(
):
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix(), default_scale=Scale.RAW)
config.add_lv("POLINS", Mode.A, c.MV("ecks", Scale.NUM), c.MV("death"),
c.MV("demo"), c.MV("inst"))
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.treat(config.filter(russa))
assert config.scaled()
for mv in ["gini", "farm", "rent"]:
assert config.scale(mv) == Scale.NUM
def test_only_single_item_constructs():
satisfaction = pd.read_csv("file:tests/data/satisfaction.csv", index_col=0)
config = c.Config(satisfaction_path_matrix())
config.add_lv("QUAL", Mode.A, c.MV("qual1"))
config.add_lv("VAL", Mode.A, c.MV("val1"))
config.add_lv("SAT", Mode.A, c.MV("sat1"))
config.add_lv("LOY", Mode.A, c.MV("loy1"))
config.add_lv("IMAG", Mode.A, c.MV("imag1"))
config.add_lv("EXPE", Mode.A, c.MV("expe1"))
plspm_calc = Plspm(satisfaction, config, Scheme.CENTROID)
with pytest.raises(ValueError):
plspm_calc.goodness_of_fit()
def test_scales_should_remain_unchanged_if_values_other_than_num_and_raw_supplied(
):
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix(),
default_scale=Scale.RAW,
scaled=False)
config.add_lv("AGRI", Mode.A, c.MV("gini", Scale.NUM),
c.MV("farm", Scale.ORD), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"),
c.MV("inst"))
config.filter(russa)
assert not config.scaled()
assert config.scale("farm") == Scale.ORD
def test_can_add_hoc_lv_paths_correctly():
structure = c.Structure()
structure.add_path(["MANDRILL", "BONOBO"], ["APE"])
structure.add_path(["APE"], ["GOAT"])
initial_path = structure.path()
config = c.Config(initial_path)
config.add_higher_order("APE", Mode.A, ["CHEDDAR", "GOUDA"])
estimator = Estimator(config)
structure = c.Structure(initial_path)
structure.add_path(["MANDRILL", "BONOBO"], ["CHEDDAR"])
structure.add_path(["MANDRILL", "BONOBO"], ["GOUDA"])
structure.add_path(["GOUDA", "CHEDDAR"], ["GOAT"])
expected = structure.path().drop("APE").drop("APE", axis=1)
actual = estimator.hoc_path_first_stage(config)
pt.assert_frame_equal(expected, actual)
def test_plspm_russa_mode_b():
satisfaction = pd.read_csv("file:tests/data/satisfaction.csv", index_col=0)
config = c.Config(satisfaction_path_matrix(), scaled=False)
config.add_lv_with_columns_named("QUAL", Mode.B, satisfaction, "qual")
config.add_lv_with_columns_named("VAL", Mode.B, satisfaction, "val")
config.add_lv_with_columns_named("SAT", Mode.B, satisfaction, "sat")
config.add_lv_with_columns_named("LOY", Mode.B, satisfaction, "loy")
config.add_lv_with_columns_named("IMAG", Mode.B, satisfaction, "imag")
config.add_lv_with_columns_named("EXPE", Mode.B, satisfaction, "expe")
plspm_calc = Plspm(satisfaction, config, Scheme.CENTROID)
expected_inner_summary = pd.read_csv("file:tests/data/satisfaction.modeb.inner-summary.csv", index_col=0)
npt.assert_allclose(util.sort_cols(expected_inner_summary.drop(["type"], axis=1)).sort_index(),
util.sort_cols(plspm_calc.inner_summary().drop(["type", "r_squared_adj"], axis=1)).sort_index())
pt.assert_series_equal(expected_inner_summary.loc[:, "type"].sort_index(),
plspm_calc.inner_summary().loc[:, "type"].sort_index())
def test_plspm_russa_mode_b():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(russa_path_matrix(), default_scale=Scale.NUM)
config.add_lv("AGRI", Mode.B, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("POLINS", Mode.B, c.MV("ecks"), c.MV("demo"), c.MV("inst"),
c.MV("death"))
config.add_lv("IND", Mode.B, c.MV("gnpr"), c.MV("labo"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
expected_inner_summary = pd.read_csv(
"file:tests/data/russa.mode_b_inner_summary.csv", index_col=0)
npt.assert_allclose(
util.sort_cols(expected_inner_summary.drop(["type"],
axis=1)).sort_index(),
util.sort_cols(plspm_calc.inner_summary().drop(
["type", "r_squared_adj"], axis=1)).sort_index())
pt.assert_series_equal(
expected_inner_summary.loc[:, "type"].sort_index(),
plspm_calc.inner_summary().loc[:, "type"].sort_index())
def test_plspm_russa_missing_data():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
russa.iloc[0, 0] = np.NaN
russa.iloc[3, 3] = np.NaN
russa.iloc[5, 5] = np.NaN
config = c.Config(russa_path_matrix(), default_scale=Scale.NUM)
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"),
c.MV("inst"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
expected_inner_summary = pd.read_csv(
"file:tests/data/russa.missing.inner_summary.csv", index_col=0)
npt.assert_allclose(
util.sort_cols(expected_inner_summary.drop(["type"],
axis=1)).sort_index(),
util.sort_cols(plspm_calc.inner_summary().drop(
["type", "r_squared_adj"], axis=1)).sort_index())
pt.assert_series_equal(
expected_inner_summary.loc[:, "type"].sort_index(),
plspm_calc.inner_summary().loc[:, "type"].sort_index())
assert plspm_calc.unidimensionality().drop(["mode", "mvs"],
axis=1).isnull().values.all()
def test_hoc_two_stage():
mobi = pd.read_csv("file:tests/data/mobi.csv", index_col=0)
structure = c.Structure()
structure.add_path(["Expectation", "Quality"], ["Satisfaction"])
structure.add_path(["Satisfaction"], ["Complaints", "Loyalty"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_higher_order("Satisfaction", Mode.A, ["Image", "Value"])
config.add_lv_with_columns_named("Expectation", Mode.A, mobi, "CUEX")
config.add_lv_with_columns_named("Quality", Mode.B, mobi, "PERQ")
config.add_lv_with_columns_named("Loyalty", Mode.A, mobi, "CUSL")
config.add_lv_with_columns_named("Image", Mode.A, mobi, "IMAG")
config.add_lv_with_columns_named("Complaints", Mode.A, mobi, "CUSCO")
config.add_lv_with_columns_named("Value", Mode.A, mobi, "PERV")
mobi_pls = Plspm(mobi, config, Scheme.PATH, 100, 0.00000001)
expected_outer_model = pd.read_csv(
"file:tests/data/seminr-mobi-hoc-ts-outer-model.csv", index_col=0)
actual_outer_model = mobi_pls.outer_model().drop(
["communality", "redundancy"], axis=1)
indices = list(
set(expected_outer_model.index.values.tolist()).intersection(
set(actual_outer_model.index.values.tolist())))
expected_outer_model = expected_outer_model.loc[indices].sort_index(
).sort_index(axis=1)
actual_outer_model = actual_outer_model.loc[indices].sort_index(
).sort_index(axis=1)
npt.assert_allclose(expected_outer_model, actual_outer_model, rtol=1e-4)
expected_paths = pd.read_csv(
"file:tests/data/seminr-mobi-hoc-ts-paths.csv",
index_col=0).transpose()
actual_paths = mobi_pls.path_coefficients()
npt.assert_allclose(expected_paths.sort_index().sort_index(axis=1),
actual_paths.sort_index().sort_index(axis=1),
rtol=1e-6)
def test_plspm_russa():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(russa_path_matrix(), default_scale=Scale.NUM)
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"),
c.MV("inst"))
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("rent"), c.MV("farm"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
expected_scores = pd.read_csv("file:tests/data/russa.scores.csv",
index_col=0)
npt.assert_allclose(util.sort_cols(expected_scores),
util.sort_cols(plspm_calc.scores()))
expected_inner_model = pd.read_csv("file:tests/data/russa.inner_model.csv",
index_col=0)
actual_inner_model = plspm_calc.inner_model()
actual_inner_model = actual_inner_model[actual_inner_model['to'].isin(
["POLINS"])].drop(["to"], axis=1)
npt.assert_allclose(
util.sort_cols(expected_inner_model).sort_index(),
util.sort_cols(actual_inner_model.set_index(["from"],
drop=True)).sort_index())
expected_outer_model = pd.read_csv("file:tests/data/russa.outer_model.csv",
index_col=0)
npt.assert_allclose(
util.sort_cols(
expected_outer_model.filter(
["weight", "loading", "communality",
"redundancy"])).sort_index(),
util.sort_cols(plspm_calc.outer_model()).sort_index())
expected_crossloadings = pd.read_csv(
"file:tests/data/russa.crossloadings.csv", index_col=0)
npt.assert_allclose(
util.sort_cols(expected_crossloadings.filter(["AGRI", "IND",
"POLINS"])).sort_index(),
util.sort_cols(plspm_calc.crossloadings()).sort_index())
expected_inner_summary = pd.read_csv(
"file:tests/data/russa.inner_summary.csv", index_col=0)
npt.assert_allclose(
util.sort_cols(expected_inner_summary.drop(["type"],
axis=1)).sort_index(),
util.sort_cols(plspm_calc.inner_summary().drop(
["type", "r_squared_adj"], axis=1)).sort_index())
pt.assert_series_equal(
expected_inner_summary.loc[:, "type"].sort_index(),
plspm_calc.inner_summary().loc[:, "type"].sort_index())
assert math.isclose(0.643594505232204, plspm_calc.goodness_of_fit())
plspm_calc_path = Plspm(russa, config, Scheme.PATH, 100, 0.0000001)
expected_outer_model_path = util.sort_cols(
pd.read_csv("file:tests/data/russa.outer_model_path.csv",
index_col=0).filter(
["weight", "loading", "communality",
"redundancy"])).sort_index()
npt.assert_allclose(
expected_outer_model_path,
util.sort_cols(plspm_calc_path.outer_model()).sort_index())
plspm_calc_factorial = Plspm(russa, config, Scheme.FACTORIAL, 100,
0.0000001)
expected_outer_model_factorial = util.sort_cols(
pd.read_csv("file:tests/data/russa.outer_model_factorial.csv",
index_col=0).filter(
["weight", "loading", "communality",
"redundancy"])).sort_index()
npt.assert_allclose(
expected_outer_model_factorial,
util.sort_cols(plspm_calc_factorial.outer_model()).sort_index())
def test_plspm_satisfaction():
satisfaction = pd.read_csv("file:tests/data/satisfaction.csv", index_col=0)
config = c.Config(satisfaction_path_matrix(), scaled=False)
config.add_lv_with_columns_named("IMAG", Mode.A, satisfaction, "imag")
config.add_lv_with_columns_named("EXPE", Mode.A, satisfaction, "expe")
config.add_lv_with_columns_named("VAL", Mode.A, satisfaction, "val")
config.add_lv_with_columns_named("QUAL", Mode.A, satisfaction, "qual")
config.add_lv_with_columns_named("SAT", Mode.A, satisfaction, "sat")
config.add_lv_with_columns_named("LOY", Mode.A, satisfaction, "loy")
plspm_calc = Plspm(satisfaction, config)
expected_scores = pd.read_csv("file:tests/data/satisfaction.scores.csv")
npt.assert_allclose(util.sort_cols(expected_scores), util.sort_cols(plspm_calc.scores()))
expected_inner_model = pd.read_csv("file:tests/data/satisfaction.inner-model.csv", index_col=0)
actual_inner_model = plspm_calc.inner_model()
actual_inner_model = actual_inner_model[actual_inner_model['to'].isin(["SAT"])].drop(["to"], axis=1)
npt.assert_allclose(util.sort_cols(expected_inner_model).sort_index(),
util.sort_cols(actual_inner_model.set_index(["from"],drop=True)).sort_index())
expected_outer_model = pd.read_csv("file:tests/data/satisfaction.outer-model.csv", index_col=0).drop(["block"], axis=1)
pt.assert_index_equal(expected_outer_model.columns, plspm_calc.outer_model().columns)
npt.assert_allclose(
util.sort_cols(expected_outer_model.sort_index()),
util.sort_cols(plspm_calc.outer_model()).sort_index())
expected_crossloadings = pd.read_csv("file:tests/data/satisfaction.crossloadings.csv", index_col=0)
npt.assert_allclose(util.sort_cols(expected_crossloadings.drop(["block"], axis=1)).sort_index(),
util.sort_cols(plspm_calc.crossloadings()).sort_index())
expected_inner_summary = pd.read_csv("file:tests/data/satisfaction.inner-summary.csv", index_col=0)
npt.assert_allclose(util.sort_cols(expected_inner_summary.drop(["type"], axis=1)).sort_index(),
util.sort_cols(plspm_calc.inner_summary().drop(["type", "r_squared_adj"], axis=1)).sort_index())
pt.assert_series_equal(expected_inner_summary.loc[:, "type"].sort_index(),
plspm_calc.inner_summary().loc[:, "type"].sort_index())
expected_effects = pd.read_csv("file:tests/data/satisfaction.effects.csv", index_col=0)
pt.assert_frame_equal(expected_effects.loc[:, ["from", "to"]].sort_index(),
plspm_calc.effects().loc[:, ["from", "to"]].sort_index())
npt.assert_allclose(expected_effects.drop(["from", "to"], axis=1).sort_index(),
plspm_calc.effects().drop(["from", "to"], axis=1).sort_index())
expected_unidimensionality = pd.read_csv("file:tests/data/satisfaction_unidim.csv", index_col=0)
npt.assert_allclose(util.sort_cols(expected_unidimensionality.drop(["mode"], axis=1)).sort_index(),
util.sort_cols(plspm_calc.unidimensionality().drop(["mode"], axis=1)).sort_index())
assert math.isclose(0.609741624338411,plspm_calc.goodness_of_fit())
plspm_calc_path = Plspm(satisfaction, config, Scheme.PATH)
expected_outer_model_path = util.sort_cols(
pd.read_csv("file:tests/data/satisfaction.outer-model-path.csv", index_col=0).drop(["block"],
axis=1)).sort_index()
npt.assert_allclose(expected_outer_model_path,
util.sort_cols(plspm_calc_path.outer_model()).sort_index())
plspm_calc_factorial = Plspm(satisfaction, config, Scheme.FACTORIAL)
expected_outer_model_factorial = util.sort_cols(
pd.read_csv("file:tests/data/satisfaction.outer-model-factorial.csv", index_col=0).drop(["block"],
axis=1)).sort_index()
npt.assert_allclose(expected_outer_model_factorial,
util.sort_cols(plspm_calc_factorial.outer_model()).sort_index())
def test_config_rejects_adding_lv_not_present_in_path():
config = c.Config(config_test_path_matrix())
with pytest.raises(ValueError):
config.add_lv("POO", Mode.A, c.MV("test"))
def test_config_returns_correct_mode_and_mvs():
config = c.Config(config_test_path_matrix())
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
assert config.mode("AGRI") == Mode.A
npt.assert_array_equal(config.mvs("AGRI"), ["gini", "farm", "rent"])
def test_config_rejects_missing_mvs():
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
config = c.Config(config_test_path_matrix())
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("poo"))
with pytest.raises(ValueError):
config.filter(russa)
