def test_plspm_russa_categorical_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("IND", Mode.B, c.MV("gnpr", Scale.ORD),
c.MV("labo", Scale.ORD))
config.add_lv("POLINS", Mode.B, c.MV("ecks"), c.MV("death"),
c.MV("demo", Scale.NOM), c.MV("inst"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
expected_inner_summary = pd.read_csv(
"file:tests/data/russa.categorical.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_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_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_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)
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_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_cannot_add_mv_with_same_name_as_lv():
structure = c.Structure()
structure.add_path(source=["BONOBO"], target=["APE"])
config = c.Config(structure.path())
with pytest.raises(ValueError):
config.add_lv("BONOBO", Mode.A, c.MV("BONOBO"))