Esempio n. 1
0
def func(maxrt):
    stop = datetime.datetime.now() + maxrt
    while datetime.datetime.now() < stop:
        (
            model_spec_init_dict,
            random_model_params_df,
            exog_educ_shares,
            exog_child_age_shares,
            exog_partner_shares,
            exog_exper_shares_pt,
            exog_exper_shares_ft,
            exog_child_info,
            exog_partner_arrival_info,
            exog_partner_separation_info,
        ) = random_init()

        exog_educ_shares.to_pickle("test.soepy.educ.shares.pkl")
        exog_child_age_shares.to_pickle("test.soepy.child.age.shares.pkl")
        exog_child_info.to_pickle("test.soepy.child.pkl")
        exog_partner_shares.to_pickle("test.soepy.partner.shares.pkl")
        exog_exper_shares_pt.to_pickle("test.soepy.pt.exp.shares.pkl")
        exog_exper_shares_ft.to_pickle("test.soepy.ft.exp.shares.pkl")
        exog_partner_arrival_info.to_pickle("test.soepy.partner.arrival.pkl")
        exog_partner_separation_info.to_pickle(
            "test.soepy.partner.separation.pkl")

        simulate(random_model_params_df, model_spec_init_dict)
Esempio n. 2
0
def test_simulation_func_exp(input_vault, test_id):
    """This test runs a random selection of test regression tests from
    our regression test battery.
    """
    (
        model_spec_init_dict,
        random_model_params_df,
        exog_educ_shares,
        exog_child_age_shares,
        exog_partner_shares,
        exog_exper_shares_pt,
        exog_exper_shares_ft,
        exog_child_info,
        exog_partner_arrival_info,
        exog_partner_separation_info,
        expected_df_sim_func,
        expected_df_sim_sol,
    ) = input_vault[test_id]

    exog_educ_shares.to_pickle("test.soepy.educ.shares.pkl")
    exog_child_age_shares.to_pickle("test.soepy.child.age.shares.pkl")
    exog_child_info.to_pickle("test.soepy.child.pkl")
    exog_partner_shares.to_pickle("test.soepy.partner.shares.pkl")
    exog_exper_shares_pt.to_pickle("test.soepy.pt.exp.shares.pkl")
    exog_exper_shares_ft.to_pickle("test.soepy.ft.exp.shares.pkl")
    exog_partner_arrival_info.to_pickle("test.soepy.partner.arrival.pkl")
    exog_partner_separation_info.to_pickle("test.soepy.partner.separation.pkl")
    #

    calculated_df_false = simulate(random_model_params_df,
                                   model_spec_init_dict,
                                   is_expected=False)

    for edu_type in ["low", "middle", "high"]:
        random_model_params_df.loc[(
            "exp_returns_p_bias", f"gamma_p_bias_{edu_type}"), "value"] = (
                random_model_params_df.loc[("exp_returns_p",
                                            f"gamma_p_{edu_type}"), "value"] /
                random_model_params_df.loc[("exp_returns_f",
                                            f"gamma_f_{edu_type}"), "value"])

    calculated_df_true = simulate(random_model_params_df,
                                  model_spec_init_dict,
                                  is_expected=True)

    pd.testing.assert_series_equal(
        calculated_df_false.sum(axis=0),
        calculated_df_true.sum(axis=0),
    )
    cleanup()
Esempio n. 3
0
def test_simulation_func(input_vault, test_id):
    """This test runs a random selection of test regression tests from
    our regression test battery.
    """
    (
        model_spec_init_dict,
        random_model_params_df,
        exog_educ_shares,
        exog_child_age_shares,
        exog_partner_shares,
        exog_exper_shares_pt,
        exog_exper_shares_ft,
        exog_child_info,
        exog_partner_arrival_info,
        exog_partner_separation_info,
        expected_df_sim_func,
        expected_df_sim_sol,
    ) = input_vault[test_id]

    exog_educ_shares.to_pickle("test.soepy.educ.shares.pkl")
    exog_child_age_shares.to_pickle("test.soepy.child.age.shares.pkl")
    exog_child_info.to_pickle("test.soepy.child.pkl")
    exog_partner_shares.to_pickle("test.soepy.partner.shares.pkl")
    exog_exper_shares_pt.to_pickle("test.soepy.pt.exp.shares.pkl")
    exog_exper_shares_ft.to_pickle("test.soepy.ft.exp.shares.pkl")
    exog_partner_arrival_info.to_pickle("test.soepy.partner.arrival.pkl")
    exog_partner_separation_info.to_pickle("test.soepy.partner.separation.pkl")

    calculated_df = simulate(random_model_params_df, model_spec_init_dict)

    pd.testing.assert_series_equal(
        expected_df_sim_func,
        calculated_df.sum(axis=0),
    )
    cleanup()
Esempio n. 4
0
def update_sim_objectes():
    vault_file = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    vault = {}
    with open(vault_file, "rb") as file:
        tests_sim_func = pickle.load(file)

    for i in range(0, 100):
        print(i)

        (
            model_spec_init_dict,
            random_model_params_df,
            exog_educ_shares,
            exog_child_age_shares,
            exog_partner_shares,
            exog_exper_shares_pt,
            exog_exper_shares_ft,
            exog_child_info,
            exog_partner_arrival_info,
            exog_partner_separation_info,
            expected_df_sim_func,
            expected_df_sim_sol,
        ) = tests_sim_func[i]

        exog_educ_shares.to_pickle("test.soepy.educ.shares.pkl")
        exog_child_age_shares.to_pickle("test.soepy.child.age.shares.pkl")
        exog_child_info.to_pickle("test.soepy.child.pkl")
        exog_partner_shares.to_pickle("test.soepy.partner.shares.pkl")
        exog_exper_shares_pt.to_pickle("test.soepy.pt.exp.shares.pkl")
        exog_exper_shares_ft.to_pickle("test.soepy.ft.exp.shares.pkl")
        exog_partner_arrival_info.to_pickle("test.soepy.partner.arrival.pkl")
        exog_partner_separation_info.to_pickle("test.soepy.partner.separation.pkl")

        calculated_df_sim = simulate(random_model_params_df, model_spec_init_dict)

        vault[i] = (
            model_spec_init_dict,
            random_model_params_df,
            exog_educ_shares,
            exog_child_age_shares,
            exog_partner_shares,
            exog_exper_shares_pt,
            exog_exper_shares_ft,
            exog_child_info,
            exog_partner_arrival_info,
            exog_partner_separation_info,
            calculated_df_sim.sum(axis=0),
            expected_df_sim_sol,
        )

    with open(vault_file, "wb") as file:
        pickle.dump(vault, file)

    cleanup(options="regression")
Esempio n. 5
0
def test_unit_data_frame_shape():
    """This test ensures that the shape of the simulated data frame corresponds to the
    to the random specifications of our initialization file.
    """
    for _ in range(5):
        constr = dict()
        constr["AGENTS"] = np.random.randint(10, 100)
        constr["PERIODS"] = np.random.randint(1, 6)
        constr["EDUC_MAX"] = np.random.randint(10, min(10 + constr["PERIODS"], 12))

        random_init(constr)
        df = simulate("test.soepy.pkl", "test.soepy.yml")

        np.testing.assert_array_equal(df.shape[0], constr["AGENTS"] * constr["PERIODS"])
Esempio n. 6
0
def create_vault(num_test=1000, seed=123456):
    """This function creates our regression vault."""
    np.random.seed(seed)
    seeds = np.random.randint(0, 1000, size=num_test)
    vault = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    tests = []

    for counter, seed in enumerate(seeds):

        np.random.seed(seed)

        (
            model_spec_init_dict,
            random_model_params_df,
            exog_educ_shares,
            exog_child_age_shares,
            exog_partner_shares,
            exog_exper_shares_pt,
            exog_exper_shares_ft,
            exog_child_info,
            exog_partner_arrival_info,
            exog_partner_separation_info,
        ) = random_init()

        df = simulate("test.soepy.pkl", "test.soepy.yml")

        tests += [
            (
                model_spec_init_dict,
                random_model_params_df,
                exog_educ_shares,
                exog_child_age_shares,
                exog_partner_shares,
                exog_exper_shares_pt,
                exog_exper_shares_ft,
                exog_child_info,
                exog_partner_arrival_info,
                exog_partner_separation_info,
                df,
            )
        ]

    cleanup("regression")

    with open(vault, "wb") as file:
        pickle.dump(tests, file)
Esempio n. 7
0
def check_vault(num_test):
    """This function runs another simulation for each init file in our regression vault.
    """
    vault = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    with open(vault, "rb") as file:
        tests = pickle.load(file)

    for test in tests[:num_test]:

        model_spec_init_dict, random_model_params_df, expected_df = test

        calculated_df = simulate(random_model_params_df, model_spec_init_dict)

        for col in expected_df.columns.tolist():
            expected_df[col].equals(calculated_df[col])

    cleanup("regression")
Esempio n. 8
0
def test_unit_nan():
    """This test ensures that the data frame only includes individuals that have
    completed education.
    """
    constr = {
        "AGENTS": 200,
        "PERIODS": 7,
        "EDUC_YEARS": [0, np.random.randint(1, 3),
                       np.random.randint(4, 6)],
    }
    random_init(constr)
    df = simulate("test.soepy.pkl", "test.soepy.yml")

    np.testing.assert_equal(
        df[df["Education_Level"] == 1]["Period"].min(),
        constr["EDUC_YEARS"][1],
    )
    np.testing.assert_equal(
        df[df["Education_Level"] == 2]["Period"].min(),
        constr["EDUC_YEARS"][2],
    )
Esempio n. 9
0
def test1(idx):
    """This test runs a random selection of five regression tests from
    our regression test battery.
    """

    vault = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    with open(vault, "rb") as file:
        tests = pickle.load(file)

    test = tests[idx]

    model_spec_init_dict, random_model_params_df, expected_df = test

    calculated_df = simulate(random_model_params_df, model_spec_init_dict)

    for col in expected_df.columns.tolist():
        np.testing.assert_array_almost_equal(
            expected_df[col][expected_df[col].notna()],
            calculated_df[col][calculated_df[col].notna()],
        )
Esempio n. 10
0
def create_vault(num_test=1000, seed=123456):
    """This function creates our regression vault."""
    np.random.seed(seed)
    seeds = np.random.randint(0, 1000, size=num_test)
    vault = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    tests = []

    for counter, seed in enumerate(seeds):

        np.random.seed(seed)

        model_spec_init_dict, random_model_params_df = random_init()

        df = simulate("test.soepy.pkl", "test.soepy.yml")

        tests += [(model_spec_init_dict, random_model_params_df, df)]

    cleanup("regression")

    with open(vault, "wb") as file:
        pickle.dump(tests, file)
Esempio n. 11
0
def check_vault(num_test):
    """This function runs another simulation for each init file in our regression vault."""
    vault = TEST_RESOURCES_DIR / "regression_vault.soepy.pkl"

    with open(vault, "rb") as file:
        tests = pickle.load(file)

    for test in tests[:num_test]:

        (
            model_spec_init_dict,
            random_model_params_df,
            exog_educ_shares,
            exog_child_age_shares,
            exog_partner_shares,
            exog_exper_shares_pt,
            exog_exper_shares_ft,
            exog_child_info,
            exog_partner_arrival_info,
            exog_partner_separation_info,
            expected_df,
        ) = test

        exog_educ_shares.to_pickle("test.soepy.educ.shares.pkl")
        exog_child_age_shares.to_pickle("test.soepy.child.age.shares.pkl")
        exog_child_info.to_pickle("test.soepy.child.pkl")
        exog_partner_shares.to_pickle("test.soepy.partner.shares.pkl")
        exog_exper_shares_pt.to_pickle("test.soepy.pt.exp.shares.pkl")
        exog_exper_shares_ft.to_pickle("test.soepy.ft.exp.shares.pkl")
        exog_partner_arrival_info.to_pickle("test.soepy.partner.arrival.pkl")
        exog_partner_separation_info.to_pickle("test.soepy.partner.separation.pkl")

        calculated_df = simulate(random_model_params_df, model_spec_init_dict)

        for col in expected_df.columns.tolist():
            expected_df[col].equals(calculated_df[col])

    cleanup("regression")
Esempio n. 12
0
def test_unit_nan():
    """This test ensures that the data frame contain only nan values if individuals are
     still a in education.
    """
    constr = {"AGENTS": 200}
    random_init(constr)
    df = simulate("test.soepy.pkl", "test.soepy.yml")

    for year in [11, 12]:

        df2 = df[(df["Years_of_Education"] == year) & (df["Period"] < year - 10)]

        df2 = df2[
            [
                col
                for col in df2.columns.values
                if col not in ["Identifier", "Period", "Years_of_Education"]
            ]
        ]
        a = np.empty(df2.shape)
        a[:] = np.nan

        np.testing.assert_array_equal(df2.values, a)
Esempio n. 13
0
def test_1():
    """This test makes sure the full package works for random initialization files."""
    random_init()

    simulate("test.soepy.pkl", "test.soepy.yml")
Esempio n. 14
0
def func(maxrt):
    stop = datetime.datetime.now() + maxrt
    while datetime.datetime.now() < stop:
        random_init()
        simulate("test.soepy.pkl", "test.soepy.yml")