Python generate_data Examples

Example #1

File: test_fama_macbeth.py Project: nhmatheson/linearmodels

def data(request):
    missing, datatype, const = request.param
    return generate_data(missing,
                         datatype,
                         const=const,
                         other_effects=1,
                         ntk=(25, 200, 5))

Example #2

def large_data(request):
    missing, datatype, const = request.param
    return generate_data(missing,
                         datatype,
                         const=const,
                         ntk=(51, 71, 5),
                         other_effects=2)

Example #3

def data(request):
    missing, datatype, const = request.param
    return generate_data(missing,
                         datatype,
                         const=const,
                         ntk=(91, 15, 5),
                         other_effects=2)

Example #4

File: test_panel_ols.py Project: caozq19/linearmodels

def singleton_data(request):
    missing, datatype, const = request.param
    return generate_data(missing,
                         datatype,
                         const=const,
                         ntk=(91, 15, 5),
                         other_effects=2,
                         num_cats=[5 * 91, 15])

Example #5

File: test_panel_ols.py Project: limingbei/linearmodels

def const_data(request):
    missing, datatype = request.param
    data = generate_data(missing, datatype, ntk=(91, 7, 1))
    y = PanelData(data.y).dataframe
    x = y.copy()
    x.iloc[:, :] = 1
    x.columns = ['Const']
    return AttrDict(y=y, x=x, w=PanelData(data.w).dataframe)

Example #6

def test_limited_redundancy():
    data = generate_data(0,
                         datatype="numpy",
                         const=False,
                         other_effects=1,
                         ntk=(25, 200, 5))
    for i in range(0, data.x.shape[1], 7):
        data.x[1, i, :] = data.x[0, i, :]
    mod = FamaMacBeth(data.y, data.x)
    res = mod.fit()
    assert np.any(np.isnan(res.all_params))

Example #7

def absorbed_data(request):
    datatype = request.param
    rng = np.random.RandomState(12345)
    data = generate_data(0, datatype, ntk=(131, 4, 3), rng=rng)
    x = data.x
    if isinstance(data.x, np.ndarray):
        absorbed = np.arange(x.shape[2])
        absorbed = np.tile(absorbed, (1, x.shape[1], 1))
        data.x = np.concatenate([data.x, absorbed])
    elif isinstance(data.x, pd.DataFrame):
        codes = get_codes(data.x.index)
        absorbed = np.array(codes[0]).astype(np.double)
        data.x["x_absorbed"] = absorbed
    return data

Example #8

def absorbed_data(request):
    datatype = request.param
    rng = np.random.RandomState(12345)
    data = generate_data(0, datatype, ntk=(131, 4, 3), rng=rng)
    x = data.x
    if isinstance(data.x, np.ndarray):
        absorbed = np.arange(x.shape[2])
        absorbed = np.tile(absorbed, (1, x.shape[1], 1))
        data.x = np.concatenate([data.x, absorbed])
    else:
        try:
            codes = data.x.index.codes
        except AttributeError:
            # pandas < 0.24
            codes = data.x.index.labels
        absorbed = np.array(codes[0]).astype(np.double)
        data.x["x_absorbed"] = absorbed
    return data

Example #9

File: test_firstdifference_ols.py Project: paulkahura/linearmodels

def data(request):
    missing, datatype = request.param
    return generate_data(missing, datatype, other_effects=1)

Example #10

def data(request):
    missing, datatype = request.param
    rng = np.random.RandomState(12345)
    return generate_data(missing, datatype, ntk=(131, 4, 3), rng=rng)

Example #11

def data(request):
    missing, datatype, const = request.param
    return generate_data(missing, datatype, ntk=(1000, 3, 5), const=const)

Example #12

def data_gen(missing, datatype):
    missing = 0.20 if missing else 0.0
    return generate_data(missing, datatype)

Example #13

File: test_between_ols.py Project: chine007/linearmodels

def missing_data(request):
    return generate_data(0.20, request.param)

Example #14

File: test_between_ols.py Project: chine007/linearmodels

def data(request):
    return generate_data(0.0, request.param)

Example #15

File: experiment.py Project: chrinide/linearmodels

    joined = {}
    for n in (2000, ):
        beta = {}
        std_errs = {}
        std_errs_no = {}
        std_errs_u = {}
        std_errs_u_no = {}
        std_errs_r = {}
        std_errs_r_no = {}
        vals = np.zeros((NUM_REPS, 5, 7))
        for b in range(NUM_REPS):
            if b % 25 == 0:
                print(key, n, b)
            data = generate_data(0.00,
                                 'pandas',
                                 ntk=(n, 3, 5),
                                 other_effects=1,
                                 const=False,
                                 rng=rs)
            mo, fo = options[key]

            mod_type, cluster_type = key.split(':')

            y = PanelData(data.y)
            random_effects = np.random.randint(0,
                                               n // 3,
                                               size=y.dataframe.shape)
            other_random = np.random.randint(0, n // 5, size=y.dataframe.shape)

            if mod_type == 'random':
                effects = y.copy()
                effects.dataframe.iloc[:, :] = random_effects

Example #16

zz.groupby(level=0).sum()
zz.groupby(level=0).sum().T
o = zz.groupby(level=0).sum().values
o
o.T @ o
(x.T @ z) @ (x.T @ z).T
a = x.T @ p @ x
b = (x.T @ z) @ (x.T @ z).T
a
b
np.linalg.inv(a) @ b
np.trace(np.linalg.inv(a) @ b)
30
30

data = generate_data(0, 'pandas', ntk=(101, 3, 5), other_effects=1, const=False)

y = PanelData(data.y)
x = PanelData(data.x)
w = PanelData(data.w)

x.dataframe.iloc[:, 0] = 1
mod = PanelOLS(data.y, data.x, weights=data.w)
mod.fit()
mod = PanelOLS(y, x, weights=data.w, entity_effects=True)
mod.fit()
mod = PanelOLS(data.y, data.x, weights=data.w, time_effects=True)
mod.fit()
mod = PanelOLS(data.y, data.x, weights=data.w, time_effects=True, entity_effects=True)
mod.fit()

Example #17

def data(request):
    missing, datatype = request.param
    return generate_data(missing, datatype, ntk=(231, 7, 5))

Example #18

def test_uneuqal_samples():
    data = generate_data(False, "pandas")
    with pytest.raises(ValueError, match="dependent and exog must have"):
        PooledOLS(data.y.iloc[::2], data.x)