Esempi in Python per get_prediction

Esempio n. 1

    def test_ci(self):
        res_wls = self.res_wls
        prstd, iv_l, iv_u = wls_prediction_std(res_wls)
        pred_res = get_prediction(res_wls)
        ci = pred_res.conf_int(obs=True)

        assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf = pred_res.summary_frame()

        col_names = [
            'mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper',
            'obs_ci_lower', 'obs_ci_upper'
        ]
        assert_equal(sf.columns.tolist(), col_names)

        pred_res2 = res_wls.get_prediction()
        ci2 = pred_res2.conf_int(obs=True)

        assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci2, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf2 = pred_res2.summary_frame()
        assert_equal(sf2.columns.tolist(), col_names)

Esempio n. 2

File: test_predict.py Progetto: GonzaloUlla/Bankruptcy-Prediction-using-Machine-Learning-Algorithms-in-Python

    def test_ci(self):
        res_wls = self.res_wls
        prstd, iv_l, iv_u = wls_prediction_std(res_wls)
        pred_res = get_prediction(res_wls)
        ci = pred_res.conf_int(obs=True)

        assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf = pred_res.summary_frame()

        col_names = [
            'mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper',
            'obs_ci_lower', 'obs_ci_upper'
        ]
        assert_equal(sf.columns.tolist(), col_names)

        pred_res2 = res_wls.get_prediction()
        ci2 = pred_res2.conf_int(obs=True)

        assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci2, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf2 = pred_res2.summary_frame()
        assert_equal(sf2.columns.tolist(), col_names)

        # check that list works, issue 4437
        x = res_wls.model.exog.mean(0)
        pred_res3 = res_wls.get_prediction(x)
        ci3 = pred_res3.conf_int(obs=True)
        pred_res3b = res_wls.get_prediction(x.tolist())
        ci3b = pred_res3b.conf_int(obs=True)
        assert_allclose(pred_res3b.se_obs, pred_res3.se_obs, rtol=1e-13)
        assert_allclose(ci3b, ci3, rtol=1e-13)
        res_df = pred_res3b.summary_frame()
        assert_equal(res_df.index.values, [0])

        x = res_wls.model.exog[-2:]
        pred_res3 = res_wls.get_prediction(x)
        ci3 = pred_res3.conf_int(obs=True)
        pred_res3b = res_wls.get_prediction(x.tolist())
        ci3b = pred_res3b.conf_int(obs=True)
        assert_allclose(pred_res3b.se_obs, pred_res3.se_obs, rtol=1e-13)
        assert_allclose(ci3b, ci3, rtol=1e-13)
        res_df = pred_res3b.summary_frame()
        assert_equal(res_df.index.values, [0, 1])

Esempio n. 3

File: test_predict.py Progetto: ChadFulton/statsmodels

    def test_ci(self):
        res_wls = self.res_wls
        prstd, iv_l, iv_u = wls_prediction_std(res_wls)
        pred_res = get_prediction(res_wls)
        ci = pred_res.conf_int(obs=True)

        assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf = pred_res.summary_frame()

        col_names = ['mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper',
                      'obs_ci_lower', 'obs_ci_upper']
        assert_equal(sf.columns.tolist(), col_names)

        pred_res2 = res_wls.get_prediction()
        ci2 = pred_res2.conf_int(obs=True)

        assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci2, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf2 = pred_res2.summary_frame()
        assert_equal(sf2.columns.tolist(), col_names)

        # check that list works, issue 4437
        x = res_wls.model.exog.mean(0)
        pred_res3 = res_wls.get_prediction(x)
        ci3 = pred_res3.conf_int(obs=True)
        pred_res3b = res_wls.get_prediction(x.tolist())
        ci3b = pred_res3b.conf_int(obs=True)
        assert_allclose(pred_res3b.se_obs, pred_res3.se_obs, rtol=1e-13)
        assert_allclose(ci3b, ci3, rtol=1e-13)
        res_df = pred_res3b.summary_frame()
        assert_equal(res_df.index.values, [0])

        x = res_wls.model.exog[-2:]
        pred_res3 = res_wls.get_prediction(x)
        ci3 = pred_res3.conf_int(obs=True)
        pred_res3b = res_wls.get_prediction(x.tolist())
        ci3b = pred_res3b.conf_int(obs=True)
        assert_allclose(pred_res3b.se_obs, pred_res3.se_obs, rtol=1e-13)
        assert_allclose(ci3b, ci3, rtol=1e-13)
        res_df = pred_res3b.summary_frame()
        assert_equal(res_df.index.values, [0, 1])

Esempio n. 4

File: tests_predict.py Progetto: 0ceangypsy/statsmodels

    def test_ci(self):
        res_wls = self.res_wls
        prstd, iv_l, iv_u = wls_prediction_std(res_wls)
        pred_res = get_prediction(res_wls)
        ci = pred_res.conf_int(obs=True)

        assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf = pred_res.summary_frame()

        col_names = ['mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper',
                      'obs_ci_lower', 'obs_ci_upper']
        assert_equal(sf.columns.tolist(), col_names)

        pred_res2 = res_wls.get_prediction()
        ci2 = pred_res2.conf_int(obs=True)

        assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)
        assert_allclose(ci2, np.column_stack((iv_l, iv_u)), rtol=1e-13)

        sf2 = pred_res2.summary_frame()
        assert_equal(sf2.columns.tolist(), col_names)

Esempio n. 5

File: ex_predict_results.py Progetto: gongyg1/stat-model1

beta = [5., 0.5, -0.01]
sig = 0.5
w = np.ones(nsample)
w[nsample * 6 / 10:] = 3
y_true = np.dot(X, beta)
e = np.random.normal(size=nsample)
y = y_true + sig * w * e
X = X[:, [0, 1]]

# ### WLS knowing the true variance ratio of heteroscedasticity

mod_wls = WLS(y, X, weights=1. / w)
res_wls = mod_wls.fit()

prstd, iv_l, iv_u = wls_prediction_std(res_wls)
pred_res = get_prediction(res_wls)
ci = pred_res.conf_int(obs=True)

from numpy.testing import assert_allclose

assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

print pred_res.summary_frame().head()

pred_res2 = res_wls.get_prediction()
ci2 = pred_res2.conf_int(obs=True)

from numpy.testing import assert_allclose

assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)

Esempio n. 6

File: ex_predict_results.py Progetto: ChadFulton/statsmodels

w[nsample * 6/10:] = 3
y_true = np.dot(X, beta)
e = np.random.normal(size=nsample)
y = y_true + sig * w * e
X = X[:,[0,1]]


# ### WLS knowing the true variance ratio of heteroscedasticity

mod_wls = WLS(y, X, weights=1./w)
res_wls = mod_wls.fit()



prstd, iv_l, iv_u = wls_prediction_std(res_wls)
pred_res = get_prediction(res_wls)
ci = pred_res.conf_int(obs=True)

from numpy.testing import assert_allclose
assert_allclose(pred_res.se_obs, prstd, rtol=1e-13)
assert_allclose(ci, np.column_stack((iv_l, iv_u)), rtol=1e-13)

print(pred_res.summary_frame().head())

pred_res2 = res_wls.get_prediction()
ci2 = pred_res2.conf_int(obs=True)

from numpy.testing import assert_allclose
assert_allclose(pred_res2.se_obs, prstd, rtol=1e-13)
assert_allclose(ci2, np.column_stack((iv_l, iv_u)), rtol=1e-13)