def test_xprob_yprob(self):
p2 = self.probs + numpy.random.uniform(-1, 1, size=len(self.probs))
scales = {'fitlogs': None, 'fitprobs': 'both'}
x, y = self.probs, p2,
x_, y_, res = viz.fit_line(x,
y,
xhat=x[::8],
estimate_ci=True,
**scales)
nptest.assert_allclose(y_, self.known_y_probprob, rtol=0.0001)
known_res = {
'slope':
0.98467862838225351,
'intercept':
0.0013327049076583583,
'yhat_lo':
numpy.array([
1.96759603, 24.66922946, 46.88723664, 68.88913508, 91.58436332
]),
'yhat_hi':
numpy.array([
2.28593917, 25.24921351, 47.60781632, 70.11543855, 92.54803847
]),
}
self.check_res(res, known_res)
def test_xlinear_ylog(self):
scales = {"fitlogs": "y", "fitprobs": None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linlog, rtol=0.0001)
known_res = {
"slope": 0.55515014824534514,
"intercept": 2.1749556618678434,
"yhat_lo": numpy.array([2.4355, 5.6436, 8.1653, 11.3136, 18.1000]),
"yhat_hi": numpy.array([3.1348, 6.3072, 8.7495, 12.2324, 21.2824]),
}
self.check_res(res, known_res)
def test_xlinear_ylinear(self):
scales = {"fitlogs": None, "fitprobs": None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linlin, rtol=0.0001)
known_res = {
"slope": 5.3404377026700995,
"intercept": 10.114857142857147,
"yhat_lo": numpy.array([-2.9223, 5.4807, 9.1090, 12.0198, 16.2376]),
"yhat_hi": numpy.array([0.4983, 7.0448, 10.2715, 13.4877, 18.8306]),
}
self.check_res(res, known_res)
def test_xlinear_yprob(self):
scales = {"fitlogs": None, "fitprobs": "y"}
x, y = self.data, self.probs
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linprob, rtol=0.0001)
known_res = {
"slope": 0.16920340891421964,
"intercept": -1.7114683092517717,
"yhat_lo": numpy.array([5.6382, 18.9842, 36.0326, 54.0282, 92.8391]),
"yhat_hi": numpy.array([12.6284, 28.2687, 44.6934, 61.8816, 97.1297]),
}
self.check_res(res, known_res)
def test_xlinear_ylinear(self):
scales = {'fitlogs': None, 'fitprobs': None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linlin, rtol=0.0001)
known_res = {
'slope': 5.3404377026700995,
'intercept': 10.114857142857147,
'yhat_lo': numpy.array([ -2.9223, 5.4807, 9.109 , 12.0198, 16.2376]),
'yhat_hi': numpy.array([ 0.4983, 7.0448, 10.2715, 13.4877, 18.8306]),
}
self.check_res(res, known_res)
def test_xlog_ylog(self):
scales = {'fitlogs': 'both', 'fitprobs': None}
x, y = self.data, self.y
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_loglog, rtol=0.0001)
known_res = {
'slope': 1.9695339470891058,
'intercept': -4.4267200322534261,
'yhat_lo': numpy.array([ 0.033559, 0.32797 , 0.777473, 1.331504, 3.811647]),
'yhat_hi': numpy.array([ 0.061867, 0.422956, 0.892383, 1.48953 , 4.842235]),
}
self.check_res(res, known_res)
def test_xlog_yprob(self):
scales = {'fitlogs': 'x', 'fitprobs': 'y'}
x, y = self.data, self.probs
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_logprob, rtol=0.0001)
known_res = {
'slope': 1.7385543724819046,
'intercept': -3.7812786758946113,
'yhat_lo': numpy.array([0.187555, 19.859832, 46.267537, 64.085292, 89.551801]),
'yhat_hi': numpy.array([1.030230, 26.174702, 50.963065, 68.949137, 94.089655]),
}
self.check_res(res, known_res)
def test_xlog_ylog(self):
scales = {"fitlogs": "both", "fitprobs": None}
x, y = self.data, self.y
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_loglog, rtol=0.0001)
known_res = {
"slope": 1.9695339470891058,
"intercept": -4.4267200322534261,
"yhat_lo": numpy.array([0.033559, 0.327970, 0.777473, 1.331504, 3.811647]),
"yhat_hi": numpy.array([0.061867, 0.422956, 0.892383, 1.489530, 4.842235]),
}
self.check_res(res, known_res)
def test_xlog_ylinear(self):
scales = {'fitlogs': 'x', 'fitprobs': None}
x, y = self.data, self.zscores
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_loglin, rtol=0.0001)
known_res = {
'slope': 1.7385543724819053,
'intercept': -3.7812786758946122,
'yhat_lo': numpy.array([-2.88948 , -0.846565, -0.093696, 0.360738, 1.255963]),
'yhat_hi': numpy.array([-2.310246, -0.63795 , 0.024143, 0.494404, 1.561183]),
}
self.check_res(res, known_res)
def test_xprob_ylinear(self):
scales = {'fitlogs': None, 'fitprobs': 'x'}
x, y = self.probs, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_problin, rtol=0.0001)
known_res = {
'slope': 5.3404377026700995,
'intercept': 10.114857142857147,
'yhat_lo': numpy.array([-2.92233134, 5.48065673, 9.1090198 , 12.01977856, 16.23762957]),
'yhat_hi': numpy.array([ 0.49826723, 7.04480065, 10.27146083, 13.48770383, 18.83061329]),
}
self.check_res(res, known_res)
def test_xlinear_ylog(self):
scales = {'fitlogs': 'y', 'fitprobs': None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linlog, rtol=0.0001)
known_res = {
'slope': 0.55515014824534514,
'intercept': 2.1749556618678434,
'yhat_lo': numpy.array([ 2.4355, 5.6436, 8.1653, 11.3136, 18.1000]),
'yhat_hi': numpy.array([ 3.1348, 6.3072, 8.7495, 12.2324, 21.2824]),
}
self.check_res(res, known_res)
def test_xprob_ylog(self):
scales = {'fitlogs': 'y', 'fitprobs': 'x'}
x, y = self.probs, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_problog, rtol=0.0001)
known_res = {
'intercept': 2.1749556618678434,
'slope': 0.55515014824534525,
'yhat_lo': numpy.array([2.43550106, 5.6436203 , 8.16525601, 11.31358231, 18.09998664]),
'yhat_hi': numpy.array([3.13484803, 6.30722509, 8.74945323, 12.23244498, 21.28240831]),
}
self.check_res(res, known_res)
def test_xlinear_yprob(self):
scales = {'fitlogs': None, 'fitprobs': 'y'}
x, y = self.data, self.probs
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_linprob, rtol=0.0001)
known_res = {
'slope': 0.16920340891421964,
'intercept': -1.7114683092517717,
'yhat_lo': numpy.array([ 5.6382, 18.9842, 36.0326, 54.0282, 92.8391]),
'yhat_hi': numpy.array([ 12.6284, 28.2687, 44.6934, 61.8816, 97.1297]),
}
self.check_res(res, known_res)
def test_xlinear_ylinear_no_ci(self):
known_y_linlin_no_ci = numpy.array(
[
-0.89650596,
1.20256093,
2.45912768,
3.39459245,
4.15976331,
4.81895346,
5.40596572,
5.94094748,
6.43698357,
6.90313142,
7.34598503,
7.77055185,
8.18077912,
8.57988686,
8.97059045,
9.35525614,
9.73601589,
10.11485714,
10.49369839,
10.87445814,
11.25912384,
11.64982743,
12.04893516,
12.45916243,
12.88372926,
13.32658287,
13.79273071,
14.28876680,
14.82374857,
15.41076083,
16.06995097,
16.83512184,
17.77058661,
19.02715336,
21.12622025,
]
)
scales = {"fitlogs": None, "fitprobs": None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, known_y_linlin_no_ci)
known_res = {
"slope": 5.3404377026700995,
"intercept": 10.114857142857147,
"yhat_lo": None,
"yhat_hi": None,
}
self.check_res(res, known_res)
def test_xprob_yprob(self):
p2 = self.probs + numpy.random.uniform(-1, 1, size=len(self.probs))
scales = {'fitlogs': None, 'fitprobs': 'both'}
x, y = self.probs, p2,
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_probprob, rtol=0.0001)
known_res = {
'slope': 0.98467862838225351,
'intercept': 0.0013327049076583583,
'yhat_lo': numpy.array([1.96759603, 24.66922946, 46.88723664, 68.88913508, 91.58436332]),
'yhat_hi': numpy.array([2.28593917, 25.24921351, 47.60781632, 70.11543855, 92.54803847]),
}
self.check_res(res, known_res)
def test_xprob_ylog(self):
scales = {"fitlogs": "y", "fitprobs": "x"}
x, y = self.probs, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_problog, rtol=0.0001)
known_res = {
"intercept": 2.1749556618678434,
"slope": 0.55515014824534525,
"yhat_lo": numpy.array(
[2.43550106, 5.64362030, 8.16525601, 11.31358231, 18.09998664]
),
"yhat_hi": numpy.array(
[3.13484803, 6.30722509, 8.74945323, 12.23244498, 21.28240831]
),
}
self.check_res(res, known_res)
def test_xprob_ylinear(self):
scales = {"fitlogs": None, "fitprobs": "x"}
x, y = self.probs, self.data
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_problin, rtol=0.0001)
known_res = {
"slope": 5.3404377026700995,
"intercept": 10.114857142857147,
"yhat_lo": numpy.array(
[-2.92233134, 5.48065673, 9.10901980, 12.01977856, 16.23762957]
),
"yhat_hi": numpy.array(
[0.49826723, 7.04480065, 10.27146083, 13.48770383, 18.83061329]
),
}
self.check_res(res, known_res)
def test_xlog_yprob(self):
scales = {"fitlogs": "x", "fitprobs": "y"}
x, y = self.data, self.probs
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_logprob, rtol=0.0001)
known_res = {
"slope": 1.7385543724819046,
"intercept": -3.7812786758946113,
"yhat_lo": numpy.array(
[0.187555, 19.859832, 46.267537, 64.085292, 89.551801]
),
"yhat_hi": numpy.array(
[1.030230, 26.174702, 50.963065, 68.949137, 94.089655]
),
}
self.check_res(res, known_res)
def test_xlog_ylinear(self):
scales = {"fitlogs": "x", "fitprobs": None}
x, y = self.data, self.zscores
x_, y_, res = viz.fit_line(x, y, xhat=x[::8], estimate_ci=True, **scales)
nptest.assert_allclose(y_, self.known_y_loglin, rtol=0.0001)
known_res = {
"slope": 1.7385543724819053,
"intercept": -3.7812786758946122,
"yhat_lo": numpy.array(
[-2.889480, -0.846565, -0.093696, 0.360738, 1.255963]
),
"yhat_hi": numpy.array(
[-2.310246, -0.637950, 0.024143, 0.494404, 1.561183]
),
}
self.check_res(res, known_res)
def test_xlinear_ylinear_no_ci(self):
known_y_linlin_no_ci = numpy.array([
-0.89650596, 1.20256093, 2.45912768, 3.39459245,
4.15976331, 4.81895346, 5.40596572, 5.94094748,
6.43698357, 6.90313142, 7.34598503, 7.77055185,
8.18077912, 8.57988686, 8.97059045, 9.35525614,
9.73601589, 10.11485714, 10.49369839, 10.87445814,
11.25912384, 11.64982743, 12.04893516, 12.45916243,
12.88372926, 13.32658287, 13.79273071, 14.2887668 ,
14.82374857, 15.41076083, 16.06995097, 16.83512184,
17.77058661, 19.02715336, 21.12622025
])
scales = {'fitlogs': None, 'fitprobs': None}
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, known_y_linlin_no_ci)
known_res = {
'slope': 5.3404377026700995,
'intercept': 10.114857142857147,
'yhat_lo': None,
'yhat_hi': None,
}
self.check_res(res, known_res)
def test_custom_xhat(self):
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=self.custom_xhat)
nptest.assert_array_almost_equal(y_, self.known_custom_yhat)
def test_bad_fitprobs(self):
with pytest.raises(ValueError):
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, fitprobs="junk")
def test_bad_fitprobs(self):
with pytest.raises(ValueError):
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, fitprobs='junk')
def test_custom_xhat(self):
x, y = self.zscores, self.data
x_, y_, res = viz.fit_line(x, y, xhat=self.custom_xhat)
nptest.assert_array_almost_equal(y_, self.known_custom_yhat)
