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Abbreviations fixed #17 #18

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1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -60,3 +60,4 @@ venv.bak/
docs/.ipynb_checkpoints/*


/tst.py
8 changes: 8 additions & 0 deletions .idea/.gitignore
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14 changes: 14 additions & 0 deletions .idea/piecewise-regression.iml
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36 changes: 18 additions & 18 deletions piecewise_regression/main.py
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Expand Up @@ -227,18 +227,18 @@ def calculate_all_standard_errors(self):
Save to the self.estimates dictionary
"""
const_ses = self.get_const_standard_error()
self.estimates["const"]["se"] = const_ses
self.estimates["const"]["standard_error"] = const_ses

beta_ses = self.get_beta_standard_errors()
bp_ses = self.get_bp_standard_errors()
for bp_i in range(self.n_breakpoints):
self.estimates["beta{}".format(bp_i + 1)]["se"] = beta_ses[bp_i]
self.estimates["beta{}".format(bp_i + 1)]["standard_error"] = beta_ses[bp_i]
self.estimates["breakpoint{}".format(
bp_i + 1)]["se"] = bp_ses[bp_i]
bp_i + 1)]["standard_error"] = bp_ses[bp_i]
alpha_ses = self.get_alpha_standard_errors()
for alpha_i in range(self.n_breakpoints + 1):
self.estimates["alpha{}".format(
alpha_i + 1)]["se"] = alpha_ses[alpha_i]
alpha_i + 1)]["standard_error"] = alpha_ses[alpha_i]

def calculate_all_confidence_intervals(self):
"""
Expand All @@ -253,8 +253,8 @@ def calculate_all_confidence_intervals(self):
# to all estimators
for estimator_name, details in self.estimates.items():
confidence_interval = (
details["estimate"] - t_const * details["se"],
details["estimate"] + t_const * details["se"])
details["estimate"] - t_const * details["standard_error"],
details["estimate"] + t_const * details["standard_error"])
details["confidence_interval"] = confidence_interval

def calculate_all_t_stats(self):
Expand All @@ -268,15 +268,15 @@ def calculate_all_t_stats(self):
# H_0 isn't bp=0, it's that bp doesn't exist
if "breakpoint" in estimator_name:
details["t_stat"] = "-"
details["p_t"] = "-"
details["p_t_stat"] = "-"
else:
t_stat = details["estimate"] / details["se"]
t_stat = details["estimate"] / details["standard_error"]
p_t = scipy.stats.t.sf(np.abs(t_stat), dof) * 2
details["t_stat"] = t_stat
if "beta" in estimator_name:
details["p_t"] = "-"
details["p_t_stat"] = "-"
else:
details["p_t"] = p_t
details["p_t_stat"] = p_t

def get_predicted_yy(self):
"""
Expand Down Expand Up @@ -724,14 +724,14 @@ def get_results(self):

if self.best_muggeo:
results["estimates"] = self.best_muggeo.best_fit.estimates
results["bic"] = self.best_muggeo.best_fit.bic
results["rss"] = self.best_muggeo.best_fit.residual_sum_squares
results["bayesian_information_criterion"] = self.best_muggeo.best_fit.bic
results["residual_sum_of_squares"] = self.best_muggeo.best_fit.residual_sum_squares
results["converged"] = True
else:
results["converged"] = False
results["estimates"] = None
results["bic"] = None
results["rss"] = None
results["bayesian_information_criterion"] = None
results["residual_sum_of_squares"] = None
return results

def get_params(self):
Expand Down Expand Up @@ -1115,9 +1115,9 @@ def summary(self):
estimator_row = table_row_template.format(
est_name,
estimates[est_name]["estimate"],
estimates[est_name]["se"],
estimates[est_name]["standard_error"],
estimates[est_name]["t_stat"],
estimates[est_name]["p_t"],
estimates[est_name]["p_t_stat"],
estimates[est_name]["confidence_interval"][0],
estimates[est_name]["confidence_interval"][1])
table_contents += estimator_row
Expand All @@ -1136,8 +1136,8 @@ def summary(self):
for est_name in alpha_names:
estimator_row = table_row_template.format(
est_name, estimates[est_name]["estimate"],
estimates[est_name]["se"],
estimates[est_name]["t_stat"], estimates[est_name]["p_t"],
estimates[est_name]["standard_error"],
estimates[est_name]["t_stat"], estimates[est_name]["p_t_stat"],
estimates[est_name]["confidence_interval"][0],
estimates[est_name]["confidence_interval"][1])
table_contents += estimator_row
Expand Down
10 changes: 6 additions & 4 deletions piecewise_regression/model_selection.py
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Expand Up @@ -73,11 +73,13 @@ def summary(self):
for model_summary in self.model_summaries:

if model_summary["converged"]:
print("=" * 200)
print(model_summary)
model_row = table_row_template.format(
model_summary["n_breakpoints"],
model_summary["bic"],
model_summary["bayesian_information_criterion"],
str(model_summary["converged"]),
model_summary["rss"])
model_summary["residual_sum_of_squares"])
else:
model_row = table_row_template.format(
model_summary["n_breakpoints"], "",
Expand Down Expand Up @@ -110,11 +112,11 @@ def no_breakpoint_fit(self, xx, yy):
bic = n * np.log(rss / n) + k * np.log(n)

fit_data = {
"bic": bic,
"bayesian_information_criterion": bic,
"n_breakpoints": 0,
"estimates": {},
"converged": True,
"rss": rss
"residual_sum_of_squares": rss
}

fit_data["estimates"]["const"] = results.params[0]
Expand Down
44 changes: 22 additions & 22 deletions tests/test_fit.py
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Expand Up @@ -94,17 +94,17 @@ def test_against_muggeo_r_package_data_1(self):
self.assertAlmostEqual(
muggeo_bp2, estimates["breakpoint2"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta2_se, estimates["beta2"]["se"], places=1)
muggeo_beta2_se, estimates["beta2"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp2_se, estimates["breakpoint2"]["se"], places=1)
muggeo_bp2_se, estimates["breakpoint2"]["standard_error"], places=1)

self.assertAlmostEqual(
muggeo_c_t, estimates["const"]["t_stat"], delta=1)
Expand Down Expand Up @@ -138,17 +138,17 @@ def test_against_muggeo_r_package_data_1(self):
self.assertAlmostEqual(
muggeo_bp2, estimates["breakpoint2"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta2_se, estimates["beta2"]["se"], places=1)
muggeo_beta2_se, estimates["beta2"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp2_se, estimates["breakpoint2"]["se"], places=1)
muggeo_bp2_se, estimates["breakpoint2"]["standard_error"], places=1)

self.assertAlmostEqual(
muggeo_c_t, estimates["const"]["t_stat"], delta=1)
Expand Down Expand Up @@ -214,13 +214,13 @@ def test_against_muggeo_r_package_data_2(self):
self.assertAlmostEqual(
muggeo_bp1, estimates["breakpoint1"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)

self.assertAlmostEqual(
muggeo_c_t, estimates["const"]["t_stat"], delta=1)
Expand Down Expand Up @@ -289,13 +289,13 @@ def test_against_muggeo_r_package_data_3(self):
self.assertAlmostEqual(
muggeo_bp1, estimates["breakpoint1"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)

self.assertAlmostEqual(
muggeo_c_t, estimates["const"]["t_stat"], delta=1)
Expand Down Expand Up @@ -393,8 +393,8 @@ def test_non_converging(self):

self.assertEqual(results["converged"], False)
self.assertEqual(results["estimates"], None)
self.assertEqual(results["bic"], None)
self.assertEqual(results["rss"], None)
self.assertEqual(results["bayesian_information_criterion"], None)
self.assertEqual(results["residual_sum_of_squares"], None)


class TestPlots(unittest.TestCase):
Expand Down
5 changes: 3 additions & 2 deletions tests/test_model_selection.py
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Expand Up @@ -7,6 +7,7 @@

import os
import sys

sys.path.insert(1, os.path.join(sys.path[0], '..'))

DATA_SOURCE = "tests/data/data.txt"
Expand All @@ -32,15 +33,15 @@ def test_it_works_with_differnet_options(self):
ms = ModelSelection(xx, yy, n_boot=20)

# For each n_breakpoints, chekc the ModelSelection vs fit results
for n_breakpoints in range(1,10):
for n_breakpoints in range(1, 10):
fit = Fit(xx, yy, n_breakpoints=n_breakpoints, verbose=False, n_boot=20)

fit_converged = fit.get_results()["converged"]
ms_converged = ms.model_summaries[n_breakpoints]["converged"]
print(n_breakpoints, fit_converged, ms_converged)


self.assertEqual(fit_converged, ms_converged)


if __name__ == '__main__':
unittest.main()
20 changes: 10 additions & 10 deletions tests/test_muggeo.py
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Expand Up @@ -78,17 +78,17 @@ def test_against_muggeo_r_package_data_1(self):
self.assertAlmostEqual(
muggeo_bp2, estimates["breakpoint2"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta2_se, estimates["beta2"]["se"], places=1)
muggeo_beta2_se, estimates["beta2"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp2_se, estimates["breakpoint2"]["se"], places=1)
muggeo_bp2_se, estimates["breakpoint2"]["standard_error"], places=1)

print(estimates)

Expand Down Expand Up @@ -163,13 +163,13 @@ def test_against_muggeo_r_package_data_2(self):
self.assertAlmostEqual(
muggeo_bp1, estimates["breakpoint1"]["estimate"], places=1)

self.assertAlmostEqual(muggeo_c_se, estimates["const"]["se"], places=1)
self.assertAlmostEqual(muggeo_c_se, estimates["const"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_alpha_se, estimates["alpha1"]["se"], places=1)
muggeo_alpha_se, estimates["alpha1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_beta1_se, estimates["beta1"]["se"], places=1)
muggeo_beta1_se, estimates["beta1"]["standard_error"], places=1)
self.assertAlmostEqual(
muggeo_bp1_se, estimates["breakpoint1"]["se"], places=1)
muggeo_bp1_se, estimates["breakpoint1"]["standard_error"], places=1)

self.assertAlmostEqual(
muggeo_c_t, estimates["const"]["t_stat"], delta=1)
Expand Down
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