change naming, add in description

AmpersandTV · Jul 7, 2020 · 6b6789f · 6b6789f
1 parent 09f936e
commit 6b6789f
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Showing 4 changed files with 24 additions and 13 deletions.
diff --git a/tests/test_sampling.py b/tests/test_sampling.py
@@ -1,5 +1,5 @@
 from pymc3_hmm.distributions import HMMStateSeq, SwitchingProcess
-from tests.utils import simulate_poiszero_hmm, check_metrics
+from tests.utils import simulate_poiszero_hmm, check_metrics_for_sampling
 from pymc3_hmm.step_methods import FFBSStep, TransMatConjugateStep
 import pymc3 as pm
 import theano.tensor as tt
@@ -52,7 +52,7 @@ def test_sampling(N: int = 200, off_param=1):
         steps = [ffbs, mu_step, transitions]
         trace_ = pm.sample(N, step=steps, return_inferencedata=True, chains=1)
         posterior = pm.sample_posterior_predictive(trace_.posterior)
-        check_metrics(trace_, posterior, simulation)
+        check_metrics_for_sampling(trace_, posterior, simulation)
 
 
 # def test_PriorRobust():

diff --git a/tests/test_sampling_seasonality.py b/tests/test_sampling_seasonality.py
@@ -1,7 +1,7 @@
 from pymc3_hmm.distributions import HMMStateSeq, SwitchingProcess
 from tests.utils import (
     simulate_poiszero_hmm,
-    check_metrics,
+    check_metrics_for_sampling,
 )
 from pymc3_hmm.step_methods import FFBSStep, TransMatConjugateStep
 import pymc3 as pm
@@ -74,4 +74,4 @@ def test_seasonality_sampling(N: int = 200, off_param=1):
         trace_ = pm.sample(N, step=steps, return_inferencedata=True, chains=1)
         posterior = pm.sample_posterior_predictive(trace_.posterior)
 
-    check_metrics(trace_, posterior, simulation)
+    check_metrics_for_sampling(trace_, posterior, simulation)
diff --git a/tests/test_ztp.py b/tests/test_ztp.py
diff --git a/tests/utils.py b/tests/utils.py
@@ -38,7 +38,20 @@ def simulate_poiszero_hmm(
     return y_test_point, test_model
 
 
-def check_metrics(trace_, posterior, simulation):
+def check_metrics_for_sampling(trace_, posterior, simulation):
+    """
+    this function is to check the how the posterior generated matches matched with the simulated toy series
+
+    Parameters
+    ----------
+    trace_ : trace object returned from pm.sampling,
+    posterior : posterior prediction
+    simulation : simulated toy series generated from `simulate_poiszero_hmm` funcion
+
+    Returns None
+    -------
+
+    """
 
     ## checking for state prediction
     st_trace = trace_.posterior["S_t"].mean(axis=0).mean(axis=0)
@@ -53,13 +66,13 @@ def check_metrics(trace_, posterior, simulation):
     positive_sim = simulation["Y_t"][positive_index]
     ## point metric
     y_trace = posterior["Y_t"].mean(axis=0)
-    MAPE = np.mean(abs(y_trace[positive_index] - positive_sim) / positive_sim)
+    mape = np.mean(abs(y_trace[positive_index] - positive_sim) / positive_sim)
 
     ## confidence_metrics_
     az_post_trace = az.from_pymc3(posterior_predictive=posterior)
-    CI_CONF = 0.95
+    ci_conf = 0.95
     post_pred_imps_hpd_df = az.hdi(
-        az_post_trace, hdi_prob=CI_CONF, group="posterior_predictive", var_names=["Y_t"]
+        az_post_trace, hdi_prob=ci_conf, group="posterior_predictive", var_names=["Y_t"]
     ).to_dataframe()
 
     post_pred_imps_hpd_df = post_pred_imps_hpd_df.unstack(level="hdi")
@@ -69,13 +82,11 @@ def check_metrics(trace_, posterior, simulation):
     pred_range = post_pred_imps_hpd_df[positive_index]["Y_t"]
     pred_range["T_Y"] = simulation["Y_t"][positive_index]
 
-    pred_CI = sum(
+    pred_ci = sum(
         (pred_range["T_Y"] <= pred_range["upper"])
         & (pred_range["T_Y"] >= pred_range["lower"]) * 1
     ) / len(pred_range)
 
-    print(mean_error_rate, MAPE, pred_CI)
-    print(pred_range)
     assert mean_error_rate <= 0.05
-    assert MAPE <= 0.05
-    assert pred_CI >= CI_CONF - 0.05
+    assert mape <= 0.05
+    assert pred_ci >= ci_conf - 0.05