update tests

better · Mar 31, 2020 · 84ec1a2 · 84ec1a2
1 parent 6043eb0
commit 84ec1a2
Showing 1 changed file with 8 additions and 8 deletions.
diff --git a/test_convoys.py b/test_convoys.py
@@ -64,7 +64,7 @@ def test_output_shapes(c=0.3, lambd=0.1, n=1000, k=5):
     B, T = generate_censored_data(N, E, C)
 
     # Fit model with ci
-    model = convoys.regression.Exponential(ci=True)
+    model = convoys.regression.Exponential(mcmc=True)
     model.fit(X, B, T)
 
     # Generate output without ci
@@ -91,7 +91,7 @@ def test_output_shapes(c=0.3, lambd=0.1, n=1000, k=5):
     assert model.cdf(X[0], 0, ci=0.8).shape == (3,)
 
     # Fit model without ci (should be the same)
-    model = convoys.regression.Exponential(ci=False)
+    model = convoys.regression.Exponential(mcmc=False)
     model.fit(X, B, T)
     assert model.predict(X[0], 0).shape == ()
     assert model.predict([X[0], X[1]], [0, 1]).shape == (2,)
@@ -104,7 +104,7 @@ def test_exponential_regression_model(c=0.3, lambd=0.1, n=10000):
     N = scipy.stats.uniform.rvs(scale=5./lambd, size=(n,))  # time now
     E = scipy.stats.expon.rvs(scale=1./lambd, size=(n,))  # time of event
     B, T = generate_censored_data(N, E, C)
-    model = convoys.regression.Exponential(ci=True)
+    model = convoys.regression.Exponential(mcmc=True)
     model.fit(X, B, T)
     assert 0.80*c < model.predict([1], float('inf')) < 1.30*c
     for t in [1, 3, 10]:
@@ -126,7 +126,7 @@ def test_exponential_regression_model(c=0.3, lambd=0.1, n=10000):
         assert 0.70*d < (convert_times < t).mean() < 1.30*d
 
     # Fit a linear model
-    model = convoys.regression.Exponential(ci=False, flavor='linear')
+    model = convoys.regression.Exponential(mcmc=False, flavor='linear')
     model.fit(X, B, T)
     model_c = model.params['map']['b'] + model.params['map']['beta'][0]
     assert 0.9*c < model_c < 1.1*c
@@ -162,7 +162,7 @@ def test_weibull_regression_model(cs=[0.3, 0.5, 0.7],
         assert 0.80 * c < model.predict(x, float('inf')) < 1.30 * c
 
     # Fit a linear model
-    model = convoys.regression.Weibull(ci=False, flavor='linear')
+    model = convoys.regression.Weibull(mcmc=False, flavor='linear')
     model.fit(X, B, T)
     model_cs = model.params['map']['b'] + model.params['map']['beta']
     for model_c, c in zip(model_cs, cs):
@@ -184,7 +184,7 @@ def test_gamma_regression_model(c=0.3, lambd=0.1, k=3.0, n=10000):
     assert 0.80*k < numpy.mean(model.params['map']['k']) < 1.30*k
 
     # Fit a linear model
-    model = convoys.regression.Gamma(ci=False, flavor='linear')
+    model = convoys.regression.Gamma(mcmc=False, flavor='linear')
     model.fit(X, B, T)
     model_c = model.params['map']['b'] + model.params['map']['beta'][0]
     assert 0.9*c < model_c < 1.1*c
@@ -201,7 +201,7 @@ def test_linear_model(n=10000, m=5, k=3.0, lambd=0.1):
     E = numpy.array([sample_weibull(k, lambd) for r in range(n)])
     B, T = generate_censored_data(N, E, C)
 
-    model = convoys.regression.Weibull(ci=False, flavor='linear')
+    model = convoys.regression.Weibull(mcmc=False, flavor='linear')
     model.fit(X, B, T)
 
     # Check the fitted parameters
@@ -365,7 +365,7 @@ def _test_plot_cohorts(model='weibull', extra_model=None):
 def test_plot_cohorts_model():
     df = _generate_dataframe()
     unit, groups, (G, B, T) = convoys.utils.get_arrays(df)
-    model = convoys.multi.Exponential(ci=None)
+    model = convoys.multi.Exponential(mcmc=None)
     model.fit(G, B, T)
     matplotlib.pyplot.clf()
     convoys.plotting.plot_cohorts(G, B, T, model=model, groups=groups)