inference.rst: Update doctests

doc/demos/inference/inference.rst

@@ -456,7 +456,7 @@ case of the `C. elegans` network we have
    :options: +NORMALIZE_WHITESPACE
 
    Non-degree-corrected DL:	 8507.97432099
-   Degree-corrected DL:	 8228.11609772
+   Degree-corrected DL:	 8249.76252971
 
 Since it yields the smallest description length, the degree-corrected
 fit should be preferred. The statistical significance of the choice can
@@ -482,12 +482,12 @@ fits. In our particular case, we have
 .. testoutput:: model-selection
    :options: +NORMALIZE_WHITESPACE
 
-   ln Λ:  -279.858223272
+   ln Λ:  -258.211791282
 
 The precise threshold that should be used to decide when to `reject a
 hypothesis <https://en.wikipedia.org/wiki/Hypothesis_testing>`_ is
 subjective and context-dependent, but the value above implies that the
-particular degree-corrected fit is around :math:`e^{280} \sim 10^{121}`
+particular degree-corrected fit is around :math:`e^{258} \sim 10^{112}`
 times more likely than the non-degree corrected one, and hence it can be
 safely concluded that it provides a substantially better fit.
 
@@ -509,12 +509,12 @@ example, for the American football network above, we have:
 .. testoutput:: model-selection
    :options: +NORMALIZE_WHITESPACE
 
-   Non-degree-corrected DL:	 1749.51938237
-   Degree-corrected DL:	 1780.57671694
-   ln Λ:			 -31.0573345685
+   Non-degree-corrected DL:	 1738.71605436
+   Degree-corrected DL:	 1795.27140372
+   ln Λ:			 -56.5553493622
 
-Hence, with a posterior odds ratio of :math:`\Lambda \sim e^{-36} \sim
-10^{-16}` in favor of the non-degree-corrected model, it seems like the
+Hence, with a posterior odds ratio of :math:`\Lambda \sim e^{-56} \sim
+10^{-24}` in favor of the non-degree-corrected model, it seems like the
 degree-corrected variant is an unnecessarily complex description for
 this network.
 
@@ -1068,8 +1068,8 @@ evidence efficiently, as we show below, using
 
 .. testoutput:: model-evidence
 
-   Model evidence for deg_corr = True: -575.864972067 (mean field), -802.39062289 (Bethe)
-   Model evidence for deg_corr = False: -584.307313493 (mean field), -707.827204203 (Bethe)
+   Model evidence for deg_corr = True: -575.889667037 (mean field), -794.733483508 (Bethe)
+   Model evidence for deg_corr = False: -618.120673252 (mean field), -729.104723167 (Bethe)
 
 If we consider the more accurate approximation, the outcome shows a
 preference for the non-degree-corrected model.
@@ -1133,8 +1133,8 @@ approach for the same network, using the nested model.
 
 .. testoutput:: model-evidence
 
-   Model evidence for deg_corr = True: -346.618790006 (mean field), -601.313781849 (Bethe)
-   Model evidence for deg_corr = False: -374.614350884 (mean field), -563.256840699 (Bethe)
+   Model evidence for deg_corr = True: -333.948404832 (mean field), -651.91054824 (Bethe)
+   Model evidence for deg_corr = False: -360.235692786 (mean field), -549.293909028 (Bethe)
 
 The results are similar: If we consider the most accurate approximation,
 the non-degree-corrected model possesses the largest evidence. Note also
@@ -1363,11 +1363,11 @@ above).
 
 .. testoutput:: missing-edges
 
-   likelihood-ratio for (101, 102): 0.372308
-   likelihood-ratio for (17, 56): 0.627692
+   likelihood-ratio for (101, 102): 0.436558
+   likelihood-ratio for (17, 56): 0.563442
 
-From which we can conclude that edge :math:`(17, 56)` is around twice as
-likely as :math:`(101, 102)` to be a missing edge.
+From which we can conclude that edge :math:`(17, 56)` is more likely
+than :math:`(101, 102)` to be a missing edge.
 
 The prediction using the non-nested model can be performed in an
 entirely analogous fashion.

doc/test_inference.py

@@ -28,23 +28,26 @@ rec_p = g.new_ep("double", random(g.num_edges()))
 rec_s = g.new_ep("double", normal(0, 10, g.num_edges()))
 
 
-def gen_state(directed, deg_corr, layers, overlap, rec, allow_empty):
+def gen_state(directed, deg_corr, layers, overlap, rec, rec_type, allow_empty):
     u = GraphView(g, directed=directed)
     if layers != False:
         state = graph_tool.inference.LayeredBlockState(u, B=u.num_vertices(),
                                                        deg_corr=deg_corr,
                                                        ec=ec.copy(),
-                                                       rec=rec,
+                                                       recs=[rec] if rec is not None else [],
+                                                       rec_types=[rec_type] if rec is not None else [],
                                                        overlap=overlap,
                                                        layers=layers == True,
                                                        allow_empty=allow_empty)
     elif overlap:
         state = graph_tool.inference.OverlapBlockState(u, B=2 * u.num_edges(),
-                                                       rec=rec,
+                                                       recs=[rec] if rec is not None else [],
+                                                       rec_types=[rec_type] if rec is not None else [],
                                                        deg_corr=deg_corr)
     else:
         state = graph_tool.inference.BlockState(u, B=u.num_vertices(),
-                                                rec=rec,
+                                                recs=[rec] if rec is not None else [],
+                                                rec_types=[rec_type] if rec is not None else [],
                                                 deg_corr=deg_corr,
                                                 allow_empty=allow_empty)
     return state
@@ -53,7 +56,7 @@ def gen_state(directed, deg_corr, layers, overlap, rec, allow_empty):
 pranges = [("directed", [False, True]),
            ("overlap", [False, True]),
            ("layered", [False, "covariates", True]),
-           ("rec", [None, "positive", "signed"]),
+           ("rec", [None, "real-exponential", "real-normal"]),
            ("deg_corr", [False, True]),
            ("dl", [False, True]),
            ("degree_dl_kind", ["uniform", "distributed", "entropy"]),
@@ -84,13 +87,13 @@ for pvals in iter_ranges(pranges):
     print(params, file=out)
 
     rec_ = None
-    if rec == "positive":
+    if rec == "real-exponential":
         rec_ = rec_p
-    elif rec == "signed":
+    elif rec == "real-normal":
         rec_ = rec_s
 
     print("\t mcmc (unweighted)", file=out)
-    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, rec, allow_empty)
 
     print("\t\t",
           state.mcmc_sweep(beta=0,
@@ -107,7 +110,7 @@ for pvals in iter_ranges(pranges):
                                                  exact=exact)),
               state.get_nonempty_B(), file=out)
 
-    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, rec, allow_empty)
 
     if not overlap:
         print("\t mcmc", file=out)
@@ -136,7 +139,7 @@ for pvals in iter_ranges(pranges):
 
     print("\t merge", file=out)
 
-    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, rec, allow_empty)
 
     if not overlap:
         bstate = state.get_block_state(vweight=True, deg_corr=deg_corr)
@@ -174,7 +177,7 @@ for pvals in iter_ranges(pranges):
 
     print("\t shrink", file=out)
 
-    state = gen_state(directed, deg_corr, layered, overlap, rec_, allow_empty)
+    state = gen_state(directed, deg_corr, layered, overlap, rec_, rec, allow_empty)
     state = state.shrink(B=5, entropy_args=dict(dl=dl,
                                                 degree_dl_kind=degree_dl_kind,
                                                 multigraph=False,
@@ -185,7 +188,7 @@ for pvals in iter_ranges(pranges):
 pranges = [("directed", [False, True]),
            ("overlap", [False, True]),
            ("layered", [False, "covariates", True]),
-           ("rec", [None, "positive", "signed"]),
+           ("rec", [None, "real-exponential", "real-normal"]),
            ("deg_corr", [False, True]),
            ("degree_dl_kind", ["uniform", "distributed", "entropy"]),
            ("exact", [True])]
@@ -205,16 +208,17 @@ for pvals in iter_ranges(pranges):
 
     print(params, file=out)
 
-    rec_ = None
-    if rec == "positive":
+    rec_ = []
+    if rec == "real-exponential":
         rec_ = rec_p
-    elif rec == "signed":
+    elif rec == "real-normal":
         rec_ = rec_s
 
     if layered != False:
-        state_args = dict(ec=ec, layers=(layered == True), rec=rec_)
+        state_args = dict(ec=ec, layers=(layered == True), recs=[rec_],
+                          rec_types=[rec])
     else:
-        state_args = dict(rec=rec_)
+        state_args = dict(recs=[rec_], rec_types=[rec])
 
     entropy_args = dict(exact=exact)