Merge pull request #241 from SciML/loglevels

Adding log levels to the functions and using internal logger

Project.toml

@@ -23,6 +23,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
+TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 
 [compat]
 AbstractAlgebra = "0.13, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33"

benchmarking/IdentifiableFunctions/experiments.jl

@@ -988,14 +988,17 @@ begin
     )
 
     using Nemo, Logging
-    using JuliaInterpreter
+    # using JuliaInterpreter
     Groebner = StructuralIdentifiability.Groebner
     # ParamPunPam = StructuralIdentifiability.ParamPunPam
     Base.global_logger(ConsoleLogger(Logging.Info))
 end
 
-dennums, ring =
-    StructuralIdentifiability.initial_identifiable_functions(Pivastatin, p = 0.99);
+begin
+    StructuralIdentifiability.find_identifiable_functions(Pivastatin)
+    StructuralIdentifiability.print_timings_table()
+end
+
 fracs = StructuralIdentifiability.dennums_to_fractions(dennums);
 
 rff = StructuralIdentifiability.RationalFunctionField(dennums);

docs/src/identifiability/identifiability.md

@@ -12,12 +12,6 @@ assess_identifiability
 assess_local_identifiability
 ```
 
-## Assessing Global Identifiability
-
-```@docs
-assess_global_identifiability
-```
-
 ## Finding Identifiable Functions
 
 ```@docs

docs/src/tutorials/discrete_time.md

@@ -71,6 +71,9 @@ assess_local_identifiability(sir; measured_quantities = [I], funcs_to_check = [
     principle it may produce incorrect result but the probability of correctness of the returned result is guaranteed to be at least `p`
     (in fact, the employed bounds are quite conservative, so in practice incorrect result is almost never produced).
 
+As other main functions in the package, `assess_local_identifiability` accepts an optional parameter `loglevel` (default: `Logging.Info`)
+to adjust the verbosity of logging.
+
 The implementation is based on a version of the observability rank criterion and will be described in a forthcoming paper.
 
 [^1]: > S. Nõmm, C. Moog, [*Identifiability of discrete-time nonlinear systems*](https://doi.org/10.1016/S1474-6670(17)31245-4), IFAC Proceedings Volumes, 2004.

docs/src/tutorials/identifiability.md

@@ -64,6 +64,8 @@ Function `assess_local_identifiability` has several optional parameters
     likely to be much higher.
   - `type` (default `:SE`). By default, the algorithm checks the standard single-experiment identifiability. If one sets `type = :ME`, then the algorithm
     checks multi-experiment identifiability, that is, identifiability from several experiments with independent initial conditions (the algorithm from [^2] is used).
+  - `loglevel` (default `Logging.Info`). The minimal level of logging messages to be displayed. Available options: `Logging.Debug`,
+    `Logging.Info`, `Logging.Warn`, and `Logging.Error`.
 
 Note that the scaling symmetry given above suggests that $b x_2(t)$ may in fact be identifiable. This can be checked using `funcs_to_check` parameter:
 
@@ -107,6 +109,8 @@ Similarly to `assess_local_identifiability`, this function has optional paramete
     error probability is much lower than 1%.
     Also, currently, the probability of correctness does not include the probability of correctness of the modular reconstruction for Groebner bases.
     This probability is ensured by an additional check modulo a large prime, and can be neglected for practical purposes.
+  - `loglevel` (default `Logging.Info`). The minimal level of logging messages to be displayed. Available options: `Logging.Debug`,
+    `Logging.Info`, `Logging.Warn`, and `Logging.Error`.
 
 Using `funcs_to_check` parameter, one can further inverstigate the nature of the lack of identifiability in the model at hand.
 For example, for the Goodwin oscillator, we can check if `beta + delta` and `beta * delta` are identifiable:

docs/src/tutorials/identifiable_functions.md

@@ -40,4 +40,7 @@ By default, `find_identifiable_functions` tries to simplify the output functions
 the degree of simplification. The default value is `:standard` but one could use `:strong` to try to simplify further
 (at the expense of heavier computation) or use `:weak` to simplify less (but compute faster).
 
+As `assess_identifiability` and `assess_local_identifiability`, `find_identifiable_functions` accepts an optional parameter `loglevel` (default: `Logging.Info`)
+to adjust the verbosity of logging.
+
 [^1]: > Y. Lecourtier, F. Lamnabhi-Lagarrigue, and E. Walter, [*A method to prove that nonlinear models can be unidentifiable*](https://doi.org/10.1109/CDC.1987.272467), Proceedings of the 26th Conference on Decision and Control, December 1987, 2144-2145;

examples/CRN.jl

@@ -1,13 +1,8 @@
 # Conradi, C., Shiu, A.,
 # Dynamics of post-translational modification systems: recent progress and future directions
 # Eq. 3.4
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     x1'(t) = -k1 * x1(t) * x2(t) + k2 * x4(t) + k4 * x6(t),
     x2'(t) = -k1 * x1(t) * x2(t) + k2 * x4(t) + k3 * x4(t),
@@ -19,4 +14,4 @@ ode = @ODEmodel(
     y2(t) = x2(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))

examples/Crauste.jl

@@ -1,10 +1,5 @@
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     N'(t) = -N(t) * mu_N - N(t) * P(t) * delta_NE,
     E'(t) =
@@ -17,4 +12,8 @@ ode = @ODEmodel(
     y3(t) = M(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/Fujita.jl

@@ -2,13 +2,8 @@
 # Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics
 # https://doi.org/10.1126/scisignal.2000810
 
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     EGFR'(t) =
         EGFR_turnover * pro_EGFR(t) + EGF_EGFR(t) * reaction_1_k2 -
@@ -42,4 +37,8 @@ ode = @ODEmodel(
     y3(t) = a3 * pS6(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/Goodwin.jl

@@ -2,13 +2,8 @@
 # Oscillatory behavior in enzymatic control processes
 # https://doi.org/10.1016/0065-2571(65)90067-1
 
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     x1'(t) = -b * x1(t) + 1 / (c + x4(t)),
     x2'(t) = alpha * x1(t) - beta * x2(t),
@@ -17,4 +12,8 @@ ode = @ODEmodel(
     y(t) = x1(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/HIV.jl

@@ -2,13 +2,8 @@
 # Specific therapy regimes could lead to long-term immunological control of HIV
 # https://doi.org/10.1073/pnas.96.25.14464
 # Page 1
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     x'(t) = lm - d * x(t) - beta * x(t) * v(t),
     y'(t) = beta * x(t) * v(t) - a * y(t),
@@ -19,4 +14,8 @@ ode = @ODEmodel(
     y2(t) = z(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/LV.jl

@@ -1,15 +1,14 @@
 # Lotka-Volterra model
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     x1'(t) = a * x1(t) - b * x1(t) * x2(t) + u(t),
     x2'(t) = -c * x2(t) + d * x1(t) * x2(t),
     y(t) = x1(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/MAPK.jl

@@ -1,13 +1,8 @@
 # A. K. Manrai and J. Gunawardena. 
 # The geometry of multisite phosphorylation
 # https://dx.doi.org/10.1529%2Fbiophysj.108.140632
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     KS00'(t) =
         -a00 * K(t) * S00(t) +
@@ -52,4 +47,4 @@ ode = @ODEmodel(
     y5(t) = S11(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))

examples/NFkB.jl

@@ -2,13 +2,8 @@
 # An iterative identification procedure for dynamic modeling of biochemical networks
 # https://doi.org/10.1186/1752-0509-4-11
 # The model is out of reach at the moment but can be analyzed by SIAN (https://github.com/pogudingleb/SIAN)
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     x1'(t) = k_prod - k_deg * x1(t) - k1 * x1(t) * u(t),
     x2'(t) =

examples/Pharm.jl

@@ -1,13 +1,8 @@
 # Demignot, S., D., D., 
 # Effect of prosthetic sugar groups on the pharmacokinetics of glucose-oxidase
 # https://pubmed.ncbi.nlm.nih.gov/2855567/
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     x0'(t) =
         a1 * (x1(t) - x0(t)) - (ka * n * x0(t)) / (kc * ka + kc * x2(t) + ka * x0(t)),
@@ -18,4 +13,4 @@ ode = @ODEmodel(
     y1(t) = x0(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))

examples/QWWC.jl

@@ -1,11 +1,5 @@
-using Logging
-
 using StructuralIdentifiability
 
-#QWWC
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     x'(t) = a * (y(t) - x(t)) + y(t) * z(t),
     y'(t) = b * (x(t) + y(t)) - x(t) * z(t),

examples/SEAIJRC.jl

@@ -2,13 +2,8 @@
 # Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models
 # https://doi.org/10.1186/s12976-018-0097-6
 # Model 2
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     S'(t) = -b * S(t) * (I(t) + J(t) + q * A(t)) * Ninv,
     E'(t) = b * S(t) * (I(t) + J(t) + q * A(t)) * Ninv - k * E(t),
@@ -20,4 +15,8 @@ ode = @ODEmodel(
     y2(t) = Ninv
 )
 
-@time println(assess_global_identifiability(ode))
+println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/SIAR.jl

@@ -1,12 +1,10 @@
 # SIAR model
-# https://arxiv.org/pdf/2012.00443.pdf
-using Logging
-
+# L. Gallo, M.Frasca, V. Latora, G. Russo
+# Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models
+# Science Advances, DOI: 10.1126/sciadv.abg5234
+# Equation (21)
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     S'(t) =
         -S(t) * (betaIa * Ia(t) + betaIs * Is(t) + betaH * H(t) + betaT * T(t)) * Ninv,
@@ -28,4 +26,4 @@ ode = @ODEmodel(
     y5(t) = Ninv
 )
 
-@time println(assess_identifiability(ode))
+println(find_identifiable_functions(ode, funcs_to_check = ode.parameters))

examples/SIRS_forced.jl

@@ -2,13 +2,8 @@
 # Parameter Estimation of Some Epidemic Models. The Case of Recurrent Epidemics Caused by Respiratory Syncytial Virus
 # doi.org/10.1007/s11538-009-9429-3
 # Equations (7)-(11)
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     s'(t) = mu - mu * s(t) - b0 * (1 + b1 * x1(t)) * i(t) * s(t) + g * r(t),
     i'(t) = b0 * (1 + b1 * x1(t)) * i(t) * s(t) - (nu + mu) * i(t),
@@ -20,3 +15,7 @@ ode = @ODEmodel(
 )
 
 @time println(assess_identifiability(ode))
+
+# Not everything is identifiable, so we may wonder what are the identifiable functions
+
+println(find_identifiable_functions(ode, with_states = true))

examples/SIWR.jl

@@ -2,13 +2,8 @@
 # Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity
 # https://doi.org/10.1016/j.jtbi.2017.01.032
 # Eq. (3)
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Info)
-global_logger(logger)
-
 ode = @ODEmodel(
     S'(t) = mu - bi * S(t) * I(t) - bw * S(t) * W(t) - mu * S(t) + a * R(t),
     I'(t) = bw * S(t) * W(t) + bi * S(t) * I(t) - (gam + mu) * I(t),
@@ -17,4 +12,4 @@ ode = @ODEmodel(
     y(t) = k * I(t)
 )
 
-@time println(assess_identifiability(ode))
+println(assess_identifiability(ode))

examples/SIWR_multioutput.jl

@@ -2,13 +2,8 @@
 # Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity
 # https://doi.org/10.1016/j.jtbi.2017.01.032
 # Eq. (3) + extra output
-using Logging
-
 using StructuralIdentifiability
 
-logger = Logging.SimpleLogger(stdout, Logging.Debug)
-global_logger(logger)
-
 ode = @ODEmodel(
     x_0'(t) =
         mu - bi * x_0(t) * x_1(t) - bw * x_0(t) * x_2(t) - mu * x_0(t) + a * x_3(t),
@@ -19,4 +14,4 @@ ode = @ODEmodel(
     y2(t) = x_0(t) + x_1(t) + x_3(t)
 )
 
-@time println(assess_global_identifiability(ode))
+println(assess_identifiability(ode))