Revision a6a40d62517655a8f72002ab395e1e63b6251886 authored by Marge Bot on 06 February 2024, 08:47:30 UTC, committed by Marge Bot on 06 February 2024, 08:47:30 UTC
Co-authored-by: Alain Mebsout <alain.mebsout@functori.com> Approved-by: Sylvain R. <sylvain.ribstein@nomadic-labs.com> Approved-by: Nic Volanschi <nic.volanschi@nomadic-labs.com> Approved-by: Killian Delarue <killian.delarue@nomadic-labs.com> Approved-by: Victor Allombert <victor.allombert@nomadic-labs.com> See merge request https://gitlab.com/tezos/tezos/-/merge_requests/11829
inference.mli
(*****************************************************************************)
(* *)
(* Open Source License *)
(* Copyright (c) 2018 Dynamic Ledger Solutions, Inc. <contact@tezos.com> *)
(* Copyright (c) 2018 Nomadic Labs. <contact@nomadic-labs.com> *)
(* *)
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(* *)
(* The above copyright notice and this permission notice shall be included *)
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(*****************************************************************************)
module NMap : Stats.Finbij.S with type elt = Free_variable.t
type measure = Measure of Maths.vector
type constrnt = Full of Costlang.affine * measure
type problem =
| Non_degenerate of {
lines : constrnt list;
input : Maths.matrix;
output : Maths.matrix;
nmap : NMap.t;
}
| Degenerate of {predicted : Maths.matrix; measured : Maths.matrix}
type scores = {
(* R2 score is uninformative when the input is constant. (e.g. constant model)
We use `None` for the R2 score of such models. *)
r2_score : float option;
rmse_score : float;
tvalues : (Free_variable.t * float) list;
}
val scores_encoding : scores Data_encoding.t
val pp_scores : Format.formatter -> scores -> unit
val scores_to_csv_column : string * Namespace.t -> scores -> Csv.csv
type solution = {
mapping : (Free_variable.t * float) list;
weights : Maths.matrix;
intercept_lift : float;
(** The diff required to overestimate all measurements by the predictions.
This diff should be applied to the intercept parameter
when this solution is for the allocation costs. *)
scores : scores;
}
type solver =
| Ridge of {alpha : float}
| Lasso of {alpha : float; positive : bool}
| NNLS
type error_statistics
val pp_error_statistics : Format.formatter -> error_statistics -> unit
(** Compute prediction error *)
val compute_error_statistics :
predicted:Maths.matrix -> measured:Maths.matrix -> error_statistics
(** [make_problem ~data ~model ~overrides] makes a benchmark problem for a solver
from the workload [data] and the model [model]. [overrides] specify the variables
whose values are already known.
*)
val make_problem :
data:'workload Measure.workload_data ->
model:'workload Model.t ->
overrides:(Free_variable.t -> float option) ->
problem
(** [solve_problem problem solver] solves [problem] using [solver]. *)
val solve_problem : problem -> solver -> solution
val problem_to_csv : problem -> Csv.csv
val mapping_to_csv : (Free_variable.t * float) trace -> Csv.csv
val solution_to_csv : solution -> Csv.csv option
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