pyinference.ml
(*****************************************************************************)
(* *)
(* Open Source License *)
(* Copyright (c) 2018 Dynamic Ledger Solutions, Inc. <contact@tezos.com> *)
(* Copyright (c) 2022 Nomadic Labs. <contact@nomadic-labs.com> *)
(* *)
(* Permission is hereby granted, free of charge, to any person obtaining a *)
(* copy of this software and associated documentation files (the "Software"),*)
(* to deal in the Software without restriction, including without limitation *)
(* the rights to use, copy, modify, merge, publish, distribute, sublicense, *)
(* and/or sell copies of the Software, and to permit persons to whom the *)
(* Software is furnished to do so, subject to the following conditions: *)
(* *)
(* The above copyright notice and this permission notice shall be included *)
(* in all copies or substantial portions of the Software. *)
(* *)
(* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR*)
(* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, *)
(* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL *)
(* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER*)
(* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING *)
(* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER *)
(* DEALINGS IN THE SOFTWARE. *)
(* *)
(*****************************************************************************)
module Numpy = struct
let transpose x =
let npy_transpose = Py.Module.get_function (Pyinit.numpy ()) "transpose" in
npy_transpose [|x|]
end
module LinearModel = struct
let assert_matrix_nontrivial (m : Scikit_matrix.t) =
let l, c = Scikit_matrix.shape m in
assert (l <> 0 && c <> 0)
let ridge ~(alpha : float) ?(fit_intercept : bool = false)
~(input : Scikit_matrix.t) ~(output : Scikit_matrix.t) () =
assert_matrix_nontrivial input ;
assert_matrix_nontrivial output ;
let input = Scikit_matrix.to_numpy input in
let output = Scikit_matrix.to_numpy output in
let ridge_object =
Py.Module.get_function_with_keywords
(Pyinit.linear_model ())
"Ridge"
[||]
[
("alpha", Py.Float.of_float alpha);
("fit_intercept", Py.Bool.of_bool fit_intercept);
]
in
let _ =
match Py.Object.get_attr_string ridge_object "fit" with
| None -> Stdlib.failwith "Scikit.LinearModel.ridge: method fit not found"
| Some meth -> Py.Callable.to_function meth [|input; output|]
in
match Py.Object.get_attr_string ridge_object "coef_" with
| None ->
Stdlib.failwith "Scikit.LinearModel.ridge: attribute coef_ not found"
| Some coef -> Scikit_matrix.of_numpy (Numpy.transpose coef)
let lasso ~(alpha : float) ?(fit_intercept : bool = false)
?(positive : bool = false) ~(input : Scikit_matrix.t)
~(output : Scikit_matrix.t) () =
assert_matrix_nontrivial input ;
assert_matrix_nontrivial output ;
let input = Scikit_matrix.to_numpy input in
let output = Scikit_matrix.to_numpy output in
let lasso_object =
Py.Module.get_function_with_keywords
(Pyinit.linear_model ())
"Lasso"
[||]
[
("alpha", Py.Float.of_float alpha);
("fit_intercept", Py.Bool.of_bool fit_intercept);
("positive", Py.Bool.of_bool positive);
]
in
let _ =
match Py.Object.get_attr_string lasso_object "fit" with
| None -> Stdlib.failwith "Scikit.LinearModel.lasso: method fit not found"
| Some meth -> Py.Callable.to_function meth [|input; output|]
in
match Py.Object.get_attr_string lasso_object "coef_" with
| None ->
Stdlib.failwith "Scikit.LinearModel.lasso: attribute coef_ not found"
| Some coef -> Scikit_matrix.of_numpy coef
let nnls ~(input : Scikit_matrix.t) ~(output : Scikit_matrix.t) =
assert_matrix_nontrivial input ;
assert_matrix_nontrivial output ;
let len = Scikit_matrix.dim1 output in
let input = Scikit_matrix.to_numpy input in
let output = Scikit_matrix.to_numpy output in
let output =
Py.Module.get_function
(Pyinit.numpy ())
"reshape"
[|output; Py.Int.of_int len|]
in
let nnls_outcome =
Py.Module.get_function (Pyinit.scipy_optimize ()) "nnls" [|input; output|]
in
let array = Py.Tuple.to_array nnls_outcome in
if Array.length array <> 2 then
Stdlib.failwith "Scikit.nnls: invalid outcome"
else
let res = array.(0) in
Scikit_matrix.of_numpy res
end
let predict_output ~(input : Scikit_matrix.t) ~(weights : Scikit_matrix.t) =
let weights = Scikit_matrix.to_numpy weights in
let input = Scikit_matrix.to_numpy input in
Py.Module.get_function (Pyinit.numpy ()) "matmul" [|input; weights|]
let r2_score ~output ~prediction =
let len = Scikit_matrix.dim1 output in
let output = Scikit_matrix.to_numpy output in
if len <= 1 then
(* The following warning will be raised from `r2_score` of Python. *)
(* `R^2 score is not well-defined with less than two samples.` *)
(* see https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score *)
(* For this case, we use `None` as the score. *)
None
else
Py.Module.get_function
(Pyinit.sklearn_metrics ())
"r2_score"
[|output; prediction|]
|> Py.Float.to_float |> Option.some
let rmse_score ~output ~prediction =
let output = Scikit_matrix.to_numpy output in
Py.Module.get_function_with_keywords
(Pyinit.sklearn_metrics ())
"mean_squared_error"
[|output; prediction|]
[("squared", Py.Bool.f)]
|> Py.Float.to_float
let benchmark_score ~input ~output =
let input = Scikit_matrix.to_numpy input in
let output = Scikit_matrix.to_numpy_vector output in
let model =
Py.Module.get_function (Pyinit.statsmodels_api ()) "OLS" [|output; input|]
in
let result =
Py.Module.get_function model "fit" [||]
(* We couldn't get tvalue from fit_reguralized for now *)
(* Py.Module.get_function_with_keywords model "fit_regularized" [||]
[("method",Py.String.of_string "elastic_net");
("alpha", Py.Float.of_float 0.03);
("L1_wt", Py.Float.of_float 1.)] *)
in
let tvalues =
Py.Object.find_attr_string result "tvalues" |> Scikit_matrix.of_numpy
in
let params =
Py.Object.find_attr_string result "params" |> Scikit_matrix.of_numpy
in
(params, tvalues)