<Model>Solution
allows access to the solution of an sklearn.<Model>
. The fitted model is considered a "solution". You may create a solution object from a model and export it to a file. At any moment, the solution (fitted model) can be loaded from a file that holds the correct information.
Consider the same scripts shown in the model section and statistics section, which fit, predict and obtain statistics with the diabetes dataset. You may use the <Model>Solution
to obtain and write the solution of the model. After it has been exported, you can load the solution, create a fitted model from it, and make a basic prediction.
Dummy
Write the solution.
from nextmv_sklearn import dummy import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = dummy.DummyRegressorSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "strategy": null, "constant": null, "quantile": null }, "solution": { "class": { "module": "nextmv_sklearn.dummy.solution", "name": "DummyRegressorSolution" }, "attributes": { "constant_": [ [ 152.13348416289594 ] ], "n_features_in_": 10, "n_outputs_": 1 } }, "statistics": { "run": { "duration": 0.0009088516235351562 }, "result": { "custom": { "score": 0.0 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script:
Ensemble
Gradient boosting
Write the solution.
from nextmv_sklearn import ensemble import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = ensemble.GradientBoostingRegressorSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "loss": null, "learning_rate": null, "n_estimators": null, "subsample": null, "criterion": null, "min_samples_split": null, "min_samples_leaf": null, "min_weight_fraction_leaf": null, "max_depth": null, "min_impurity_decrease": null, "random_state": null, "max_features": null, "alpha": null, "max_leaf_nodes": null, "warm_start": null, "validation_fraction": null, "n_iter_no_change": null, "tol": null, "ccp_alpha": null }, "solution": { "class": { "module": "nextmv_sklearn.ensemble.solution", "name": "GradientBoostingRegressorSolution" }, "attributes": { "n_estimators_": 100, "n_trees_per_iteration_": 1, "oob_score_": 0.0, "train_score_": [ 5365.788686570168, ... 1195.018648214559, 1191.6744015438958 ], "init_": { "constant_": [ [ 152.13348416289594 ] ], "n_features_in_": 10, "n_outputs_": 1 }, "estimators_": [ { "max_features_": 10, "n_features_in_": 10, "n_outputs_": 1, "tree_": "gASVqwYAAAAA....QJR0lGJ1Yi4=" } ], "n_features_in_": 10, "max_features_": 10, "loss": "gASVYwEAAAA....AGgZiWgaiXVidWIu" } }, "statistics": { "run": { "duration": 0.06821298599243164 }, "result": { "custom": { "depth": 3, "feature_importances_": [ 0.04868904207548975, 0.013906683009910865, ... 0.041790175253534984 ], "score": 0.7990392018966865 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script:
Random forest
Write the solution.
from nextmv_sklearn import ensemble import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = ensemble.RandomForestRegressorSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "n_estimators": null, "criterion": null, "max_depth": null, "min_samples_split": null, "min_samples_leaf": null, "min_weight_fraction_leaf": null, "max_features": null, "max_leaf_nodes": null, "min_impurity_decrease": null, "bootstrap": null, "oob_score": null, "n_jobs": null, "random_state": null, "verbose": null, "warm_start": null, "ccp_alpha": null, "max_samples": null, "monotonic_cst": null }, "solution": { "class": { "module": "nextmv_sklearn.ensemble.solution", "name": "RandomForestRegressorSolution" }, "attributes": { "estimator_": { "max_features_": 0, "n_features_in_": 0, "n_outputs_": 0 }, "estimators_": [ { "max_features_": 10, "n_features_in_": 10, "n_outputs_": 1, "tree_": "gASVAHFAAAAAA....BtQJR0lGJ1Yi4=" } ], "n_features_in_": 10, "n_outputs_": 1, "oob_score_": 0.0 } }, "statistics": { "run": { "duration": 0.15402698516845703 }, "result": { "custom": { "feature_importances_": [ 0.05862192557250181, 0.012334872517744695, 0.28574949264657873, 0.09883856630804999, 0.047828611880482576, 0.05744602179282225, 0.05022167910320828, 0.0236904772411412, 0.2967779353746083, 0.06849041756286224 ], "score": 0.9184641072754306 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script:
Linear model
Write the solution.
from nextmv_sklearn import linear_model import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = linear_model.LinearRegressionSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "fit_intercept": null, "copy_X": null, "n_jobs": null, "positive": null }, "solution": { "class": { "module": "nextmv_sklearn.linear_model.solution", "name": "LinearRegressionSolution" }, "attributes": { "coef_": [ -10.009866299810263, -239.81564367242262, 519.8459200544598, 324.38464550232345, -792.1756385522301, 476.7390210052578, 101.04326793803399, 177.0632376713463, 751.273699557104, 67.62669218370486 ], "rank_": 10, "singular_": [ 2.006043556394722, 1.2216053690118982, 1.0981649507815319, 0.9774847330082032, 0.8137452864786221, 0.7763485529194514, 0.7325064179373295, 0.6585453943089912, 0.2798571500982059, 0.092524212112576 ], "intercept_": 152.13348416289597, "n_features_in_": 10 } }, "statistics": { "run": { "duration": 0.0015370845794677734 }, "result": { "custom": { "score": 0.5177484222203499 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script:
Neural network
Write the solution.
from nextmv_sklearn import neural_network import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = neural_network.MLPRegressorSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "hidden_layer_sizes": null, "activation": null, "solver": null, "alpha": null, "batch_size": null, "learning_rate": null, "learning_rate_init": null, "power_t": null, "max_iter": 2500, "shuffle": null, "random_state": null, "tol": null, "verbose": null, "warm_start": null, "momentum": null, "nesterovs_momentum": null, "early_stopping": null, "validation_fraction": null, "beta_1": null, "beta_2": null, "epsilon": null, "n_iter_no_change": null, "max_fun": null }, "solution": { "class": { "module": "nextmv_sklearn.neural_network.solution", "name": "MLPRegressorSolution" }, "attributes": { "loss_": 1446.176859835485, "best_loss_": 1445.790195444945, "loss_curve_": [ 14531.186602180687, ... 1446.024979302395, 1446.176859835485 ], "t_": 893724, "coefs_": [ [ [ -0.017255350152175514, ... 0.04931994332298196 ], ... ], [ [ -0.021388414548190945 ], ... ] ], "intercepts_": [ [ -0.03335657429574056, ... -0.03597971253262282 ], ... ], "n_features_in_": 10, "n_iter_": 2022, "n_layers_": 3, "n_outputs_": 1, "out_activation_": "identity" } }, "statistics": { "run": { "duration": 0.9377310276031494 }, "result": { "custom": { "score": 0.5123771786534843 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script:
Tree
Write the solution.
from nextmv_sklearn import tree import nextmv # Model and statistics code here. # Create the solution object from the model and construct an output. solution = tree.DecisionTreeRegressorSolution.from_model(fit) output = nextmv.Output( options=options, solution=solution.to_dict(), statistics=statistics, ) # Export the output, with the solution, to a file. file_path = "regressor.json" nextmv.write_local(output, file_path)
After running the script, inspect the output:
cat regressor.json { "options": { "criterion": "squared_error", "splitter": "best", "max_depth": null, "min_samples_split": null, "min_samples_leaf": null, "min_weight_fraction_leaf": null, "max_features": null, "random_state": null, "max_leaf_nodes": null, "min_impurity_decrease": null, "ccp_alpha": null }, "solution": { "class": { "module": "nextmv_sklearn.tree.solution", "name": "DecisionTreeRegressorSolution" }, "attributes": { "max_features_": 10, "n_features_in_": 10, "n_outputs_": 1, "tree_": "gASVMfUAAAAAAACMEnNrbGVhcm4udH...R0lGJ1Yi4=" } }, "statistics": { "run": { "duration": 0.0044820308685302734 }, "result": { "custom": { "depth": 20, "feature_importances_": [ 0.03869204610835461, 0.010414593479079177, 0.23623009367609873, 0.08523716847272961, 0.06178429111080888, 0.07221426644525238, 0.07449166752232912, 0.012499198167788545, 0.3394458949538394, 0.06899078006371957 ], "n_leaves": 432, "score": 1.0 } }, "series_data": {}, "schema": "v1" }, "assets": [] }
Load the solution and make a prediction.
import nextmv # Load the solution from the file. input = nextmv.load_local(path=file_path) loaded_solution = nextmv.from_dict(input.data["solution"]) # Convert the solution to a model and make a prediction. loaded_fit = loaded_solution.to_model() print(loaded_fit.predict(X[:1]))
Run the script: