Solution¶
<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¶
Reference
Find the reference for the dummy.solution module here.
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(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(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¶
Reference
Find the reference for the ensemble.solution module here.
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(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(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(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(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¶
Reference
Find the reference for the linear_model.solution module here.
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(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(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¶
Reference
Find the reference for the neural_network.solution module here.
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(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(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¶
Reference
Find the reference for the tree.solution module here.
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(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(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: