Solution

Solution

Learn how to work with a 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

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py
[152.13348416]
Copy

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py
[200.87337372]
Copy

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py
[176.1]
Copy

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py
[206.11667725]
Copy

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py -max_iter 2500
[199.38432603]
Copy

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)
Copy

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": []
}
Copy

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]))
Copy

Run the script:

python main.py
[151.]
Copy

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