test_search.py

"""
Module for testing the model_selection.search module.
"""


import os

import numpy as np
import pytest
from scipy.stats import randint, uniform

from surprise import Dataset, Reader, SVD
from surprise.model_selection import (
    cross_validate,
    GridSearchCV,
    KFold,
    PredefinedKFold,
    RandomizedSearchCV,
)


# Tests for GridSearchCV class
def test_basesearchcv_parse_options():
    """Check that _parse_options explodes nested dictionaries."""
    param_grid = {
        "bsl_options": {"method": ["als", "sgd"], "reg": [1, 2]},
        "k": [2, 3],
        "sim_options": {
            "name": ["msd", "cosine"],
            "min_support": [1, 5],
            "user_based": [False],
        },
    }
    gs = GridSearchCV(SVD, param_grid)
    parsed_params = gs.param_grid
    assert len(parsed_params["sim_options"]) == 4  # 2 method x 2 reg
    assert len(parsed_params["bsl_options"]) == 4  # 2 method x 2 reg


def test_gridsearchcv_parameter_combinations():
    """Make sure that parameter_combinations attribute is correct (has correct
    size).  Dict parameters like bsl_options and sim_options require special
    treatment in the param_grid argument. We here test both in one shot with
    KNNBaseline."""

    param_grid = {
        "bsl_options": {"method": ["als", "sgd"], "reg": [1, 2]},
        "k": [2, 3],
        "sim_options": {
            "name": ["msd", "cosine"],
            "min_support": [1, 5],
            "user_based": [False],
        },
    }

    gs = GridSearchCV(SVD, param_grid)
    assert len(gs.param_combinations) == 32


def test_gridsearchcv_best_estimator(u1_ml100k):
    """Ensure that the best estimator is the one giving the best score (by
    re-running it)"""

    param_grid = {
        "n_epochs": [5],
        "lr_all": [0.002, 0.005],
        "reg_all": [0.4, 0.6],
        "n_factors": [1],
        "init_std_dev": [0],
    }
    gs = GridSearchCV(
        SVD, param_grid, measures=["mae"], cv=PredefinedKFold(), joblib_verbose=100
    )
    gs.fit(u1_ml100k)
    best_estimator = gs.best_estimator["mae"]

    # recompute MAE of best_estimator
    mae = cross_validate(
        best_estimator, u1_ml100k, measures=["MAE"], cv=PredefinedKFold()
    )["test_mae"]

    assert mae == gs.best_score["mae"]


def test_gridsearchcv_same_splits():
    """Ensure that all parameter combinations are tested on the same splits (we
    check their RMSE scores are the same once averaged over the splits, which
    should be enough)."""

    data_file = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(data_file, reader=Reader("ml-100k"))
    kf = KFold(3, shuffle=True, random_state=4)

    # all RMSE should be the same (as param combinations are the same)
    param_grid = {
        "n_epochs": [5],
        "lr_all": [0.2, 0.2],
        "reg_all": [0.4, 0.4],
        "n_factors": [5],
        "random_state": [0],
    }
    gs = GridSearchCV(SVD, param_grid, measures=["RMSE"], cv=kf, n_jobs=1)
    gs.fit(data)

    rmse_scores = [m for m in gs.cv_results["mean_test_rmse"]]
    assert len(set(rmse_scores)) == 1  # assert rmse_scores are all equal

    # Note: actually, even when setting random_state=None in kf, the same folds
    # are used because we use product(param_comb, kf.split(...)). However, it's
    # needed to have the same folds when calling fit again:
    gs.fit(data)
    rmse_scores += [m for m in gs.cv_results["mean_test_rmse"]]
    assert len(set(rmse_scores)) == 1  # assert rmse_scores are all equal


def test_gridsearchcv_cv_results():
    """Test the cv_results attribute"""

    f = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(f, Reader("ml-100k"))
    kf = KFold(3, shuffle=True, random_state=4)
    param_grid = {
        "n_epochs": [5],
        "lr_all": [0.2, 0.4],
        "reg_all": [0.4, 0.6],
        "n_factors": [5],
        "random_state": [0],
    }
    gs = GridSearchCV(
        SVD,
        param_grid,
        measures=["RMSE", "mae", "fcp"],
        cv=kf,
        return_train_measures=True,
    )
    gs.fit(data)

    # test keys split*_test_rmse, mean and std dev.
    assert gs.cv_results["split0_test_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["split1_test_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["split2_test_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["mean_test_rmse"].shape == (4,)  # 4 param comb.
    assert np.allclose(
        gs.cv_results["mean_test_rmse"],
        np.mean(
            [
                gs.cv_results["split0_test_rmse"],
                gs.cv_results["split1_test_rmse"],
                gs.cv_results["split2_test_rmse"],
            ],
            axis=0,
        ),
    )
    assert np.allclose(
        gs.cv_results["std_test_rmse"],
        np.std(
            [
                gs.cv_results["split0_test_rmse"],
                gs.cv_results["split1_test_rmse"],
                gs.cv_results["split2_test_rmse"],
            ],
            axis=0,
        ),
    )

    # test keys split*_train_mae, mean and std dev.
    assert gs.cv_results["split0_train_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["split1_train_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["split2_train_rmse"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["mean_train_rmse"].shape == (4,)  # 4 param comb.
    assert np.allclose(
        gs.cv_results["mean_train_rmse"],
        np.mean(
            [
                gs.cv_results["split0_train_rmse"],
                gs.cv_results["split1_train_rmse"],
                gs.cv_results["split2_train_rmse"],
            ],
            axis=0,
        ),
    )
    assert np.allclose(
        gs.cv_results["std_train_rmse"],
        np.std(
            [
                gs.cv_results["split0_train_rmse"],
                gs.cv_results["split1_train_rmse"],
                gs.cv_results["split2_train_rmse"],
            ],
            axis=0,
        ),
    )

    # test fit and train times dimensions.
    assert gs.cv_results["mean_fit_time"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["std_fit_time"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["mean_test_time"].shape == (4,)  # 4 param comb.
    assert gs.cv_results["std_test_time"].shape == (4,)  # 4 param comb.

    assert gs.cv_results["params"] is gs.param_combinations

    # assert that best parameter in gs.cv_results['rank_test_measure'] is
    # indeed the best_param attribute.
    best_index = np.argmin(gs.cv_results["rank_test_rmse"])
    assert gs.cv_results["params"][best_index] == gs.best_params["rmse"]
    best_index = np.argmin(gs.cv_results["rank_test_mae"])
    assert gs.cv_results["params"][best_index] == gs.best_params["mae"]
    best_index = np.argmin(gs.cv_results["rank_test_fcp"])
    assert gs.cv_results["params"][best_index] == gs.best_params["fcp"]


def test_gridsearchcv_refit(u1_ml100k):
    """Test refit function of GridSearchCV."""

    data_file = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(data_file, Reader("ml-100k"))

    param_grid = {
        "n_epochs": [5],
        "lr_all": [0.002, 0.005],
        "reg_all": [0.4, 0.6],
        "n_factors": [2],
    }

    # assert gs.fit() and gs.test will use best estimator for mae (first
    # appearing in measures)
    gs = GridSearchCV(SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=True)
    gs.fit(data)
    gs_preds = gs.test(data.construct_testset(data.raw_ratings))
    mae_preds = gs.best_estimator["mae"].test(data.construct_testset(data.raw_ratings))
    assert gs_preds == mae_preds

    # assert gs.fit() and gs.test will use best estimator for rmse
    gs = GridSearchCV(SVD, param_grid, measures=["mae", "rmse"], cv=2, refit="rmse")
    gs.fit(data)
    gs_preds = gs.test(data.construct_testset(data.raw_ratings))
    rmse_preds = gs.best_estimator["rmse"].test(
        data.construct_testset(data.raw_ratings)
    )
    assert gs_preds == rmse_preds
    # test that predict() can be called
    gs.predict(2, 4)

    # assert test() and predict() cannot be used when refit is false
    gs = GridSearchCV(SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=False)
    gs.fit(data)
    with pytest.raises(ValueError):
        gs_preds = gs.test(data.construct_testset(data.raw_ratings))
    with pytest.raises(ValueError):
        gs.predict("1", "2")

    # test that error is raised if used with load_from_folds
    gs = GridSearchCV(SVD, param_grid, measures=["mae", "rmse"], cv=2, refit=True)
    with pytest.raises(ValueError):
        gs.fit(u1_ml100k)


# Tests for RandomizedSearchCV


def test_randomizedsearchcv_parameter_combinations_all_lists():
    """Ensure the parameter_combinations attribute populates correctly by
    checking its length."""
    param_distributions = {
        "bsl_options": {"method": ["als", "sgd"], "reg": [1, 2]},
        "k": [2, 3],
        "sim_options": {
            "name": ["msd", "cosine"],
            "min_support": [1, 5],
            "user_based": [False],
        },
    }
    rs = RandomizedSearchCV(SVD, param_distributions, n_iter=10)
    assert len(rs.param_combinations) == 10


def test_randomizedsearchcv_parameter_combinations_with_distribution():
    """Ensure the parameter_combinations attribute populates correctly by
    checking its length."""
    param_distributions = {
        "bsl_options": {"method": ["als", "sgd"], "reg": [1, 2]},
        "k": randint(2, 4),  # min inclusive, max exclusive
        "sim_options": {
            "name": ["msd", "cosine"],
            "min_support": [1, 5],
            "user_based": [False],
        },
    }
    rs = RandomizedSearchCV(SVD, param_distributions, n_iter=10)
    assert len(rs.param_combinations) == 10


def test_randomizedsearchcv_best_estimator(u1_ml100k):
    """Ensure that the best estimator is the one that gives the best score (by
    re-running it)"""

    param_distributions = {
        "n_epochs": [5],
        "lr_all": uniform(0.002, 0.003),
        "reg_all": uniform(0.04, 0.02),
        "n_factors": [1],
        "init_std_dev": [0],
    }
    rs = RandomizedSearchCV(
        SVD,
        param_distributions,
        measures=["mae"],
        cv=PredefinedKFold(),
        joblib_verbose=100,
    )
    rs.fit(u1_ml100k)
    best_estimator = rs.best_estimator["mae"]

    # recompute MAE of best_estimator
    mae = cross_validate(
        best_estimator, u1_ml100k, measures=["MAE"], cv=PredefinedKFold()
    )["test_mae"]

    assert mae == rs.best_score["mae"]


def test_randomizedsearchcv_same_splits():
    """Ensure that all parameter combinations are tested on the same splits (we
    check their RMSE scores are the same once averaged over the splits, which
    should be enough). We use as much parallelism as possible."""

    data_file = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(data_file, reader=Reader("ml-100k"))
    kf = KFold(3, shuffle=True, random_state=4)

    # all RMSE should be the same (as param combinations are the same)
    param_distributions = {
        "n_epochs": [5],
        "lr_all": uniform(0.2, 0),
        "reg_all": uniform(0.4, 0),
        "n_factors": [5],
        "random_state": [0],
    }
    rs = RandomizedSearchCV(
        SVD, param_distributions, measures=["RMSE"], cv=kf, n_jobs=1
    )
    rs.fit(data)

    rmse_scores = [m for m in rs.cv_results["mean_test_rmse"]]
    assert len(set(rmse_scores)) == 1  # assert rmse_scores are all equal

    # Note: actually, even when setting random_state=None in kf, the same folds
    # are used because we use product(param_comb, kf.split(...)). However, it's
    # needed to have the same folds when calling fit again:
    rs.fit(data)
    rmse_scores += [m for m in rs.cv_results["mean_test_rmse"]]
    assert len(set(rmse_scores)) == 1  # assert rmse_scores are all equal


def test_randomizedsearchcv_cv_results():
    """Test the cv_results attribute"""

    f = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(f, Reader("ml-100k"))
    kf = KFold(3, shuffle=True, random_state=4)
    param_distributions = {
        "n_epochs": [5],
        "lr_all": uniform(0.2, 0.3),
        "reg_all": uniform(0.4, 0.3),
        "n_factors": [5],
        "random_state": [0],
    }
    n_iter = 5
    rs = RandomizedSearchCV(
        SVD,
        param_distributions,
        n_iter=n_iter,
        measures=["RMSE", "mae"],
        cv=kf,
        return_train_measures=True,
    )
    rs.fit(data)

    # test keys split*_test_rmse, mean and std dev.
    assert rs.cv_results["split0_test_rmse"].shape == (n_iter,)
    assert rs.cv_results["split1_test_rmse"].shape == (n_iter,)
    assert rs.cv_results["split2_test_rmse"].shape == (n_iter,)
    assert rs.cv_results["mean_test_rmse"].shape == (n_iter,)
    assert np.allclose(
        rs.cv_results["mean_test_rmse"],
        np.mean(
            [
                rs.cv_results["split0_test_rmse"],
                rs.cv_results["split1_test_rmse"],
                rs.cv_results["split2_test_rmse"],
            ],
            axis=0,
        ),
    )
    assert np.allclose(
        rs.cv_results["std_test_rmse"],
        np.std(
            [
                rs.cv_results["split0_test_rmse"],
                rs.cv_results["split1_test_rmse"],
                rs.cv_results["split2_test_rmse"],
            ],
            axis=0,
        ),
    )

    # test keys split*_train_mae, mean and std dev.
    assert rs.cv_results["split0_train_rmse"].shape == (n_iter,)
    assert rs.cv_results["split1_train_rmse"].shape == (n_iter,)
    assert rs.cv_results["split2_train_rmse"].shape == (n_iter,)
    assert rs.cv_results["mean_train_rmse"].shape == (n_iter,)
    assert np.allclose(
        rs.cv_results["mean_train_rmse"],
        np.mean(
            [
                rs.cv_results["split0_train_rmse"],
                rs.cv_results["split1_train_rmse"],
                rs.cv_results["split2_train_rmse"],
            ],
            axis=0,
        ),
    )
    assert np.allclose(
        rs.cv_results["std_train_rmse"],
        np.std(
            [
                rs.cv_results["split0_train_rmse"],
                rs.cv_results["split1_train_rmse"],
                rs.cv_results["split2_train_rmse"],
            ],
            axis=0,
        ),
    )

    # test fit and train times dimensions.
    assert rs.cv_results["mean_fit_time"].shape == (n_iter,)
    assert rs.cv_results["std_fit_time"].shape == (n_iter,)
    assert rs.cv_results["mean_test_time"].shape == (n_iter,)
    assert rs.cv_results["std_test_time"].shape == (n_iter,)

    assert rs.cv_results["params"] is rs.param_combinations

    # assert that best parameter in rs.cv_results['rank_test_measure'] is
    # indeed the best_param attribute.
    best_index = np.argmin(rs.cv_results["rank_test_rmse"])
    assert rs.cv_results["params"][best_index] == rs.best_params["rmse"]
    best_index = np.argmin(rs.cv_results["rank_test_mae"])
    assert rs.cv_results["params"][best_index] == rs.best_params["mae"]


def test_randomizedsearchcv_refit(u1_ml100k):
    """Test refit method of RandomizedSearchCV class."""

    data_file = os.path.join(os.path.dirname(__file__), "./u1_ml100k_test")
    data = Dataset.load_from_file(data_file, Reader("ml-100k"))

    param_distributions = {
        "n_epochs": [5],
        "lr_all": uniform(0.002, 0.003),
        "reg_all": uniform(0.4, 0.2),
        "n_factors": [2],
    }

    # assert rs.fit() and rs.test will use best estimator for mae (first
    # appearing in measures)
    rs = RandomizedSearchCV(
        SVD, param_distributions, measures=["mae", "rmse"], cv=2, refit=True
    )
    rs.fit(data)
    rs_preds = rs.test(data.construct_testset(data.raw_ratings))
    mae_preds = rs.best_estimator["mae"].test(data.construct_testset(data.raw_ratings))
    assert rs_preds == mae_preds

    # assert rs.fit() and rs.test will use best estimator for rmse
    rs = RandomizedSearchCV(
        SVD, param_distributions, measures=["mae", "rmse"], cv=2, refit="rmse"
    )
    rs.fit(data)
    rs_preds = rs.test(data.construct_testset(data.raw_ratings))
    rmse_preds = rs.best_estimator["rmse"].test(
        data.construct_testset(data.raw_ratings)
    )
    assert rs_preds == rmse_preds
    # test that predict() can be called
    rs.predict(2, 4)

    # assert test() and predict() cannot be used when refit is false
    rs = RandomizedSearchCV(
        SVD, param_distributions, measures=["mae", "rmse"], cv=2, refit=False
    )
    rs.fit(data)
    with pytest.raises(ValueError):
        rs.test(data.construct_testset(data.raw_ratings))
    with pytest.raises(ValueError):
        rs.predict("1", "2")

    # test that error is raised if used with load_from_folds
    rs = RandomizedSearchCV(
        SVD, param_distributions, measures=["mae", "rmse"], cv=2, refit=True
    )
    with pytest.raises(ValueError):
        rs.fit(u1_ml100k)