How to use test_outer method in hypothesis

Best Python code snippet using hypothesis

test_spherical_tensor.py

Source:test_spherical_tensor.py

1import torch2from torch_gauge.o3 import O3Tensor, SphericalTensor3def test_spherical_tensor_creation1d():4    dten = torch.rand(4, 6, 12, 101, 7)5    metadata = torch.LongTensor([[7, 9, 1, 5, 3]])6    SphericalTensor(dten, (3,), metadata)7    return 08def test_spherical_tensor_creation2d():9    dten = torch.rand(4, 6, 12, 101, 7)10    metadata = torch.LongTensor([[1, 2, 1, 0, 0], [7, 9, 1, 5, 3]])11    SphericalTensor(dten, (2, 3), metadata)12    return 013def test_spherical_tensor_layout1d():14    dten = torch.rand(4, 19)15    metadata = torch.LongTensor([[1, 2, 1, 1]])16    test_sp_ten = SphericalTensor(dten, (1,), metadata)17    assert test_sp_ten.num_channels == (5,)18    assert torch.all(19        test_sp_ten.rep_layout[0][0].eq(20            torch.LongTensor([0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3])21        )22    )23    assert torch.all(24        test_sp_ten.rep_layout[0][1].eq(25            torch.LongTensor(26                [0, -1, -1, 0, 0, 1, 1, -2, -1, 0, 1, 2, -3, -2, -1, 0, 1, 2, 3]27            )28        )29    )30    assert torch.all(31        test_sp_ten.rep_layout[0][2].eq(32            torch.LongTensor([0, 1, 2, 1, 2, 1, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4])33        )34    )35    return 036def test_spherical_tensor_scalar_product_1d():37    dten = torch.tensor(38        [39            [1.1, 2.2, 0.5, 0.6, -0.6, 0.9, 0.7, 0.3, 0.1, 0.2, 0.3, 0.4, 0.5],40            [0.0, 0.0, 0.2, 0.0, 0.2, 0.0, 0.3, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0],41        ]42    )43    metadata = torch.LongTensor([[2, 2, 1]])44    test_sp_ten = SphericalTensor(dten, (1,), metadata)45    scale_ten = torch.tensor([[2.0, 0.5, 3.0, 4.0, 1.0], [101.0, 7.0, 8.0, 82.0, 4.0]])46    dten_outplace = test_sp_ten.scalar_mul(scale_ten)47    assert torch.allclose(48        dten_outplace.ten,49        torch.tensor(50            [51                [2.2, 1.1, 1.5, 2.4, -1.8, 3.6, 2.1, 1.2, 0.1, 0.2, 0.3, 0.4, 0.5],52                [0.0, 0.0, 1.6, 0.0, 1.6, 0.0, 2.4, 0.0, 4.0, 4.0, 4.0, 4.0, 4.0],53            ]54        ),55        atol=1e-10,56        rtol=1e-7,57    )58    assert torch.all(dten_outplace.rep_layout[0].eq(test_sp_ten.rep_layout[0]))59    dten_inplace = test_sp_ten.scalar_mul(scale_ten, inplace=True)60    assert torch.allclose(61        dten_inplace.ten,62        torch.tensor(63            [64                [2.2, 1.1, 1.5, 2.4, -1.8, 3.6, 2.1, 1.2, 0.1, 0.2, 0.3, 0.4, 0.5],65                [0.0, 0.0, 1.6, 0.0, 1.6, 0.0, 2.4, 0.0, 4.0, 4.0, 4.0, 4.0, 4.0],66            ]67        ),68        atol=1e-10,69        rtol=1e-7,70    )71    assert torch.all(dten_inplace.rep_layout[0].eq(test_sp_ten.rep_layout[0]))72def test_spherical_tensor_scalar_product_2d():73    metadata2d = torch.LongTensor([[2, 1], [1, 1]])74    test_2d = SphericalTensor(75        torch.tensor(76            [77                [0.1, 0.3, 0.3, 0.3],78                [0.2, -0.7, 0.1, -0.2],79                [0.3, 0.2, -0.3, 0.4],80                [0.2, 0.3, 0.4, -0.5],81                [0.1, 0.2, 0.3, 0.4],82            ]83        ),84        (85            0,86            1,87        ),88        metadata2d,89    )90    scale_2d = torch.tensor(91        [92            [1.0, 2.0],93            [3.0, 4.0],94            [5.0, 6.0],95        ]96    )97    out_ten = test_2d.scalar_mul(scale_2d).ten98    ref_ten = torch.tensor(99        [100            [0.1, 0.6, 0.6, 0.6],101            [0.6, -2.8, 0.4, -0.8],102            [1.5, 1.2, -1.8, 2.4],103            [1.0, 1.8, 2.4, -3.0],104            [0.5, 1.2, 1.8, 2.4],105        ]106    )107    assert torch.allclose(out_ten, ref_ten, atol=1e-7, rtol=1e-5)108def test_spherical_tensor_dot_product():109    dten1 = torch.rand(4, 6, 1, 101, 7)110    metadata = torch.LongTensor([[7, 9, 1, 5, 3]])111    test1 = SphericalTensor(dten1, (3,), metadata)112    dten2 = torch.rand(4, 6, 12, 101, 7)113    metadata = torch.LongTensor([[1, 2, 1, 0, 0], [7, 9, 1, 5, 3]])114    test2 = SphericalTensor(dten2, (2, 3), metadata)115    test_dot = test2.dot(test1, dim=3)116    assert torch.all(test_dot.ten.eq(test1.ten.mul(test2.ten).sum(3)))117    assert test_dot.shape == (4, 6, 12, 7)118    assert test_dot.rep_dims == (2,)119    assert torch.all(test_dot.metadata.eq(torch.LongTensor([[1, 2, 1, 0, 0]])))120    assert torch.all(test_dot.rep_layout[0].eq(test2.rep_layout[0]))121def test_spherical_tensor_rep_dot():122    # Minimal 1d example123    metadata = torch.LongTensor([[1, 2]])124    test_1d_1 = SphericalTensor(125        torch.tensor([0.2, 0.1, 0.2, 0.3, 0.2, 0.3, 0.4]), (0,), metadata126    )127    test_1d_2 = SphericalTensor(128        torch.tensor([0.4, 0.2, 0.3, -0.4, -0.4, -0.3, 0.4]), (0,), metadata129    )130    test_1d_out = test_1d_1.rep_dot(test_1d_2, dim=0)131    assert torch.allclose(132        test_1d_out,133        torch.tensor([0.08, -0.19, 0.14]),134        atol=1e-9,135        rtol=1e-7,136    )137    # 2d shape test138    dten1 = torch.rand(4, 6, 1, 101, 7)139    metadata = torch.LongTensor([[7, 9, 1, 5, 3]])140    test1 = SphericalTensor(dten1, (3,), metadata)141    dten2 = torch.rand(4, 6, 12, 101, 7)142    metadata = torch.LongTensor([[1, 2, 1, 0, 0], [7, 9, 1, 5, 3]])143    test2 = SphericalTensor(dten2, (2, 3), metadata)144    test_dot = test2.rep_dot(test1, dim=3)145    assert test_dot.shape == (4, 6, 12, 25, 7)146    assert test_dot.rep_dims == (2,)147    assert torch.all(test_dot.metadata.eq(torch.LongTensor([[1, 2, 1, 0, 0]])))148    assert torch.all(test_dot.rep_layout[0].eq(test2.rep_layout[0]))149    # When L=0 entries are positive, self rep-dot should return a150    # tensor almost the same as the invariant content within threshold _norm_eps151    test3 = SphericalTensor(152        torch.rand(32, 11, 5, 101, 7), (3,), torch.LongTensor([[7, 9, 1, 5, 3]])153    )154    dot_3 = test3.rep_dot(test3, dim=3)155    assert torch.allclose(156        dot_3,157        test3.invariant().pow(2),158        atol=1e-4,159        rtol=1e-3,160    )161def test_spherical_tensor_rep_outer():162    dten1 = torch.rand(101)163    metadata = torch.LongTensor([[7, 9, 1, 5, 3]])164    test1 = SphericalTensor(dten1, (0,), metadata)165    dten2 = torch.rand(101)166    test2 = SphericalTensor(dten2, (0,), metadata)167    test_outer = test1.rep_outer(test2)168    assert test_outer.shape == (101, 101)169    assert torch.all(test_outer.ten.eq(torch.outer(dten1, dten2)))170    assert torch.all(171        test_outer.metadata.eq(torch.tensor([[7, 9, 1, 5, 3], [7, 9, 1, 5, 3]]))172    )173    test3 = SphericalTensor(torch.rand(4, 6, 12, 101, 7), (3,), metadata)174    test4 = SphericalTensor(175        torch.rand(4, 6, 12, 12, 7), (3,), torch.LongTensor([[1, 2, 1, 0, 0]])176    )177    test_outer = test3.rep_outer(test4)178    assert test_outer.shape == (4, 6, 12, 101, 12, 7)179    assert test_outer.rep_dims == (3, 4)180    assert torch.all(181        test_outer.metadata.eq(torch.tensor([[7, 9, 1, 5, 3], [1, 2, 1, 0, 0]]))182    )183def test_spherical_tensor_invariant_contraction():184    metadata1d = torch.LongTensor([[2, 2]])185    test_1d = SphericalTensor(186        torch.tensor([0.2, -0.7, 0.1, -0.2, 0.3, 0.2, -0.3, 0.4]), (0,), metadata1d187    )188    assert torch.allclose(189        test_1d.invariant(),190        torch.tensor([0.2, -0.7, 0.4358, 0.4898]),191        atol=1e-4,192        rtol=0,193    )194    metadata2d = torch.LongTensor([[1, 1], [1, 1]])195    test_2d = SphericalTensor(196        torch.tensor(197            [198                [0.2, -0.7, 0.1, -0.2],199                [0.3, 0.2, -0.3, 0.4],200                [0.2, 0.3, 0.4, -0.5],201                [0.1, 0.2, 0.3, 0.4],202            ]203        ),204        (205            0,206            1,207        ),208        metadata2d,209    )210    assert torch.allclose(211        test_2d.invariant(),212        torch.tensor([[0.2, 0.73484], [0.37416, 1.03923]]),213        atol=1e-4,214        rtol=0,215    )216def test_spherical_tensor_fold_dim():217    metadata = torch.LongTensor([[8, 4, 2, 4, 0]])218    test = SphericalTensor(torch.rand(4, 6, 12, 58, 7), (3,), metadata)219    folded = test.fold(stride=2)220    assert folded.shape == (4, 6, 12, 29, 2, 7)221    assert torch.all(folded.metadata[0].eq(torch.LongTensor([4, 2, 1, 2, 0])))222    assert folded.num_channels == (9,)223    # Test error capturing224    metadata = torch.LongTensor([[8, 4, 2, 3, 1]])225    test = SphericalTensor(torch.rand(4, 6, 12, 60, 7), (3,), metadata)226    try:227        test.fold(stride=2)228    except AssertionError as e:229        assert (230            str(e)231            == f"The number of channels for the SphericalTensor to be folded must be multiples of "232            "stride, got (tensor([[8, 4, 2, 3, 1]]), 2) instead"233        )234def test_to_o3():235    dten1 = torch.rand(4, 6, 1, 101, 7)236    metadata = torch.LongTensor([[7, 9, 1, 5, 3]])237    test1 = SphericalTensor(dten1, (3,), metadata)238    o3_ten1 = O3Tensor.from_so3(test1, parity=1)239    assert o3_ten1.rep_dims == (3,)240    assert torch.all(241        o3_ten1.metadata.eq(torch.LongTensor([[7, 0, 9, 0, 1, 0, 5, 0, 3, 0]]))242    )243    assert torch.all(o3_ten1.rep_layout[0][3].eq(torch.ones(101, dtype=torch.long)))244    assert torch.all(o3_ten1.rep_layout[0][:3].eq(test1.rep_layout[0]))245    o3_ten2 = O3Tensor.from_so3(test1, parity=-1)246    assert o3_ten2.rep_dims == (3,)247    assert torch.all(248        o3_ten2.metadata.eq(torch.LongTensor([[0, 7, 0, 9, 0, 1, 0, 5, 0, 3]]))249    )250    assert torch.all(o3_ten2.rep_layout[0][3].eq(-torch.ones(101, dtype=torch.long)))...

helpers.py

Source:helpers.py

1# import libraries2import numpy as np3from sklearn.utils import shuffle4from sklearn.model_selection import GridSearchCV, GroupKFold, StratifiedKFold5import matplotlib.pyplot as plt6from sklearn.linear_model import LogisticRegression7from sklearn.model_selection import train_test_split8from sklearn.calibration import CalibratedClassifierCV, calibration_curve9#from keras.models import Sequential10#from keras.layers import Dense11#from keras.layers import Flatten12#from keras.layers import Dropout13#from keras.layers.convolutional import Conv1D14#from keras.layers.convolutional import MaxPooling1D15def plot_calibration_curve(X, y, model, model_name, outer_cv):16    """17    Plot calibration curve for model w/o and with calibration. 18    """19    # Calibrated with isotonic/sigmoid calibration20    isotonic = CalibratedClassifierCV(model, cv=outer_cv, method='isotonic')21    sigmoid = CalibratedClassifierCV(model, cv=outer_cv, method='sigmoid')22    lr = LogisticRegression(C=1.) # baseline23    fig = plt.figure(figsize=(10, 10))24    ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)25    ax2 = plt.subplot2grid((3, 1), (2, 0))26    ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")27    28    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)29    for clf, name in [(lr, 'Logistic'), (model, model_name), (isotonic, model_name + ' + Isotonic'), 30                      (sigmoid, model_name + ' + Sigmoid')]:31        clf.fit(X_train, y_train)32        y_pred = clf.predict(X_test)33        if hasattr(clf, "predict_proba"):34            prob_pos = clf.predict_proba(X_test)[:, 1]35        else:  # use decision function36            prob_pos = clf.decision_function(X_test)37            prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())38        fraction_of_positives, mean_predicted_value = calibration_curve(y_test, prob_pos, n_bins=10)39        ax1.plot(mean_predicted_value, fraction_of_positives, "s-",40                 label=name)41        ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,42                 histtype="step", lw=2)43    ax1.set_ylabel("Fraction of positives")44    ax1.set_ylim([-0.05, 1.05])45    ax1.legend(loc="lower right")46    ax1.set_title('Calibration plots  (reliability curve)')47    ax2.set_xlabel("Mean predicted value")48    ax2.set_ylabel("Count")49    ax2.legend(loc="upper center", ncol=2)50    51    fig.savefig('calibration'+model_name+'.png', dpi=300)52    #plt.tight_layout()53def process_testdata(file):54    """55    Process the testdata from an .xlsx file into a transformed np.array that is ready for predictions.56    """57    # import libraries58    import re59    import pandas as pd60    from scipy import signal, integrate61    # get the data62    raw_data = pd.ExcelFile(file)63    sheetname = re.search('data/.+xlsx', file).group(0)[5:-5]64    test_data = np.matrix(raw_data.parse(sheetname, header=3)).T[1:,0:58] # first column (thus row in DF) is timestamp65    66    # get blanks and substract67    blanks = np.concatenate((test_data[34, :], test_data[35, :], test_data[46, :], test_data[47, :], test_data[58, :],68                             test_data[59, :], test_data[70, :], test_data[71, :]), axis=0)69    blanks_median = np.median(blanks, axis=0)70    test_data = np.delete(test_data, obj=[34, 35, 46, 47, 58, 59, 70, 71], axis=0)71    processed_data = np.asarray(test_data) #- blanks_median72    # transform the data73    transformed_sav1 = signal.savgol_filter(processed_data, window_length=11, polyorder=5, deriv=1)74    transformed_sav5 = signal.savgol_filter(processed_data, window_length=21, polyorder=2, deriv=2)75    transformed_int = np.zeros((processed_data.shape[0], processed_data.shape[1]))76    for i in range(transformed_int.shape[0]):77        transformed_int[i,:] = integrate.cumtrapz(processed_data[i,:], initial=0)78    79    return processed_data, transformed_sav1, transformed_sav5, transformed_int, blanks_median80def predict_testdata(trained_model, testdata, name):81    """82    Predict testcases on processed/transformed testdata for a given model.83    """84    import pandas as pd85    preds = trained_model.predict(testdata)86    probs = trained_model.predict_proba(testdata)87    probs = [probs[i][1] for i in range(probs.shape[0])]88    predictions_df = pd.DataFrame({name+'_probability': probs, name+'_prediction': preds})89    return predictions_df90def NestedGroupKFoldProba(model, X, y, parameter_grid, groups, n_classes, scorer, 91                     inner_cv=GroupKFold(n_splits=4), outer_cv=GroupKFold(n_splits=4), weights=[], cpus=1):92    """93    Implements a nested version of GroupKFold cross-validation using GridSearchCV to evaluate models 94    that need hyperparameter tuning in settings where different groups exist in the available data.95    96    Dependencies: sklearn.model_selection, numpy97    98    Input:99    - X, y: features and labels (must be NumPy arrays).100    - model, parameter_grid: the model instance and its parameter grid to be optimized.101    - groups: the groups to use in both inner- and outer loop.102    - n_classes: the number of classes in the prediction problem103    - scorer: the scoring to use in inner loop.104    - inner_cv, outer_cv: the iterators for both CV-loops (default: GroupKFold(n_splits=4))105    - weights: sample weights to account for more important samples.106    107    Output: cross-validated predicted class probabilities108    """109    # define empty matrix to store performances (n CV runs and four performance metrics)110    probabilities = np.zeros((X.shape[0], n_classes))111    preds = np.zeros(X.shape[0])112    113    # define outer loop114    for train_outer, test_outer in outer_cv.split(X, y, groups):115        X_train, X_test = X[train_outer], X[test_outer]116        y_train, y_test = y[train_outer], y[test_outer]117        groups_train, groups_test = groups[train_outer], groups[test_outer]118        119        # define inner loop (in GridSearchCV)120        tuned_model = GridSearchCV(model, cv=inner_cv, param_grid=parameter_grid, scoring=scorer, njobs=cpus)121        if len(weights) == 0:122            tuned_model.fit(X_train, y_train, groups=groups_train)123        else:124            weights_train = weights[train_outer]125            tuned_model.fit(X_train, y_train, groups=groups_train, **{'sample_weight': weights_train})126        127        # make predictions for test set (outer loop)128        y_probs = tuned_model.predict_proba(X_test)129        y_preds = tuned_model.predict(X_test)130        131        for i, index in enumerate(test_outer):132            probabilities[index,:] = y_probs[i,:]133            preds[index] = y_preds[i]134    135    return probabilities, preds136def NestedShuffledKFoldProba(model, X, y, parameter_grid, n_classes, scorer, 137                     inner_cv=StratifiedKFold(n_splits=10), outer_cv=StratifiedKFold(n_splits=10), 138                     weights=[], cpus=1):139    """140    Implements a nested version of GroupKFold cross-validation using GridSearchCV to evaluate models 141    that need hyperparameter tuning in settings where different groups exist in the available data.142    143    Dependencies: sklearn.model_selection, numpy144    145    Input:146    - X, y: features and labels (must be NumPy arrays).147    - model, parameter_grid: the model instance and its parameter grid to be optimized.148    - n_classes: the number of classes in the prediction problem149    - scorer: the scoring to use in inner loop.150    - inner_cv, outer_cv: the StratifiedShuffleSplit iterators for both CV-loops (default n_splits: 10)151    - weights: sample weights to account for more important samples152    153    Output: cross-validated predicted class probabilities154    """155    # define empty matrix to store performances (n CV runs and four performance metrics)156    probabilities = np.zeros((X.shape[0], n_classes))157    preds = np.zeros(X.shape[0])158    159    # shuffle data160    indices = np.asarray(range(X.shape[0]), dtype=int)161    if len(weights) == 0:162        X, y, indices = shuffle(X, y, indices, random_state=0)163    else:164        X, y, weights, indices = shuffle(X, y, weights, indices, random_state=0)165    166    # define outer loop167    for train_outer, test_outer in outer_cv.split(X, y):168        X_train, X_test = X[train_outer], X[test_outer]169        y_train, y_test = y[train_outer], y[test_outer]170        indices_train, indices_test = indices[train_outer], indices[test_outer]171        172        # define inner loop (in GridSearchCV)173        tuned_model = GridSearchCV(model, cv=inner_cv, param_grid=parameter_grid, scoring=scorer, n_jobs=cpus)174        if len(weights) == 0:175            tuned_model.fit(X_train, y_train)176        else:177            weights_train = weights[train_outer]178            tuned_model.fit(X_train, y_train, **{'sample_weight': weights_train})179        180        # make predictions for test set (outer loop)181        y_probs = tuned_model.predict_proba(X_test)182        y_preds = tuned_model.predict(X_test)183        184        for i, index in enumerate(test_outer):185            original_index = indices_test[i]186            probabilities[original_index,:] = y_probs[i,:]187            preds[original_index] = y_preds[i]188    189    return probabilities, preds   ...

model_selection-checkpoint.py

Source:model_selection-checkpoint.py

1from sklearn.model_selection import KFold, GridSearchCV2import numpy as np3from sklearn.metrics import roc_auc_score4from utils.fairness_functions import compute_fairness5from sklearn.calibration import CalibratedClassifierCV6def cross_validate(X, Y, estimator, c_grid, seed):7    """Performs cross validation and selects a model given X and Y dataframes, 8    an estimator, a dictionary of parameters, and a random seed. 9    """10    # settings 11    n_splits = 512    scoring = 'roc_auc'13    cross_validation = KFold(n_splits=n_splits, shuffle=True, random_state=seed)14    clf = GridSearchCV(estimator=estimator, param_grid=c_grid, scoring=scoring,15                       cv=cross_validation, return_train_score=True).fit(X, Y)16    mean_train_score = clf.cv_results_['mean_train_score']17    mean_test_score = clf.cv_results_['mean_test_score']18    test_std = clf.cv_results_['std_test_score']19    # scores20    best_auc = clf.best_score_21    best_std = test_std[np.where(mean_test_score == clf.best_score_)[0][0]]22    best_param = clf.best_params_23    auc_diff = mean_train_score[np.where(mean_test_score == clf.best_score_)[24        0][0]] - clf.best_score_25    return mean_train_score, mean_test_score, test_std, best_auc, best_std, best_param, auc_diff26def nested_cross_validate(X, Y, estimator, c_grid, seed, index = None):27    28    ## outer cv29    train_outer = []30    test_outer = []31    outer_cv = KFold(n_splits=5, random_state=seed, shuffle=True)32    33    ## 5 sets of train & test index34    for train, test in outer_cv.split(X, Y):35        train_outer.append(train)36        test_outer.append(test)37        38    ## storing lists39    holdout_auc = []40    best_params = []41    auc_diffs = []42    fairness_overviews = []43    ## inner cv44    inner_cv = KFold(n_splits=5, shuffle=True, random_state=seed)45    for i in range(len(train_outer)):46        47        ## subset train & test sets in inner loop48        train_x, test_x = X.iloc[train_outer[i]], X.iloc[test_outer[i]]49        train_y, test_y = Y[train_outer[i]], Y[test_outer[i]]50        51        ## holdout test with "race" for fairness52        holdout_with_attrs = test_x.copy()53        54        ## remove unused feature in modeling55        train_x = train_x.drop(['person_id', 'screening_date', 'race'], axis=1)56        test_x = test_x.drop(['person_id', 'screening_date', 'race'], axis=1)57        58        ## GridSearch: inner CV59        clf = GridSearchCV(estimator=estimator, param_grid=c_grid, scoring='roc_auc',60                           cv=inner_cv, return_train_score=True).fit(train_x, train_y)61        ## best parameter & scores62        mean_train_score = clf.cv_results_['mean_train_score']63        mean_test_score = clf.cv_results_['mean_test_score']        64        best_param = clf.best_params_65        auc_diffs.append(mean_train_score[np.where(mean_test_score == clf.best_score_)[0][0]] - clf.best_score_)66        ## train model on best param67        if index == 'svm':68            best_model = CalibratedClassifierCV(clf, cv=5)69            best_model.fit(train_x, train_y)70            prob = best_model.predict_proba(test_x)[:, 1]71            holdout_pred = best_model.predict(test_x)72        else:73            best_model = clf.fit(train_x, train_y)74            prob = best_model.predict_proba(test_x)[:, 1]75            holdout_pred = best_model.predict(test_x)76        ## fairness 77        holdout_fairness_overview = compute_fairness(df=holdout_with_attrs,78                                                     preds=holdout_pred,79                                                     labels=test_y)80        fairness_overviews.append(holdout_fairness_overview)81        ## store results82        holdout_auc.append(roc_auc_score(test_y, prob))83        best_params.append(best_param)...

Automation Testing Tutorials

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