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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)...
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