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Maintenance for tests (LogisticRegression default arguments)
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@vecxoz
vecxoz committed Jun 21, 2019
1 parent d41c4af commit 564d272
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Showing 4 changed files with 148 additions and 148 deletions.
78 changes: 39 additions & 39 deletions tests/test_func_api_classification_binary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,13 +83,13 @@ def tearDown(self):

def test_oof_pred_mode(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)
_ = model.fit(X_train, y_train)
S_test_1 = model.predict(X_test).reshape(-1, 1)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'oof_pred', random_state = 0, verbose = 0, stratified = True)
Expand All @@ -110,12 +110,12 @@ def test_oof_pred_mode(self):

def test_oof_mode(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)
S_test_1 = None

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'oof', random_state = 0, verbose = 0, stratified = True)
Expand All @@ -136,12 +136,12 @@ def test_oof_mode(self):

def test_pred_mode(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = None
_ = model.fit(X_train, y_train)
S_test_1 = model.predict(X_test).reshape(-1, 1)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'pred', random_state = 0, verbose = 0, stratified = True)
Expand Down Expand Up @@ -171,16 +171,16 @@ def test_oof_pred_bag_mode(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
S_test_temp[:, fold_counter] = model.predict(X_test)
S_test_1 = st.mode(S_test_temp, axis = 1)[0]

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'oof_pred_bag', random_state = 0, verbose = 0, stratified = True)
Expand Down Expand Up @@ -210,14 +210,14 @@ def test_pred_bag_mode(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
S_test_temp[:, fold_counter] = model.predict(X_test)
S_test_1 = st.mode(S_test_temp, axis = 1)[0]

S_train_1 = None

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'pred_bag', random_state = 0, verbose = 0, stratified = True)
Expand All @@ -242,13 +242,13 @@ def test_pred_bag_mode(self):

def test_oof_pred_mode_proba(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict_proba')
_ = model.fit(X_train, y_train)
S_test_1 = model.predict_proba(X_test)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, stratified = True,
mode = 'oof_pred', random_state = 0, verbose = 0, needs_proba = True, save_dir=temp_dir)
Expand All @@ -269,12 +269,12 @@ def test_oof_pred_mode_proba(self):

def test_oof_mode_proba(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict_proba')
S_test_1 = None

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, stratified = True,
mode = 'oof', random_state = 0, verbose = 0, needs_proba = True, save_dir=temp_dir)
Expand All @@ -295,12 +295,12 @@ def test_oof_mode_proba(self):

def test_pred_mode_proba(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = None
_ = model.fit(X_train, y_train)
S_test_1 = model.predict_proba(X_test)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, stratified = True,
mode = 'pred', random_state = 0, verbose = 0, needs_proba = True, save_dir=temp_dir)
Expand Down Expand Up @@ -331,18 +331,18 @@ def test_oof_pred_bag_mode_proba(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes, fold_counter * n_classes + n_classes)
S_test_temp[:, col_slice_fold] = model.predict_proba(X_test)
for class_id in range(n_classes):
S_test_1[:, class_id] = np.mean(S_test_temp[:, class_id::n_classes], axis = 1)

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict_proba')

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'oof_pred_bag', random_state = 0, verbose = 0, stratified = True, needs_proba = True)
Expand Down Expand Up @@ -382,7 +382,7 @@ def test_pred_bag_mode_proba(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes, fold_counter * n_classes + n_classes)
S_test_temp[:, col_slice_fold] = model.predict_proba(X_test)
Expand All @@ -391,7 +391,7 @@ def test_pred_bag_mode_proba(self):

S_train_1 = None

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
mode = 'pred_bag', random_state = 0, verbose = 0, stratified = True, needs_proba = True)
Expand Down Expand Up @@ -425,17 +425,17 @@ def test_oof_pred_bag_mode_shuffle(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
S_test_temp[:, fold_counter] = model.predict(X_test)
S_test_1 = st.mode(S_test_temp, axis = 1)[0]

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
# !!! Important. Here we pass CV-generator not number of folds <cv = kf>
S_train_1 = cross_val_predict(model, X_train, y = y_train, cv = kf,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)

models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = True, save_dir=temp_dir,
mode = 'oof_pred_bag', random_state = 0, verbose = 0, stratified = True)
Expand All @@ -462,15 +462,15 @@ def test_oof_pred_bag_mode_shuffle(self):
#---------------------------------------------------------------------------
def test_oof_mode_metric(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
scorer = make_scorer(accuracy_score)
scores = cross_val_score(model, X_train, y = y_train, cv = n_folds,
scoring = scorer, n_jobs = 1, verbose = 0)
mean_str_1 = '%.8f' % np.mean(scores)
std_str_1 = '%.8f' % np.std(scores)


models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train, S_test = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, save_dir=temp_dir,
mode = 'oof', random_state = 0, verbose = 0, stratified = True)
Expand Down Expand Up @@ -499,15 +499,15 @@ def test_oof_mode_metric(self):
#---------------------------------------------------------------------------
def test_oof_mode_metric_proba(self):

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
scorer = make_scorer(log_loss, needs_proba = True)
scores = cross_val_score(model, X_train, y = y_train, cv = n_folds,
scoring = scorer, n_jobs = 1, verbose = 0)
mean_str_1 = '%.8f' % np.mean(scores)
std_str_1 = '%.8f' % np.std(scores)


models = [LogisticRegression(random_state=0)]
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')]
S_train, S_test = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, save_dir=temp_dir,
mode = 'oof', random_state = 0, verbose = 0, stratified = True,
Expand Down Expand Up @@ -536,7 +536,7 @@ def test_oof_mode_metric_proba(self):
def test_oof_pred_mode_2_models(self):

# Model a
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1_a = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)
_ = model.fit(X_train, y_train)
Expand All @@ -552,7 +552,7 @@ def test_oof_pred_mode_2_models(self):
S_train_1 = np.c_[S_train_1_a, S_train_1_b]
S_test_1 = np.c_[S_test_1_a, S_test_1_b]

models = [LogisticRegression(random_state=0),
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr'),
GaussianNB()]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
Expand Down Expand Up @@ -584,12 +584,12 @@ def test_oof_pred_bag_mode_2_models(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
S_test_temp[:, fold_counter] = model.predict(X_test)
S_test_1_a = st.mode(S_test_temp, axis = 1)[0]

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1_a = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict').reshape(-1, 1)

Expand All @@ -615,7 +615,7 @@ def test_oof_pred_bag_mode_2_models(self):
S_train_1 = np.c_[S_train_1_a, S_train_1_b]
S_test_1 = np.c_[S_test_1_a, S_test_1_b]

models = [LogisticRegression(random_state=0),
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr'),
GaussianNB()]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
Expand All @@ -639,7 +639,7 @@ def test_oof_pred_bag_mode_2_models(self):
def test_oof_pred_mode_proba_2_models(self):

# Model a
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1_a = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict_proba')
_ = model.fit(X_train, y_train)
Expand All @@ -655,7 +655,7 @@ def test_oof_pred_mode_proba_2_models(self):
S_train_1 = np.c_[S_train_1_a, S_train_1_b]
S_test_1 = np.c_[S_test_1_a, S_test_1_b]

models = [LogisticRegression(random_state=0),
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr'),
GaussianNB()]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, stratified = True,
Expand Down Expand Up @@ -689,14 +689,14 @@ def test_oof_pred_bag_mode_proba_2_models(self):
y_tr = y_train[tr_index]
X_te = X_train[te_index]
y_te = y_train[te_index]
model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
_ = model.fit(X_tr, y_tr)
col_slice_fold = slice(fold_counter * n_classes, fold_counter * n_classes + n_classes)
S_test_temp[:, col_slice_fold] = model.predict_proba(X_test)
for class_id in range(n_classes):
S_test_1_a[:, class_id] = np.mean(S_test_temp[:, class_id::n_classes], axis = 1)

model = LogisticRegression(random_state=0)
model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')
S_train_1_a = cross_val_predict(model, X_train, y = y_train, cv = n_folds,
n_jobs = 1, verbose = 0, method = 'predict_proba')

Expand Down Expand Up @@ -727,7 +727,7 @@ def test_oof_pred_bag_mode_proba_2_models(self):



models = [LogisticRegression(random_state=0),
models = [LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr'),
GaussianNB()]
S_train_2, S_test_2 = stacking(models, X_train, y_train, X_test,
regression = False, n_folds = n_folds, shuffle = False, save_dir=temp_dir,
Expand Down

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