def test_repeated_stratified_kfold_determinstic_split():
X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
y = [1, 1, 1, 0, 0]
random_state = 1944695409
rskf = RepeatedStratifiedKFold(
n_splits=2,
n_repeats=2,
random_state=random_state)
# split should produce same and deterministic splits on
# each call
for _ in range(3):
splits = rskf.split(X, y)
train, test = next(splits)
assert_array_equal(train, [1, 4])
assert_array_equal(test, [0, 2, 3])
train, test = next(splits)
assert_array_equal(train, [0, 2, 3])
assert_array_equal(test, [1, 4])
train, test = next(splits)
assert_array_equal(train, [2, 3])
assert_array_equal(test, [0, 1, 4])
train, test = next(splits)
assert_array_equal(train, [0, 1, 4])
assert_array_equal(test, [2, 3])
assert_raises(StopIteration, next, splits)
def main(dataset_name):
dataset = load_dataset()
raw_data = np.asarray(dataset['raw']['data'])
raw_label = np.asarray(dataset['raw']['label'])
num_classes = len(np.unique(raw_label))
rskf = RepeatedStratifiedKFold(n_splits=k_folds,
n_repeats=k_fold_reps,
random_state=42)
print('L2X-Method')
cont_seed = 0
nfeats = []
accuracies = []
model_accuracies = []
svc_accuracies = []
fs_time = []
BAs = []
svc_BAs = []
model_BAs = []
mAPs = []
svc_mAPs = []
model_mAPs = []
mus = []
name = dataset_name + '_' + kernel + '_mu_' + str(mu)
print(name)
for j, (train_index,
test_index) in enumerate(rskf.split(raw_data, raw_label)):
print('k_fold', j, 'of', k_folds * k_fold_reps)
train_data, train_labels = raw_data[train_index], raw_label[
train_index]
test_data, test_labels = raw_data[test_index], raw_label[test_index]
train_labels = to_categorical(train_labels, num_classes=num_classes)
test_labels = to_categorical(test_labels, num_classes=num_classes)
valid_features = np.where(np.abs(train_data).sum(axis=0) > 0)[0]
if len(valid_features) < train_data.shape[1]:
print('Removing', train_data.shape[1] - len(valid_features),
'zero features')
train_data = train_data[:, valid_features]
test_data = test_data[:, valid_features]
model_kwargs = {
'mu': mu / len(train_data),
'kernel': kernel,
'degree': 3
}
svc_kwargs = {'C': 1.0, 'solver': 0.}
for i, n_features in enumerate([10, 50, 100, 150, 200]):
n_accuracies = []
n_svc_accuracies = []
n_model_accuracies = []
n_BAs = []
n_svc_BAs = []
n_model_BAs = []
n_mAPs = []
n_svc_mAPs = []
n_model_mAPs = []
n_train_accuracies = []
n_time = []
print('n_features : ', n_features)
heatmaps = []
for r in range(reps):
np.random.seed(cont_seed)
K.tf.set_random_seed(cont_seed)
cont_seed += 1
model = train_Keras(
train_data,
train_labels,
test_data,
test_labels,
model_kwargs,
l2x_model_func=get_l2x_model,
n_features=n_features,
)
heatmaps.append(model.heatmap)
n_time.append(model.fs_time)
test_data_norm = model.normalization.transform(test_data)
train_data_norm = model.normalization.transform(train_data)
test_pred = model.predict(test_data_norm)
n_model_accuracies.append(
model.evaluate(test_data_norm, test_labels, verbose=0)[-1])
n_model_BAs.append(balance_accuracy(test_labels, test_pred))
n_model_mAPs.append(
average_precision_score(test_labels[:, -1], test_pred))
train_acc = model.evaluate(train_data_norm,
train_labels,
verbose=0)[-1]
print('n_features : ', n_features, ', accuracy : ',
n_model_accuracies[-1], ', BA : ', n_model_BAs[-1],
', mAP : ', n_model_mAPs[-1], ', train_accuracy : ',
train_acc, ', time : ', n_time[-1], 's')
del model
K.clear_session()
heatmap = np.mean(heatmaps, axis=0)
best_features = np.argsort(heatmap)[::-1][:n_features]
svc_train_data = train_data[:, best_features]
svc_test_data = test_data[:, best_features]
norm = normalization_func()
svc_train_data_norm = norm.fit_transform(svc_train_data)
svc_test_data_norm = norm.transform(svc_test_data)
bestcv = -1
bestc = None
bestSolver = None
for s in [0, 1, 2, 3]:
for my_c in [
0.001, 0.1, 0.5, 1.0, 1.4, 1.5, 1.6, 2.0, 2.5, 5.0,
100.0
]:
cmd = '-v 5 -s ' + str(s) + ' -c ' + str(my_c) + ' -q'
cv = liblinearutil.train(
(2 * train_labels[:, -1] - 1).tolist(),
svc_train_data_norm.tolist(), cmd)
if cv > bestcv:
# print('Best -> C:', my_c, ', s:', s, ', acc:', cv)
bestcv = cv
bestc = my_c
bestSolver = s
svc_kwargs['C'] = bestc
svc_kwargs['solver'] = bestSolver
print('Best -> C:', bestc, ', s:', bestSolver, ', acc:', bestcv)
for r in range(reps):
np.random.seed(cont_seed)
K.tf.set_random_seed(cont_seed)
cont_seed += 1
model = train_SVC(svc_train_data_norm, train_labels,
svc_kwargs)
_, accuracy, test_pred = liblinearutil.predict(
(2 * test_labels[:, -1] - 1).tolist(),
svc_test_data_norm.tolist(), model, '-q')
test_pred = np.asarray(test_pred)
n_svc_accuracies.append(accuracy[0])
n_svc_BAs.append(balance_accuracy(test_labels, test_pred))
n_svc_mAPs.append(
average_precision_score(test_labels[:, -1], test_pred))
del model
model = train_Keras(svc_train_data, train_labels,
svc_test_data, test_labels, model_kwargs)
train_data_norm = model.normalization.transform(svc_train_data)
test_data_norm = model.normalization.transform(svc_test_data)
test_pred = model.predict(test_data_norm)
n_BAs.append(balance_accuracy(test_labels, test_pred))
n_mAPs.append(
average_precision_score(test_labels[:, -1], test_pred))
n_accuracies.append(
model.evaluate(test_data_norm, test_labels, verbose=0)[-1])
n_train_accuracies.append(
model.evaluate(train_data_norm, train_labels,
verbose=0)[-1])
del model
K.clear_session()
print(
'n_features : ',
n_features,
', acc : ',
n_accuracies[-1],
', BA : ',
n_BAs[-1],
', mAP : ',
n_mAPs[-1],
', train_acc : ',
n_train_accuracies[-1],
', svc_acc : ',
n_svc_accuracies[-1],
', svc_BA : ',
n_svc_BAs[-1],
', svc_mAP : ',
n_svc_mAPs[-1],
)
if i >= len(accuracies):
accuracies.append(n_accuracies)
svc_accuracies.append(n_svc_accuracies)
model_accuracies.append(n_model_accuracies)
BAs.append(n_BAs)
mAPs.append(n_mAPs)
fs_time.append(n_time)
svc_BAs.append(n_svc_BAs)
svc_mAPs.append(n_svc_mAPs)
model_BAs.append(n_model_BAs)
model_mAPs.append(n_model_mAPs)
nfeats.append(n_features)
mus.append(model_kwargs['mu'])
else:
accuracies[i] += n_accuracies
svc_accuracies[i] += n_svc_accuracies
model_accuracies[i] += n_model_accuracies
fs_time[i] += n_time
BAs[i] += n_BAs
mAPs[i] += n_mAPs
svc_BAs[i] += n_svc_BAs
svc_mAPs[i] += n_svc_mAPs
model_BAs[i] += n_model_BAs
model_mAPs[i] += n_model_mAPs
output_filename = directory + 'LinearSVC_' + kernel + '_L2X.json'
if not os.path.isdir(directory):
os.makedirs(directory)
info_data = {
'kernel': kernel,
'reps': reps,
'classification': {
'mus': mus,
'n_features': nfeats,
'accuracy': accuracies,
'mean_accuracy': np.array(accuracies).mean(axis=1).tolist(),
'svc_accuracy': svc_accuracies,
'mean_svc_accuracy':
np.array(svc_accuracies).mean(axis=1).tolist(),
'model_accuracy': model_accuracies,
'mean_model_accuracy':
np.array(model_accuracies).mean(axis=1).tolist(),
'BA': BAs,
'mean_BA': np.array(BAs).mean(axis=1).tolist(),
'mAP': mAPs,
'mean_mAP': np.array(mAPs).mean(axis=1).tolist(),
'svc_BA': svc_BAs,
'svc_mean_BA': np.array(svc_BAs).mean(axis=1).tolist(),
'svc_mAP': svc_mAPs,
'svc_mean_mAP': np.array(svc_mAPs).mean(axis=1).tolist(),
'model_BA': model_BAs,
'model_mean_BA': np.array(model_BAs).mean(axis=1).tolist(),
'model_mAP': model_mAPs,
'model_mean_mAP': np.array(model_mAPs).mean(axis=1).tolist(),
'fs_time': fs_time
}
}
for k, v in info_data['classification'].items():
if 'mean' in k:
print(k, v)
with open(output_filename, 'w') as outfile:
json.dump(info_data, outfile)
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
else:
training_transform, test_transform = 2 * [
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
]
if args.dataset == 'cars':
from data import cars
stratified_crossvalidation = RepeatedStratifiedKFold(
n_splits=3, n_repeats=5, random_state=args.seed)
data = cars.Calltech101(images_folder='images',
input_transform=training_transform)
data.shuffle(seed=args.seed)
data_size = len(data)
indexes = np.arange(0, data_size, 1, dtype=int)
targets = np.asarray(data.targets)
kfolds = list(stratified_crossvalidation.split(indexes, targets))
train_dataset = cars.Calltech101(images_folder='images',
input_transform=training_transform)
train_dataset.shuffle(seed=args.seed)
train_dataset.prune_dataset(indexes=kfolds[args.cross_validation_split][1])
test_dataset = cars.Calltech101(images_folder='images',
input_transform=test_transform)
train_dataset.shuffle(seed=args.seed)
test_dataset.prune_dataset(indexes=kfolds[args.cross_validation_split][0])
def generate_kfold(X,
y=None,
n_splits=5,
random_state=0,
stratified=False,
n_repeats=1):
if stratified and (y is not None):
if n_repeats > 1:
kf = RepeatedStratifiedKFold(n_splits=n_splits,
n_repeats=n_repeats,
random_state=random_state)
else:
kf = StratifiedKFold(n_splits=n_splits,
shuffle=True,
random_state=random_state)
kf.get_n_splits(X, y)
return [[train_index, test_index]
for train_index, test_index in kf.split(X, y)]
else:
if n_repeats > 1:
kf = RepeatedKFold(n_splits=n_splits,
n_repeats=n_repeats,
random_state=random_state)
else:
kf = KFold(n_splits=n_splits,
shuffle=True,
random_state=random_state)
kf.get_n_splits(X)
return [[train_index, test_index]
for train_index, test_index in kf.split(X)]
def rfe_feature_selection(self, input, output, dict_of_models,
list_number_of_features_to_select):
"""
Performs models evaluation within the No_outer times repeated No_inner-fold cross-validation procedure for different
number of features selected by RFE algorithm with nested 10-times cross-validation for model hyperparameters' tuning
----------
:param input : array-like, shape (n_samples, n_features)
The training input samples.
:param output : array-like, shape (n_samples, 1)
The target values.
:param dict_of_models: dictionary
Models with details for grid-search.
:param list_number_of_features_to_select - list
Number of features to select.
:return df_aucs : DataFrame object, shape (No_outer x No_inner, number of models x length of list_number_of_features)
AUC values for every step of No_outer x No_inner-times CV are provided.
:return df_res : DataFrame object, shape ([number of models x length of list_number_of_features_to_select], 9)
For every model and every No. of selected features best classifier's parameters and averaged classification
metrics are provided : Accuracy, Sensitivity, Specificity, Precision, F1-Score, AUC.
:return df_stds : DataFrame object, shape ([number of models x length of list_number_of_features_to_select], 8)
For every model and every No. of selected features standard deviations of classification metrics
are provided.
"""
df_res = pd.DataFrame(columns=[
'FS Method', 'Classifier', 'Selected features', 'Best parameters',
'Accuracy', 'Sensitivity', 'Specificity', 'Precision', 'F1-score',
'ROC_AUC'
])
df_stds = pd.DataFrame(columns=[
'FS Method', 'Classifier', 'Selected features', 'Acc_std',
'Sens_std', 'Spec_std', 'Prec_std', 'F1_std', 'ROC_AUC_std'
])
df_aucs = pd.DataFrame()
for m in dict_of_models:
for k in list_number_of_features_to_select:
accuracy = []
aucs = []
sensitivity = []
specificity = []
precision = []
f1score = []
cohen_kappa = []
tprs = []
params = []
X, y = input, output
skf = RepeatedStratifiedKFold(n_splits=self.N_inner,
n_repeats=self.N_outer,
random_state=88)
clf = m['classifier']
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
best_params = []
rfe = RFE(estimator=clf, n_features_to_select=k, step=1)
rfe_smote_clf = Pipeline([('oversampling',
SMOTE(random_state=88)),
('feature_selection', rfe),
('classifier', clf)])
param_grid = m['grid']
gridsearch_cv = GridSearchCV(rfe_smote_clf,
param_grid,
cv=10,
scoring='roc_auc')
gridsearch_cv.fit(X_train, y_train)
best_params.append(gridsearch_cv.best_params_)
# predicted class
y_predict = gridsearch_cv.predict(X_test)
# predicted probabilities
probas_ = gridsearch_cv.predict_proba(X_test)
# accuracy
acc = accuracy_score(y_predict, y_test)
accuracy.append(acc)
# sensitivity = recall
sens = recall_score(y_test, y_predict)
sensitivity.append(sens)
# specificity
spec = self.get_specificity(y_test, y_predict)
specificity.append(spec)
# precision
prec = precision_score(y_test, y_predict)
precision.append(prec)
# f1-score
f1 = f1_score(y_test, y_predict)
f1score.append(f1)
# cohen-kappa-score
kappa = cohen_kappa_score(y_test, y_predict)
cohen_kappa.append(kappa)
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test_index], probas_[:,
1])
tprs.append(interp(self.mean_fprs, fpr, tpr))
tprs[-1][0] = 0.0
roc_auc = auc(fpr, tpr)
aucs.append(roc_auc)
# best parameters
params.append(best_params)
df_aucs[m['name'] + str(k)] = aucs
df_stds = df_stds.append(
{
'Classifier': m['name'],
'Selected features': k,
'Acc_std': np.std(accuracy),
'Sens_std': np.std(sensitivity),
'Spec_std': np.std(specificity),
'Prec_std': np.std(precision),
'F1_std': np.std(f1score),
'ROC_AUC_std': np.std(aucs)
},
ignore_index=True)
df_res = df_res.append(
{
'Classifier': m['name'],
'Selected features': k,
'Best parameters': params,
'Accuracy': np.mean(accuracy),
'Sensitivity': np.mean(sensitivity),
'Specificity': np.mean(specificity),
'Precision': np.mean(precision),
'F1-score': np.mean(f1score),
'ROC_AUC': np.mean(aucs)
},
ignore_index=True)
return df_aucs, df_res, df_stds
def best_models_ROC_curves(self, input, output, models_dict, show_plot):
"""
Plot ROC curves for the best evaluated methods or returns averaged predicted probabilities for every sample
by all selected models
Parameters
----------
:param input : array-like, shape (n_samples, n_features)
The training input samples.
:param output : array-like, shape (n_samples, 1)
The target values.
:param models_dict : dictionary
Models with details for grid-search
:param show_plot - boolean
Indicator whether plot should be rendered
:return [selected_models]_proba_[number_of_selected_features] : array-like, shape (1, n_samples)
Averaged predicted probabilities for every sample by every selected model
:return plot
"""
X, y = input, output
mean_fpr = np.linspace(0, 1, 100)
instances = X.shape[0]
proba = np.zeros((len(models_dict), 10, instances))
skf = RepeatedStratifiedKFold(n_splits=self.N_inner,
n_repeats=self.N_outer,
random_state=88)
plt.figure(1)
plt.rcParams["figure.figsize"] = (10, 6)
plt.plot([0, 1], [0, 1],
linestyle='--',
lw=2,
color='brown',
label='Chance',
alpha=.8)
j = 0
for m in models_dict:
tprs = []
aucs = []
clf = m['classifier']
fs = m['fs_method']
color_line = m['color_line']
color_shadow = m['color_shadow']
k = 1
i = 0
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
fs_smote_clf = Pipeline([('oversampling',
SMOTE(random_state=88)),
('feature_selection', fs),
('classifier', clf)])
param_grid = m['grid']
# for survival problem
if m['name'] == 'MLP_mRMR' or m['name'] == 'SVM_mRMR_50':
gridsearch_cv = fs_smote_clf
else:
gridsearch_cv = GridSearchCV(fs_smote_clf,
param_grid,
cv=10,
scoring='roc_auc')
gridsearch_cv.fit(X_train, y_train)
# predicted probabilities
probas_ = gridsearch_cv.predict_proba(X_test)
if k > self.N_outer:
break
elif i >= k * self.N_inner:
k += 1
proba[j, k - 1, test_index] = probas_[:, 1]
# Compute ROC curve
fpr, tpr, thresholds = roc_curve(y[test_index], probas_[:, 1])
tprs.append(interp(self.mean_fprs, fpr, tpr))
tprs[-1][0] = 0.0
roc_auc = auc(fpr, tpr)
aucs.append(roc_auc)
i += 1
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
std_auc = np.std(aucs)
plt.plot(mean_fpr,
mean_tpr,
color=color_line,
label=r'Mean ROC %s (AUC = %0.2f $\pm$ %0.2f)' %
(m['name'], mean_auc, std_auc),
lw=2,
alpha=.8)
std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
plt.fill_between(mean_fpr,
tprs_lower,
tprs_upper,
color=color_shadow,
alpha=.2,
label=r'$\pm$ 1 std. dev. for ' + m['name'])
j += 1
if show_plot:
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
return
else:
mean_proba = np.mean(proba, axis=1)
return mean_proba
def cross_val(
self,
X=None,
y=None,
X_test=None,
model=None,
folds=10,
score_folds=5,
n_repeats=2,
print_metric=False,
metric_round=4,
predict=False,
get_feature_importance=False,
):
"""
Description of cross_val:
Cross-validation function
Args:
X=None (undefined):
y=None (undefined):
X_test=None (undefined):
model=None (undefined):
folds=10 (undefined):
score_folds=5 (undefined):
n_repeats=2 (undefined):
print_metric=False (undefined):
metric_round=4 (undefined):
predict=False (undefined):
get_feature_importance=False (undefined):
Returns:
result (dict)
"""
if model is None:
model = self
if X is None:
X = model._data.X_train
if y is None:
y = model._data.y_train
if X_test is None:
X_test = model._data.X_test
if predict and (X_test is None):
raise Exception("No X_test for predict")
if model.type_of_estimator == 'classifier':
skf = RepeatedStratifiedKFold(
n_splits=folds,
n_repeats=n_repeats,
random_state=model._random_state,
)
else:
skf = RepeatedKFold(
n_splits=folds,
n_repeats=n_repeats,
random_state=model._random_state,
)
folds_scores = []
stacking_y_pred_train = np.zeros(len(X))
stacking_y_pred_test = np.zeros(len(X_test))
feature_importance_df = pd.DataFrame(np.zeros(len(X.columns)),
index=X.columns)
for i, (train_idx, valid_idx) in enumerate(skf.split(X, y)):
train_x, train_y = X.iloc[train_idx], y.iloc[train_idx]
val_x, val_y = X.iloc[valid_idx], y.iloc[valid_idx]
# TargetEncoders
train_x, val_x, X_test = model.preproc_data_in_cv(
train_x, train_y, val_x, X_test)
# Fit
model._fit(
model=model,
X_train=train_x.reset_index(drop=True),
y_train=train_y.reset_index(drop=True),
X_test=val_x.reset_index(drop=True),
y_test=val_y.reset_index(drop=True),
)
# Predict
if (model.metric.__name__ in predict_proba_metrics) and (
model.is_possible_predict_proba()):
y_pred = model._predict_proba(val_x)
if predict:
y_pred_test = model._predict_proba(X_test)
else:
y_pred = model._predict(val_x)
if predict:
y_pred_test = model._predict(X_test)
score_model = model.metric(val_y, y_pred)
folds_scores.append(score_model)
if get_feature_importance:
feature_importance_df += model._get_feature_importance(train_x)
if predict:
stacking_y_pred_train[valid_idx] += y_pred
stacking_y_pred_test += y_pred_test
else:
# score_folds
if i + 1 >= score_folds:
break
if predict:
stacking_y_pred_train = stacking_y_pred_train / n_repeats
stacking_y_pred_test = stacking_y_pred_test / (folds * n_repeats)
if score_folds > 1 or predict:
score = round(np.mean(folds_scores), metric_round)
score_std = round(np.std(folds_scores), metric_round + 2)
else:
score = round(score_model, metric_round)
score_std = 0
if print_metric:
print(
f'\n Mean Score {model.metric.__name__} on {i+1} Folds: {score} std: {score_std}'
)
# Total
result = {
'Score': score,
'Score_Std': score_std,
'Test_predict': stacking_y_pred_test,
'Train_predict': stacking_y_pred_train,
'Feature_importance': dict(feature_importance_df[0]),
}
return (result)
def cross_validate(feature_name, classifier_name, X, y, cv_num_folds,
cv_num_repeats):
"""Runs repeated stratified $k$-fold cross-validation.
Returns multiple cross-validation metrics as a dictionary, where for each
metric mean and variance across multiple repeats and folds is summarized.
Args:
feature_name: (string) Name of the WALS feature.
classifier_name: (string) Classifier name.
X: (numpy array) Input features.
y: (numpy array) Labels.
cv_num_folds: (int) Number of folds ($k$).
cv_num_repeats: (int) Number of repetitions.
Returns:
Dictionary containing cross-validation scores and stats.
"""
model = _make_classifier(classifier_name)
scoring = ["f1_micro", "precision_micro", "recall_micro", "accuracy"]
try:
# Really primitive logic to figure out class distribution.
_, y_counts = np.unique(y, return_counts=True)
y_max_freq = np.max(y_counts)
# Check if the class counts are not reliable to run cross-validation.
if y_max_freq < cv_num_folds:
logging.warning(
"[%s] %s: Not enough data. Fitting the model instead "
"of running CV", feature_name, classifier_name)
# Simply fit the model.
model.fit(X, y)
cv_scores = {}
cv_scores["accuracy"] = (model.score(X, y), 0.0)
cv_scores[MODEL_INFO_SPARSITY_KEY] = True
return cv_scores
else:
logging.info(
"[%s] Running cross-validation of %s (k=%d, n=%d) ...",
feature_name, classifier_name, cv_num_folds, cv_num_repeats)
# Run cross-validation.
cv = RepeatedStratifiedKFold(n_splits=cv_num_folds,
n_repeats=cv_num_repeats,
random_state=_RANDOM_STATE)
cv_scores = model_selection.cross_validate(model,
X,
y,
cv=cv,
scoring=scoring,
n_jobs=cv_num_folds)
cv_scores[MODEL_INFO_SPARSITY_KEY] = False
except Exception as e: # pylint: disable=broad-except
logging.error("[%s] %s: CV: Exception: %s", feature_name,
classifier_name, e)
return None
del cv_scores["fit_time"]
del cv_scores["score_time"]
for score_name in scoring:
scores_vec_key = "test_" + score_name
cv_scores[score_name] = (np.mean(cv_scores[scores_vec_key]),
np.var(cv_scores[scores_vec_key]))
del cv_scores[scores_vec_key]
# Sanity check.
if math.isnan(cv_scores["accuracy"][0]):
return None
logging.info("[train] %s: CV scores for %s: %s", feature_name,
classifier_name, cv_scores)
return cv_scores
'use_particle_clamp_each_iteration': False,
'unchanged_iterations_stop': 20000,
'use_only_early_stopping': False
# no early stopping used, that is why 20k
},
'pso_velocity_clamp': (-1, 1),
'n_particles':
100,
'pso_iters':
5000,
'pso_optimizer':
PSO,
}
CONFIG['cv'] = StratifiedKFold(n_splits=CONFIG['n_splits'], shuffle=True, random_state=CONFIG['random_state']) \
if CONFIG['n_repeats'] == 1 else RepeatedStratifiedKFold(n_splits=CONFIG['n_splits'],
n_repeats=CONFIG['n_repeats'],
random_state=CONFIG['random_state'])
INPUT_FEATURES = torch.load(CONFIG['labels_features_common_name'] +
"_features.tr").numpy()
INPUT_LABELS = torch.load(CONFIG['labels_features_common_name'] +
"_labels.tr").numpy()
make_experiment_reproducible(CONFIG['random_state'])
def run_cross_validation_psobp(file_to_print) -> torch.Tensor:
logger = logging.getLogger('10_fold_cv')
configure_logger_by_default(logger)
logger.info("START run_cross_validation")
def print_info(info):
logger.info(info)
np.set_printoptions(suppress=True)
# Pobieramy zestaw danych
digits = load_digits()
# Obrazy
images = digits.images
# Etykiety
y = digits.target
# Spłaszczenie obrazów do wektora
X = images.reshape((images.shape[0], -1))
print(X.shape)
#Podział na zbiór treningowy i testowy stratyfikowany na 5 foldów
kf = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=1410)
results = []
# iterowanie po zbiorach treningowych i testowych po podzieleniu na foldy
for train_index, test_index in kf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
#ekstrakcja cech PCA
pca = PCA(n_components=X_train.shape[1], random_state=1410)
pca.fit(X_train)
# procent objasnioniej wariacji
evr = pca.explained_variance_ratio_
evr_acc = np.add.accumulate(evr)
print(evr_acc)
def CueDesc_SegDecAnalysis(dat):
nPe = 100
nRepeats = 10
nSh = 50
njobs = 20
trConds = dat['TrialConds']
trDat = dat['TrialLongMat']
nUnits = dat['fitTable2'].shape[0]
gTrialsIDs = trConds['Good']
Trials = trConds[gTrialsIDs].index.values
nTrials = len(Trials)
allZoneFR,unitIDs = reformatFRDat(dat,Trials)
CoTrials = trConds[gTrialsIDs & (trConds['Co']=='Co')].index.values
InCoTrials = trConds[gTrialsIDs & (trConds['Co']=='InCo')].index.values
nInCo = len(InCoTrials)
TrSets = {}
TrSets['all'] = np.arange(nTrials)
_,idx,_=np.intersect1d(np.array(Trials),np.array(CoTrials),return_indices=True)
TrSets['co'] = idx
_,idx,_=np.intersect1d(np.array(Trials),np.array(InCoTrials),return_indices=True)
TrSets['inco'] = idx
cueVec = trConds.loc[gTrialsIDs]['Cues'].values
descVec = trConds.loc[gTrialsIDs]['Desc'].values
predVec = {'Cue':cueVec, 'Desc':descVec}
nFeatures = {'h':np.arange(1),'a':np.arange(2),'center':np.arange(3),'be':np.arange(4),'int':np.arange(5),'cdfg':np.arange(6),'goal':np.arange(7)}
def correctTrials_Decoder(train,test):
res = pd.DataFrame(np.zeros((3,4)),columns=['Test','BAc','P','Z'])
temp = mod.fit(X_train[train],y_train[train])
res.loc[0,'Test'] = 'Model'
y_hat = temp.predict(X_train[test])
res.loc[0,'BAc'] = bac(y_train[test],y_hat)*100
# shuffle for held out train set
mod_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm_hat = np.random.permutation(y_hat)
mod_sh[sh] = bac(y_train[test],y_perm_hat)*100
res.loc[0,'Z'] = getPerm_Z(mod_sh, res.loc[0,'BAc'] )
res.loc[0,'P'] = getPerm_Pval(mod_sh, res.loc[0,'BAc'] )
# predictions on x test
y_hat = temp.predict(X_test)
res.loc[1,'Test'] = 'Cue'
res.loc[1,'BAc'] = bac(y_test_cue,y_hat)*100
res.loc[2,'Test'] = 'Desc'
res.loc[2,'BAc'] = bac(y_test_desc,y_hat)*100
# shuffles for ytest cue/desc
cue_sh = np.zeros(nSh)
desc_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm_hat = np.random.permutation(y_hat)
cue_sh[sh] = bac(y_test_cue,y_perm_hat)*100
desc_sh[sh] = bac(y_test_desc,y_perm_hat)*100
res.loc[1,'Z'] = getPerm_Z(cue_sh, res.loc[1,'BAc'] )
res.loc[1,'P'] = getPerm_Pval(cue_sh, res.loc[1,'BAc'] )
res.loc[2,'Z'] = getPerm_Z(desc_sh, res.loc[2,'BAc'] )
res.loc[2,'P'] = getPerm_Pval(desc_sh, res.loc[2,'BAc'] )
res['nSeUnits'] = nUnits
return res
def balancedCoIncoTrial_Decoder(pe,feats):
res = pd.DataFrame(np.zeros((2,4)),columns=['Test','BAc','P','Z'])
# sample correct trials to match the number of incorrect trials.
samp_co_trials = np.random.choice(TrSets['co'],nInCo,replace=False)
train = np.concatenate( (TrSets['inco'], samp_co_trials ))
test = np.setdiff1d(TrSets['co'], samp_co_trials)
X_train = allZoneFR.loc[train,feats].values
X_test = allZoneFR.loc[test,feats].values
Y_cue_train = predVec['Cue'][train]
Y_desc_train = predVec['Desc'][train]
Y_test = predVec['Cue'][test] # cue and desc trials are the on the test set.
# model trained on the cue
res.loc[0,'Test'] = 'Cue'
cue_mod = mod.fit(X_train,Y_cue_train)
y_cue_hat = cue_mod.predict(X_test)
res.loc[0,'BAc'] = bac(Y_test,y_cue_hat)*100
cue_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm = np.random.permutation(Y_test)
cue_sh[sh] = bac(y_perm,y_cue_hat)*100
res.loc[0,'Z'] = getPerm_Z(cue_sh, res.loc[0,'BAc'] )
res.loc[0,'P'] = getPerm_Pval(cue_sh, res.loc[0,'BAc'] )
# model trained on the desc
res.loc[1,'Test'] = 'Desc'
desc_mod = mod.fit(X_train,Y_desc_train)
y_desc_hat = desc_mod.predict(X_test)
res.loc[1,'BAc'] = bac(Y_test,y_desc_hat)*100
desc_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm = np.random.permutation(Y_test)
desc_sh[sh] = bac(y_perm,y_desc_hat)*100
res.loc[1,'Z'] = getPerm_Z(cue_sh, res.loc[1,'BAc'] )
res.loc[1,'P'] = getPerm_Pval(cue_sh, res.loc[1,'BAc'] )
return res
def IncoTrial_Decoder(train,test):
res = pd.DataFrame(np.zeros((3,4)),columns=['Test','BAc','P','Z'])
temp = mod.fit(X_train[train],y_train[train])
res.loc[0,'Test'] = 'Model'
y_hat = temp.predict(X_train[test])
res.loc[0,'BAc'] = bac(y_train[test],y_hat)*100
# shuffle for held out train set
mod_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm_hat = np.random.permutation(y_hat)
mod_sh[sh] = bac(y_train[test],y_perm_hat)*100
res.loc[0,'Z'] = getPerm_Z(mod_sh, res.loc[0,'BAc'] )
res.loc[0,'P'] = getPerm_Pval(mod_sh, res.loc[0,'BAc'] )
# predictions on x test
y_hat = temp.predict(X_test)
res.loc[1,'Test'] = 'Cue'
res.loc[1,'BAc'] = bac(y_test_cue,y_hat)*100
res.loc[2,'Test'] = 'Desc'
res.loc[2,'BAc'] = 100-res.loc[1,'BAc']
# shuffles for ytest cue/desc
cue_sh = np.zeros(nSh)
for sh in np.arange(nSh):
y_perm_hat = np.random.permutation(y_hat)
cue_sh[sh] = bac(y_test_cue,y_perm_hat)*100
res.loc[1,'Z'] = getPerm_Z(cue_sh, res.loc[1,'BAc'] )
res.loc[1,'P'] = getPerm_Pval(cue_sh, res.loc[1,'BAc'] )
res.loc[2,'Z'] = getPerm_Z(100-cue_sh, res.loc[2,'BAc'] )
res.loc[2,'P'] = getPerm_Pval(100-cue_sh, res.loc[2,'BAc'] )
return res
with Parallel(n_jobs=njobs) as parallel:
# correct trials Model:
coModsDec = pd.DataFrame()
popCoModsDec = pd.DataFrame()
try:
nFolds = 10
y_train = predVec['Cue'][TrSets['co']]
y_test_cue = predVec['Cue'][TrSets['inco']]
y_test_desc = predVec['Desc'][TrSets['inco']]
rskf = RepeatedStratifiedKFold(n_splits=nFolds,n_repeats=nRepeats, random_state=0)
t0=time.time()
for unitNum in np.arange(nUnits):
for p,nF in nFeatures.items():
feats = unitIDs[unitNum][nF]
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
X_train = allZoneFR.loc[TrSets['co'], feats ].values
X_test = allZoneFR.loc[TrSets['inco'], feats ].values
cnt=0
r = parallel(delayed(correctTrials_Decoder)(train,test) for train,test in rskf.split(X_train,y_train))
t1=time.time()
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
res['unit'] = unitNum
coModsDec = pd.concat((coModsDec,res))
print(end='.')
coModsDec['Decoder'] = 'Correct'
# -population
for p,nF in nFeatures.items():
feats=np.array([])
for f in nF:
feats=np.concatenate((feats,np.arange(f,nUnits*7,7)))
feats=feats.astype(int)
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
X_train = allZoneFR.loc[TrSets['co'], feats ].values
X_test = allZoneFR.loc[TrSets['inco'], feats ].values
cnt=0
r = parallel(delayed(correctTrials_Decoder)(train,test) for train,test in rskf.split(X_train,y_train))
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
popCoModsDec = pd.concat((popCoModsDec,res))
print(end='.')
print('\nDecoding Correct Model Completed. Time = {0:.2f}s \n'.format(time.time()-t0))
popCoModsDec['Decoder'] = 'Correct'
except:
print('CorrectTrials Model Failed.')
print ("Error", sys.exc_info()[0],sys.exc_info()[1],sys.exc_info()[2].tb_lineno)
# balanced correct/inco model:
baModsDec = pd.DataFrame()
popBaModsDec = pd.DataFrame()
try:
t0=time.time()
for unitNum in np.arange(nUnits):
for p,nF in nFeatures.items():
feats = unitIDs[unitNum][nF]
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
r = parallel(delayed(balancedCoIncoTrial_Decoder)(pe, feats) for pe in np.arange(nPe))
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
res['unit'] = unitNum
baModsDec = pd.concat((baModsDec,res))
print(end='.')
baModsDec['Decoder'] = 'Balanced'
# -population
for p,nF in nFeatures.items():
feats=np.array([])
for f in nF:
feats=np.concatenate((feats,np.arange(f,nUnits*7,7)))
feats=feats.astype(int)
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
r = parallel(delayed(balancedCoIncoTrial_Decoder)(pe, feats) for pe in np.arange(nPe))
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
popBaModsDec = pd.concat((popBaModsDec,res))
print(end='.')
print('\nDecoding Balanced Model Completed. Time = {0:.2f}s \n'.format(time.time()-t0))
popBaModsDec['Decoder'] = 'Balanced'
except:
print('Balanced Model Failed.')
print ("Error", sys.exc_info()[0],sys.exc_info()[1],sys.exc_info()[2].tb_lineno)
# incorrect trials model:
InCoModsDec = pd.DataFrame()
popInCoModsDec = pd.DataFrame()
try:
t0=time.time()
nFolds = 5
y_train = predVec['Cue'][TrSets['inco']]
y_test_cue = predVec['Cue'][TrSets['co']]
y_test_desc = predVec['Desc'][TrSets['co']]
rskf = RepeatedStratifiedKFold(n_splits=nFolds,n_repeats=nRepeats, random_state=0)
for unitNum in np.arange(nUnits):
for p,nF in nFeatures.items():
feats = unitIDs[unitNum][nF]
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
X_train = allZoneFR.loc[TrSets['inco'], feats ].values
X_test = allZoneFR.loc[TrSets['co'], feats ].values
cnt=0
r = parallel(delayed(IncoTrial_Decoder)(train,test) for train,test in rskf.split(X_train,y_train))
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
res['unit'] = unitNum
InCoModsDec = pd.concat((InCoModsDec,res))
print(end='.')
InCoModsDec['Decoder'] = 'Incorrect'
#-population
for p,nF in nFeatures.items():
feats=np.array([])
for f in nF:
feats=np.concatenate((feats,np.arange(f,nUnits*7,7)))
feats=feats.astype(int)
mod = lm.LogisticRegression(class_weight='balanced',C=1/np.sqrt(len(feats)))
X_train = allZoneFR.loc[TrSets['inco'], feats ].values
X_test = allZoneFR.loc[TrSets['co'], feats ].values
cnt=0
r = parallel(delayed(IncoTrial_Decoder)(train,test) for train,test in rskf.split(X_train,y_train))
res = pd.DataFrame()
for jj in r:
res = pd.concat((jj,res))
res['Loc'] = p
res['-log(P)'] = -np.log(res['P'])
popInCoModsDec = pd.concat((popInCoModsDec,res))
print(end='.')
print('\nDecoding Incorrect Model Completed. Time = {0:.2f}s \n'.format(time.time()-t0))
popInCoModsDec['Decoder'] = 'Incorrect'
except:
print('Incorrect Model Failed.')
print ("Error", sys.exc_info()[0],sys.exc_info()[1],sys.exc_info()[2].tb_lineno)
# group results.
singCellDec = pd.concat((coModsDec,baModsDec,InCoModsDec))
popDec = pd.concat((popCoModsDec,popBaModsDec,popInCoModsDec))
singCellDecSummary = singCellDec.groupby(['Loc','Test','unit','Decoder']).mean()
singCellDecSummary = singCellDecSummary.reset_index()
singCellDecSummary['Test'] = pd.Categorical(singCellDecSummary['Test'],categories=['Model','Cue','Desc'],ordered=True)
singCellDecSummary.sort_values('Test',inplace=True)
singCellDecSummary['Loc'] = pd.Categorical(singCellDecSummary['Loc'],categories=nFeatures.keys(),ordered=True)
singCellDecSummary.sort_values('Loc',inplace=True)
return singCellDec,singCellDecSummary, popDec
# Creamos un ColumnTransformer para el StandardScaler
scaler = ColumnTransformer([('scaler_media', scaler_media, slice(0, 8)),
('scaler_moda', scaler_moda,
slice(8, len(X.columns)))])
# Creamos el Pipeline incorporando ColumnTransformer y Clasificador
pipeline = Pipeline([('imputer', imputer), ('scaler', scaler),
('svm',
SVC(random_state=RANDOM_STATE,
class_weight=CLASS_WEIGHT,
probability=True))])
# InnerCV (GridSearchCV de 2-folds 5-times (stratified) para obtener mejores parámetros)
rskf = RepeatedStratifiedKFold(n_splits=2,
n_repeats=5,
random_state=RANDOM_STATE) # inner
grid_search = GridSearchCV(estimator=pipeline,
param_grid=PARAM_GRID,
scoring=SCORING,
cv=rskf)
# # OuterCV (Validación cruzada de 5 folds (stratified) para estimar Accuracy)
# scores = cross_validate(estimator=grid_search, X=X, y=y, cv=5, error_score='raise', return_estimator=True, scoring=SCORING) # outer
# print('Scores: {}' .format(scores['test_score']))
# print('Mean score: {}' .format(np.mean(scores['test_score'])))
# # Creamos clasificador 'tonto' y obtenemos resultados también con validación cruzada (CV=5) para tener resultados más realistas
# dummy_clf = DummyClassifier(strategy='most_frequent', random_state=RANDOM_STATE)
# dummy_scores = cross_validate(estimator=dummy_clf, X=X, y=y, cv=5, error_score='raise', return_estimator=True, scoring=SCORING)
# print('Dummy scores: {}' .format(dummy_scores['test_score']))
def launch(self) -> int:
"""Execute the :class:`Resampling <resampling.resampling.Resampling>` resampling.resampling.Resampling object."""
# check input/output paths and parameters
self.check_data_params(self.out_log, self.err_log)
# Setup Biobb
if self.check_restart(): return 0
self.stage_files()
# check mandatory properties
method, over, under = getCombinedMethod(self.method, self.out_log,
self.__class__.__name__)
checkResamplingType(self.type, self.out_log, self.__class__.__name__)
sampling_strategy_over = getSamplingStrategy(
self.sampling_strategy_over, self.out_log, self.__class__.__name__)
sampling_strategy_under = getSamplingStrategy(
self.sampling_strategy_under, self.out_log,
self.__class__.__name__)
# load dataset
fu.log(
'Getting dataset from %s' %
self.io_dict["in"]["input_dataset_path"], self.out_log,
self.global_log)
if 'column' in self.target:
labels = getHeader(self.io_dict["in"]["input_dataset_path"])
skiprows = 1
header = 0
else:
labels = None
skiprows = None
header = None
data = pd.read_csv(self.io_dict["in"]["input_dataset_path"],
header=None,
sep="\s+|;|:|,|\t",
engine="python",
skiprows=skiprows,
names=labels)
train_df = data
ranges = None
le = preprocessing.LabelEncoder()
cols_encoded = []
for column in train_df:
# if type object, LabelEncoder.fit_transform
if train_df[column].dtypes == 'object':
cols_encoded.append(column)
train_df[column] = le.fit_transform(train_df[column])
# defining X
X = train_df.loc[:, train_df.columns != getTargetValue(
self.target, self.out_log, self.__class__.__name__)]
# calling resample method
if self.method == 'smotetomek':
method = method(
smote=over(sampling_strategy=sampling_strategy_over),
tomek=under(sampling_strategy=sampling_strategy_under),
random_state=self.random_state_method)
elif self.method == 'smotenn':
method = method(
smote=over(sampling_strategy=sampling_strategy_over),
enn=under(sampling_strategy=sampling_strategy_under),
random_state=self.random_state_method)
fu.log(
'Target: %s' % (getTargetValue(self.target, self.out_log,
self.__class__.__name__)),
self.out_log, self.global_log)
# resampling
if self.type == 'regression':
fu.log(
'Resampling regression dataset, continuous data will be classified',
self.out_log, self.global_log)
# call resampler class for Regression ReSampling
rs = resampler()
# Create n_bins classes for the dataset
ranges, y, target_pos = rs.fit(
train_df,
target=getTargetValue(self.target, self.out_log,
self.__class__.__name__),
bins=self.n_bins,
balanced_binning=self.balanced_binning,
verbose=0)
# Get the re-sampled data
final_X, final_y = rs.resample(method, train_df, y)
elif self.type == 'classification':
# get X and y
y = getTarget(self.target, train_df, self.out_log,
self.__class__.__name__)
# fit and resample
final_X, final_y = method.fit_resample(X, y)
target_pos = None
# evaluate resampling
if self.evaluate:
fu.log(
'Evaluating data before resampling with RandomForestClassifier',
self.out_log, self.global_log)
cv = RepeatedStratifiedKFold(
n_splits=self.evaluate_splits,
n_repeats=self.evaluate_repeats,
random_state=self.random_state_evaluate)
# evaluate model
scores = cross_val_score(
RandomForestClassifier(class_weight='balanced'),
X,
y,
scoring='accuracy',
cv=cv,
n_jobs=-1)
if not np.isnan(np.mean(scores)):
fu.log(
'Mean Accuracy before resampling: %.3f' %
(np.mean(scores)), self.out_log, self.global_log)
else:
fu.log(
'Unable to calculate cross validation score, NaN was returned.',
self.out_log, self.global_log)
# log distribution before resampling
dist = ''
for k, v in Counter(y).items():
per = v / len(y) * 100
rng = ''
if ranges: rng = str(ranges[k])
dist = dist + 'Class=%d, n=%d (%.3f%%) %s\n' % (k, v, per, rng)
fu.log('Classes distribution before resampling:\n\n%s' % dist,
self.out_log, self.global_log)
# join final_X and final_y in the output dataframe
if header is None:
# numpy
out_df = np.column_stack((final_X, final_y))
else:
# pandas
out_df = final_X.join(final_y)
# if no header, convert np to pd
if header is None: out_df = pd.DataFrame(data=out_df)
# if cols encoded, decode them
if cols_encoded:
for column in cols_encoded:
if header is None:
out_df = out_df.astype({column: int})
out_df[column] = le.inverse_transform(
out_df[column].values.ravel())
# if no header, target is in a different column
if target_pos: t = target_pos
else:
t = getTargetValue(self.target, self.out_log,
self.__class__.__name__)
# log distribution after resampling
if self.type == 'regression':
ranges, y_out, _ = rs.fit(out_df,
target=t,
bins=self.n_bins,
balanced_binning=self.balanced_binning,
verbose=0)
elif self.type == 'classification':
y_out = getTarget(self.target, out_df, self.out_log,
self.__class__.__name__)
dist = ''
for k, v in Counter(y_out).items():
per = v / len(y_out) * 100
rng = ''
if ranges: rng = str(ranges[k])
dist = dist + 'Class=%d, n=%d (%.3f%%) %s\n' % (k, v, per, rng)
fu.log('Classes distribution after resampling:\n\n%s' % dist,
self.out_log, self.global_log)
# evaluate resampling
if self.evaluate:
fu.log(
'Evaluating data after resampling with RandomForestClassifier',
self.out_log, self.global_log)
cv = RepeatedStratifiedKFold(n_splits=3,
n_repeats=3,
random_state=42)
# evaluate model
scores = cross_val_score(
RandomForestClassifier(class_weight='balanced'),
final_X,
y_out,
scoring='accuracy',
cv=cv,
n_jobs=-1)
if not np.isnan(np.mean(scores)):
fu.log(
'Mean Accuracy after resampling a %s dataset with %s method: %.3f'
% (self.type, resampling_methods[self.method]['method'],
np.mean(scores)), self.out_log, self.global_log)
else:
fu.log(
'Unable to calculate cross validation score, NaN was returned.',
self.out_log, self.global_log)
# save output
hdr = False
if header == 0: hdr = True
fu.log(
'Saving resampled dataset to %s' %
self.io_dict["out"]["output_dataset_path"], self.out_log,
self.global_log)
out_df.to_csv(self.io_dict["out"]["output_dataset_path"],
index=False,
header=hdr)
return 0
#evaluate knn with uncalibrated probabilities for imbalanced classification
from numpy import mean
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
#generate dataset
X, y = make_classification(n_samples=10000,
n_features=2,
n_redundant=0,
n_clusters_per_class=1,
weights=[0.99],
flip_y=0,
random_state=4)
#define model
model = KNeighborsClassifier()
#define evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
#evaluate model
scores = cross_val_score(model, X, y, scoring='roc_auc', cv=cv, n_jobs=-1)
#summarize performance
print('Mean ROC AUC: %.3f' % mean(scores))
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=test_split,
stratify=y)
x_train = x_train[:int(x_train.shape[0] * training_fraction)]
y_train = y_train[:int(y_train.shape[0] * training_fraction)]
# Train model
print('Training model...')
# history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=val_split)
train_accuracies = []
train_losses = []
val_accuracies = []
val_losses = []
rskf = RepeatedStratifiedKFold(n_splits=kfolds_splits,
n_repeats=kfolds_repeats)
for train_index, test_index in rskf.split(x_train, y_train):
history = model.fit(x_train[train_index],
y_train[train_index],
batch_size=batch_size,
epochs=epochs,
validation_data=(x_train[test_index],
y_train[test_index]))
train_accuracies.append(history.history['acc'])
train_losses.append(history.history['loss'])
val_accuracies.append(history.history['val_acc'])
val_losses.append(history.history['val_loss'])
train_accuracies = np.array(train_accuracies)
train_losses = np.array(train_losses)
val_accuracies = np.array(val_accuracies)
def test_get_n_splits_for_repeated_stratified_kfold():
n_splits = 3
n_repeats = 4
rskf = RepeatedStratifiedKFold(n_splits, n_repeats)
expected_n_splits = n_splits * n_repeats
assert_equal(expected_n_splits, rskf.get_n_splits())
def evaluate_model(X, y, model): # define evaluation procedure cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=1) # evaluate model scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) return scores
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1337)
# lgb =========================================================================
import lightgbm as lgb
clf_lgb = lgb.LGBMClassifier(objective='binary',
boosting_type='dart',
verbose=-1,
random_state=1337)
scorer = make_scorer(roc_auc_score, greater_is_better=True, needs_proba=True)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1337)
rskf = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=1337)
acc = cross_val_score(estimator=clf_lgb,
X=X_train,
y=y_train,
cv=rskf,
scoring='roc_auc')
acc.mean(), acc.std()
# GridSearchCV needs a predefined plan of the experiments
param_grid = {
'learning_rate': [0.1],
'max_depth': [5, 7, 9, -1],
'min_data_in_leaf': [5, 10, 15],
'num_leaves': [10, 20, 30],
'bagging_freq': [7],
print(">> loading dataset ... ")
path=Path("data/")
train=pd.read_csv(path/"train.csv")
#train = train[:100]
train_ID_code = train["ID_code"].tolist()
train=train.drop("ID_code",axis=1)
test=pd.read_csv(path/"test.csv")
test_ID_code = test["ID_code"].tolist()
test=test.drop("ID_code",axis=1)
##
valid_df = pd.DataFrame({"ID_code": train_ID_code , 'target':-1})
result=np.zeros(test.shape[0])
#
rskf = RepeatedStratifiedKFold(n_splits=4, n_repeats=1,random_state=SEED)
for counter,(train_index, valid_index) in enumerate(rskf.split(train, train.target),1):
K.clear_session()
model = None # Clearing the NN.
model , model_name = create_model(init_dim=200,n0=200,n1=100,n2=50,act='relu')
print ("fold:",counter, " -- model name:",model_name)
sys.stdout.flush()
#Train data
t=train.iloc[train_index]
v = train.iloc[valid_index]
early_stopping = EarlyStopping(monitor='val_auc_roc', patience=2 , mode='max')
model_path = model_name + '.h5'
model_checkpoint = ModelCheckpoint(model_path, monitor='val_auc_roc' , mode='max', save_best_only=True, verbose=1)
results = model.fit(t.drop("target",axis=1),
t.target,
validation_data=(v.drop("target",axis=1),v.target),
def __init__(self, n_splits=10, n_repeats=2, groupcount=10, random_state=0, strategy='quantile'):
self.groupcount = groupcount
self.strategy = strategy
self.cvkwargs = dict(n_splits=n_splits, n_repeats=n_repeats, random_state=random_state)
self.cv = RepeatedStratifiedKFold(**self.cvkwargs)
self.discretizer = KBinsDiscretizer(n_bins=self.groupcount, encode='ordinal', strategy=self.strategy)
def logi(request):
hospital.objects.all().delete()
inp = request.FILES['testinput'].name
print(inp)
train_df = pd.read_csv("junapp/static/junapp/data/train.csv")
# Read CSV test data file into DataFrame
test_df = pd.read_csv("junapp/static/junapp/data/" + inp)
print('The number of samples into the train data is {}.'.format(
train_df.shape[0]))
a = train_df.isnull().sum()
train_data = train_df.copy()
train_data["How many days, immunization service is provided?"].fillna(
train_df["How many days, immunization service is provided?"].median(
skipna=True),
inplace=True)
train_data["How many bed are available in this hospital?"].fillna(
train_df["How many bed are available in this hospital?"].median(
skipna=True),
inplace=True)
train_data.isnull().sum()
test_data = test_df.copy()
test_data["How many days, immunization service is provided?"].fillna(
test_df["How many days, immunization service is provided?"].median(
skipna=True),
inplace=True)
a = test_data.isnull().sum()
print(a)
cols = [
"Does this health facility have its own building?",
"Infrastructure Needs Repairing",
"Number of rooms available in the health facilities? Number",
"How many bed are available in this hospital?",
"OPD service avaliable?", "Immunization Service Avaliable",
"How many days, immunization service is provided?",
"Laboraotry Service Avaliable",
"ASRH (Adolescent Friendly Services) Service Avaliable",
"Mental health Service Avaliable", "Substance abuse Service Avaliable",
"Oral Health Service Avaliable"
]
X = train_data[cols]
y = train_data['Passed Threshold']
# Build a logreg and compute the feature importances
model = LogisticRegression()
# create the RFE model and select 8 attributes
rfe = RFE(model, 13)
rfe = rfe.fit(X, y)
# summarize the selection of the attributes
print('Selected features: %s' % list(X.columns[rfe.support_]))
# -------------------------
rfecv = RFECV(estimator=LogisticRegression(),
step=1,
cv=10,
scoring='accuracy')
rfecv.fit(X, y)
print("Optimal number of features: %d" % rfecv.n_features_)
print('Selected features: %s' % list(X.columns[rfecv.support_]))
Selected_features = [
"Does this health facility have its own building?",
"Infrastructure Needs Repairing",
"Number of rooms available in the health facilities? Number",
"How many bed are available in this hospital?",
"OPD service avaliable?", "Immunization Service Avaliable",
"How many days, immunization service is provided?",
"Laboraotry Service Avaliable",
"ASRH (Adolescent Friendly Services) Service Avaliable",
"Mental health Service Avaliable", "Substance abuse Service Avaliable",
"Oral Health Service Avaliable"
]
X = train_data[Selected_features]
C = np.arange(1e-05, 5.5, 0.1)
scoring = {
'Accuracy': 'accuracy',
'AUC': 'roc_auc',
'Log_loss': 'neg_log_loss'
}
log_reg = LogisticRegression()
# Simple pre-processing estimators
###############################################################################
std_scale = StandardScaler(with_mean=False, with_std=False)
# std_scale = StandardScaler()
# Defining the CV method: Using the Repeated Stratified K Fold
###############################################################################
n_folds = 5
n_repeats = 5
rskfold = RepeatedStratifiedKFold(n_splits=n_folds,
n_repeats=n_repeats,
random_state=2)
# Creating simple pipeline and defining the gridsearch
###############################################################################
log_clf_pipe = Pipeline(steps=[('scale', std_scale), ('clf', log_reg)])
log_clf = GridSearchCV(estimator=log_clf_pipe,
cv=rskfold,
scoring=scoring,
return_train_score=True,
param_grid=dict(clf__C=C),
refit='Accuracy')
log_clf.fit(X, y)
results = log_clf.cv_results_
# print('=' * 20)
print("best params: " + str(log_clf.best_estimator_))
print("best params: " + str(log_clf.best_params_))
print('best score:', (log_clf.best_score_) * 100)
# print('=' * 20)
test_data['Passed Threshold'] = log_clf.predict(
test_data[Selected_features])
test_data['Ward No'] = test_df['Ward No']
test_data['Address'] = test_df['Address']
test_data['Does this health facility have its own building?'] = test_df[
'Does this health facility have its own building?']
test_data['Infrastructure Needs Repairing'] = test_df[
'Infrastructure Needs Repairing']
test_data[
'Number of rooms available in the health facilities? Number'] = test_df[
'Number of rooms available in the health facilities? Number']
test_data['How many bed are available in this hospital?'] = test_df[
'How many bed are available in this hospital?']
test_data['OPD service avaliable?'] = test_df['OPD service avaliable?']
test_data['Immunization Service Avaliable'] = test_df[
'Immunization Service Avaliable']
test_data['Oral Health Service Avaliable'] = test_df[
'Oral Health Service Avaliable']
test_data['Type of Health facility'] = test_df['Type of Health facility']
test_data['longt'] = test_df['longt']
test_data['lat'] = test_df['lat']
submission = test_data[[
'Ward No', 'Address', 'Type of Health facility', 'Passed Threshold',
'longt', 'lat', 'Does this health facility have its own building?',
'Infrastructure Needs Repairing',
'Number of rooms available in the health facilities? Number',
'How many bed are available in this hospital?',
'OPD service avaliable?', 'Immunization Service Avaliable',
'Oral Health Service Avaliable', 'Type of Health facility'
]]
submission.to_csv("submission.csv", index=False)
submission.tail()
# dict = {}
result = pd.read_csv("submission.csv")
# dict = {
# 'ward': result['Ward No'],
# 'address': result['Address'],
# 'type': result['Type of Health facility'],
# 'pass': result['Passed Threshold'],
# 'longt': result['longt'],
# 'lat': result['lat']
# }
# out = hospital.objects.create(ward_no= dict['ward'], address= dict['address'], type= dict['type'], passed= dict['pass'])
# out.save()
print('Here')
a = len(result['Ward No'])
for i in range(a):
hospitals = hospital.objects.create(
ward_no=result['Ward No'][i],
address=result['Address'][i],
type=result['Type of Health facility'][i],
passed=result['Passed Threshold'][i],
lat=result['lat'][i],
log=result['longt'][i],
building=result['Does this health facility have its own building?']
[i],
repair=result['Infrastructure Needs Repairing'][i],
noofrooms=result[
'Number of rooms available in the health facilities? Number']
[i],
beds=result['How many bed are available in this hospital?'][i],
optservice=result['OPD service avaliable?'][i],
immunizationservice=result['Immunization Service Avaliable'][i],
oralhealth=result['Oral Health Service Avaliable'][i]
# lat=str(round(result['lat'][i],4)),
# log=str(round(result['long'][i],4))
)
hospitals.save()
# print(result['Ward No'][i])
# gethospitals = hospital.objects.all()
# finaldata = []
# for i in gethospitals:
# finaldata.append(
# {'address': i.address, 'building': i.building, 'repair': i.repair, 'beds': i.beds, 'room': i.noofrooms})
#
# return render(request, 'junapp/logistic.html', {'result': finaldata})
# return render(request, 'junapp/logistic.html')
passed = hospital.objects.filter(passed=1).count()
failed = hospital.objects.filter(passed=0).count()
total = passed + failed
gethospitals = hospital.objects.all()
finaldata = []
for i in gethospitals:
finaldata.append({
'address': i.address,
'building': i.building,
'immunizationservice': i.immunizationservice,
'beds': i.beds,
'optservice': i.optservice,
'oralhealth': i.oralhealth,
'type': i.type,
'repair': i.repair,
'pass': i.passed,
'room': i.noofrooms
})
finalresult = {'finaldata': finaldata, 'pass': passed, 'fail': failed}
return render(request, 'junapp/logistic.html', {'result': finalresult})
def calculate_reliability(self, input, output, models_dict, file_path):
"""
Calculates reliability estimations for selected models based on test data within the single CV procedure.
The calculated estimations are compared with prediction accuracy by using Pearson's test.
:param input: array-like, shape (n_samples, n_features)
Training data.
:param output: array-like, shape (n_samples, )
True labels
:param models_dict: dictionary
Selected classifiers with details for grid-search and feature selection.
:param file_path: String
File path for saving data
:return: DataFrame
Calculated correlation coefficients.
"""
from ReliabilityEstimation import ReliabilityEstimation
X, y = input, output
df_res = pd.DataFrame(columns=[
'Classifier', 'Corr_Oref', 'p_Oref', 'Corr_DENS', 'p_DENS',
'Corr_CNK', 'p_CNK', 'Corr_LCV', 'p_LCV'
])
for m in models_dict:
rel_ref = []
rel_dens = []
rel_cnk = []
rel_lcv = []
acc_probabilities = []
predicted_label = []
true_label = []
clf = m['classifier']
fs = m['fs_method']
df_raw_res = pd.DataFrame(columns=[
'Oref', 'DENS', 'CNK', 'LCV', 'Accuracy', 'Predicted_label',
'True_label'
])
# leave one out cross - validation
skf = RepeatedStratifiedKFold(n_splits=self.N_inner,
n_repeats=1,
random_state=88)
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
fs_smote_clf = Pipeline([('oversampling',
SMOTE(random_state=88,
k_neighbors=3)),
('feature_selection', fs),
('classifier', clf)])
param_grid = m['grid']
classifier = copy.deepcopy(fs_smote_clf)
if m['name'] == 'MLP_mRMR_50':
gridsearch_cv = fs_smote_clf
else:
gridsearch_cv = GridSearchCV(fs_smote_clf,
param_grid,
cv=10,
scoring='roc_auc')
gridsearch_cv.fit(X_train, y_train)
# predicted class
y_predict = gridsearch_cv.predict(X_test)
predicted_label.append(y_predict)
# predicted probabilities
probas_ = gridsearch_cv.predict_proba(X_test)
acc = self.ind_classification_accuracy(probas_, y_test)
acc_probabilities.append(acc)
true_label.append(y_test)
rel = ReliabilityEstimation()
ref = list(
map(lambda prob: rel.o_ref(prob), np.max(probas_, axis=1)))
dens = list(map(lambda test: rel.DENS(X_train, test), X_test))
cnk = list(
map(
lambda test: rel.CNK(
X_train, y_train, test,
gridsearch_cv.predict_proba(test.reshape(1, -1))),
X_test))
lcv = list(
map(
lambda test: rel.LCV(X_train, y_train, test, 40,
classifier), X_test))
rel_ref.append(ref)
rel_dens.append(dens)
rel_cnk.append(cnk)
rel_lcv.append(lcv)
merged_rel_ref = np.concatenate(rel_ref).ravel()
merged_rel_dens = np.concatenate(rel_dens).ravel()
merged_rel_cnk = np.concatenate(rel_cnk).ravel()
merged_rel_lcv = np.concatenate(rel_lcv).ravel()
merged_acc = np.concatenate(acc_probabilities).ravel()
merged_predicted_labels = np.concatenate(predicted_label).ravel()
merged_true_labels = np.concatenate(true_label).ravel()
df_raw_res["Oref"] = merged_rel_ref
df_raw_res["DENS"] = merged_rel_dens
df_raw_res["CNK"] = merged_rel_cnk
df_raw_res["LCV"] = merged_rel_lcv
df_raw_res["Accuracy"] = merged_acc
df_raw_res["Predicted_label"] = merged_predicted_labels
df_raw_res["True_label"] = merged_true_labels
df_raw_res.to_csv(file_path + 'Reliability_data_' + m['name'] +
'.csv',
header=True)
correlation_ref, p_ref = spearmanr(merged_rel_ref, merged_acc)
correlation_dens, p_dens = spearmanr(merged_rel_dens, merged_acc)
correlation_cnk, p_cnk = spearmanr(merged_rel_cnk, merged_acc)
correlation_lcv, p_lcv = spearmanr(merged_rel_lcv, merged_acc)
df_res = df_res.append(
{
'Classifier': m['name'],
'Corr_Oref': correlation_ref,
'p_Oref': p_ref,
'Corr_DENS': correlation_dens,
'p_DENS': p_dens,
'Corr_CNK': correlation_cnk,
'p_CNK': p_cnk,
'Corr_LCV': correlation_lcv,
'p_LCV': p_lcv
},
ignore_index=True)
return df_res
def fit_model_kfold(
features,
model,
analysis_type="classification",
reduce_set=True,
reduced_set_size=100,
reduced_set_max_correlation=0.9,
n_repeats=1,
random_state=42,
n_splits=None,
compute_shap=True,
):
"""Classify graphs from extracted features with kfold.
Args:
features (dataframe): extracted features
model (str): model to preform analysis
analysis_type (str): 'classification' or 'regression'
reduce_set (bool): is True, the classification will be rerun
on a reduced set of top features (from shapely analysis)
reduce_set_size (int): number of features to keep for reduces set
reduced_set_max_correlation (float): to discared highly correlated top features
in reduced set of features
n_repeats (int): number of k-fold repeats
random_state (int): rng seed
n_splits (int): numbere of split for k-fold, None=automatic estimation
compute_shap (bool): compute SHAP values or not
Returns:
(dict): dictionary with results
"""
if model is None:
raise Exception("Please provide a model for classification")
X, y = features_to_Xy(features)
if analysis_type == "classification":
if n_splits is None:
n_splits = _number_folds(y)
L.info("Using %s splits", str(n_splits))
folds = RepeatedStratifiedKFold(n_splits=n_splits,
n_repeats=n_repeats,
random_state=random_state)
elif analysis_type == "regression":
if n_splits is None:
n_splits = _number_folds(y)
L.info("Using %s splits", str(n_splits))
folds = RepeatedKFold(n_splits=n_splits,
n_repeats=n_repeats,
random_state=random_state)
acc_scores, shap_values = _evaluate_kfold(X, y, model, folds,
analysis_type, compute_shap)
_print_accuracy(acc_scores, analysis_type)
if compute_shap:
mean_shap_values, shap_feature_importance = _get_shap_feature_importance(
shap_values)
else:
mean_shap_values = None
shap_feature_importance = None
analysis_results = {
"X": X,
"y": y,
"acc_scores": acc_scores,
"mean_shap_values": mean_shap_values,
"shap_values": shap_values,
"shap_feature_importance": shap_feature_importance,
"reduced_features": None,
}
if not reduce_set:
return analysis_results
if not compute_shap:
return analysis_results
reduced_features = _get_reduced_feature_set(
X,
shap_feature_importance,
n_top_features=reduced_set_size,
alpha=reduced_set_max_correlation,
)
reduced_acc_scores, reduced_shap_values = _evaluate_kfold(
X[reduced_features], y, model, folds, analysis_type, compute_shap)
_print_accuracy(reduced_acc_scores, analysis_type, reduced=True)
(
reduced_mean_shap_values,
reduced_shap_feature_importance,
) = _get_shap_feature_importance(reduced_shap_values)
analysis_results.update({
"reduced_features":
reduced_features,
"reduced_shap_values":
reduced_shap_values,
"shap_values":
shap_values,
"reduced_acc_scores":
reduced_acc_scores,
"reduced_mean_shap_values":
reduced_mean_shap_values,
"reduced_shap_feature_importance":
reduced_shap_feature_importance,
})
return analysis_results
)
param_grid = [
{
"regressor__C": np.logspace(-3, 0, 4),
"regressor__solver": ["liblinear"],
"regressor__penalty": ["l1"]
},
{
"regressor__C": np.logspace(-3, 0, 4),
"regressor__solver": ["lbfgs"],
"regressor__penalty": ["l2"] # these are actually just the defaults
}
]
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5)
grid = GridSearchCV(
pipe,
param_grid,
scoring="recall",
cv=cv,
return_train_score=True,
verbose=10
)
### Scoring ###
def print_model_scores(model,
scoring_list = [
accuracy_score,
def main(dataset_name):
dataset = load_dataset()
raw_data = np.asarray(dataset['raw']['data'])
raw_label = np.asarray(dataset['raw']['label'])
num_classes = len(np.unique(raw_label))
rskf = RepeatedStratifiedKFold(n_splits=k_folds, n_repeats=k_fold_reps, random_state=42)
for fs_method, fs_range in fs_methods:
print('FS-Method : ', fs_method.__name__)
nfeats = []
accuracies = []
svc_accuracies = []
BAs = []
svc_BAs = []
mAPs = []
svc_mAPs = []
mus = []
name = dataset_name + '_mu_' + str(mu)
print(name)
for j, (train_index, test_index) in enumerate(rskf.split(raw_data, raw_label)):
print('k_fold', j, 'of', k_folds*k_fold_reps)
train_data, train_labels = raw_data[train_index].copy(), raw_label[train_index].copy()
test_data, test_labels = raw_data[test_index].copy(), raw_label[test_index].copy()
train_labels = to_categorical(train_labels, num_classes=num_classes)
test_labels = to_categorical(test_labels, num_classes=num_classes)
valid_features = np.where(np.abs(train_data).sum(axis=0) > 0)[0]
if len(valid_features) < train_data.shape[1]:
print('Removing', train_data.shape[1] - len(valid_features), 'zero features')
train_data = train_data[:, valid_features]
test_data = test_data[:, valid_features]
model_kwargs = {
# 'nclasses': num_classes,
'mu': mu / len(train_data),
'degree': 3
}
print('mu :', model_kwargs['mu'], ', batch_size :', batch_size)
svc_kwargs = {
'C': 1.0,
'solver': 0.
}
print('Starting feature selection')
best_fs = 0
best_value = None
for fs_value in fs_range:
fs_class = fs_method(10, fs_value, matlab_engine=matlab_engine)
fs_class.fit(train_data, 2. * train_labels[:, -1] - 1.)
svc_train_data = fs_class.transform(train_data)
norm = normalization_func()
svc_train_data_norm = norm.fit_transform(svc_train_data)
for s in [0, 1, 2, 3]:
for my_c in [0.001, 0.01, 0.1, 0.5, 1.0, 1.4, 1.5, 1.6, 2.0, 2.5, 5.0, 25.0, 50.0, 100.0]:
cmd = '-v 5 -s ' + str(s) + ' -c ' + str(my_c) + ' -q'
cv = liblinearutil.train((2 * train_labels[:, -1] - 1).tolist(), svc_train_data_norm.tolist(), cmd)
if cv > best_fs:
best_fs = cv
best_value = fs_value
print('best fs_value: ', best_value)
fs_class = fs_method(200, best_value, matlab_engine=matlab_engine)
fs_class.fit(train_data, 2. * train_labels[:, -1] - 1.)
print('Finishing feature selection')
for i, n_features in enumerate([10, 50, 100, 150, 200]):
n_accuracies = []
n_svc_accuracies = []
n_BAs = []
n_svc_BAs = []
n_mAPs = []
n_svc_mAPs = []
n_train_accuracies = []
print('n_features : ', n_features)
fs_class.n_features_to_select = n_features
svc_train_data = fs_class.transform(train_data)
svc_test_data = fs_class.transform(test_data)
norm = normalization_func()
svc_train_data_norm = norm.fit_transform(svc_train_data)
svc_test_data_norm = norm.transform(svc_test_data)
bestcv = -1
bestc = None
bestSolver = None
for s in [0, 1, 2, 3]:
for my_c in [0.001, 0.01, 0.1, 0.5, 1.0, 1.4, 1.5, 1.6, 2.0, 2.5, 5.0, 25.0, 50.0, 100.0]:
cmd = '-v 5 -s ' + str(s) + ' -c ' + str(my_c) + ' -q'
cv = liblinearutil.train((2 * train_labels[:, -1] - 1).tolist(), svc_train_data_norm.tolist(), cmd)
if cv > bestcv:
bestcv = cv
bestc = my_c
bestSolver = s
svc_kwargs['C'] = bestc
svc_kwargs['solver'] = bestSolver
print('Best -> C:', bestc, ', s:', bestSolver, ', acc:', bestcv)
for r in range(reps):
model = train_SVC(svc_train_data_norm, train_labels, svc_kwargs)
_, accuracy, test_pred = liblinearutil.predict(
(2 * test_labels[:, -1] - 1).tolist(), svc_test_data_norm.tolist(), model, '-q'
)
test_pred = np.asarray(test_pred)
n_svc_accuracies.append(accuracy[0])
n_svc_BAs.append(balance_accuracy(test_labels, test_pred))
n_svc_mAPs.append(average_precision_score(test_labels[:, -1], test_pred))
del model
model = train_Keras(svc_train_data, train_labels, svc_test_data, test_labels, model_kwargs)
train_data_norm = model.normalization.transform(svc_train_data)
test_data_norm = model.normalization.transform(svc_test_data)
test_pred = model.predict(test_data_norm)
n_BAs.append(balance_accuracy(test_labels, test_pred))
n_mAPs.append(average_precision_score(test_labels[:, -1], test_pred))
n_accuracies.append(model.evaluate(test_data_norm, test_labels, verbose=0)[-1])
n_train_accuracies.append(model.evaluate(train_data_norm, train_labels, verbose=0)[-1])
del model
K.clear_session()
print(
'n_features : ', n_features,
', acc : ', n_accuracies[-1],
', BA : ', n_BAs[-1],
', mAP : ', n_mAPs[-1],
', train_acc : ', n_train_accuracies[-1],
', svc_acc : ', n_svc_accuracies[-1],
', svc_BA : ', n_svc_BAs[-1],
', svc_mAP : ', n_svc_mAPs[-1],
)
if i >= len(accuracies):
accuracies.append(n_accuracies)
svc_accuracies.append(n_svc_accuracies)
BAs.append(n_BAs)
mAPs.append(n_mAPs)
svc_BAs.append(n_svc_BAs)
svc_mAPs.append(n_svc_mAPs)
nfeats.append(n_features)
mus.append(model_kwargs['mu'])
else:
accuracies[i] += n_accuracies
svc_accuracies[i] += n_svc_accuracies
BAs[i] += n_BAs
mAPs[i] += n_mAPs
svc_BAs[i] += n_svc_BAs
svc_mAPs[i] += n_svc_mAPs
output_filename = directory + 'LinearSVC_' + fs_method.__name__ + '.json'
if not os.path.isdir(directory):
os.makedirs(directory)
info_data = {
'reps': reps,
'classification': {
'mus': mus,
'n_features': nfeats,
'accuracy': accuracies,
'mean_accuracy': np.array(accuracies).mean(axis=1).tolist(),
'svc_accuracy': svc_accuracies,
'mean_svc_accuracy': np.array(svc_accuracies).mean(axis=1).tolist(),
'BA': BAs,
'mean_BA': np.array(BAs).mean(axis=1).tolist(),
'mAP': mAPs,
'mean_mAP': np.array(mAPs).mean(axis=1).tolist(),
'svc_BA': svc_BAs,
'svc_mean_BA': np.array(svc_BAs).mean(axis=1).tolist(),
'svc_mAP': svc_mAPs,
'svc_mean_mAP': np.array(svc_mAPs).mean(axis=1).tolist(),
}
}
for k, v in info_data['classification'].items():
if 'mean' in k:
print(k, v)
with open(output_filename, 'w') as outfile:
json.dump(info_data, outfile)
sbsMinIdx = np.random.choice(minIdx,
int(majSize * imbRatio),
replace=False)
minDel = np.setdiff1d(minIdx, sbsMinIdx)
if len(minDel) > 0:
imbY = np.delete(imbY, minDel)
imbX = np.delete(imbX, minDel, axis=0)
print("to size: " + str(np.sum(imbY == c)))
techNames = []
imbSizesTxt = []
for techType, a in [['none', 0], ['WeightedBase', 0], ['SMOTE', 0],
['mixup', 0.1], ['remix', 0.1]]:
techNames = np.append(techNames, techType + str(a))
imbSizesTxt = np.append(imbSizesTxt, str(imbRatio))
rskf = RepeatedStratifiedKFold(n_splits=2,
n_repeats=10,
random_state=36851234)
tmpGm = np.array([])
tmpFm = np.array([])
tmpBa = np.array([])
tmpBp = np.array([])
tmpBmc = np.array([])
tmpBb = np.array([])
for train_index, test_index in rskf.split(imbX, imbY):
X_train, X_test = imbX[train_index, :], imbX[test_index, :]
y_train, y_test = imbY[train_index], imbY[test_index]
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = np.clip(X_train, -5, 5)
X_test = np.clip(X_test, -5, 5)
def runBS(input, output, classifier, oDim, lenTrainer):
inputClass = input[:lenTrainer, :]
inputValid = input[lenTrainer:, :]
outputClass = output[:lenTrainer]
outputValid = output[lenTrainer:]
decision = []
success = 0
probs = np.zeros((len(outputClass), oDim))
nt = np.zeros(len(outputClass))
#preds = [[]]*len(outputClass)
random_state = np.random.RandomState(0)
rskf = RepeatedStratifiedKFold(n_splits=2,
n_repeats=np.power(2, 10),
random_state=random_state)
for train, test in rskf.split(inputClass, outputClass):
random_state = np.random.RandomState(0)
clf = None
if classifier['type'] == 'svm':
clf = svm.SVC(kernel=classifier['kernel'],
degree=classifier['degree'],
probability=True,
random_state=random_state)
elif classifier['type'] == 'lda':
clf = LDA(solver="svd", store_covariance=True)
elif classifier['type'] == 'knn':
clf = KNeighborsClassifier(5)
probas_ = clf.fit(inputClass[train],
outputClass[train]).predict_proba(inputClass[test])
#pred_ = clf.fit(inputClass[train], outputClass[train]).predict(inputClass[test])
'''
print(probas_)
print(pred_)
print(outputClass[train])
'''
for ip, it in enumerate(test):
probs[it, :] += probas_[ip, :]
#preds[it].append(pred_[ip])
nt[it] += 1.0
# Validation part
clf_v = None
if classifier['type'] == 'svm':
clf_v = svm.SVC(kernel=classifier['kernel'],
degree=classifier['degree'],
probability=True,
random_state=random_state)
elif classifier['type'] == 'lda':
clf_v = LDA(solver="svd", store_covariance=True)
elif classifier['type'] == 'knn':
clf_v = KNeighborsClassifier(5)
probas_v = clf_v.fit(inputClass, outputClass).predict_proba(inputValid)
#pred_v = clf_v.fit(inputClass, outputClass).predict(inputValid)
prob_v = []
pred_v = []
for ipv in probas_v:
prob_v.append(max(ipv))
pred_v.append(np.argmax(ipv))
prob_v = np.array(prob_v)
pred_v = np.array(pred_v)
'''
prob_t = []; pred_t = []
for ie in range(len(nt)):
med = probs[it,:]/nt[it]
prob_t.append(max(med))
(values, counts) = np.unique(preds[it], return_counts=True)
#pred_t.append(values[np.argmax(counts)])
pred_t.append(np.argmax(med))
prob_t = np.array(prob_t)
pred_t = np.array(pred_t)
'''
score_t = np.zeros((len(outputClass), oDim))
for ip in range(len(outputClass)):
score_t[ip, :] = probs[ip, :] / nt[ip]
pred_t = []
prob_t = []
for il in score_t:
posLab = np.argmax(il)
prob_t.append(max(il))
if posLab == 0:
pred_t.append(0)
elif posLab == 1:
pred_t.append(1)
else:
pred_t.append(2)
pred_t = np.array(pred_t)
prob_t = np.array(prob_t)
return pred_t, prob_t, pred_v, prob_v
# Define models and parameters
model = RandomForestClassifier(random_state=0,
n_jobs=-1,
class_weight='balanced_subsample')
n_estimators = [400, 600, 700, 1000]
max_features = [2, 3]
max_depth = [4, 5]
# Define grid search
grid = dict(n_estimators=n_estimators,
max_features=max_features,
max_depth=max_depth)
# 3 - Define how it will be seaarched, being stratified to preserve the two calsses, splitting in 10 and repeating 3 random times
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=6, random_state=0)
# Create model with grid and CV structure
grid_search = GridSearchCV(estimator=model,
param_grid=grid,
n_jobs=-1,
cv=cv,
error_score=0,
verbose=2)
# 4 - Find hyperparameters
grid_result = grid_search.fit(X_train, y_train.values.ravel())
# Summarize results ################################
print("!! Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
def main():
#if dataset is not provided on call terminate
if len(sys.argv) < 3:
print(
"usage: python classifier_metrics.py <train_data_file> <test_data_file> "
)
sys.exit()
#pass dataset and get the matrix containing the data vectors and data targets
ret_value = data_preprocessing(sys.argv[1])
data_matrix = ret_value[0]
category_labels = ret_value[1]
#create stratified k_fold iterator to calculate metrics
k_fold = RepeatedStratifiedKFold(n_splits=10, n_repeats=3)
sk_fold = StratifiedKFold(n_splits=10)
metrics = [
'accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted'
]
#create RandomForest classifier and calculate metrics
rf_clf = RandomForestClassifier(n_jobs=-1)
rf_result = cross_validate(rf_clf,
data_matrix,
category_labels,
cv=k_fold,
scoring=metrics,
return_train_score=False,
n_jobs=-1)
print "RANDOM FOREST:"
for key, value in rf_result.iteritems():
print key + " : " + str(np.round_(np.mean(value), decimals=5))
print("\n")
#create MNB classifier and calculate metrics
#scale data matrix to positive values because MNB does not accept negative values
#increasing scaling range increases accuracy up until scale is around 10
scaler = preprocessing.MinMaxScaler(feature_range=(0, 10), copy=True)
scaled_data_matrix = scaler.fit_transform(data_matrix)
mnb_clf = MultinomialNB()
mnb_result = cross_validate(mnb_clf,
scaled_data_matrix,
category_labels,
cv=k_fold,
scoring=metrics,
return_train_score=False,
n_jobs=-1)
print "MULTINOMIAL NAIVE BAYES:"
for key, value in mnb_result.iteritems():
print key + " : " + str(np.round_(np.mean(value), decimals=5))
print("\n")
#load hyperparameters for svc classifier from file hyperparameter_values.py
kernel_hp = HYPERPARAMETER_VALUES['kernel']
c_hp = HYPERPARAMETER_VALUES['C']
gamma_hp = 'auto'
if kernel_hp == 'rbf':
gamma_hp = HYPERPARAMETER_VALUES['gamma']
#create svc classifier and calculate metrics
svc_clf = SVC(kernel=kernel_hp, C=c_hp, gamma=gamma_hp)
svc_result = cross_validate(svc_clf,
data_matrix,
category_labels,
cv=k_fold,
scoring=metrics,
return_train_score=False,
n_jobs=-1)
print "svm.SVC (kernel=" + kernel_hp + " ,C=" + str(
c_hp) + ", gamma=" + str(gamma_hp) + ")"
for key, value in svc_result.iteritems():
print key + " : " + str(np.round_(np.mean(value), decimals=5))
print("\n")
#create KNN(my implementation) classifier and calculate metrics
knn_clf = MyKNN(k=10)
knn_result = cross_validate(knn_clf,
data_matrix,
category_labels,
cv=sk_fold,
scoring=metrics,
return_train_score=False)
print "My implementation of KNN(brute force):"
for key, value in knn_result.iteritems():
print key + " : " + str(np.round_(np.mean(value), decimals=5))
print("\n")
#Beat the benchmark
TITLE_WEIGHT = 5
#preprocess the data differently to achieve better score
btb_ret_value = btb_data_preprocessing(sys.argv[1],
title_weight=TITLE_WEIGHT,
n_comp=250,
ret_vectorizers=True)
btb_data_matrix = btb_ret_value[0]
btb_category_labels = btb_ret_value[1]
#i chose svc because it was the better scoring classifier
#calculate metrics
btb_clf = SVC(kernel=kernel_hp,
C=c_hp,
gamma=gamma_hp,
class_weight='balanced',
probability=False)
btb_result = cross_validate(btb_clf,
btb_data_matrix,
btb_category_labels,
cv=k_fold,
scoring=metrics,
return_train_score=False,
n_jobs=-1)
print "(Beat the benchmark)svm.SVC (kernel=" + kernel_hp + " ,C=" + str(
c_hp) + ", gamma=" + str(gamma_hp) + ")"
for key, value in btb_result.iteritems():
print key + " : " + str(np.round_(np.mean(value), decimals=5))
print("\n")
#train the classifier with the train data
#cross_validate() does not train the classifier object passed to it but a copy of it
btb_clf.fit(btb_data_matrix, btb_category_labels) #refit
#get the vectorizers and transformers used to fit and trasform the train data
count_vectorizer = btb_ret_value[2]
tfidf_transformer = btb_ret_value[3]
svd = btb_ret_value[4]
le = btb_ret_value[5]
#read test data and trasform them using the above vectorizers/transformers
test_data = pd.read_csv(sys.argv[2], sep="\t")
test_redu_matrix = test_data_transformation(test_data, count_vectorizer,
tfidf_transformer, svd,
TITLE_WEIGHT)
#do the class predictions for the test data
test_category_pred = btb_clf.predict(test_redu_matrix)
#store predictions to file
create_pred_file(test_data, test_category_pred, le)
#store metrics to file
create_eval_file(mnb_result, rf_result, svc_result, knn_result, btb_result,
metrics)
def train_job(train_cfg, train_dmatrix, val_dmatrix, train_val_dmatrix,
model_dir, checkpoint_dir, is_master):
"""Train and save XGBoost model using data on current node.
If doing distributed training, XGBoost will use rabit to sync the trained model between each boosting iteration.
Trained model is only saved if 'is_master' is True.
:param train_cfg: Training hyperparameter configurations
:param train_dmatrix: Training Data Matrix
:param val_dmatrix: Validation Data Matrix
:param train_val_dmatrix: Training + Validation Data Matrix
:param model_dir: Directory where model will be saved
:param is_master: True if single node training, or the current node is the master node in distributed training.
"""
# Parse arguments for train() API
num_round = train_cfg.pop("num_round")
# Parse arguments for intermediate model callback
save_model_on_termination = train_cfg.pop('save_model_on_termination',
"false")
# Evaluation metrics to use with train() API
tuning_objective_metric_param = train_cfg.pop("_tuning_objective_metric",
None)
eval_metric = train_cfg.get("eval_metric")
cleaned_eval_metric, configured_feval, tuning_objective_metric = train_utils.get_eval_metrics_and_feval(
tuning_objective_metric_param, eval_metric)
if cleaned_eval_metric:
train_cfg['eval_metric'] = cleaned_eval_metric
else:
train_cfg.pop('eval_metric', None)
early_stopping_rounds = train_cfg.pop('early_stopping_rounds', None)
early_stopping_data_name = 'validation' if val_dmatrix else None
early_stopping_metric = None
if early_stopping_rounds:
if tuning_objective_metric:
early_stopping_metric = tuning_objective_metric[-1]
elif eval_metric:
early_stopping_metric = eval_metric[-1]
logging.info("Train matrix has {} rows and {} columns".format(
train_dmatrix.num_row(), train_dmatrix.num_col()))
if val_dmatrix:
logging.info("Validation matrix has {} rows".format(
val_dmatrix.num_row()))
try:
kfold = train_cfg.pop("_kfold", None)
if kfold is None:
xgb_model, iteration, callbacks, watchlist = get_callbacks_watchlist(
train_dmatrix=train_dmatrix,
val_dmatrix=val_dmatrix,
model_dir=model_dir,
checkpoint_dir=checkpoint_dir,
early_stopping_data_name=early_stopping_data_name,
early_stopping_metric=early_stopping_metric,
early_stopping_rounds=early_stopping_rounds,
save_model_on_termination=save_model_on_termination,
is_master=is_master)
add_debugging(callbacks=callbacks,
hyperparameters=train_cfg,
train_dmatrix=train_dmatrix,
val_dmatrix=val_dmatrix)
bst = xgb.train(train_cfg,
train_dmatrix,
num_boost_round=num_round - iteration,
evals=watchlist,
feval=configured_feval,
callbacks=callbacks,
xgb_model=xgb_model,
verbose_eval=False)
else:
num_cv_round = train_cfg.pop("_num_cv_round", 1)
logging.info(
"Run {}-round of {}-fold cross validation with {} rows".format(
num_cv_round, kfold, train_val_dmatrix.num_row()))
bst = []
evals_results = []
num_class = train_cfg.get("num_class", None)
objective = train_cfg.get("objective", None)
# RepeatedStratifiedKFold expects X as array-like of shape (n_samples, n_features)
X = range(train_val_dmatrix.num_row())
y = train_val_dmatrix.get_label(
) if num_class or objective.startswith("binary:") else None
rkf = RepeatedStratifiedKFold(n_splits=kfold, n_repeats=num_cv_round) if y is not None \
else RepeatedKFold(n_splits=kfold, n_repeats=num_cv_round)
for train_index, val_index in rkf.split(X=X, y=y):
cv_train_dmatrix = train_val_dmatrix.slice(train_index)
cv_val_dmatrix = train_val_dmatrix.slice(val_index)
xgb_model, iteration, callbacks, watchlist = get_callbacks_watchlist(
train_dmatrix=cv_train_dmatrix,
val_dmatrix=cv_val_dmatrix,
model_dir=model_dir,
checkpoint_dir=checkpoint_dir,
early_stopping_data_name=early_stopping_data_name,
early_stopping_metric=early_stopping_metric,
early_stopping_rounds=early_stopping_rounds,
save_model_on_termination=save_model_on_termination,
is_master=is_master,
fold=len(bst))
add_debugging(callbacks=callbacks,
hyperparameters=train_cfg,
train_dmatrix=cv_train_dmatrix,
val_dmatrix=cv_val_dmatrix)
evals_result = {}
logging.info(
"Train cross validation fold {}".format((len(bst) %
kfold) + 1))
booster = xgb.train(train_cfg,
cv_train_dmatrix,
num_boost_round=num_round - iteration,
evals=watchlist,
feval=configured_feval,
evals_result=evals_result,
callbacks=callbacks,
xgb_model=xgb_model,
verbose_eval=False)
bst.append(booster)
evals_results.append(evals_result)
if len(bst) % kfold == 0:
logging.info(
"The metrics of round {} cross validation".format(
int(len(bst) / kfold)))
print_cv_metric(num_round, evals_results[-kfold:])
if num_cv_round > 1:
logging.info(
"The overall metrics of {}-round cross validation".format(
num_cv_round))
print_cv_metric(num_round, evals_results)
except Exception as e:
for customer_error_message in CUSTOMER_ERRORS:
if customer_error_message in str(e):
raise exc.UserError(str(e))
exception_prefix = "XGB train call failed with exception"
raise exc.AlgorithmError("{}:\n {}".format(exception_prefix, str(e)))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
if is_master:
if type(bst) is not list:
model_location = os.path.join(model_dir, MODEL_NAME)
bst.save_model(model_location)
logging.debug("Stored trained model at {}".format(model_location))
else:
for fold in range(len(bst)):
model_location = os.path.join(model_dir,
f"{MODEL_NAME}-{fold}")
bst[fold].save_model(model_location)
logging.debug("Stored trained model {} at {}".format(
fold, model_location))
traindataall = datapreparing['traindataall']
testdataall = datapreparing['testdataall']
WHO_train = traindataall['WHO']
WHO_test = testdataall['WHO']
print("Preparing data done.")
ALLgroup_bestmodscore = []
ALLgroup_bestmodmeanscore = []
ALLgroupresults = {}
for key in select_features.keys():
val = select_features[key]
select_features_mod = val
select_U_features_mod = list(val.values())[0]
select_U_data_mod = list(val.values())[1]
select_U_data_test_mod = list(val.values())[2]
rskf = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)
k = 0
ALLmodscores = []
ALLmod = {}
LRscores = []
SVMscores = []
KNNscores = []
NBscores = []
RFscores = []
Stackscores = []
Lassoscores = []
param_grid_svm = {
'kernel': ['linear', 'rbf'],
'C': [0.1, 1, 10, 50],
# Creamos un ColumnTransformer para el StandardScaler
scaler = ColumnTransformer([
('scaler_media', scaler_media, slice(0, 8)),
('scaler_moda', scaler_moda, slice(8, len(X.columns)))
])
# Creamos el Pipeline incorporando ColumnTransformer y Clasificador
pipeline = Pipeline([
('imputer', imputer),
('scaler', scaler),
('svm', SVC(random_state=random_state, class_weight=class_weight))
])
# InnerCV (GridSearchCV de 2-folds 5-times (stratified) para obtener mejores parámetros)
rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=5, random_state=random_state) # inner
grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, scoring=SCORING, cv=rskf)
# # OuterCV (Validación cruzada de 5 folds (stratified) para estimar Accuracy)
# scores = cross_validate(estimator=grid_search, X=X, y=y, cv=5, error_score='raise', return_estimator=True, scoring=SCORING) # outer
# print('Scores: {}' .format(scores['test_score']))
# print('Mean score: {}' .format(np.mean(scores['test_score'])))
#
# # Creamos clasificador 'tonto' y obtenemos resultados también con validación cruzada (CV=5) para tener resultados más realistas
# dummy_clf = DummyClassifier(strategy='most_frequent', random_state=random_state)
# dummy_scores = cross_validate(estimator=dummy_clf, X=X, y=y, cv=5, error_score='raise', return_estimator=True, scoring=SCORING)
# print('Dummy scores: {}' .format(dummy_scores['test_score']))
# print('Dummy mean score: {}' .format(np.mean(dummy_scores['test_score'])))
# Matriz de confusion
results = cross_val_predict(grid_search, X, y, cv=5)
'feature_fraction': 0.02,
'learning_rate': 0.001,
'max_depth': 6,
'metric':'auc',
'min_data_in_leaf': 100,
'min_sum_hessian_in_leaf': 10.0,
'num_leaves': 13,
'n_jobs': 30,
'tree_learner': 'serial',
'objective': 'binary',
'verbosity': -1
}
result=np.zeros(test.shape[0])
rskf = RepeatedStratifiedKFold(n_splits=5, n_repeats=5,random_state=10)
best_iteration , best_valid_auc = 0, 0
for counter,(train_index, valid_index) in enumerate(rskf.split(train, train.target),1):
print ("Rep-Fold:",counter)
sys.stdout.flush()
#Train data
t=train.iloc[train_index]
trn_data = lgb.Dataset(t.drop("target",axis=1), label=t.target)
#Validation data
v=train.iloc[valid_index]
val_data = lgb.Dataset(v.drop("target",axis=1), label=v.target)
#Training
model = lgb.train(param, trn_data, 1000000, feature_name=train.columns.tolist()[1:], valid_sets = [trn_data, val_data], verbose_eval=500, early_stopping_rounds = 4000)
result +=model.predict(test)
## feat imp
gain = model.feature_importance('gain')