def stacking_proba(clf,X_train,y,X_test,nfolds=5,random_seed=2017,return_score=False,
shuffle=True,metric='acc',clf_name='UnKnown'):
folds = StratifiedKFold(n_splits=nfolds, shuffle=shuffle, random_state=random_seed)
folds.get_n_splits(X_train,y)
#return stacking_proba for train set
train_stacking_proba=np.zeros((X_train.shape[0],np.unique(y).shape[0]))
score=0
for i,(train_index, validate_index) in enumerate(folds.split(X_train, y)):
# print(str(clf_name)+" folds:"+str(i+1)+"/"+str(nfolds))
X_train_fold=X_train[train_index,:]
y_train_fold=y[train_index]
X_validate_fold=X_train[validate_index,:]
y_validate_fold=y[validate_index]
clf.fit(X_train_fold,y_train_fold)
fold_preds=clf.predict_proba(X_validate_fold)
train_stacking_proba[validate_index,:]=fold_preds
#validation
fold_preds_a = np.argmax(fold_preds, axis=1)
fold_score=len(np.nonzero(y_validate_fold - fold_preds_a == 0)[0]) / len(y_validate_fold)
# print('validate '+metric+":"+str(fold_score))
score+=fold_score
score/=nfolds
#return stacking_proba for test set
clf.fit(X_train,y)
test_stacking_proba=clf.predict_proba(X_test)
if np.unique(y).shape[0] == 2: # when binary classification only return positive class proba
train_stacking_proba=train_stacking_proba[:,1]
test_stacking_proba=test_stacking_proba[:,1]
if return_score:
return train_stacking_proba,test_stacking_proba,score
else:
return train_stacking_proba,test_stacking_proba
def test_kfold_valueerrors():
X1 = np.array([[1, 2], [3, 4], [5, 6]])
X2 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
# Check that errors are raised if there is not enough samples
assert_raises(ValueError, next, KFold(4).split(X1))
# Check that a warning is raised if the least populated class has too few
# members.
y = np.array([3, 3, -1, -1, 2])
skf_3 = StratifiedKFold(3)
assert_warns_message(Warning, "The least populated class",
next, skf_3.split(X2, y))
# Check that despite the warning the folds are still computed even
# though all the classes are not necessarily represented at on each
# side of the split at each split
with warnings.catch_warnings():
check_cv_coverage(skf_3, X2, y, labels=None, expected_n_iter=3)
# Error when number of folds is <= 1
assert_raises(ValueError, KFold, 0)
assert_raises(ValueError, KFold, 1)
assert_raises(ValueError, StratifiedKFold, 0)
assert_raises(ValueError, StratifiedKFold, 1)
# When n_folds is not integer:
assert_raises(ValueError, KFold, 1.5)
assert_raises(ValueError, KFold, 2.0)
assert_raises(ValueError, StratifiedKFold, 1.5)
assert_raises(ValueError, StratifiedKFold, 2.0)
# When shuffle is not a bool:
assert_raises(TypeError, KFold, n_folds=4, shuffle=None)
def __init__(self, fm_decoder, n_iter=5, n_folds=3,
random_state=None):
self.fm_decoder = fm_decoder
StratifiedKFold.__init__(
self,
n_folds=n_folds,
random_state=random_state)
def test_datasets(dataset_names):
from sklearn.svm import SVC
data = Data(dataset_names=dataset_names)
def separate_sets(x, y, test_fold_id, test_folds):
x_test = x[test_folds == test_fold_id, :]
y_test = y[test_folds == test_fold_id]
x_train = x[test_folds != test_fold_id, :]
y_train = y[test_folds != test_fold_id]
return [x_train, y_train, x_test, y_test]
n_folds = 2
accuracies = {}
for name, dataset in data.datasets.items():
dataset.print_summary()
skf = StratifiedKFold(dataset.target, n_folds=n_folds, shuffle=True)
test_folds = skf.test_folds
accuracies[name] = np.zeros(n_folds)
test_fold = 0
for train_idx, test_idx in skf.split(X=dataset.data, y=dataset.target):
x_train, y_train = dataset.data[train_idx], dataset.target[train_idx]
x_test, y_test = dataset.data[test_idx], dataset.target[test_idx]
svc = SVC(C=1.0, kernel='rbf', degree=1, tol=0.01)
svc.fit(x_train, y_train)
prediction = svc.predict(x_test)
accuracies[name][test_fold] = 100*np.mean((prediction == y_test))
print("Acc = {0:.2f}%".format(accuracies[name][test_fold]))
test_fold += 1
return accuracies
def stratified_cross_validate(self, k):
attributes = np.append(self.training_attributes, self.testing_attributes, axis=0)
labels = np.append(self.training_labels, self.testing_labels, axis=0)
all_data = np.array([np.append(attributes[i], labels[i]) for i in range(len(attributes))])
#print("all data : %s" % all_data)
#print("")
np.random.shuffle(all_data)
X = all_data[:, :-1]
y = all_data[:, -1]
print(X.shape, y.shape)
skf = StratifiedKFold(n_splits=2)
print(skf.get_n_splits(X, y))
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]
yield (X_train, y_train, X_test, y_test)
#print("shuffled data : %s" % all_data)
#print("")
for i in range(k):
split = len(all_data) / k
#print("split : %s" % split)
test_data = all_data[i * split:(i + 1) * split, :]
train_data = np.delete(all_data, np.arange(i * split, (i + 1) * split), axis=0)
train_input, train_output = train_data[:, :-1], train_data[:, -1]
test_input, test_output = test_data[:, :-1], test_data[:, -1]
yield (train_input, train_output, test_input, test_output)
def cv_score(X, y, n_epochs = 10, n_folds=10, random_state=1999):
kf = StratifiedKFold(n_folds, shuffle=True, random_state=random_state)
scores = np.zeros((n_folds, n_epochs))
val_scores = np.zeros((n_folds, n_epochs))
best_epochs = np.zeros(n_folds)
clfs = [KerasWrapper(num_features=X.shape[1], label='keras_{}'.format(i)) for i in range(n_folds)]
folds = kf.split(X, y_train)
#iteratively train epochs
kfsplit = [(itrain, itest) for itrain, itest in folds]
for i in range(n_epochs):
print('=============Epoch {}================'.format(i))
i_fold = 0
for itrain, itest in kfsplit:
print('Fold ', i_fold)
train = X[itrain,:]
test = X[itest,:]
ytrain, ytest = y[itrain], y[itest]
clf, score, num_epoch = clfs[i_fold].fit(train, ytrain, nb_epoch=1,
validation_split=None, batch_size=64,
patience=1)
print('score: {}'.format(score))
scores[i_fold, i] = score
best_epochs[i_fold] = num_epoch
# predict on oof
pred = clf.predict_proba(test)
val_score = log_loss(ytest, pred)
print('Validation score: ', val_score)
val_scores[i_fold, i] = val_score
i_fold += 1
return scores, val_scores, best_epochs
def get_cv_results(design, data, cv_splits=10):
test_df, unit_onehot, unit_x = data
cv_results = []
for i in range(design.shape[0]):
lambda_int, lambda_x = design[i, :]
val_losses = []
for rep in range(3): # Almost like bootstrap. Reshuffling
cv_val_losses = []
skf = StratifiedKFold(n_splits=10, shuffle=True)
for train_index, test_index in skf.split(unit_x, test_df['unit']):
re_model = create_model(unit_onehot.shape[1], lambda_int, lambda_x,
.01, .0001, .92)
X_train = [test_df["x"][train_index], unit_onehot[train_index],
unit_x[train_index]]
X_test = [test_df["x"][test_index], unit_onehot[test_index],
unit_x[test_index]]
y_train, y_test = test_df["y"][train_index], test_df["y"][test_index]
h = re_model.fit(X_train, y_train,
epochs = 15000, batch_size = 450,
validation_data = (X_test, y_test),
callbacks = callbacks, verbose = 0)
cv_val_losses.append(np.min(h.history['val_loss']))
val_losses.append(np.mean(cv_val_losses))
cv_results.append(np.mean(val_losses))
return cv_results
def split_data(self, X, y, stratified = True, bad_chess = False):
if bad_chess:
n_points = int(X.shape[0] / self.nodes)
for node in range(self.nodes):
start_slice = node * n_points
final_slice = start_slice + n_points
dx = X[start_slice:final_slice]
dy = y[start_slice:final_slice]
frame_dx = pd.DataFrame(dx)
frame_dy = pd.DataFrame(dy)
file_data = datas_path.joinpath('data_' + str(node) + '.csv')
file_class = datas_path.joinpath('class_' + str(node) + '.csv')
frame_dx.to_csv(file_data, index = False)
frame_dy.to_csv(file_class, index = False)
else:
node = 0
if stratified:
skf = StratifiedKFold(n_splits = self.nodes)
else:
skf = KFold(n_splits = self.nodes, shuffle = True, random_state = 17)
for splited_index in skf.split(X, y):
new_X = pd.DataFrame(X[splited_index[1]])
new_y = pd.DataFrame(y[splited_index[1]])
X_path = datas_path.joinpath("data_" + str(node) + ".csv")
y_path = datas_path.joinpath("class_" + str(node) + ".csv")
new_X.to_csv(X_path, index = False)
new_y.to_csv(y_path, index = False)
node += 1
def cv(X_train, y_train):
kfold = StratifiedKFold(n_splits=5, shuffle=True)
scores_f = []
scores_p = []
scores_r = []
for train, test in kfold.split(X_train, y_train):
model = TargetEnsembler(features)
X_train_cv = pd.DataFrame(X_train.values[train], columns=X_train.columns)
y_train_cv = pd.DataFrame(y_train.values[train], columns=["PCL_Strict3"])
X_test_cv = pd.DataFrame(X_train.values[test], columns=X_train.columns)
y_test_cv = pd.DataFrame(y_train.values[test], columns=["PCL_Strict3"])
model.fit(X_train_cv, y_train_cv)
y_pred = model.predict(X_test_cv)
s_f = f1_score(y_test_cv, y_pred)
s_p = precision_score(y_test_cv, y_pred)
s_r = recall_score(y_test_cv, y_pred)
print("\tscores f1", (s_f))
print("\tscores p", (s_p))
print("\tscores r", (s_r))
scores_f.append(s_f)
scores_p.append(s_p)
scores_r.append(s_r)
print("mean scores f1", np.mean(scores_f))
print("mean scores p", np.mean(scores_p))
print("mean scores r", np.mean(scores_r))
def classify(X,y, clf,**para):
# y = profile["Loss"].as_matrix()
# X = profile[features].as_matrix()
kf = KFold(n_splits=10)
skf = StratifiedKFold(n_splits=6)
# print(**para)
classifier = clf(**para)
name = str(classifier).split("(")[0]
# dt = tree.DecisionTreeClassifier(min_samples_split=min_split, max_depth=max_dep)
print("{0} has been established with {1}".format(name, para))
# lr = LogisticRegression(penalty='l1')
for train_index, test_index in skf.split(X, y):
# print("TRAIN:",train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
score = accuracy_score(y_test, y_pred)
print("10-fold Score is: {0}".format(score))
return classifier,y_test, y_pred
def test_grid_search_correct_score_results():
# test that correct scores are used
n_splits = 3
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
for score in ['f1', 'roc_auc']:
grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits)
results = grid_search.fit(X, y).cv_results_
# Test scorer names
result_keys = list(results.keys())
expected_keys = (("mean_test_score", "rank_test_score") +
tuple("split%d_test_score" % cv_i
for cv_i in range(n_splits)))
assert_true(all(in1d(expected_keys, result_keys)))
cv = StratifiedKFold(n_splits=n_splits)
n_splits = grid_search.n_splits_
for candidate_i, C in enumerate(Cs):
clf.set_params(C=C)
cv_scores = np.array(
list(grid_search.cv_results_['split%d_test_score'
% s][candidate_i]
for s in range(n_splits)))
for i, (train, test) in enumerate(cv.split(X, y)):
clf.fit(X[train], y[train])
if score == "f1":
correct_score = f1_score(y[test], clf.predict(X[test]))
elif score == "roc_auc":
dec = clf.decision_function(X[test])
correct_score = roc_auc_score(y[test], dec)
assert_almost_equal(correct_score, cv_scores[i])
def split(dependent, independent, n_folds):
skf = StratifiedKFold(n_splits=n_folds, random_state=RANDOM_STATE)
for train_indices, test_indices in skf.split(dependent, independent):
train_x = dependent[train_indices]
train_y = independent[train_indices]
test_x = dependent[test_indices]
test_y = independent[test_indices]
yield train_x, train_y, test_x, test_y
def test_ovr_multinomial_iris():
# Test that OvR and multinomial are correct using the iris dataset.
train, target = iris.data, iris.target
n_samples, n_features = train.shape
# The cv indices from stratified kfold (where stratification is done based
# on the fine-grained iris classes, i.e, before the classes 0 and 1 are
# conflated) is used for both clf and clf1
n_cv = 2
cv = StratifiedKFold(n_cv)
precomputed_folds = list(cv.split(train, target))
# Train clf on the original dataset where classes 0 and 1 are separated
clf = LogisticRegressionCV(cv=precomputed_folds)
clf.fit(train, target)
# Conflate classes 0 and 1 and train clf1 on this modified dataset
clf1 = LogisticRegressionCV(cv=precomputed_folds)
target_copy = target.copy()
target_copy[target_copy == 0] = 1
clf1.fit(train, target_copy)
# Ensure that what OvR learns for class2 is same regardless of whether
# classes 0 and 1 are separated or not
assert_array_almost_equal(clf.scores_[2], clf1.scores_[2])
assert_array_almost_equal(clf.intercept_[2:], clf1.intercept_)
assert_array_almost_equal(clf.coef_[2][np.newaxis, :], clf1.coef_)
# Test the shape of various attributes.
assert_equal(clf.coef_.shape, (3, n_features))
assert_array_equal(clf.classes_, [0, 1, 2])
coefs_paths = np.asarray(list(clf.coefs_paths_.values()))
assert_array_almost_equal(coefs_paths.shape, (3, n_cv, 10, n_features + 1))
assert_equal(clf.Cs_.shape, (10,))
scores = np.asarray(list(clf.scores_.values()))
assert_equal(scores.shape, (3, n_cv, 10))
# Test that for the iris data multinomial gives a better accuracy than OvR
for solver in ['lbfgs', 'newton-cg', 'sag', 'saga']:
max_iter = 2000 if solver in ['sag', 'saga'] else 15
clf_multi = LogisticRegressionCV(
solver=solver, multi_class='multinomial', max_iter=max_iter,
random_state=42, tol=1e-5 if solver in ['sag', 'saga'] else 1e-2,
cv=2)
clf_multi.fit(train, target)
multi_score = clf_multi.score(train, target)
ovr_score = clf.score(train, target)
assert_greater(multi_score, ovr_score)
# Test attributes of LogisticRegressionCV
assert_equal(clf.coef_.shape, clf_multi.coef_.shape)
assert_array_equal(clf_multi.classes_, [0, 1, 2])
coefs_paths = np.asarray(list(clf_multi.coefs_paths_.values()))
assert_array_almost_equal(coefs_paths.shape, (3, n_cv, 10,
n_features + 1))
assert_equal(clf_multi.Cs_.shape, (10,))
scores = np.asarray(list(clf_multi.scores_.values()))
assert_equal(scores.shape, (3, n_cv, 10))
def gen_folds(X, y, n_folds=5, random_state=0):
from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_folds, shuffle=True, random_state=random_state)
folds = kf.split(X, y)
# iteratively train epochs
kfsplit = [(itrain, itest) for itrain, itest in folds]
return kfsplit
def categorical_average(variable, y, pred_0, feature_name):
def calculate_average(sub1, sub2):
s = pd.DataFrame(data = {
variable: sub1.groupby(variable, as_index = False).count()[variable],
'sumy': sub1.groupby(variable, as_index = False).sum()['y'],
'avgY': sub1.groupby(variable, as_index = False).mean()['y'],
'cnt': sub1.groupby(variable, as_index = False).count()['y']
})
tmp = sub2.merge(s.reset_index(), how='left', left_on=variable, right_on=variable)
del tmp['index']
tmp.loc[pd.isnull(tmp['cnt']), 'cnt'] = 0.0
tmp.loc[pd.isnull(tmp['cnt']), 'sumy'] = 0.0
def compute_beta(row):
cnt = row['cnt'] if row['cnt'] < 200 else float('inf')
return 1.0 / (g + exp((cnt - k) / f))
if lambda_val is not None:
tmp['beta'] = lambda_val
else:
tmp['beta'] = tmp.apply(compute_beta, axis = 1)
tmp['adj_avg'] = tmp.apply(lambda row: (1.0 - row['beta']) * row['avgY'] + row['beta'] * row['pred_0'],
axis = 1)
tmp.loc[pd.isnull(tmp['avgY']), 'avgY'] = tmp.loc[pd.isnull(tmp['avgY']), 'pred_0']
tmp.loc[pd.isnull(tmp['adj_avg']), 'adj_avg'] = tmp.loc[pd.isnull(tmp['adj_avg']), 'pred_0']
tmp['random'] = np.random.uniform(size = len(tmp))
tmp['adj_avg'] = tmp.apply(lambda row: row['adj_avg'] *(1 + (row['random'] - 0.5) * r_k),
axis = 1)
return tmp['adj_avg'].ravel()
#cv for training set
k_fold = StratifiedKFold(5)
X_train[feature_name] = -999
for (train_index, cv_index) in k_fold.split(np.zeros(len(X_train)),
X_train['interest_level'].ravel()):
sub = pd.DataFrame(data = {variable: X_train[variable],
'y': X_train[y],
'pred_0': X_train[pred_0]})
sub1 = sub.iloc[train_index]
sub2 = sub.iloc[cv_index]
X_train.loc[cv_index, feature_name] = calculate_average(sub1, sub2)
#for test set
sub1 = pd.DataFrame(data = {variable: X_train[variable],
'y': X_train[y],
'pred_0': X_train[pred_0]})
sub2 = pd.DataFrame(data = {variable: X_test[variable],
'y': X_test[y],
'pred_0': X_test[pred_0]})
X_test.loc[:, feature_name] = calculate_average(sub1, sub2)
def stratifiedCV(X, y, n_splits = 6):
skf = StratifiedKFold(n_splits=n_splits)
for train_index, test_index in skf.split(X, y):
# print("TRAIN:",train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
yield X_train, y_train, X_test, y_test
def cv_stats(self):
"""Perform cross-validation for model evaluation.
Returns
-------
(list[int], list[int], list[float])
Tuple containing three lists of the same size:
true labels
predicted labels
prediction probabilities
"""
if 'y_true' in self._cache:
return self._cache['y_true'], self._cache['y_pred'], self._cache['y_prob'], self._cache['sigfeatures']
X = self._fe.X
y = self._fe.y
kf = StratifiedKFold(n_splits=10, shuffle=True)
y_true, y_pred, y_prob = [], [], []
sigfeatures = []
order_indices = []
for train_index, test_index in kf.split(X, y):
order_indices.extend(test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
clf = self.get_new_classifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
prob = clf.predict_proba(X_test)
prob = np.choose(pred, prob.T)
for predy in pred:
sigfeatures.append(get_sig_features(predy, clf.coef_, 20))
y_true.extend(y_test)
y_pred.extend(pred)
y_prob.extend(prob)
# reorder the results so they match the order of original data
y_true = [v for i, v in sorted(zip(order_indices, y_true))]
y_pred = [v for i, v in sorted(zip(order_indices, y_pred))]
y_prob = [v for i, v in sorted(zip(order_indices, y_prob))]
assert list(y_true) == list(y)
# cache the results
self._cache['y_true'] = y_true
self._cache['y_pred'] = y_pred
self._cache['y_prob'] = y_prob
self._cache['sigfeatures'] = sigfeatures
return (y_true, y_pred, y_prob, sigfeatures)
def test_shuffle_stratifiedkfold():
# Check that shuffling is happening when requested, and for proper
# sample coverage
X_40 = np.ones(40)
y = [0] * 20 + [1] * 20
kf0 = StratifiedKFold(5, shuffle=True, random_state=0)
kf1 = StratifiedKFold(5, shuffle=True, random_state=1)
for (_, test0), (_, test1) in zip(kf0.split(X_40, y),
kf1.split(X_40, y)):
assert_not_equal(set(test0), set(test1))
check_cv_coverage(kf0, X_40, y, labels=None, expected_n_iter=5)
def get_cross_validated_confusion_matrix(data, label, estimator, index, nfolds=10):
# nfolds = get_least_class(label)
skf = StratifiedKFold(n_splits=nfolds)
con_matrix = np.zeros((len(np.unique(label)), len(np.unique(label))))
for train_index, test_index in skf.split(data, label):
train_data, test_data = data[train_index], data[test_index]
train_label, test_label = np.array(label)[train_index], np.array(label)[test_index]
estimator.train_matrix(train_data, train_label)
pred_label = estimator.predict(test_data)
con_matrix = con_matrix + confusion_matrix(test_label, pred_label, labels = index)
return con_matrix
def run_cv_evaluation(data, n_folds, nlu_config):
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold
from collections import defaultdict
# type: (List[rasa_nlu.training_data.Message], int, RasaNLUConfig) -> Dict[Text, List[float]]
"""Stratified cross validation on data
:param data: list of rasa_nlu.training_data.Message objects
:param n_folds: integer, number of cv folds
:param nlu_config: nlu config file
:return: dictionary with key, list structure, where each entry in list
corresponds to the relevant result for one fold
"""
trainer = Trainer(nlu_config)
results = defaultdict(list)
y_true = [e.get("intent") for e in data]
skf = StratifiedKFold(n_splits=n_folds, random_state=11, shuffle=True)
counter = 1
logger.info("Evaluation started")
for train_index, test_index in skf.split(data, y_true):
train = [data[i] for i in train_index]
test = [data[i] for i in test_index]
logger.debug("Fold: {}".format(counter))
logger.debug("Training ...")
trainer.train(TrainingData(training_examples=train))
model_directory = trainer.persist("projects/") # Returns the directory the model is stored in
logger.debug("Evaluation ...")
interpreter = Interpreter.load(model_directory, nlu_config)
test_y = [e.get("intent") for e in test]
preds = []
for e in test:
res = interpreter.parse(e.text)
if res.get('intent'):
preds.append(res['intent'].get('name'))
else:
preds.append(None)
# compute fold metrics
results["Accuracy"].append(metrics.accuracy_score(test_y, preds))
results["F1-score"].append(metrics.f1_score(test_y, preds, average='weighted'))
results["Precision"] = metrics.precision_score(test_y, preds, average='weighted')
# increase fold counter
counter += 1
return dict(results)
def runCrossValidation(train, RFfile): train_tracks = [] for feature in train: if feature[0] != 0.: train_tracks.append(feature) train_tracks = np.array(train_tracks) # Gets parameter values for training data trainArr = train_tracks[:,1:] # Gets class label of all training data trainRes = train_tracks[:,0] # Convert all NaNs to 0 for RF to work properly trainArr = np.nan_to_num(trainArr) trainRes = np.nan_to_num(trainRes) # Load the classifier rf = joblib.load(RFfile) # Stratified KFolds cross validation cv = StratifiedKFold(n_splits = 5) precision = [] accuracy = [] sensitivity = [] matthews = [] r2 = [] f1 = [] auroc = [] cm = [[0, 0], [0, 0]] for train_index, test_index in cv.split(trainArr, trainRes): probas_ = rf.fit(trainArr[train_index], trainRes[train_index]).predict_proba(trainArr[test_index]) classes = rf.fit(trainArr[train_index], trainRes[train_index]).predict(trainArr[test_index]) # r2 = np.append(r2, (r2_score(trainRes[test_index], probas_[:, 1]))) precision = np.append(precision, (precision_score(trainRes[test_index], classes))) # auroc = np.append(auroc, (roc_auc_score(trainRes[test_index], classes))) accuracy = np.append(accuracy, (accuracy_score(trainRes[test_index], classes))) sensitivity = np.append(sensitivity, (recall_score(trainRes[test_index], classes))) f1 = np.append(f1, (f1_score(trainRes[test_index], classes))) # matthews = np.append(matthews, (matthews_corrcoef(trainRes[test_index], classes))) #cma = np.add(cma, (confusion_matrix(trainRes[test_index], classes))) # cma = np.array(cma) # r2 = np.array(r2) precision = np.array(precision) accuracy = np.array(accuracy) sensitivity = np.array(sensitivity) f1 = np.array(f1) # auroc = np.array(auroc) # matthews = np.array(matthews) return accuracy, precision, sensitivity, f1
def generate_folds(dataset_path, output_folder, n_folds=10, random_state=None):
"""
Given a dataset df, generate n_folds for it and store them in <output_folder>/<dataset_name>.
:type dataset_path: str
:param dataset_path: Path to dataset with .arff file extension (i.e my_dataset.arff)
:type output_folder: str
:param output_folder: Path to store both index file with folds and fold files.
:type n_folds: int
:param n_folds: Optional - Number of folds to split the dataset into. Defaults to 10.
:type random_state: int
:param random_state: Optional - Seed to use in the splitting process. Defaults to None (no seed).
"""
import warnings
warnings.filterwarnings('error')
dataset_name = dataset_path.split('/')[-1].split('.')[0]
af = load_arff(dataset_path)
df = load_dataframe(af)
skf = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=random_state)
fold_iter = skf.split(df[df.columns[:-1]], df[df.columns[-1]])
fold_index = dict()
jvm.start()
csv_loader = Loader(classname="weka.core.converters.CSVLoader")
arff_saver = Saver(classname='weka.core.converters.ArffSaver')
for i, (arg_rest, arg_test) in enumerate(fold_iter):
fold_index[i] = list(arg_test)
_temp_path = 'temp_%s_%d.csv' % (dataset_name, i)
fold_data = df.loc[arg_test] # type: pd.DataFrame
fold_data.to_csv(_temp_path, sep=',', index=False)
java_arff_dataset = csv_loader.load_file(_temp_path)
java_arff_dataset.relationname = af['relation']
java_arff_dataset.class_is_last()
arff_saver.save_file(java_arff_dataset, os.path.join(output_folder, '%s_fold_%d.arff' % (dataset_name, i)))
os.remove(_temp_path)
json.dump(
fold_index, open(os.path.join(output_folder, dataset_name + '.json'), 'w'), indent=2
)
jvm.stop()
warnings.filterwarnings('default')
def Kfold(dataset, k, shuffle=False, stratify=False):
"""
Envelop function for folding operation
"""
# remove class labels
data = dataset[0]
if stratify:
kf = StratifiedKFold(k, shuffle)
return kf.split(dataset[0], dataset[1])
kf = KFold(k, shuffle)
return kf.split(data)
def rmseCvMean(model, X, y, cv=5, random_state=41):
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=cv, random_state=random_state)
scr = 0
for train_index, test_index in skf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
model.fit(X_train, y_train)
pred = model.predict(X_test)
scr += rmse(y_test, pred)
print('\t', rmse(y_test, pred))
return scr/cv
def cross_validation(sgd_clf, x_train, y_train):
skfolds = StratifiedKFold(n_splits=5, random_state=42)
for train_index, test_index in skfolds.split(x_train, y_train): #40000, 20000
clone_clf = clone(sgd_clf)
x_train_folds = x_train[train_index]
y_train_folds = y_train[train_index]
x_test_fold = x_train[test_index]
y_test_fold = y_train[test_index]
clone_clf.fit(x_train_folds, y_train_folds)
y_pred = clone_clf.predict(x_test_fold)
n_correct = sum(y_pred == y_test_fold)
print(n_correct / len(y_pred))
def test_kfold_valueerrors():
X1 = np.array([[1, 2], [3, 4], [5, 6]])
X2 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
# Check that errors are raised if there is not enough samples
assert_raises(ValueError, next, KFold(4).split(X1))
# Check that a warning is raised if the least populated class has too few
# members.
y = np.array([3, 3, -1, -1, 3])
skf_3 = StratifiedKFold(3)
assert_warns_message(Warning, "The least populated class",
next, skf_3.split(X2, y))
# Check that despite the warning the folds are still computed even
# though all the classes are not necessarily represented at on each
# side of the split at each split
with warnings.catch_warnings():
warnings.simplefilter("ignore")
check_cv_coverage(skf_3, X2, y, labels=None, expected_n_splits=3)
# Check that errors are raised if all n_labels for individual
# classes are less than n_splits.
y = np.array([3, 3, -1, -1, 2])
assert_raises(ValueError, next, skf_3.split(X2, y))
# Check that errors are raised if all n_labels for individual
# classes are less than n_folds.
y = np.array([3, 3, -1, -1, 2])
assert_raises(ValueError, next, skf_3.split(X2, y))
# Error when number of folds is <= 1
assert_raises(ValueError, KFold, 0)
assert_raises(ValueError, KFold, 1)
error_string = ("k-fold cross-validation requires at least one"
" train/test split")
assert_raise_message(ValueError, error_string,
StratifiedKFold, 0)
assert_raise_message(ValueError, error_string,
StratifiedKFold, 1)
# When n_splits is not integer:
assert_raises(ValueError, KFold, 1.5)
assert_raises(ValueError, KFold, 2.0)
assert_raises(ValueError, StratifiedKFold, 1.5)
assert_raises(ValueError, StratifiedKFold, 2.0)
# When shuffle is not a bool:
assert_raises(TypeError, KFold, n_splits=4, shuffle=None)
def run_cv_model(self, alpha=0.0001, batch_size=200, learning_rate_init=0.001, power_t=0.5, max_iter=200, momentum=0.9, beta_1=0.9, beta_2=0.999, hidden_layer_sizes=(100,), do_plot=True):
# use k-fold cross validation
# we need to standardize the data for the KNN learner
pipe_clf = Pipeline([ ('scl', StandardScaler() ),
('clf', MLPClassifier(alpha=alpha,
batch_size=batch_size,
learning_rate_init=learning_rate_init,
power_t=power_t,
max_iter=max_iter,
momentum=momentum,
beta_1=beta_1,
beta_2=beta_2,
hidden_layer_sizes=hidden_layer_sizes))])
# resample the test data without replacement. This means that each data point is part of a test a
# training set only once. (paraphrased from Raschka p.176). In Stratified KFold, the features are
# evenly disributed such that each test and training set is an accurate representation of the whole
# this is the 0.17 version
#kfold = StratifiedKFold(y=self.y_train, n_folds=self.cv, random_state=0)
# this is the 0.18dev version
skf = StratifiedKFold(n_folds=self.cv, random_state=0)
# do the cross validation
train_scores = []
test_scores = []
#for k, (train, test) in enumerate(kfold):
for k, (train, test) in enumerate(skf.split(X=self.x_train, y=self.y_train)):
# run the learning algorithm
pipe_clf.fit(self.x_train[train], self.y_train[train])
train_score = pipe_clf.score(self.x_train[test], self.y_train[test])
train_scores.append(train_score)
test_score = pipe_clf.score(self.x_test, self.y_test)
test_scores.append(test_score)
print('Fold:', k+1, ', Training score:', train_score, ', Test score:', test_score)
train_score = np.mean(train_scores)
print('Training score is', train_score)
test_score = np.mean(test_scores)
print('Test score is', test_score)
if do_plot:
self.__plot_learning_curve(pipe_clf)
return train_score, test_score
def evaluate_classifier(clf, features, labels):
"""
Evaluates the classifier using StratifiedKFold cross validation. The
precision and recall scores are used to evaluate the algorithm's
performance.
clf = classifier
features = features list as returned by the targetFeatureSplit script
labels = target list as returned by the targetFeatureSplit script
"""
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.model_selection import StratifiedKFold
### Use StratifiedKFold cross validation with 10 folds
skf = StratifiedKFold(n_splits = 10, random_state = 42)
precision = []
recall = []
count = 0
### Split the features and labels into training and testing sets.
for train_index, test_index in skf.split(features, labels):
features_train = []
features_test = []
labels_train = []
labels_test = []
for i in train_index:
features_train.append(features[i])
labels_train.append(labels[i])
for j in test_index:
features_test.append(features[j])
labels_test.append(labels[j])
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
precision.append(precision_score(labels_test, pred))
recall.append(recall_score(labels_test, pred))
count += 1
print clf
print "Folds:", count
print "Average Precision:", sum(precision) / count
print "Average Recall:", sum(recall) / count
print ""
def kfold_sklearn_model_train(train_X,
train_y,
parameters,
n_fold,
sklearn_model,
logger):
best_auc = 0
best_param = None
for params in tqdm(list(ParameterGrid(parameters))):
logger.info('params: {}'.format(params))
auc_lst = []
skf = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=0)
for trn_i, val_i in skf.split(train_X, train_y):
trn_x = train_X[trn_i]
val_x = train_X[val_i]
trn_y = train_y[trn_i]
val_y = train_y[val_i]
model = sklearn_model(**params)
model.fit(trn_x, trn_y)
pred = model.predict_proba(val_x)
pred = np.array([p[1] for p in pred])
fpr, tpr, thresholds = metrics.roc_curve(val_y, pred)
auc = metrics.auc(fpr, tpr)
logger.info('AUC: {}'.format(auc))
auc_lst.append(auc)
auc_avg = sum(auc_lst) / len(auc_lst)
logger.info('AUC AVG: {}'.format(auc_avg))
if best_auc < auc_avg:
best_auc = auc_avg
best_param = params
logger.info('best params: {}'.format(best_param))
logger.info('AUC: {}'.format(best_auc))
logger.info('train by best parameters')
model = sklearn_model(**best_param)
model.fit(train_X, train_y)
return model
def CrossVal(estimator, X, y,procsessor=None,cv=3,times=10,random_state=0,imb=False):
"""
交叉验证
estimator:
模型
X:
数据集X部分
y:
数据集的label
procsessor:
预处理器，其实就是做特征选择
cv:
做cv折交叉验证
times:
重复times次交叉验证
random_state:
随机数种子
imb:
是否使用SMOTE使得正负样本数平衡
"""
res=[]
for t in range(times):
skf=StratifiedKFold(n_splits=cv, shuffle=True, random_state=random_state+t)
indices=list(skf.split(X=X,y=y))
for k in indices:
x_train,y_train,x_test,y_test=X[k[0]],y[k[0]],X[k[1]],y[k[1]]
if(imb==True):
n,p=__lableCount(y_train)
rus=RandomUnderSampler(random_state=random_state+t)
x_train,y_train=rus.fit_sample(x_train,y_train)
if(procsessor is not None):
procsessor.fit(x_train,y_train)
x_train,y_train=procsessor.transform(x_train,y_train)
x_test,y_test=procsessor.transform(x_test,y_test)
estimator.fit(x_train,y_train)
res.append(Metrics.Score(estimator,x_test,y_test))
res=np.array(res)
return res
plt.subplot(2,2,2)
plt.imshow(tf.squeeze(resizing[311]), cmap = 'jet')
plt.title(f'{tf.squeeze(resizing[311]).shape}')
plt.tight_layout()
plt.show()
# ---------------------------------------------------------------------
'''
# 이미지 증폭 정의 / idg2는 증폭없이 형태만 맞춰줌
idg = ImageDataGenerator(height_shift_range=(-1, 1), width_shift_range=(-1, 1))
idg2 = ImageDataGenerator()
# kfold 정의
skf = StratifiedKFold(n_splits=40, random_state=42, shuffle=True)
# callback 정의
redu_lr = ReduceLROnPlateau(patience= 80, verbose=1, factor=0.5)
stop = EarlyStopping(monitor='val_loss', patience=160, verbose=1, mode='min')
mc = ModelCheckpoint(filepath= '../data/modelcheckpoint/dacon3/1st_01.h5', save_best_only=True, verbose=1)
result = 0
nth = 0
# for문으로 모델 + 컴파일 + 훈련 + 평가
for train_index, valid_index in skf.split(train2, train['digit']) :
x_train = train2[train_index]
x_val = train2[valid_index]
def kfold_lightgbm(df, num_folds, stratified = False, debug= False):
# Divide in training/validation and test data
train_df = df[df['TARGET'].notnull()]
test_df = df[df['TARGET'].isnull()]
print("Starting LightGBM. Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape))
del df
gc.collect()
# Cross validation model
if stratified:
folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=SEED)
else:
folds = KFold(n_splits= num_folds, shuffle=True, random_state=SEED)
# Create arrays and dataframes to store results
oof_preds = np.zeros(train_df.shape[0])
sub_preds = np.zeros(test_df.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in train_df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]
lgb_params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric':'auc',
'n_estimators': 10000,
'learning_rate': 0.05,
'num_leaves': 34, # we should let it be smaller than 2^(max_depth)
'max_depth': 8,
'subsample': 0.8715623, # Subsample ratio of the training instance.
'subsample_freq': 1, # frequence of subsample, <=0 means no enable
'colsample_bytree': 0.9497036, # Subsample ratio of columns when constructing each tree.
'min_child_weight': 60,
'min_split_gain': 0.0222415, # lambda_l1, lambda_l2 and min_gain_to_split to regularization
'reg_alpha': 0.041545473, # L1 regularization term on weights
'reg_lambda': 0.0735294, # L2 regularization term on weights
'nthread': 8,
'seed':42,
'verbose': -1,
}
# lgb_params = {
# 'boosting_type': 'gbdt',
# 'objective': 'binary',
# 'metric':'auc',
# 'n_estimators': 10000,
# 'learning_rate': 0.05,
# 'num_leaves': 15, # we should let it be smaller than 2^(max_depth)
# #'max_depth': 8,
# 'subsample': 0.7225, # Subsample ratio of the training instance.
# #'subsample_freq': 1, # frequence of subsample, <=0 means no enable
# 'colsample_bytree': 0.8443, # Subsample ratio of columns when constructing each tree.
# 'min_child_weight': np.power(10, -1.7449),
# 'min_split_gain': np.power(10, 0.1397), # lambda_l1, lambda_l2 and min_gain_to_split to regularization
# 'reg_alpha': np.power(10, -3.1527), # L1 regularization term on weights
# 'reg_lambda': np.power(10, 1.4779), # L2 regularization term on weights
# 'nthread': 8,
# 'random_state':42,
# 'verbose': -1,
# }
CV_score = pd.DataFrame()
FOLDS = []
SCORE = []
for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train_df[feats], train_df['TARGET'])):
train_x, train_y = train_df[feats].iloc[train_idx], train_df['TARGET'].iloc[train_idx]
valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['TARGET'].iloc[valid_idx]
xgtrain = lgb.Dataset(train_x, label=train_y,
feature_name=feats
)
xgvalid = lgb.Dataset(valid_x, label=valid_y,
feature_name=feats
)
evals_results = {}
clf = lgb.train(lgb_params,
xgtrain,
valid_sets=[xgvalid],
valid_names=['valid'],
evals_result=evals_results,
num_boost_round=10000,
early_stopping_rounds=200,
verbose_eval=200)
oof_preds[valid_idx] = clf.predict(valid_x, num_iteration=clf.best_iteration)
sub_preds += clf.predict(test_df[feats], num_iteration=clf.best_iteration)
fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = feats
fold_importance_df["importance"] = clf.feature_importance()
fold_importance_df["fold"] = n_fold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))
FOLDS.append(str(n_fold + 1))
SCORE.append(roc_auc_score(valid_y, oof_preds[valid_idx]))
del clf, train_x, train_y, valid_x, valid_y
gc.collect()
FOLDS.append('Full AUC score')
SCORE.append(roc_auc_score(train_df['TARGET'], oof_preds))
CV_score['folds'] = FOLDS
CV_score['score'] = SCORE
CV_score.to_csv('CV_SCORE.csv', index=False)
print('Full AUC score %.6f' % roc_auc_score(train_df['TARGET'], oof_preds))
# Write submission file and plot feature importance
if not debug:
test_df['TARGET'] = sub_preds / num_folds
test_df[['SK_ID_CURR', 'TARGET']].to_csv(submission_file_name, index= False)
pd.DataFrame(data=oof_preds, columns=['TARGET']).to_csv('lgb_baseline_val_oof.csv', index=False)
display_importances(feature_importance_df)
return feature_importance_df
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
#Load Breast Cancer Dataset
from sklearn.metrics import roc_auc_score
from sklearn.metrics import average_precision_score
from sklearn.model_selection import RepeatedStratifiedKFold
breast_cancer = load_breast_cancer()
X = breast_cancer.data
y = breast_cancer.target
sum_l=0
n=5
kf = StratifiedKFold(n_splits=n, random_state=None)
auc_score=0
auc_score_2=0
for train_index, test_index in kf.split(X,y):
# print("Train:", train_index, "Validation:",test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
gnb = LogisticRegression()
classifier = gnb.fit(X_train, y_train)
y_pred = gnb.predict_proba( X_test)[:,1]
y_class = gnb.predict( X_test)
sum_l += metrics.accuracy_score( y_class, y_test)
fpr, tpr, _ = metrics.roc_curve( y_test, y_pred)
auc_score += metrics.auc(fpr, tpr)
y = y.astype(np.uint8)
xtrain, xtest, ytrain, ytest = X[:60000], X[60000:], y[:60000], y[60000:]
ytrain_5 = (ytrain == 5)
ytest_5 = (ytest == 5)
sgd = SGDClassifier(random_state=42)
sgd.fit(xtrain, ytrain_5)
##Medir la presicion usando cross-validation
from sklearn.base import clone
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
skfold = StratifiedKFold(n_splits=3, random_state=42)
for train_index, test_index in skfold.split(xtrain, ytrain_5):
clone_sgd = clone(sgd)
xtrain_folds = xtrain[train_index]
ytrain_folds = ytrain_5[train_index]
xtest_folds = xtrain[test_index]
ytest_folds = ytrain_5[test_index]
clone_sgd.fit(xtrain_folds, ytrain_folds)
y_pred = clone_sgd.predict(xtest_folds)
n_correct = sum(y_pred == ytest_folds)
print(n_correct / len(y_pred))
print(cross_val_score(sgd, xtrain, ytrain_5, cv=3, scoring="accuracy"))
pipelines = {}
pipelines['fgMDM-Coh'] = make_pipeline(
FeatConn("Coh", s, "test"),
FgMDM2(metric='logeuclid', tsupdate=True, n_jobs=n_jobs))
pipelines['fgMDM-PLV'] = make_pipeline(
FeatConn("PLV", s, "test"),
FgMDM2(metric='logeuclid', tsupdate=True, n_jobs=n_jobs))
pipelines['fgMDM-Cov'] = make_pipeline(
FeatConn("Cov", s, "test"),
FgMDM2(metric='logeuclid', tsupdate=True, n_jobs=n_jobs))
estimators = [('cov', pipelines['fgMDM-Cov']),
('coh', pipelines['fgMDM-Coh']),
('plv', pipelines['fgMDM-PLV'])
]
final_estimator = RidgeClassifier(class_weight="balanced")
cvkf = StratifiedKFold(n_splits=5, shuffle=True)
scl = StackingClassifier(estimators=estimators,
cv=cvkf, n_jobs=n_jobs, final_estimator=final_estimator,
stack_method='predict_proba')
pipelines['Ensemble'] = scl
pipelines['Ensemble'].fit(X_train, y_train)
y_pred = pipelines['Ensemble'].predict(X_test)
y_pred = le.inverse_transform(y_pred)
for i, yp in enumerate(y_pred):
res = {"subject name": "P{:02d}".format(s+1),
"trial index": i+1,
"prediction": yp}
all_pred.append(res)
df_pred = pd.DataFrame(all_pred)
def train(infile):
with gzip.open(infile, 'r') as file:
data = np.genfromtxt(file, delimiter='\t', dtype=str)
## Split the data up into features and answers
answers = []
features = []
for row in data[1:, ]:
answers.append(row[1])
features.append(row[2:])
features = np.array(features)
answers = np.array(answers)
y_test_final = np.array([])
predictions_final = np.array([])
y_prob_final = np.ndarray(shape=(0, 2), dtype=int)
##first way to cross validate, not very easy to scale
# scores = cross_val_score(mlp, features, answers, cv=10)
# print(scores)
## Second way that is applicable, problem is that it just takes the first certain number. We don't know if there is a correlation
# kf = KFold(n_splits=10)
# for train, test in kf.split(features):
## Third way is the shuffle split.
# ss = ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)
# for train, test in ss.split(features):
## Fouth way is the stradified fold.
skf = StratifiedKFold(n_splits=10)
for train, test in skf.split(features, answers):
print("Training: %s \n Test: %s" % (train, test))
scaler = StandardScaler()
X_train, X_test, y_train, y_test = features[train], features[
test], answers[train], answers[test]
## This sets the size of the scaler object
scaler.fit(X_train)
## The MLP is super senesitive to feature scaling, so it is highly recommended to scale your data.
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
####### This is where we want to implement the Ensemble method ######
predictions, y_prob = mlp(X_train, X_test, y_train)
# predictions, y_prob = rf(X_train, X_test, y_train)
# predictions, y_prob = naiveBayes(X_train, X_test, y_train)
# predictions, y_prob = kNearestNeighbor(X_train, X_test, y_train)
# predictions, y_prob = supportVM(X_train, X_test, y_train) ##Attention, this returns a y_prob of 0 because it doesn't work with the SVM
# predictions, y_prob = logisticRegression(X_train, X_test, y_train)
## This will show the confusion in a matrix that will tell how often we were correct
y_test_final = np.concatenate([y_test_final, y_test])
predictions_final = np.concatenate([predictions_final, predictions])
y_prob_final = np.concatenate([y_prob_final, y_prob])
print(confusion_matrix(y_test_final, predictions_final))
print(classification_report(y_test_final, predictions_final))
for i in range(len(y_prob_final)):
print("Predicted value for item " + str(i + 1) + " : " +
str(predictions_final[i]) + ", actual: " + str(y_test_final[i]))
print("Probability : " + str(y_prob_final[i]))
def train_kfold(log_dir,
hparams,
model_name,
k,
state,
X,
Y,
X_test=None,
Y_test=None,
X_train_add=None,
Y_train_add=None,
batch_size=20,
epochs=200):
'''
X: The training set
X_test: The testing set
X_train_add: The additional set for model training, used for the 2nd experiment in paper.
'''
''' Training '''
# Log
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')
model_dir = os.path.join(log_dir, timestamp, 'models')
os.makedirs(model_dir)
hist_dir = os.path.join(log_dir, timestamp, 'history')
os.makedirs(hist_dir)
eval_dir = os.path.join(log_dir, timestamp, 'evaluate')
os.makedirs(eval_dir)
params_savename = os.path.join(log_dir, timestamp, 'params.json')
summary_savename = os.path.join(log_dir, timestamp, 'summary.json')
test_hist_savename = os.path.join(log_dir, timestamp, 'test.json')
# Start training
kfold = StratifiedKFold(n_splits=k, shuffle=True, random_state=state)
fold = 1
accs = []
for train_index, val_index in kfold.split(X, Y):
model, params = compile_model(model_name, hparams)
if fold == 1:
print(model.summary())
print(params)
print('\n' + '=' * 60 + ' Fold: ' + str(fold) + ' ' + '=' * 60 + '\n')
# Callback functions
model_savename = os.path.join(model_dir,
'model{0}.h5'.format(str(fold)))
hist_savename = os.path.join(hist_dir,
'history{0}.json'.format(str(fold)))
val_savename = os.path.join(eval_dir,
'evaluate{0}.json'.format(str(fold)))
cb_list = [
callbacks.ModelCheckpoint(filepath=model_savename,
monitor='val_acc',
save_best_only=True),
callbacks.EarlyStopping(
monitor='acc',
patience=6,
)
]
# Add new training sets
try:
if X_train_add.any() and Y_train_add.any():
x_train = np.concatenate([X[train_index], X_train_add], axis=0)
y_train = np.concatenate([Y[train_index], Y_train_add], axis=0)
index = list(range(len(y_train)))
random.seed(state + 1)
random.shuffle(index)
x_train = x_train[index]
y_train = y_train[index]
except AttributeError:
x_train = X[train_index]
y_train = Y[train_index]
history = model.fit(x_train,
y_train,
validation_data=(X[val_index], Y[val_index]),
batch_size=batch_size,
epochs=epochs,
callbacks=cb_list,
verbose=2)
# Log
hist_dict = history.history
m = models.load_model(model_savename, custom_objects={'tf': tf})
val_dict = evaluate_model(m, X[val_index], Y[val_index])
accs.append(val_dict['accuracy'])
log_to_json(hist_dict, hist_savename)
log_to_json(val_dict, val_savename)
fold += 1
K.clear_session()
print('Session cleared.')
# Summary
try:
if X_test.any() and Y_test.any():
model_path = os.path.join(
model_dir, 'model{0}.h5'.format(accs.index(max(accs)) + 1))
m = models.load_model(model_path, custom_objects={'tf': tf})
test_dict = evaluate_model(m, X_test, Y_test)
log_to_json(test_dict, test_hist_savename)
except AttributeError:
pass
log_to_json(hparams, params_savename)
summary = summary_kfold(eval_dir)
print(summary)
log_to_json(summary, summary_savename)
C_vals = [
0.0001, 0.001, 0.01, 0.1, 0.13, 0.2, .15, .25, .275, .33, 0.5, .66, 0.75,
1.0, 2.5, 4.0, 4.5, 5.0, 5.1, 5.5, 6.0, 10.0, 100.0, 1000.0
]
penalties = ['l1', 'l2']
param = {
'penalty': penalties,
'C': C_vals,
}
grid = GridSearchCV(logreg,
param,
verbose=False,
cv=StratifiedKFold(n_splits=5,
random_state=10,
shuffle=True),
n_jobs=1,
scoring='accuracy')
# In[ ]:
grid.fit(X_train, y_train)
print(grid.best_params_)
print(grid.best_score_)
print(grid.best_estimator_)
# In[ ]:
#grid.best_estimator_.fit(X_train,y_train)
#predict=grid.best_estimator_.predict(X_test)
imputer = ColumnTransformer([('imputer_media', imputer_media, num_cols),
('imputer_moda', imputer_moda, cat_cols)])
# Creamos un ColumnTransformer para el StandardScaler
scaler = ColumnTransformer([('scaler_media', scaler_media, num_cols),
('scaler_moda', scaler_moda, cat_cols)])
# Creamos el Pipeline incorporando ColumnTransformer
pipeline = Pipeline([('imputer', imputer), ('trans', trans),
('scaler', scaler), ('trans2', trans)])
# TRAMPA. Problemas con el pipeline. RFE y RFECV tienen un 'check_X_y()' antes de llamar al pipeline (que contiene el imputer)
X = pipeline.fit_transform(X)
# 5 folds estratificadas para el RFECV
skf = StratifiedKFold(n_splits=5)
# Diccionario que mapea la RFE Accuracy con un índice
dict_1 = {}
# Diccionario que mapea un índice con el objeto RFECV
dict_2 = {}
time_prebucle = time.time()
# Itero sobre los posibles valores de C
for i, c in enumerate(C):
time_temp1 = time.time()
clf_temp = SVC(C=c,
kernel=kernel,
class_weight=class_weight,
random_state=random_state)
# def build_fn():
# model = MobileNet(
# include_top=True,
# input_shape=(64, 690, 1),
# classes=2,
# classifier_activation='softmax',
# pooling=None,
# weights=None,
# )
# return model
model = build_fn()
model.summary()
split = StratifiedKFold(n_splits = 3, shuffle = True, random_state = 10)
pred = []
pred_ = []
for train_idx, val_idx in split.split(train_x, train_y):
x_train, y_train = train_x[train_idx], train_y[train_idx]
x_val, y_val = train_x[val_idx], train_y[val_idx]
model = build_fn()
model.compile(optimizer = keras.optimizers.Adam(0.002),
loss = keras.losses.SparseCategoricalCrossentropy(),
metrics = ['acc'])
history = model.fit(x = x_train, y = y_train, validation_data = (x_val, y_val), epochs = 8)
print("*******************************************************************")
#print nX.shape
#nX = SelectKBest(f_classif, k=10000).fit_transform(nX, ny)
#print nX.shape
#PCA
#print nX.shape
#nX = PCA(n_components=0.99, svd_solver="full").fit_transform(nX)
#print nX.shape
#Filter out NaN values
indices = np.array([not np.any(np.isnan(vec)) for vec in nX])
nX = nX[indices]
ny = ny[indices]
#Begin k-fold cross validation
kf = StratifiedKFold(n_splits=10)
#Optional - parameter evaluation using grid search
"""
param_grid = [
{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'kernel': ['linear']},
{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf']},
{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 'kernel': ['sigmoid']},
{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 'degree': [2,3], 'kernel': ['poly']}
]
grid = GridSearchCV(svm.SVC(class_weight="balanced"), param_grid=param_grid, cv=kf, scoring="f1_macro", verbose=10)
grid.fit(nX,ny)
print("The best parameters are %s with a score of %0.2f"
% (grid.best_params_, grid.best_score_))
"""
numpy.random.seed(seed)
tf.random.set_seed(3)
df=pd.read_csv('./data/dataset/sonar.csv', header=None)
dataset=df.values
X=dataset[:,0:60].astype(float)
Y_obj=dataset[:,60]
e=LabelEncoder()
e.fit(Y_obj)
Y=e.transform(Y_obj)
n_fold=10
skf=StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=seed)
accuracy=[]
for train, test in skf.split(X, Y):
model= Sequential()
model.add(Dense(24, input_dim=60, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.fit(X[train], Y[train], epochs=100, batch_size=5)
k_accuracy="%.4f" % (model.evaluate(X[test], Y[test])[1])
accuracy.append(k_accuracy)
print("\n %.f fold_accuracy : " % n_fold, accuracy)
def smape_objective(preds, train_data):
labels = train_data.get_label()
grad = fgrad(preds, labels)
hess = fhess(preds, labels)
return grad, hess
def smape_error(preds, train_data):
labels = train_data.get_label()
return 'error', 100 * np.mean(
np.fabs(preds - labels) / (preds + labels) * 2), False
from sklearn.model_selection import StratifiedKFold
folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=2019)
y_pred_lgb = np.zeros(len(X_test))
params = {
'objective': 'regression',
'num_leaves': 5,
'learning_rate': 0.05,
'n_estimators': 720,
'max_bin': 55,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'feature_fraction': 0.2319,
'feature_fraction_seed': 9,
'bagging_seed': 9,
'min_data_in_leaf': 6,
'min_sum_hessian_in_leaf': 11
from comet_ml import Experiment
from pytorch_lightning.callbacks import ModelCheckpoint
from src.transforms import ImageTransform
from src.utils import summarize_submit
import warnings
warnings.filterwarnings('ignore')
# Config ###########################
# Input Data
data_dir = './input'
# TTA
test_num = 20
# CV
cv = StratifiedKFold(n_splits=4, shuffle=True)
@hydra.main('config.yml')
def main(cfg: DictConfig):
cur_dir = hydra.utils.get_original_cwd()
os.chdir(cur_dir)
# Random Seed
seed_everything(cfg.train.seed)
# Model ####################################################################
net = ENet(model_name=cfg.train.model_name)
transform = ImageTransform(img_size=cfg.data.img_size)
# Comet.ml
experiment = Experiment(api_key=cfg.comet_ml.api_key,
def test_permutation_score():
iris = load_iris()
X = iris.data
X_sparse = coo_matrix(X)
y = iris.target
svm = SVC(kernel='linear')
cv = StratifiedKFold(2)
score, scores, pvalue = permutation_test_score(svm,
X,
y,
n_permutations=30,
cv=cv,
scoring="accuracy")
assert_greater(score, 0.9)
assert_almost_equal(pvalue, 0.0, 1)
score_group, _, pvalue_group = permutation_test_score(svm,
X,
y,
n_permutations=30,
cv=cv,
scoring="accuracy",
groups=np.ones(
y.size),
random_state=0)
assert_true(score_group == score)
assert_true(pvalue_group == pvalue)
# check that we obtain the same results with a sparse representation
svm_sparse = SVC(kernel='linear')
cv_sparse = StratifiedKFold(2)
score_group, _, pvalue_group = permutation_test_score(svm_sparse,
X_sparse,
y,
n_permutations=30,
cv=cv_sparse,
scoring="accuracy",
groups=np.ones(
y.size),
random_state=0)
assert_true(score_group == score)
assert_true(pvalue_group == pvalue)
# test with custom scoring object
def custom_score(y_true, y_pred):
return (((y_true == y_pred).sum() - (y_true != y_pred).sum()) /
y_true.shape[0])
scorer = make_scorer(custom_score)
score, _, pvalue = permutation_test_score(svm,
X,
y,
n_permutations=100,
scoring=scorer,
cv=cv,
random_state=0)
assert_almost_equal(score, .93, 2)
assert_almost_equal(pvalue, 0.01, 3)
# set random y
y = np.mod(np.arange(len(y)), 3)
score, scores, pvalue = permutation_test_score(svm,
X,
y,
n_permutations=30,
cv=cv,
scoring="accuracy")
assert_less(score, 0.5)
assert_greater(pvalue, 0.2)
def RandomizedSearchCV_load_or_make(model, data, labels, random_grid, cv="5", scoring="accuracy", n_iter=20, random_state=47):
import xgboost as xgb
from xgboost import XGBClassifier
from sklearn.model_selection import RandomizedSearchCV
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold
# we use the RandomizedSearchCV to find the best parameters for our XGB model
load_or_make = input("Load or make RandomizedSearchCV?")
if load_or_make == "load":
# pick RandomizedSearchCV to load
print('Izaberi RandomizedSearchCV:')
print('1. RandomizedSearchCV10_basic_all_features_neg_log_loss ')
option = input()
if option == '1':
filename = '.../dataset/basic_modeli/RandomizedSearchCV10_basic_all_features_neg_log_loss.sav'
else:
print('Ne postoji zatražena opcija!')
raise ValueError('Ne postoji zatražena opcija - pri učitavanju RandomizedSearchCV!')
return
rand_XGB = pickle.load(open(filename, 'rb'))
rand_XGB.get_params()
# show results
rand_XGB_results_df = pd.DataFrame(rand_XGB.cv_results_)[['mean_test_score', 'std_test_score', 'params','rank_test_score']]
rand_XGB_results_df
# plot of randomized search results
rand_XGB_mean_scores = rand_XGB.cv_results_['mean_test_score']
plt.plot(list(range(1, 21)), rand_XGB_mean_scores)
plt.xlabel('k-ti Model Randomized Search CV treniranja (XGB)')
plt.ylabel('Točnost unakrsne validacije')
return [rand_XGB, rand_XGB_results_df]
elif load_or_make == "make":
# getting ready for saving later
name = input('Unesi ime novo-pokrenutoga RandomizedSearchCV? ')
filename = 'data/RandomizedSearchCV_' + name + '.sav'
model_XGB = model
# RandomizedSearchCV
rand_XGB = RandomizedSearchCV(model_XGB,
param_distributions = random_grid,
cv=StratifiedKFold(n_splits=cv),
scoring=scoring,
n_iter=20,
random_state=random_state,
return_train_score=False,
verbose=True,
n_jobs=-1)
# fit
rand_XGB.fit(data, labels)
# save
pickle.dump(rand_XGB, open(filename, 'wb'))
print('RandomizedSearchCV je spremljen u: ' + filename )
# show results
rand_XGB_results_df = pd.DataFrame(rand_XGB.cv_results_)[['mean_test_score', 'std_test_score', 'params', 'rank_test_score']]
rand_XGB_results_df
# plot of randomized search results
rand_XGB_mean_scores = rand_XGB.cv_results_['mean_test_score']
plt.plot(list(range(1, 21)), rand_XGB_mean_scores)
plt.xlabel('k-ti Model Randomized Search CV treniranja (XGB)')
plt.ylabel('Točnost unakrsne validacije')
return [rand_XGB, rand_XGB_results_df]
else:
print("Krivi unos! Upiši 'load' ili 'make'!")
raise ValueError('Nije upisano load ili make - pri učitavanju/izradi RandomizedSearchCV!')
return
def fit_predict(X, y, X_pred):
predictors = [i for i in X.columns]
stacking_num = 5
bagging_num = 3
bagging_test_size = 0.33
num_boost_round = 500
early_stopping_rounds = 100
stacking_model = []
bagging_model = []
l2_error = []
X = X.values
y = y.values
layer_train = np.zeros((X.shape[0], 2))
SK = StratifiedKFold(n_splits=stacking_num, shuffle=True, random_state=1)
for k, (train_index, test_index) in enumerate(SK.split(X, y)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test)
gbm = lgb.train(param,
lgb_train,
num_boost_round=num_boost_round,
valid_sets=lgb_eval,
early_stopping_rounds=early_stopping_rounds)
stacking_model.append(gbm)
X = np.hstack((X, layer_train[:, 1].reshape((-1, 1))))
predictors.append('lgb_result')
for bn in range(bagging_num):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=bagging_test_size, random_state=bn)
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test)
gbm = lgb.train(param,
lgb_train,
num_boost_round=10000,
valid_sets=lgb_eval,
early_stopping_rounds=200)
bagging_model.append(gbm)
l2_error.append(
mean_squared_error(
gbm.predict(X_test, num_iteration=gbm.best_iteration), y_test))
feat_imp = pd.Series(gbm.feature_importance(),
predictors).sort_values(ascending=False)
test_pred = np.zeros((X_pred.shape[0], stacking_num))
for sn, gbm in enumerate(stacking_model):
pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration)
test_pred[:, sn] = pred
X_pred = np.hstack((X_pred, test_pred.mean(axis=1).reshape((-1, 1))))
for bn, gbm in enumerate(bagging_model):
pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration)
if bn == 0:
pred_out = pred
else:
pred_out += pred
return pred_out / bagging_num, feat_imp
verbose=False)
score = model.best_score_["valid_0"]["multi_logloss"]
return {'loss': score, 'status': STATUS_OK, 'model': model}
trials = Trials()
best = fmin(fn=objective, space=space, trials=trials,
algo=tpe.suggest, max_evals=10, verbose=1)
hyperparams = space_eval(space, best)
n_best = trials.best_trial['result']['model'].best_iteration_
params.update(hyperparams)
print(params)
# 하이퍼 파라미터 튜닝 끝
cv = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=seed)
p_val = np.zeros((trn.shape[0], n_class))
p_tst = np.zeros((tst.shape[0], n_class))
for i, (i_trn, i_val) in enumerate(cv.split(trn, y), 1):
print(f'training model for CV #{i}')
clf = lgb.LGBMClassifier(**params)
clf.fit(trn[i_trn], y[i_trn],
eval_set=[(trn[i_val], y[i_val])],
eval_metric='multiclass',
verbose=3,
early_stopping_rounds=20)
p_val[i_val, :] = clf.predict_proba(trn[i_val])
p_tst += clf.predict_proba(tst) / n_fold
def discriminate(X, y, nDmax):
CVFOLDS = 10
MINCOUNT = 10
MINCOUNTTRAINING = 5
# Initialize Variables and clean up data
classes, classesCount = np.unique(
y, return_counts=True
) # Classes to be discriminated should be same as ldaMod.classes_
goodIndClasses = np.array([n >= MINCOUNT for n in classesCount])
goodInd = np.array([b in classes[goodIndClasses] for b in y])
yGood = y[goodInd]
XGood = X[goodInd]
classes, classesCount = np.unique(yGood, return_counts=True)
nClasses = classes.size # Number of classes or groups
cvFolds = min(min(classesCount), CVFOLDS)
if (cvFolds < CVFOLDS):
print(
'Warning in ldaPlot: Cross-validation performed with %d folds (instead of %d)'
% (cvFolds, CVFOLDS))
# Data size and color values
nD = XGood.shape[1] # number of features in X
nX = XGood.shape[0] # number of data points in X
# Use a uniform prior
myPrior = np.ones(nClasses) * (1.0 / nClasses)
# Perform a PCA for dimensionality reduction so that the covariance matrix can be fitted.
# nDmax = int(np.fix(np.sqrt(nX//5)))
if nDmax < nD:
print('Warning: Insufficient data for', nD,
'parameters. PCA projection to', nDmax, 'dimensions.')
nDmax = min(nD, nDmax)
pca = PCA(n_components=nDmax)
Xr = pca.fit_transform(XGood)
print('Variance explained is %.2f%%' %
(sum(pca.explained_variance_ratio_) * 100.0))
# Initialise Classifiers
ldaMod = LDA(n_components=min(nDmax, nClasses - 1),
priors=myPrior,
shrinkage=None,
solver='svd')
qdaMod = QDA(priors=myPrior)
rfMod = RF() # by default assumes equal weights
# Perform CVFOLDS fold cross-validation to get performance of classifiers.
ldaYes = 0
qdaYes = 0
rfYes = 0
cvCount = 0
skf = StratifiedKFold(n_splits=cvFolds)
skfList = skf.split(Xr, yGood)
for train, test in skfList:
# Enforce the MINCOUNT in each class for Training
trainClasses, trainCount = np.unique(yGood[train], return_counts=True)
goodIndClasses = np.array([n >= MINCOUNTTRAINING for n in trainCount])
goodIndTrain = np.array(
[b in trainClasses[goodIndClasses] for b in yGood[train]])
# Specity the training data set, the number of groups and priors
yTrain = yGood[train[goodIndTrain]]
XrTrain = Xr[train[goodIndTrain]]
trainClasses, trainCount = np.unique(yTrain, return_counts=True)
ntrainClasses = trainClasses.size
# Skip this cross-validation fold because of insufficient data
if ntrainClasses < 2:
continue
goodInd = np.array([b in trainClasses for b in yGood[test]])
if (goodInd.size == 0):
continue
# Fit the data
trainPriors = np.ones(ntrainClasses) * (1.0 / ntrainClasses)
ldaMod.priors = trainPriors
qdaMod.priors = trainPriors
ldaModself = ldaMod.fit(XrTrain, yTrain)
qdaMod.fit(XrTrain, yTrain)
rfMod.fit(XrTrain, yTrain)
ldaYes += np.around(
(ldaMod.score(Xr[test[goodInd]], yGood[test[goodInd]])) *
goodInd.size)
qdaYes += np.around(
(qdaMod.score(Xr[test[goodInd]], yGood[test[goodInd]])) *
goodInd.size)
rfYes += np.around(
(rfMod.score(Xr[test[goodInd]], yGood[test[goodInd]])) *
goodInd.size)
cvCount += goodInd.size
ldaYes = int(ldaYes)
qdaYes = int(qdaYes)
rfYes = int(rfYes)
p = 1.0 / nClasses
ldaP = 0
qdaP = 0
rfP = 0
for k in range(ldaYes, cvCount + 1):
ldaP += binom.pmf(k, cvCount, p)
for k in range(qdaYes, cvCount + 1):
qdaP += binom.pmf(k, cvCount, p)
for k in range(rfYes, cvCount + 1):
rfP += binom.pmf(k, cvCount, p)
print("Number of classes %d. Chance level %.2f %%" %
(nClasses, 100.0 / nClasses))
print("LDA: %.2f %% (%d/%d p=%.4f)" %
(100.0 * ldaYes / cvCount, ldaYes, cvCount, ldaP))
print("QDA: %.2f %% (%d/%d p=%.4f)" %
(100.0 * qdaYes / cvCount, qdaYes, cvCount, qdaP))
print("RF: %.2f %% (%d/%d p=%.4f)" %
(100.0 * rfYes / cvCount, rfYes, cvCount, rfP))
# return ldaYes, qdaYes, rfYes, cvCount, ldaP, qdaP, rfP, nClasses, weights
return 100.0 * ldaYes / cvCount, 100.0 * qdaYes / cvCount, 100.0 * rfYes / cvCount
def kfold_lightgbm(df, num_folds, stratified=False):
# Divide in training/validation and test data
train_df = df[df['FLAG'] != -1]
test_df = df[df['FLAG'] == -1]
print("Starting LightGBM. Train shape: {}, test shape: {}".format(
train_df.shape, test_df.shape))
del df
gc.collect()
# Cross validation model
if stratified:
folds = StratifiedKFold(n_splits=num_folds,
shuffle=False,
random_state=1001)
else:
folds = KFold(n_splits=num_folds, shuffle=True, random_state=1001)
# Create arrays and dataframes to store results
oof_preds = np.zeros(train_df.shape[0])
sub_preds = np.zeros(test_df.shape[0])
feature_importance_df = pd.DataFrame()
feats = [f for f in train_df.columns if f not in ['FLAG', 'USRID']]
for n_fold, (train_idx, valid_idx) in enumerate(
folds.split(train_df[feats], train_df['FLAG'])):
train_x, train_y = train_df[feats].iloc[train_idx], train_df[
'FLAG'].iloc[train_idx]
valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df[
'FLAG'].iloc[valid_idx]
clf = lgb.LGBMClassifier(nthread=4,
n_estimators=3000,
learning_rate=0.02,
num_leaves=31,
colsample_bytree=0.997212866002197,
bagging_fraction=0.7733927534732657,
min_data_in_leaf=37,
min_child_weight=13.05659547343758,
min_split_gain=0.027258234021548238,
reg_lambda=0.12367585365238067,
verbose=0)
clf.fit(train_x,
train_y,
eval_set=[(train_x, train_y), (valid_x, valid_y)],
eval_metric='auc',
verbose=100,
early_stopping_rounds=150)
oof_preds[valid_idx] = clf.predict_proba(
valid_x, num_iteration=clf.best_iteration_)[:, 1]
sub_preds += clf.predict_proba(
test_df[feats],
num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits
fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = feats
fold_importance_df["importance"] = clf.feature_importances_
fold_importance_df["fold"] = n_fold + 1
feature_importance_df = pd.concat(
[feature_importance_df, fold_importance_df], axis=0)
print('Fold %2d AUC : %.6f' %
(n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))
del clf, train_x, train_y, valid_x, valid_y
gc.collect()
print('Full AUC score %.6f' % roc_auc_score(train_df['FLAG'], oof_preds))
# Write submission file and plot feature importance
train_df['RST'] = oof_preds
test_df['RST'] = sub_preds
test_df[['USRID', 'RST']].to_csv('submission6.csv', index=False, sep='\t')
display_importances(feature_importance_df)
return train_df[['USRID', 'RST']], test_df[['USRID',
'RST']], feature_importance_df
MAX_FEATURES= 195000
MAX_LEN = 150
MODEL_IDENTIFIER = "fastext_minimum_preproc_reg"
train = pd.read_csv(TRAIN_DATA_FILE)
test = pd.read_csv(TEST_DATA_FILE)
print(train.shape, test.shape)
list_classes = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
y = train[list_classes].values
#Get validation folds
train['target_str'] = reduce(lambda x,y: x+y, [train[col].astype(str) for col in list_classes])
train['target_str'] = train['target_str'].replace('110101', '000000').replace('110110','000000')
cvlist1 = list(StratifiedKFold(n_splits=10, random_state=786).split(train, train['target_str'].astype('category')))
cvlist2 = list(StratifiedShuffleSplit(n_splits=5, test_size=0.05, random_state=786).split(train, train['target_str'].astype('category')))
#NOrmalize text
for df in train, test:
df["comment_text"] = normalizeString(df["comment_text"])
#stemmer = PorterStemmer()
#def custom_tokenize(text):
# tokens = wordpunct_tokenize(text)
# tokens = [stemmer.stem(token) for token in tokens]
# return tokens
#Tokenize comments S
tok = Tokenizer(max_features=MAX_FEATURES, max_len=MAX_LEN, tokenizer=wordpunct_tokenize)
X = tok.fit_transform(pd.concat([train["comment_text"].astype(str).fillna("na"), test["comment_text"].astype(str).fillna("na")]))
X_train = X[:len(train), :]
path, dataset),
dtype=np.dtype(np.int))
nb_rows = len(clust)
Data = np.zeros((nb_rows, nb_columns), dtype=np.float32)
for i in range(nb_rows):
row = prepare_activity_score_feature_vector(features, labels, clust[i],
clusters)
Data[i, :] = row
X = np.transpose(Data)
#Activity score features are sorted as label 0 then label 1, so we need to rearrange the labels (0s first then 1s)
labels.sort()
y = np.asarray(labels, dtype=np.int)
# Run classifier with cross-validation and plot ROC curves
cv = StratifiedKFold(n_splits=5, shuffle=True)
classifier = LogisticRegression(solver='lbfgs', max_iter=500)
max_iter = 100
if (False):
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)
i = 0
for train, test in cv.split(X, y):
probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
tprs.append(interp(mean_fpr, fpr, tpr))
tprs[-1][0] = 0.0
ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],
180 + angle, color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
iris = datasets.load_iris()
# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(n_splits=4)
# Only take the first fold.
train_index, test_index = next(iter(skf.split(iris.data, iris.target)))
X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]
print( X_train.shape )
print( X_test.shape )
#exit(1)
n_classes = len(np.unique(y_train))
# Try GMMs using different types of covariances.
def fit(self, data):
# Split training data for phase 1 and phase 2
if self.task_type in CLS_TASKS:
kf = StratifiedKFold(n_splits=self.kfold)
else:
kf = KFold(n_splits=self.kfold)
# Train basic models using a part of training data
model_cnt = 0
suc_cnt = 0
feature_p2 = None
for algo_id in self.stats["include_algorithms"]:
model_to_eval = self.stats[algo_id]['model_to_eval']
for idx, (node, config) in enumerate(model_to_eval):
X, y = node.data
if self.base_model_mask[model_cnt] == 1:
for j, (train, test) in enumerate(kf.split(X, y)):
x_p1, x_p2, y_p1, _ = X[train], X[test], y[train], y[
test]
estimator = fetch_predict_estimator(
self.task_type,
config,
x_p1,
y_p1,
weight_balance=data.enable_balance,
data_balance=data.data_balance)
with open(
os.path.join(
self.output_dir, '%s-model%d_part%d' %
(self.timestamp, model_cnt, j)),
'wb') as f:
pkl.dump(estimator, f)
if self.task_type in CLS_TASKS:
pred = estimator.predict_proba(x_p2)
n_dim = np.array(pred).shape[1]
if n_dim == 2:
# Binary classificaion
n_dim = 1
# Initialize training matrix for phase 2
if feature_p2 is None:
num_samples = len(train) + len(test)
feature_p2 = np.zeros(
(num_samples, self.ensemble_size * n_dim))
if n_dim == 1:
feature_p2[test,
suc_cnt * n_dim:(suc_cnt + 1) *
n_dim] = pred[:, 1:2]
else:
feature_p2[test, suc_cnt *
n_dim:(suc_cnt + 1) * n_dim] = pred
else:
pred = estimator.predict(x_p2).reshape(-1, 1)
n_dim = 1
# Initialize training matrix for phase 2
if feature_p2 is None:
num_samples = len(train) + len(test)
feature_p2 = np.zeros(
(num_samples, self.ensemble_size * n_dim))
feature_p2[test, suc_cnt * n_dim:(suc_cnt + 1) *
n_dim] = pred
suc_cnt += 1
model_cnt += 1
# Train model for stacking using the other part of training data
self.meta_learner.fit(feature_p2, y)
return self
def plot_learning_curve( model, X_train, y_train, X_test, y_test, cv, seed ):
import warnings
warnings.filterwarnings("ignore")
# load libraries
import numpy as np
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split as tts
from sklearn.metrics import accuracy_score, make_scorer, log_loss
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve, StratifiedKFold
#plt.style.use('ggplot')
malware_dict = { 1 : 'Ramnit', 2 : 'Lollipop', 3 : 'Kelihos_ver3', 4 : 'Vundo', 5 : 'Simba',
6 : 'Tracur', 7 : 'Kelihos_ver1', 8 : 'Obfuscator.ACY', 9 : 'Gatak'}
# Create CV training and test scores for various training set sizes
train_sizes, train_scores, test_scores = learning_curve(model,
X_train, y_train, cv=StratifiedKFold(n_splits=cv),
scoring="accuracy",
#scoring=make_scorer(log_loss, needs_proba=True, labels=list(malware_dict.keys())),
n_jobs=-1,
random_state=seed)
# Create means and standard deviations of training set scores
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
# Create means and standard deviations of test set scores
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
# Draw lines
plt.subplots(1, figsize=(12,12))
plt.plot(train_sizes, train_mean, '--', color="#111111", label="Uspješnost treniranja")
plt.plot(train_sizes, test_mean, color="#111111", label="Uspješnost unakrsne validacije")
# Draw bands
plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color="#DDDDDD")
plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color="#DDDDDD")
# Create plot
plt.title("Krivulja učenja")
plt.xlabel("Veličina skupa za treniranje"), plt.ylabel("Točnost"), plt.legend(loc="best")
plt.tight_layout(); plt.show()
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
# retrieve performance metrics
results = model.evals_result()
epochs = len(results['validation_0']['merror'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots(figsize=(12,12))
ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')
ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = plt.subplots(figsize=(12,12))
ax.plot(x_axis, results['validation_0']['merror'], label='Train')
ax.plot(x_axis, results['validation_1']['merror'], label='Test')
ax.legend()
plt.ylabel('Pogreška klasifikacije')
plt.title('XGBoost pogreška klasifikacije')
plt.show()
# learning and prediction -------------------------------------------------------------
print("BayesSearch")
bayes_cv_tuner = BayesSearchCV(estimator=ExtraTreesClassifier(
n_estimators=300, random_state=0, class_weight="balanced"),
search_spaces={
'criterion': ["gini", "entropy"],
'splitter': ["random", "best"],
'min_samples_split': (2, 100),
'min_samples_leaf': (1, 100),
'min_weight_fraction': (0, 1),
'max_depth': (1, 50),
},
scoring="roc_auc",
cv=StratifiedKFold(n_splits=3,
shuffle=True,
random_state=42),
n_jobs=-3,
n_iter=10,
verbose=0,
refit=True,
random_state=42)
result = bayes_cv_tuner.fit(train_mod[selected_features].values,
target_mod.values,
callback=status_print)
#Model
#Best ROC-AUC:
#Best params:
def mean_encode(train_data,
test_data,
columns,
target_col,
reg_method=None,
alpha=0,
add_random=False,
rmean=0,
rstd=0.1,
folds=1):
length_train = len(train_data)
'''Returns a DataFrame with encoded columns'''
encoded_cols = []
target_mean_global = train_data[target_col].mean()
for col in columns:
# Getting means for test data
nrows_cat = train_data.groupby(col)[target_col].count()
target_means_cats = train_data.groupby(col)[target_col].mean()
target_means_cats_adj = (target_means_cats * nrows_cat +
target_mean_global * alpha) / (nrows_cat +
alpha)
# Mapping means to test data
encoded_col_test = test_data[col].map(target_means_cats_adj)
# Getting a train encodings
if reg_method == 'expanding_mean':
train_data_shuffled = train_data.sample(frac=1, random_state=1)
cumsum = train_data_shuffled.groupby(
col)[target_col].cumsum() - train_data_shuffled[target_col]
cumcnt = train_data_shuffled.groupby(col).cumcount()
encoded_col_train = cumsum / (cumcnt)
encoded_col_train.fillna(target_mean_global, inplace=True)
if add_random:
encoded_col_train = encoded_col_train + normal(
loc=rmean, scale=rstd, size=(encoded_col_train.shape[0]))
elif (reg_method == 'k_fold') and (folds > 1):
kfold = StratifiedKFold(n_splits=folds,
shuffle=True,
random_state=1).split(
train_data[target_col].values,
train_data[target_col])
parts = []
for tr_in, val_ind in kfold:
# divide data
df_for_estimation, df_estimated = train_data.iloc[
tr_in], train_data.iloc[val_ind]
# getting means on data for estimation (all folds except estimated)
nrows_cat = df_for_estimation.groupby(col)[target_col].count()
target_means_cats = df_for_estimation.groupby(
col)[target_col].mean()
target_means_cats_adj = (target_means_cats * nrows_cat +
target_mean_global * alpha) / (
nrows_cat + alpha)
# Mapping means to estimated fold
encoded_col_train_part = df_estimated[col].map(
target_means_cats_adj)
if add_random:
encoded_col_train_part = encoded_col_train_part + normal(
loc=rmean,
scale=rstd,
size=(encoded_col_train_part.shape[0]))
# Saving estimated encodings for a fold
parts.append(encoded_col_train_part)
encoded_col_train = pd.concat(parts, axis=0)
encoded_col_train.fillna(target_mean_global, inplace=True)
else:
encoded_col_train = train_data[col].map(target_means_cats_adj)
if add_random:
encoded_col_train = encoded_col_train + normal(
loc=rmean, scale=rstd, size=(encoded_col_train.shape[0]))
# Saving the column with means
encoded_col = pd.concat([encoded_col_train, encoded_col_test], axis=0)
encoded_col[encoded_col.isnull()] = target_mean_global
encoded_cols.append(
pd.DataFrame({'mean_' + target_col + '_' + col: encoded_col}))
all_encoded = pd.concat(encoded_cols, axis=1)
#Modified to reindex
all_encoded = all_encoded.reset_index()
return (all_encoded.iloc[:length_train].reset_index(drop=True),
all_encoded.iloc[length_train:].reset_index(drop=True))
def kfold_xgb(df, num_folds, stratified=False):
# Divide in training/validation and test data
train_df = df[df['FLAG'] != -1]
test_df = df[df['FLAG'] == -1]
print("Starting LightGBM. Train shape: {}, test shape: {}".format(
train_df.shape, test_df.shape))
del df
gc.collect()
# Cross validation model
if stratified:
folds = StratifiedKFold(n_splits=num_folds,
shuffle=False,
random_state=1001)
else:
folds = KFold(n_splits=num_folds, shuffle=True, random_state=1001)
# Create arrays and dataframes to store results
oof_preds = np.zeros(train_df.shape[0])
sub_preds = np.zeros(test_df.shape[0])
feats = [f for f in train_df.columns if f not in ['FLAG', 'USRID']]
feature_importance_df = pd.DataFrame()
for n_fold, (train_idx, valid_idx) in enumerate(
folds.split(train_df[feats], train_df['FLAG'])):
train_x, train_y = train_df[feats].iloc[train_idx], train_df[
'FLAG'].iloc[train_idx]
valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df[
'FLAG'].iloc[valid_idx]
train_x = xgb.DMatrix(train_x, label=train_y)
valid_x = xgb.DMatrix(valid_x, label=valid_y)
params = {
'booster': 'gbtree',
'objective': 'rank:pairwise',
'eval_metric': 'auc',
'max_depth': 4,
'subsample': 0.85,
'colsample_bytree': 0.8,
'colsample_bylevel': 0.8,
'tree_method': 'exact',
'seed': 0,
'nthread': 4,
'gamma': 0.5,
'min_child_weight': 50,
}
watchlist = [(train_x, 'train'), (valid_x, 'val')]
clf = xgb.train(params,
train_x,
num_boost_round=3000,
evals=watchlist,
early_stopping_rounds=90)
test = xgb.DMatrix(test_df[feats])
oof_preds[valid_idx] = clf.predict(valid_x,
ntree_limit=clf.best_ntree_limit)
sub_preds += clf.predict(
test, ntree_limit=clf.best_ntree_limit) / folds.n_splits
print('Fold %2d AUC : %.6f' %
(n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))
xgb.plot_importance(clf)
fscore = clf.get_fscore()
a = list(fscore.keys())
v = list(fscore.values())
fold_importance_df = pd.DataFrame()
fold_importance_df['feature'] = a
fold_importance_df['importance'] = v
fold_importance_df['fold'] = n_fold + 1
feature_importance_df = pd.concat(
[feature_importance_df, fold_importance_df], axis=0)
del clf, train_x, train_y, valid_x, valid_y
gc.collect()
print('Full AUC score %.6f' % roc_auc_score(train_df['FLAG'], oof_preds))
# Write submission file and plot feature importance
train_df['RST'] = oof_preds
test_df['RST'] = sub_preds
# test_df[['USRID', 'RST']].to_csv('submission6.csv', index= False,sep='\t')
return train_df[['USRID', 'RST']], test_df[['USRID',
'RST']], feature_importance_df
model = RandomForestClassifier(n_estimators=300,
bootstrap=True,
max_features='sqrt',
n_jobs=2,
random_state=1)
elif args.m == 'dt':
model = DecisionTreeClassifier(max_depth=10, random_state=1)
elif args.m == 'svm':
model = SVC(kernel='linear', C=1.0, random_state=1)
elif args.m == 'nb':
model = GaussianNB()
elif args.m == 'knn':
model = KNeighborsClassifier(n_neighbors=1)
elif args.m == 'all':
for mm in ['rf', 'dt', 'svm', 'nb', 'knn']:
cv = StratifiedKFold(n_splits=5, random_state=123, shuffle=True)
acc, recall, prec, f1, TN, TP, FP, FN = 0, 0, 0, 0, 0, 0, 0, 0
for (train, test), i in zip(cv.split(x, y), range(5)):
if mm == 'rf':
model = RandomForestClassifier(n_estimators=300,
bootstrap=True,
max_features='sqrt',
n_jobs=2,
random_state=1)
elif mm == 'dt':
model = DecisionTreeClassifier(max_depth=10, random_state=1)
elif mm == 'svm':
model = SVC(kernel='linear', C=1.0, random_state=1)
elif mm == 'nb':
model = GaussianNB()
elif mm == 'knn':
"Ubicación"]] = cases_covid[[
"Ciudad.de.residencia", "Sexo", "Tipo.de.caso", "Ubicación"
]].astype(str)
cases_covid.replace(dict, inplace=True)
# print(cases_covid.columns)
print(cases_covid.info())
# print(cases_covid)
x = cases_covid.loc[:, cases_covid.columns != 'Estado']
y = cases_covid.loc[:, 'Estado']
n_samples, n_features = x.shape
random_state = np.random.RandomState(0)
x = np.c_[x, random_state.randn(n_samples, 200 * n_features)]
cv = StratifiedKFold(n_splits=10)
classifier = svm.SVC(kernel='linear',
probability=True,
random_state=random_state)
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)
fig, ax = plt.subplots()
for i, (train, test) in enumerate(cv.split(x, y)):
classifier.fit(x[train], y[train])
viz = plot_roc_curve(classifier,
x[test],
y[test],
name='ROC fold {}'.format(i),
