def calc_results_for_ensamble(X, y, train_index, test_index, nrun, clf):
all_rez = []
for i in range(nrun):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
all_rez.append(y_pred)
y_pred = np.array([
Counter(ys).most_common()[0][0]
for ys in np.array(all_rez).transpose()
])
acc = np.mean(y_test == y_pred)
recall_0 = recall_score(y_test, y_pred, pos_label=0)
recall_1 = recall_score(y_test, y_pred, pos_label=1)
return acc, recall_0, recall_1
def calc_results_onlystudy(X, y, train_index, test_index, clf):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
return calc_results_simple(X_train, X_test, y_train, y_test, clf)
def get_balanced_split_for_study(full_dataset, study, y_field="posOutcome"):
X, y = prepare_dataset(full_dataset, study, y_field=y_field)
kf = RepeatedStratifiedKFold(n_splits=5)
(train_index, test_index) = next(kf.split(X, y))
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
return X_train, X_test, y_train, y_test
def calc_results_withfull_simple(X, y, train_index, test_index, full_dataset, test_study, clf):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
X_train_other, y_train_other = get_balanced_studies_except_test_study(full_dataset, test_study)
X_train = np.concatenate([X_train, X_train_other])
y_train = np.concatenate([y_train, y_train_other])
return calc_results_simple(X_train, X_test, y_train, y_test, clf)
def calc_results_for_fold(X, y, train_index, test_index, clf):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = np.mean(y_test == y_pred)
recall_0 = recall_score(y_test, y_pred, pos_label=0)
recall_1 = recall_score(y_test, y_pred, pos_label=1)
return acc, recall_0, recall_1
def calc_results_withfull_balanced2(X, y, train_index, test_index, full_dataset, test_study, clf):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, y_train = random_upsample_balance(X_train, y_train)
X_test, y_test = random_upsample_balance(X_test, y_test)
X_train_other, y_train_other = get_balanced_studies_except_test_study(full_dataset, test_study)
N_rep = int(len(y_train_other) / len(y_train))
X_train_rep = np.repeat(X_train, N_rep, axis=0)
y_train_rep = np.repeat(y_train, N_rep, axis=0)
X_train = np.concatenate([X_train_rep, X_train_other])
y_train = np.concatenate([y_train_rep, y_train_other])
return calc_results_simple(X_train, X_test, y_train, y_test, clf)
def get_balanced_study(full_dataset, study):
X, y = prepare_dataset(full_dataset, study)
return random_upsample_balance(X, y)
from funs_balance import random_upsample_balance X = [[1, 1], [2, 1], [3, 1], [4, 1], [1, 0], [2, 0]] y = [1, 1, 1, 1, 0, 0] Xb, yb = random_upsample_balance(X, y) print(Xb) print(yb)
def print_results(dataset, set1, set2):
X_set1, y_set1 = prepare_full_dataset(
dataset.loc[dataset['patient_ID'].isin(set1)])
X_set2, y_set2 = prepare_full_dataset(
dataset.loc[dataset['patient_ID'].isin(set2)])
# X_set1 = np.random.rand(*X_set1.shape)
# X_set2 = np.random.rand(*X_set2.shape)
X_set1_wf = add_one_features(X_set1, 0)
X_set2_wf = add_one_features(X_set2, 1)
X_genes_wf = np.concatenate([X_set1_wf, X_set2_wf])
y_all = np.concatenate([y_set1, y_set2])
kf = RepeatedStratifiedKFold(n_splits=5, n_repeats=10)
print_order = [
"genes", "genes_set", "genes_biased", "genes_double", "study"
]
max_len_order = max(map(len, print_order))
rez = defaultdict(list)
for i, (train_index, test_index) in enumerate(kf.split(X_genes_wf, y_all)):
X_genes_wf_train, X_genes_wf_test = X_genes_wf[
train_index], X_genes_wf[test_index]
y_train, y_test = y_all[train_index], y_all[test_index]
print("before balanced")
print_count_two_sets(X_genes_wf_train[:, 0], y_train)
print_count_two_sets(X_genes_wf_test[:, 0], y_test)
# print("counter before balance", Counter(X_genes_wf_train[:,0]), Counter(X_genes_wf_test[:,0]), Counter(y_train), Counter(y_test))
X_genes_wf_train, y_train = random_upsample_balance(
X_genes_wf_train, y_train)
X_genes_wf_test, y_test = random_upsample_balance(
X_genes_wf_test, y_test)
# print("counter after balance", Counter(X_genes_wf_train[:,0]), Counter(X_genes_wf_test[:,0]), Counter(y_train), Counter(y_test))
print("after balanced")
print_count_two_sets(X_genes_wf_train[:, 0], y_train)
print_count_two_sets(X_genes_wf_test[:, 0], y_test)
X_genes_train = X_genes_wf_train[:, 1:]
X_genes_test = X_genes_wf_test[:, 1:]
Xs_train = X_genes_wf_train[:, :1]
Xs_test = X_genes_wf_test[:, :1]
rez["genes"].append(
calc_results_simple(X_genes_train, X_genes_test, y_train, y_test,
XGBClassifier()))
rez["genes_set"].append(
calc_results_simple(X_genes_wf_train, X_genes_wf_test, y_train,
y_test, XGBClassifier()))
rez["genes_biased"].append(
calc_results_simple(X_genes_wf_train, X_genes_wf_test, y_train,
y_test, BiasedXgboost()))
rez["genes_double"].append(
calc_results_simple(X_genes_wf_train, X_genes_wf_test, y_train,
y_test, DoubleXgboost()))
rez["study"].append(
calc_results_simple(Xs_train, Xs_test, y_train, y_test,
XGBClassifier()))
for order in print_order:
print(order, " " * (max_len_order - len(order)), ": ",
list_to_4g_str(rez[order][-1]))
print("")
for order in print_order:
print("==> ", order, " " * (max_len_order - len(order)), ": ",
list2d_to_4g_str_pm(rez[order]))
