diff --git a/feature_importance/01_run_ablation_classification.py b/feature_importance/01_run_ablation_classification.py
index b2a1448..8b388b5 100644
--- a/feature_importance/01_run_ablation_classification.py
+++ b/feature_importance/01_run_ablation_classification.py
@@ -19,10 +19,10 @@
from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
-from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import LogisticRegressionCV
from sklearn.svm import SVC
import xgboost as xgb
-
+from imodels.importance import RandomForestPlusRegressor, RandomForestPlusClassifier
sys.path.append(".")
sys.path.append("..")
sys.path.append("../..")
@@ -77,6 +77,22 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features):
data_copy[i, indices[i,j]] = train_mean[indices[i,j]]
return data_copy
+def ablation_by_addition(data, feature_importance, mode, num_features):
+ """
+ Initialize the data with zeros and add the top num_features max feature importance data for each sample
+ """
+ assert mode in ["max", "min"]
+ fi = feature_importance.to_numpy()
+ if mode == "max":
+ indices = np.argsort(-fi)
+ else:
+ indices = np.argsort(fi)
+ data_copy = np.zeros(data.shape)
+ for i in range(data.shape[0]):
+ for j in range(num_features):
+ data_copy[i, indices[i,j]] = data[i, indices[i,j]]
+ return data_copy
+
def compare_estimators(estimators: List[ModelConfig],
fi_estimators: List[FIModelConfig],
X, y, support: List,
@@ -121,7 +137,6 @@ def compare_estimators(estimators: List[ModelConfig],
y_tune = y
y_test = y
- normalizer = preprocessing.Normalizer()
normalizer = preprocessing.Normalizer()
if splitting_strategy == "train-test":
X_train = normalizer.fit_transform(X_train)
@@ -138,10 +153,12 @@ def compare_estimators(estimators: List[ModelConfig],
test_all_auprc = auprc_score(y_test, est.predict_proba(X_test)[:, 1])
test_all_f1 = f1_score(y_test, est.predict_proba(X_test)[:, 1] > 0.5)
- if model.name == "RF_plus":
- indices = np.random.choice(X_test.shape[0], 100, replace=False)
- X_test = X_test[indices]
- y_test = y_test[indices]
+ indices_train = np.random.choice(X_train.shape[0], 100, replace=False)
+ indices_test = np.random.choice(X_test.shape[0], 100, replace=False)
+ X_train_subset = X_train[indices_train]
+ y_train_subset = y_train[indices_train]
+ X_test_subset = X_test[indices_test]
+ y_test_subset = y_test[indices_test]
# loop over fi estimators
rng = np.random.RandomState()
@@ -160,101 +177,167 @@ def compare_estimators(estimators: List[ModelConfig],
'test_all_f1': test_all_f1
}
for i in range(100):
- if model.name == "RF_plus":
- metric_results[f'sample_test_{i}'] = indices[i]
- else:
- metric_results[f'sample_test_{i}'] = None
+ metric_results[f'sample_train_{i}'] = indices_train[i]
+ metric_results[f'sample_test_{i}'] = indices_test[i]
for i in range(len(seeds)):
metric_results[f'ablation_seed_{i}'] = seeds[i]
start = time.time()
- local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train,
+ local_fi_score_train_subset = fi_est.cls(X_train=X_train, y_train=y_train,
X_test=X_test, y_test=y_test,
fit=copy.deepcopy(est), data_fit_on="train", **fi_est.kwargs)
local_fi_score_test = fi_est.cls(X_train=X_train, y_train=y_train,
X_test=X_test, y_test=y_test,
fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs)
+ local_fi_score_test_subset = None
end = time.time()
metric_results['fi_time'] = end - start
- feature_importance_list.append(local_fi_score_train)
+ feature_importance_list.append(local_fi_score_train_subset)
feature_importance_list.append(local_fi_score_test)
+ feature_importance_list.append(local_fi_score_test_subset)
- ablation_models = {"RF_classifier": RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42),
- "Logistic": LogisticRegression(),
+ ablation_models = {"RF_Classifier": RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42),
+ "Logistic": LogisticRegressionCV(),
"SVM": SVC(probability=True),
- "XGBoost_classifier": xgb.XGBClassifier(n_estimators=100, min_child_weight=1, max_depth=None,
- subsample=1.0, colsample_bytree=np.sqrt(X_train.shape[0])/X_train.shape[0], random_state=42)}
+ "XGBoost_Classifier": xgb.XGBClassifier(random_state=42),
+ "RF_Plus_Classifier": RandomForestPlusClassifier(rf_model=RandomForestClassifier(n_estimators=100, min_samples_leaf=1, max_features='sqrt', random_state=42))}
- # Train data ablation
+ # Subset Train data ablation for all FI methods
start = time.time()
for a_model in ablation_models:
- est = ablation_models[a_model]
- est.fit(X_train, y_train)
- y_pred = est.predict_proba(X_train)[:, 1]
- metric_results[a_model+'_train_AUROC_before_ablation'] = roc_auc_score(y_train, y_pred)
- metric_results[a_model+'_train_AUPRC_before_ablation'] = auprc_score(y_train, y_pred)
- metric_results[a_model+'_train_F1_before_ablation'] = f1_score(y_train, y_pred > 0.5)
- imp_vals = copy.deepcopy(local_fi_score_train)
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ y_pred = ablation_est.predict_proba(X_train_subset)[:, 1]
+ metric_results[a_model+'_train_subset_AUROC_before_ablation'] = roc_auc_score(y_train_subset, y_pred)
+ metric_results[a_model+'_train_subset_AUPRC_before_ablation'] = auprc_score(y_train_subset, y_pred)
+ metric_results[a_model+'_train_subset_F1_before_ablation'] = f1_score(y_train_subset, y_pred > 0.5)
+ imp_vals = copy.deepcopy(local_fi_score_train_subset)
imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
- ablation_results_auroc_list = [0] * X_train.shape[1]
- ablation_results__auprc_list = [0] * X_train.shape[1]
- ablation_results_f1_list = [0] * X_train.shape[1]
+ ablation_results_auroc_list = [0] * X_train_subset.shape[1]
+ ablation_results_auprc_list = [0] * X_train_subset.shape[1]
+ ablation_results_f1_list = [0] * X_train_subset.shape[1]
for seed in seeds:
- for i in range(X_train.shape[1]):
+ for i in range(X_train_subset.shape[1]):
if fi_est.ascending:
- ablation_X_train = ablation_to_mean(X_train, X_train, imp_vals, "max", i+1)
+ ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1)
else:
- ablation_X_train = ablation_to_mean(X_train, X_train, imp_vals, "min", i+1)
- ablation_results_auroc_list[i] += roc_auc_score(y_train, est.predict_proba(ablation_X_train)[:, 1])
- ablation_results__auprc_list[i] += auprc_score(y_train, est.predict_proba(ablation_X_train)[:, 1])
- ablation_results_f1_list[i] += f1_score(y_train, est.predict_proba(ablation_X_train)[:, 1] > 0.5)
+ ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1)
+ ablation_results_auroc_list[i] += roc_auc_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1])
+ ablation_results_auprc_list[i] += auprc_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1])
+ ablation_results_f1_list[i] += f1_score(y_train_subset, ablation_est.predict_proba(ablation_X_train_subset)[:, 1] > 0.5)
ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list]
ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list]
- ablation_results__auprc_list = [x / number_of_ablations for x in ablation_results__auprc_list]
- for i in range(X_train.shape[1]):
- metric_results[f'{a_model}_train_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i]
- metric_results[f'{a_model}_train_AUPRC_after_ablation_{i+1}'] = ablation_results__auprc_list[i]
- metric_results[f'{a_model}_train_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i]
+ ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list]
+ for i in range(X_train_subset.shape[1]):
+ metric_results[f'{a_model}_train_subset_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i]
+ metric_results[f'{a_model}_train_subset_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i]
+ metric_results[f'{a_model}_train_subset_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i]
end = time.time()
- metric_results['train_data_ablation_time'] = end - start
+ metric_results['train_subset_data_ablation_time'] = end - start
# Test data ablation
+ # Subset test data ablation for all FI methods - removal
start = time.time()
for a_model in ablation_models:
- est = ablation_models[a_model]
- est.fit(X_train, y_train)
- y_pred = est.predict_proba(X_test)[:, 1]
- metric_results[a_model+'_test_AUROC_before_ablation'] = roc_auc_score(y_test, y_pred)
- metric_results[a_model+'_test_AUPRC_before_ablation'] = auprc_score(y_test, y_pred)
- metric_results[a_model+'_test_F1_before_ablation'] = f1_score(y_test, y_pred > 0.5)
- imp_vals = copy.deepcopy(local_fi_score_test)
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ y_pred_subset = est.predict_proba(X_test_subset)[:, 1]
+ metric_results[a_model+'_test_subset_AUROC_before_ablation'] = roc_auc_score(y_test_subset, y_pred_subset)
+ metric_results[a_model+'_test_subset_AUPRC_before_ablation'] = auprc_score(y_test_subset, y_pred_subset)
+ metric_results[a_model+'_test_subset_F1_before_ablation'] = f1_score(y_test_subset, y_pred_subset > 0.5)
+ imp_vals = copy.deepcopy(local_fi_score_test_subset)
imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
- ablation_results_auroc_list = [0] * X_test.shape[1]
- ablation_results__auprc_list = [0] * X_test.shape[1]
- ablation_results_f1_list = [0] * X_test.shape[1]
+ ablation_results_auroc_list = [0] * X_test_subset.shape[1]
+ ablation_results_auprc_list = [0] * X_test_subset.shape[1]
+ ablation_results_f1_list = [0] * X_test_subset.shape[1]
for seed in seeds:
- for i in range(X_test.shape[1]):
+ for i in range(X_test_subset.shape[1]):
if fi_est.ascending:
- ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1)
+ ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1)
else:
- ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1)
- ablation_results_auroc_list[i] += roc_auc_score(y_test, est.predict_proba(ablation_X_test)[:, 1])
- ablation_results__auprc_list[i] += auprc_score(y_test, est.predict_proba(ablation_X_test)[:, 1])
- ablation_results_f1_list[i] += f1_score(y_test, est.predict_proba(ablation_X_test)[:, 1] > 0.5)
+ ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1)
+ ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1])
+ ablation_results_auprc_list[i] += auprc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1])
+ ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset)[:, 1] > 0.5)
ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list]
ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list]
- ablation_results__auprc_list = [x / number_of_ablations for x in ablation_results__auprc_list]
- for i in range(X_test.shape[1]):
- metric_results[f'{a_model}_test_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i]
- metric_results[f'{a_model}_test_AUPRC_after_ablation_{i+1}'] = ablation_results__auprc_list[i]
- metric_results[f'{a_model}_test_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i]
+ ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list]
+ for i in range(X_test_subset.shape[1]):
+ metric_results[f'{a_model}_test_subset_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i]
+ metric_results[f'{a_model}_test_subset_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i]
+ metric_results[f'{a_model}_test_subset_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i]
end = time.time()
- metric_results['test_data_ablation_time'] = end - start
-
+ metric_results['test_subset_ablation_time'] = end - start
- print(f"data_size: {X_test.shape[0]}, fi: {fi_est.name}, done with time: {end - start}")
+ # Subset test data ablation for all FI methods - addition
+ start = time.time()
+ for a_model in ablation_models:
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ metric_results[a_model+'_test_subset_AUROC_before_ablation_blank'] = roc_auc_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)))
+ metric_results[a_model+'_test_subset_AUPRC_before_ablation_blank'] = auprc_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)))
+ metric_results[a_model+'_test_subset_F1_before_ablation_blank'] = f1_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)) > 0.5)
+ imp_vals = copy.deepcopy(local_fi_score_test_subset)
+ imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
+ imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
+ ablation_results_auroc_list = [0] * X_test_subset.shape[1]
+ ablation_results_auprc_list = [0] * X_test_subset.shape[1]
+ ablation_results_f1_list = [0] * X_test_subset.shape[1]
+ for seed in seeds:
+ for i in range(X_test_subset.shape[1]):
+ if fi_est.ascending:
+ ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "max", i+1)
+ else:
+ ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "min", i+1)
+ ablation_results_auroc_list[i] += roc_auc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1])
+ ablation_results_auprc_list[i] += auprc_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1])
+ ablation_results_f1_list[i] += f1_score(y_test_subset, ablation_est.predict_proba(ablation_X_test_subset_blank)[:, 1] > 0.5)
+ ablation_results_list = [x / len(seeds) for x in ablation_results_list]
+ ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2]
+ for i in range(X_test_subset.shape[1]):
+ metric_results[f'{a_model}_test_subset_AUROC_after_ablation_{i+1}_blank'] = ablation_results_auroc_list[i]
+ metric_results[f'{a_model}_test_subset_AUPRC_after_ablation_{i+1}_blank'] = ablation_results_auprc_list[i]
+ metric_results[f'{a_model}_test_subset_F1_after_ablation_{i+1}_blank'] = ablation_results_f1_list[i]
+ end = time.time()
+ metric_results['test_subset_blank_ablation_time'] = end - start
+
+ # Whole test data ablation for all FI methods except for KernelSHAP and LIME
+ if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]:
+ start = time.time()
+ for a_model in ablation_models:
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ y_pred = est.predict_proba(X_test)[:, 1]
+ metric_results[a_model+'_test_AUROC_before_ablation'] = roc_auc_score(y_test, y_pred)
+ metric_results[a_model+'_test_AUPRC_before_ablation'] = auprc_score(y_test, y_pred)
+ metric_results[a_model+'_test_F1_before_ablation'] = f1_score(y_test, y_pred > 0.5)
+ imp_vals = copy.deepcopy(local_fi_score_test)
+ imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
+ imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
+ ablation_results_auroc_list = [0] * X_test.shape[1]
+ ablation_results_auprc_list = [0] * X_test.shape[1]
+ ablation_results_f1_list = [0] * X_test.shape[1]
+ for seed in seeds:
+ for i in range(X_test.shape[1]):
+ if fi_est.ascending:
+ ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1)
+ else:
+ ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1)
+ ablation_results_auroc_list[i] += roc_auc_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1])
+ ablation_results_auprc_list[i] += auprc_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1])
+ ablation_results_f1_list[i] += f1_score(y_test, ablation_est.predict_proba(ablation_X_test)[:, 1] > 0.5)
+ ablation_results_f1_list = [x / number_of_ablations for x in ablation_results_f1_list]
+ ablation_results_auroc_list = [x / number_of_ablations for x in ablation_results_auroc_list]
+ ablation_results_auprc_list = [x / number_of_ablations for x in ablation_results_auprc_list]
+ for i in range(X_test.shape[1]):
+ metric_results[f'{a_model}_test_AUROC_after_ablation_{i+1}'] = ablation_results_auroc_list[i]
+ metric_results[f'{a_model}_test_AUPRC_after_ablation_{i+1}'] = ablation_results_auprc_list[i]
+ metric_results[f'{a_model}_test_F1_after_ablation_{i+1}'] = ablation_results_f1_list[i]
+ end = time.time()
+ metric_results['test_data_ablation_time'] = end - start
+ print(f"fi: {fi_est.name} ablation done with time: {end - start}")
# initialize results with metadata and metric results
kwargs: dict = model.kwargs # dict
diff --git a/feature_importance/01_run_ablation_regression.py b/feature_importance/01_run_ablation_regression.py
index 2c049a9..970f233 100644
--- a/feature_importance/01_run_ablation_regression.py
+++ b/feature_importance/01_run_ablation_regression.py
@@ -21,6 +21,7 @@
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
import xgboost as xgb
+from imodels.importance import RandomForestPlusRegressor, RandomForestPlusClassifier
sys.path.append(".")
sys.path.append("..")
@@ -76,6 +77,22 @@ def ablation_to_mean(train, data, feature_importance, mode, num_features):
data_copy[i, indices[i,j]] = train_mean[indices[i,j]]
return data_copy
+def ablation_by_addition(data, feature_importance, mode, num_features):
+ """
+ Initialize the data with zeros and add the top num_features max feature importance data for each sample
+ """
+ assert mode in ["max", "min"]
+ fi = feature_importance.to_numpy()
+ if mode == "max":
+ indices = np.argsort(-fi)
+ else:
+ indices = np.argsort(fi)
+ data_copy = np.zeros(data.shape)
+ for i in range(data.shape[0]):
+ for j in range(num_features):
+ data_copy[i, indices[i,j]] = data[i, indices[i,j]]
+ return data_copy
+
def compare_estimators(estimators: List[ModelConfig],
fi_estimators: List[FIModelConfig],
@@ -133,10 +150,12 @@ def compare_estimators(estimators: List[ModelConfig],
test_all_mse = mean_squared_error(y_test, est.predict(X_test))
test_all_r2 = r2_score(y_test, est.predict(X_test))
- if model.name == "RF_plus":
- indices = np.random.choice(X_test.shape[0], 100, replace=False)
- X_test = X_test[indices]
- y_test = y_test[indices]
+ indices_train = np.random.choice(X_train.shape[0], 100, replace=False)
+ indices_test = np.random.choice(X_test.shape[0], 100, replace=False)
+ X_train_subset = X_train[indices_train]
+ y_train_subset = y_train[indices_train]
+ X_test_subset = X_test[indices_test]
+ y_test_subset = y_test[indices_test]
# loop over fi estimators
rng = np.random.RandomState()
@@ -154,88 +173,147 @@ def compare_estimators(estimators: List[ModelConfig],
'test_all_r2': test_all_r2
}
for i in range(100):
- if model.name == "RF_plus":
- metric_results[f'sample_test_{i}'] = indices[i]
- else:
- metric_results[f'sample_test_{i}'] = None
+ metric_results[f'sample_train_{i}'] = indices_train[i]
+ metric_results[f'sample_test_{i}'] = indices_test[i]
for i in range(len(seeds)):
metric_results[f'ablation_seed_{i}'] = seeds[i]
start = time.time()
- local_fi_score_train = fi_est.cls(X_train=X_train, y_train=y_train,
+ local_fi_score_train_subset = fi_est.cls(X_train=X_train, y_train=y_train,
X_test=X_test, y_test=y_test,
fit=copy.deepcopy(est), data_fit_on="train", **fi_est.kwargs)
local_fi_score_test = fi_est.cls(X_train=X_train, y_train=y_train,
X_test=X_test, y_test=y_test,
fit=copy.deepcopy(est), data_fit_on="test", **fi_est.kwargs)
+ local_fi_score_test_subset = None
end = time.time()
metric_results['fi_time'] = end - start
- feature_importance_list.append(local_fi_score_train)
+ feature_importance_list.append(local_fi_score_train_subset)
feature_importance_list.append(local_fi_score_test)
+ feature_importance_list.append(local_fi_score_test_subset)
- ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33),
+ ablation_models = {"RF_Regressor": RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=42),
"Linear": LinearRegression(),
- "XGB_Regressor": xgb.XGBRegressor(n_estimators=100, max_depth=None, min_child_weight=5,
- subsample=1.0, colsample_bytree=0.33, random_state=42)}
+ "XGB_Regressor": xgb.XGBRegressor(random_state=42),
+ "RF_Plus_Regressor":RandomForestPlusRegressor(rf_model=RandomForestRegressor(n_estimators=100,min_samples_leaf=5,max_features=0.33,random_state=42))}
- # Train data ablation
+ # Subset Train data ablation for all FI methods
start = time.time()
for a_model in ablation_models:
ablation_est = ablation_models[a_model]
ablation_est.fit(X_train, y_train)
- y_pred = ablation_est.predict(X_train)
- metric_results[a_model + '_MSE_before_ablation'] = mean_squared_error(y_train, y_pred)
- metric_results[a_model + '_R_2_before_ablation'] = r2_score(y_train, y_pred)
- imp_vals = copy.deepcopy(local_fi_score_train)
+ y_pred_subset = ablation_est.predict(X_train_subset)
+ metric_results[a_model + '_train_subset_MSE_before_ablation'] = mean_squared_error(y_train_subset, y_pred_subset)
+ metric_results[a_model + '_train_subset_R_2_before_ablation'] = r2_score(y_train_subset, y_pred_subset)
+ imp_vals = copy.deepcopy(local_fi_score_train_subset)
imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
- ablation_results_list = [0] * X_train.shape[1]
- ablation_results_list_r2 = [0] * X_train.shape[1]
+ ablation_results_list = [0] * X_train_subset.shape[1]
+ ablation_results_list_r2 = [0] * X_train_subset.shape[1]
for seed in seeds:
- for i in range(X_train.shape[1]):
+ for i in range(X_train_subset.shape[1]):
if fi_est.ascending:
- ablation_X_train = ablation_to_mean(X_train, X_train, imp_vals, "max", i+1)
+ ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "max", i+1)
else:
- ablation_X_train = ablation_to_mean(X_train, X_train, imp_vals, "min", i+1)
- ablation_results_list[i] += mean_squared_error(y_train, ablation_est.predict(ablation_X_train))
- ablation_results_list_r2[i] += r2_score(y_train, ablation_est.predict(ablation_X_train))
+ ablation_X_train_subset = ablation_to_mean(X_train, X_train_subset, imp_vals, "min", i+1)
+ ablation_results_list[i] += mean_squared_error(y_train_subset, ablation_est.predict(ablation_X_train_subset))
+ ablation_results_list_r2[i] += r2_score(y_train_subset, ablation_est.predict(ablation_X_train_subset))
ablation_results_list = [x / len(seeds) for x in ablation_results_list]
ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2]
for i in range(X_train.shape[1]):
- metric_results[f'{a_model}_MSE_after_ablation_{i+1}'] = ablation_results_list[i]
- metric_results[f'{a_model}_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i]
+ metric_results[f'{a_model}_train_subset_MSE_after_ablation_{i+1}'] = ablation_results_list[i]
+ metric_results[f'{a_model}_train_subset_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i]
end = time.time()
- metric_results['train_data_ablation_time'] = end - start
+ metric_results['train_subset_ablation_time'] = end - start
# Test data ablation
+ # Subset test data ablation for all FI methods - removal
start = time.time()
for a_model in ablation_models:
ablation_est = ablation_models[a_model]
ablation_est.fit(X_train, y_train)
- y_pred = ablation_est.predict(X_test)
- metric_results[a_model + '_MSE_before_ablation'] = mean_squared_error(y_test, y_pred)
- metric_results[a_model + '_R_2_before_ablation'] = r2_score(y_test, y_pred)
- imp_vals = copy.deepcopy(local_fi_score_test)
+ y_pred_subset = ablation_est.predict(X_test_subset)
+ metric_results[a_model + '_test_subset_MSE_before_ablation'] = mean_squared_error(y_test_subset, y_pred_subset)
+ metric_results[a_model + '_test_subset_R_2_before_ablation'] = r2_score(y_test_subset, y_pred_subset)
+ imp_vals = copy.deepcopy(local_fi_score_test_subset)
imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
- ablation_results_list = [0] * X_test.shape[1]
- ablation_results_list_r2 = [0] * X_test.shape[1]
+ ablation_results_list = [0] * X_test_subset.shape[1]
+ ablation_results_list_r2 = [0] * X_test_subset.shape[1]
for seed in seeds:
- for i in range(X_test.shape[1]):
+ for i in range(X_test_subset.shape[1]):
if fi_est.ascending:
- ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1)
+ ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "max", i+1)
else:
- ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1)
- ablation_results_list[i] += mean_squared_error(y_test, ablation_est.predict(ablation_X_test))
- ablation_results_list_r2[i] += r2_score(y_test, ablation_est.predict(ablation_X_test))
+ ablation_X_test_subset = ablation_to_mean(X_train, X_test_subset, imp_vals, "min", i+1)
+ ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset))
+ ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset))
ablation_results_list = [x / len(seeds) for x in ablation_results_list]
ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2]
- for i in range(X_test.shape[1]):
- metric_results[f'{a_model}_MSE_after_ablation_{i+1}'] = ablation_results_list[i]
- metric_results[f'{a_model}_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i]
+ for i in range(X_test_subset.shape[1]):
+ metric_results[f'{a_model}_test_subset_MSE_after_ablation_{i+1}'] = ablation_results_list[i]
+ metric_results[f'{a_model}_test_subset_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i]
end = time.time()
- metric_results['test_data_ablation_time'] = end - start
+ metric_results['test_subset_ablation_time'] = end - start
- print(f"data_size: {X_test.shape[0]}, fi: {fi_est.name}, done with time: {end - start}")
+
+ # Subset test data ablation for all FI methods - addition
+ start = time.time()
+ for a_model in ablation_models:
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ metric_results[a_model + '_test_subset_MSE_before_ablation_blank'] = mean_squared_error(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)))
+ metric_results[a_model + '_test_subset_R_2_before_ablation_blank'] = r2_score(y_test_subset, ablation_est.predict(np.zeros(X_test_subset.shape)))
+ imp_vals = copy.deepcopy(local_fi_score_test_subset)
+ imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
+ imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
+ ablation_results_list = [0] * X_test_subset.shape[1]
+ ablation_results_list_r2 = [0] * X_test_subset.shape[1]
+ for seed in seeds:
+ for i in range(X_test_subset.shape[1]):
+ if fi_est.ascending:
+ ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "max", i+1)
+ else:
+ ablation_X_test_subset_blank = ablation_by_addition(X_test_subset, imp_vals, "min", i+1)
+ ablation_results_list[i] += mean_squared_error(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank))
+ ablation_results_list_r2[i] += r2_score(y_test_subset, ablation_est.predict(ablation_X_test_subset_blank))
+ ablation_results_list = [x / len(seeds) for x in ablation_results_list]
+ ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2]
+ for i in range(X_test_subset.shape[1]):
+ metric_results[f'{a_model}_test_subset_MSE_after_ablation_{i+1}_blank'] = ablation_results_list[i]
+ metric_results[f'{a_model}_test_subset_R_2_after_ablation_{i+1}_blank'] = ablation_results_list_r2[i]
+ end = time.time()
+ metric_results['test_subset_blank_ablation_time'] = end - start
+
+ # Whole test data ablation for all FI methods except for KernelSHAP and LIME
+ if fi_est.name not in ["LIME_RF_plus", "Kernel_SHAP_RF_plus"]:
+ start = time.time()
+ for a_model in ablation_models:
+ ablation_est = ablation_models[a_model]
+ ablation_est.fit(X_train, y_train)
+ y_pred = ablation_est.predict(X_test)
+ metric_results[a_model + '_test_MSE_before_ablation'] = mean_squared_error(y_test, y_pred)
+ metric_results[a_model + '_test_R_2_before_ablation'] = r2_score(y_test, y_pred)
+ imp_vals = copy.deepcopy(local_fi_score_test)
+ imp_vals[imp_vals == float("-inf")] = -sys.maxsize - 1
+ imp_vals[imp_vals == float("inf")] = sys.maxsize - 1
+ ablation_results_list = [0] * X_test.shape[1]
+ ablation_results_list_r2 = [0] * X_test.shape[1]
+ for seed in seeds:
+ for i in range(X_test.shape[1]):
+ if fi_est.ascending:
+ ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "max", i+1)
+ else:
+ ablation_X_test = ablation_to_mean(X_train, X_test, imp_vals, "min", i+1)
+ ablation_results_list[i] += mean_squared_error(y_test, ablation_est.predict(ablation_X_test))
+ ablation_results_list_r2[i] += r2_score(y_test, ablation_est.predict(ablation_X_test))
+ ablation_results_list = [x / len(seeds) for x in ablation_results_list]
+ ablation_results_list_r2 = [x / len(seeds) for x in ablation_results_list_r2]
+ for i in range(X_test.shape[1]):
+ metric_results[f'{a_model}_test_MSE_after_ablation_{i+1}'] = ablation_results_list[i]
+ metric_results[f'{a_model}_test_R_2_after_ablation_{i+1}'] = ablation_results_list_r2[i]
+ end = time.time()
+ metric_results['test_data_ablation_time'] = end - start
+ print(f"fi: {fi_est.name} ablation done with time: {end - start}")
# initialize results with metadata and metric results
kwargs: dict = model.kwargs # dict
diff --git a/feature_importance/fi_config/mdi_local/real_data_regression/models.py b/feature_importance/fi_config/mdi_local/real_data_regression/models.py
index 8b83659..3b9d3c9 100644
--- a/feature_importance/fi_config/mdi_local/real_data_regression/models.py
+++ b/feature_importance/fi_config/mdi_local/real_data_regression/models.py
@@ -11,9 +11,9 @@
ESTIMATORS = [
[ModelConfig('RF', RandomForestRegressor, model_type='tree',
- other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33})],
+ other_params={'n_estimators': 100, 'min_samples_leaf': 5, 'max_features': 0.33, 'random_state': 42})],
[ModelConfig('RF_plus', RandomForestPlusRegressor, model_type='t_plus',
- other_params={'rf_model': RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33)})],
+ other_params={'rf_model': RandomForestRegressor(n_estimators=100, min_samples_leaf=5, max_features=0.33, random_state=42)})],
]
FI_ESTIMATORS = [
diff --git a/feature_importance/real_data_ablation_visulization_version3.ipynb b/feature_importance/real_data_ablation_visulization_version3.ipynb
index 128ea01..449bd4f 100644
--- a/feature_importance/real_data_ablation_visulization_version3.ipynb
+++ b/feature_importance/real_data_ablation_visulization_version3.ipynb
@@ -21,7 +21,7 @@
"source": [
"# directory = './results/mdi_local.real_data_regression/diabetes_regression_parallel/varying_sample_row_n/'\n",
"# directory = './results/mdi_local.real_data_classification/diabetes_classification_parallel/varying_sample_row_n/'\n",
- "directory = './results/mdi_local.real_data_regression/diabetes_regression_parallel/varying_sample_row_n'\n",
+ "directory = './results/mdi_local.real_data_regression/diabetes_regression/varying_sample_row_n'\n",
"folder_names = [folder for folder in os.listdir(directory) if os.path.isdir(os.path.join(directory, folder))]\n",
"experiments_seeds = []\n",
"for folder_name in folder_names:\n",
@@ -78,18 +78,18 @@
"
n_estimators | \n",
" min_samples_leaf | \n",
" max_features | \n",
+ " random_state | \n",
+ " include_raw | \n",
" cv_ridge | \n",
" calc_loo_coef | \n",
- " include_raw | \n",
" sample_split | \n",
" fit_on | \n",
" model | \n",
" fi | \n",
- " splitting_strategy | \n",
" train_size | \n",
+ " test_size | \n",
" num_features | \n",
" data_split_seed | \n",
- " test_size | \n",
" test_all_mse | \n",
" test_all_r2 | \n",
" sample_test_0 | \n",
@@ -193,44 +193,187 @@
" sample_test_98 | \n",
" sample_test_99 | \n",
" ablation_seed_0 | \n",
- " ablation_seed_1 | \n",
- " ablation_seed_2 | \n",
- " ablation_seed_3 | \n",
- " ablation_seed_4 | \n",
- " ablation_seed_5 | \n",
- " ablation_seed_6 | \n",
- " ablation_seed_7 | \n",
- " ablation_seed_8 | \n",
- " ablation_seed_9 | \n",
" fi_time | \n",
- " MSE_before_ablation | \n",
- " R_2_before_ablation | \n",
- " MSE_after_ablation_1 | \n",
- " R_2_after_ablation_1 | \n",
- " MSE_after_ablation_2 | \n",
- " R_2_after_ablation_2 | \n",
- " MSE_after_ablation_3 | \n",
- " R_2_after_ablation_3 | \n",
- " MSE_after_ablation_4 | \n",
- " R_2_after_ablation_4 | \n",
- " MSE_after_ablation_5 | \n",
- " R_2_after_ablation_5 | \n",
- " MSE_after_ablation_6 | \n",
- " R_2_after_ablation_6 | \n",
- " MSE_after_ablation_7 | \n",
- " R_2_after_ablation_7 | \n",
- " MSE_after_ablation_8 | \n",
- " R_2_after_ablation_8 | \n",
- " MSE_after_ablation_9 | \n",
- " R_2_after_ablation_9 | \n",
- " MSE_after_ablation_10 | \n",
- " R_2_after_ablation_10 | \n",
- " ablation_time | \n",
+ " RF_Regressor_train_MSE_before_ablation | \n",
+ " RF_Regressor_train_R_2_before_ablation | \n",
+ " RF_Regressor_train_MSE_after_ablation_1 | \n",
+ " RF_Regressor_train_R_2_after_ablation_1 | \n",
+ " RF_Regressor_train_MSE_after_ablation_2 | \n",
+ " RF_Regressor_train_R_2_after_ablation_2 | \n",
+ " RF_Regressor_train_MSE_after_ablation_3 | \n",
+ " RF_Regressor_train_R_2_after_ablation_3 | \n",
+ " RF_Regressor_train_MSE_after_ablation_4 | \n",
+ " RF_Regressor_train_R_2_after_ablation_4 | \n",
+ " RF_Regressor_train_MSE_after_ablation_5 | \n",
+ " RF_Regressor_train_R_2_after_ablation_5 | \n",
+ " RF_Regressor_train_MSE_after_ablation_6 | \n",
+ " RF_Regressor_train_R_2_after_ablation_6 | \n",
+ " RF_Regressor_train_MSE_after_ablation_7 | \n",
+ " RF_Regressor_train_R_2_after_ablation_7 | \n",
+ " RF_Regressor_train_MSE_after_ablation_8 | \n",
+ " RF_Regressor_train_R_2_after_ablation_8 | \n",
+ " RF_Regressor_train_MSE_after_ablation_9 | \n",
+ " RF_Regressor_train_R_2_after_ablation_9 | \n",
+ " RF_Regressor_train_MSE_after_ablation_10 | \n",
+ " RF_Regressor_train_R_2_after_ablation_10 | \n",
+ " Linear_train_MSE_before_ablation | \n",
+ " Linear_train_R_2_before_ablation | \n",
+ " Linear_train_MSE_after_ablation_1 | \n",
+ " Linear_train_R_2_after_ablation_1 | \n",
+ " Linear_train_MSE_after_ablation_2 | \n",
+ " Linear_train_R_2_after_ablation_2 | \n",
+ " Linear_train_MSE_after_ablation_3 | \n",
+ " Linear_train_R_2_after_ablation_3 | \n",
+ " Linear_train_MSE_after_ablation_4 | \n",
+ " Linear_train_R_2_after_ablation_4 | \n",
+ " Linear_train_MSE_after_ablation_5 | \n",
+ " Linear_train_R_2_after_ablation_5 | \n",
+ " Linear_train_MSE_after_ablation_6 | \n",
+ " Linear_train_R_2_after_ablation_6 | \n",
+ " Linear_train_MSE_after_ablation_7 | \n",
+ " Linear_train_R_2_after_ablation_7 | \n",
+ " Linear_train_MSE_after_ablation_8 | \n",
+ " Linear_train_R_2_after_ablation_8 | \n",
+ " Linear_train_MSE_after_ablation_9 | \n",
+ " Linear_train_R_2_after_ablation_9 | \n",
+ " Linear_train_MSE_after_ablation_10 | \n",
+ " Linear_train_R_2_after_ablation_10 | \n",
+ " XGB_Regressor_train_MSE_before_ablation | \n",
+ " XGB_Regressor_train_R_2_before_ablation | \n",
+ " XGB_Regressor_train_MSE_after_ablation_1 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_1 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_2 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_2 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_3 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_3 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_4 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_4 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_5 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_5 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_6 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_6 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_7 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_7 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_8 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_8 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_9 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_9 | \n",
+ " XGB_Regressor_train_MSE_after_ablation_10 | \n",
+ " XGB_Regressor_train_R_2_after_ablation_10 | \n",
+ " RF_Plus_Regressor_train_MSE_before_ablation | \n",
+ " RF_Plus_Regressor_train_R_2_before_ablation | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_1 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_1 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_2 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_2 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_3 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_3 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_4 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_4 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_5 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_5 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_6 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_6 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_7 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_7 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_8 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_8 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_9 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_9 | \n",
+ " RF_Plus_Regressor_train_MSE_after_ablation_10 | \n",
+ " RF_Plus_Regressor_train_R_2_after_ablation_10 | \n",
+ " train_data_ablation_time | \n",
+ " RF_Regressor_test_MSE_before_ablation | \n",
+ " RF_Regressor_test_R_2_before_ablation | \n",
+ " RF_Regressor_test_MSE_after_ablation_1 | \n",
+ " RF_Regressor_test_R_2_after_ablation_1 | \n",
+ " RF_Regressor_test_MSE_after_ablation_2 | \n",
+ " RF_Regressor_test_R_2_after_ablation_2 | \n",
+ " RF_Regressor_test_MSE_after_ablation_3 | \n",
+ " RF_Regressor_test_R_2_after_ablation_3 | \n",
+ " RF_Regressor_test_MSE_after_ablation_4 | \n",
+ " RF_Regressor_test_R_2_after_ablation_4 | \n",
+ " RF_Regressor_test_MSE_after_ablation_5 | \n",
+ " RF_Regressor_test_R_2_after_ablation_5 | \n",
+ " RF_Regressor_test_MSE_after_ablation_6 | \n",
+ " RF_Regressor_test_R_2_after_ablation_6 | \n",
+ " RF_Regressor_test_MSE_after_ablation_7 | \n",
+ " RF_Regressor_test_R_2_after_ablation_7 | \n",
+ " RF_Regressor_test_MSE_after_ablation_8 | \n",
+ " RF_Regressor_test_R_2_after_ablation_8 | \n",
+ " RF_Regressor_test_MSE_after_ablation_9 | \n",
+ " RF_Regressor_test_R_2_after_ablation_9 | \n",
+ " RF_Regressor_test_MSE_after_ablation_10 | \n",
+ " RF_Regressor_test_R_2_after_ablation_10 | \n",
+ " Linear_test_MSE_before_ablation | \n",
+ " Linear_test_R_2_before_ablation | \n",
+ " Linear_test_MSE_after_ablation_1 | \n",
+ " Linear_test_R_2_after_ablation_1 | \n",
+ " Linear_test_MSE_after_ablation_2 | \n",
+ " Linear_test_R_2_after_ablation_2 | \n",
+ " Linear_test_MSE_after_ablation_3 | \n",
+ " Linear_test_R_2_after_ablation_3 | \n",
+ " Linear_test_MSE_after_ablation_4 | \n",
+ " Linear_test_R_2_after_ablation_4 | \n",
+ " Linear_test_MSE_after_ablation_5 | \n",
+ " Linear_test_R_2_after_ablation_5 | \n",
+ " Linear_test_MSE_after_ablation_6 | \n",
+ " Linear_test_R_2_after_ablation_6 | \n",
+ " Linear_test_MSE_after_ablation_7 | \n",
+ " Linear_test_R_2_after_ablation_7 | \n",
+ " Linear_test_MSE_after_ablation_8 | \n",
+ " Linear_test_R_2_after_ablation_8 | \n",
+ " Linear_test_MSE_after_ablation_9 | \n",
+ " Linear_test_R_2_after_ablation_9 | \n",
+ " Linear_test_MSE_after_ablation_10 | \n",
+ " Linear_test_R_2_after_ablation_10 | \n",
+ " XGB_Regressor_test_MSE_before_ablation | \n",
+ " XGB_Regressor_test_R_2_before_ablation | \n",
+ " XGB_Regressor_test_MSE_after_ablation_1 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_1 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_2 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_2 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_3 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_3 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_4 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_4 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_5 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_5 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_6 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_6 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_7 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_7 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_8 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_8 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_9 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_9 | \n",
+ " XGB_Regressor_test_MSE_after_ablation_10 | \n",
+ " XGB_Regressor_test_R_2_after_ablation_10 | \n",
+ " RF_Plus_Regressor_test_MSE_before_ablation | \n",
+ " RF_Plus_Regressor_test_R_2_before_ablation | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_1 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_1 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_2 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_2 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_3 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_3 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_4 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_4 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_5 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_5 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_6 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_6 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_7 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_7 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_8 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_8 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_9 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_9 | \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_10 | \n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_10 | \n",
+ " test_data_ablation_time | \n",
" split_seed | \n",
" rf_model | \n",
- " index | \n",
- " var | \n",
- " true_support | \n",
" \n",
" \n",
" \n",
@@ -242,20 +385,20 @@
" 100.0 | \n",
" 5.0 | \n",
" 0.33 | \n",
- " 5.0 | \n",
- " False | \n",
- " NaN | \n",
+ " 42.0 | \n",
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+ " False | \n",
+ " oob | \n",
+ " test | \n",
" RF | \n",
- " LFI_with_raw_CV_RF | \n",
- " train-test | \n",
+ " LFI_with_raw_OOB_RF | \n",
" 296 | \n",
- " 10 | \n",
- " 7 | \n",
" 146 | \n",
- " 3015.657705 | \n",
- " 0.493287 | \n",
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+ " 4 | \n",
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- " oob | \n",
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" RandomForestRegressor(max_features=0.33, min_s... | \n",
- " 7 | \n",
- " 0 | \n",
- " 1.0 | \n",
"
\n",
" \n",
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- "80 rows × 159 columns
\n",
+ "70 rows × 302 columns
\n",
""
],
"text/plain": [
@@ -2027,535 +3743,2381 @@
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- "\n",
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+ "4 100.0 96.0 14.0 39.0 \n",
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+ "69 15.0 66.0 68.0 37.0 \n",
+ "\n",
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+ "68 64.0 17.0 4.0 145.0 \n",
+ "69 64.0 17.0 4.0 145.0 \n",
+ "\n",
+ " sample_test_98 sample_test_99 ablation_seed_0 fi_time \\\n",
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+ "68 47.0 63.0 188 1.481301 \n",
+ "69 47.0 63.0 188 618.747853 \n",
+ "\n",
+ " RF_Regressor_train_MSE_before_ablation \\\n",
+ "0 1635.151982 \n",
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+ "66 1599.333649 \n",
+ "67 1599.333649 \n",
+ "68 1599.333649 \n",
+ "69 1599.333649 \n",
+ "\n",
+ " RF_Regressor_train_R_2_before_ablation \\\n",
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+ "67 0.737912 \n",
+ "68 0.737912 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_1 \\\n",
+ "0 2928.895040 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_1 \\\n",
+ "0 0.514040 \n",
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+ "68 0.519927 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_2 \\\n",
+ "0 4043.923756 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_2 \\\n",
+ "0 0.329035 \n",
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+ "68 0.296492 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_3 \\\n",
+ "0 4919.486722 \n",
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+ "2 5012.797456 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_3 \\\n",
+ "0 0.183763 \n",
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+ "65 0.149503 \n",
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+ "67 0.176684 \n",
+ "68 0.181712 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_4 \\\n",
+ "0 5437.029551 \n",
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+ "2 5621.910172 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_4 \\\n",
+ "0 0.097892 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_5 \\\n",
+ "0 5901.392876 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_5 \\\n",
+ "0 0.020846 \n",
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+ "65 0.020094 \n",
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+ "67 0.064186 \n",
+ "68 0.048656 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_6 \\\n",
+ "0 6048.520615 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_6 \\\n",
+ "0 -0.003566 \n",
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+ "68 0.032948 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_7 \\\n",
+ "0 6074.659511 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_7 \\\n",
+ "0 -0.007903 \n",
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+ "66 -0.012579 \n",
+ "67 0.029589 \n",
+ "68 0.021374 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_8 \\\n",
+ "0 6149.594281 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_8 \\\n",
+ "0 -0.020336 \n",
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+ "67 0.018395 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_9 \\\n",
+ "0 6175.817312 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_9 \\\n",
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+ "68 -0.011974 \n",
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+ "\n",
+ " RF_Regressor_train_MSE_after_ablation_10 \\\n",
+ "0 6202.436470 \n",
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+ "66 6136.821625 \n",
+ "67 6136.821625 \n",
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+ "\n",
+ " RF_Regressor_train_R_2_after_ablation_10 \\\n",
+ "0 -0.029103 \n",
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+ "66 -0.005659 \n",
+ "67 -0.005659 \n",
+ "68 -0.005659 \n",
+ "69 -0.005659 \n",
+ "\n",
+ " Linear_train_MSE_before_ablation Linear_train_R_2_before_ablation \\\n",
+ "0 2888.523636 0.520738 \n",
+ "1 2888.523636 0.520738 \n",
+ "2 2888.523636 0.520738 \n",
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+ "66 2821.915630 0.537564 \n",
+ "67 2821.915630 0.537564 \n",
+ "68 2821.915630 0.537564 \n",
+ "69 2821.915630 0.537564 \n",
+ "\n",
+ " Linear_train_MSE_after_ablation_1 Linear_train_R_2_after_ablation_1 \\\n",
+ "0 3761.316620 0.375925 \n",
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+ "67 4042.670841 0.337516 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_2 Linear_train_R_2_after_ablation_2 \\\n",
+ "0 4957.235173 0.177500 \n",
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+ "2 4885.192723 0.189453 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_3 Linear_train_R_2_after_ablation_3 \\\n",
+ "0 5736.434644 0.048215 \n",
+ "1 5721.361272 0.050716 \n",
+ "2 5492.817224 0.088636 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_4 Linear_train_R_2_after_ablation_4 \\\n",
+ "0 6023.733378 0.000547 \n",
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+ "\n",
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+ "0 6302.387576 -0.045687 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_6 Linear_train_R_2_after_ablation_6 \\\n",
+ "0 6410.100574 -0.063559 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_7 Linear_train_R_2_after_ablation_7 \\\n",
+ "0 6404.359728 -0.062606 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_8 Linear_train_R_2_after_ablation_8 \\\n",
+ "0 6297.330585 -0.044848 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_9 Linear_train_R_2_after_ablation_9 \\\n",
+ "0 6131.286306 -0.017298 \n",
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+ "\n",
+ " Linear_train_MSE_after_ablation_10 Linear_train_R_2_after_ablation_10 \\\n",
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+ "67 6102.287573 0.0 \n",
+ "68 6102.287573 0.0 \n",
+ "69 6102.287573 0.0 \n",
+ "\n",
+ " XGB_Regressor_train_MSE_before_ablation \\\n",
+ "0 0.668153 \n",
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+ "2 0.668153 \n",
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+ "66 0.760518 \n",
+ "67 0.760518 \n",
+ "68 0.760518 \n",
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+ "\n",
+ " XGB_Regressor_train_R_2_before_ablation \\\n",
+ "0 0.999889 \n",
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+ "65 0.999875 \n",
+ "66 0.999875 \n",
+ "67 0.999875 \n",
+ "68 0.999875 \n",
+ "69 0.999875 \n",
+ "\n",
+ " XGB_Regressor_train_MSE_after_ablation_1 \\\n",
+ "0 1216.244402 \n",
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+ "0 4632.400024 \n",
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+ "\n",
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+ "0 5426.927997 \n",
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+ "\n",
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+ "0 5750.819366 \n",
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+ "\n",
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+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_2 \\\n",
+ "0 0.168047 \n",
+ "1 0.198474 \n",
+ "2 0.181837 \n",
+ "3 0.188864 \n",
+ "4 0.106060 \n",
+ ".. ... \n",
+ "65 0.270445 \n",
+ "66 0.244006 \n",
+ "67 0.221602 \n",
+ "68 0.230631 \n",
+ "69 0.264471 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_3 \\\n",
+ "0 5282.813050 \n",
+ "1 5044.353110 \n",
+ "2 5165.809068 \n",
+ "3 5247.502613 \n",
+ "4 5596.675389 \n",
+ ".. ... \n",
+ "65 4786.645423 \n",
+ "66 4765.872128 \n",
+ "67 4822.571646 \n",
+ "68 4853.249242 \n",
+ "69 4713.345462 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_3 \\\n",
+ "0 0.075127 \n",
+ "1 0.116875 \n",
+ "2 0.095611 \n",
+ "3 0.081309 \n",
+ "4 0.062209 \n",
+ ".. ... \n",
+ "65 0.140728 \n",
+ "66 0.144457 \n",
+ "67 0.121601 \n",
+ "68 0.116013 \n",
+ "69 0.141496 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_4 \\\n",
+ "0 5661.928879 \n",
+ "1 5456.091373 \n",
+ "2 5579.673507 \n",
+ "3 5637.322633 \n",
+ "4 5937.622020 \n",
+ ".. ... \n",
+ "65 5216.701738 \n",
+ "66 5236.877977 \n",
+ "67 5169.557621 \n",
+ "68 5184.298032 \n",
+ "69 5046.789311 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_4 \\\n",
+ "0 0.008755 \n",
+ "1 0.044791 \n",
+ "2 0.023155 \n",
+ "3 0.013063 \n",
+ "4 0.005079 \n",
+ ".. ... \n",
+ "65 0.063527 \n",
+ "66 0.059905 \n",
+ "67 0.058400 \n",
+ "68 0.055715 \n",
+ "69 0.080761 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_5 \\\n",
+ "0 5833.559426 \n",
+ "1 5793.795173 \n",
+ "2 5866.425027 \n",
+ "3 5960.763458 \n",
+ "4 6111.152277 \n",
+ ".. ... \n",
+ "65 5425.473666 \n",
+ "66 5625.037238 \n",
+ "67 5364.977537 \n",
+ "68 5471.391949 \n",
+ "69 5304.597975 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_5 \\\n",
+ "0 -0.021293 \n",
+ "1 -0.014331 \n",
+ "2 -0.027047 \n",
+ "3 -0.043563 \n",
+ "4 -0.023998 \n",
+ ".. ... \n",
+ "65 0.026049 \n",
+ "66 -0.009775 \n",
+ "67 0.022805 \n",
+ "68 0.003422 \n",
+ "69 0.033803 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_6 \\\n",
+ "0 5865.509482 \n",
+ "1 6010.746038 \n",
+ "2 5899.293811 \n",
+ "3 5977.057139 \n",
+ "4 6143.624744 \n",
+ ".. ... \n",
+ "65 5590.326152 \n",
+ "66 5521.985960 \n",
+ "67 5362.147595 \n",
+ "68 5443.474923 \n",
+ "69 5388.787525 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_6 \\\n",
+ "0 -0.026887 \n",
+ "1 -0.052313 \n",
+ "2 -0.032801 \n",
+ "3 -0.046415 \n",
+ "4 -0.029439 \n",
+ ".. ... \n",
+ "65 -0.003544 \n",
+ "66 0.008724 \n",
+ "67 0.023321 \n",
+ "68 0.008507 \n",
+ "69 0.018468 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_7 \\\n",
+ "0 5966.941781 \n",
+ "1 6049.385658 \n",
+ "2 6021.113226 \n",
+ "3 6028.927315 \n",
+ "4 6163.867887 \n",
+ ".. ... \n",
+ "65 5562.124282 \n",
+ "66 5545.446533 \n",
+ "67 5383.491593 \n",
+ "68 5455.547129 \n",
+ "69 5465.219576 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_7 \\\n",
+ "0 -0.044645 \n",
+ "1 -0.059078 \n",
+ "2 -0.054128 \n",
+ "3 -0.055496 \n",
+ "4 -0.032831 \n",
+ ".. ... \n",
+ "65 0.001518 \n",
+ "66 0.004512 \n",
+ "67 0.019433 \n",
+ "68 0.006308 \n",
+ "69 0.004547 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_8 \\\n",
+ "0 6001.779722 \n",
+ "1 5998.481445 \n",
+ "2 6042.816515 \n",
+ "3 6074.807174 \n",
+ "4 6244.000528 \n",
+ ".. ... \n",
+ "65 5644.709360 \n",
+ "66 5653.778023 \n",
+ "67 5405.576376 \n",
+ "68 5499.562086 \n",
+ "69 5509.129123 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_8 \\\n",
+ "0 -0.050744 \n",
+ "1 -0.050166 \n",
+ "2 -0.057928 \n",
+ "3 -0.063529 \n",
+ "4 -0.046258 \n",
+ ".. ... \n",
+ "65 -0.013307 \n",
+ "66 -0.014935 \n",
+ "67 0.015410 \n",
+ "68 -0.001709 \n",
+ "69 -0.003451 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_9 \\\n",
+ "0 6030.147576 \n",
+ "1 6036.697699 \n",
+ "2 6017.320805 \n",
+ "3 6086.136079 \n",
+ "4 6396.107074 \n",
+ ".. ... \n",
+ "65 5619.576540 \n",
+ "66 5661.451333 \n",
+ "67 5547.677265 \n",
+ "68 5564.283426 \n",
+ "69 5521.703510 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_9 \\\n",
+ "0 -0.055710 \n",
+ "1 -0.056857 \n",
+ "2 -0.053464 \n",
+ "3 -0.065512 \n",
+ "4 -0.071746 \n",
+ ".. ... \n",
+ "65 -0.008795 \n",
+ "66 -0.016312 \n",
+ "67 -0.010472 \n",
+ "68 -0.013497 \n",
+ "69 -0.005741 \n",
+ "\n",
+ " RF_Regressor_test_MSE_after_ablation_10 \\\n",
+ "0 6067.018319 \n",
+ "1 6067.018319 \n",
+ "2 6067.018319 \n",
+ "3 6067.018319 \n",
+ "4 6465.545412 \n",
+ ".. ... \n",
+ "65 5664.609603 \n",
+ "66 5664.609603 \n",
+ "67 5648.814576 \n",
+ "68 5648.814576 \n",
+ "69 5648.814576 \n",
+ "\n",
+ " RF_Regressor_test_R_2_after_ablation_10 Linear_test_MSE_before_ablation \\\n",
+ "0 -0.062165 3121.854972 \n",
+ "1 -0.062165 3121.854972 \n",
+ "2 -0.062165 3121.854972 \n",
+ "3 -0.062165 3121.854972 \n",
+ "4 -0.083381 3465.585233 \n",
+ ".. ... ... \n",
+ "65 -0.016879 3213.858553 \n",
+ "66 -0.016879 3213.858553 \n",
+ "67 -0.028894 3438.229965 \n",
+ "68 -0.028894 3438.229965 \n",
+ "69 -0.028894 3438.229965 \n",
+ "\n",
+ " Linear_test_R_2_before_ablation Linear_test_MSE_after_ablation_1 \\\n",
+ "0 0.453451 3802.338138 \n",
+ "1 0.453451 3803.232973 \n",
+ "2 0.453451 3782.799439 \n",
+ "3 0.453451 3739.288445 \n",
+ "4 0.419299 4161.596525 \n",
+ ".. ... ... \n",
+ "65 0.423066 4037.406723 \n",
+ "66 0.423066 4218.066221 \n",
+ "67 0.373749 4269.577351 \n",
+ "68 0.373749 4250.446715 \n",
+ "69 0.373749 4154.543994 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_1 Linear_test_MSE_after_ablation_2 \\\n",
+ "0 0.334317 4944.247196 \n",
+ "1 0.334160 4703.872125 \n",
+ "2 0.337738 4640.935068 \n",
+ "3 0.345355 4678.676433 \n",
+ "4 0.302674 5338.566511 \n",
+ ".. ... ... \n",
+ "65 0.275227 5200.381743 \n",
+ "66 0.242796 5356.336721 \n",
+ "67 0.222325 5222.916628 \n",
+ "68 0.225809 5168.535770 \n",
+ "69 0.243278 5271.242084 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_2 Linear_test_MSE_after_ablation_3 \\\n",
+ "0 0.134401 5658.191138 \n",
+ "1 0.176484 5324.761627 \n",
+ "2 0.187502 5146.024758 \n",
+ "3 0.180895 5452.652247 \n",
+ "4 0.105458 5866.474772 \n",
+ ".. ... ... \n",
+ "65 0.066456 5884.153057 \n",
+ "66 0.038460 5997.063026 \n",
+ "67 0.048681 5812.610512 \n",
+ "68 0.058586 5904.704003 \n",
+ "69 0.039878 5923.989444 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_3 Linear_test_MSE_after_ablation_4 \\\n",
+ "0 0.009409 6066.312661 \n",
+ "1 0.067783 5773.709727 \n",
+ "2 0.099075 5560.543096 \n",
+ "3 0.045393 5808.699092 \n",
+ "4 0.017001 6058.949364 \n",
+ ".. ... ... \n",
+ "65 -0.056290 6556.102398 \n",
+ "66 -0.076559 6524.414652 \n",
+ "67 -0.058728 6323.222346 \n",
+ "68 -0.075502 6346.397918 \n",
+ "69 -0.079015 6127.787889 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_4 Linear_test_MSE_after_ablation_5 \\\n",
+ "0 -0.062042 6021.472063 \n",
+ "1 -0.010815 5977.826552 \n",
+ "2 0.026504 5746.434317 \n",
+ "3 -0.016941 6069.614343 \n",
+ "4 -0.015251 6186.505044 \n",
+ ".. ... ... \n",
+ "65 -0.176915 6885.746921 \n",
+ "66 -0.171227 7015.708280 \n",
+ "67 -0.151733 6789.017275 \n",
+ "68 -0.155954 6559.339739 \n",
+ "69 -0.116136 6595.262987 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_5 Linear_test_MSE_after_ablation_6 \\\n",
+ "0 -0.054191 6101.326312 \n",
+ "1 -0.046550 5986.809965 \n",
+ "2 -0.006040 5832.336892 \n",
+ "3 -0.062620 5915.356963 \n",
+ "4 -0.036624 6078.148133 \n",
+ ".. ... ... \n",
+ "65 -0.236091 6917.122114 \n",
+ "66 -0.259421 7106.531998 \n",
+ "67 -0.236574 6888.956923 \n",
+ "68 -0.194740 6702.852473 \n",
+ "69 -0.201283 6947.470195 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_6 Linear_test_MSE_after_ablation_7 \\\n",
+ "0 -0.068172 6090.259226 \n",
+ "1 -0.048123 5983.111169 \n",
+ "2 -0.021079 5952.085807 \n",
+ "3 -0.035613 6006.034824 \n",
+ "4 -0.018468 6079.932688 \n",
+ ".. ... ... \n",
+ "65 -0.241723 6905.819125 \n",
+ "66 -0.275725 6990.755293 \n",
+ "67 -0.254778 6342.579611 \n",
+ "68 -0.220880 6652.209705 \n",
+ "69 -0.265435 6558.722886 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_7 Linear_test_MSE_after_ablation_8 \\\n",
+ "0 -0.066234 6039.050269 \n",
+ "1 -0.047475 5939.508997 \n",
+ "2 -0.042044 5999.332220 \n",
+ "3 -0.051489 5993.584111 \n",
+ "4 -0.018767 6124.462336 \n",
+ ".. ... ... \n",
+ "65 -0.239694 6489.716483 \n",
+ "66 -0.254941 6853.979093 \n",
+ "67 -0.155259 5805.435651 \n",
+ "68 -0.211656 6360.483378 \n",
+ "69 -0.194628 6060.428514 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_8 Linear_test_MSE_after_ablation_9 \\\n",
+ "0 -0.057269 5875.871694 \n",
+ "1 -0.039842 5810.251492 \n",
+ "2 -0.050315 5843.222894 \n",
+ "3 -0.049309 5835.219347 \n",
+ "4 -0.026228 6098.814817 \n",
+ ".. ... ... \n",
+ "65 -0.164998 5999.861947 \n",
+ "66 -0.230388 6484.153190 \n",
+ "67 -0.057421 5647.333250 \n",
+ "68 -0.158520 5807.268863 \n",
+ "69 -0.103867 5729.046317 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_9 Linear_test_MSE_after_ablation_10 \\\n",
+ "0 -0.028701 5743.289993 \n",
+ "1 -0.017212 5743.289993 \n",
+ "2 -0.022985 5743.289993 \n",
+ "3 -0.021584 5743.289993 \n",
+ "4 -0.021931 6050.073529 \n",
+ ".. ... ... \n",
+ "65 -0.077062 5585.176699 \n",
+ "66 -0.163999 5585.176699 \n",
+ "67 -0.028624 5535.318508 \n",
+ "68 -0.057755 5535.318508 \n",
+ "69 -0.043508 5535.318508 \n",
+ "\n",
+ " Linear_test_R_2_after_ablation_10 XGB_Regressor_test_MSE_before_ablation \\\n",
+ "0 -0.005489 3565.479582 \n",
+ "1 -0.005489 3565.479582 \n",
+ "2 -0.005489 3565.479582 \n",
+ "3 -0.005489 3565.479582 \n",
+ "4 -0.013764 3931.667854 \n",
+ ".. ... ... \n",
+ "65 -0.002620 3725.748845 \n",
+ "66 -0.002620 3725.748845 \n",
+ "67 -0.008221 3761.115159 \n",
+ "68 -0.008221 3761.115159 \n",
+ "69 -0.008221 3761.115159 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_before_ablation \\\n",
+ "0 0.375784 \n",
+ "1 0.375784 \n",
+ "2 0.375784 \n",
+ "3 0.375784 \n",
+ "4 0.341201 \n",
+ ".. ... \n",
+ "65 0.331174 \n",
+ "66 0.331174 \n",
+ "67 0.314938 \n",
+ "68 0.314938 \n",
+ "69 0.314938 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_1 \\\n",
+ "0 4362.897831 \n",
+ "1 4417.125378 \n",
+ "2 4491.207741 \n",
+ "3 4237.764376 \n",
+ "4 4787.549007 \n",
+ ".. ... \n",
+ "65 4237.968847 \n",
+ "66 4433.523117 \n",
+ "67 4551.081588 \n",
+ "68 4484.427719 \n",
+ "69 4499.283634 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_1 \\\n",
+ "0 0.236179 \n",
+ "1 0.226685 \n",
+ "2 0.213715 \n",
+ "3 0.258086 \n",
+ "4 0.197788 \n",
+ ".. ... \n",
+ "65 0.239223 \n",
+ "66 0.204119 \n",
+ "67 0.171051 \n",
+ "68 0.183191 \n",
+ "69 0.180486 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_2 \\\n",
+ "0 5082.947380 \n",
+ "1 5059.030994 \n",
+ "2 5325.002424 \n",
+ "3 5120.520406 \n",
+ "4 5654.969990 \n",
+ ".. ... \n",
+ "65 5811.342427 \n",
+ "66 5404.224345 \n",
+ "67 5212.816093 \n",
+ "68 5230.083512 \n",
+ "69 4657.567836 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_2 \\\n",
+ "0 0.110118 \n",
+ "1 0.114305 \n",
+ "2 0.067741 \n",
+ "3 0.103540 \n",
+ "4 0.052441 \n",
+ ".. ... \n",
+ "65 -0.043220 \n",
+ "66 0.029864 \n",
+ "67 0.050520 \n",
+ "68 0.047375 \n",
+ "69 0.151655 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_3 \\\n",
+ "0 5443.059555 \n",
+ "1 5123.918062 \n",
+ "2 5404.375561 \n",
+ "3 5318.162829 \n",
+ "4 5475.757988 \n",
+ ".. ... \n",
+ "65 7035.886520 \n",
+ "66 6252.311144 \n",
+ "67 5727.578800 \n",
+ "68 6147.504065 \n",
+ "69 5698.794307 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_3 \\\n",
+ "0 0.047073 \n",
+ "1 0.102945 \n",
+ "2 0.053845 \n",
+ "3 0.068939 \n",
+ "4 0.082470 \n",
+ ".. ... \n",
+ "65 -0.263043 \n",
+ "66 -0.122380 \n",
+ "67 -0.043240 \n",
+ "68 -0.119727 \n",
+ "69 -0.037997 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_4 \\\n",
+ "0 5747.170461 \n",
+ "1 5524.767115 \n",
+ "2 6105.030874 \n",
+ "3 5673.859855 \n",
+ "4 5690.431182 \n",
+ ".. ... \n",
+ "65 6466.436133 \n",
+ "66 6135.927928 \n",
+ "67 6311.016611 \n",
+ "68 6596.737184 \n",
+ "69 6048.504854 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_4 \\\n",
+ "0 -0.006169 \n",
+ "1 0.032768 \n",
+ "2 -0.068820 \n",
+ "3 0.006666 \n",
+ "4 0.046499 \n",
+ ".. ... \n",
+ "65 -0.160819 \n",
+ "66 -0.101488 \n",
+ "67 -0.149510 \n",
+ "68 -0.201552 \n",
+ "69 -0.101695 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_5 \\\n",
+ "0 5782.250032 \n",
+ "1 5776.289705 \n",
+ "2 6161.928219 \n",
+ "3 6080.111206 \n",
+ "4 5921.843788 \n",
+ ".. ... \n",
+ "65 6318.690304 \n",
+ "66 6393.470622 \n",
+ "67 6614.314282 \n",
+ "68 6460.519172 \n",
+ "69 6602.231271 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_5 \\\n",
+ "0 -0.012310 \n",
+ "1 -0.011267 \n",
+ "2 -0.078781 \n",
+ "3 -0.064457 \n",
+ "4 0.007723 \n",
+ ".. ... \n",
+ "65 -0.134296 \n",
+ "66 -0.147720 \n",
+ "67 -0.204753 \n",
+ "68 -0.176740 \n",
+ "69 -0.202552 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_6 \\\n",
+ "0 5830.867264 \n",
+ "1 6083.961808 \n",
+ "2 6084.476971 \n",
+ "3 6056.849952 \n",
+ "4 5940.795730 \n",
+ ".. ... \n",
+ "65 6248.073414 \n",
+ "66 6529.782934 \n",
+ "67 6483.378328 \n",
+ "68 6583.652258 \n",
+ "69 6440.659192 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_6 \\\n",
+ "0 -0.020822 \n",
+ "1 -0.065131 \n",
+ "2 -0.065222 \n",
+ "3 -0.060385 \n",
+ "4 0.004547 \n",
+ ".. ... \n",
+ "65 -0.121619 \n",
+ "66 -0.172190 \n",
+ "67 -0.180904 \n",
+ "68 -0.199168 \n",
+ "69 -0.173123 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_7 \\\n",
+ "0 5832.799073 \n",
+ "1 5755.751419 \n",
+ "2 6072.037858 \n",
+ "3 6114.234944 \n",
+ "4 6009.317532 \n",
+ ".. ... \n",
+ "65 6390.760993 \n",
+ "66 6440.441698 \n",
+ "67 5960.523436 \n",
+ "68 6573.851529 \n",
+ "69 6118.444678 \n",
+ "\n",
+ " XGB_Regressor_test_R_2_after_ablation_7 \\\n",
+ "0 -0.021160 \n",
+ "1 -0.007671 \n",
+ "2 -0.063044 \n",
+ "3 -0.070431 \n",
+ "4 -0.006934 \n",
+ ".. ... \n",
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+ "67 -0.085670 \n",
+ "68 -0.197383 \n",
+ "69 -0.114434 \n",
+ "\n",
+ " XGB_Regressor_test_MSE_after_ablation_8 \\\n",
+ "0 5886.180420 \n",
+ "1 5791.839463 \n",
+ "2 6164.297845 \n",
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+ "\n",
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+ "0 5843.408829 \n",
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+ "\n",
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+ "0 6007.922958 \n",
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"\n",
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"\n",
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"\n",
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- "79 80.0 17.0 131.0 108.0 \n",
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"\n",
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"\n",
- " sample_test_30 sample_test_31 sample_test_32 sample_test_33 \\\n",
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- "79 28.0 46.0 69.0 107.0 \n",
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+ "0 4914.443118 \n",
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"\n",
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"\n",
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- "79 34.0 144.0 105.0 89.0 \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_3 \\\n",
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"\n",
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"\n",
- " sample_test_46 sample_test_47 sample_test_48 sample_test_49 \\\n",
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- "78 16.0 12.0 137.0 101.0 \n",
- "79 16.0 12.0 137.0 101.0 \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_4 \\\n",
+ "0 5832.603810 \n",
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"\n",
- " sample_test_50 sample_test_51 sample_test_52 sample_test_53 \\\n",
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"\n",
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- "79 79.0 64.0 59.0 119.0 \n",
+ " RF_Plus_Regressor_test_MSE_after_ablation_5 \\\n",
+ "0 5784.351708 \n",
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"\n",
- " sample_test_58 sample_test_59 sample_test_60 sample_test_61 \\\n",
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"\n",
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+ " RF_Plus_Regressor_test_MSE_after_ablation_6 \\\n",
+ "0 5793.649629 \n",
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"\n",
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"\n",
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+ "0 5799.895490 \n",
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"\n",
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"\n",
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- "78 38.0 13.0 129.0 22.0 \n",
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+ "0 5799.531819 \n",
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"\n",
- " sample_test_82 sample_test_83 sample_test_84 sample_test_85 \\\n",
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"\n",
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"\n",
- " sample_test_90 sample_test_91 sample_test_92 sample_test_93 \\\n",
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+ "1 -0.011659 \n",
+ "2 -0.008687 \n",
+ "3 -0.016648 \n",
+ "4 -0.018975 \n",
+ ".. ... \n",
+ "65 -0.016863 \n",
+ "66 -0.029910 \n",
+ "67 -0.024743 \n",
+ "68 -0.023738 \n",
+ "69 -0.018208 \n",
"\n",
- " sample_test_94 sample_test_95 sample_test_96 sample_test_97 \\\n",
- "0 NaN NaN NaN NaN \n",
- "1 NaN NaN NaN NaN \n",
- "2 NaN NaN NaN NaN \n",
- "3 NaN NaN NaN NaN \n",
- "4 NaN NaN NaN NaN \n",
- ".. ... ... ... ... \n",
- "75 NaN NaN NaN NaN \n",
- "76 NaN NaN NaN NaN \n",
- "77 103.0 63.0 18.0 19.0 \n",
- "78 103.0 63.0 18.0 19.0 \n",
- "79 103.0 63.0 18.0 19.0 \n",
- "\n",
- " sample_test_98 sample_test_99 ablation_seed_0 ablation_seed_1 \\\n",
- "0 NaN NaN 224 4847 \n",
- "1 NaN NaN 224 4847 \n",
- "2 NaN NaN 224 4847 \n",
- "3 NaN NaN 224 4847 \n",
- "4 NaN NaN 224 4847 \n",
- ".. ... ... ... ... \n",
- "75 NaN NaN 3861 146 \n",
- "76 NaN NaN 3861 146 \n",
- "77 43.0 99.0 6734 8731 \n",
- "78 43.0 99.0 6734 8731 \n",
- "79 43.0 99.0 6734 8731 \n",
- "\n",
- " ablation_seed_2 ablation_seed_3 ablation_seed_4 ablation_seed_5 \\\n",
- "0 6229 7033 4246 4462 \n",
- "1 6229 7033 4246 4462 \n",
- "2 6229 7033 4246 4462 \n",
- "3 6229 7033 4246 4462 \n",
- "4 6229 7033 4246 4462 \n",
- ".. ... ... ... ... \n",
- "75 5855 1493 3971 5711 \n",
- "76 5855 1493 3971 5711 \n",
- "77 5921 9043 526 8382 \n",
- "78 5921 9043 526 8382 \n",
- "79 5921 9043 526 8382 \n",
- "\n",
- " ablation_seed_6 ablation_seed_7 ablation_seed_8 ablation_seed_9 \\\n",
- "0 2467 704 6974 7100 \n",
- "1 2467 704 6974 7100 \n",
- "2 2467 704 6974 7100 \n",
- "3 2467 704 6974 7100 \n",
- "4 2467 704 6974 7100 \n",
- ".. ... ... ... ... \n",
- "75 8760 4156 1273 9581 \n",
- "76 8760 4156 1273 9581 \n",
- "77 3923 2646 9942 5732 \n",
- "78 3923 2646 9942 5732 \n",
- "79 3923 2646 9942 5732 \n",
- "\n",
- " fi_time MSE_before_ablation R_2_before_ablation MSE_after_ablation_1 \\\n",
- "0 87.258065 3015.657705 0.493287 4431.464328 \n",
- "1 2.991500 3015.657705 0.493287 4403.317011 \n",
- "2 3.529108 3015.657705 0.493287 4350.242507 \n",
- "3 1.448320 3015.657705 0.493287 4450.729741 \n",
- "4 0.166827 3015.657705 0.493287 4425.135054 \n",
- ".. ... ... ... ... \n",
- "75 0.651751 3364.534109 0.472198 4589.284370 \n",
- "76 0.071407 3364.534109 0.472198 4644.131916 \n",
- "77 29.754058 3058.202408 0.478782 4223.564334 \n",
- "78 0.437314 3058.202408 0.478782 4118.945235 \n",
- "79 68.105400 3058.202408 0.478782 4213.920895 \n",
- "\n",
- " R_2_after_ablation_1 MSE_after_ablation_2 R_2_after_ablation_2 \\\n",
- "0 0.255393 5395.476701 0.093413 \n",
- "1 0.260123 5510.475704 0.074090 \n",
- "2 0.269041 5528.366306 0.071084 \n",
- "3 0.252156 5401.335532 0.092429 \n",
- "4 0.256457 5571.449691 0.063845 \n",
- ".. ... ... ... \n",
- "75 0.280069 5510.440643 0.135565 \n",
- "76 0.271465 5625.114534 0.117576 \n",
- "77 0.280166 5540.213154 0.055766 \n",
- "78 0.297996 5522.440952 0.058795 \n",
- "79 0.281809 5618.801778 0.042372 \n",
- "\n",
- " MSE_after_ablation_3 R_2_after_ablation_3 MSE_after_ablation_4 \\\n",
- "0 5961.563954 -0.001705 6127.256083 \n",
- "1 6062.766174 -0.018710 6420.107254 \n",
- "2 6008.104990 -0.009525 6190.662160 \n",
- "3 5872.831654 0.013205 6229.387859 \n",
- "4 6052.897939 -0.017051 6391.870115 \n",
- ".. ... ... ... \n",
- "75 6269.197133 0.016537 6802.626607 \n",
- "76 6479.673837 -0.016480 7087.133723 \n",
- "77 6100.799366 -0.039777 6404.189310 \n",
- "78 6139.370419 -0.046350 6561.148099 \n",
- "79 6167.544944 -0.051152 6554.712486 \n",
- "\n",
- " R_2_after_ablation_4 MSE_after_ablation_5 R_2_after_ablation_5 \\\n",
- "0 -0.029546 6329.519071 -0.063531 \n",
- "1 -0.078753 6550.611119 -0.100681 \n",
- "2 -0.040200 6400.736746 -0.075498 \n",
- "3 -0.046707 6497.063836 -0.091683 \n",
- "4 -0.074008 6662.404069 -0.119465 \n",
- ".. ... ... ... \n",
- "75 -0.067143 7182.245130 -0.126694 \n",
- "76 -0.111774 7414.971343 -0.163203 \n",
- "77 -0.091484 6705.799392 -0.142889 \n",
- "78 -0.118235 6779.508319 -0.155451 \n",
- "79 -0.117138 6770.764955 -0.153961 \n",
- "\n",
- " MSE_after_ablation_6 R_2_after_ablation_6 MSE_after_ablation_7 \\\n",
- "0 6440.261355 -0.082139 6543.210590 \n",
- "1 6699.697074 -0.125731 6768.142298 \n",
- "2 6540.444309 -0.098973 6621.351976 \n",
- "3 6638.438945 -0.115438 6708.991358 \n",
- "4 6724.232910 -0.129854 6712.329030 \n",
- ".. ... ... ... \n",
- "75 7383.240571 -0.158225 7500.322118 \n",
- "76 7523.826113 -0.180279 7625.821932 \n",
- "77 6918.319441 -0.179109 7039.701464 \n",
- "78 6876.299938 -0.171947 6935.213602 \n",
- "79 6933.509996 -0.181698 6983.510919 \n",
- "\n",
- " R_2_after_ablation_7 MSE_after_ablation_8 R_2_after_ablation_8 \\\n",
- "0 -0.099437 6598.214822 -0.108680 \n",
- "1 -0.137232 6787.349203 -0.140459 \n",
- "2 -0.112567 6725.345707 -0.130041 \n",
- "3 -0.127293 6692.120871 -0.124458 \n",
- "4 -0.127854 6746.616969 -0.133615 \n",
- ".. ... ... ... \n",
- "75 -0.176592 7560.531068 -0.186037 \n",
- "76 -0.196279 7658.318127 -0.201377 \n",
- "77 -0.199796 7041.718704 -0.200140 \n",
- "78 -0.181988 7002.733329 -0.193496 \n",
- "79 -0.190220 7023.012024 -0.196952 \n",
- "\n",
- " MSE_after_ablation_9 R_2_after_ablation_9 MSE_after_ablation_10 \\\n",
- "0 6668.503871 -0.120490 6754.906732 \n",
- "1 6725.982194 -0.130148 6754.906732 \n",
- "2 6728.526559 -0.130575 6754.906732 \n",
- "3 6704.867772 -0.126600 6754.906732 \n",
- "4 6754.743785 -0.134981 6754.906732 \n",
- ".. ... ... ... \n",
- "75 7623.677720 -0.195943 7620.889900 \n",
- "76 7654.377608 -0.200759 7620.889900 \n",
- "77 7051.753892 -0.201851 7018.716589 \n",
- "78 7019.751192 -0.196396 7018.716589 \n",
- "79 7014.183445 -0.195447 7018.716589 \n",
- "\n",
- " R_2_after_ablation_10 ablation_time split_seed \\\n",
- "0 -0.135008 2.449821 7 \n",
- "1 -0.135008 2.449706 7 \n",
- "2 -0.135008 2.461490 7 \n",
- "3 -0.135008 2.445521 7 \n",
- "4 -0.135008 2.448545 7 \n",
- ".. ... ... ... \n",
- "75 -0.195506 1.181488 5 \n",
- "76 -0.195506 1.182896 5 \n",
- "77 -0.196220 23.125059 5 \n",
- "78 -0.196220 23.535761 5 \n",
- "79 -0.196220 22.972130 5 \n",
- "\n",
- " rf_model index var \\\n",
- "0 NaN 0 0 \n",
- "1 NaN 1 0 \n",
- "2 NaN 2 0 \n",
- "3 NaN 3 0 \n",
- "4 NaN 4 0 \n",
- ".. ... ... ... \n",
- "75 NaN 3 0 \n",
- "76 NaN 4 0 \n",
- "77 RandomForestRegressor(max_features=0.33, min_s... 5 0 \n",
- "78 RandomForestRegressor(max_features=0.33, min_s... 6 0 \n",
- "79 RandomForestRegressor(max_features=0.33, min_s... 7 0 \n",
- "\n",
- " true_support \n",
- "0 1.0 \n",
- "1 1.0 \n",
- "2 1.0 \n",
- "3 1.0 \n",
- "4 1.0 \n",
- ".. ... \n",
- "75 1.0 \n",
- "76 1.0 \n",
- "77 1.0 \n",
- "78 1.0 \n",
- "79 1.0 \n",
- "\n",
- "[80 rows x 159 columns]"
+ " RF_Plus_Regressor_test_MSE_after_ablation_10 \\\n",
+ "0 5775.798475 \n",
+ "1 5775.798475 \n",
+ "2 5775.798475 \n",
+ "3 5775.798475 \n",
+ "4 6099.151078 \n",
+ ".. ... \n",
+ "65 5655.363927 \n",
+ "66 5655.363927 \n",
+ "67 5636.734002 \n",
+ "68 5636.734002 \n",
+ "69 5636.734002 \n",
+ "\n",
+ " RF_Plus_Regressor_test_R_2_after_ablation_10 test_data_ablation_time \\\n",
+ "0 -0.011181 8.891757 \n",
+ "1 -0.011181 8.881537 \n",
+ "2 -0.011181 8.914796 \n",
+ "3 -0.011181 8.867040 \n",
+ "4 -0.021987 8.382616 \n",
+ ".. ... ... \n",
+ "65 -0.015219 7.412112 \n",
+ "66 -0.015219 7.402059 \n",
+ "67 -0.026693 7.036491 \n",
+ "68 -0.026693 7.033576 \n",
+ "69 -0.026693 6.885340 \n",
+ "\n",
+ " split_seed rf_model \n",
+ "0 4 NaN \n",
+ "1 4 NaN \n",
+ "2 4 NaN \n",
+ "3 4 NaN \n",
+ "4 4 RandomForestRegressor(max_features=0.33, min_s... \n",
+ ".. ... ... \n",
+ "65 6 NaN \n",
+ "66 6 NaN \n",
+ "67 6 RandomForestRegressor(max_features=0.33, min_s... \n",
+ "68 6 RandomForestRegressor(max_features=0.33, min_s... \n",
+ "69 6 RandomForestRegressor(max_features=0.33, min_s... \n",
+ "\n",
+ "[70 rows x 302 columns]"
]
},
"execution_count": 4,
@@ -2564,6 +6126,7 @@
}
],
"source": [
+ "pd.set_option('display.max_columns', None)\n",
"combined_df"
]
},
@@ -2577,14 +6140,13 @@
"output_type": "stream",
"text": [
" fi fi_time\n",
- "0 Kernel_SHAP_RF_plus 53.592039\n",
- "1 LFI_with_raw_CV_RF 70.349461\n",
- "2 LFI_with_raw_OOB_RF 2.535222\n",
- "3 LFI_with_raw_RF 3.045225\n",
- "4 LFI_with_raw_RF_plus 0.706900\n",
- "5 LIME_RF_plus 124.873722\n",
- "6 MDI_RF 1.098270\n",
- "7 TreeSHAP_RF 0.108346\n"
+ "0 Kernel_SHAP_RF_plus 277.088223\n",
+ "1 LFI_with_raw_OOB_RF 6.107385\n",
+ "2 LFI_with_raw_RF 6.842227\n",
+ "3 LFI_with_raw_RF_plus 1.639075\n",
+ "4 LIME_RF_plus 612.684298\n",
+ "5 MDI_RF 2.537897\n",
+ "6 TreeSHAP_RF 0.334840\n"
]
}
],
@@ -2603,21 +6165,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " fi ablation_time\n",
- "0 Kernel_SHAP_RF_plus 37.999997\n",
- "1 LFI_with_raw_CV_RF 1.801137\n",
- "2 LFI_with_raw_OOB_RF 1.750226\n",
- "3 LFI_with_raw_RF 1.777164\n",
- "4 LFI_with_raw_RF_plus 38.591149\n",
- "5 LIME_RF_plus 37.662260\n",
- "6 MDI_RF 1.775139\n",
- "7 TreeSHAP_RF 1.757286\n"
+ " fi train_data_ablation_time\n",
+ "0 Kernel_SHAP_RF_plus 10.402297\n",
+ "1 LFI_with_raw_OOB_RF 10.521922\n",
+ "2 LFI_with_raw_RF 10.535834\n",
+ "3 LFI_with_raw_RF_plus 10.385392\n",
+ "4 LIME_RF_plus 10.394773\n",
+ "5 MDI_RF 10.534780\n",
+ "6 TreeSHAP_RF 10.418015\n"
]
}
],
"source": [
"# Print the ablation time\n",
- "averages = combined_df.groupby('fi')['ablation_time'].mean().reset_index()\n",
+ "averages = combined_df.groupby('fi')['train_data_ablation_time'].mean().reset_index()\n",
"print(averages)"
]
},
@@ -2632,6 +6193,7 @@
"########################################################################################\n",
"methods_rf = [\"LFI_with_raw_RF\", \"LFI_with_raw_CV_RF\", \"LFI_with_raw_OOB_RF\", \"MDI_RF\", \"TreeSHAP_RF\"]\n",
"methods_rf_plus = [\"Kernel_SHAP_RF_plus\",\"LFI_with_raw_RF_plus\", \"LIME_RF_plus\"]\n",
+ "methods_all = methods_rf + methods_rf_plus\n",
"n_testsize = combined_df[['train_size', 'test_size']].drop_duplicates()\n",
"num_features = combined_df['num_features'].drop_duplicates()[0]\n",
"metrics = {\"regression\": [\"MSE\", \"R_2\"], \"classification\": [\"AUROC\",\"AUPRC\", \"F1\"]}"
@@ -2647,12 +6209,12 @@
"output_type": "stream",
"text": [
"Model: RF\n",
- "MSE before ablation: 3161.100213831893\n",
- "R2 before ablation: 0.4470820005009606\n",
+ "MSE before ablation: 3160.159277609703\n",
+ "R2 before ablation: 0.4474091923958154\n",
"\n",
"Model: RF_plus\n",
- "MSE before ablation: 2943.1783568546443\n",
- "R2 before ablation: 0.4856215513988536\n",
+ "MSE before ablation: 3058.492966541393\n",
+ "R2 before ablation: 0.4653868933792268\n",
"\n"
]
}
@@ -2679,10 +6241,86 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "ablation_models = {\"regression\": [\"RF_Regressor\", \"Linear\", \"XGB_Regressor\", \"RF_Plus_Regressor\"], \"classification\": [\"RF_Classifier\",\"Logistic\", \"SVM\", \"XGBoost_Classifier\", \"RF_Plus_Classifier\"]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Training Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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QVq/Xi7KysmPuc+DAgc16n7S0S8rxmhfffvttERAQIAoKCoQQTXfp0Ol0wtfXVwQEBIh33nlHrFy5UjzzzDNCpVKJm2++uUXHdMGFroEDBwovLy9RUVHhXnbnnXcKQGRmZrqX1X+gPvDAAw22cfvttwutVivsdrsQovHQVVhYKMaPHy+ioqKEUqn0eGO8+uqr7nItCV1jxowRBoOhQd+CwsJCAYgnn3zSvQwQY8eObbDN8PBwMWHChKafIHH4g+CDDz7wWP7BBx8IQPz8888ey6dOnSoAUV1dLYQQ4oknnhCAKCoq8ijndDqFwWAQN954oxBCiPfee08A4p9//vEoZ7PZhFqt9vgFioyMFFdeeaU71NTf0tPTBSDee+89j3NvTlDatm2b2LBhw3Fvubm5x91WUwGuPtwfqf75fe211xrsy2w2H3df9erfd0ff2rVrJ8rLy93ldu/eLQDxn//8p8HzV/8a7NixQwghRGhoaIMPfCGEeOGFFxoNXV26dGlQtrmvVf0fQGPHjhXff/+9R9Crd9dddwmdTieefPJJsWrVqgZ9xnbs2NFowBdCiIkTJwqlUun+Yqz/gt+6devxn9xmOFbo+r//+79G13n//fdFly5dhE6n83jN2rZt61GuqdC1bNkyj3L1f+CsW7euyeN0Op0iISFBREZGijfeeENs2rTJI6DWO1bo+u2334ROpxPjxo3zWN63b1/RsWPHBq91dXW1UCgU4oknnmjyuIRw9blqzu/gtm3bjrmdxs75tttuEyqVSnz//fcej1ksFpGYmCgiIiLEL7/8IsrLy8XSpUtFWFiYUKlUHq/FsGHDBCDGjx/vsY364PbRRx+16LiOdnToWr58uQDEN9980+A5ffLJJ4VCoRA1NTVCCCGGDBki/P39xcsvvyzWrl0rrFZrg+23JHTt2bNHaLVacckll4hZs2aJvXv3NlruWJ+v06dPF4D4+OOP3ctMJpNQq9XiwQcfbHBOS5YsaVY437VrV7PeJy3tG32s79+srCzh4+PjcS5NhS6NRiMA8eWXX3osr6+oaMkf0xdU8+KePXtYvXo11113HUIId9X69ddfz2effcann37K9OnTPdYJDw9vsJ3w8HCsVis1NTUYjcYGjzudTi677DLy8vJ49tln6dChAwaDAafTSa9evZoclnw8paWlhIeHN6iuDw0NRa1WezS5QOMj4XQ6XbP3HxgY6HG/voq0qeVmsxkfHx9KS0tRq9UNRuAoFArCw8Pdx1n/8+jnWK1WNzj2wsJCFi9ejEajafRYj+wr11wpKSnNrrJujn79+vHf//4Xh8PB7t27efbZZ3nggQdITU1t0MQFEB8f7+5/dTK++OIL2rVrR3V1NQsWLOCDDz7gpptuYunSpcDhvl1TpkxhypQpjW6j/vkrLS0lLCysweONLQPcTeJHau5rNW7cOOx2Ox999BHXXXcdTqeTHj168MorrzBs2DDANbVLVFQUCxYs4LXXXkOv1zN8+HBef/112rRp434PNXYcEREROJ1OysvLPZpGGit7qjW2jzfffJPHHnuMCRMm8PLLLxMcHIxKpeLZZ59l586dzdru0b8X9U1ix/qdVigUrFixgpdeeokZM2bw2GOPERgYyC233MK///1vfH19j7nP9PR0rr76avr37+/uD1uvsLCQPXv2nPDvZXh4eKN9sRo7h+YSh5qE5syZw+eff85VV13l8bhWq2Xp0qWMGzeOyy67DACDwcC0adN4+eWXiYyMdJetf76HDx/usY3hw4e7+weeSvW/q8eaOqasrAyDwcCCBQt45ZVX+Pjjj3n22Wfx8fHhmmuuYcaMGY1+bx1PQkICv/76KzNmzOD++++ntraW+Ph4HnroIR5++OHjrj9nzhyefvppnnvuOe6++2738tLSUux2O2+//TZvv/12o+se732SmJh4Sj+rm+P++++nffv2XHfdde6sUN+3raamhsrKSvf3f1BQEAUFBQ3eJ5dffjlvvfUWmzZtIjExsVn7vaBC16effooQgm+++YZvvvmmweOff/45r7zyikffgYKCggblCgoK0Gq1+Pj4NLqftLQ0tm7dyqxZszz6ie3Zs+ekjj8oKIj169cjhPD4ECoqKsJutxMcHHxS2z9VgoKCsNvtFBcXewQvIQQFBQX06NHDXQ5cz+eRH3R2u71BgAwODqZjx478+9//bnSfJ9K3IiEhgQMHDhy33PPPP9+seWmMRqM7RPXs2ZOePXvSqVMnJk2axJYtW07pB8KR2rVr597v4MGDcTgcfPzxx3zzzTdcf/317vfF1KlTufbaaxvdRnJyMuB6TRrr+N/Y7wE0/mXYktfqzjvv5M4776S2tpbVq1fz/PPPM2rUKDIzM4mNjcVgMPDiiy/y4osvUlhYyNKlS3nqqae48sor2bVrl/s9lJ+f32A/eXl5KJXKBtNmnIkO0Y3tY86cOQwaNIj333/fY/nR/RdPh9jYWHdgyszM5KuvvuKFF17AarUyc+bMJtfLyclhxIgRxMTEsHDhwgbhKjg4GC8vL/cUPEc73mfSSy+95B58cbzjb85cSvWB67PPPuOTTz7h1ltvbbRcYmIia9euJTc3l7KyMhISEqisrOThhx9mwIAB7nIdO3Zk/vz5Te7vVP9O1z9fb7/9Nr169Wq0TP0fQMHBwbz11lu89dZbHDx4kEWLFvHUU09RVFTEsmXLTmj//fv3p3///jgcDv755x/efvttJk+eTFhYGGPHjm1yveXLl3PXXXdxxx13NHg9AwICUKlUjBs3jvvvv7/R9ePi4o55XEOHDuX3338/7vHffvvtTfa7aqm0tDQOHDjQ6LQ7gwcPxmg0usNYx44dG/2MrA+KLXmfXDChy+Fw8Pnnn5OQkMDHH3/c4PEff/yRN954g6VLl7pHsYBrwr3XX38dvV4PuD4gFy9eTP/+/Zvs2Fn/gXt0R80PPvigQdnm/KVab+jQoXz11Vd8//33XHPNNe7lX3zxhfvxc8HQoUOZMWMGc+bM4ZFHHnEvX7hwIbW1te7jrO84PHfuXLp16+Yu99VXXzXoYD5q1CiWLFlCQkLCKZt7avHixQ1GfTbmRDvLtmnThieeeIIXX3yRBQsWcNNNN53QdlpqxowZLFy4kOeee45rr72W5ORk2rRpw9atW5k2bdox1x04cCBLliyhpKTE/QXgdDr5+uuvm73/E3mtDAYDl19+OVarlauvvpr09HRiY2M9yoSFhXHHHXewdetW3nrrLerq6khOTiYyMpJ58+YxZcoU9+9ebW0tCxcudI9oPBcoFIoGnwnbtm1j7dq1REdHn7HjSEpK4l//+hcLFy48Zk1NZWUll19+OQqFgiVLljQ6cmvUqFFMmzaNoKCg435xNua+++7z+LxtytHPW2OEENx777189tlnfPDBB9x5553HXScyMtL9B9+//vUvDAaDRy3NNddcwzPPPMPSpUs9PnOXLl2KEKLJYHSi+vbti7+/Pzt27OCBBx5o9noxMTE88MADrFixwj06E1rWsnEklUpFz549adu2LXPnzmXTpk1Nhq4tW7Zw3XXXMWTIkEYnAvX29mbw4MFs3ryZjh07tqxT+SEffPBBs/44OZUVD/Pnz8dsNnssW7ZsGa+99hozZ870mPftuuuu45dffmHp0qUec/YtWbIEpVLprmhojgsmdC1dupS8vDxee+21RkcJtW/fnnfeeYdPPvnE40NApVIxbNgwHn30UZxOJ6+99hpVVVXH/Ousbdu2JCQk8NRTTyGEIDAwkMWLF7N8+fIGZTt06ADA//3f/3H77bej0WhITk5utMr/tttu49133+X2228nKyuLDh068OeffzJt2jSuuOIKLr300hN4Zk69YcOGMXz4cJ588kmqqqro27eve/Rily5dGDduHOCqobn11lt566230Gg0XHrppaSlpfGf//ynwQf8Sy+9xPLly+nTpw8PPfQQycnJmM1msrKyWLJkCTNnzmzxhH31z/3pNGXKFGbOnMmLL77ImDFjTmgEVksFBAQwdepUnnjiCebNm8ett97KBx98wOWXX87w4cO54447iIyMpKysjJ07d7Jp0yZ3qHrmmWdYvHgxQ4cO5ZlnnsHLy4uZM2e6R/k15y+25r5W9957L15eXvTt25dWrVpRUFDA9OnTMRqN7g+pnj17MmrUKDp27EhAQAA7d+5k9uzZHmFqxowZ3HLLLYwaNYrx48djsVh4/fXXqaio4NVXX23Wc5aVlUVcXNwp/Uv5aKNGjeLll1/m+eefZ+DAgWRkZPDSSy8RFxd3Wqc92bZtGw888AA33HADbdq0QavVsnLlSrZt28ZTTz3V5Ho333wzO3bs4MMPPyQ7O9tj2oWoqCiioqKYPHkyCxcuZMCAATzyyCN07NgRp9PJwYMH+eWXX3jssceOOfF0RETEKRsB+NBDD/HJJ59w11130aFDB9atW+d+TKfT0aVLF/f9+ia4mJgYCgsL3X/Mzp4926PWvW3bttx///289957+Pr6cvnll5OZmcm//vUvunTpwpgxY9xlf/vtNwYPHtzsmvHG+Pj48Pbbb3P77bdTVlbG9ddf7x75unXrVoqLi3n//feprKxk8ODB3HzzzbRt2xZfX182bNjAsmXLPGqzO3TowLfffsv7779Pt27dUCqVTXZpmDlzJitXrmTkyJHExMRgNpvdNZhNfbdUVVVxxRVX4OXlxZQpUxrM1J6SkoKfnx//93//R79+/ejfvz8TJ06kdevWVFdXs2fPHhYvXnzc0bf1NfGnQnFxsbvWbPv27YArH4SEhBASEsLAgQMBGg3U9bWt3bp183ge77zzTj744AMmTZpESUkJKSkp/Prrr7z77rtMmjSpwR+Qx9Ts3l/nuKuvvlpotdoGnbuPNHbsWKFWq0VBQYG7k+xrr70mXnzxRREVFSW0Wq3o0qVLg47kjXWk37Fjhxg2bJh7RMMNN9wgDh482GgnxKlTp4qIiAh3h/tVq1YJIRrv2FpaWiomTJggWrVqJdRqtYiNjRVTp05t0AEbEPfff3+Dc2zOpKNNTdjXVCfC+g7KR3aENplM4sknnxSxsbFCo9GIVq1aiYkTJ3p08BbC1an1scceE6GhoUKv14tevXqJtWvXNnqcxcXF4qGHHhJxcXFCo9GIwMBA0a1bN/HMM8+4O5fWn/u5MDlqvXfffVcA4vPPPxdCnP7JUYVwPf8xMTGiTZs27gEfW7duFWPGjBGhoaFCo9GI8PBwMWTIkAad/f/44w/Rs2dPodPpRHh4uHj88cfdI2SPHIByrHNuzmv1+eefi8GDB4uwsDCh1WpFRESEGDNmjEen6aeeekp0795dBAQECJ1OJ+Lj48UjjzwiSkpKPPb3/fffi549ewq9Xi8MBoMYOnSo+OuvvzzKNPY+rbd9+3YBiKeeeupYT7mHY3Wkb+y1tVgsYsqUKSIyMlLo9XrRtWtX8f3334vbb7+9QWfno9/DTb3W9fur/8xoTGFhobjjjjtE27ZthcFgED4+PqJjx47iv//9r/u9IUTDz5tjje498thqamrEv/71L5GcnCy0Wq0wGo2iQ4cO4pFHHnGP+joTjnW8Rz+/L774okhISBA6nU74+/uLESNGiNWrVze6XbvdLl599VWRmJh4zM+yxYsXNzl4pilNvV9+//13MXLkSBEYGCg0Go2IjIwUI0eOdJczm81iwoQJomPHjsLPz094eXmJ5ORk8fzzz3uMqCwrKxPXX3+98Pf3FwqFQhzrK33t2rXimmuuEbGxsUKn04mgoCAxcOBAsWjRIo9yR77+9b8DTd2OfF/u379f3HXXXSIyMlJoNBoREhIi+vTpI1555ZVmP1+nwrFGjx9r9K4Qx/7MLS0tFePHjxdhYWFCo9GIpKQk8frrrzc6aOVYFEI0o/eaJEkXtMsuu4ysrCwyMzPP9qGcFu+99x5PPPEEe/fubXLQgCQdyxNPPMGXX37J7t273d1RJKmlLpjmRUmSmufRRx+lS5cuREdHU1ZWxty5c1m+fHmDkWsXklWrVvHQQw/JwCWdsFWrVvHss8/KwCWdFFnTJUlnWHNm6T4VM9c35eGHH2bRokUUFBSgUChISUlh8uTJTY4EkyRJkk4NGbok6Qx74YUXjjuMfv/+/bRu3frMHJAkSZJ0RsjQJUlnWF5e3nEvYnyiQ68lSZKkc5cMXZIkSZIkSWfA6ZlCW5IkSZIkSfIgRy82k9PpJC8vD19f3zNymRFJkiRJkk6eEILq6moiIiJO2+XamkuGrmbKy8s7o5fykCRJkiTp1MnOzm7xlU1ONRm6mqn+sj3Z2dmNXqNMkiRJkqRzT1VVFdHR0Y1efu9Mk6GrmeqbFP38/GTokiRJkqTzzLnQNUh2pJckSZIkSToDZOiSJEmSJEk6A2TokiRJkiRJOgNk6JIkSZIkSToDZOiSJEmSJEk6A2TokiRJkiRJOgNk6JIkSZIkSToDZOiSJEmSJEk6A2TokiRJkiRJOgNk6JIkSZIkSToDZOiSJEmSJEk6A2TokiRJkiRJOgPkBa8lSZIk6ShCCOw2J1aTHYfdiVKpQKFQoFAq0BvUKFWuOguHzYnD4UShVKBQuC6qrFBw6P7Zv8CydG6RoUuSJEm6YDjsTmxmB1azHZ9APUqlK/gU7K+kNKcGq8mB1WLHZnKVcd0cDLszBS9fLQBrv9vD5l8OIkTj+xj77CUERfoAsOmXA/y9eH+Tx3PdE90IjzcCsHVFNut+2OsOZO6QpnT9HH5feyIS/QHI/LuA9Yv3Hwp7wFHl+49pQ0SbAAAOpJeycUmWO/ihUKDRqfAJ0OEbqKd1x2ACWxlO/smVTpoMXZIkSdI5o7rMTG2FxRWGTA5sFtdPq9mOzezgktFxqDUqADYuy2LPxiKsJjs2iwOryYHD7nRv647X+mIw6gDI/LuQ7atymtyvudbmDl1KlfJw4FKASqVECIEQrhowjqjAEs4mkln96kfUdjkcTuxWZ5NlhePwtix1dqqKTU2WtZod7v/XVVrJ31vZZFnfIL07dGVtK+G3eRn4BurwCdDjE6h3hzPfQD3GUC+0ehkNThf5zEqSJEknTTgFFpMdc40Nc52N8Dij+7Fda/PJ31uJudaGpc6O7VDtUn0t092v90OtdQWp9Yv2kbGuoMn9dB4W4w5dtRVWSrJrGi2n1iqxWw8Hk+AoH1p3DEbrpUKrV6PVq9Do1a7/e6ncgQug09Bo2g+IRKNXodGqUCibbibsfkVrug6PdQcyIVzPhRAC4QSd4fDXbGr/SBK6hLqC26HyTufh//sFe7nLxncJITja91BZ17bEET9DYnzdZSOT/Rkxvr37MQRYTHZqys3UlFk8armqSk3UVliorbAAVQ3O57J7UmnTPQyA/L2V7PgzF59APb4Benzqg1qATgazEySfNUmSJMmDw+HEUnsoQNVaMdfYsZhstOsT4S6z4af9HEwvw1xrw1xjw1Jn82iOm/D2IFQaV7+nnF3lZKxvOkhZzQ536DIYdfgF6w8FoiMCkpcrIClVhwNQav8IWncIOlzW61BZncrd56peSt8IUvpG0Bx6g6ZZ5cBVK6ZUNa+szkuNzqt5X7sGo85dS9cUIQTCZsOgteMdo0JYLAirFafFCn4CZYwOhVcQSp0dp9mMQqcjuWc4YXFGasrM1JRbqC4zU1NmprrcQk2ZGd9AvXv7xQer2LW28ddN561m2N2pxKYGAVBRWEfRgSp3zZnBX4dKJcfqHU2GLkmSpAuY3eY4FJ5sh37aMddYXbVOJgd9r0t0l105eyd7NxZ5NF0dKalHuDtIVRaZKNjXsElLq1eh99Fgtdjx0rhqj+I7h2AM9UJv0KDzVh8OR4dqmvRH1Ab1viaB3tckNOvcgiJ93H2rzqT6sFMfco4MO8JqRVjr79c/fni5a9kR61otOI8sU78t61HlLIfKHVFGWK0tO3CFAoVOh1KvR6HX46PX46vXu+8r9Xrs/9OTp9ej0OvQKoJoHxyESeipc+ios2mos6iw2RVY6uw49+7CZDOg0OvZl2Zh7a+lR+4Kb6PO3YzZ7fJYgqNctXMWkx2n3YneR3PRDTaQoUuSJOk8U1thobyglroqV3gy1diwHApWNouDkfd3cpdd9mEaB7aXNrmtnkf0kXI6xOHApXDVZnj5aNEb1OgNGux2pzt0tR8YSVznYPQGDXofjeunQYNK3bB2I75LCPFdQk7hM3CYOwCZzTjN5sM/LZZD/7cgLEf8NJkP3TcjzBacFtfP+sfc981mV0Aym10B6KhQdC5SaDQodDoUWi0oFO7nAseh11QIhNmMw2xuZGUVKJSgVIJChULh+hmmVLqW1y9TqnCovLDqjZi3V5ONExQqbAHJxAd3wKbxwa7xQaFUoai1oqi1YMkuZ++qhRygFodaTY0ujFp9OA61HrvChE1pAmEi3O7E9/J2dB8x9Mw+cWeQDF2SJEnnGCEEpmobFUV1VBTWUVthocfIOPfjv87aQc6u8ibXt9sc7iClN2hc0xy4g9ERQcpHgziiX/clo+LoNiIWvY8GnbfGPfKvMfUj8pp7Po6KCmw5OTgqKg+HILPpiFDkGYKcZpNnKDoyBNWHq0P3mxxmeIYotFrX7VDgUR51X6HTHlquO2K5BmX941pd48sObceh1mBVqrGgxGSBGrONWosVi9mG1WrHaXGAVaCyOtFaQW9TonUoUQoFCgEqpwKlUKA6dFOidP+s/3eyvIEIBCasmBRW6hQWBBDjDAY0YBjCcs02ShXVmBSVOBSe718/pxf9rX1Iz8yEESd9OOessx66cnNzefLJJ1m6dCkmk4mkpCQ++eQTunXrBrh+WV988UU+/PBDysvL6dmzJ++++y6pqanubVgsFqZMmcKXX36JyWRi6NChvPfee0RFRbnLlJeX89BDD7Fo0SIARo8ezdtvv42/v/8ZPV9JkqTG7FqXz8H0MioK66gsqmvQxNdpSDTaQ/2BAiMMVJeZ8QnQoTdo0fto8KoPVT4aFEcMrxt8S1uG3t6uWc04R3bkbimnxYItNxdbdjbWnBxs2TnYcnOwZudgy87GWVt7wttuNqXS1Uym07mbyzzuu5frUOj0KL30KHSupjRl/U+9a5lSr0Oh9zpUVncoBB0RinSu5ajV2J1gsjqos9ldP60OTDbHEf+3Y7I6qbFYqLRUU2WtxmSqwWmqQ2GxoLLa0Jps6KsseNkteDuU+DrU+DjV+Dl1+Dn1+Dm88RY6vHEFHFAdup0+TuFE4MSitFGrsFCrNCMcgjCbDwInQjhZr8uiQmXCrLBhVtgRR7zNvJxqhlTEIHDiFE7KjRXUqGzux9VCgdapRudU4+VQsb9mI8bE1EaO5MJxVkNXeXk5ffv2ZfDgwSxdupTQ0FD27t3rEYRmzJjBm2++yaxZs0hKSuKVV15h2LBhZGRk4Ovrah+ePHkyixcvZv78+QQFBfHYY48xatQoNm7ciErlelPefPPN5OTksGzZMgDuu+8+xo0bx+LFi8/4eUuSdPGwWRxUFNVRWWRyB6qKojoqi02Me6UPGp3rMyp/byW7NxQeXlEBvoF6/EO9MIZ6e0yF0H9MEv3HNG//9c2BJ0s4ndiLiz1DVU421hxX0LIXFR13G+rQUFRBQU2HokNhyB2CvPSNh6H6n15eHoEKTfP6CNkdTmotDqrMNqrNdmosdqoP/b/abKPaYnf9v8RGjdlOjcVCna2KWmsttfYaTI5azPZarM46LM469AorRgT+CoEfCoyoMAoVfkKNUWjxc+owOvVEOrzxc/jg6zCgF36AX4tfBwdOapS11CrqMAkzVqcZu92C3WYiI1aJRQNWrYKQPYX4FpYjEO7wJIQT56GfddcOR+3thcIpEJu2I3aku5pqcVIX0gqH3gs0WpxqDeKIkQLePjpSfXXovQzovL0pO1BHpcmzudJgMODj44O/0UjqqJFo9V6oNBoisrNRKBT4+Pjg4+ODRtP8AQsXCoUQZ69e9qmnnuKvv/7ijz/+aPRxIQQRERFMnjyZJ598EnDVaoWFhfHaa68xfvx4KisrCQkJYfbs2dx4440A5OXlER0dzZIlSxg+fDg7d+4kJSWFdevW0bNnTwDWrVtH79692bVrF8nJycc91qqqKoxGI5WVlfj5tfwXRZKkC5fD5qSy2ERFUR2xqUHuoPPnN7vZ+mt2k+vd+K8e7s7FB3eUUpJTg3+oN/6h3viF6N1NhGeKo6amkVBVX2uVe9y+TEpvbzTR0Wiio9BGRaOJikIbHeVaFhGBUq8/5vrHI4TAYne6w1FNfTgy26gy26kxH75fH6YaC1Z17qkkBCgtKNVVKA7dlJoqtKoaghVOQlEQKtQECC1Gpxe+DgNGhwFfhw9+DoP7phEnFh7sCgc1KjN1agsmjQ2L1oFNJ3DqFaABhbCjiwzG4G/A19+XnD/+ZPefv2EzNT5/1z1vf4wxNByr1cqGJYso2bMLjd4Lrd6LvDoLJocTm1NgsTuwOZ2YLRYAjH5+3D52DFq9Fxq9F5/PmU1eXr7HtuvDUmBgIHfeead7+Z49e3A4HPj4+ODr64vBYHBXdpwrzqXv77Na07Vo0SKGDx/ODTfcwO+//05kZCSTJk3i3nvvBWD//v0UFBRw2WWXudfR6XQMHDiQNWvWMH78eDZu3IjNZvMoExERQfv27VmzZg3Dhw9n7dq1GI1Gd+AC6NWrF0ajkTVr1jQrdEmSJAGU5NSQm1lOZaGrxqqiyER1mRkO/fl65Gzl3ofmftL7aNw1Vv6h3hhDvfAP9SYg7PD8STEpQcSkBJ3WYxc2G7b8fGw5h5r9jgxVOTk4KiqOvQGVCk2rVo2HqqgoVP7+TdY0OZyCKnN9zdHhoFTtDksNg5JnsHLdtzmaWU+gsKJQV7sCleZQoNJXoTXUEKcQhAoVIWgItfsSbA8g2OZPsDWO4Fp/Ah3N769Wz650YNU5sOkcOPUKhJcChbcatUGLxkeHzscbva8Bg9EXrY8XSm81Cp2KisJ8CvfuxlGQR11+HpX5eZQX5GGuqQbg3nc/xS84FIDKzVtdgUuhwC84BHVoJCo/f9DpsStVzP/2eyqrqqirq8Pf35/JU/7lPr4PP/yQ/Ly8BsetVqvR6nQERhzujtO3bz+sVqs7SPn4+ODt7Y1S2bDWNDExscEyqWlnNXTt27eP999/n0cffZSnn36av//+m4ceegidTsdtt91GQYFrfpCwsDCP9cLCwjhw4AAABQUFaLVaAgICGpSpX7+goIDQ0NAG+w8NDXWXOZrFYsFy6K8AcCVlSZIubMIpqKmwHNEM6Kq96j+mDcYQV0+arO0lrP9hX4N1NXoV/qHe2G2HmwFT+0eQ0i+iRfM+ndTxC4GjrKzxUJWdja2gAJxNz4gOoAoIQBMdjToyEsIjcIRHYAkJwxQUTq1/EDV2qLXYXTerg1qLnZosO3UZ2dRY9x9+zOKg1nr4/yZb49NQtJwdpaYGg1cteq8atLoaVNpqFOoqVFTj63DiZ1Xgb9MTYgs4FKgCCK6LIcQWQICjeTUdTqXA7gMKoxq1rx6tjx6trxcagw6lQY3SW+O61f9f23jtjs1qoaIgn/L8XPL2plFRkEf/m27HW+8PwPaVv7Dhh2/c5QUgVGqE3oDWP5C1a9dhV6qorKzEbnVwxxvvYQwNR63V8uGHH3IgLw+obrBfq9WKEMIdgrt3747JZPIIUr6+vuj1+gZB+cg+09KpdVZDl9PppHv37kybNg2ALl26kJ6ezvvvv89tt93mLnf0G+LIN1JTji7TWPljbWf69Om8+OKLzT4XSZLOX5t/OciudflUFptw2BqGktR+Ee7QFdbaj4QuIRjra6zCXLVXXr4N+xPpvE9P2LKXlWFOT8e6P8vdWd2anY0tJwfRRNNTPYdGS01AKFX+IZQZgyn2CabIEEiOVyDZen/KnRpqLHYsdieU47rtNANZh24nR6NS4KvX4KtX46NT46tX46vX4KNTotHWotJUI1SV2BWVWKnALMqpsZdislSiqLGiMyldYcrmT7A1gODaQILt8S0LVCqB01eJyk+HLtCAzt8blVGLyqhz3fx1rpqoZs4h5bDbcDpAeahZbff6NWz55UfK8vOoKS3xKCuAmG698A4Np6KigkqllojkFALCIwhoFcE/B3IprnDNf1YHrN24yb2uSqUiICLKXeMUHx+P0WjE39+/wU8vL89BEV27dm3WuUin11kNXa1atSIlJcVjWbt27Vi4cCEA4eHhgKumqlWrVu4yRUVF7tqv8HBX+3V5eblHbVdRURF9+vRxlyksPKKD6iHFxcUNatHqTZ06lUcffdR9v6qqiujo6BM5TUmSzjEOmxOHw+m+lIl/mBdlea7RdUqlAr8Qr8PNgWHeBEUdnoAzul0g0e0Cz9yxVlVh3rED07btlG3einl7GpqShp9n9ZwoKPXyo8A7kALvIAoMR/4MpFzvi1A00rneBtgE4NlvS6tSYtCpMOjUGLTqo/6vxqf+vk6NQXv4/95aFTqNA6eiDpQm7NRhE7VUWcspNmVTbCqmqK6I4rpiDtaUQrWdQJvxcKA6VEMVYo8m2Obf7EAlVICfCo3RC02AF2qjHpW/FpXfiQUq9/PqcFBVXER5fi7lBXmU5+dRUZBHeX4uVcXFjHlhOlFtU7Hb7ZQUF3EwbZt7XUerWIRfAA61BptTsOCnpe7HVCoVz7zwqjtIHfjqK4orKvH19W00TB3p0ksvbdE5SGffWQ1dffv2JSMjw2NZZmYmsbGxAMTFxREeHs7y5cvp0qUL4Koy/f3333nttdcA6NatGxqNhuXLlzNmjGs4T35+PmlpacyYMQOA3r17U1lZyd9//80ll1wCwPr166msrHQHs6PpdDp0umNfgkGSpPNLXZWVtNW5pK3Opf2ASC4Z5Zr7KrZDMKMe7IR/qBe+gfoGl5A5U5x1dZh37sS0fTvVW7dTs3UbqjzPizTX153l+IRwwDeMAkMQ+d6BFBiCKPAOpNIvCJ23F946FQatqzbJoFPTWqci9VBQqg9OPkcHqSPu++jU6DVKrKKOKksVVdYqqq3Vh38eWlZlraLEWsV+azVVlVVUWQ6XszltKIWSILuRUFsgIbYAd5Nfki2Avva4lgUqNSj9tGj8vVC7a6UOBSp/1/0TCVTgClU1ZaVUlRRRXVJMVUkxbXr2cfd12rbiZ1Z88p67vMPLgEPnjdBocbZqzbc/LcP641JqampQqVRcM3EygRGR+IdHsHjpMnbu3AmHLo6tVCoxGo3uIGW329FqXf3/rrzySq699lrU6rM+o5N0GpzVV/WRRx6hT58+TJs2jTFjxvD333/z4Ycf8uGHHwKuJsHJkyczbdo02rRpQ5s2bZg2bRre3t7cfPPNABiNRu6++24ee+wxgoKCCAwMZMqUKXTo0MH9V0C7du0YMWIE9957Lx988AHgmjJi1KhRshO9JF0ESnKq2boyh8y/C3DaXV98WdtK6DGyNQqFAqVS4b6G3JnitFiwZGRg2r4d0/Y0qrZuR2TtR3HEbKX1vYQKvAPY7R/N3sAYbInJBHftSGpSJN0Cvd2hqr52SXNUYLQ6rO5w5ApEZR4BqshSRXVVtUdYqr/VWGsQHLvjut6pI9QWQKgtkEhbIF1srd0BK9QWSLDdH1Vz5pNSK1AZdUeEKZ2rye8UBCoAS10d1SVFGAIC8fJ1hbyDaVv566u5VJcUU1NWijj03AuVCofWi2KzDW1QCGVlZZQWFqLSaAkIb4V/eAQ5aCiuqXNvv6z6cL8qhUJBXI/eGAyugRI9evSgXbt27horX1/fRjulAw2aBaULy1kNXT169OC7775j6tSpvPTSS8TFxfHWW29xyy23uMs88cQTmEwmJk2a5J4c9ZdffnHP0QXw3//+F7VazZgxY9yTo86aNctj2OrcuXN56KGH3KMcR48ezTvvvHPmTlaSpDMua3sJW349SG5GhXtZaGs/Og+NJr5ryBm77puw2bDs2YMpLQ3z9jSqt23Hvmc3Crvdo5wCKNX7kekfzW7/KCpiEvHt2IF27WLoEe3P7RF+aFVKsquz2VG6ne3VRVSVNl0LVW2txuxo5JIvzaQQCgLsfkQ5w4kVkUQ4QglzBBFiDcDf4oOfyRudrRn91pQKVP461P5HBar6gHWSgepIFQX5ZK7/i6qSYqpLig79LMZS52o+HjHpEVIHDsXpdFJZWUVuxg73VLLWsGhs/sE4D81L9U/GbmC3e9uPvTcL30NTDqxdu5asrCx3bdWRTYDe3t4e5xIfH3/S5yVdGM7qPF3nk3Npng9Jkppn1eyd7PgrH4VSQULXEDoNiW7R5WtOhHA4sO7f7w5YddvTMO/aiaKROa4qtd6HAlY02aGt0bVPoU27eDpH+9M5xp9gHx2FtYWklaaRXpJOWkka6aXpVFlbNppagQIfrQ9+Wj/3zVfrS4DanzB7MME2I4EWP/xM3njX6dDXqlBXg6LaCc2YokGhV7sCVYDucLjy17v+H6BD6aNFcYxLCh2P3WqlsqjAHaCqjghTVSXF9Bs7jnb9BgGQtWUjC6c/715XKBQ4tXqcWj1KXyOBCcnYVGpKS0ux2+1cM6g/oZFR+AaH8NffG1i7di3gakUJDg4mODiYwMBA/P39iYuLczcDSuePc+n7WzYaS5J0QagsNrF9VQ7JvcIJiXHVhHccGo3eR0P7gVH4Bp7cxJyNEUJgO3jQFbDS0l1Nhek7wFTnUU4B1Kr17PaPYndAFHsCorEltqV1agJdYgIZF+NPQogP1dZK0krTSCtZxQ/r00kvTafYVNxgvxqlhraBbYnyjfIIUn46V5jy0/rhp/HF12HAYNKhrVHhrLDiqLDgqDBjr7DgqLDgrLE12LbLEdM7KDjcxHcoRKkOhSr1oWVK/Yl/lQink9rKCqqKi6guLXaHqcQevYlp3xGAnB3bPYLU0SoLC6irq6OkpISciiqS+g4iIDQMv+AQduYVsnPffnfZupLDF/9WqVQEt2lLq8hIwNX60qFDB4KCgmSfXum0kKFLkqTzlhCCvN0VbF2Rzf5tJSDAXGfj0jtco6KDInzofc2pmbxRCIG9oADT9u2Y09Ixp22nbnsaorrhHElmlYa9xkgyA1y1WCVRCbRKSaRzbCDDogN4LMoISgs7SneQXrKCD3elkfZnGrk1uQ22pVKoSPBPoH1we1KDUkkNTiXJPwk1KhyV1sMhqtCCo9KCvdx8KFyVI2yl1ALHuuqhQqM8VEOlP6K2Su/qWxWgQ+WnRXESAwucDgfVpcVUFhXiGxxCQHgEAAV7MvnxfzOoLinB6bA3WM/bz+gOXb7BoegMBvyCQvANDkFpDMSk0mBFQZ3Vzu97DrDs0MApgLvvvts92tz6zz/sz8snODiYkJAQd+1VcHAw/v7+Ht1QAgPP3KhU6eIkQ5ckSecdh83J7n8K2boym5LsGvfymJRAki5pfBqYlrKXlLgDlinN9dNRWtqgnE2pYp9fxKGAFcX+4FgC2rahY2wQXWL8uS3an2BfJZnlmaSV/M2P+em8uj2N/ZX7G+2kHusXS2pQKu2D29M+uD1tA9vipXZ1rraXmjBnllOZkYllbwWikTnFjqb01XjUSrma/w43/Sm8Tk1fKoCaslLSVi2nsriQyiLXrbq0GHFoQta+Y26l13VjAdDovagsdE1OrVAo8QkMwi8kBN+gEAyBQejDI0hLS6O4uJiSkhLGvPq2e5Lr9evXs3Tp0gb7r28SPPJ8unbtSrdu3c5Y/z1JOhYZuiRJOu8sfH0jxQddNUxqjZLkXuF0HBxNYIThOGs2zlFZ6e6DZU5Pw7Q9DXsjV6twKJRk+Ya7A9bugGhE63g6xoXQOdqfETH+JIZ6k12zn/TSdDaVpPHFn2nsrtiN3dmwNifcEO4OWKlBqaQEpWDUHe5zJmwOLHsrqcjIw5xZjr3kqIlP1Qp3gFIZG2n6M+pQnOQFr4UQmGuqqSwqpOqIMFUfrDoMuYweV14LgKWulr++mtNgGyqNBr+QMNRHNNkZw8IZ8/x0jKFh+AQEcTA7mz///JPdJSVU7DkIew56bCMpKckduiIiIkhJSXHXWIWEhBAUFNRof6umRglK0tkgQ5ckSee8kpwaAlt5u+fPSugaQl2VlQ6DIkntF4nep+UzvwshMG3eQvncuVT9/DMcNZLQiYIc3xB3R/fMgCiKQmNIjQulS7Q/Y2IC6BDlR42j4FBH9595fVsau8p2NTpiMEAXQGrwoRqsoPakBqcS7BXc4JhsJSbMGWWYM8qx7KsE+xG1WUoF2lhf9MmB6JMC0IQbTqqDej2b2UxlUYE7SAVGRtO6o2tuxLLcHGY9NrHJdctyD88j5hcSSuqgSzGGhmEMDccYEoYxNAyDfwBV1dVkZmayaNEid+3VyJEjiT50XUGbzcaePXvc29Lr9R7NgREREe7HoqOj5WTV0nlJhi5Jks5JTqcga1sJ21Zmk5tZwfB725PYzfUF3XFINJ2HxaA6gb5GTrOZqp+WUD53LuYdO9zL872DyAyIJvNQDdb+gChiokLoEuNP/2h/Hoz2x+Bdw46ydNJK1jL3YBo7Nu2g2tawT5dBYyAlKMUdrtoHtyfCENFoE5fT6sCytwJzZjnmjHIcZZ6BTWXUok8KRJ8cgC7R/4Q6rTvsNuxWKzpvV01gXVUlKz+d6Q5ZpqpKj/LtB1/mDl1+ISGgUGAw+uMXGuYOUn6HfgZGHr5QskanZ8TEye771dXV/LNxI7t27Wr0OrfFxYcHCURERDBq1Ch3yDIYDLJJULrgyNAlSdI5xWqys3NNPttWZVNV4gogCqWC8oLD3cE1TVxc+FhsubmUz59Pxdff4KiocO1Lqea3qC4siu9LdXQCXWMC6Bzjz43R/kQFO9hbtYv0kj/5rTSNd/emU2pu2KdLq9TSNqgt7YNcfbBSg1Np7dcaZWOX2uFQh/ziQ7VZmYdqs46clkGlQNfaz12bpQ7zblb4cNht5GdmHApSBR7NgDVlpXQYchmX3fcgAGqtloy1f3isrzMYMIaEYwwNo1Viknu5RqfnoS++QaM9/mg+u92OyWRyz6NosVj47bff3I9HR0cTFxfnrsEKCjo8Ia3BYKB79+7H3Ycknc9k6JIk6ZzgcDhZu3AvO9bkYTO7pizQeatJ7R9xwlM+CCGoW7+e8rlzqV6xEg516C7yCuDHuN78HNuTpKQonhgUib9/AemlW0krSeO7Tenk1eY12J5KoSLRP9EdrtoHtScxIBGN8tjNm06LHcueSsyZrmZDR4XFc7v+OvTJAeiTA9ElGFHqGv9oFkJQVVxEXuZO8jJ3EdAqgq6Xj3Y9f3Y7C158qsljOPLCy1q9F0PumoBPQKC7xkpv8Gly3WMFLpPJxJ49e9i1axd79uwhPj6eG2+8EYDg4GB69OhBREQESUlJ7hnaJeliJUOXJEnnBJVKScH+SmxmBwHh3nQcEk1yz3A0upbXajlra6lctIiyuXOx7tnrXr4lJJHFcX1ZF55CrzahvNrXn63V3/H0P99gdTacvLS1X2v3KMLUoFSSA5PdIwmPRQiBvbAOc0Y55owyLAeqGtZmxRvdzYbqEK9Ga7OE00nBvt3kZewiL2MHeZk7qSkvcz8e27GLO3Rp9V6EJyah9fJ29akKCfNoDvQ2+ntsu8vwUcc9j6ZUVFSQkZHBrl27OHDgAE7n4X5nBQUFOJ1Odwf2kSNHnvB+JOlCI0OXJElnXP2UDzv+zOOKSR3RG1w1Rb2vScBucxLTLvCEOohbs7Io//JLKr79Dueh+bOsGh2/RHVlcVxfDvqFMyg5hA/7GtlQ/i3PbjwctsIN4XQI7uAeTZgSlIKv1vdYu/PgNNsx765whazMchxVniFOFaRHn3SoNiveiLKRJlJTTTVVxUWExSW4l307/QXMNYf7jSlVKkLjEohIakdUu1SP9W/595vNPt6WEEJ4hMJvvvmGnJzDHehDQkJITk4mOTmZyMhIOWJQkpogQ5ckSWdMXZWVtNW5pK3OxXQolOz4M4+uw2MBiEwKaPE2hdNJ7R9/UDZ3LrWrD/dTKg8IY0FUL36N7k6t1othKWG81MfImtKveXL9QnfY6hralYmdJ9IzvGeLOm4LIbDl17prs6wHq+DIabPUSvQJRvRJAeiSA9EEezVYvzw/l9yMHe6arLK8HHyDQ7jv3c8AUCiVxHXpjqW2hoikdkQmpxCWkIhGd+pn1z+a3W4nKyvL3Ww4fvx498WY27Vrh0qlcgetI/tmSZLUNBm6JEk67Upyqtm6MofMvwtw2l3NbAZ/HR0GRZLSN+I4azfOUVVF5XffUTZvHrYDh+d02hPfiVlhPdgUmgRKJVd0aMVNvf34vXABj65diM3puvRN19CuTOo8iUvCL2l22HLW2TDvqXAFrcwynNWel9FRB3sd7psV54dC03jT6G9ffEz66pWYqxteQ1Gj1WGpq0Pn7Q3AFQ881qxjOxVMJhO7d+92By3rEdeL3L17Nx07umaI79OnD3379j1jxyVJFwoZuiRJOq3MNTa+nv4PzkN9msLi/Og0JJr4riEnNOWDOTOT8nnzqFy0GFHnusah0+DD32378GFAV/J9glEq4OrOkdzYy4df8+fz0J/feoSt+zvfT4/wHscNW8IpsOXVHApZ5a7arCO6Zik0SnQJ/q6glRSAOuhwbVZNWemhDu87yd+dyfXPvuLukO50ODBXV6HWaAlPTCIiqS0RySlEJLXFy/fsXJB3165dLFiwACEOn6CPj4+7NisuLs69XE7lIEknRoYuSZJOKavJTvbOMhK6uubU0vtoSOoRht3upNOQaMLjjcfZQkPCbqd65UrK586jbv1693J7TBw/JfZjlndbzGodaqWCG7tGcX1PH37Oncek1YfDVrewbkzqNOm4YctRa8Oyu9wdtJy1R9VmhXof6psVgC7OiELtCo4VBflk/bzS1VyYuYuq4kKP9Qr37CYqpT0AnYePpF3/QYS2jkelbvnErifD6XSSn59PRkYGYWFhpKa6+oVFREQghCA0NNQdtCIiImT/LEk6hWTokiTplNmzsYhVc3ZhNdkZ+9wlBEW4piEYcnu7E6odsZeXU/H1N5TP/xJ7Xr5roVJJ3SX9mBN+Cd+JMFAo0KqUjOsRzTU9vPnp4Fzu++1b92V3uod1Z1JnV9hqijWnGtNO17xZtpxqz9osrQpd4hG1WQF6LHW15O/OILQ23j0qcO/G9fz2xceH11MoCY5t7eqLldSWoOgY92OBEYcnFD0T7HY7+/fvZ9euXWRmZlJ9aJBBfHy8O3T5+fnxyCOPYDS2PBRLktQ8MnRJknTSnE7B34v2sXHZAQACwr0x1xyuIWpp4DKlpbsuz/PTT4hD/YpUAQGUDLyct306sLbGVTuk1yq5+ZJYru7hxff7Z3P3yu/cYatHeA8mdprYZNgSToF5VxnVv+dgPeDZt0oT7o3u0OSk2hhfqsqKOZixg7yFO8nL2Elx9gEQghGTHiF14FAAotq1J7ZjF3eH9/DEJHe/rLNFCMHChQvJzMz06J+l1WpJTEykXbt2HuVl4JKk00uGLkmSToqlzsYvn+zgYLprtvbOw2LofXW8+zqJzSWsVqp+/oXyuXMxbdniXq5LTSWr/0hmWKPZVWaBGjBoVYzr3ZpRXfUs3Pc5ty//3h22Lgm/hAmdJjQdtuxO6jYXUb06B3vxoQtIqxR4tQ1E39YVtFRGV9+r/D0ZfH//A9RVVjTYjjEsHKfT4b4fFp/I9c+83KJzPtXKysrIzs6mU6dOgCvs1tXVYbVa8fX1dTcbtm7dGo3mzDZrSpIkQ5ckSSehLK+WJTO3UVlkQqVRMmRcW5IuCW/RNmyFRVR89RXlXy3AUXxo1nSNBp/LLmNL98t4I1tNVoEJsOCrV3Nnn9aM6Kzjqz2zGPfzD9iFK2z1DO/JhE4T6B7e+KVknGY7NevyqfkrD2e1q9ZHoVfhfUkY5f6l7Nu/nrwfdxLXpQc9r74BAGNIGHWVFShVasLiE4hITiEyqR2tktriExB4Yk/aKeR0OsnLy2PXrl1kZGS4r2UYFxeHn5+rQ/7gwYMZOnQorVq1kv2zJOksk6FLkqQTlre7nMoiEz6BOq6Y0JGQmOZNJiqEwLR5M+Vz5lL1yy9gdwUndUgIvmPG8Fubvry9pZzcrSbARoC3hrv7xXFpRw3zM2dxy7IjwlarnkzsNJFuYd0a3Zej0kL1X3nUrs9HWFw1U0o/LSJZQ0bpBnZ++47H1A0qjdYduryN/tz87zcIiYlDrdWe6NN0yuXm5rJx40YyMzOpqalxL1coFLRu3RqTyeQOXdHR0WfrMCVJOooMXZIknbDUAZHYbU6Se4bj5Xv8UOI0m6n6aQllc+dg2bHTvdyra1d8xt7EYr8kZq45SOFvruseBvvouG9AHINSVczN+Iyblixyh61erXoxsdNEuoZ1bXRftsJaqlfnUrelyH0JHnWYNz79I/huwXSKvtrnLmvwDyC+aw9XTVbbFI/ttEpMbtmTchrU1NSgVCrxPtRHrLS0lE2bNgGu/llt2rQhOTmZNm3auCcwlSTp3CNDlyRJzWY12Vn3/V56XhWPzluDQqGg86Uxx13PlptL+fz5VHz9DY6KCgAUOh1+o0aiv2EsX1V68/Ef+yip2Q1AuJ+e8QPj6Z+i5IsdnzDmp0U4hKuWqner3kzsPJEuoV0a7EcIgTWriurfczDvOnyNQkcQhF6Zij4pAIVSgf8fEZQV5tKmR29S+g8mpkNnlKqWX+PxdBFCUFxcTEZGBhkZGeTk5HDppZfSr18/ABITE+nRo4e7f5ZaLT/KJel8oBBHzoQnNamqqgqj0UhlZaW72l6SLiblBbUsnbmd8oI64juHcPmEDscsL4Sgbv16yubMoWblKjh0UWRNRAQBN9+EctRVzEmv4JO/9lNR5xrpGBXgxcRBCfRKEsza8QmL9y52h60+EX2Y2GkinUM7N9yXU2DeUUr16hysB13TIQigUlfCpoO/UFyXzR1vvk9QpKupraqkGL3BgNbr7I4uPJLT6eTAgQPuoFVeXu7xeJcuXbjqqqvO0tFJ0vnrXPr+ln8eSZJ0XFnbSlj+aTpWswODv46uI2KbLOusraVy0SLK5s7Fumeve7l3714E3north59+GzdQWbN3ES12dVUGBdsYNKgBLomOvgs7SNeX/yjO2z1jejLhE4TGg9bNie1mwupWZ2LvcQ1EtGpcHKwbifpJX9RY3cFl+DoWExVlXAodPkFh5yS5+VkORwOVIdq2BwOB/PmzcNmcwVQlUpFfHw8ycnJJCUlnfUvC0mSTp4MXZIkNUk4BRuXZbF+8X4Q0CrRyIj7OuDt13j/reqVq8h76imcVa6O6Qpvb/yvvoqAm2+mKiyad/7Yx+z//E6d1RWo2oT68MCQRDq0tvFJ2kz+veinw2Ersi8TO02kU0inBvtxmuzUrM+n5q9c9/UPhQZ2Fq9hd9UmzI5aDP4BdOt7NSkDhhASG3fOXLqmvLzcXZtVV1fHxIkTAdBoNHTs2BGHw0FycjLx8fHodLqzfLSSJJ1KMnRJktQoq9nOis93sm+zaxqC9gMj6XdDG1TqhtMOCCEonz2bwumvghBoY2MJuOUWjNdcTbFTw6ur9/Ll5ysx21xNjO1a+fHQkETaRlv4OO09nlv0I07heqxfZD8mdppIx5CODfZjr7BQ82cutX/nI6yu8iqjFp9+Uei6BLHkiU+I693jnOqnVT+tQ33QKioq8ni8vLycgIAAAK688sqzcYiSJJ0hMnRJktQoh81J0YEqlGoFA8cmk9IvotFywm6ncNp0yufNA8B/zBjCn/0XuTU23vh1L1//k4PV4QpInaKMPDikDfERtXy0/X88tXmJO2z1j+zPxE4T6RDSsK+YraCWqt+yqdtajOJQL9QKazEHRQYjHn8CpdoVru59+5NzImgd6eeff2b9EdeLVCgUxMTEuCcqrQ9ckiRd+GTokiSpUV6+Wq6Y0BGH3dnkRaodNbXkPvYotb+vBiD08cepGj2GJ3/YwbebcrE7XQmpR+sAHhzShqjQaj7c/l+W/rPUHbYGRA1gYqeJtA9u77FtIQTW/VWULMtAHLQAoAAKTQfYVfk3tiA7KQOHIHACrqB1NgNXdXU1u3fvJiMjg4EDBxIR4QqpcXFxbN682T2tQ2JionvqB0mSLi5y9GIznUujHyTpdBBCsOnnA3j76WjXp9Vxy9sKCsieMBHLrl0odDpazXiN73ySePmnnTgOha2+iUE8OKQNoUGVfLD1A5ZlLXOHrYFRA5nYaSKpwamex+EUmNJLqVmdgzW72n1sOXUZZNl30Kp3Cu36DyG0dfxZ7aclhKCoqMjdbJibm+t+bMCAAQwZMgRwdZAXQshpHSTpLDmXvr/lp4AkSdgsDlZ+sZM9G4tQqhVEtPHHGNL0JJum9HRyJk7CXlSEKiiIVu+8w/QsFfNW7QCgf5tgJl+aRICxnJnbXmPZH8sQuILYoKhBTOg8gdQgz7BlrqrmwPd/o9xpxUv4uBaqlRCvZlfFehKH9KVnh7vOiebD8vJyPv/8cyoOzTlWLyIiguTkZFJSDk+wqjoHjleSpHODDF2SdJGrLDaxdOY2SnNrUaoU9B+TdMzAVb1yFbmPPYYwmdAmJmB8623Grypkzd5SFAp4akRbhnYUfLhtOsuyjghb0YOY0MkzbDnsdrI2bKR0xW4CK4MxqAyAFofSgf+g1vj0iUDloyWKnqf7aWiSyWRiz5492O12unRxTcjq5+eHxWKR0zpIktQiMnRJ0kUse0cZP3+chqXOjpeflsvva0+rRP9Gyx49QtHQpw/WZ//N9d/sJKu0DoNWxcvXtWZd5Qe8u+hw2BocPZgJnSaQEnS49sdSV8f6uV8i0kzE6NoRoYwFFZhELZbWDqKv6o4xIuxMPAWNOnJahwMHDuB0OvHz86Nz584oFApUKhXjxo0jODgY7Tl0TUZJks5tMnRJ0kVq8/KDrP12D0JAaGs/Lh/fAZ+AxueFEnY7hdNfpXzuXAD8b7iBPTdPZNLnW6k224n09+KJq/T8L+1+iupcUyIMiR7ChE4TaBfUDgCr2YRW74WtoJaa37JpvScRpZdr+gmLzox3n3AShvZxj0Q8G9atW8emTZsaTOsQEhJCcnIydrsdjUYD4O4oL0mS1FwydEnSRcpmtiMEtO3TioE3JaHWNB52Go5QnMJP7Ybw4uzNOJyCrrH+XNFnPy/88wZ2p504Yxyv9n+VlKAULHV1bF/1Czt/X4WiRDCo6y1YMisAUCqUOEOVBF+RjFdy0FnpFO90OlEqD887Vl5eTlFREQqFgtjYWHezYVBQ0Bk/NkmSLjxy9GIznUujHyTpVBBOQVZaKa07NB14jh6hGPbqq7xpiWL2ugMAXNU5GK+I7/lp/48ADIsdxgs9n6dkZyY7Vq9k7z9/E65pTVv/SwjSHaoZUoBXh2B8B0ShjfI9I+d6NKfTyfbt2/ntt9+49tpriY52XR6ooKCAoqIi2rRpg5dX0/3aJEk6f5xL39+ypkuSLhI5GeVs/vkAl0/ogFqrQqFUENcxuMnyR49QDPjv/3hgm5W/9hxAoYDxQ/z5x/Q6GfszUCqUPNL1Efqb2zFv8gOYK6tp7dOey8Jux1dzaPJPtQJD93B8+0eiDjo7gUYIQUZGBitXrnQ3Ia5du9YdusLDwwkPDz8rxyZJ0oVPhi5JusAJIdi2Moe/Fu5xXUvx5wP0vDL+mOscPUKRaW9y4y957CupxVurYvxwCwsOPk61tZpAfSCvD3idS1pdQk5aGjG0JTmmBzqlK1gpvNT49InAp3crVD5nr9P5vn37WLFihXs+Lb1eT9++fenZ8+yNjJQk6eIiQ5ckXcDsVge/zcsgY10BAEk9w+g2PLbJ8o2NUMx5+FkmfJtBldlOK6OWEf3S+GTPZwgEHYM78lLHZ0holYK9zIxmlY2OAQMAUAXo8O0fhXf3MJTasztX1XfffcfWrVsB14Wle/XqRZ8+fWQToiRJZ5QMXZJ0gaouM7Psg+0UHahGoVTQ97pEOg6JarL/VmMjFFcMv53nF+zA4RR0jNEQGr+Qb/b9BcCNra+j2w4jP85/lpsnTMe6vBRhtqP0VmO8MgHvjiEoVGdvxvgjxcTEsH37drp3786AAQPw8fE524ckSdJFSIYuSboAFe6v4qf3tmKqtqE3aLjs3lSi2wY2Wf7oEYrBjz3G2yE9+eLHXQAM7WQjR/M26wtz0al0PBZyF9VfbWBnSQntA/pjWVwIgDbal8Bb2qH2b3zqiTOhvLyc3377jfj4eDp16gRA586dSUhIwN/f/6wdlyRJkgxdknQB8vLTIAQERflwxYQO+AU33Yx29AhF/1em8WhREH+sOwjA6L65rKn8EIvVQrQ+krvKBnJg8RJ0Sm+GxowjSOW6TqNPnwiMV8ShUCub3NfpVF1dzerVq9m4cSNOp5MDBw7QoUMHlEolKpVKBi5Jks46Gbok6QIhnAKF0tWc5xfkxVWTO2MM8Uaja7o/1dEjFFXT3uDmf0zsKy7BSyPo33sNq4oXA3Cp+hJS/oIDhX8RrItkQPSNaBwaFFolAde1wbtT6Bk5zwbnYDLx119/sW7dOux2OwDx8fEMHTrUYw4uSZKks02GLkm6ANSUW/j5o+10HR5LXKcQAIKPMwfW0SMUi6dOZ/yv+VSabIQFmIlI+oq1xTtQoGBip4l02mNkbeE8OrQaRDuvnigcoA71IujWFDSh3mfiNBvYtm0bS5YswWw2AxAVFcXQoUOJi4s7K8cjSZJ0LDJ0SdJ5Ln9PBcs+TKOuysofC3YTkxqE6jhNfGVfzKZw+nT3CMW/xj3Gsz9lYXcKklsXUuc/iz1V5fipfZk+8FUGRA3AlmgmeE8Q3hXeIMCrUwgB17ZBeYyatNPNz88Ps9lMaGgoQ4YMITk5+azMbC9JktQcMnRJ0nks/Y9cVs/PxOkQBEYYuGJih2MGrqNHKPpdfz0fd7uez37eDwg6t9/CfufXCJOToXkJdKyKpO+NvbEV1FI6Z6crcKkU+I+Kx9Cr1RkNOE6nk23btmE2m+nVqxcArVu3Zty4ccTFxcmmREmSznkydEnSechhd/LHgkzS/8gDIKFrCENua4dW3/Sv9NEjFP0mP8JT6o78sT4HlBY6dl7KXtM6jNUarsxIQl1kopwD7PtmDd7paoTNicqoI/CWtuhiztylNIQQ7Ny5k5UrV1JSUoJGoyE1NRVfX1fzaUJCwhk7FkmSpJMhQ5cknWfsVgc/vLWFgn2VoIBeV8XTdXjsMWudjh6hqHn2JW474Mfe4lK8vEuISFpAVl02HbL86bY7AOwmvA1Ghncfj3aLAoETXVIAgTcmozJozsh5CiHcs8jn5bnCpV6vp1+/fuh0Z29KCkmSpBMlQ5cknWfUWhUh0T6U5ddy2d2pxLYPOmb5o0colj/9b8ZvtFBRV0tw6C4UoQswldm4Mi2KwFIV4CS5Y1+6eV+K46AZFOA3NAbfITHu0ZGnW3FxMT/99BNZWVmAaxb53r1706dPH/R6/Rk5BkmSpFNNhi5JOk84bE5UGle/pb5j2tB5WMwx59+ChiMUt0x8jif/KsHutBOV8BuV2uXggJF72uBbakWj92LY5ffhl+GDo9CM0ltN4Ni26JMCzsAZHqbRaMjOzkalUtGjRw/69esnZ5GXJOm8J0OXJJ3jHA4nf321m7KCOkY/1AmlSolKpTxu4DpyhKJX7958eflEPvijCIWqmuiUb6kQOwG4M/VObr9sDKs//4S+Sddj/bsMgf3Q7PJtUfuf/pqlsrIyMjMz3R3k/f39ueaaa4iKipKTmkqSdMGQoUuSzmF1VVZ+/iiNvN0VAORklBOTcuzmxKNHKHpfex0vxF3Bqk1FKPUHCU2YT9BBC0nmYG68eyqXtb4MR42VfkHXYPm7DABD71b4j4w/7bPLV1VVsXr1ajZt2oTT6SQ6OprIyEgA2rdvf1r3LUmSdKbJ0CVJ56jygloW/d8WasotaPQqht2ZctzAdfQIRc2kh7jb1o49e8vwDlqPn/8Sem4wElMYDEB7awyWrEpK5+3CWWU9Y7PL19XV8ddff7F+/Xr3LPIJCQloNGemk74kSdLZIEOXJJ2DbBYHSz9Io6bcgn+YN1dM7EBAuOHY6xw1QrHmsWe5J8uXClM5AbGLiaxMp/fqUPRWFUqVij7X34JPgYHiZdvBKc7I7PJWq5W1a9eyZs0aLBYLANHR0QwdOpTWrVuftv1KkiSdC2TokqRzjBCC3+dlUJ5fi7dRyzWPdcXbT3vMdY4eobjrged4dLsdh6qA8NZf0nWXmYQ81+WBQmLjGHHvZFTrLVQtyQLO7OzyGzZswGKxEBYWxpAhQ0hKSpKzyEuSdFGQoUuSzjHVpWb2bSlGoVQw/J7U4wau6pWryJ0yBVFXhzYhge9vnMLbW2tRGXbhH/0Vl/5hJKDGAAoFPa8eQ/d+o6mYvxtrick1u/zIeAy9T8/s8g6Hg507d5KSkoJSqUSr1TJixAiEEKSmpspZ5CVJuqgohBDibB/E+aCqqgqj0UhlZSV+fmduNm7p4lRRWEf+3kra9Wl1zHJlX8ym8NVXwelE17MXM3rdxrIDNWiDV6ALWQkIBlW1pcNuP6588An8qv2p+G7PaZ9d3ul0snPnTlatWkVJSQnXXnstHTt2POX7kSRJOp5z6ftb1nRJ0jnIP8wb/7Cm+1YdPUJRfeXVTAwbRkZ2CQl+X6JSZJOP4MbkG3m8++MorVDz80HK12cCoGvjT+DYtqdldvk9e/awYsUK8vPzAfDy8sLpdJ7y/UiSJJ1vZOiSpHOAEILV8zNJ6BpKVPKxJyI9eoSi5a5J3GJOproyk6G2b0nZqsWkCyb18Tu5psMY7GVmSufuxJZbAwrwHRKD39BTP7u83W5n2bJl/PPPPwBotVp69+5N79695SzykiRJyNAlSeeEbatySPs9l51r8rnt332a7Md19AjFrPFP8GCuPyGKXxmVtxVjrWu9pB59GJk4CtOuMsoWZCBM9tM+u/x3331Heno6AD179mTAgAEYDMcecSlJknQxkaFLks6ygv2VrFm4B4De1yQ0GbiOHqG44tbHef2Agz7Wz+mUW4MSDXZvFSMnPkpq9wFULT9A9apsgDMyu3zfvn05cOAAo0ePJikp6bTtR5Ik6XwlQ5cknUXmGhs/f5SG0yFI6BpKx8FRjZY7coSiOj6B9y6bxE/ZxYwt/oqgGgWgQJUawcRHXken8KLk0zQseyqA0ze7vBCCkpISQkJcU1FERETw8MMPywlOJUmSmiBDlySdJcIp+HXWDmrKLBhDvBgyrm2j0zYcOUJR1aMnj6eOZXtVJl4J8yg36/GxepNy0zVcfcW9WA5UUTR3M4762eWvbYN351M/u7zVauXHH38kPT2du+++m4iICAAZuCRJko5Bhi5JOks2/XKAA2mlqDRKRoxvj9bL89dROBwUTpvuHqFoG3Elk7w7U+r4Be+YX0EhKO8bxaM9XyQxMoXqP3OpXLL/8Ozyt7RDE3bq+1SVl5ezYMECCgoKUCgUFBQUuEOXJEmS1DQZuiTpLBBCUF5QB8CAsUkER/k2KFP0xpvuwJV/4928U2JixP555AfXsSJSMDphNP/q9S90dg1l83Zh2l4CnN7Z5ffs2cPChQsxmUx4e3tzww03EBcXd8r3I0mSdCGSoUuSzgKFQsHQ29uR3Cu80SkiqlesoOzTTwFYf/29bD2wnb6mYgBUTiVPd3mSsR1vwV5YR9GcLdhP8+zyQgj+/PNPVqxYAUBkZCRjxozBaDSe0v1IkiRdyM7qNTheeOEFFAqFxy08PNz9uBCCF154gYiICLy8vBg0aJB7SHo9i8XCgw8+SHBwMAaDgdGjR5OTk+NRpry8nHHjxmE0GjEajYwbN46KioozcYqS5MHpFNRfBEKhUBDdNrBBQLLm5JA39WkE8EfXXuTv/Z0oUzE2lZPtXa2Me/4/3NTpVuq2FFP0ritwqYw6QsZ3xKdPxGm5nE96ero7cHXt2pU77rhDBi5JkqQWOusXPktNTSU/P9992759u/uxGTNm8Oabb/LOO++wYcMGwsPDGTZsGNXV1e4ykydP5rvvvmP+/Pn8+eef1NTUMGrUKBwOh7vMzTffzJYtW1i2bBnLli1jy5YtjBs37oyepyQBbPhxP0ve34651tbo406rldxHHsVZVcWWxASqHcVonQ4KAszsvyaENx76kk4BHSn/bjflCzIQNie6Nv6EPtTltFzOp15KSgrt2rXjyiuvZPTo0bLDvCRJ0gk4682LarXao3arnhCCt956i2eeeYZrr70WgM8//5ywsDDmzZvH+PHjqays5JNPPmH27NlceumlAMyZM4fo6Gh+/fVXhg8fzs6dO1m2bBnr1q2jZ8+eAHz00Uf07t2bjIwMkpOTz9zJShe1g+ml/LM0CwTk7ConsVvDUYVFM17HvH07Jl8/9huV6O1ONiaV0+3Ka5ne/WGosFM0d+tpn10eXP23YmNj0Wg0KJVKxowZc1pq0SRJki4WZ72ma/fu3URERBAXF8fYsWPZt28fAPv376egoIDLLrvMXVan0zFw4EDWrFkDwMaNG7HZbB5lIiIiaN++vbvM2rVrMRqN7sAF0KtXL4xGo7tMYywWC1VVVR43STpRNeVmln+2AwSkDohsNHBVLVtG+Zw5ALzZoy+L+h9kfbsyxt7xJI9d8hi2zCoK396MLbcGpbea4DvbYxwWe8oDl9PpZMWKFcyZM4cff/zRozlUkiRJOnFntaarZ8+efPHFFyQlJVFYWMgrr7xCnz59SE9Pp6CgAICwsDCPdcLCwjhw4AAABQUFaLVaAgICGpSpX7+goIDQ0IZfcKGhoe4yjZk+fTovvvjiSZ2fJAE4HE5+/igdc42NkBhf+t2Q2KCMNSuL/Gf+BcAvnQezuds6lGoH7YeP4Mq4K6n8Ocs9u7wm2peg0zS7fF1dHQsXLmTv3r2A62LVQggZuCRJkk6Bsxq6Lr/8cvf/O3ToQO/evUlISODzzz+nV69eQMO/rpvzBXB0mcbKH287U6dO5dFHH3Xfr6qqIjo6+tgnJEmNWPfdXgr2VaL1UjP83vaoNZ5TOTjNZnIeeRRHbS1r2yXyc3I+SnU1sb7xPJYy+YzMLg+Qn5/PggULqKioQK1WM3r0aDp27HjK9yNJknSxOuvNi0cyGAx06NCB3bt3u/t5HV0bVVRU5K79Cg8Px2q1Ul5efswyhYWFDfZVXFzcoBbtSDqdDj8/P4+bJLXUvi3FbPnVVUM19PZ2GEO8GpQpnDYdy86d7IptRYVWMGC3BR+rjjfbT6Pq3Z1Y9lSg0CoJHJtMwFWJpyVwbdu2jU8++YSKigoCAgK45557ZOCSJEk6xc6p0GWxWNi5cyetWrUiLi6O8PBwli9f7n7carXy+++/06dPHwC6deuGRqPxKJOfn09aWpq7TO/evamsrOTvv/92l1m/fj2VlZXuMpJ0ungbtfgE6Oh0aTTxnUMaPF65eDEVX31Fvr8P+/29AVjToZSHL3kUvx/qcFRZUYd4EXp/59NyOR8Ak8nE0qVLsdvtJCYmcu+99zY6uEWSJEk6OQpR30v2LJgyZQpXXnklMTExFBUV8corr/D777+zfft2YmNjee2115g+fTqfffYZbdq0Ydq0afz2229kZGTg6+uawXvixIn8+OOPzJo1i8DAQKZMmUJpaSkbN25EpXI141x++eXk5eXxwQcfAHDfffcRGxvL4sWLm32sVVVVGI1GKisrZa2X1CLmWhsavQqVyvNvHMvevey/YQyVTjt/JscAgu3xlXgP6chLuRMx7ypH5a8j9MEuqAynd4qGffv2kZWVxaBBg1Aqz6m/xSRJkk7KufT9fVb7dOXk5HDTTTdRUlJCSEgIvXr1Yt26dcTGxgLwxBNPYDKZmDRpEuXl5fTs2ZNffvnFHbgA/vvf/6JWqxkzZgwmk4mhQ4cya9Ysd+ACmDt3Lg899JB7lOPo0aN55513zuzJSheVmnILPgE6APSNBCZnXR25kydjsZj5OyUeEOQGm9jfUcVXjgcx7yoGtZKgcSmnJXBlZ2djNptp06YNAPHx8cTHx5/y/UiSJEmHndWarvPJuZSUpXNb5t8FrJy9iwFjk0jp2/BC0EII8p+aSvkPP/B3UgxlXhqqvG382KeAj9q9R+hPThAQcEMShm5N9zs8EUIINm7cyJIlS9BoNNx3330EBQWd0n1IkiSdS86l7++zPjmqJF1IyvJrWTU3A4fNSXWpudEyld9+S+UPP4BSSXpAa4JtOazsVsx9be4l7FclQjgx9Gp1ygOXzWZjyZIlbN68GXDVbvn4+JzSfUiSJElNk6FLkk4Rm8XBzx+lYbc4iEz2p8eouAZlzBkZFLz0MgC/XHIlP12yCz9lLonhKVy1uTd2cx3aGF/8R53apr6Kigq++uor8vLyUCgUDBkyhH79+sn5tyRJks4gGbok6RQQQvD7lxmU5dXi7adl2F2pKI+aKd5RU0vuw5OpFQ4q2nZmZoodrWEvDqWe6VWPYi+sQ+mjIejWdqd0Woj9+/fz9ddfU1dXh5eXF9dddx2JiQ0naJUkSZJOLxm6JOkU2Lkmn4x1BSgUcNk9qRiMOo/HhRAUPPcc1TnZrEuO4YCPCh/jCqzA/3xeQrneBEoFQbe0Q+Wna3wnJ3psO3dSV1dHeHg4N954Y4MrOEiSJElnhgxdknSSKgrrWD0/E4CeV8UTmdQw1FQsWEDFkiVsSozErFaiU5YhVA7u9L2JmA2uflX+I+PQxRlP+fFddtll+Pj40Lt3bzSa0zv1hCRJktQ0OSGPJJ0kvxAvug6PpXXHYLpeFtvgcVN6OoX/nsbOyGDKDXqsKgUru+eT6NWGG9MHgxO8u4Ri6NNwpOOJKC0t5ccff8ThcACgVqsZMGCADFySJElnmazpkqSTpFQquGRUHMIpUBzdj6u6mtzJj3DQV8+BYCMCWN25ELO3kteKH0XU2tG0MuB/TeIp6dSemZnJt99+i9lsxmAwMHjw4JPepiRJknRqyNAlSScoZ1cZ4QlG9wWsjw5cQgjyn36GotIi0hIjAdicWE1OmIn3rS+jKXCi8FITNC4FpVbVYPst4XQ6+eOPP1i1ahUA0dHRdOvW7aS2KUmSJJ1aMnRJ0gko2F/J4re3Ehhh4KrJXRqddb589myqli9nW9sYhELB/mDBtjZl3OsYS+u9QaCAoJvaog7Un9SxmM1mvv32WzIzXf3KunfvzogRI1Cr5a+3JEnSuUR+KktSC5lrbfz8URpOh8AY7IXOu+GvkWnLFgpnvI4CqA3rzj7FHtZ03Ulnezuu3T8AAL/LYtE30um+JYqKipg/fz5lZWWoVCpGjRpFly5dTmqbkiRJ0ukhQ5cktYBwClbM2kFNmQVjiBeDb2vXoC+Wo6KCnEcfBbudwi59eS8iDK/Yxfg7DLyUfz84QJ8ShO/A6JM/HiGorq7GaDRy4403EhFxajrjS5IkSaeeDF2S1AKblx8ka3spKrWS4fe1R+fl+SsknE7ynprKflMNutho7o8eiD7yA1QoeKPsKTS1StTBXgSOSWrQB+xEhIWFcdNNNxEWFobBYDjp7UmSJEmnj5wyQpKaKW93Oet+2AdA/xvbEBLt26BM2aefcnDDOtKiQ9jgryMgYBFKTSUPV44jsjQQhVZF0Lh2KPUn9vdObW0tc+bM4eDBg+5l8fHxMnBJkiSdB2TokqRmEE7B719mIpyC5J7hpPRr2IxXt3EjB955m02x4QiFgj3+/lS22sWg6h5clt8TgIAbktCEnVhAys3N5cMPP2TPnj18//337nm4JEmSpPODbF6UpGZQKBWMvL8jfy/az8Cbkxv047KXlXHwkUfZGB2CVaOiWGdkbY8dxFpa8VjB7QD4DozCu0PwCe1/8+bN7glPAwMDGTt2LCrVyU0zIUmSJJ1ZMnRJUjP5BXlx6Z0pDZYLh4PcKY+zWQdV3nrMSh0ruxbjpVTx79yHUduV6BL98busdYv3abfbWbZsGf/88w8AycnJXHPNNej1JzfNhCRJknTmydAlSceQvbMMh91J62PUUJV88AHpmenkRQYjULA8yQ+z/25eznuAIJMfKn8dgTe1RaFqWcf52tpavvzyS3JycgAYPHgw/fv3R6mUvQIkSZLORzJ0SVITasrN/PJJOuYaG5dP6EB855AGZWrXraP47XeoinKFsj9bJVIa/ys3FV9Ot6p2oFYQdGs7VI1Mnno8Go0GX19f9Ho91157LUlJSSd9TpIkSdLZI0OXJDXC4XDyy8euwBUc7UNMamCDMraiInKnPI5CCFrFduZ9n1hK23/DJTWpjCsZBUDA1W3QRjUc5dgcWq2WG264gZqaGvz8/E7qfCRJkqSzT7ZTSFIj1n2/j/y9lWj1Kkbc1959fcV6wm4n+9HHsJeUIOISuD9oMEVt1hAh9DyVdzcKFBh6tcLQPazF+967dy9CCACUSqUMXJIkSRcIGbok6Sj7thSzZblrHqwht7fDGOLdoEzR22+zrjibTQmRvNx5DJbAvzF4Z/FczgS8HXq0Mb74j4pv8b43bdrE7Nmz+fbbb3E6nSd9LpIkSdK5QzYvStIRqkpMrPxiJwCdhkST0CW0QZmaP/5gw7dfkR8RBCjIcuSjD13Kw/njaG2JQOmjIeiWdijULfubZt++ffz4448ABAQEyA7zkiRJFxj5qS5JR9i7qRhLnZ2wOD96X5vQ4HFbfj5bnn2GjFauPl4rg/tQlfgzV1UMYHDVJaBUEHRLO1RGXYv2W1RUxIIFC3A6nXTo0IHBgwefkvORJEmSzh2ypkuSjtDlshgM/lpaJfqjOqqmSths7HpkMhuDDKBQkOHXlj1t9tDN7s+9hdcBYBwZhy7O2KJ91tTUMG/ePCwWC9HR0YwePbrB5KuSJEnS+U/WdEnSUZIuCcc3sOHko7n/+Q9rTOXY1SqqvUL5LSaIVj4ZTM29GxVKvDuH4NOn4eWBjsVmszF//nwqKioICAhg7NixaDQtn15CkiRJOvfJ0CVd9MoLalny/jbqqqxNlqleuZLVq5ZRo9eCWs/X4X3wiVjMM7n3EuDwQ9PKgP+1bVpcQ5WTk0NeXh56vZ5bbrlFXrhakiTpAiabF6WLms3qYNmHaZTl1aLWZHLZPe0blLHm5JL31FTibBbKgoOYHXo5IvYHxhePpJ0pHoVeRdCt7VBqW34txLi4OG677TYAgoNP7LqMkiRJ0vlBhi7porb6ywzK8mrx8tPS94Y2DR4XViu5jzyCs6qKgJT2fNz6VuoCVnOlNZyRFQMQQNBNbVEHebVovw6Hw33B6tatW5+CM5EkSZLOdbJ5Ubpo7VyTx661BSgUMPzuVAyNjDjMfPklCvdkoDQaeanrLVTpcmnvk8n9BWMBMA6LxSu54Wz1x7Jv3z7effddioqKTsl5SJIkSecHGbqki1JJTg2/f5kJwCWj44lMDmhQpviHRazc/jdrEyNZPvQa1tQ5iAhfzL9y70ErNOjbBeI7OLpF+y0uLmbBggWUlZWxbt26U3IukiRJ0vlBhi7pomM12fn5ozQcNicxqYF0Gx7boIx5/36WfvQ2dTotGo2ed2zxeIf9wJOF1xJqD0QZpCPwxmQUyuZ3nK+pqWHu3LnuqSEuv/zyU3lakiRJ0jlOhi7polNXbUUIgU+AjkvvTGkQnJwWC8sff5higw4lsDDqKuwBu7jXGkuXurYIDYTclopS3/wukXJqCEmSJEl2pJcuOv6h3oyZ2oPqcjNePtoGj2946nEyVa7rHu5vPYQsrYLLfbK4Ie8OAIJuaIsmrPlTOzidTr7//ntycnLk1BCSJEkXMVnTJV00HPbDF5DWeqkJivBpUGbfF7NYm73HVaZVOxYpEkkJ+5VH8l0d5w39I/DuGNKi/a5bt4709HSUSiU33nijnBpCkiTpIiVDl3RRMNfa+PKl9WxdkY0QotEyln372Dj3cxwqJf7e/rypG0BA0G88WzQaL6FH0doL/xHxLd53ly5daN26NaNHjyYuLu4kz0SSJEk6X8nQJV0U1i/aR2WRiW2rsrFZHA0ed5pM5D78MKkHCmin9OKjqOtRee9jqiWeKGsYNh8n4bd2RKFq+TURvby8uO222+jcufMpOBNJkiTpfCVDl3TBq620sPOvfAAG39oWbSMd4AteehnL7j2og4P5uvvdFDit3GUopk9NJ+xKB5F3dEXVSP+vphQXF/PPP/+47yuV8ldNkiTpYic70ksXvK0rsnHYnYTH+zU6H9c/b77O3g1/kqpSse6mh1mVbWdgyD/cWjwSAOPoeLRRvs3eX21tLXPnzqWiogKHw0HPnj1P2blIkiRJ5y8ZuqQLmrnWRtrvuQB0G9G6wQWpD65awR9rf8MZ5IdXh+68lONFjO9WniodghIl1k56onrFNHt/NpuNL7/80j01RPv2Da/lKEmSJF2cZJuHdEFL+z0Hm8VBUKSB2A5BHo9V5+Xx43tv4lQqCFNqeSXgMvTqIl6xxuHrNFAZbCbuhm7N3tfRU0PcfPPNcmoISZIkyU2GLumC5XA42bYqB4CuI2I9arnsNhvfPfkwJqUCg93JHz3uoqjOzNN6E/GWKGo0JpLu6YdC3fxfkVWrVnlMDRES0rKpJSRJkqQLm2xelC5YKpWSax7ryo6/8knsGurx2C/PPUWx1YTa4UTXfyw/Fyi4yW8fg6t64MBB4M3JaPy9mr2vzZs388cffwDIqSEkSZKkRsnQJV3QAsIN9L0u0WPZpjmz2LkvA4B2yd2ZUBhEF68cxld1BaCin4rYdg2vx3gsZrMZgP79+8upISRJkqRGydAlXZDsNgdqjarBckd1Nea5X6LxVpLoE8Bz3gMIrCvlJWs0alRkRRXTd+Q1Ld5f7969iY6OJiIi4lQcviRJknQBkn26pAuO0yn46t8b+OWTdGorLe7lQgjyn36GgP0HGVxlY3GveympNjFdrSDA4Uuedwk97r68wQjHptTW1rpruACioqLkfFySJElSk+Q3hHTB2bupiPKCOg7uKEWjc9V22W02Dn4wk+rly0GjIX3cFJYeqONRfQXtLK2oUdYRPC4VnVfz+nHVTw3x6aefUlFRcRrPRpIkSbpQyOZF6YIihGDjsgMAdBoS7Z59/q8P3mbLb7/SxdcL/9sm8q9dTq5QVzPa3BonTrKHmhkWl9SsfRw9NYTNZjtt5yNJkiRdOGRNl3RBOZBWSmlODRqdig6DogAw1VSz5Y+V2FVKFJ06M6k2ngSnnSmOMADWJuzi0iGjm70POTWEJEmSdCJk6JIuGEIINi511XKlDohEb9AAsOHDd7EDPhYbP3S9ieoKC9NVGrRCzRZjBsNvvbHZ/biOnBriyiuvlFNDSJIkSc0mQ5d0wcjfU0HBvkqUagWdL40GwGKqY+u6PwEIDk3gu4NWXlI5CHX4kKstJPymjvh7+Tdr+/v372fx4sWAa2qILl26nJbzkCRJki5MMnRJF4xtK12zz7fr3QqDUQfAhvf+h1UB3lY7r/oO5F6FoJvDH5PCzM7BZXRvfUmztu10Olm6dClOp5PU1FQGDx582s5DkiRJujDJjvTSBWPIbe0Ibe1HwqHZ562mOras/xMU4PSJIUXjx63CB4Bvk//g0YHPNnvbSqWSW2+9ld9++43LL79cTg0hSZIktZgMXdIFQ+ulpuvwwzPJZ37+GRYEepuTxZHD+Z9CBwJ+CP6Nm6+7F41S06Lt+/n5MXp08zvcS5IkSdKR5J/r0nnPZnUghPBYJux29N8ton9GNkr/JO7S6/EWGrZ4ZxB7dReifaOPu936qSHS0tJO16FLkiRJFxEZuqTz3ur5mXw9/R/y91S4l1X+8AO2Awfx0RtYEjCEPk4/HDjZfkkeoxJHNWu7q1atYsuWLXz33XdUVlaepqOXJEmSLhayeVE6r1WXmclcV4DTKVAoXdM+2E0mst5/Dx3wV48ruEmnBCf8YdzEXf3HN2u7R04NMWrUKIxG4+k6BUmSJOkiIUOXdF7bsvwgTqcgMtmf8HhXMNr8vzf5I1BPrDaKhd6d+djpjxMnJd3shBnCjrtNOTWEJEmSdDrI5kXpvFVXZWXHn3kAdBvRGgBHbS2b1v+JUCjIj0zmJrWr9usvvy1c12vscbdZXFzMggUL5NQQkiRJ0iknQ5d03tq2Mhu7zUlorC9RbQMA2PJ/b1KjUaF2Cn70H8pgpx8AOZ1qiPY7dud5k8nEvHnzMJvNREVFcfXVV8upISRJkqRTRn6jSOcli8nO9t9zAeh2eWsUCgX26ho2bfgLAFVgAtdrtChRsMZnK1f3GXPcbep0OlJSUvD39+emm25Co2nZlBKSJEmSdCyyT5d0Xtr9dwFWk52AVgbiOgYDsP3/3qBKq0YlBIuNg/jQ6ZoIdU/7YsYEtDnuNpVKJcOGDaN///7o9frTevySJEnSxUeGLum8lNo/Em+jDpVGiUKpwF5RwaYNa0CvQR2cyDUqPSqh5G9DGqP73XDMbaWnp5OUlOSu2ZKBS5IkSTodZPOidF5SKBXEdw4hNjUIgIMz36dGo0QpYJHvQEYIAwDb22bTPrh9k9vZvHkzX3/9NbNnz8Zut5+RY5ckSZIuTudM6Jo+fToKhYLJkye7lwkheOGFF4iIiMDLy4tBgwaRnp7usZ7FYuHBBx8kODgYg8HA6NGjycnJ8ShTXl7OuHHjMBqNGI1Gxo0bR0VFxRk4K+lUczic2KwOj2X2sjKsX33D4B0H8U7sy2ilF2qUbDLs5Ir+1zS5rSOnhoiNjUWtlhW/kiRJ0ulzToSuDRs28OGHH9KxY0eP5TNmzODNN9/knXfeYcOGDYSHhzNs2DCqq6vdZSZPnsx3333H/Pnz+fPPP6mpqWHUqFE4HIe/mG+++Wa2bNnCsmXLWLZsGVu2bGHcuHFn7PykU2f334XMfmYNab8fDtalH32MqKvDKymZ2YouXCG8ANiQsJvuYd0b3Y6cGkKSJEk601oUumbMmIHJZHLfX716NRaLxX2/urqaSZMmtegAampquOWWW/joo48ICAhwLxdC8NZbb/HMM89w7bXX0r59ez7//HPq6uqYN28eAJWVlXzyySe88cYbXHrppXTp0oU5c+awfft2fv31VwB27tzJsmXL+Pjjj+nduze9e/fmo48+4scffyQjI6NFxyqdXU6nYOOyA5iqbVjNrlBtKywia+HXCGDNwOu5zglaVGz33s2wAVeiUCgabKe2tlZODSFJkiSdcS36ppk6dapHLdOoUaPIzc1136+rq+ODDz5o0QHcf//9jBw5kksvvdRj+f79+ykoKOCyyy5zL9PpdAwcOJA1a9YAsHHjRmw2m0eZiIgI2rdv7y6zdu1ajEYjPXv2dJfp1asXRqPRXaYxFouFqqoqj5t0du3fUkxFYR06bzXtB0QCkPnWG/wZF8b6Tkl8WhrAaOHqBP9nXBr9I/s32IbNZmP+/PmUl5fLqSEkSZKkM6pFoUsIccz7LTV//nw2bdrE9OnTGzxWUFAAQFiY52VbwsLC3I8VFBSg1Wo9asgaKxMaGtpg+6Ghoe4yjZk+fbq7D5jRaCQ6+tgTa0qnlxCuWi6ADoOi0HqpseXmsnnLBgDqwuIYaVeiQ8VOr30M6De80VqusrIyiouL0el03HLLLRgMhjN6HpIkSdLF66y1qWRnZ/Pwww8zZ86cYw7RP/qLUwjR6Jfpsco0Vv5425k6dSqVlZXuW3Z29jH3KZ1e2TvLKD5YjVqrpOOQKAB2//cNCv28AVji04dr0AKwImYTl8Ze2uh2wsLCuOeee7jpppsICQk5MwcvSZIkSZzF0LVx40aKioro1q0barUatVrN77//zv/+9z/UarW7huvo2qiioiL3Y+Hh4VitVsrLy49ZprCwsMH+i4uLG9SiHUmn0+Hn5+dxk86ejUtdtVwp/SLw8tFi2b+fLds3AaAPi6ev04gXajL1B+jVbzAqparJbQUHB9O6deszcdiSJEmS5NbiMfIff/wxPj6umb7tdjuzZs0iONg1I/iR/b2OZ+jQoWzfvt1j2Z133knbtm158skniY+PJzw8nOXLl9OlSxcArFYrv//+O6+99hoA3bp1Q6PRsHz5csaMcV3mJT8/n7S0NGbMmAFA7969qays5O+//+aSSy4BYP369VRWVtKnT5+Wnr50FpQX1JK3uwKlSkHnS2MA2PfWf8k3upoGf/Hpwwynq5br58j1vJLwpsf6VquVuXPn0qNHD1JTU49bUypJkiRJp0OLQldMTAwfffSR+354eDizZ89uUKY5fH19ad/ec9JKg8FAUFCQe/nkyZOZNm0abdq0oU2bNkybNg1vb29uvvlmAIxGI3fffTePPfYYQUFBBAYGMmXKFDp06ODumN+uXTtGjBjBvffe6+7kf9999zFq1CiSk5NbcvrSWRIQbmDsc5dQuL8K30A95sxMtu7YCoG+6MPj6eYMxICafbocuvTri0bp2TF+7dq1HDhwgIqKCpKTk2XHeUmSJOmsaFHoysrKOk2H0bgnnngCk8nEpEmTKC8vp2fPnvzyyy/4+vq6y/z3v/9FrVYzZswYTCYTQ4cOZdasWahUh5uX5s6dy0MPPeQe5Th69GjeeeedM3ou0skJivAhKMJVw1r4v7ep8nLVbK306ckrDtfb+MdWf/JSmzc81quurubPP/8E4NJLL5WBS5IkSTprFOJkhyBeJKqqqjAajVRWVsr+XWeQudaG3nA4KJnS0sm6/nqEQsHmh55m14FQxuPFQW0+GTfUcmeHOz3WX7RoEZs2bSIyMpJ77rlHNi1KkiRdZM6l7+8WdaRfv349S5cu9Vj2xRdfEBcXR2hoKPfdd5/HZKmSdDIqiuqY9eRfrJi1A6fDCUDx//4PAL9Ro/i0MoKxClct1w/hvzOm7RiP9QsKCti8eTMAw4c3PoWEJEmSJJ0pLQpdL7zwAtu2bXPf3759O3fffTeXXnopTz31FIsXL250zi1JOhGblx/EYXdiqrGhVCmp27SJ7I0bcKjVbBp0HZdU2DEKDbmaIlr3SsWgOTznlhCCX375BSEEKSkpze5rKEmSJEmnS4tC15YtWxg6dKj7/vz58+nZsycfffQRjz76KP/73//46quvTvlBShefmnILu9bmA9BtRCwA2W+9xd/xrfi9Y8L/t3fncVWU+wPHP3MO+3bYQRQBxRUUtzRXsFTcM+1auXJdyqu567Wy0izNdjUzc8VfWVpXM5fCNRA1NxQ1cRfFFHNDRNnPmd8fXM71BCgochC/79drXi9n5plnvjNHOF+eeeZ5WBz/Fy8pef32fvLcRp+gvibHX7x4kbNnz6LVagvMdiCEEEKYQ4k60qekpJiMbRUTE0PHjh2N60899ZQMIipKRfzWJAy5KpUCdVQKdObO7t0kJJ3B4OWC4upFjVvWuClWXLa8hmez6uisdSbHV6lShQEDBnD16lVcXV3NdBVCCCHE/5SopcvLy4vExEQgb+yjAwcO0Lx5c+P+tLQ0eTtMPLTM2zkc3Z43p2fjTv6oqsrFz2dx3j0vsdrn0oy+mry/F1a7b6N/cP9C66lWrZrJnJtCCCGEOZUo6erYsSOvv/46sbGxvPHGG9jZ2dG69f8mFT58+DDVq1cv9SDFk+XQbxfIzTbg7utA1bqu3I6J4djlJPRaDTbuldBlV8FDteKqRQoOTbzxsPvfdD5ZWVncvn3bjNELIYQQhSvR48X333+fnj17EhoaioODA5GRkVhZWRn3L1myxDgWlhAPwmBQOfF73tRPjTv6g6pyac5szv23lSverRmvZuf15VrlvoV/hUwyOT42Npa9e/cSHh5O48aNyzR2IR4lg8FAdna2ucMQotyxtLQ0GZuzPCtR0uXh4UFsbCypqak4ODgUuMgff/zRZOBSIUpKo1HoPfkpTuy+TLWGHqRt3sypa5fJreSGjasn1lm+eCtW3NCmommoo7JDZeOxN2/e5Pfff0ev12Nvb3+PswjxeMnOziYxMRGDwWDuUIQol5ydnfH29i73QwOVKOkaNGhQscotWbLkgYIRAsDG3pKQZ31R9XqufjGHO1Z5/QT/8GhGRHbef9lVbluIaDDK5LgtW7ag1+vx9/eXKZ5EhaGqKsnJyWi1Wnx9fdFoStQrRIgKTVVV0tPTuXLlCgCVKlUyc0T3VqKkKzIyEj8/Pxo2bIgMZC9KW/qtbGwdLY1/qdz65ReyT5+hgU6H+6Q3OLUljcqKFanaNDLqW1JNV8147J9//skff/wBQIcOHcr9XztCFFdubi7p6en4+PhgZ2dn7nCEKHdsbW0BuHLlCp6enuX6UWOJkq5hw4axYsUKzp49y6BBg+jXr5+8ji9KhUFvYNVH+7F1tKL9oLo4OVty9b/zY7oNGsR75zT8S8lr8VrlupV/NhxiPFZVVTZu3AhASEgIPj4+ZX8BQjwier0ewKT/rBDCVP4fJDk5OeU66SpRO/W8efNITk5m0qRJrFu3Dl9fX3r37s3GjRul5Us8lFP7r3DrWiapVzOw01lzc80a/rp2hWwPN86HdsHqzC38sCJNc4frQdnUcatjPDYhIYELFy5gYWFhMnivEBWJtN4KUbTH5eejxJ0DrK2tefnll9m8eTMJCQkEBQUxfPhw/Pz85FV98UBUg8qBjecBCHnGFy16rsz7ikNVPdlW2YXIdTsY+N9G2TWu24hoZNq38ObNm2g0Glq2bGn2yUyFEEKIopTo8eLfKYqCoiioqipv1YgHdu7INW5cuoOljZZ6YZW5+cMPJGXeJsPKEyt7R9KuOlJdY8UdTQbna92koWdDk+NbtmxJ7dq1cXBwMNMVCCEeFxEREdy8eZM1a9YUWSY6Opq2bduSkpKCs7NzmcUmKr4St3RlZWXx/fff0759e2rVqsWRI0eYO3cuSUlJ8qUnSkxVVeKi8lq56oVWxlLJ5erX8znt5QzApSrNGKCxBmCtSzQDGkUUWo+bmxvW1tZlEbIQopgiIiLo0aNHofv8/f2Nf7jnL1WqVDHZP2vWrFKPafbs2URGRhrXw8LCGDNmTKmfx9wiIiKM99XCwoKqVavyr3/9i5SUFJNy9/scROkqUUvX8OHDWbFiBVWrVuWf//wnK1aswM3N7VHFJp4AF0/e5K/EW2gtNYQ8W5WU777lz5xM0q11WNrZ82dGdWpqrMhQMjkWeJEJPv+bdmrfvn1UrlxZOs4L8ZiaNm0aQ4cONa6XRQdonU53/0KlLCcnxyxT5HXs2JGlS5eSm5tLQkICgwYN4ubNm3z//fcm5czxOTypStTSNX/+fJycnAgICCAmJoahQ4fSs2fPAosQxXViT97o83VbVMJak821hQs57ekMwNWqzeivyXsjZb3rdvo2GmDsLHnt2jV+/fVXFixYYByfRQjxeHF0dMTb29u4eHh43P+gvxk/fjzdunUzrs+aNQtFUdiwYYNxW61atfj6668B09a3iIgIYmJimD17trGV59y5c8bj4uLiaNKkCXZ2drRo0YITJ04UK6apU6fSoEEDlixZQrVq1bC2tkZVVaKiomjVqhXOzs64ubnRtWtXzpw5YzyuV69ejBw50rg+ZswYFEXh6NGjQN7wIY6Ojsa3te/H2toab29vqlSpQocOHXjxxRfZtGlTgXKl8TmI4ilR0jVgwADatm2Ls7MzOp2uyEWI4mrbrzbhQ4Np0KEqN/7v/7hkyOa2rTUWNraczahBXSzJVLKJ8z9N26ptjcdt3rwZg8FAzZo18fT0NOMVCFG2VFUlPTvXLEt5fEs9LCyM2NhYY7/imJgY3N3diYmJAeDy5cucPHmS0NDQAsfOnj2b5s2bM3ToUJKTk0lOTsbX19e4f/LkyXz66afs378fCwuLYg8QDnD69Gl++OEHVq1aRXx8PAB37txh3Lhx7Nu3j61bt6LRaHj++eeNsYeFhREdHW2s4+/Xsm/fPjIzM2nZsmWJ7hHA2bNniYqKMkuLm/ifEg+OKkRp0mgUAht7or95k+QlS8mytsDCwoKbfk3pk503lc+vLrG82KgPGiXvb4TExEROnDiBoii0b9/enOELUeYycvTUfad4LR2lLWFaOHZWD/X+lYlJkybx1ltvGddnzJjBqFGj7nFEQW3atCEtLY2DBw/SqFEjYmNjmTBhAqtXrwbgt99+w8vLi9q1axc4VqfTYWVlhZ2dHd7e3gX2T58+3Zisvf7663Tp0oXMzExsbGzuG1d2djbffPONSatRr169TMosXrwYT09PEhISCA4OJiwsjNGjR3Pt2jW0Wi1Hjx5lypQpREdHM3z4cKKjo2ncuHGx+0+vX78eBwcH9Ho9mZmZAHz22WcFypXG5yCKp/R+eoQogYzb2Vhaa7GwzOs7cH1pJIbbt6npU5PAGfN4a94BBiuWZCs57PD9g38F5P1CMBgMxqb1Jk2aSDO4EI+xiRMnEhERYVx3d3cvcR06nY4GDRoQHR2NpaUlGo2GV199lSlTppCWlkZ0dHShrVzFUb9+feO/86eXuXLlClWrVr3vsX5+fgV+P505c4a3336b3bt3c+3aNWMLV1JSEsHBwQQHB+Pm5kZMTAyWlpaEhITQvXt35syZA1Dia2nbti1fffUV6enpLFq0iJMnT5o8vsxXGp+DKB5JuoRZ7PzxNBeO3yCsb218Kyvc+OYbADxGj+LDg9d5Scnry7XReRe9GvXGQpP3X/Xw4cNcvnwZa2trwsLCzBW+EGZja6klYVq42c5dmtzd3QkMDHzoevIfy1lZWREaGoqLiwtBQUHs3LmT6OjoB3478e5Hcfn9SYs7PJK9vX2Bbd26dcPX15eFCxfi4+ODwWAgODiY7Oxs4znatGljvJawsDCCg4PR6/UcOXKEXbt2leha7O3tjfd3zpw5tG3blnfffZf33nvPpFxpfQ7i/iTpEmXu1rUMTu77C9WgYq+z4vrCr7ip5qINrsOdJi04+uF2hmFHDrlsq3yA5YH/BvKa67du3QrkPVIo7JeaEBWdoiil+oivIggLC2Px4sVYWFjQrl07AEJDQ1mxYkWR/bnyWVlZGadaepSuX7/OsWPH+Prrr2ndujUAO3bsKFAuLCyMBQsWYGVlxbRp01AUhdatW/PJJ5+QkZHxQP258k2ZMoVOnTrxr3/9S976NhOZrl6UuYObklANKr51XHCxySDlu+84XsmN7dpsln4dSV9D3l+XW5x3061hD6y0eXPOabVa2rRpQ+XKlWnatKk5L0EIUUypqanEx8ebLElJSaV6jvx+XevWrTO2gIeFhfHtt9/i4eFB3bp1izzW39+fPXv2cO7cOZNHfqXNxcUFNzc3FixYwOnTp9m2bRvjxo0rUC4sLIyjR49y5MgRY3IWFhbG8uXLadSo0UPNuhEWFkZQUBAzZsx44DrEw5GkS5SpO6lZHNuVDEDjjv5cmz+fGxYK1x3tULRaDt/w4mks0aMnqtLvvFDzBeOxWq2Wp556iiFDhsgbOEI8JqKjo2nYsKHJ8s4775TqOXQ6HQ0bNsTV1dWYYLVu3RqDwXDfPlATJkxAq9VSt25dPDw8Sj0hzKfRaFixYgVxcXEEBwczduxYPv744wLlgoODcXd3JyQkxJhghYaGotfrH7hv2t3GjRvHwoULuXDhwkPXJUpOUcvjO8Dl0K1bt9DpdKSmpsr8fg9h1+rTHNyUhHc1J7q+7MXZzl3YV8WNq072GGo0xTe3Fa2wZItuN1mdnXg15FUgrx+FRiN/I4gnT2ZmJomJiQQEBBTrrTkhnkT3+jkpT9/f8i0mykzmnRz+iLkIQKOO/lz/aj6pFgpXnexRFA0HcoNohSUGDKz12s5LtV8C8sbZmTt3rnGAQCGEEOJxJEmXKDNJR6+Tk6XHrbI9lRxukbpmDWc8XfJ2VmtAZ/L+vd0pjjYhz6Kz1qGqKhs3buTGjRskJCSYMXohhDksX74cBweHQpegoCCzxBQUFFRkTMuXL3/k58+f67io5VE9IhUPT16BEWWmZlNvXCrZk5Ol5/qX75NmqeWyc94gf/sM9fiYvH5aqzy3sajuMgBOnTpFYmIiWq3W+FaSEOLJ0b17d5o1a1boPnP17fzll1/IyckpdJ+Xl9cjP7+Pj49xlPui9ovySZIuUaY8fB3JPHGSxF9+IdvOGicXV+44V6EDeROn73A8QNN6LXGzdUOv1xvnCXv66adxcXExZ+hCCDNwdHTE0dHR3GGY8PPzM+v5LSwsZFytx5Q8XhSPnD7XwO2UTOP61S/mgKri3yaMPp8vItayBc/8N///wWMz/wz+J5A32ey1a9ews7MzvjothBBCPK6kpUs8cif2XCbmuxM0bF+VkOoZ3N6yFTQaPF57jeVxFwnPsUWLwm6HwwQHNcLb3pvMzEzjxK9hYWHy1pYQQojHnrR0iUfKYFA5sPE8Br2KjYMlV+fMIcNSy9WwluT6VGZN9Bk6/Lcv1wqPKAbXGwxAQkIC6enpuLu707hxY3NeghBCCFEqpKVLPFJnDlwh9UoG1vYWBDhc4VJsLGereHD++iUOf/ABHbPbYIHCfvujVKtdBz+nvL4SjRo1QqfTodFo0GpLd743IYQQwhykpUs8MqqqEhd1HoD6batw88s5ZFloueCuAyAutwad/tvK9b37r8ZWrnzVq1cnICCgbIMWQgghHhFJusQjk3T0Btf/vI2ltZZA+2TS9+0j0dsVg6pi5VONhnpvLFGItzuBZ82q1HKtxbVr17h9+7a5QxdCVFARERH06NHjnmWio6NRFIWbN2+WSUziySFJl3hk4qLOARDU2ofU+XPI1mpI8nAG4LBdY7ooeRNZf+/+K0PqD0FVVdasWcOcOXM4efKkmaIWQpSWeyU4/v7+KIpislSpUsVk/6xZs0o9ptmzZxMZGWlcDwsLY8yYMaV+nvLg8uXLjBw5kmrVqmFtbY2vry/dunVj69atZGdn4+7uzvvvv1/osR988AHu7u5kZ2ff8xyRkZEmn6GXlxfdunUrMINIREREgc9bURROnz5datf7OJCkSzwSt65ncDUpDY2FQqDdBTIPHeZcJXdyDQasvXypnVsZaxSO2p7GtpoLIR4hHD16lD///BNVVfH29jb3JQghHrFp06aRnJxsXA4ePPjIz6nT6XB2dn7k57lbUQOpPkrnzp2jcePGbNu2jY8++ogjR44QFRVF27ZtGTFiBFZWVvTr14/IyEgKm4J56dKl9O/fHysrq/uey8nJieTkZC5dusSGDRu4c+cOXbp0KZCwdezY0eTzTk5OfuK6kEjSJR4JJzdbBsxoQfiQIO4snEuORsN5T2cAjjg9xXNKXl+u79x/ZWjIUHJyctiyZQsALVu2NPukpEKIR8/R0RFvb2/j4uHhUeI6xo8fT7du3Yzrs2bNQlEUNmzYYNxWq1Ytvv76a8C09S0iIoKYmBhmz55tbHk5d+6c8bi4uDiaNGmCnZ0dLVq04MSJE8WKaerUqTRo0IAlS5YYW5lUVSUqKopWrVrh7OyMm5sbXbt25cyZM8bjevXqxciRI43rY8aMQVEUY6tRbm4ujo6ObNy48b4xDB8+HEVR2Lt3Ly+88AI1a9YkKCiIcePGsXv3bgAGDx7MmTNn2L59u8mxsbGxnDp1isGDBxdWdQGKouDt7U2lSpVo0qQJY8eO5fz58wXul7W1tcnn7e3t/cS9KCVJl3hkbB2scL98kKxjx9A7OuBTqy7WHj5Uy6mKDRqO2ySS62dJU++m7N27l5s3b+Lo6EiLFi3MHboQ5ZeqQvYd8yyFtIiYW1hYGLGxsRgMBgBiYmJwd3cnJiYGyHvEdvLkSUJDQwscO3v2bJo3b87QoUONLS++vr7G/ZMnT+bTTz9l//79WFhYMGjQoGLHdfr0aX744QdWrVplnLLnzp07jBs3jn379rF161Y0Gg3PP/+8MfawsDDj+ISFXcu+ffvIzMykZcuW9zz3jRs3iIqKYsSIEdjb2xfYn9/SV69ePZ566imWLl1qsn/JkiU0bdqU4ODgYl9vvps3b/Ldd98B5pumqTyTISNEqUu5fAcXb3tUvZ6rX3wBQJV+/Wnw2gj6fraVqVf/98bi0JBXSU9PN/6l9eyzzxarOVuIJ1ZOOsww09x6b14Cq4Jf4g9q0qRJvPXWW8b1GTNmMGrUqBLV0aZNG9LS0jh48CCNGjUiNjaWCRMmsHr1agB+++03vLy8qF27doFjdTodVlZW2NnZFdqlYfr06cZk7fXXX6dLly5kZmYWa7Dm7OxsvvnmG5PWu169epmUWbx4MZ6eniQkJBAcHExYWBijR4/m2rVraLVajh49ypQpU4iOjmb48OFER0fTuHFjHBwc7nnu06dPo6pqodf8d4MGDWLChAnMnTsXBwcHbt++zY8//shnn31232Pzpaam4uDggKqqpKenA3lzZv79/OvXrzeJvVOnTvz444/FPk9FIC1dolSlXL7Dd+/u4adPD5Dy83qyz5xBq9PhGjGQ6JNXqXsV7NBw2jqJFN8s2lRpQ3R0NFlZWVSqVIn69eub+xKEEGVk4sSJxMfHG5cBAwaUuA6dTkeDBg2Ijo7myJEjaDQaXn31VQ4dOkRaWhrR0dGFtnIVx92/jypVqgTAlStXinWsn59fgcelZ86coU+fPlSrVg0nJydjf6akpCQAgoODcXNzIyYmhtjYWEJCQujevbuxpau415LfR0tRlPuWffnllzEYDKxcuRKAlStXoqoqL730UrGuE/IeE8fHxxMXF8f8+fOpXr068+fPL1Cubdu2Jp/3nDlzin2OikJaukSpOrApCVSwstFw46sv0SsKf7ZtQaWcbBZtPsnbxnG5ohhafygKChYWFmg0Gjp06IBGI38HCHFPlnZ5LU7mOncpcnd3L5WJm/Mfy1lZWREaGoqLiwtBQUHs3LmT6OjoB3478e7HY/kJTP6jwPsp7LFet27d8PX1ZeHChfj4+GAwGAgODjZ2OFcUhTZt2hivJSwsjODgYPR6PUeOHGHXrl3FupYaNWqgKArHjh277/AYOp2OF154gaVLlzJ48GCWLl3KCy+8UKJ+tRqNxvg51q5dm8uXL/Piiy8W6Ctmb2//xE/ULd9wotSk3cjk5O7LANS0OkvOhQtc9PXmcOJJ/u/tSdS4kIEDGhKtL3LR5wYd/DqgKArh4eGMGTPmiXuLRYgHoih5j/jMsRSj5cQc8vt1bdu2jbCwMABCQ0NZsWJFkf258llZWaHX6x95jNevX+fYsWO89dZbPPvss9SpU4eUlJQC5fITyOjoaMLCwlAUhdatW/PJJ5+QkZFx3/5cAK6uroSHh/Pll19y586dAvv/Pv7Y4MGD2blzJ+vXr2fnzp3F7kBflLFjx3Lo0CF++umnh6qnIpKkS5Sa+M1JGAwqPjV0sPwLDAokVnIHING1Eb3/Oy7XSrcoBtUfjFbzv7dW5G1FISqm1NRUk0dK8fHxxsdppSW/X9e6deuMSVdYWBjffvstHh4e1K1bt8hj/f392bNnD+fOnePatWvFbskqKRcXF9zc3FiwYAGnT59m27ZtjBs3rkC5sLAwjh49ypEjR2jdurVx2/Lly2nUqFGxf1fOmzcPvV5P06ZNWbVqFadOneLYsWPMmTOH5s2bm5QNDQ0lMDCQAQMGEBgYSJs2bR7qWp2cnBgyZAhTpkwpdDiKJ5kkXaJUpN/KJmFH3iOPWpZnyL18mWS/KtzJTMfKUYdtVnWc0HDB6jInvC/Sxb8L69evJzk52cyRCyEepejoaBo2bGiyvPPOO6V6Dp1OR8OGDXF1dTUmWK1bt8ZgMNy3D9SECRPQarXUrVsXDw+PUk8I82k0GlasWEFcXBzBwcGMHTuWjz/+uEC54OBg3N3dCQkJMSZYoaGh6PX6EvVNCwgI4MCBA7Rt25bx48cTHBxM+/bt2bp1K1999VWB8oMGDSIlJaVEb2jey+jRozl27NgT11H+fhRV0tBiuXXrFjqdjtTUVGmVKcTuNWeIizqPp6899X+dQO61a+xs0YC0O2lcrRlO35z6uKDhk0rLeKpDW+pk1GHt2rXY2toyduxYeWNRiCJkZmaSmJhIQEBAsd6aE+JJdK+fk/L0/S0tXeKhqapKUsINAGpancFw7RpXqvuRdicNSzsHlKwauKAh2fIqh7zO0NWvK9u2bQPy/hqVhEsIIcSTQJIu8dAUReGFSY3pMDAQm/98gQqc9cnry/VXlWa8qLEFYIX7RvoG9eXAngPcvn0bFxcXmjZtasbIhRDl3fLly3FwcCh0CQoKMktMQUFBRca0fPnyR37+pKSkIs/v4OBQqo9IzX2tFY0MGSFKhUarwfnAOq6l3kRbrRoBzVuR8PsOcrJq4a7R8JfFdfa6JTDeZwpLfl4CQLt27bCwkP+CQoiide/enWbNmhW6z1wjnv/yyy9Fzqfo5eX1yM/v4+NjHOW+qP2lxdzXWtHIN554KDeS76DzsIXbt7ixNBKASiNfo1anTqy1aET/hAwAfnTfRO+g3uyN3Utubi6+vr73fKNICCEgb+BNR0dHc4dhws/Pz6znt7CwKLPxrsx9rRWNJF3igen1BtZ9EQ8qNHc8hOH2baxr18YxPJwzV29jSLiJF7Zcs7hJrFs8S13H8N3PeXNyhYeHF2u0ZCGEEKKikKRLPLBTe//i9o0sbB0syPlPJApw5qkQbM+cYv6BDPphDcB/3DbzXO0eVK9Snc6dO3Pt2jWqVKli3uCFEEKIMiZJl3ggBoNKXNR5AAItzqJJT+NW/SAOH97P0WOHyKk8BB/FiRTtLba47uHnupPRarXScV4IIcQTS95eFA8kMf4qN/9Kx8pGg+vGeQCcreIJwB2/Jryo5M07tsptCx0DO+Fm7Wa2WIUQQojyQJIuUWKq+r9WrgBtItqMNNIbNeDSxSQ0Wi1Xc+rhi5ZU7W1+dd3B07lP8+WXX3Ly5EkzRy6EEEKYjyRdosQuHLvB1aQ0tBYKHlu/BuBM1bxXh7P8G9NLkzfi7xrXbbSt+iyHdh8iJSWF1NRUs8UshBAAERER9OjR455loqOjURSlwMTQQjwsSbpEiSUdzRt93l9zHqvMVLKaN+VC4mkUjYZkfX0C0JKmSWetSzQNbzckIyMDDw8PGjVqZObIhRBl6V4Jjr+/P4qimCx3v2Dj7+/PrFmzSj2m2bNnExkZaVwPCwtjzJgxpX6e8uDChQsMHjwYHx8frKys8PPzY/To0Vy/fr1A2aNHj9K7d288PDywtramRo0avP3226Snp5uUu/tz02q1+Pj4MHjwYFJSUooVU35Cm7+4ubnxzDPPsHPnTpNyU6dOLfD/Q1EUtmzZ8uA3pByQpEuUWKt/1OD5CB+8ty8A4KxvXitXrn8DnlecAVjr+hstvVtz8lDeI8UOHTqg1WrNEq8QonyaNm0aycnJxuXgwYOP/Jw6nQ5nZ+dHfp67FTW46KN09uxZmjRpwsmTJ/n+++85ffo08+fPZ+vWrTRv3pwbN24Yy+7evZtmzZqRnZ3Nhg0bOHnyJDNmzGDZsmW0b9+e7Oxsk7rzP7ekpCSWL1/O9u3bGTVqVIniO3HiBMnJyURHR+Ph4UGXLl24cuWKSZmgoCCT/x/Jycm0adPmwW9KOSBJl3gg6g8LsM5Mwf6ZZ6jeOgwHdy/+NDQgEC3pmgzWuP5G3et10ev1VK9evcwG8hNCPD4cHR3x9vY2Lh4eHiWuY/z48XTr1s24PmvWLBRFYcOGDcZttWrV4uuv87pC3N36FhERQUxMDLNnzza2pJw7d854XFxcHE2aNMHOzo4WLVpw4sSJYsU0depUGjRowJIlS6hWrRrW1taoqkpUVBStWrXC2dkZNzc3unbtypkzZ4zH9erVi5EjRxrXx4wZg6IoHD16FIDc3FwcHR3ZuHHjfWMYMWIEVlZWbNq0idDQUKpWrUqnTp3YsmULFy9eZPLkyUBeH93BgwdTp04dVq9eTdOmTfHz8+Mf//gH69at4/fff+fzzz83qTv/c6tcuTJt27ZlwIABHDhwoFj3Jp+npyfe3t7Uq1ePt956i9TUVPbs2WNSxsLCwuT/h7e392M/V68kXaLYDHoDmXdyyDxxglu//AqA5+hRhLTvzO3uE+iuuAKw1iWGxrqn+PPMnyiKQocOHWQgVCFKiaqqpOekm2VRVdXcl19AWFgYsbGxGAwGAGJiYnB3dycmJgaAy5cvc/LkSUJDQwscO3v2bJo3b87QoUONLSm+vr7G/ZMnT+bTTz9l//79WFhYMGjQoGLHdfr0aX744QdWrVplnLLnzp07jBs3jn379rF161Y0Gg3PP/+8MfawsDCio6ONdfz9Wvbt20dmZiYtW7a857lv3LjBxo0bGT58OLa2tib7vL296du3LytXrkRVVeLj40lISGDcuHFoNKYpQUhICO3ateP7778v8lwXL15k/fr1RU7VdD/p6eksXboUMN+0TmVJxukSxXb+j+tsXHgUf07jBzh17oRNrVrcyszh6I4/eREbMpUs1rhu4xXNvzjLWRo2bCjzcwlRijJyM2j23YN9wT2sPX32YGdpV2r1TZo0ibfeesu4PmPGjBI/pmrTpg1paWkcPHiQRo0aERsby4QJE1i9ejUAv/32G15eXtSuXbvAsTqdDisrK+zs7PD29i6wf/r06cZk7fXXX6dLly5kZmZiY2Nz37iys7P55ptvTFrvevXqZVJm8eLFeHp6kpCQQHBwMGFhYYwePZpr166h1Wo5evQoU6ZMITo6muHDhxMdHU3jxo1xcHC457lPnTqFqqrUqVOn0P116tQhJSWFq1evGt8qv1fZHTt2mGzL/9z0ej2ZmZk0a9aMzz777L735G75/ffS0/OS+caNG/Pss8+alDly5IjJtdatW5e9e/eW6DzljSRdotiO7riEPtdAzp+JAJwJ9ONmzFZ+y65M75y8/0obXGKp5lOD/h37c6beGUm4hBBFmjhxIhEREcZ1d3f3Eteh0+lo0KAB0dHRWFpaotFoePXVV5kyZQppaWlER0cX2spVHPXr1zf+u1KlSgBcuXKFqlWr3vdYPz+/Ao9Lz5w5w9tvv83u3bu5du2asYUrKSmJ4OBggoODcXNzIyYmBktLS0JCQujevTtz5swBeKhruVt+i2VxnkCoqlqgXP7npqoqFy5c4M0336RLly5s37692H13Y2Njsbe35+DBg0yaNInIyMgCLV21atVi7dq1xnVra+ti1V2eSdIliiXtRiZJf+S98eJzcQeGBiHsj92KGrOZ8wED6YE3WUoOq9y2ML3eTBRFkX5cQjwCtha27Omz5/4FH9G5S5O7u3up/J7IfyxnZWVFaGgoLi4uBAUFsXPnTqKjox/47cS7k4D8xCM/Ubofe3v7Atu6deuGr68vCxcuxMfHB4PBQHBwsLGjuqIotGnTxngtYWFhBAcHo9frOXLkCLt27SrWtQQGBqIoCgkJCYW+PXr8+HFcXFxwd3enZs2aACQkJNCgQYNCy9aoUcNk292fW40aNZg1axbNmzfnt99+o127dveNDyAgIABnZ2dq1qxJZmYmzz//PH/88YdJYmVlZVXhvkfM2qfrq6++on79+jg5OeHk5ETz5s359ddfjftVVWXq1Kn4+Phga2tLWFiYsUNhvqysLEaOHIm7uzv29vZ0796dP//806RMSkoK/fv3R6fTodPp6N+/v4y/UkLHdl5CVcEl/Rx2GVc4F1gV1WDAwrcW7cn76zTKeQfVbWrS0LmhmaMVouJSFAU7SzuzLOW1b2Z+v65t27YRFhYGQGhoKCtWrCiyP1c+Kysr9Hr9I4/x+vXrHDt2jLfeeotnn33W+Ijv7/ITyOjoaMLCwlAUhdatW/PJJ5+QkZFx3/5cAG5ubrRv35558+aRkZFhsu/y5cssX76cF198EUVRaNCgAbVr1+bzzz8vkFAeOnSILVu28PLLL9/zfPmtW38/V3H1798fg8HAvHnzHuj4x4lZk64qVaowc+ZM9u/fz/79+3nmmWd47rnnjInVRx99xGeffcbcuXPZt28f3t7etG/fnrS0NGMdY8aM4aeffmLFihXs2LGD27dv07VrV5Mfoj59+hAfH09UVBRRUVHEx8fTv3//Mr/ex5XBoHJsVzIAlc5tI9vdlVOJef0Azlo2pSEW5Ci5/OwcTbVz1ZgzZ06BV3+FEE+m1NRU4uPjTZakpKRSPUd+v65169YZk66wsDC+/fZbPDw8qFu3bpHH+vv7s2fPHs6dO2fyyK+0ubi44ObmxoIFCzh9+jTbtm1j3LhxBcrlNy4cOXKE1q1bG7ctX76cRo0a4eTkVKzzzZ07l6ysLMLDw9m+fTsXLlwgKiqK9u3bU7lyZaZPnw7kJfGLFi0iISGBXr16sXfvXpKSkvjxxx/p1q0bzZs3L9C6lpaWxuXLl0lOTmbv3r1MnDgRd3d3WrRo8UD3RqPRMGbMGGbOnFlgXLCKxqxJV7du3ejcuTM1a9akZs2aTJ8+HQcHB3bv3o2qqsyaNYvJkyfTs2dPgoODWbZsGenp6Xz33XdA3g/z4sWL+fTTT2nXrh0NGzbk22+/5ciRI8YB1I4dO0ZUVBSLFi2iefPmNG/enIULF7J+/fpiv/77pEs6ep3bKVlYqpl4XDvExcb10efmYlk5kDA1b77FTbrfqZFdB32OHjc3twfqmyGEqHiio6Np2LChyfLOO++U6jl0Oh0NGzbE1dXVmGC1bt0ag8Fw3z5QEyZMQKvVUrduXTw8PEo9Icyn0WhYsWIFcXFxBAcHM3bsWD7++OMC5YKDg3F3dyckJMSYYIWGhqLX60vUn6tGjRrs37+f6tWr8+KLL1K9enVeeeUV2rZty++//46rq6uxbMuWLdm9ezdarZbOnTsTGBjIG2+8wcCBA9m8eXOBvlTvvPMOlSpVwsfHh65du2Jvb8/mzZtxc3vwOXYHDRpETk4Oc+fOfeA6HgeKWk7eAdbr9fz4448MHDiQgwcPYmNjQ/Xq1Tlw4AANG/7vcdVzzz2Hs7Mzy5YtY9u2bTz77LPcuHEDFxcXY5mQkBB69OjBu+++y5IlSxg3blyBx4nOzs58/vnn/POf/yxWfLdu3UKn05GamlrsvzQqil++OkzioWv4XthKwLk1RDepQ3ZmJhcDXmYcVclFz8iqH9L4ajNQ88a+8ff3N3fYQlQImZmZJCYmEhAQUKy35oR4Et3r56Q8fX+bvSP9kSNHaN68OZmZmTg4OPDTTz9Rt25ddu3aBVDg7TcvLy/On8+bbPny5ctYWVmZJFz5ZS5fvmws4+npWeC8np6exjKFycrKIisry7h+69atB7vACiC0Ty3sk+Kx37ODq081IDsjFQtXL55WK4EC23R7qJZeE1SoXbu2JFxCCCFEIcw+OGqtWrWIj49n9+7d/Otf/2LgwIEkJCQY9/+942Zhr6/+3d/LFFb+fvV88MEHxo73Op3OZMC8J42ttUqlbfOwz7hC5fbh+AaHcNmhKS0USwwY+NVhDy63XdBoNLRv397c4QohKpDly5fj4OBQ6BIUFGSWmIKCgoqMafny5Y/8/ElJSUWe38HB4ZE9Ii2OTp06FRnXjBkzzBZXeWH2lq67Xwlt0qQJ+/btY/bs2UyaNAnIa6nKHx8F8sZIyW/98vb2Jjs7m5SUFJPWritXrhg79Hl7e/PXX38VOO/Vq1fvOYbUG2+8YdLJ8datW09s4nXr1yj0qalY+vhQ/aW+HDt8mXor8vrDbXPci9/tagA89dRTD/VMXwgh/q579+5FjnZurhHMf/nllyLnUyyLsQl9fHyMo9wXtd9cFi1aVORbjHf3I3tSmT3p+jtVVcnKyiIgIABvb282b95s7NOVnZ1NTEwMH374IQCNGzfG0tKSzZs307t3bwCSk5P5448/+OijjwBo3rw5qamp7N27l6ZNmwKwZ88eUlNT7/mmhbW1dYUYiO1hJB29zoFN5/Hevwsd4PzSS6DRsH7LGaYqlhhQ+dFtM884hGNIvn+HVSGEKClHR0ccHR3NHYYJPz8/s57fwsKi3I5fVblyZXOHUK6ZNel688036dSpE76+vqSlpbFixQqio6OJiopCURTGjBnDjBkzqFGjBjVq1GDGjBnY2dnRp08fIO+NlcGDBzN+/Hjc3NxwdXVlwoQJ1KtXzzhAW506dejYsSNDhw41Tnj6yiuv0LVrV2rVqmW2a38cHN1xiYsnbqKkWqO1t+WaFWQdOE2r67mAJbGOB7ipu8OwF4ZhYbB44pNUIYQQ4l7MmnT99ddf9O/fn+TkZHQ6HfXr1zeOIwLw73//m4yMDIYPH05KSgrNmjVj06ZNJn/1fP7551hYWNC7d28yMjJ49tlniYyMNJmKYPny5YwaNYoOHToAec3VFf211Id1JzWLc4euAeCTvJOLIXU58+vP6A9c4EW7vIT2B/eN9KvTD3vLgiMvCyGEEMJUuRkyorwrT6+cloW4qHPsXnMWXdo56sV/SnSDmuTm5uDkO5ROFq78bneE32z+YNxL4wisWj6buYWoCGTICCHu73EZMsLsby+K8kc1qCTsuASAz8VYkmtVIzc3B8XZjzBt3gsLu61O4JnuSezWWHOGKoQQQjw2JOkSBfx5PIVb1zKxMGTifiWOc052ANjYtcJWUThgfRLHzLzkK/+RrRBCCCHuTZIuUcDR/7ZyeSXv4Ya7I3cy7qCxdqK1Vd5ryLst8+ZdrFevnrypIoQQQhSTJF2igGoN3XFVruNzaSdJ1asCYOnSFhdFwwnLC1jl2qIoCm3btjVzpEKI8iwiIgJFURg2bFiBfcOHD0dRFCIiIkzKKoqCpaUlXl5etG/fniVLlhSYhNrf359Zs2YVKwZ/f39jvba2ttSuXZuPP/6Yu7sznzt3zljm7qVfv34PfO1CFEaSLlFAQFWFBtvfxf7ORTyDglGs7Ghqk9dZPsb6DwDq168vA90JIe7L19eXFStWmAyYmZmZyffff0/VqlVNynbs2JHk5GTOnTvHr7/+Stu2bRk9ejRdu3YlNzf3gWOYNm0aycnJHDt2jAkTJvDmm2+yYMGCAuW2bNlCcnKycfnyyy8f+JxCFEaSLlHAzR//A3o99k0a8+zYN7gY+Ao+igUXtFfR5FoB0KpVKzNHKYR4HDRq1IiqVauyevVq47bVq1fj6+trHPg6n7W1Nd7e3lSuXJlGjRrx5ptv8vPPP/Prr78SGRn5wDE4Ojri7e2Nv78/Q4YMoX79+mzatKlAOTc3N7y9vY2LTqd74HMKURhJuoTR5bOpHIxK5MqqdQC4vPwyvxy+RHhG3nBu23X7SKueRrNmzfDw8DBnqEI8sVRVxZCebpblQUcY+uc//8nSpUuN60uWLGHQoEHFOvaZZ54hJCTEJGl7UKqqEh0dzbFjx8w2hZB4spW7aYCE+RzedoFT+69Q2aE5LpWz0FXz57cNiUxAS7aSwzq3aL4In0dDz4b3rUsI8WioGRmcaNTYLOeudSAOxc6uxMf179+fN954w9h3aufOncYZSIqjdu3aHD58uMTnzTdp0iTeeustsrOzycnJwcbGhlGjRhUo16JFCzSa/7VFxMbGFmiNE+JhSNIlAMi4nc2Z+KsAeCfv5EBtF/ZPncSzVf8FWic2Of1OdZ+aknAJIUrM3d2dLl26sGzZMlRVpUuXLri7uxf7eFVVURTlgc8/ceJEIiIiuHr1KpMnT+aZZ54pdO7dlStXUqdOHeO6r6/vA59TiMJI0iUAOLH7MoZcFce089y2uklGjhV29lVpqHXiFumc0afQ2abzQ//yE0I8HMXWlloH4sx27gc1aNAgXnvtNYASd1A/duwYAQEBD3xud3d3AgMDCQwMZNWqVQQGBvL0008b5+jN5+vrW24nkhYVgyRdAlVVORqbNzZXpUs7SPKvAvpsPHVhAGyyO4B9rgNZF7Ik4RLCzBRFeaBHfObWsWNHsrOzAQgPDy/2cdu2bePIkSOMHTu2VOJwcXFh5MiRTJgwgYMHD8rvNFGmpCO9IPn0TW7+lY5Wn4VN2hFS9NnYWDrTyMqbdLK4Ych71bt169ZmjlQI8bjSarUcO3aMY8eOodVqCy2TlZXF5cuXuXjxIgcOHGDGjBk899xzdO3alQEDBpRaLCNGjODEiROsWrWq1OoUojikpUsYW7k8/9rPn1XcAHB3fRZLRWGjzSE0aKjiWwV/f38zRimEeNzdb7LhqKgoKlWqhIWFBS4uLoSEhDBnzhwGDhxo0sH9YXl4eNC/f3+mTp1Kz549S61eIe5HUR/0HeAnTHmapbw0qarK1mXHOPn7RYIPfcJ+fysssCTcbxRaRc//2cSgQUPfvn2pUaOGucMV4omTmZlJYmIiAQEB2NjYmDscIcqle/2clKfvb3m8+IRTFIWWjXJptfMNtGoKds4ueLiFYq9o2WV9DA0avL29pXOpEEII8ZAk6RKkfPc9lrl3CAh7Bru+71DHIYRscjmjXAEgNDRUOpsKIcqd5cuX4+DgUOgSFBRk7vCEKED6dD3Bbl5JJycllVu//gqA00svcXL9nzyjaLlmkcKhyofo7z6AWrVqmTlSIYQoqHv37jRr1qzQfTLivCiPJOl6gsX9co7juy8T4BWKzvE8iaoLHe7cBOBn12jaNWlPr4a9zBukEEIUwdHREUdHR3OHIUSxyePFJ1TmnRxOxeU9PtRknmS3RS5/fb2Aami5o0lnm9s++tTpY+YohRBCiIpDkq4n1Mm9f6HPMWB/+yJXnXIBqO7wFLnoWWm9i3aaDtgr9maOUgghhKg4JOl6AqmqSsKOiwC4/RXDFXtrXK0qUc3SjePaixhUBdsrtkUOYCiEEEKIkpOk6wn017lbXL94B40+m0z1NAC+bmEYMLDPMm+9davWWFhIlz8hhBCitEjS9QRK+O8I9O5X93HJzREHC2dqWvtyWnsZPSo2djY0atTIzFEKIYQQFYskXU8Yg97AhWM3ANDe3k0uKv4urUCB3ZYnAWjdsrW8bi2EEEKUMkm6njAarYbuLW9R78jX5NjmYK2xo4Z9bRI1f5GNHitrK5o0aWLuMIUQjzFFUe65REREPJLz3rlzh0mTJlGtWjVsbGzw8PAgLCyM9evXG8uEhYUxZsyYAsdGRkbi7OxcYHtGRgYuLi64urqSkZFRYL+/v7/xuuzs7AgODubrr78uVryRkZEm98XLy4tu3bpx9OhRk3IRERGF3sfTp08X6zyi/JCk6wl0a+X3eFw/TJtnu4DfACwVDXutTgHQskVLrK2tzRyhEOJxlpycbFxmzZqFk5OTybbZs2eblM/JySmV8w4bNow1a9Ywd+5cjh8/TlRUFL169eL69esPXOeqVasIDg6mbt26rF69utAy06ZNIzk5mcOHD9OjRw+GDRvGypUri1V//r25dOkSGzZs4M6dO3Tp0oXs7GyTch07djS5h8nJyQQEBDzwdQnzkKTrCZKdmUvmqVOk79sHWi27AlvSFkcUFM45n8YvyI+mTZuaO0whxGPO29vbuOh0OhRFMa5nZmbi7OzMDz/8QFhYGDY2Nnz77bcALF26lDp16mBjY0Pt2rWZN2+eSb0XL17kxRdfxMXFBTc3N5577jnOnTtn3L9u3TrefPNNOnfujL+/P40bN2bkyJEMHDjwga9l8eLF9OvXj379+rF48eJCyzg6OhrnqH3//fepUaMGa9asKVb9+femUqVKNGnShLFjx3L+/HlOnDhhUs7a2trkvnp7e8sb5o8hSbqeIDv/c5oVs05xySMIy9A2nDh4Bx0aki2vklI9k4gXIrC1tTV3mEKIYsjJ0he55Oboi182u3hlS9ukSZMYNWoUx44dIzw8nIULFzJ58mSmT5/OsWPHmDFjBm+//TbLli0DID09nbZt2+Lg4MD27dvZsWMHDg4OdOzY0dgq5O3tzS+//EJaWlqpxHjmzBl+//13evfuTe/evdm1axdnz56973E2NjYP1Hp38+ZNvvvuO0CmMaqoZEyAJ0R2Zi4n914mV29DsmMmh1Mu0dkhG73GilWuWxlcf7BMai3EY2TB6Jgi9/kFu9H1tRDj+pKJseRmGwot61PDmefH/+9t5f+bvIvM2wUThhHzn3mIaAsaM2YMPXv2NK6/9957fPrpp8ZtAQEBJCQk8PXXXzNw4EBWrFiBRqNh0aJFxt9VS5cuxdnZmejoaDp06MCCBQvo27cvbm5uhISE0KpVK1544QVatmxpcu558+axaNEik225ubnY2NiYbFuyZAmdOnXCxcUFyHvEt2TJEt5///1Cryk3N5dvv/2WI0eO8K9//atY9yE1NRUHBwdUVSU9PR3Im1Oydu3aJuXWr1+Pg4ODcb1Tp078+OOPxTqHKD+kpesJcWrfX+RmG7C5c55rDiqVbQNx19iw3mo/WaoVDRwbmDtEIcQT5O4Xdq5evcqFCxcYPHgwDg4OxuX999/nzJkzAMTFxXH69GkcHR2N+11dXcnMzDSWadOmDWfPnmXr1q306tWLo0eP0rp1a9577z2Tc/ft25f4+HiTZdq0aSZl9Ho9y5Yto1+/fsZt/fr1Y9myZej1pi1/kyZNwsHBAVtbW0aMGMHEiRN59dVXi3UfHB0diY+PJy4ujvnz51O9enXmz59foFzbtm1N4p0zZ06x6hfli7R0PSGO7sgbm8s6ZSs37RUCnVuQrKRwVZOG821nLC2kKVuIx8krs0OL3Kf87c/pQR+3Lrrs3xq4B0xv8TBhFZu9/f+mGTMY8lrhFi5cSLNmzUzK5fdbMhgMNG7cmOXLlxeoy8PDw/hvS0tLWrduTevWrXn99dd5//33mTZtGpMmTcLKygoAnU5HYGCgSR2enp4m6xs3bjT2IbubXq9n06ZNdOrUybht4sSJREREYGdnR6VKlUr01ECj0RhjqV27NpcvX+bFF19k+/btJuXs7e0LxCweP5J0PQGuJqVx9Xwa6LNItbqBp40/nlYebLCMA6Bhw4Y4OTmZOUohRElYWhe/E/WjKltavLy8qFy5MmfPnqVv376FlmnUqBErV67E09OzRL+v6tatS25uLpmZmcakqzgWL17MSy+9xOTJk022z5w5k8WLF5skXe7u7qWWEI0dO5bPPvuMn376ieeff75U6hTlhyRdT4D8Vi6H69u4ZqmhiXNzriipJGtugpI35Y8QQpjT1KlTGTVqFE5OTnTq1ImsrCz2799PSkoK48aNo2/fvnz88cc899xzTJs2jSpVqpCUlMTq1auZOHEiVapUISwsjJdffpkmTZrg5uZGQkICb775Jm3bti1Ronb16lXWrVvH2rVrCQ4ONtk3cOBAunTpwtWrV01a2EqLk5MTQ4YMYcqUKfTo0UP62lYw0qergsvJ0nNyTzIAWfpj6Cw9qGzrz0GLcwAE1ws2dhIVQghzGTJkCIsWLSIyMpJ69eoRGhpKZGSkcSwqOzs7tm/fTtWqVenZsyd16tRh0KBBZGRkGBOq8PBwli1bRocOHahTpw4jR44kPDycH374oUSx/N///R/29vY8++yzBfa1bdsWR0dHvvnmm4e/6CKMHj2aY8eOSUf5CkhRVVU1dxCPg1u3bqHT6UhNTX2sHsWpqsrxWctJ2HqIJLtjNPHsipNjVX6y3gvAa6+9hru7u5mjFEIUJTMzk8TERAICAgq8XSeEyHOvn5Py9P0tLV0VncGA5YZl1D29noA6nfC1r8NR7QUAatWpJQmXEEIIUUYk6arg7uzYQc6ff6JxcuJybi0sFA32lgrWda15Jqx0x90RQgjxP0FBQSZDYNy9FPYWpqj4pCN9Bbb75zPc+PU4rvYe6Fu05Rl93se9ziOGD7rOwsOu9DuBCiGEyPPLL78UOTK9l5dXGUcjygNJuiqonGw9R7ZdIEsN4HSAE1VyXPCxgbPWf1K1QS1JuIQQ4hHz8/MzdwiinJGkq4I6E3eF7CwD2jtx5FhoCbarzR6LUxy1SKSbroe5wxNCCCGeONKnq4I6uv1PVFXFkLEff/sgVK0FJ7WX0Oqt8LT3vH8FQgghhChV0tJVAV2/dJvLiWmoOX+SZZFFTeemHLE4j0FR8fDxwN/f39whCiGEEE8caemqgBL+OwK9Ni0aH7tArCwdOaa9CED4M+FmjEwIIYR4cknSVcHkZus5vvMiqiGNDOUqtXXN+MPiAnrFgLOHM9WrVzd3iEIIIcQTSZKuCkafa8Df4jzaW7G42VTGycaLhP8Ohhr+TLjM4yWEeOKdO3cORVGIj483dygV1tSpU2nQoIG5wyh3JOmqYCwMWVT97QtaHdqAr9uznNX+RY6ix97Fnlq1apk7PCHEEyQiIoIePXqYbPvPf/6DjY0NH330kXmCegCrVq2iWbNm6HQ6HB0dCQoKYvz48cb9kZGRODs7F3qsoiisWbOmwPZXXnkFrVbLihUrCuybOnUqiqKgKAparRZfX1+GDBnC1atXixVv/rGKouDg4EBISAiRkZEmZaKjo03K5S9vvfVWsc4hHox0pK9gUteuxZCejt4/hBrW3ih6+CVgJ/3b9kejkRxbCGE+ixYtYsSIEXz55ZcMGTKkxMdnZ2djZWX1CCIr2pYtW3jppZeYMWMG3bt3R1EUEhIS2Lp16wPXmZ6ezsqVK5k4cSKLFy/mpZdeKlAmKCiILVu2oNfrOXjwIIMHD+bixYv8+uuvxTrH0qVL6dixI3fu3GHlypX885//pFKlSoSHm/brPXHihMl8hA4ODg98XeL+5Fu4Ajn2ezInV+/CgMKVGt3QoLDb4TAdnu0ifbmEEGb10Ucf8dprr/Hdd98ZE65du3bRpk0bbG1t8fX1ZdSoUdy5c8d4jL+/P++//z4RERHodDqGDh1qbFXauHEjderUwcHBgY4dO5KcnGxyvqVLl1KnTh1sbGyoXbs28+bNe6C4169fT6tWrZg4cSK1atWiZs2a9OjRgy+++OKB78WPP/5I3bp1eeONN9i5cyfnzp0rUMbCwgJvb28qV65M165dGTVqFJs2bSIjI6NY53B2dsbb25vq1avz5ptv4urqyqZNmwqU8/T0xNvb27gUJ+nK/wzWrFlDzZo1sbGxoX379ly4cKHIY8LCwhgzZozJth49ehAREWFcnzdvHjVq1MDGxgYvLy9eeOGFYl3r40SSrgoiN0fPzhXH2atrwq769aluV4lsctle5TDt/dqbOzwhRCnLycwscsnNzi522ZzsrGKVfRivv/467733HuvXr6dXr14AHDlyhPDwcHr27Mnhw4dZuXIlO3bs4LXXXjM59uOPPyY4OJi4uDjefvttIK+l6JNPPuGbb75h+/btJCUlMWHCBOMxCxcuZPLkyUyfPp1jx44xY8YM3n77bZYtW1bi2L29vTl69Ch//PHHQ9wBU4sXL6Zfv37odDo6d+7M0qVL73uMra0tBoOB3NzcEp1Lr9fzww8/cOPGDSwtLR805ALS09OZPn06y5YtY+fOndy6davQFrvi2r9/P6NGjWLatGmcOHGCqKgo2rRpU2rxlhfyeLGCOBt/lawsFTV9D36uDTlj8Rd7LU8R7N4YC418zEJUNHMGFt0KENCwCT1fn2pcn/dKX3KzsgotW6VuMC9OmWlcX/jaIDLSbhUoN37l+geK89dff+Xnn39m69atPPPMM8btH3/8MX369DG2ftSoUYM5c+YQGhrKV199hY2NDQDPPPOMSUK1Y8cOcnJymD9/vrEF/7XXXmPatGnGMu+99x6ffvopPXv2BCAgIICEhAS+/vprBg4cWKL4R44cSWxsLPXq1cPPz4+nn36aDh060LdvX6ytrY3lUlNTi9VKdOrUKXbv3s3q1asB6NevH6NGjWLKlClFdgE5fvw4X331FU2bNsXR0bFYcb/88stotVoyMzPR6/W4uroW+ki3SpUqJuvnz5/Hzc3tvvXn5OQwd+5cmjVrBsCyZcuoU6cOe/fupWnTpsWK8W5JSUnY29vTtWtXHB0d8fPzo2HDhiWup7yTlq4K4ui28xj0N8HwFwGODThscZ5cDNT3qG/u0IQQT7D69evj7+/PO++8Q1pamnF7XFwckZGRODg4GJfw8HAMBgOJiYnGck2aNClQp52dnUmXiUqVKnHlyhUArl69yoULFxg8eLBJ3e+//z5nzpwpcfz29vZs2LCB06dP89Zbb+Hg4MD48eNp2rQp6enpxnKOjo7Ex8cXWP5u8eLFhIeH4+7uDkDnzp25c+cOW7ZsMSl35MgRHBwcsLW1pW7duvj6+rJ8+fJix/35558THx/P5s2badCgAZ9//jmBgYEFysXGxprE6+LiUqz6LSwsTD6b2rVr4+zszLFjx4od493at2+Pn58f1apVo3///ixfvtzk/lYU0gRSAdz8K51LiXfQZx6gulN9kqxuclvJRGutpUmjgr+whBCPv1HL/lPkPuVvLSbDF9zjy1pjOozM0LlLHiquv6tcuTKrVq2ibdu2dOzYkaioKBwdHTEYDLz66quMGjWqwDFVq1Y1/tve3r7A/r8/JlMUBVVVATAYDEDeI8b8Vph8Wq32ga+jevXqVK9enSFDhjB58mRq1qxp7KAOoNFoCk1q7qbX6/m///s/Ll++jIWFhcn2xYsX06FDB+O2WrVqsXbtWrRaLT4+PiatasXh7e1NYGAggYGB/PjjjzRs2JAmTZpQt25dk3IBAQFFvnl5P4UNQVTUsEQajcb4GeXLyckx/tvR0ZEDBw4QHR3Npk2beOedd5g6dSr79u174PjKI0m6KoCjsX+iqlmoWUcJ9BrMr9qjALRu1bpUn+ELIcoPy/8+fjNn2eKqWrUqMTExtG3blg4dOrBx40YaNWrE0aNH75uolJSXlxeVK1fm7Nmz9O3bt1Trzufv74+dnZ1Jp//i+OWXX0hLS+PgwYMmCeDx48fp27cv169fNz7as7KyKrV7ExgYSK9evXjjjTf4+eefS6XO3Nxc9u/fb3yUeOLECW7evEnt2rULLe/h4WHysoNer+ePP/6gbdu2xm0WFha0a9eOdu3aMWXKFJydndm2bZvxMXFFIEnXY06fa+B47J/os47i61CDK1YZ3NJkoFgqNG/a3NzhCSEEkNd3KDo62ph4ff311zRv3pwRI0YwdOhQ7O3tOXbsGJs3b36oNwMhb5yrUaNG4eTkRKdOncjKymL//v2kpKQwbty4EteVnp5O586d8fPz4+bNm8yZM4ecnBzaty/ZS0qLFy+mS5cuhISEmGwPCgpizJgxfPvtt4wePbpEdRbX+PHjCQkJYf/+/YU+si0pS0tLRo4cyZw5c7C0tOS1117j6aefLrI/1zPPPMO4cePYsGED1atX5/PPP+fmzZvG/evXr+fs2bO0adMGFxcXfvnlFwwGQ4UbX1L6dD3mbqdkYpGRgiHzALV0TYm3OAfA008/XeLmaCGEeJQqV65MTEwMN2/eZOjQocTExHDq1Clat25Nw4YNefvtt6lUqdJDn2fIkCEsWrSIyMhI6tWrR2hoKJGRkQQEBJS4rtDQUM6ePcuAAQOoXbs2nTp14vLly2zatKlECcFff/3Fhg0bjG9v3k1RFHr27MnixYtLHF9x1atXj3bt2vHOO++USn12dnZMmjSJPn360Lx5c2xtbQsd6DXfoEGDGDhwIAMGDCA0NJSAgACTVi5nZ2dWr17NM888Q506dZg/fz7ff/89QUFBpRJveaGof3/IKgp169YtdDodqampJgPJmVt2UhKnwjty268ZVg1f4GerfWABkyZMwtbW1tzhCSEeUmZmJomJiQQEBBjf6BPCnCIjIxkzZoxJS5W53evnpDx9f8vjxcdcysqVaFQVu4C2uKhOONsqPNWhnSRcQgghRDkjjxcfY3+dvs6Nn9ah0VXFRReAHj0ZjbS0bNTS3KEJIcRjYdiwYSZDS9y9DBs2zNzhFTBjxowi4+3UqVOpnKNTp05FnmPGjBmlco4nlTxeLKby1DwJeR3oI8dt4/aN32jhUBVH52rsczpCyxE9qKarZu7whBClRB4vPlpXrlzh1q2Cg8ECODk54enpWcYR3duNGze4ceNGoftsbW2pXLnyQ5/j4sWLRU435Orqiqur60Ofo7TJ40XxSJ07fI2MzCys1QsYXJ5ipdVOcFDp51TyjqJCCPGk8vT0LHeJ1b2URdJTGombKJw8XnxMHYk6iT77CDWdGnLYIglVgWo+gUUOTCeEEEII85Kk6zF061oGf57PQJN9HEedPxe1NwCVrs92NXdoQgghhCiCJF2PoaO/ncOQc4ZAx5ocsbwIQOWaVYo9Z5YQQgghyp4kXY8Zvd7Ase1/omYdwt25Fknaa4DK8x2eN3doQgghhLgHsyZdH3zwAU899RSOjo54enrSo0cPTpw4YVJGVVWmTp2Kj48Ptra2hIWFcfToUZMyWVlZjBw5End3d+zt7enevTt//vmnSZmUlBT69++PTqdDp9PRv3//cjWwW3H9lZjKnfSr+Nm6kWD1FwDu/h7GGeuFEEIIUT6ZNemKiYlhxIgR7N69m82bN5Obm0uHDh1MJhH96KOP+Oyzz5g7dy779u3D29ub9u3bk5aWZiwzZswYfvrpJ1asWMGOHTu4ffs2Xbt2Ra/XG8v06dOH+Ph4oqKiiIqKIj4+nv79+5fp9ZYG3bWTNIn/ikDHxlzSpADQq2PBaSWEEEIUFBERQY8ePe5ZJjo6GkVRHss/zMuTc+fOoSgK8fHx5g6l/FDLkStXrqiAGhMTo6qqqhoMBtXb21udOXOmsUxmZqaq0+nU+fPnq6qqqjdv3lQtLS3VFStWGMtcvHhR1Wg0alRUlKqqqpqQkKAC6u7du41lfv/9dxVQjx8/XqzYUlNTVUBNTU196Ot8GBdGj1FPtu2jXpi0XT30xjo1cuNSs8YjhHi0MjIy1ISEBDUjI8PcoZTYwIED1eeee67QfX5+fipgslSuXNlk/+eff17qMd28eVNNSUkxroeGhqqjR482KfPbb7+pgEm5x83AgQON91Wr1aq+vr7qsGHD1Bs3bpiUu9/n8DASExNVQD148GCp1Hcv9/o5KS/f36qqquWqT1dqaiqAcQySxMRELl++TIcOHYxlrK2tCQ0NZdeuXQDExcWRk5NjUsbHx4fg4GBjmd9//x2dTkezZs2MZZ5++ml0Op2xzN9lZWVx69Ytk8XcMi5eJm3LFqwCwwGIcT9En3Z9zRyVEEI8mGnTppGcnGxcDh48+MjPqdPpcHZ2fuTnuVtOTk6Zni9fx44dSU5O5ty5cyxatIh169YxfPjwAuXM8Tk8qcpN0qWqKuPGjaNVq1YEBwcDcPnyZQC8vLxMynp5eRn3Xb58GSsrqwJv7v29TGGD33l6ehrL/N0HH3xg7P+l0+nw9fV9uAt8SAa9ge8/OMChht255eZBlpKNR5sALDWWZo1LCCEelKOjI97e3sbFw8OjxHWMHz+ebt26GddnzZqFoihs2LDBuK1WrVp8/fXXgOnjxYiICGJiYpg9ezaKoqAoCufOnTMeFxcXR5MmTbCzs6NFixYF+hwXZerUqTRo0IAlS5ZQrVo1rK2tUVWVqKgoWrVqhbOzM25ubnTt2pUzZ84Yj+vVqxcjR440ro8ZMwZFUYz9mHNzc3F0dGTjxo3FisPa2hpvb2+qVKlChw4dePHFF9m0aVOBcg/6OSiKwldffUWnTp2wtbUlICCAH3/8scjykZGRBRLeNWvWmIwveejQIdq2bYujoyNOTk40btyY/fv3Fyuex0G5Sbpee+01Dh8+zPfff19g398H/FRV9b6DgP69TGHl71XPG2+8QWpqqnG5cOFCcS7jkTl/+Cq375zF29Wfn6z2ssJmJ+G1S2eeLSHE40NVVQzZerMsajmcNS4sLIzY2FgMBgOQ11fY3d2dmJgYIO+P7pMnTxIaGlrg2NmzZ9O8eXOGDh1qbOW5+w/syZMn8+mnn7J//34sLCwYNGhQseM6ffo0P/zwA6tWrTL2abpz5w7jxo1j3759bN26FY1Gw/PPP2+MPSwsjOjoaGMdf7+Wffv2kZmZScuWJZ9f9+zZs0RFRWFpWbp/qL/99tv06tWLQ4cO0a9fP15++WWOHTv2wPX17duXKlWqsG/fPuLi4nj99ddLPWZzKhfTAI0cOZK1a9eyfft2qlSpYtzu7e0N5P3QVKpUybj9ypUrxtYvb29vsrOzSUlJMWntunLlCi1atDCW+euvvwqc9+rVqwVa0fJZW1tjbW398BdXSg79fAQ7/VmuOgZhUFQsnaxwcZBxuYR40qg5Bi69U3i3iEfNZ1oLFCttqdU3adIk3nrrLeP6jBkzGDVqVInqaNOmDWlpaRw8eJBGjRoRGxvLhAkTWL16NQC//fYbXl5e1K5du8CxOp0OKysr7OzsjN83d5s+fboxWXv99dfp0qULmZmZxZoDMzs7m2+++cak1ahXL9OXnhYvXoynpycJCQkEBwcTFhbG6NGjuXbtGlqtlqNHjzJlyhSio6MZPnw40dHRNG7cGAcHh2Ldm/Xr1+Pg4IBeryczMxOAzz77rEC5h/kc/vGPfzBkyBAA3nvvPTZv3swXX3zBvHnzinX83yUlJTFx4kTj51WjRo0Hqqe8MmtLl6qqvPbaa6xevZpt27YREGA6b2BAQADe3t5s3rzZuC07O5uYmBhjQtW4cWMsLS1NyiQnJ/PHH38YyzRv3pzU1FT27t1rLLNnzx5SU1ONZcqz2ymZXLhwCX+nAI5r8wZD7dKpi5mjEkKIhzNx4kTi4+ONy4ABA0pch06no0GDBkRHR3PkyBE0Gg2vvvoqhw4dIi0tjejo6EJbuYqjfv36xn/n/+F/5cqVYh3r5+dX4DHdmTNn6NOnD9WqVcPJycn4nZeUlARAcHAwbm5uxMTEEBsbS0hICN27dze2dJX0Wtq2bUt8fDx79uxh5MiRhIeHmzy+zPcwn0Pz5s0LrD9MS9e4ceMYMmQI7dq1Y+bMmSaPXysCs7Z0jRgxgu+++46ff/4ZR0dHY/8qnU6Hra0tiqIwZswYZsyYQY0aNahRowYzZszAzs6OPn36GMsOHjyY8ePH4+bmhqurKxMmTKBevXq0a9cOgDp16tCxY0eGDh1qfK7/yiuv0LVrV2rVqmWeiy+BIxsSsMg5Tqp3IHrlOpZ2FtSrVc/cYQkhzECx1OAzzTx/LCqWpft3uru7O4GBgQ9dT/5jOSsrK0JDQ3FxcSEoKIidO3cSHR3NmDFjHqjeux9r5XdFyX8UeD/29vYFtnXr1g1fX18WLlyIj48PBoOB4OBgsrOzjedo06aN8VrCwsIIDg5Gr9dz5MgRdu3aVaJrsbe3N97fOXPm0LZtW959913ee+89k3Kl9TnkK6rbjkajKfCI+u8vGUydOpU+ffqwYcMGfv31V6ZMmcKKFSt4/vmKMQC4WVu6vvrqK1JTUwkLC6NSpUrGZeXKlcYy//73vxkzZgzDhw+nSZMmXLx4kU2bNuHo6Ggs8/nnn9OjRw969+5Ny5YtsbOzY926dWi1/2sGX758OfXq1aNDhw506NCB+vXr880335Tp9T4Ig0Hl6I7T+Nu7cNwir5WrQ8dwmdhaiCeUoihorLRmWcrr7538fl3btm0jLCwMgNDQUFasWFFkf658VlZWJmM6PirXr1/n2LFjvPXWWzz77LPUqVOHlJSUAuXyE8jo6GjCwsJQFIXWrVvzySefkJGR8UD9ufJNmTKFTz75hEuXLj3MpZjYvXt3gfXCHuUCeHh4kJaWZjIWZ2FjeNWsWZOxY8eyadMmevbsydKlS0stXnMza0tXcTplKorC1KlTmTp1apFlbGxs+OKLL/jiiy+KLOPq6sq33377IGGa1fn4ZDIzTpJZ2YccJQmttZbGwY3NHZYQQhRLampqgS/W/GGBSkt+v65169bx/vvvA3nJS69evfDw8KBu3bpFHuvv78+ePXs4d+4cDg4OpR5bPhcXF9zc3FiwYAGVKlUiKSmJ119/vUC5/H5dFhYWtG7d2rht/PjxNGrUCCcnpweOISwsjKCgIGbMmMHcuXMfuJ67/fjjjzRp0oRWrVqxfPly9u7dy+LFiwst26xZM+zs7HjzzTcZOXIke/fuJTIy0rg/IyODiRMn8sILLxAQEMCff/7Jvn37CvSFe5yVm7cXReEcTu0mRLXkL4u8ccLaPNsajUY+NiHE4yE6OpqGDRuaLO+8806pnkOn09GwYUNcXV2NCVbr1q0xGAz37QM1YcIEtFotdevWxcPDw9i/qrRpNBpWrFhBXFwcwcHBjB07lo8//rhAueDgYNzd3QkJCTEmWKGhoej1+gfum3a3cePGsXDhwlJ7I//dd99lxYoV1K9fn2XLlrF8+fIik9z8xo9ffvmFevXq8f3335s0qGi1Wq5fv86AAQOoWbMmvXv3plOnTrz77rulEmt5oKjl8R3gcujWrVvodDpSU1Mf6i+NkjrdqzdWVfqh2DrzQ+XfGTtioiRdQjxBMjMzSUxMJCAgoFhvzQlRVhRF4aeffrrvtEpl4V4/J+b6/i6MfHuXYxlHjqDeckBj60KKNpWuLz4nCZcQQgjxmJJv8HJKNaisnhvN9XrtyUXPzkqnqeVZeOdEIYSoqJYvX46Dg0OhS1BQkFliCgoKKjKm5cuXP/LzJyUlFXl+BweHUnlEWh7ve0VQLgZHFQWdj/sTRX+LWMfb6DnLU82fNndIQghR5rp3724yb+7dzDVS+S+//FLkfIpFDbhdmnx8fAp96+/u/Q+rOPddeieVnCRd5dS+H7Zj6+LCHeUKWhTa1G9j7pCEEKLMOTo6mgwRVB74+fmZ9fwWFhalOq5WYcrjfa8IJOkqh+7czOTO9YvcqJL3V4RfvWoVau4pIYQQ4kkkfbrKoYPfxeLoYsMtTQZaVeHFrr3NHZIQQgghHpK0dJUzqkHldNwhblbOm2rCM9CnXE28LYQQQogHIy1d5cy5Xaewd7LgpiYdrarQr1cfc4ckhBBCiFIgSVc589fGTWjt8jov6rxdsbcrOGmqEEIIIR4/8nixHFFzctCdSKZKtY7o9AqN+laMWdWFEEIUFBERwc2bN1mzZo25QxFlRFq6ypFL66Kwr9wKgD/raXFxcjFzREII8eAiIiKKnCLG39+fWbNmmawrisKKFSsKlA0KCkJRFJPJkfPL/32ZOXPmfeM6d+6cyTE6nY6nn36adevWmZSLjIws9ByLFi0q1vUL8XfS0lVOqKpK3KZT1HAJRs25Ss+ePc0dkhBClClfX1+WLl3KSy+9ZNy2e/duLl++jL19wa4W06ZNY+jQoSbbSjK21JYtWwgKCuLmzZvMmzePXr16ceDAAYKDg41lnJycOHHihMlxOp2u2OcQ4m7S0lVOJGzYw0V3lR+sfme7SyKOtjIonRDiydK3b19iYmK4cOGCcduSJUvo27cvFhYF2wgcHR3x9vY2WQpLzori5uaGt7c3tWvXZvr06eTk5PDbb7+ZlFEUpcA5bG1t71v31KlTadCgAV9//TW+vr7Y2dnxj3/8g5s3bxZ5zN9b/wAaNGjA1KlTTeqtWrUq1tbW+Pj4MGrUqGJfrzA/SbrKiUMbd/CXRRoAz/TrYd5ghBDlXnZ2dpHL36eoKY2yZcHLy4vw8HCWLVsGQHp6OitXrmTQoEGP9Lw5OTksXLgQKN2phU6fPs0PP/zAunXriIqKIj4+nhEjRjxwff/5z3/4/PPP+frrrzl16hRr1qyhXr16pRavePTk8WI5cOvSNW45KQC45VpR06+6mSMSQpR3M2bMKHJfjRo16Nu3r3H9448/LnKuQD8/P/75z38a12fNmkV6enqBcne3tjxKgwYNYvz48UyePJn//Oc/VK9enQYNGhRadtKkSbz11lsm29avX09YWFixztWiRQs0Gg0ZGRkYDAb8/f3p3dt0MOrU1FQcHByM6w4ODly+fLlY9WdmZrJs2TKqVKkCwBdffEGXLl349NNP8fb2LlYdd0tKSsLb25t27dphaWlJ1apVadq0aYnrEeYjLV3lwPpP/4/LlmkoKjTt1d7c4QghhNl06dKF27dvs337dpYsWXLPVq6JEycSHx9vshQ1SXNhVq5cycGDB1m7di2BgYEsWrQIV1dXkzKOjo4m9e/atavY9VetWtWYcAE0b94cg8FQoI9Ycf3jH/8gIyODatWqMXToUH766Sdyc3MfqC5hHtLSZWYGg4E067w5Fj2ybWnaqImZIxJCPA7efPPNIvcpimKyPnHixGKXHTNmzEPF9bAsLCzo378/U6ZMYc+ePfz0009FlnV3d3+oiZ99fX2pUaMGNWrUwMHBgV69epGQkICnp6exjEajKbXJpfPv9d/v+d3nUlXVZNvdLZS+vr6cOHGCzZs3s2XLFoYPH87HH39MTEyMzM/7mJCWLjP76ZMlJFvm9eUKaFzbzNEIIR4XVlZWRS5//wIujbJladCgQcTExPDcc8/h4lI2Q+eEhoYSHBzM9OnTS63OpKQkLl26ZFz//fff0Wg01KxZs9DyHh4eJCcnG9dv3bpFYmKiSRlbW1u6d+/OnDlziI6O5vfff+fIkSOlFrN4tKSly8w0f2Vi6aDFJduaTi88Z+5whBCiVKWmphIfH2+y7e+P8P6uTp06XLt2DTs7u3uWS0tLK9C/ys7ODicnpweKdfz48fzjH//g3//+N5UrV36gOu5mY2PDwIED+eSTT7h16xajRo2id+/eRfbneuaZZ4iMjKRbt264uLjw9ttvo9VqjfsjIyPR6/U0a9YMOzs7vvnmG2xtbfHz83voWEXZkKTLjLKzc/DPUKhn0ZxjVf80dzhCCFHqoqOjadiwocm2gQMH3vc4Nze3+5Z55513eOedd0y2vfrqq8yfP79kQf5X165d8ff3Z/r06cybN++B6rhbYGAgPXv2pHPnzty4cYPOnTvfs9433niDs2fP0rVrV3Q6He+9955JS5ezszMzZ85k3Lhx6PV66tWrx7p164p1r0T5oKh/f4AsCnXr1i10Oh2pqakP/FdUUeJ37CKkZfMin/MLIZ5cmZmZJCYmEhAQgI2NjbnDEcU0depU1qxZU6CVTzwa9/o5eZTf3yUlLV3lQINWLcwdghBCCCEeMelIL4QQokIZNmwYDg4OhS7Dhg0rlXMEBQUVeY7ly5eXyjlExSOPF4upPDVPCiGeHPJ4seSuXLnCrVu3Ct3n5ORkMiTEgzp//nyRA856eXmVaA5I8fDk8aIQQghhBp6enqWSWN2LvDEoHoQ8XhRCCCGEKAOSdAkhxGNAeoIIUbTH5edDki4hhCjH8gfHzM7ONnMkQpRf+ZO0l/fpkKRPlxBClGMWFhbY2dlx9epVLC0t0Wjkb2Uh8qmqSnp6OleuXMHZ2dlkBP/ySJIuIYQoxxRFoVKlSiQmJnL+/HlzhyNEueTs7Fzk9ErliSRdQghRzllZWVGjRg15xChEISwtLct9C1c+SbqEEOIxoNFoZJwuIR5z0jlACCGEEKIMSNIlhBBCCFEGJOkSQgghhCgD0qermPIHXitqPi8hhBBClD/539vlYQBVSbqKKS0tDQBfX18zRyKEEEKIkkpLS0On05k1BkUtD6nfY8BgMHDp0iUcHR1RFKXU6r116xa+vr5cuHDB7LOfV3Ryr8uG3OeyIfe5bMh9LhuP8j6rqkpaWho+Pj5mH1xYWrqKSaPRUKVKlUdWv5OTk/xAlxG512VD7nPZkPtcNuQ+l41HdZ/N3cKVTzrSCyGEEEKUAUm6hBBCCCHKgCRdZmZtbc2UKVOwtrY2dygVntzrsiH3uWzIfS4bcp/LxpNyn6UjvRBCCCFEGZCWLiGEEEKIMiBJlxBCCCFEGZCkSwghhBCiDEjSJYQQQghRBiTpMrN58+YREBCAjY0NjRs3JjY21twhVSgffPABTz31FI6Ojnh6etKjRw9OnDhh7rAqvA8++ABFURgzZoy5Q6lwLl68SL9+/XBzc8POzo4GDRoQFxdn7rAqnNzcXN566y0CAgKwtbWlWrVqTJs2DYPBYO7QHmvbt2+nW7du+Pj4oCgKa9asMdmvqipTp07Fx8cHW1tbwsLCOHr0qHmCfQQk6TKjlStXMmbMGCZPnszBgwdp3bo1nTp1IikpydyhVRgxMTGMGDGC3bt3s3nzZnJzc+nQoQN37twxd2gV1r59+1iwYAH169c3dygVTkpKCi1btsTS0pJff/2VhIQEPv30U5ydnc0dWoXz4YcfMn/+fObOncuxY8f46KOP+Pjjj/niiy/MHdpj7c6dO4SEhDB37txC93/00Ud89tlnzJ07l3379uHt7U379u2N8x8/9lRhNk2bNlWHDRtmsq127drq66+/bqaIKr4rV66ogBoTE2PuUCqktLQ0tUaNGurmzZvV0NBQdfTo0eYOqUKZNGmS2qpVK3OH8UTo0qWLOmjQIJNtPXv2VPv162emiCoeQP3pp5+M6waDQfX29lZnzpxp3JaZmanqdDp1/vz5Zoiw9ElLl5lkZ2cTFxdHhw4dTLZ36NCBXbt2mSmqii81NRUAV1dXM0dSMY0YMYIuXbrQrl07c4dSIa1du5YmTZrwj3/8A09PTxo2bMjChQvNHVaF1KpVK7Zu3crJkycBOHToEDt27KBz585mjqziSkxM5PLlyybfi9bW1oSGhlaY70WZ8NpMrl27hl6vx8vLy2S7l5cXly9fNlNUFZuqqowbN45WrVoRHBxs7nAqnBUrVnDgwAH27dtn7lAqrLNnz/LVV18xbtw43nzzTfbu3cuoUaOwtrZmwIAB5g6vQpk0aRKpqanUrl0brVaLXq9n+vTpvPzyy+YOrcLK/+4r7Hvx/Pnz5gip1EnSZWaKopisq6paYJsoHa+99hqHDx9mx44d5g6lwrlw4QKjR49m06ZN2NjYmDucCstgMNCkSRNmzJgBQMOGDTl69ChfffWVJF2lbOXKlXz77bd89913BAUFER8fz5gxY/Dx8WHgwIHmDq9Cq8jfi5J0mYm7uztarbZAq9aVK1cKZPni4Y0cOZK1a9eyfft2qlSpYu5wKpy4uDiuXLlC48aNjdv0ej3bt29n7ty5ZGVlodVqzRhhxVCpUiXq1q1rsq1OnTqsWrXKTBFVXBMnTuT111/npZdeAqBevXqcP3+eDz74QJKuR8Tb2xvIa/GqVKmScXtF+l6UPl1mYmVlRePGjdm8ebPJ9s2bN9OiRQszRVXxqKrKa6+9xurVq9m2bRsBAQHmDqlCevbZZzly5Ajx8fHGpUmTJvTt25f4+HhJuEpJy5YtCwx5cvLkSfz8/MwUUcWVnp6ORmP6FanVamXIiEcoICAAb29vk+/F7OxsYmJiKsz3orR0mdG4cePo378/TZo0oXnz5ixYsICkpCSGDRtm7tAqjBEjRvDdd9/x888/4+joaGxZ1Ol02Nramjm6isPR0bFAPzl7e3vc3Nyk/1wpGjt2LC1atGDGjBn07t2bvXv3smDBAhYsWGDu0Cqcbt26MX36dKpWrUpQUBAHDx7ks88+Y9CgQeYO7bF2+/ZtTp8+bVxPTEwkPj4eV1dXqlatypgxY5gxYwY1atSgRo0azJgxAzs7O/r06WPGqEuReV+eFF9++aXq5+enWllZqY0aNZKhDEoZUOiydOlSc4dW4cmQEY/GunXr1ODgYNXa2lqtXbu2umDBAnOHVCHdunVLHT16tFq1alXVxsZGrVatmjp58mQ1KyvL3KE91n777bdCfycPHDhQVdW8YSOmTJmient7q9bW1mqbNm3UI0eOmDfoUqSoqqqaKd8TQgghhHhiSJ8uIYQQQogyIEmXEEIIIUQZkKRLCCGEEKIMSNIlhBBCCFEGJOkSQgghhCgDknQJIYQQQpQBSbqEEEIIIcqAJF1CCADOnTuHoijEx8ebOxSj48eP8/TTT2NjY0ODBg0KLaOqKq+88gqurq7lLn5zio6ORlEUbt68WWSZyMhInJ2dyyymv/P392fWrFlmO78QZU2SLiHKiYiICBRFYebMmSbb16xZg6IoZorKvKZMmYK9vT0nTpxg69athZaJiooiMjKS9evXk5ycXGrTDkVERNCjR49SqasikURJiAcnSZcQ5YiNjQ0ffvghKSkp5g6l1GRnZz/wsWfOnKFVq1b4+fnh5uZWZJlKlSrRokULvL29sbAoX1PK6vV6mSRZCAFI0iVEudKuXTu8vb354IMPiiwzderUAo/aZs2ahb+/v3E9v5VmxowZeHl54ezszLvvvktubi4TJ07E1dWVKlWqsGTJkgL1Hz9+nBYtWmBjY0NQUBDR0dEm+xMSEujcuTMODg54eXnRv39/rl27ZtwfFhbGa6+9xrhx43B3d6d9+/aFXofBYGDatGlUqVIFa2trGjRoQFRUlHG/oijExcUxbdo0FEVh6tSpBeqIiIhg5MiRJCUloSiK8R6oqspHH31EtWrVsLW1JSQkhP/85z/G4/R6PYMHDyYgIABbW1tq1arF7NmzTe7xsmXL+Pnnn1EUBUVRiI6OLvSRXXx8PIqicO7cOeB/j+zWr19P3bp1sba25vz582RnZ/Pvf/+bypUrY29vT7NmzUzu7fnz5+nWrRsuLi7Y29sTFBTEL7/8Uui9A/j2229p0qQJjo6OeHt706dPH65cuVKg3M6dOwkJCcHGxoZmzZpx5MiRIus8c+YMzz33HF5eXjg4OPDUU0+xZcsW4/6wsDDOnz/P2LFjjfcl365du2jTpg22trb4+voyatQo7ty5Y9x/5coVunXrhq2tLQEBASxfvrzIOISoqCTpEqIc0Wq1zJgxgy+++II///zzoeratm0bly5dYvv27Xz22WdMnTqVrl274uLiwp49exg2bBjDhg3jwoULJsdNnDiR8ePHc/DgQVq0aEH37t25fv06AMnJyYSGhtKgQQP2799PVFQUf/31F7179zapY9myZVhYWLBz506+/vrrQuObPXs2n376KZ988gmHDx8mPDyc7t27c+rUKeO5goKCGD9+PMnJyUyYMKHQOvITt+TkZPbt2wfAW2+9xdKlS/nqq684evQoY8eOpV+/fsTExAB5CV+VKlX44YcfSEhI4J133uHNN9/khx9+AGDChAn07t2bjh07kpycTHJyMi1atCj2vU9PT+eDDz5g0aJFHD16FE9PT/75z3+yc+dOVqxYweHDh/nHP/5Bx44djdc7YsQIsrKy2L59O0eOHOHDDz/EwcGhyHNkZ2fz3nvvcejQIdasWUNiYiIREREFyk2cOJFPPvmEffv24enpSffu3cnJySm0ztu3b9O5c2e2bNnCwYMHCQ8Pp1u3biQlJQGwevVqqlSpwrRp04z3BeDIkSOEh4fTs2dPDh8+zMqVK9mxYwevvfaase6IiAjOnTvHtm3b+M9//sO8efMKTRKFqNDMO9+2ECLfwIED1eeee05VVVV9+umn1UGDBqmqqqo//fSTeveP6pQpU9SQkBCTYz///HPVz8/PpC4/Pz9Vr9cbt9WqVUtt3bq1cT03N1e1t7dXv//+e1VVVTUxMVEF1JkzZxrL5OTkqFWqVFE//PBDVVVV9e2331Y7dOhgcu4LFy6ogHrixAlVVVU1NDRUbdCgwX2v18fHR50+fbrJtqeeekodPny4cT0kJESdMmXKPev5+7Xfvn1btbGxUXft2mVSbvDgwerLL79cZD3Dhw9Xe/XqZVy/+/PI99tvv6mAmpKSYtx28OBBFVATExNVVVXVpUuXqoAaHx9vLHP69GlVURT14sWLJvU9++yz6htvvKGqqqrWq1dPnTp16j2v9V727t2rAmpaWppJrCtWrDCWuX79umpra6uuXLnSGKtOp7tnvXXr1lW/+OIL47qfn5/6+eefm5Tp37+/+sorr5hsi42NVTUajZqRkaGeOHFCBdTdu3cb9x87dkwFCtQlREVWvjo/CCEA+PDDD3nmmWcYP378A9cRFBSERvO/xmwvLy+TTuZarRY3N7cCrQ3Nmzc3/tvCwoImTZpw7NgxAOLi4vjtt98KbYE5c+YMNWvWBKBJkyb3jO3WrVtcunSJli1bmmxv2bIlhw4dKuYVFi4hIYHMzMwCjzWzs7Np2LChcX3+/PksWrSI8+fPk5GRQXZ2dpFvSJaUlZUV9evXN64fOHAAVVWN9ydfVlaWsa/aqFGj+Ne//sWmTZto164dvXr1Mqnj7w4ePMjUqVOJj4/nxo0bxn5jSUlJ1K1b11ju7s/T1dWVWrVqGT/Pv7tz5w7vvvsu69ev59KlS+Tm5pKRkWFs6SpKXFwcp0+fNnlkqKoqBoOBxMRETp48afy/lK927dpmfXNSCHOQpEuIcqhNmzaEh4fz5ptvFnhkpNFoUFXVZFthj4ssLS1N1hVFKXRbcTp55/fdMRgMdOvWjQ8//LBAmUqVKhn/bW9vf9867643n6qqD/2mZv71bNiwgcqVK5vss7a2BuCHH35g7NixfPrppzRv3hxHR0c+/vhj9uzZc8+685PYu+9/Yffe1tbW5DoMBgNarZa4uDi0Wq1J2fwEdsiQIYSHh7NhwwY2bdrEBx98wKeffsrIkSML1H/nzh06dOhAhw4d+Pbbb/Hw8CApKYnw8PBivbhQ1D2eOHEiGzdu5JNPPiEwMBBbW1teeOGF+9ZpMBh49dVXGTVqVIF9VatW5cSJE/c8rxBPCkm6hCinZs6cSYMGDQq0jnh4eHD58mWTBKU0x6bavXs3bdq0ASA3N5e4uDhj35xGjRqxatUq/P39H+otQScnJ3x8fNixY4fxXJDXGbtp06YPFX9+5/WkpCRCQ0MLLRMbG0uLFi0YPny4cduZM2dMylhZWaHX6022eXh4AHn9zVxcXIDi3fuGDRui1+u5cuUKrVu3LrKcr6+vsa/dG2+8wcKFCwtNuo4fP861a9eYOXMmvr6+AOzfv7/QOnfv3k3VqlUBSElJ4eTJk9SuXbvQsrGxsURERPD8888DeX288l8QyFfYfWnUqBFHjx4lMDCw0Hrr1KlDbm4u+/fvN36+J06cuOcYYkJURNKRXohyql69evTt25cvvvjCZHtYWBhXr17lo48+4syZM3z55Zf8+uuvpXbeL7/8kp9++onjx48zYsQIUlJSGDRoEJDX2fvGjRu8/PLL7N27l7Nnz7Jp0yYGDRpU4Iv4fiZOnMiHH37IypUrOXHiBK+//jrx8fGMHj36oeJ3dHRkwoQJjB07lmXLlnHmzBkOHjzIl19+ybJlywAIDAxk//79bNy4kZMnT/L2228bO+Hn8/f35/Dhw5w4cYJr166Rk5NDYGAgvr6+TJ06lZMnT7JhwwY+/fTT+8ZUs2ZN+vbty4ABA1i9ejWJiYns27ePDz/80PiG4pgxY9i4cSOJiYkcOHCAbdu2UadOnULrq1q1KlZWVnzxxRecPXuWtWvX8t577xVadtq0aWzdupU//viDiIgI3N3dixx/LDAwkNWrVxMfH8+hQ4fo06dPgZZQf39/tm/fzsWLF41vrU6aNInff/+dESNGEB8fz6lTp1i7dq0xYaxVqxYdO3Zk6NCh7Nmzh7i4OIYMGYKtre19750QFYkkXUKUY++9916BR4l16tRh3rx5fPnll4SEhLB3795C3+x7UDNnzuTDDz8kJCSE2NhYfv75Z9zd3QHw8fFh586d6PV6wsPDCQ4OZvTo0eh0OpP+Y8UxatQoxo8fz/jx46lXrx5RUVGsXbuWGjVqPPQ1vPfee7zzzjt88MEH1KlTh/DwcNatW0dAQAAAw4YNo2fPnrz44os0a9aM69evm7R6AQwdOpRatWrRpEkTPDw82LlzJ5aWlnz//fccP36ckJAQPvzwQ95///1ixbR06VIGDBjA+PHjqVWrFt27d2fPnj3Gliq9Xs+IESOoU6cOHTt2pFatWsybN6/Qujw8PIiMjOTHH3+kbt26zJw5k08++aTQsjNnzmT06NE0btyY5ORk1q5di5WVVaFlP//8c1xcXGjRogXdunUjPDycRo0amZSZNm0a586do3r16saWv/r16xMTE8OpU6do3bo1DRs25O233zZ55Lx06VJ8fX0JDQ2lZ8+evPLKK3h6ehbr3glRUSjq33+jCyGEEEKIUictXUIIIYQQZUCSLiGEEEKIMiBJlxBCCCFEGZCkSwghhBCiDEjSJYQQQghRBiTpEkIIIYQoA5J0CSGEEEKUAUm6hBBCCCHKgCRdQgghhBBlQJIuIYQQQogyIEmXEEIIIUQZkKRLCCGEEKIM/D/4Dp+RPApftQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "