def random_boruta(self):
with open(self.result_folder +
'/param_RF_{}.json'.format(self.epoch)) as f:
dati = json.load(f)
for data in dati:
del data['value']
brfmodel = BalancedRandomForestClassifier(**data)
cv = StratifiedKFold(n_splits=5, shuffle=True)
for train_index, test_index in cv.split(self.X, self.y):
X_train = self.X.iloc[lambda x: train_index]
X_test = self.X.iloc[lambda x: test_index]
y_train = np.take(self.y, train_index)
y_test = np.take(self.y, test_index)
median_imputer = SimpleImputer(missing_values=np.NaN,
strategy='median')
imputer = median_imputer.fit(X_train)
vX_train = imputer.transform(X_train)
imputertest = median_imputer.fit(X_test)
vX_test = imputertest.transform(X_test)
X_train = pd.DataFrame(vX_train,
columns=X_train.columns,
index=X_train.index)
X_test = pd.DataFrame(vX_test,
columns=X_test.columns,
index=X_test.index)
Feature_Selector = BorutaShap(model=brfmodel,
importance_measure='shap',
percentile=85,
pvalue=0.08,
classification=True)
Feature_Selector.fit(X_train,
y_train,
n_trials=200,
random_state=0)
Feature_Selector.TentativeRoughFix()
Feature_Selector.plot(X_size=12,
figsize=(12, 8),
y_scale='log',
which_features='all')
Xstrain = Feature_Selector.Subset()
selected = [x for x in Xstrain.columns]
print('features selected', selected)
v_test_X = median_imputer.fit_transform(self.X_test)
test_X = pd.DataFrame(v_test_X,
columns=self.X_test.columns,
index=self.X_test.index)
valx = self.X_validation
valy = self.y_validation
vvalX = imputer.transform(valx)
valx = pd.DataFrame(vvalX,
columns=valx.columns,
index=valx.index)
print('AUC')
brfmodel.fit(X_train, y_train)
roc = roc_auc_score(y_test,
brfmodel.predict_proba(X_test)[:, 1])
print(roc)
print('AUC Validation')
roc_test = roc_auc_score(
self.y_validation,
brfmodel.predict_proba(valx)[:, 1])
print(roc_test)
print('AUC ridotte')
brfmodel.fit(Xstrain, y_train)
roc = roc_auc_score(
y_test,
brfmodel.predict_proba(X_test[selected])[:, 1])
print(roc)
roc_test = roc_auc_score(
self.y_validation,
brfmodel.predict_proba(valx[selected])[:, 1])
print(roc_test)
# + [markdown] heading_collapsed=true hidden=true
# ### BorutaShap
# + [markdown] hidden=true
# This initialization takes a maximum of 5 parameters including a tree based model of your choice example a **“Decision Tree” or “XGBoost” or "CatBoost" default is a “Random Forest”**. Which importance metric you would like to evaluate the features importance with either **Shapley values (default) or Gini importance**, A flag to specify if the problem is either classification or regression, a percentile parameter which will take a percentage of the max shadow feature thus making the selector less strict and finally a p-value or significance level which a after a feature will be either rejected or accepted.
# + hidden=true
model_xgb = XGBClassifier(objective='binary:logistic')
Feature_Selector_xgb = BorutaShap(model=model_xgb,
importance_measure='shap',
classification=True)
Feature_Selector_xgb.fit(X=X_one_hot, y=y, n_trials=25, random_state=0)
# + hidden=true
#Returns a subset of the original data with the selected features
X_subset_xgb = Feature_Selector_xgb.Subset()
print(X_subset_xgb.shape)
X_subset_xgb.head()
# + hidden=true
model_cat = CatBoostClassifier()
Feature_Selector_cat = BorutaShap(model=model_cat,
importance_measure='shap',
classification=True)
Feature_Selector_cat.fit(X=X_one_hot, y=y, n_trials=25, random_state=0)
# + hidden=true
#Returns a subset of the original data with the selected features
X_subset_cat = Feature_Selector_cat.Subset()
print(X_subset_cat.shape)
X_subset_cat.head()