from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from comparator import Comparator as Tester
pd.set_option('display.max_columns', None)
df = pd.read_csv(r"./data/processed_data.csv", engine="python")
tester1 = Tester('SeriousDlqin2yrs')
tester1.addDataset('processed_data', df)
# tester1.addModel('1', RandomForestClassifier(n_estimators=100, max_depth=16, max_features='auto', min_samples_leaf=100))
# tester1.addModel('2', RandomForestClassifier(n_estimators=100, max_depth=12, max_features='auto', min_samples_leaf=150))
# tester1.addModel('3', RandomForestClassifier(n_estimators=100, max_depth=8, max_features='auto'))
# tester1.addModel('1', GradientBoostingClassifier(n_estimators=200, learning_rate=0.08, subsample=0.95, max_depth=5, min_samples_leaf=43))
tester1.addModel('2', GradientBoostingClassifier(n_estimators=200, learning_rate=0.08, subsample=0.85, max_depth=5, min_samples_leaf=550))
tester1.runTests()
# n_estimators = [30, 50, 70, 90, 110, 130]
# learning_rate = [0.03, 0.05, 0.08, 0.1, 0.13, 0.15]
# plt.subplot(1, 2, 1)
# line1 = plt.plot(n_estimators, learning_rate, 'y', label='time_spent')
#
# plt.subplot(1, 2, 2)
# line2 = plt.plot(n_estimators, learning_rate, 'y', label='time_spent')
# plt.ylabel('time_spent')
#
# plt.ylim([0.84, 0.87])
#
# df = pd.read_csv(r"./data/processed_data.csv", engine="python")
# print(df.shape[0])
tester = Comparator('SeriousDlqin2yrs')
tester.addDataset('missing data processed', df)
tester.addDataset('debt ratio outliers removed',
removed_debt_outliers) # 164 removed
tester.addDataset('debt ratio outliers replaced',
repalace_debt_ratio) # 164 removed
tester.addDataset('overdue outliers replaced', repalace98) #269 removed
tester.addDataset('utilization outliers removed', dfus) # 241 removed
tester.addDataset('overdue outliers removed', drop98)
tester.addDataset('outliers added', add_outliers)
tester.addDataset('best_data', best_data)
# rf_default = RandomForestClassifier()
# dbdt_default = GradientBoostingClassifier()
# tester.addModel('default RF', rf_default)
# tester.addModel('default GBDT ', dbdt_default)
rf = RandomForestClassifier(n_estimators=32,
max_depth=8,
random_state=0,
max_features='auto',
oob_score=True)
# dbdt = GradientBoostingClassifier(n_estimators=250, subsample=0.8, min_samples_split=1000, learning_rate=0.06, max_depth=6 )
tester.addModel('RF', rf)
# tester.addModel('GBDT', dbdt)
# tester.addModel('Simple SVM', svm.LinearSVC())
test_auc, train_auc, time_spent = tester.runTests()
from sklearn.ensemble import RandomForestClassifier
from comparator import Comparator
import numpy
df = pd.read_csv(r"./data/processed_data.csv", engine="python")
add_outliers = df.copy()
outlier_count = int(df.shape[0] * 0.05)
index = numpy.random.randint(0, df.shape[0], outlier_count)
add_outliers.reset_index(drop=True, inplace=True)
for i in index:
add_outliers.at[i, 'DebtRatio'] = numpy.random.randint(3000, 30000)
comparator = Comparator('SeriousDlqin2yrs')
comparator.addDataset('data', df)
comparator.addDataset('outliers added', add_outliers)
# comparator.addModel('tuned RF', RandomForestClassifier(n_estimators=100, max_depth=16, max_features='auto', min_samples_leaf=100))
# comparator.addModel('default RF', RandomForestClassifier())
comparator.addModel(
'tuned GBDT',
GradientBoostingClassifier(n_estimators=200,
learning_rate=0.05,
subsample=0.85,
max_depth=5,
min_samples_leaf=500))
comparator.addModel('default GBDT', GradientBoostingClassifier())
comparator.runTests()
paras["n_estimators"] = n_estimators
paras["max_depth"] = max_depth
paras["max_features"] = max_features
paras["min_samples_split"] = min_samples_split
paras["min_samples_leaf"] = min_samples_leaf
to_tuning = 'min_samples_leaf'
rfc = []
for i in range(0, len(paras[to_tuning])):
rfc.append(
RandomForestClassifier(n_estimators=100,
max_depth=16,
max_features='auto',
min_samples_leaf=int(min_samples_leaf[i])))
tester1.addModel(i, rfc[i])
test_auc, train_auc, time_spent = tester1.runTests()
# plt.subplot(121)
line1, = plt.plot(paras[to_tuning], train_auc, 'b', label='Train AUC')
line2, = plt.plot(paras[to_tuning], test_auc, 'r', label='Test AUC')
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel(to_tuning)
plt.ylim([0.85, 0.9])
# plt.subplot(122)
# line3, = plt.plot(paras[to_tuning], time_spent)
# plt.ylabel('time spent')
# plt.xlabel(to_tuning)
#
# plt.show()
for j in range(0, len(learning_rate)):
rfc = []
comparator = Comparator('SeriousDlqin2yrs')
df = pd.read_csv(r"./data/processed_data.csv", engine="python")
comparator.addDataset('processed_data', df)
to_tuning = 'n_estimators'
for i in range(0, len(paras[to_tuning])):
rfc.append(
GradientBoostingClassifier(n_estimators=n_estimators[i],
learning_rate=learning_rate[j],
subsample=0.85,
max_depth=5,
min_samples_leaf=550))
comparator.addModel(i, rfc[i])
test_auc, train_auc, time_spent = comparator.runTests()
plt.subplot(len(learning_rate) / 2, 2, j + 1)
plt.title("learning_rate=" + str(learning_rate[j]))
line1, = plt.plot(paras[to_tuning], train_auc, 'b', label='Train AUC')
line2, = plt.plot(paras[to_tuning], test_auc, 'r', label='Test AUC')
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel(to_tuning)
plt.ylim([0.85, 0.875])
plt.show()
