!pip install --upgrade pip
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!pip install xgboost
Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.1.2) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.26.4) Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.23.4) Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.13.1)
!pip install catboost
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!pip install optuna
Collecting optuna Downloading optuna-4.1.0-py3-none-any.whl.metadata (16 kB) Collecting alembic>=1.5.0 (from optuna) Downloading alembic-1.14.0-py3-none-any.whl.metadata (7.4 kB) Collecting colorlog (from optuna) Downloading colorlog-6.9.0-py3-none-any.whl.metadata (10 kB) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.26.4) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (24.2) Requirement already satisfied: sqlalchemy>=1.4.2 in /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.36) Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.66.6) Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.2) Collecting Mako (from alembic>=1.5.0->optuna) Downloading Mako-1.3.6-py3-none-any.whl.metadata (2.9 kB) Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from alembic>=1.5.0->optuna) (4.12.2) Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.4.2->optuna) (3.1.1) Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (3.0.2) Downloading optuna-4.1.0-py3-none-any.whl (364 kB) Downloading alembic-1.14.0-py3-none-any.whl (233 kB) Downloading colorlog-6.9.0-py3-none-any.whl (11 kB) Downloading Mako-1.3.6-py3-none-any.whl (78 kB) Installing collected packages: Mako, colorlog, alembic, optuna Successfully installed Mako-1.3.6 alembic-1.14.0 colorlog-6.9.0 optuna-4.1.0
!pip install lightgbm
Requirement already satisfied: lightgbm in /usr/local/lib/python3.10/dist-packages (4.5.0) Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.26.4) Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm) (1.13.1)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
import random
%matplotlib inline
from sklearn.model_selection import train_test_split, StratifiedKFold, ShuffleSplit
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve, precision_recall_curve, auc
import warnings
warnings.filterwarnings('ignore')
# Algorithms
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.preprocessing import LabelEncoder, StandardScaler
from lightgbm import LGBMClassifier
from tensorflow.keras import models, layers, callbacks
from sklearn import (
ensemble,
linear_model,
naive_bayes,
neighbors,
svm,
tree,
discriminant_analysis,
gaussian_process,
neural_network,
model_selection
)
import optuna
import optuna.visualization as oviz
# Load Data
train_df = pd.read_csv("/content/train.csv")
test_df = pd.read_csv("/content/test.csv")
train = train_df.copy()
test = test_df.copy()
Read data
train.head()
| id | person_age | person_income | person_home_ownership | person_emp_length | loan_intent | loan_grade | loan_amnt | loan_int_rate | loan_percent_income | cb_person_default_on_file | cb_person_cred_hist_length | loan_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 37 | 35000 | RENT | 0.0 | EDUCATION | B | 6000 | 11.49 | 0.17 | N | 14 | 0 |
| 1 | 1 | 22 | 56000 | OWN | 6.0 | MEDICAL | C | 4000 | 13.35 | 0.07 | N | 2 | 0 |
| 2 | 2 | 29 | 28800 | OWN | 8.0 | PERSONAL | A | 6000 | 8.90 | 0.21 | N | 10 | 0 |
| 3 | 3 | 30 | 70000 | RENT | 14.0 | VENTURE | B | 12000 | 11.11 | 0.17 | N | 5 | 0 |
| 4 | 4 | 22 | 60000 | RENT | 2.0 | MEDICAL | A | 6000 | 6.92 | 0.10 | N | 3 | 0 |
test.head()
| id | person_age | person_income | person_home_ownership | person_emp_length | loan_intent | loan_grade | loan_amnt | loan_int_rate | loan_percent_income | cb_person_default_on_file | cb_person_cred_hist_length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 58645 | 23 | 69000 | RENT | 3.0 | HOMEIMPROVEMENT | F | 25000 | 15.76 | 0.36 | N | 2 |
| 1 | 58646 | 26 | 96000 | MORTGAGE | 6.0 | PERSONAL | C | 10000 | 12.68 | 0.10 | Y | 4 |
| 2 | 58647 | 26 | 30000 | RENT | 5.0 | VENTURE | E | 4000 | 17.19 | 0.13 | Y | 2 |
| 3 | 58648 | 33 | 50000 | RENT | 4.0 | DEBTCONSOLIDATION | A | 7000 | 8.90 | 0.14 | N | 7 |
| 4 | 58649 | 26 | 102000 | MORTGAGE | 8.0 | HOMEIMPROVEMENT | D | 15000 | 16.32 | 0.15 | Y | 4 |
Visualization
numerical_features = train.select_dtypes(include=[np.number]).columns
correlation_matrix = train[numerical_features].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Heatmap of all original columns')
plt.show()
loan_status_0 = train[train['loan_status'] == 0]
loan_status_1 = train[train['loan_status'] == 1]
plt.figure(figsize=(12, 6))
sns.countplot(data=loan_status_0, x='person_age', palette='flare')
plt.title("Age's distribution with loan status = 0")
plt.xlabel('Age')
plt.ylabel('Count')
plt.show()
plt.figure(figsize=(12, 6))
sns.countplot(data=loan_status_1, x='person_age', palette='flare')
plt.title("Age's distribution with loan status = 1")
plt.xlabel('Age')
plt.ylabel('Count')
plt.show()
plt.figure(figsize=(8, 6))
sns.countplot(data=train, x='loan_status', palette='viridis')
plt.title('Distribution of Loan Status')
plt.xlabel('Loan Status')
plt.ylabel('Count')
plt.show()
plt.figure(figsize=(10, 6))
sns.countplot(data=train, x='loan_intent', palette='pastel', order=train['loan_intent'].value_counts().index)
plt.title('Distribution of Loan Intention')
plt.xlabel('Loan Intent')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
sns.countplot(data=train, x='loan_grade', hue='loan_status')
plt.title('Distribution of Loan Grade')
plt.xlabel('Loan Grade')
plt.ylabel('Count')
plt.show()
sns.countplot(data=train, x='cb_person_default_on_file', hue='loan_status')
plt.title('Loan Status by Prior Default Status')
plt.xlabel('Prior Default Status')
plt.ylabel('Count')
plt.show()
train['income_range'] = pd.qcut(train['person_income'], q=10, duplicates='drop') # q=10 means 10 bins
summary = train.groupby(['income_range', 'loan_status']).size().unstack(fill_value=0)
summary.plot(kind='bar', figsize=(12, 6), width=0.8)
plt.title('Loan Approval Status by Income Range (10 Bins)')
plt.xlabel('Income Range')
plt.ylabel('Loan Status Count')
plt.xticks(rotation=45, ha='right')
plt.legend(title='Loan Status')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
avg_loan_amount = train.groupby('income_range')['loan_amnt'].mean()
plt.figure(figsize=(12, 6))
avg_loan_amount.plot(kind='bar', color='skyblue', width=0.8)
plt.title('Average Loan Amount by Income Range')
plt.xlabel('Income Range')
plt.ylabel('Average Loan Amount')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
train = train.drop(columns=['income_range'])
Data Wrangling
test.isnull().sum()
| 0 | |
|---|---|
| id | 0 |
| person_age | 0 |
| person_income | 0 |
| person_home_ownership | 0 |
| person_emp_length | 0 |
| loan_intent | 0 |
| loan_grade | 0 |
| loan_amnt | 0 |
| loan_int_rate | 0 |
| loan_percent_income | 0 |
| cb_person_default_on_file | 0 |
| cb_person_cred_hist_length | 0 |
train.isna().sum()
| 0 | |
|---|---|
| id | 0 |
| person_age | 0 |
| person_income | 0 |
| person_home_ownership | 0 |
| person_emp_length | 0 |
| loan_intent | 0 |
| loan_grade | 0 |
| loan_amnt | 0 |
| loan_int_rate | 0 |
| loan_percent_income | 0 |
| cb_person_default_on_file | 0 |
| cb_person_cred_hist_length | 0 |
| loan_status | 0 |
train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 58645 entries, 0 to 58644 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 58645 non-null int64 1 person_age 58645 non-null int64 2 person_income 58645 non-null int64 3 person_home_ownership 58645 non-null object 4 person_emp_length 58645 non-null float64 5 loan_intent 58645 non-null object 6 loan_grade 58645 non-null object 7 loan_amnt 58645 non-null int64 8 loan_int_rate 58645 non-null float64 9 loan_percent_income 58645 non-null float64 10 cb_person_default_on_file 58645 non-null object 11 cb_person_cred_hist_length 58645 non-null int64 12 loan_status 58645 non-null int64 dtypes: float64(3), int64(6), object(4) memory usage: 5.8+ MB
test.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 39098 entries, 0 to 39097 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 39098 non-null int64 1 person_age 39098 non-null int64 2 person_income 39098 non-null int64 3 person_home_ownership 39098 non-null object 4 person_emp_length 39098 non-null float64 5 loan_intent 39098 non-null object 6 loan_grade 39098 non-null object 7 loan_amnt 39098 non-null int64 8 loan_int_rate 39098 non-null float64 9 loan_percent_income 39098 non-null float64 10 cb_person_default_on_file 39098 non-null object 11 cb_person_cred_hist_length 39098 non-null int64 dtypes: float64(3), int64(5), object(4) memory usage: 3.6+ MB
# Preprocessing data on person_age and cb_person_default_on_file (if the person used to default)
def preprocessing(df):
df = df[df['person_age'] < 100]
df['cb_person_default_on_file'] = df['cb_person_default_on_file'].map({'Y': 1, 'N': 0})
df.reset_index(drop=True, inplace=True)
return df
#Adding more features to potentially capture some patterns that can increase accuracy
def feature_engineering(df):
df['risk_flag'] = np.where((df['cb_person_default_on_file'] == 1) & (df['loan_grade'].isin(['D', 'E', 'F', 'G'])), 1, 0)
df['total_payable'] = np.floor(df['loan_amnt'] * (1 + df['loan_int_rate'] / 100))
df['interest'] = np.floor(df['loan_amnt'] * (df['loan_int_rate'] / 100))
df['credit_hist_to_age'] = np.round(df['cb_person_cred_hist_length'] / df['person_age'], 2)
df['credit_hist_to_work'] = np.round(df['person_emp_length'] / df['cb_person_cred_hist_length'], 2)
df['work_to_age'] = np.round(df['person_emp_length'] / df['person_age'], 2)
df['income_to_age'] = np.round(df['person_income'] / df['person_age'], 2)
return df
#We encode some features here to make it eaier for the models to read
def label_encode_features(df):
le = LabelEncoder()
df['person_home_ownership'] = le.fit_transform(df['person_home_ownership'])
df['loan_intent'] = le.fit_transform(df['loan_intent'])
df['loan_grade'] = le.fit_transform(df['loan_grade'])
return df
#We are normalizing the features here as that is better for neural network that we use later on
def normalize_numeric_features(df):
numeric_features = ['person_age', 'person_income', 'person_emp_length', 'loan_amnt', 'loan_int_rate',
'loan_percent_income', 'cb_person_cred_hist_length', 'total_payable', 'interest',
'credit_hist_to_work', 'credit_hist_to_age', 'work_to_age', 'income_to_age']
scaler = StandardScaler()
df[numeric_features] = scaler.fit_transform(df[numeric_features])
return df
# apply wrangling functions above to both train and test sets.
train = normalize_numeric_features(label_encode_features(feature_engineering(preprocessing(train))))
test = normalize_numeric_features(label_encode_features(feature_engineering(preprocessing(test))))
y = train['loan_status']
X = train.drop(columns=['loan_status', 'id'])
X_test = test[X.columns.tolist()]
X.head()
| person_age | person_income | person_home_ownership | person_emp_length | loan_intent | loan_grade | loan_amnt | loan_int_rate | loan_percent_income | cb_person_default_on_file | cb_person_cred_hist_length | risk_flag | total_payable | interest | credit_hist_to_age | credit_hist_to_work | work_to_age | income_to_age | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.569810 | -0.765777 | 3 | -1.187183 | 1 | 1 | -0.578310 | 0.267614 | 0.117381 | 0 | 2.031777 | 0 | -0.566549 | -0.426661 | 1.941801 | -1.031050 | -1.261311 | -1.029343 |
| 1 | -0.921749 | -0.212140 | 2 | 0.328055 | 3 | 2 | -0.937777 | 0.880525 | -0.973230 | 0 | -0.946490 | 0 | -0.912257 | -0.637252 | -1.127420 | 1.711514 | 0.706552 | 0.116883 |
| 2 | 0.240979 | -0.929232 | 2 | 0.833135 | 4 | 0 | -0.578310 | -0.585849 | 0.553625 | 0 | 1.039021 | 0 | -0.591415 | -0.637252 | 1.518460 | -0.299700 | 0.779436 | -0.995555 |
| 3 | 0.407083 | 0.156952 | 3 | 2.348373 | 5 | 1 | 0.500090 | 0.142395 | 0.117381 | 0 | -0.201923 | 0 | 0.499289 | 0.448312 | -0.280738 | 1.528676 | 2.164228 | -0.035125 |
| 4 | -0.921749 | -0.106685 | 3 | -0.682104 | 3 | 0 | -0.578310 | -1.238303 | -0.646047 | 0 | -0.698301 | 0 | -0.610505 | -0.798932 | -0.598244 | -0.418544 | -0.605357 | 0.247178 |
y.head()
| loan_status | |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
X_test.head()
| person_age | person_income | person_home_ownership | person_emp_length | loan_intent | loan_grade | loan_amnt | loan_int_rate | loan_percent_income | cb_person_default_on_file | cb_person_cred_hist_length | risk_flag | total_payable | interest | credit_hist_to_age | credit_hist_to_work | work_to_age | income_to_age | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.757007 | 0.130141 | 3 | -0.436121 | 2 | 5 | 2.824250 | 1.688238 | 2.187295 | 0 | -0.940719 | 0 | 2.992466 | 4.000630 | -1.121646 | 0.352732 | -0.318389 | 0.448398 |
| 1 | -0.259715 | 0.841503 | 0 | 0.339404 | 4 | 2 | 0.134238 | 0.668431 | -0.650135 | 1 | -0.449573 | 0 | 0.162109 | 0.359400 | -0.491800 | 0.352732 | 0.429212 | 0.950998 |
| 2 | -0.259715 | -0.897382 | 3 | 0.080895 | 5 | 4 | -0.941767 | 2.161719 | -0.322739 | 1 | -0.940719 | 1 | -0.891908 | -0.432646 | -1.226621 | 1.295066 | 0.130172 | -0.891862 |
| 3 | 0.900631 | -0.370447 | 3 | -0.177613 | 0 | 0 | -0.403765 | -0.583149 | -0.213607 | 0 | 0.287147 | 0 | -0.421677 | -0.519894 | 0.138047 | -0.523638 | -0.393149 | -0.629567 |
| 4 | -0.259715 | 0.999584 | 0 | 0.856421 | 2 | 3 | 1.030908 | 1.873657 | -0.104475 | 1 | -0.449573 | 1 | 1.151901 | 1.968030 | -0.491800 | 0.823899 | 1.027293 | 1.118531 |
# Models tuning with optuna
def tune_model_with_optuna(model_class, param_space, X, y, X_test, n_splits=5, n_trials=3, random_state=42):
# cross-validation with Stratified K-Fold
stk = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)
# Objective function for tuning
def objective(trial):
# Define hyperparameters based on the trial and provided param_space
params = {key: fn(trial) if callable(fn) else fn for key, fn in param_space.items()}
auc_scores = []
# Stratified K-Fold cross-validation
for train_idx, valid_idx in stk.split(X, y):
# Train the model with trial params
model = model_class(**params)
model.fit(X.iloc[train_idx], y.iloc[train_idx])
# Predicting and calculating AUC for this fold
val_pred = model.predict_proba(X.iloc[valid_idx])[:, 1]
auc_scores.append(roc_auc_score(y.iloc[valid_idx], val_pred))
# Mean AUC across folds for this trial
return np.mean(auc_scores)
# Optuna study and optimize
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=n_trials)
# Get best params in this study
best_params = study.best_params
print(f"Best parameters: {best_params}")
oof_predictions = np.zeros(X.shape[0])
test_predictions = np.zeros(X_test.shape[0])
# output OOF predictions and test predictions
for train_idx, valid_idx in stk.split(X, y):
model = model_class(**best_params)
model.fit(X.iloc[train_idx], y.iloc[train_idx])
oof_predictions[valid_idx] = model.predict_proba(X.iloc[valid_idx])[:, 1]
test_predictions += model.predict_proba(X_test)[:, 1] / n_splits
auc_score = roc_auc_score(y, oof_predictions)
fpr, tpr, _ = roc_curve(y, oof_predictions)
plt.figure()
plt.plot(fpr, tpr, color='forestgreen', lw=2, label=f'ROC curve (AUC = {auc_score:.4f})')
plt.plot([0, 1], [0, 1], color='firebrick', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title(f'ROC Curve for {model_class.__name__}')
plt.legend(loc="lower right")
plt.grid(True)
plt.show()
return best_params, auc_score, oof_predictions, test_predictions
# Function for reservoir sampling to select k rows
def reservoir_sampling(X, y, k=1000):
n = len(X)
indices = list(range(n))
sampled_indices = random.sample(indices, k)
return X.iloc[sampled_indices], y.iloc[sampled_indices]
X_temp, y_temp = reservoir_sampling(X, y, k=1000)
# Split the temporary subset into train and validation sets
X_temp_train, X_temp_val, y_temp_train, y_temp_val = model_selection.train_test_split(X_temp, y_temp, test_size=0.2, random_state=42)
# list of Machine Learning Algorithms (MLA)
MLA = [
# Ensemble Methods
ensemble.AdaBoostClassifier(),
ensemble.BaggingClassifier(),
ensemble.ExtraTreesClassifier(),
ensemble.GradientBoostingClassifier(),
ensemble.RandomForestClassifier(),
LGBMClassifier(),
XGBClassifier(),
CatBoostClassifier(),
#GLM
linear_model.LogisticRegression(),
linear_model.LogisticRegressionCV(),
linear_model.PassiveAggressiveClassifier(),
linear_model.RidgeClassifier(),
linear_model.RidgeClassifierCV(),
linear_model.SGDClassifier(),
linear_model.Perceptron(),
# Naive Bayes
naive_bayes.BernoulliNB(),
naive_bayes.GaussianNB(),
# Nearest Neighbors
neighbors.KNeighborsClassifier(),
# Support Vector Machines SVM
svm.SVC(probability=True),
svm.LinearSVC(),
# Decision Trees
tree.DecisionTreeClassifier(),
tree.ExtraTreeClassifier(),
# Discriminant Analysis
discriminant_analysis.LinearDiscriminantAnalysis(),
discriminant_analysis.QuadraticDiscriminantAnalysis(),
# Gaussian Processes
gaussian_process.GaussianProcessClassifier(),
# Neural Networks
neural_network.MLPClassifier(max_iter=1000)
]
cv_split = model_selection.ShuffleSplit(n_splits=5, test_size=0.3, train_size=0.6, random_state=42)
MLA_columns = ['MLA Name', 'MLA Parameters', 'MLA Test Accuracy Mean', 'MLA Test Accuracy at 99.7 Percentile', 'MLA Time']
MLA_compare = pd.DataFrame(columns=MLA_columns)
test_scores_df = []
fitted_models = {}
row_index = 0
for alg in MLA:
try:
MLA_name = alg.__class__.__name__
MLA_compare.loc[row_index, 'MLA Name'] = MLA_name
MLA_compare.loc[row_index, 'MLA Parameters'] = str(alg.get_params())
cv_results = model_selection.cross_validate(alg, X_temp, y_temp, cv=cv_split, return_train_score=True)
MLA_compare.loc[row_index, 'MLA Time'] = cv_results['fit_time'].mean()
MLA_compare.loc[row_index, 'MLA Test Accuracy Mean'] = cv_results['test_score'].mean()
MLA_compare.loc[row_index, 'MLA Test Accuracy at 99.7 Percentile'] = cv_results['test_score'].std() * 3
test_scores = pd.DataFrame({'MLA Name': [MLA_name]*len(cv_results['test_score']), 'Test Score': cv_results['test_score']})
test_scores_df.append(test_scores)
alg.fit(X_temp_train, y_temp_train)
fitted_models[MLA_name] = alg
except Exception as e:
print(f"An error occurred with {MLA_name}: {e}")
row_index += 1
all_test_scores = pd.concat(test_scores_df)
MLA_compare.sort_values(by=['MLA Test Accuracy Mean'], ascending=False, inplace=True)
MLA_compare
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| MLA Name | MLA Parameters | MLA Test Accuracy Mean | MLA Test Accuracy at 99.7 Percentile | MLA Time | |
|---|---|---|---|---|---|
| 3 | GradientBoostingClassifier | {'ccp_alpha': 0.0, 'criterion': 'friedman_mse'... | 0.918 | 0.071162 | 0.895744 |
| 7 | CatBoostClassifier | {} | 0.918 | 0.05678 | 4.105442 |
| 5 | LGBMClassifier | {'boosting_type': 'gbdt', 'class_weight': None... | 0.916667 | 0.049396 | 0.103206 |
| 1 | BaggingClassifier | {'bootstrap': True, 'bootstrap_features': Fals... | 0.915333 | 0.037736 | 0.161941 |
| 6 | XGBClassifier | {'objective': 'binary:logistic', 'base_score':... | 0.915333 | 0.059532 | 0.085116 |
| 4 | RandomForestClassifier | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_w... | 0.908667 | 0.042237 | 0.293497 |
| 2 | ExtraTreesClassifier | {'bootstrap': False, 'ccp_alpha': 0.0, 'class_... | 0.904 | 0.051923 | 0.358358 |
| 0 | AdaBoostClassifier | {'algorithm': 'SAMME.R', 'estimator': None, 'l... | 0.892667 | 0.066753 | 0.24073 |
| 24 | GaussianProcessClassifier | {'copy_X_train': True, 'kernel': None, 'max_it... | 0.889333 | 0.029257 | 0.296472 |
| 25 | MLPClassifier | {'activation': 'relu', 'alpha': 0.0001, 'batch... | 0.888667 | 0.034409 | 3.590556 |
| 9 | LogisticRegressionCV | {'Cs': 10, 'class_weight': None, 'cv': None, '... | 0.884 | 0.034871 | 0.42421 |
| 8 | LogisticRegression | {'C': 1.0, 'class_weight': None, 'dual': False... | 0.883333 | 0.04 | 0.056043 |
| 18 | SVC | {'C': 1.0, 'break_ties': False, 'cache_size': ... | 0.883333 | 0.071274 | 0.037082 |
| 17 | KNeighborsClassifier | {'algorithm': 'auto', 'leaf_size': 30, 'metric... | 0.881333 | 0.05004 | 0.005833 |
| 19 | LinearSVC | {'C': 1.0, 'class_weight': None, 'dual': 'auto... | 0.880667 | 0.047497 | 0.009348 |
| 22 | LinearDiscriminantAnalysis | {'covariance_estimator': None, 'n_components':... | 0.878 | 0.023324 | 0.010439 |
| 20 | DecisionTreeClassifier | {'ccp_alpha': 0.0, 'class_weight': None, 'crit... | 0.877333 | 0.031241 | 0.011729 |
| 23 | QuadraticDiscriminantAnalysis | {'priors': None, 'reg_param': 0.0, 'store_cova... | 0.877333 | 0.027857 | 0.01741 |
| 11 | RidgeClassifier | {'alpha': 1.0, 'class_weight': None, 'copy_X':... | 0.87 | 0.05933 | 0.016857 |
| 12 | RidgeClassifierCV | {'alphas': (0.1, 1.0, 10.0), 'class_weight': N... | 0.867333 | 0.063687 | 0.043457 |
| 14 | Perceptron | {'alpha': 0.0001, 'class_weight': None, 'early... | 0.864 | 0.073321 | 0.01909 |
| 21 | ExtraTreeClassifier | {'ccp_alpha': 0.0, 'class_weight': None, 'crit... | 0.86 | 0.035777 | 0.003724 |
| 16 | GaussianNB | {'priors': None, 'var_smoothing': 1e-09} | 0.856667 | 0.040497 | 0.014058 |
| 10 | PassiveAggressiveClassifier | {'C': 1.0, 'average': False, 'class_weight': N... | 0.846 | 0.107592 | 0.027024 |
| 13 | SGDClassifier | {'alpha': 0.0001, 'average': False, 'class_wei... | 0.842 | 0.067409 | 0.030199 |
| 15 | BernoulliNB | {'alpha': 1.0, 'binarize': 0.0, 'class_prior':... | 0.84 | 0.035777 | 0.024802 |
# Distribution of test scores for each MLA
plt.figure(figsize=(12, 8))
sns.boxplot(x='Test Score', y='MLA Name', data=all_test_scores, orient='h')
plt.title('Distribution of Test Scores by Algorithm')
plt.xlabel('Test Accuracy Score')
plt.ylabel('Algorithm')
plt.show()
# Distribution of test scores (Boxplot with KDE overlay)
plt.figure(figsize=(14, 10))
sns.boxplot(x='Test Score', y='MLA Name', data=all_test_scores, orient='h', palette="vlag", width=0.5)
plt.title('Distribution of Test Scores by Algorithm')
plt.xlabel('Test Accuracy Score')
plt.ylabel('Algorithm')
# KDE plots for each algorithm
for alg_name in all_test_scores['MLA Name'].unique():
subset = all_test_scores[all_test_scores['MLA Name'] == alg_name]
sns.kdeplot(subset['Test Score'], label=alg_name, fill=True, alpha=0.2, linewidth=1.5)
plt.legend(loc='best', fontsize='small', bbox_to_anchor=(1.05, 1))
plt.tight_layout()
plt.grid(True, axis='y', linestyle='--', alpha=0.5)
plt.show()
sns.barplot(x='MLA Test Accuracy Mean', y='MLA Name', data=MLA_compare, color='blue')
plt.title('Machine Learning Algorithm Accuracy Score \n')
plt.xlabel('Accuracy Score (%)')
plt.ylabel('Algorithm')
plt.show()
# Define the parameter search space for CatBoost
param_space = {
'iterations': lambda trial: trial.suggest_int('iterations', 100, 200),
'depth': lambda trial: trial.suggest_int('depth', 4, 10),
'learning_rate': lambda trial: trial.suggest_loguniform('learning_rate', 0.01, 0.09),
'l2_leaf_reg': lambda trial: trial.suggest_loguniform('l2_leaf_reg', 1, 10)
}
# Tune CatBoost and get best parameters
best_params_cat, auc_score_cat, oof_predictions_cat, test_predictions_cat = tune_model_with_optuna(
CatBoostClassifier,
param_space,
X,
y,
X_test
)
print(f"Best AUC: {auc_score_cat:.4f}")
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[I 2024-12-03 05:55:22,766] Trial 1 finished with value: 0.9267396398775667 and parameters: {'iterations': 137, 'depth': 7, 'learning_rate': 0.027206581104112216, 'l2_leaf_reg': 9.14223436390976}. Best is trial 0 with value: 0.9399057102052197.
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[I 2024-12-03 05:55:40,388] Trial 2 finished with value: 0.9471085905152437 and parameters: {'iterations': 200, 'depth': 5, 'learning_rate': 0.08994898424195204, 'l2_leaf_reg': 3.85171244228895}. Best is trial 2 with value: 0.9471085905152437.
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Best parameters: {'iterations': 200, 'depth': 5, 'learning_rate': 0.08994898424195204, 'l2_leaf_reg': 3.85171244228895}
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Best AUC: 0.9470
# Define the parameter search space for a model like Logistic Regression
param_space = {
'C': lambda trial: trial.suggest_loguniform('C', 0.0001, 10.0),
'solver': 'liblinear'
}
# Tune Logistic Regression and get best parameters
best_params_LR, auc_score_LR, oof_predictions_LR, test_predictions_LR = tune_model_with_optuna(
LogisticRegression,
param_space,
X,
y,
X_test
)
print(f"Best AUC: {auc_score_LR:.4f}")
[I 2024-12-03 05:56:00,225] A new study created in memory with name: no-name-7a1ebf77-40d8-4ab5-a7e5-640d1d8b5bfa
[I 2024-12-03 05:56:02,356] Trial 0 finished with value: 0.886356696471241 and parameters: {'C': 0.7436682311034553}. Best is trial 0 with value: 0.886356696471241.
[I 2024-12-03 05:56:03,633] Trial 1 finished with value: 0.8605031557093143 and parameters: {'C': 0.0010325096715192448}. Best is trial 0 with value: 0.886356696471241.
[I 2024-12-03 05:56:05,758] Trial 2 finished with value: 0.8863351588641294 and parameters: {'C': 2.4238360037007007}. Best is trial 0 with value: 0.886356696471241.
Best parameters: {'C': 0.7436682311034553}
Best AUC: 0.8863
# Define the parameter search space for XGBClassifier
param_space = {
'n_estimators': lambda trial: trial.suggest_int('n_estimators', 100, 200),
'max_depth': lambda trial: trial.suggest_int('max_depth', 3, 12),
'learning_rate': lambda trial: trial.suggest_loguniform('learning_rate', 0.01, 0.3),
'subsample': lambda trial: trial.suggest_uniform('subsample', 0.5, 1.0),
'colsample_bytree': lambda trial: trial.suggest_uniform('colsample_bytree', 0.5, 1.0)
}
# Tune XGBClassifier and get best parameters
best_params_xgb, auc_score_xgb, oof_predictions_xgb, test_predictions_xgb = tune_model_with_optuna(
XGBClassifier,
param_space,
X,
y,
X_test
)
print(f"Best AUC: {auc_score_xgb:.4f}")
[I 2024-12-03 05:56:07,678] A new study created in memory with name: no-name-57622408-46fc-4467-8e91-1c273342a5da
[I 2024-12-03 05:56:16,876] Trial 0 finished with value: 0.9507298158485373 and parameters: {'n_estimators': 139, 'max_depth': 8, 'learning_rate': 0.10327432068089816, 'subsample': 0.645392435287057, 'colsample_bytree': 0.65277852555063}. Best is trial 0 with value: 0.9507298158485373.
[I 2024-12-03 05:56:33,588] Trial 1 finished with value: 0.9444636663238167 and parameters: {'n_estimators': 116, 'max_depth': 10, 'learning_rate': 0.018654891242920864, 'subsample': 0.8907081889332935, 'colsample_bytree': 0.8751054489679084}. Best is trial 0 with value: 0.9507298158485373.
[I 2024-12-03 05:56:47,831] Trial 2 finished with value: 0.9482065320303535 and parameters: {'n_estimators': 164, 'max_depth': 11, 'learning_rate': 0.078884070115644, 'subsample': 0.5743762335947262, 'colsample_bytree': 0.5181944653459972}. Best is trial 0 with value: 0.9507298158485373.
Best parameters: {'n_estimators': 139, 'max_depth': 8, 'learning_rate': 0.10327432068089816, 'subsample': 0.645392435287057, 'colsample_bytree': 0.65277852555063}
Best AUC: 0.9507
# Define the parameter search space for LightGBM
param_space_lgbm = {
'n_estimators': lambda trial: trial.suggest_int('n_estimators', 100, 200),
'max_depth': lambda trial: trial.suggest_int('max_depth', 3, 12),
'learning_rate': lambda trial: trial.suggest_loguniform('learning_rate', 0.01, 0.3),
'num_leaves': lambda trial: trial.suggest_int('num_leaves', 20, 150)
}
best_params_lgbm, auc_score_lgbm, oof_predictions_lgbm, test_predictions_lgbm = tune_model_with_optuna(
LGBMClassifier,
param_space_lgbm,
X,
y,
X_test
)
[I 2024-12-03 05:56:58,270] A new study created in memory with name: no-name-b0b0b57b-117e-47ff-9031-f301df5a57bd
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003985 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003953 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004633 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003965 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1954 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Info] Number of positive: 6680, number of negative: 40236 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006354 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1949 [LightGBM] [Info] Number of data points in the train set: 46916, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142382 -> initscore=-1.795644 [LightGBM] [Info] Start training from score -1.795644 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[I 2024-12-03 05:57:05,450] Trial 0 finished with value: 0.926886239242972 and parameters: {'n_estimators': 174, 'max_depth': 4, 'learning_rate': 0.013683453602718283, 'num_leaves': 28}. Best is trial 0 with value: 0.926886239242972.
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006555 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003917 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003987 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004043 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1954 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40236 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004050 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1949 [LightGBM] [Info] Number of data points in the train set: 46916, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142382 -> initscore=-1.795644 [LightGBM] [Info] Start training from score -1.795644
[I 2024-12-03 05:57:11,572] Trial 1 finished with value: 0.9496627966316822 and parameters: {'n_estimators': 130, 'max_depth': 10, 'learning_rate': 0.245949481998469, 'num_leaves': 52}. Best is trial 1 with value: 0.9496627966316822.
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003934 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010423 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003930 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1948 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40235 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003969 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1954 [LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619 [LightGBM] [Info] Start training from score -1.795619 [LightGBM] [Info] Number of positive: 6680, number of negative: 40236 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006346 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1949 [LightGBM] [Info] Number of data points in the train set: 46916, number of used features: 18 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142382 -> initscore=-1.795644 [LightGBM] [Info] Start training from score -1.795644
[I 2024-12-03 05:57:20,295] Trial 2 finished with value: 0.9467724450127377 and parameters: {'n_estimators': 190, 'max_depth': 6, 'learning_rate': 0.02197647583258339, 'num_leaves': 26}. Best is trial 1 with value: 0.9496627966316822.
Best parameters: {'n_estimators': 130, 'max_depth': 10, 'learning_rate': 0.245949481998469, 'num_leaves': 52}
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011026 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1948
[LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619
[LightGBM] [Info] Start training from score -1.795619
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003976 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1948
[LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619
[LightGBM] [Info] Start training from score -1.795619
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1948
[LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619
[LightGBM] [Info] Start training from score -1.795619
[LightGBM] [Info] Number of positive: 6680, number of negative: 40235
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003999 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1954
[LightGBM] [Info] Number of data points in the train set: 46915, number of used features: 18
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142385 -> initscore=-1.795619
[LightGBM] [Info] Start training from score -1.795619
[LightGBM] [Info] Number of positive: 6680, number of negative: 40236
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003891 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1949
[LightGBM] [Info] Number of data points in the train set: 46916, number of used features: 18
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.142382 -> initscore=-1.795644
[LightGBM] [Info] Start training from score -1.795644
# Define the parameter search space for Random Forest
param_space_rf = {
'n_estimators': lambda trial: trial.suggest_int('n_estimators', 100, 200),
'max_depth': lambda trial: trial.suggest_int('max_depth', 3, 12),
'min_samples_split': lambda trial: trial.suggest_int('min_samples_split', 2, 10),
'min_samples_leaf': lambda trial: trial.suggest_int('min_samples_leaf', 1, 10)
}
best_params_rf, auc_score_rf, oof_predictions_rf, test_predictions_rf = tune_model_with_optuna(
RandomForestClassifier,
param_space_rf,
X,
y,
X_test
)
print(f"Best AUC (Random Forest): {auc_score_rf:.4f}")
[I 2024-12-03 05:57:28,648] A new study created in memory with name: no-name-6b98fc1a-7b39-4ce5-b668-cd628e673db1
[I 2024-12-03 05:57:58,640] Trial 0 finished with value: 0.9237922473088377 and parameters: {'n_estimators': 104, 'max_depth': 8, 'min_samples_split': 10, 'min_samples_leaf': 10}. Best is trial 0 with value: 0.9237922473088377.
[I 2024-12-03 05:58:23,570] Trial 1 finished with value: 0.9060855408499968 and parameters: {'n_estimators': 157, 'max_depth': 3, 'min_samples_split': 2, 'min_samples_leaf': 5}. Best is trial 0 with value: 0.9237922473088377.
[I 2024-12-03 05:58:53,620] Trial 2 finished with value: 0.9155413552779507 and parameters: {'n_estimators': 157, 'max_depth': 5, 'min_samples_split': 10, 'min_samples_leaf': 10}. Best is trial 0 with value: 0.9237922473088377.
Best parameters: {'n_estimators': 104, 'max_depth': 8, 'min_samples_split': 10, 'min_samples_leaf': 10}
Best AUC (Random Forest): 0.9246
# Define the parameter search space for k-NN
param_space_knn = {
'n_neighbors': lambda trial: trial.suggest_int('n_neighbors', 3, 15),
'weights': lambda trial: trial.suggest_categorical('weights', ['uniform', 'distance']),
'p': lambda trial: trial.suggest_int('p', 1, 2)
}
best_params_knn, auc_score_knn, oof_predictions_knn, test_predictions_knn = tune_model_with_optuna(
KNeighborsClassifier,
param_space_knn,
X,
y,
X_test
)
print(f"Best AUC (k-NN): {auc_score_knn:.4f}")
[I 2024-12-03 05:59:32,493] A new study created in memory with name: no-name-52d162c8-d3a4-4757-84cc-0b6df316f4a5
[I 2024-12-03 06:00:37,323] Trial 0 finished with value: 0.8875828582017753 and parameters: {'n_neighbors': 9, 'weights': 'distance', 'p': 1}. Best is trial 0 with value: 0.8875828582017753.
[I 2024-12-03 06:01:41,475] Trial 1 finished with value: 0.8797812074000235 and parameters: {'n_neighbors': 7, 'weights': 'uniform', 'p': 1}. Best is trial 0 with value: 0.8875828582017753.
[I 2024-12-03 06:02:48,853] Trial 2 finished with value: 0.8804855245913424 and parameters: {'n_neighbors': 7, 'weights': 'distance', 'p': 1}. Best is trial 0 with value: 0.8875828582017753.
Best parameters: {'n_neighbors': 9, 'weights': 'distance', 'p': 1}
Best AUC (k-NN): 0.8876
# Concat all predictions from previous MLA
oof_predictions = {
'Logistic_Regression_prediction': oof_predictions_LR,
'XGBoost_prediction': oof_predictions_xgb,
'CatBoost_prediction': oof_predictions_cat,
'LightGBM_prediction': oof_predictions_lgbm,
'Random_Forest_prediction': oof_predictions_rf,
'k-NN_prediction': oof_predictions_knn
}
test_predictions = {
'Logistic_Regression_prediction': test_predictions_LR,
'XGBoost_prediction': test_predictions_xgb,
'CatBoost_prediction': test_predictions_cat,
'LightGBM_prediction': test_predictions_lgbm,
'Random_Forest_prediction': test_predictions_rf,
'k-NN_prediction': test_predictions_knn
}
# oof predictions to dfs
train_final_output = pd.DataFrame(oof_predictions)
test_final_output = pd.DataFrame(test_predictions)
# add original y as a new column in train_final_output
train_final_output["loan_status"] = y
# Save outputs to CSV
train_final_output.to_csv('train_final_output.csv')
test_final_output.to_csv('test_final_output.csv')
# Read outputs from CSV and drop unnecessary index columns
train_final_output = pd.read_csv('train_final_output.csv')
test_final_output = pd.read_csv('test_final_output.csv')
train_final_output = train_final_output.drop(columns=['Unnamed: 0'])
test_final_output = test_final_output.drop(columns=['Unnamed: 0'])
print("train_final_output:")
print("")
train_final_output.iloc[20:25]
train_final_output:
| Logistic_Regression_prediction | XGBoost_prediction | CatBoost_prediction | LightGBM_prediction | Random_Forest_prediction | k-NN_prediction | loan_status | |
|---|---|---|---|---|---|---|---|
| 20 | 0.006513 | 0.002047 | 0.006073 | 0.000648 | 0.026726 | 0.099845 | 0 |
| 21 | 0.009846 | 0.005899 | 0.014292 | 0.003879 | 0.019918 | 0.000000 | 0 |
| 22 | 0.005066 | 0.001086 | 0.005430 | 0.000515 | 0.010402 | 0.000000 | 0 |
| 23 | 0.009466 | 0.031974 | 0.034399 | 0.019292 | 0.073639 | 0.087979 | 0 |
| 24 | 0.594468 | 0.826767 | 0.802221 | 0.779421 | 0.560913 | 0.488432 | 1 |
print("test_final_output:")
print("")
test_final_output.head(10)
test_final_output:
| Logistic_Regression_prediction | XGBoost_prediction | CatBoost_prediction | LightGBM_prediction | Random_Forest_prediction | k-NN_prediction | |
|---|---|---|---|---|---|---|
| 0 | 0.930206 | 0.983874 | 0.994527 | 0.999039 | 0.834261 | 0.913930 |
| 1 | 0.011600 | 0.015635 | 0.023810 | 0.007098 | 0.030765 | 0.000000 |
| 2 | 0.831930 | 0.818079 | 0.442745 | 0.891024 | 0.553316 | 0.577176 |
| 3 | 0.040439 | 0.146112 | 0.038322 | 0.122881 | 0.042685 | 0.086008 |
| 4 | 0.151623 | 0.111657 | 0.124177 | 0.057175 | 0.217699 | 0.392428 |
| 5 | 0.723189 | 0.864439 | 0.906057 | 0.947061 | 0.818046 | 0.804122 |
| 6 | 0.021303 | 0.017227 | 0.005128 | 0.005680 | 0.019043 | 0.000000 |
| 7 | 0.004601 | 0.020566 | 0.019729 | 0.008595 | 0.018651 | 0.157800 |
| 8 | 0.352682 | 0.364291 | 0.209878 | 0.278017 | 0.169266 | 0.345575 |
| 9 | 0.066477 | 0.015493 | 0.028965 | 0.003076 | 0.036869 | 0.018444 |
# Assuming train_final_output is already loaded from the earlier steps
X_ensemble = train_final_output[[col for col in train_final_output.columns if 'prediction' in col]]
y_ensemble = train_final_output['loan_status']
# Splitting data into train, test, and validation sets.
X_ensemble_train_val, X_ensemble_test, y_ensemble_train_val, y_ensemble_test = train_test_split(X_ensemble, y_ensemble, test_size=0.2, stratify=y_ensemble, random_state=42)
X_ensemble_train, X_ensemble_val, y_ensemble_train, y_ensemble_val = train_test_split(X_ensemble_train_val, y_ensemble_train_val, test_size=0.2, stratify=y_ensemble_train_val, random_state=42)
# Callbacks for training
learning_rate_reduction = callbacks.ReduceLROnPlateau(monitor='val_binary_accuracy', patience=5, verbose=1, factor=0.5, min_lr=1e-7)
early_stopping = callbacks.EarlyStopping(monitor='val_binary_accuracy', min_delta=1e-5, patience=5, restore_best_weights=True)
# Define NN model with random numbers of neurons for each layer. Then add a batch normalization after hidden layers.
# Add dropout to prevent overfitting.However when the number of neurons in the layer decreases, we are no longer using
# drop out. The last layer uses a sigmoid activation function to output a probability between 0 and 1.
nn_model = models.Sequential([
layers.Dense(256, activation='relu'),
layers.BatchNormalization(),
layers.Dropout(0.3),
layers.Dense(128, activation='relu'),
layers.BatchNormalization(),
layers.Dropout(0.3),
layers.Dense(64, activation='relu'),
layers.BatchNormalization(),
layers.Dropout(0.3),
layers.Dense(32, activation='relu'),
layers.BatchNormalization(),
layers.Dropout(0.3),
layers.Dense(16, activation='relu'),
layers.BatchNormalization(),
layers.Dense(8, activation='relu'),
layers.BatchNormalization(),
layers.Dense(4, activation='relu'),
layers.BatchNormalization(),
layers.Dense(2, activation='relu'),
layers.BatchNormalization(),
layers.Dense(1, activation='sigmoid')
])
nn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'AUC'])
history_nn = nn_model.fit(X_ensemble_train, y_ensemble_train, validation_data=(X_ensemble_val, y_ensemble_val), epochs=100, callbacks=[early_stopping, learning_rate_reduction], verbose=1)
y_pred_validation = nn_model.predict(X_ensemble_test).reshape(-1)
# Evaluate using ROC-AUC
auc_ens_deep = roc_auc_score(y_ensemble_test, y_pred_validation)
print(f'AUC Score is: {auc_ens_deep}')
# Extracting the number of epochs
best_epoch = np.argmax(history_nn.history['val_binary_accuracy']) + 1
print(f"The best model weights are at epoch number {best_epoch}.")
Epoch 1/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 17s 7ms/step - AUC: 0.8063 - binary_accuracy: 0.8453 - loss: 0.4538 - val_AUC: 0.9409 - val_binary_accuracy: 0.9529 - val_loss: 0.1669 - learning_rate: 0.0010 Epoch 2/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 9s 7ms/step - AUC: 0.9287 - binary_accuracy: 0.9431 - loss: 0.1883 - val_AUC: 0.9474 - val_binary_accuracy: 0.9530 - val_loss: 0.1571 - learning_rate: 0.0010 Epoch 3/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 9s 6ms/step - AUC: 0.9359 - binary_accuracy: 0.9481 - loss: 0.1766 - val_AUC: 0.9495 - val_binary_accuracy: 0.9542 - val_loss: 0.1537 - learning_rate: 0.0010 Epoch 4/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 11s 6ms/step - AUC: 0.9376 - binary_accuracy: 0.9488 - loss: 0.1776 - val_AUC: 0.9507 - val_binary_accuracy: 0.9539 - val_loss: 0.1528 - learning_rate: 0.0010 Epoch 5/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 8s 7ms/step - AUC: 0.9419 - binary_accuracy: 0.9473 - loss: 0.1744 - val_AUC: 0.9526 - val_binary_accuracy: 0.9533 - val_loss: 0.1517 - learning_rate: 0.0010 Epoch 6/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 10s 7ms/step - AUC: 0.9397 - binary_accuracy: 0.9489 - loss: 0.1736 - val_AUC: 0.9485 - val_binary_accuracy: 0.9537 - val_loss: 0.1552 - learning_rate: 0.0010 Epoch 7/100 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 9s 6ms/step - AUC: 0.9407 - binary_accuracy: 0.9488 - loss: 0.1739 - val_AUC: 0.9513 - val_binary_accuracy: 0.9529 - val_loss: 0.1553 - learning_rate: 0.0010 Epoch 8/100 1169/1173 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - AUC: 0.9425 - binary_accuracy: 0.9490 - loss: 0.1694 Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. 1173/1173 ━━━━━━━━━━━━━━━━━━━━ 8s 7ms/step - AUC: 0.9424 - binary_accuracy: 0.9490 - loss: 0.1694 - val_AUC: 0.9485 - val_binary_accuracy: 0.9541 - val_loss: 0.1541 - learning_rate: 0.0010 367/367 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step AUC Score is: 0.9501758189555873 The best model weights are at epoch number 3.
# ROC curve of this stacking method
fpr, tpr, thresholds = roc_curve(y_ensemble_test, y_pred_validation)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='blue', label=f'AUC Deep Learning Model = {auc_ens_deep:0.3f}')
plt.plot([0, 1], [0, 1], 'k--', label='Random Guess')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve for Deep Learning Model')
plt.legend(loc='lower right')
plt.grid(alpha=0.3)
plt.show()
# Prepare the test data for prediction
X_ensemble_whole_deep = test_final_output[[col for col in test_final_output.columns if 'prediction' in col]]
history_nn_whole = nn_model.fit(X_ensemble, y_ensemble, validation_data=(X_ensemble_test, y_ensemble_test),
epochs=200, callbacks=[early_stopping, learning_rate_reduction], verbose=1)
y_pred_deep_whole = nn_model.predict(X_ensemble_whole_deep).reshape(-1)
submission_df = pd.read_csv('/content/sample_submission.csv')
y_pred_deep_whole_df = pd.DataFrame(data=y_pred_deep_whole, columns=['loan_status'])
final_submission = pd.concat([submission_df.drop(columns=['loan_status']), y_pred_deep_whole_df], axis=1)
final_submission.to_csv('submission.csv', index=False)
final_submission.head()
Epoch 1/200 1833/1833 ━━━━━━━━━━━━━━━━━━━━ 12s 6ms/step - AUC: 0.9410 - binary_accuracy: 0.9495 - loss: 0.1714 - val_AUC: 0.9520 - val_binary_accuracy: 0.9522 - val_loss: 0.1545 - learning_rate: 5.0000e-04 Epoch 2/200 1833/1833 ━━━━━━━━━━━━━━━━━━━━ 12s 7ms/step - AUC: 0.9447 - binary_accuracy: 0.9493 - loss: 0.1706 - val_AUC: 0.9515 - val_binary_accuracy: 0.9512 - val_loss: 0.1519 - learning_rate: 5.0000e-04 Epoch 3/200 1833/1833 ━━━━━━━━━━━━━━━━━━━━ 11s 6ms/step - AUC: 0.9424 - binary_accuracy: 0.9504 - loss: 0.1690 - val_AUC: 0.9525 - val_binary_accuracy: 0.9517 - val_loss: 0.1526 - learning_rate: 5.0000e-04 Epoch 4/200 1833/1833 ━━━━━━━━━━━━━━━━━━━━ 21s 6ms/step - AUC: 0.9406 - binary_accuracy: 0.9495 - loss: 0.1721 - val_AUC: 0.9535 - val_binary_accuracy: 0.9520 - val_loss: 0.1516 - learning_rate: 5.0000e-04 Epoch 5/200 1833/1833 ━━━━━━━━━━━━━━━━━━━━ 21s 7ms/step - AUC: 0.9427 - binary_accuracy: 0.9487 - loss: 0.1723 - val_AUC: 0.9520 - val_binary_accuracy: 0.9523 - val_loss: 0.1516 - learning_rate: 5.0000e-04 1222/1222 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step
| id | loan_status | |
|---|---|---|
| 0 | 58645 | 0.963223 |
| 1 | 58646 | 0.009681 |
| 2 | 58647 | 0.584223 |
| 3 | 58648 | 0.088145 |
| 4 | 58649 | 0.072687 |