89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
import numpy as np
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import pandas as pd
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def load_data():
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df_train = pd.read_csv('train.csv')
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df_test = pd.read_csv('test.csv')
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return df_train, df_test
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def preprocess_data(df_train, df_test):
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# V4
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df_train['V4'].fillna(0, inplace=True)
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df_test['V4'].fillna(0, inplace=True)
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# V22
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df_train['V22'].fillna(df_train['V22'].mean(), inplace=True)
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df_test['V22'].fillna(df_test['V22'].mean(), inplace=True)
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# V27
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df_train['V27'].fillna(df_train['V27'].mean(), inplace=True)
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df_test['V27'].fillna(df_test['V27'].mean(), inplace=True)
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# V29
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df_train['V29'].fillna(0, inplace=True)
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df_test['V29'].fillna(0, inplace=True)
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# V37
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df_train['V37'].fillna(df_train['V37'].mean(), inplace=True)
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df_test['V37'].fillna(df_test['V37'].mean(), inplace=True)
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X_train = df_train.drop('Class', axis=1).reset_index(drop=True)
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y_train = df_train['Class'].reset_index(drop=True)
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X_test = df_test.drop('Class', axis=1).reset_index(drop=True)
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y_test = df_test['Class'].reset_index(drop=True)
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return X_train, y_train, X_test, y_test
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def predict():
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hyper_params = {
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"n_estimators": [50, 100, 200, 300, 400, 500, 600, 700],
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"learning_rate": [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5],
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"max_depth": [1, 3, 4, 5, 6, 7, 8, 9, 10],
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"min_samples_split": [2, 5, 10, 15, 100],
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"tol": [1e-4, 1e-3, 1e-2, 1e-1, 1e-0],
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"validation_fraction": [0.1, 0.2, 0.3, 0.4, 0.5],
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"min_samples_leaf": [1, 2, 5, 10],
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"subsample": [0.6, 0.7, 0.8, 0.9, 1.0],
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"max_features": ["auto", "sqrt", "log2"],
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}
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df_train, df_test = load_data()
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X_train, y_train, X_test, y_test = preprocess_data(df_train, df_test)
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# Model
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.model_selection import RandomizedSearchCV
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model = RandomizedSearchCV(
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GradientBoostingClassifier(),
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hyper_params,
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n_iter=100000,
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scoring="roc_auc",
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n_jobs=-1,
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cv=5,
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verbose=1,
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)
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model.fit(X_train, y_train)
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print(model.score(X_test, y_test))
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print('Best parameters:', model.best_params_)
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if __name__ == "__main__":
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predict()
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#### Logistic Regression
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# Best parameters: {'C': 20.0, 'class_weight': None, 'max_iter': 50, 'penalty': 'l2', 'solver': 'lbfgs', 'tol': 0.0001}
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# Accuracy: 0.8660287081339713
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# Hyperparams tuned:
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# hyper_params = {
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# 'C': np.logspace(-3, 3, 7, 10, 20),
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# 'penalty': ['l1', 'l2', 'elasticnet'],
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# 'solver': ['liblinear', 'saga', 'lbfgs', 'newton-cg'],
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# 'max_iter': [50, 100, 1000, 2500, 5000],
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# 'class_weight': ['balanced', None],
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# 'tol': [1e-4, 1e-3, 1e-2, 1e-1, 1],
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# }
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######################
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