mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-06 11:37:44 +00:00
TPLin22
9a792cc21e
* init commit for XGBoost * fix some bugs * CI issues * CI issues * CI issue * edit prompts for kaggle scenario & fix some bugs * Revised Prompts To Improve Performance on Model Type & Support of Random Forest * edit prompts & modify evaluator.py to adapt to Kaggle scenario * edit prompts * fix some bugs * CI issues --------- Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com> Co-authored-by: Xisen-Wang <xisen_application@163.com>
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
import random
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import xgboost as xgb
|
|
from model import get_num_round, get_params
|
|
from sklearn.compose import ColumnTransformer
|
|
from sklearn.impute import SimpleImputer
|
|
from sklearn.metrics import accuracy_score
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.pipeline import Pipeline
|
|
from sklearn.preprocessing import OneHotEncoder
|
|
|
|
# Set random seed for reproducibility
|
|
SEED = 42
|
|
random.seed(SEED)
|
|
np.random.seed(SEED)
|
|
|
|
|
|
def compute_metrics_for_classification(y_true, y_pred):
|
|
"""Compute accuracy metric for classification."""
|
|
accuracy = accuracy_score(y_true, y_pred)
|
|
return accuracy
|
|
|
|
|
|
def train_model(X_train, y_train, X_valid, y_valid):
|
|
"""Define and train the model."""
|
|
dtrain = xgb.DMatrix(X_train, label=y_train)
|
|
dvalid = xgb.DMatrix(X_valid, label=y_valid)
|
|
|
|
params = get_params()
|
|
num_round = get_num_round()
|
|
|
|
evallist = [(dtrain, "train"), (dvalid, "eval")]
|
|
bst = xgb.train(params, dtrain, num_round, evallist)
|
|
|
|
return bst
|
|
|
|
|
|
def predict(model, X):
|
|
dtest = xgb.DMatrix(X)
|
|
y_pred_prob = model.predict(dtest)
|
|
return y_pred_prob > 0.5 # Apply threshold to get boolean predictions
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Load and preprocess the data
|
|
data_df = pd.read_csv("/root/.data/train.csv")
|
|
data_df = data_df.drop(["PassengerId", "Name"], axis=1)
|
|
|
|
X = data_df.drop(["Transported"], axis=1)
|
|
y = data_df.Transported.to_numpy()
|
|
|
|
# Identify numerical and categorical features
|
|
numerical_cols = [cname for cname in X.columns if X[cname].dtype in ["int64", "float64"]]
|
|
categorical_cols = [cname for cname in X.columns if X[cname].dtype == "object"]
|
|
|
|
# Define preprocessors for numerical and categorical features
|
|
categorical_transformer = Pipeline(
|
|
steps=[
|
|
("imputer", SimpleImputer(strategy="most_frequent")),
|
|
("onehot", OneHotEncoder(handle_unknown="ignore")),
|
|
]
|
|
)
|
|
|
|
numerical_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="mean"))])
|
|
|
|
# Combine preprocessing steps
|
|
preprocessor = ColumnTransformer(
|
|
transformers=[
|
|
("cat", categorical_transformer, categorical_cols),
|
|
("num", numerical_transformer, numerical_cols),
|
|
]
|
|
)
|
|
|
|
# Split the data into training and validation sets
|
|
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.10, random_state=SEED)
|
|
|
|
# Fit the preprocessor on the training data and transform both training and validation data
|
|
preprocessor.fit(X_train)
|
|
X_train = preprocessor.transform(X_train)
|
|
X_valid = preprocessor.transform(X_valid)
|
|
|
|
# Train the model
|
|
model = train_model(X_train, y_train, X_valid, y_valid)
|
|
|
|
# Evaluate the model on the validation set
|
|
y_valid_pred = predict(model, X_valid)
|
|
accuracy = compute_metrics_for_classification(y_valid, y_valid_pred)
|
|
print("Final Accuracy on validation set: ", accuracy)
|
|
|
|
# Save the validation accuracy
|
|
pd.Series(data=[accuracy], index=["ACC"]).to_csv("./submission_score.csv")
|
|
|
|
# Load and preprocess the test set
|
|
submission_df = pd.read_csv("/root/.data/test.csv")
|
|
passenger_ids = submission_df["PassengerId"]
|
|
submission_df = submission_df.drop(["PassengerId", "Name"], axis=1)
|
|
X_test = preprocessor.transform(submission_df)
|
|
|
|
# Make predictions on the test set and save them
|
|
y_test_pred = predict(model, X_test)
|
|
submission_result = pd.DataFrame({"PassengerId": passenger_ids, "Transported": y_test_pred})
|
|
# submit predictions for the test set
|
|
submission_result.to_csv("./submission.csv", index=False)
|