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import pandas as pd | |
import numpy as np | |
import xgboost as xgb | |
from sklearn.multioutput import MultiOutputRegressor | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import mean_squared_error, mean_absolute_error | |
# Read your data file | |
datafile_path = "data/chat_transcripts_with_embeddings_and_scores.csv" | |
df = pd.read_csv(datafile_path) | |
# Convert embeddings to numpy arrays | |
df['embedding'] = df['embedding'].apply(lambda x: np.array(eval(x))) | |
# Split the data into features (X) and labels (y) | |
X = list(df.embedding.values) | |
y = df[['avoide', 'avoida', 'avoidb', 'avoidc', 'avoidd', 'anxietye', 'anxietya', 'anxietyb', 'anxietyc', 'anxietyd']].values | |
# Split data into training, validation, and testing sets | |
X_train, X_val_test, y_train, y_val_test = train_test_split(X, y, test_size=0.3, random_state=42) | |
X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=0.5, random_state=42) | |
# Train your regression model | |
xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1, max_depth = 5, alpha = 10, n_estimators = 10) | |
multioutput_reg = MultiOutputRegressor(xg_reg) | |
multioutput_reg.fit(np.array(X_train).tolist(), y_train) | |
# Make predictions on the validation data and tune your model parameters accordingly | |
val_preds = multioutput_reg.predict(np.array(X_val).tolist()) | |
val_mse = mean_squared_error(y_val, val_preds) | |
val_mae = mean_absolute_error(y_val, val_preds) | |
print(f"Validation MSE: {val_mse:.2f}, Validation MAE: {val_mae:.2f}") | |
# After tuning your model, make predictions on the test data | |
test_preds = multioutput_reg.predict(np.array(X_test).tolist()) | |
# Evaluate your model | |
test_mse = mean_squared_error(y_test, test_preds) | |
test_mae = mean_absolute_error(y_test, test_preds) | |
print(f"Test MSE: {test_mse:.2f}, Test MAE: {test_mae:.2f}") | |