使用嵌入进行回归

import pandas as pd
import numpy as np
from ast import literal_eval

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error

datafile_path = "data/fine_food_reviews_with_embeddings_1k.csv"

df = pd.read_csv(datafile_path)
df["embedding"] = df.embedding.apply(literal_eval).apply(np.array)

X_train, X_test, y_train, y_test = train_test_split(list(df.embedding.values), df.Score, test_size=0.2, random_state=42)

rfr = RandomForestRegressor(n_estimators=100)
rfr.fit(X_train, y_train)
preds = rfr.predict(X_test)

mse = mean_squared_error(y_test, preds)
mae = mean_absolute_error(y_test, preds)

print(f"text-embedding-3-small performance on 1k Amazon reviews: mse={mse:.2f}, mae={mae:.2f}")

bmse = mean_squared_error(y_test, np.repeat(y_test.mean(), len(y_test)))
bmae = mean_absolute_error(y_test, np.repeat(y_test.mean(), len(y_test)))
print(
    f"Dummy mean prediction performance on Amazon reviews: mse={bmse:.2f}, mae={bmae:.2f}"
)