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import json
import os
import numpy as np
import openai
import pandas as pd
import requests
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
np.set_printoptions(threshold=10000)
def get_embedding_from_api(word, model="vicuna-7b-v1.1"):
if "ada" in model:
resp = openai.Embedding.create(
model=model,
input=word,
)
embedding = np.array(resp["data"][0]["embedding"])
return embedding
url = "http://localhost:8000/v1/embeddings"
headers = {"Content-Type": "application/json"}
data = json.dumps({"model": model, "input": word})
response = requests.post(url, headers=headers, data=data)
if response.status_code == 200:
embedding = np.array(response.json()["data"][0]["embedding"])
return embedding
else:
print(f"Error: {response.status_code} - {response.text}")
return None
def create_embedding_data_frame(data_path, model, max_tokens=500):
df = pd.read_csv(data_path, index_col=0)
df = df[["Time", "ProductId", "UserId", "Score", "Summary", "Text"]]
df = df.dropna()
df["combined"] = (
"Title: " + df.Summary.str.strip() + "; Content: " + df.Text.str.strip()
)
top_n = 1000
df = df.sort_values("Time").tail(top_n * 2)
df.drop("Time", axis=1, inplace=True)
df["n_tokens"] = df.combined.apply(lambda x: len(x))
df = df[df.n_tokens <= max_tokens].tail(top_n)
df["embedding"] = df.combined.apply(lambda x: get_embedding_from_api(x, model))
return df
def train_random_forest(df):
X_train, X_test, y_train, y_test = train_test_split(
list(df.embedding.values), df.Score, test_size=0.2, random_state=42
)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
preds = clf.predict(X_test)
report = classification_report(y_test, preds)
accuracy = accuracy_score(y_test, preds)
return clf, accuracy, report
input_datapath = "amazon_fine_food_review.csv"
if not os.path.exists(input_datapath):
raise Exception(
f"Please download data from: https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews"
)
df = create_embedding_data_frame(input_datapath, "vicuna-7b-v1.1")
clf, accuracy, report = train_random_forest(df)
print(f"Vicuna-7b-v1.1 accuracy:{accuracy}")
df = create_embedding_data_frame(input_datapath, "text-similarity-ada-001")
clf, accuracy, report = train_random_forest(df)
print(f"text-similarity-ada-001 accuracy:{accuracy}")
df = create_embedding_data_frame(input_datapath, "text-embedding-ada-002")
clf, accuracy, report = train_random_forest(df)
print(f"text-embedding-ada-002 accuracy:{accuracy}")
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