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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}") | |