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