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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
# Define the router for text tasks
router = APIRouter()
DESCRIPTION_NAIVE_BAYES = "Naive Bayes Text Classifier"
DESCRIPTION_SVM = "SVM Text Classifier with TF-IDF"
# Naive Bayes Endpoint
@router.post("/text", tags=["Text Task"], description=DESCRIPTION_NAIVE_BAYES)
async def evaluate_text(request: TextEvaluationRequest):
"""
Evaluate text classification using Naive Bayes.
"""
username, space_url = get_space_info()
# Label Mapping
LABEL_MAPPING = {
"0_not_relevant": 0,
"1_not_happening": 1,
"2_not_human": 2,
"3_not_bad": 3,
"4_solutions_harmful_unnecessary": 4,
"5_science_unreliable": 5,
"6_proponents_biased": 6,
"7_fossil_fuels_needed": 7
}
# Load and prepare dataset
dataset = load_dataset(request.dataset_name)
dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
# Train-Test Split
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
train_texts = [x["text"] for x in train_test["train"]]
train_labels = [x["label"] for x in train_test["train"]]
test_texts = [x["text"] for x in train_test["test"]]
test_labels = [x["label"] for x in train_test["test"]]
# TF-IDF Vectorization
vectorizer = TfidfVectorizer(max_features=5000)
train_vectors = vectorizer.fit_transform(train_texts)
test_vectors = vectorizer.transform(test_texts)
# Train Naive Bayes Classifier
model = MultinomialNB()
model.fit(train_vectors, train_labels)
# Track emissions
tracker.start()
tracker.start_task("inference")
predictions = model.predict(test_vectors)
emissions_data = tracker.stop_task()
# Calculate Accuracy
accuracy = accuracy_score(test_labels, predictions)
return {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION_NAIVE_BAYES,
"accuracy": float(accuracy),
"energy_consumed_wh": emissions_data.energy_consumed * 1000,
"emissions_gco2eq": emissions_data.emissions * 1000,
"emissions_data": clean_emissions_data(emissions_data),
"api_route": "/text",
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
# SVM Endpoint
@router.post("/text_svm", tags=["Text Task"], description=DESCRIPTION_SVM)
async def evaluate_text_svm(request: TextEvaluationRequest):
"""
Evaluate text classification using SVM.
"""
username, space_url = get_space_info()
# Label Mapping
LABEL_MAPPING = {
"0_not_relevant": 0,
"1_not_happening": 1,
"2_not_human": 2,
"3_not_bad": 3,
"4_solutions_harmful_unnecessary": 4,
"5_science_unreliable": 5,
"6_proponents_biased": 6,
"7_fossil_fuels_needed": 7
}
# Load and prepare dataset
dataset = load_dataset(request.dataset_name)
dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
# Train-Test Split
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
train_texts = [x["text"] for x in train_test["train"]]
train_labels = [x["label"] for x in train_test["train"]]
test_texts = [x["text"] for x in train_test["test"]]
test_labels = [x["label"] for x in train_test["test"]]
# TF-IDF Vectorization
vectorizer = TfidfVectorizer(max_features=5000)
train_vectors = vectorizer.fit_transform(train_texts)
test_vectors = vectorizer.transform(test_texts)
# Train SVM Classifier
model = SVC(kernel="linear", probability=True)
model.fit(train_vectors, train_labels)
# Track emissions
tracker.start()
tracker.start_task("inference")
predictions = model.predict(test_vectors)
emissions_data = tracker.stop_task()
# Calculate Accuracy
accuracy = accuracy_score(test_labels, predictions)
return {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION_SVM,
"accuracy": float(accuracy),
"energy_consumed_wh": emissions_data.energy_consumed * 1000,
"emissions_gco2eq": emissions_data.emissions * 1000,
"emissions_data": clean_emissions_data(emissions_data),
"api_route": "/text_svm",
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
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