File size: 4,277 Bytes
4d6e8c2 243d40e 296146e 9012700 4d6e8c2 296146e 4d6e8c2 70f5f26 1c33274 70f5f26 3b83e0c b562460 3b83e0c 9bcb67c 3b83e0c 296146e f5aa578 3b83e0c 9012700 1c33274 70f5f26 3b83e0c 8589546 4d6e8c2 70f5f26 4d6e8c2 70f5f26 4d6e8c2 243d40e 3b83e0c 320940c 70f5f26 4d6e8c2 70f5f26 4d6e8c2 1c33274 4d6e8c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random
from skops.io import load
# Textpreprocessor defined in this scope
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
from .utils.text_preprocessor import preprocess
router = APIRouter()
DESCRIPTION = "Random Baseline"
ROUTE = "/text"
models_description = {
"baseline": "random baseline",
"tfidf_xgb": "TF-IDF vectorizer and XGBoost classifier",
}
# Some code borrowed from Nonnormalizable
def baseline_model(dataset_length: int):
# Make random predictions (placeholder for actual model inference)
predictions = [random.randint(0, 7) for _ in range(dataset_length)]
return predictions
def tree_classifier(test_dataset: dict, model: str):
texts = test_dataset["quote"]
texts = preprocess(texts)
model_path = f"tasks/text_models/{model}.skops"
model = load(model_path,
trusted=[
'xgboost.core.Booster',
'xgboost.sklearn.XGBClassifier'])
predictions = model.predict(texts)
return predictions
@router.post(ROUTE, tags=["Text Task"],
description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest,
model: str = "tfidf_xgb"):
"""
Evaluate text classification for climate disinformation detection.
Current Model: Random Baseline
- Makes random predictions from the label space (0-7)
- Used as a baseline for comparison
"""
# Get space info
username, space_url = get_space_info()
# Define the 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 the dataset
dataset = load_dataset(request.dataset_name)
# Convert string labels to integers
dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
# Split dataset
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE CODE HERE
# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
#--------------------------------------------------------------------------------------------
# Make random predictions (placeholder for actual model inference)
true_labels = test_dataset["label"]
if model == "baseline":
predictions = baseline_model(len(true_labels))
elif model == "tfidf_xgb":
predictions = tree_classifier(test_dataset, model='xgb_pipeline')
#--------------------------------------------------------------------------------------------
# YOUR MODEL INFERENCE STOPS HERE
#--------------------------------------------------------------------------------------------
# Stop tracking emissions
emissions_data = tracker.stop_task()
# Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
# Prepare results dictionary
results = {
"username": username,
"space_url": space_url,
"submission_timestamp": datetime.now().isoformat(),
"model_description": DESCRIPTION,
"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": ROUTE,
"dataset_config": {
"dataset_name": request.dataset_name,
"test_size": request.test_size,
"test_seed": request.test_seed
}
}
return results |