Spaces:
Sleeping
Sleeping
File size: 3,879 Bytes
4d6e8c2 ad1b79a 4d6e8c2 ad1b79a 1c33274 70f5f26 1c33274 70f5f26 4d6e8c2 70f5f26 d248f3d 70f5f26 4d6e8c2 ad1b79a d248f3d 4d6e8c2 d248f3d 4d6e8c2 86c2fd7 4d6e8c2 86c2fd7 d248f3d 4d6e8c2 70f5f26 4d6e8c2 86c2fd7 70f5f26 86c2fd7 4ab93f7 86c2fd7 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 |
from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
router = APIRouter()
DESCRIPTION = "Logistic Regression"
ROUTE = "/text"
@router.post(ROUTE, tags=["Text Task"],
description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
"""
Evaluate text classification for climate disinformation detection.
Current Model: Logistic regression
- 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_test_split(test_size=.33, seed=42)
train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
test_dataset = train_test["test"]
#tfidf_vect = TfidfVectorizer(stop_words = 'english')
#tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
#tfidf_test = tfidf_vect.transform(test_dataset['quote'])
# 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"]
predictions = [random.randint(0, 7) for _ in range(len(true_labels))]
#LR = LogisticRegression(class_weight='balanced', max_iter=20, random_state=1234,
# solver='liblinear')
#LR.fit(pd.DataFrame.sparse.from_spmatrix(tfidf_train), pd.DataFrame(y_train_v))
#predictions=LR.predict(pd.DataFrame.sparse.from_spmatrix(tfidf_test))
#--------------------------------------------------------------------------------------------
# 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 |