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from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
import os
import re
import pandas as pd
import tensorflow as tf
from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
router = APIRouter()
DESCRIPTION = " XGBOOST classification"
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: Bidirectional LSTM with Attention layer classification
- Current Model: Bidirectional LSTM with Attention layer classification classification 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)
train_dataset = train_test["train"]
test_dataset = train_test["test"]
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from datetime import datetime
from sklearn.feature_extraction.text import CountVectorizer
tfidf_vect = TfidfVectorizer(stop_words = 'english')
tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
tfidf_train = tfidf_vect.transform(train_dataset['quote'])
tfidf_test = tfidf_vect.fit_transform(test_dataset['quote'])
tfidf_test = tfidf_vect.transform(test_dataset['quote'])
true_labels = test_dataset["label"]
y_train = train_dataset["label"]
y_test = test_dataset["label"]
# Model
import xgboost as xgb
#Parameters: {'colsample_bytree': 0.7039283369765, 'gamma': 0.3317686860083553, 'learning_rate': 0.08341079006092542, 'max_depth': 5, 'n_estimators': 140, 'subsample': 0.6594650911012452}
#Parameters: {'colsample_bytree': 0.7039283369765, 'gamma': 0.3317686860083553, 'learning_rate': 0.08341079006092542, 'max_depth': 5, 'n_estimators': 140, 'subsample': 0.6594650911012452}
#Parameters: {'colsample_bytree': 0.7498850106268238, 'gamma': 0.3690168082131852, 'learning_rate': 0.054839600377537934, 'max_depth': 5, 'n_estimators': 125, 'subsample': 0.6272998821416366}
#xgb_model = xgb.XGBRegressor(max_depth=5, objective='multi:softprob',
# n_estimators=125, num_class=8, colsample_bytree=0.7498850106268238,gamma=0.3690168082131852,
# learning_rate=0.054839600377537934, subsample=0.6272998821416366)
#xgb_model.fit(tfidf_train, y_train)
#y_pred = xgb_model.predict(tfidf_train)
# Start tracking emissions
tracker.start()
tracker.start_task("inference")
xgb_model = xgb.XGBRegressor(max_depth=6, objective='multi:softprob',
n_estimators=500, num_class=8, colsample_bytree=0.75,gamma=0.35,
learning_rate=0.06, subsample=0.63)
xgb_model.fit(tfidf_test, y_test)
predictions = np.argmax(xgb_model.predict(tfidf_test), axis=1)
# 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 |