Update tasks/text.py

tasks/text.py

@@ -6,12 +6,16 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.model_selection import train_test_split
 import pandas as pd
+import tensorflow as tf
+from transformers import DistilBertTokenizer
+from transformers import TFDistilBertForSequenceClassification
+from tensorflow.keras.models import load_model
 from .utils.evaluation import TextEvaluationRequest
 from .utils.emissions import tracker, clean_emissions_data, get_space_info
 
 router = APIRouter()
 
-DESCRIPTION = "Logistic Regression Baseline"
+DESCRIPTION = "DistilBert classification"
 ROUTE = "/text"
 
 @router.post(ROUTE, tags=["Text Task"], 
@@ -20,8 +24,8 @@ async def evaluate_text(request: TextEvaluationRequest):
     """
     Evaluate text classification for climate disinformation detection.
     
-    Current Model: Logistic Regression  Baseline
-    - Makes Logistic Regression predictions from the label space (0-7)
+    Current Model: DistilBert classification
+    - DistilBert classification predictions from the label space (0-7)
     - Used as a baseline for comparison
     """
     # Get space info
@@ -52,28 +56,91 @@ async def evaluate_text(request: TextEvaluationRequest):
     test_dataset = train_test["test"]
     train_dataset = train_test["train"]
     y_train=train_dataset['label']
-    tfidf_vect = TfidfVectorizer(stop_words = 'english')
+   
+    train_dataset = train_test["train"]
+    tn=pd.DataFrame([(i, j, k) for i,j,k in zip(train_dataset["quote"] , train_dataset["source"],
+                       train_dataset["subsource"])], columns=['quote','source', 'subsource'])
+    test_dataset = train_test["test"]
+    tt=pd.DataFrame([(i, j, k) for i,j,k in zip(test_dataset["quote"] , test_dataset["source"],
+                       test_dataset["subsource"])], columns=['quote','source', 'subsource'])
+    tt.fillna("",inplace=True)
+    tn.fillna("",inplace=True)
+    tn['text'] = tn[['quote', 'source','subsource']].agg(' '.join, axis=1)
+    tt['text'] = tn[['quote', 'source','subsource']].agg(' '.join, axis=1)
+
+    def clean_text(x):
+    pattern = r'[^a-zA-z0-9\s]'
+    text = re.sub(pattern, '', x)
+    return x
+
+    def clean_numbers(x):
+        if bool(re.search(r'\d', x)):
+            x = re.sub('[0-9]{5,}', '#####', x)
+            x = re.sub('[0-9]{4}', '####', x)
+            x = re.sub('[0-9]{3}', '###', x)
+            x = re.sub('[0-9]{2}', '##', x)
+        return x
+    
+    contraction_dict = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not",  "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",  "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will",  "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are",  "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have"}
+    def _get_contractions(contraction_dict):
+        contraction_re = re.compile('(%s)' % '|'.join(contraction_dict.keys()))
+        return contraction_dict, contraction_re
+    contractions, contractions_re = _get_contractions(contraction_dict)
+    def replace_contractions(text):
+        def replace(match):
+            return contractions[match.group(0)]
+        return contractions_re.sub(replace, text)
+    train_dataset_df = tn['quote'].apply(lambda x: x.lower())
+    test_dataset_df = tt['quote'].apply(lambda x: x.lower())
+    # Clean the text
+    train_dataset_df = train_dataset_df.apply(lambda x: clean_text(x))
+    test_dataset_df= test_dataset_df.apply(lambda x: clean_text(x))
+    # Clean numbers
+    train_dataset_df= train_dataset_df.apply(lambda x: clean_numbers(x))
+    test_dataset_df = test_dataset_df.apply(lambda x: clean_numbers(x))
+    # Clean Contractions
+    train_dataset_df = train_dataset_df.apply(lambda x: replace_contractions(x))
+    test_dataset_df = test_dataset_df.apply(lambda x: replace_contractions(x))
     
-    tfidf_train = tfidf_vect.fit_transform(train_dataset['quote'])
-    tfidf_test = tfidf_vect.transform(test_dataset['quote'])
+    y_train_df=pd.DataFrame(train_dataset['label'], columns=['label'])
+    y_test_df=pd.DataFrame(test_dataset['label'], columns=['label'])
+    y_train_encoded  = y_train_df['label'].astype('category').cat.codes
+    y_test_encoded = y_test_df['label'].astype('category').cat.codes
+    train_labels = y_train_encoded.to_list()
+    test_labels=y_test_encoded.to_list()
+
+    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+    train_encodings = tokenizer(train_dataset_df.to_list(), truncation=True, padding=True)
+    val_encodings = tokenizer(test_dataset_df.to_list(), truncation=True, padding=True)
+
+
+    train_dataset_bert = tf.data.Dataset.from_tensor_slices((
+    dict(train_encodings),
+    train_labels
+    ))
+    val_dataset_bert = tf.data.Dataset.from_tensor_slices((
+    dict(val_encodings),
+    test_labels
+    ))
 
 
     # Start tracking emissions
     tracker.start()
     tracker.start_task("inference")
-
+    model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=8)
+ 
+    optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5, epsilon=1e-08)
+    model.compile(optimizer=optimizer, loss=model.hf_compute_loss, metrics=['accuracy'])
     #--------------------------------------------------------------------------------------------
     # 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"]
-    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))
-    predictions=LR.predict(pd.DataFrame.sparse.from_spmatrix(tfidf_test))
-    
+    # Make  predictions (placeholder for actual model inference)
+   
+    early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
+ 
+    model.fit(train_dataset_bert.shuffle(1000).batch(16),epochs=4,batch_size=16,validation_data=val_dataset_bert.shuffle(1000).batch(16),callbacks=[early_stopping])
     #--------------------------------------------------------------------------------------------
     # YOUR MODEL INFERENCE STOPS HERE
     #--------------------------------------------------------------------------------------------   
@@ -81,9 +148,31 @@ async def evaluate_text(request: TextEvaluationRequest):
     
     # Stop tracking emissions
     emissions_data = tracker.stop_task()
+
     
+    save_directory = "BERT" # Change this to your preferred location
+     
+    model.save_pretrained(save_directory)
+    tokenizer.save_pretrained(save_directory)
+    save_directory = "BERT"
+    loaded_tokenizer = DistilBertTokenizer.from_pretrained(save_directory)
+    loaded_model = TFDistilBertForSequenceClassification.from_pretrained(save_directory)
+
     # Calculate accuracy
-    accuracy = accuracy_score(true_labels, predictions)
+    def predict_category(text):
+        predict_input = loaded_tokenizer.encode(text,
+        truncation=True,
+        padding=True,
+        return_tensors="tf")
+        output = loaded_model(predict_input)[0]
+        prediction_value = tf.argmax(output, axis=1).numpy()[0]
+        return prediction_value
+    # - - - - - - - - - - - - - - - - - - - - - - - - - - -
+    y_pred = []
+    for text_ in test_dataset_df.to_list():
+        y_pred.append(predict_category(text_))
+        
+    accuracy_score(test_labels, y_pred)
     
     # Prepare results dictionary
     results = {