Update feature_engineer.py

blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/utils/feature_engineer.py

@@ -1,11 +1,63 @@
 import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.preprocessing import StandardScaler
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 class FeatureEngineer:
-    def __init__(self, data):
+    def __init__(self, data, target_variable):
         self.data = data
+        self.target_variable = target_variable
 
-    def engineer_features(self):
+    def preprocess_text(self, text_data):
+        # Convert text data to lowercase
+        text_data = text_data.apply(lambda x: x.lower())
+
+        # Remove punctuation and special characters
+        text_data = text_data.apply(lambda x: re.sub(r'[^a-zA-Z0-9\s]', '', x))
+
+        return text_data
+
+    def extract_tfidf_features(self, text_data):
+        vectorizer = TfidfVectorizer(max_features=5000)
+        tfidf_features = vectorizer.fit_transform(text_data)
+
+        return tfidf_features
+
+    def extract_bert_features(self, text_data, model_name):
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+        features = []
+        for text in text_data:
+            inputs = tokenizer.encode_plus(
+                text,
+                add_special_tokens=True,
+                max_length=512,
+                padding='max_length',
+                truncation=True,
+                return_attention_mask=True,
+                return_tensors='pt'
+            )
+
+            outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+            features.append(outputs.last_hidden_state[:, 0, :].detach().numpy())
+
+        features = np.array(features)
+
+        return features
+
+    def scale_features(self, features):
         scaler = StandardScaler()
-        self.data[['feature1', 'feature2', ...]] = scaler.fit_transform(self.data[['feature1', 'feature2', ...]])
-        return self.data
+        scaled_features = scaler.fit_transform(features)
+
+        return scaled_features
+
+    def engineer_features(self, text_data, model_name):
+        text_data = self.preprocess_text(text_data)
+        tfidf_features = self.extract_tfidf_features(text_data)
+        bert_features = self.extract_bert_features(text_data, model_name)
+        features = np.concatenate((tfidf_features.toarray(), bert_features), axis=1)
+        scaled_features = self.scale_features(features)
+
+        return scaled_features