From 1bd6089a330e00be6ae0e69f2e7131955af19c33 Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Wed, 14 Aug 2024 11:28:55 +0700
Subject: [PATCH] Create reputation_system.py

---
 .../PiNetAI/ai/models/reputation_system.py    | 98 +++++++++++++++++++
 1 file changed, 98 insertions(+)
 create mode 100644 projects/PiNetAI/ai/models/reputation_system.py

diff --git a/projects/PiNetAI/ai/models/reputation_system.py b/projects/PiNetAI/ai/models/reputation_system.py
new file mode 100644
index 000000000..712a80d5c
--- /dev/null
+++ b/projects/PiNetAI/ai/models/reputation_system.py
@@ -0,0 +1,98 @@
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+import seaborn as sns
+from xgboost import XGBClassifier
+from catboost import CatBoostClassifier
+from lightgbm import LGBMClassifier
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.callbacks import EarlyStopping
+import optuna
+from scipy.stats import entropy
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+class ReputationSystem:
+    def __init__(self, data, num_users, num_items, embedding_dim=128):
+        self.data = data
+        self.num_users = num_users
+        self.num_items = num_items
+        self.embedding_dim = embedding_dim
+        self.user_embeddings = np.random.rand(num_users, embedding_dim)
+        self.item_embeddings = np.random.rand(num_items, embedding_dim)
+        self.models = {
+            'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
+            'XGBoost': XGBClassifier(objective='binary:logistic', max_depth=6, learning_rate=0.1, n_estimators=1000),
+            'CatBoost': CatBoostClassifier(iterations=1000, learning_rate=0.1, depth=6),
+            'LightGBM': LGBMClassifier(objective='binary', max_depth=6, learning_rate=0.1, n_estimators=1000),
+            'Neural Network': self.create_neural_network()
+        }
+
+    def create_neural_network(self):
+        model = Sequential()
+        model.add(Dense(64, activation='relu', input_shape=(self.embedding_dim*2,)))
+        model.add(Dropout(0.2))
+        model.add(Dense(32, activation='relu'))
+        model.add(Dropout(0.2))
+        model.add(Dense(1, activation='sigmoid'))
+        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+        return model
+
+    def preprocess_data(self):
+        self.data['user_id'] = self.data['user_id'].astype(str)
+        self.data['item_id'] = self.data['item_id'].astype(str)
+        self.data['rating'] = self.data['rating'].astype(float)
+        self.data['timestamp'] = pd.to_datetime(self.data['timestamp'])
+        self.data.sort_values(by='timestamp', inplace=True)
+
+    def create_user_item_matrices(self):
+        user_item_matrix = np.zeros((self.num_users, self.num_items))
+        for i, row in self.data.iterrows():
+            user_item_matrix[row['user_id'], row['item_id']] = row['rating']
+        return user_item_matrix
+
+    def calculate_reputation_scores(self, user_item_matrix):
+        reputation_scores = np.zeros((self.num_users, self.num_items))
+        for i in range(self.num_users):
+            for j in range(self.num_items):
+                if user_item_matrix[i, j] > 0:
+                    reputation_scores[i, j] = self.calculate_reputation_score(i, j, user_item_matrix)
+        return reputation_scores
+
+    def calculate_reputation_score(self, user_id, item_id, user_item_matrix):
+        user_ratings = user_item_matrix[user_id, :]
+        item_ratings = user_item_matrix[:, item_id]
+        user_entropy = entropy(user_ratings)
+        item_entropy = entropy(item_ratings)
+        reputation_score = user_entropy + item_entropy
+        return reputation_score
+
+    def train_models(self, reputation_scores):
+        X_train, X_test, y_train, y_test = train_test_split(reputation_scores, self.data['rating'], test_size=0.2, random_state=42)
+        for model_name, model in self.models.items():
+            if model_name == 'Neural Network':
+                y_train = to_categorical(y_train)
+                early_stopping = EarlyStopping(monitor='val_loss', patience=5, min_delta=0.001)
+                model.fit(X_train, y_train, epochs=100, batch_size=128, validation_data=(X_test, y_train), callbacks=[early_stopping])
+            else:
+                model.fit(X_train, y_train)
+
+    def predict_reputation_scores(self, reputation_scores):
+        predictions = {}
+        for model_name, model in self.models.items():
+            if model_name == 'Neural Network':
+                predictions[model_name] = model.predict(reputation_scores)[:, 0]
+            else:
+                predictions[model_name] = model.predict(reputation_scores)
+        return predictions
+
+    def evaluate_models(self, predictions):
+        metrics = {}
+        for model_name, prediction in