Create pi_stablecoin_stabilizer.py

blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/pi_stablecoin_stabilizer.py

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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from xgboost import XGBRegressor
+from catboost import CatBoostRegressor
+from lightgbm import LGBMRegressor
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+
+class PiStablecoinStabilizer:
+    def __init__(self, data, target_variable, test_size=0.2, random_state=42):
+        self.data = data
+        self.target_variable = target_variable
+        self.test_size = test_size
+        self.random_state = random_state
+        self.X_train = None
+        self.X_test = None
+        self.y_train = None
+        self.y_test = None
+        self.models = []
+
+    def preprocess_data(self):
+        # Drop missing values
+        self.data.dropna(inplace=True)
+
+        # Scale features using StandardScaler
+        scaler = StandardScaler()
+        self.data[['feature1', 'feature2', ...]] = scaler.fit_transform(self.data[['feature1', 'feature2', ...]])
+
+        # Select top k features using SelectKBest
+        selector = SelectKBest(f_classif, k=10)
+        self.data = selector.fit_transform(self.data.drop([self.target_variable], axis=1), self.data[self.target_variable])
+
+        # Split data into training and testing sets
+        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data.drop([self.target_variable], axis=1),
+                                                                              self.data[self.target_variable],
+                                                                              test_size=self.test_size,
+                                                                              random_state=self.random_state)
+
+    def train_models(self):
+        # Create and train multiple models
+        models = [
+            RandomForestRegressor(n_estimators=100, max_depth=5),
+            XGBRegressor(objective='reg:squarederror', max_depth=5, n_estimators=100),
+            CatBoostRegressor(iterations=100, depth=5, learning_rate=0.1),
+            LGBMRegressor(objective='regression', max_depth=5, n_estimators=100),
+            self._train_lstm_model(),
+            self._train_arima_model(),
+            self._train_sarimax_model(),
+            self._train_kalman_filter_model()
+        ]
+
+        for model in models:
+            model.fit(self.X_train, self.y_train)
+            self.models.append(model)
+
+    def _train_lstm_model(self):
+        # Train an LSTM model
+        model = Sequential()
+        model.add(LSTM(units=50, return_sequences=True, input_shape=(self.X_train.shape[1], 1)))
+        model.add(LSTM(units=50))
+        model.add(Dense(1))
+        model.compile(loss='mean_squared_error', optimizer='adam')
+        return model
+
+    def _train_arima_model(self):
+        # Train an ARIMA model
+        model = ARIMA(self.y_train, order=(1,1,1))
+        model_fit = model.fit(disp=0)
+        return model_fit
+
+    def _train_sarimax_model(self):
+        # Train a SARIMAX model
+        model = SARIMAX(self.y_train, order=(1,1,1), seasonal_order=(1,1,1,12))
+        model_fit = model.fit(disp=0)
+        return model_fit
+
+    def _train_kalman_filter_model(self):
+        # Train a Kalman filter model
+        kf = KalmanFilter(transition_matrices=[1],
+                          observation_matrices=[1],
+                          initial_state_mean=0,
+                          initial_state_covariance=1,
+                          observation_covariance=1,
+                          transition_covariance=0.1)
+        kf = kf.em(self.y_train, n_iter=50)
+        return kf
+
+    def evaluate_models(self):
+        # Evaluate each model using mean squared error and R-squared score
+        results = []
+        for model in self.models:
+            y_pred = model.predict(self.X_test)
+            mse = mean_squared_error(self.y_test, y_pred)
+            r2 = r2_score(self.y_test, y_pred)
+            results.append((model.__class__.__name__, mse, r2))
+
+        return results
+
+    def make_predictions(self, input_data):
+        # Make predictions using the best model
+        best_model = max(self.models, key=lambda x: x.score(self.X_test, self.y_test))
+        predictions = best_model.predict(input_data)
+        return predictions