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import numpy as np
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from tensorflow.keras.models import load_model
import joblib
class VAVAnomalizer:
def __init__(
self,
rtu_id,
prediction_model_path,
clustering_model_path,
pca_model_path,
num_inputs,
num_outputs,
):
"""
Initializes a VAVAnomalizer object.
Args:
rtu_id (int): The ID of the RTU (Roof Top Unit) associated with the VAV (Variable Air Volume) system.
prediction_model_path (str): The file path to the prediction model.
pca_model_path (str): The file path to the PCA model.
clustering_model_path (str): The file path to the clustering model.
num_inputs (int): The number of input features for the prediction model.
num_outputs (int): The number of output features for the prediction model.
"""
self.rtu_id = rtu_id
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.load_models(prediction_model_path, clustering_model_path, pca_model_path)
self.actual_list, self.pred_list, self.resid_list, self.resid_pca_list = (
self.initialize_lists()
)
def load_models(self, prediction_model_path, clustering_model_path, pca_model_path):
"""
Loads the prediction model and clustering model.
Args:
prediction_model_path (str): The file path to the prediction model.
pca_model_path (str): The file path to the PCA model.
clustering_model_path (str): The file path to the clustering model.
"""
self.model = load_model(prediction_model_path)
self.pca_model: PCA = joblib.load(pca_model_path)
self.kmeans_model: KMeans = joblib.load(clustering_model_path)
def initialize_lists(self, size=30):
"""
Initialize lists for storing actual, predicted, and residual values.
Args:
size (int): Size of the lists.
Returns:
tuple: A tuple containing three lists initialized with zeros.
"""
initial_values = [[0] * self.num_outputs] * size
initial_values1 = [[0] * 2] * size
return (
initial_values.copy(),
initial_values.copy(),
initial_values.copy(),
initial_values1.copy(),
)
def predict(self, df_new):
"""
Makes predictions using the prediction model.
Args:
df_new (numpy.ndarray): The new data for prediction.
Returns:
numpy.ndarray: The predicted values.
"""
return self.model.predict(df_new)
def calculate_residuals(self, df_trans, pred):
"""
Calculates the residuals between the actual values and the predicted values.
Args:
df_trans (numpy.ndarray): The transformed data.
pred (numpy.ndarray): The predicted values.
Returns:
numpy.ndarray: The actual values.
numpy.ndarray: The residuals.
"""
actual = df_trans[30, : self.num_outputs]
resid = pred - actual
return actual, resid
def calculate_distances(self, resid):
"""
Calculate the distances between residuals and cluster centers.
Args:
resid (array): Residual values.
Returns:
array: Array of distances.
"""
dist = []
dist.append(
np.linalg.norm(
self.pca_model.transform(resid.reshape(1, -1))
- self.kmeans_model.cluster_centers_[0]
)
)
return np.array(dist)
def residual_pca(self, resid):
"""
Perform PCA on the residuals.
Args:
resid (array): Residual values.
Returns:
array: Transformed residuals.
"""
return self.pca_model.transform(resid.reshape(1, -1))
def resize_prediction(self, pred, df_trans):
"""
Resize the predicted values to match the shape of the transformed input data.
Args:
pred (array): Predicted values.
df_trans (DataFrame): Transformed input data.
Returns:
array: Resized predicted values.
"""
pred = np.resize(
pred, (pred.shape[0], pred.shape[1] + len(df_trans[30, self.num_outputs :]))
)
pred[:, -len(df_trans[30, self.num_outputs :]) :] = df_trans[
30, self.num_outputs :
]
return pred
def inverse_transform(self, scaler, pred, df_trans):
"""
Inverse transform the predicted and actual values.
Args:
scaler (object): Scaler object for inverse transformation.
pred (array): Predicted values.
df_trans (DataFrame): Transformed input data.
Returns:
tuple: A tuple containing the actual and predicted values after inverse transformation.
"""
pred = scaler.inverse_transform(np.array(pred))
actual = scaler.inverse_transform(np.array([df_trans[30, :]]))
return actual, pred
def update_lists(self, actual, pred, resid, resid_pca):
"""
Update the lists of actual, predicted, and residual values.
Args:
actual_list (list): List of actual values.
pred_list (list): List of predicted values.
resid_list (list): List of residual values.
resid_pca_list (list): List of PCA-transformed residual values.
actual (array): Actual values.
pred (array): Predicted values.
resid (array): Residual values.
Returns:
tuple: A tuple containing the updated lists of actual, predicted, and residual values.
"""
self.actual_list.pop(0)
self.pred_list.pop(0)
self.resid_list.pop(0)
self.resid_pca_list.pop(0)
self.actual_list.append(actual.flatten().tolist())
self.pred_list.append(pred.flatten().tolist())
self.resid_list.append(resid.flatten().tolist())
self.resid_pca_list.append(resid_pca.flatten().tolist())
return self.actual_list, self.pred_list, self.resid_list, self.resid_pca_list
def pipeline(self, df_new, df_trans, scaler):
"""
Perform the anomaly detection pipeline.
Args:
df_new (DataFrame): Input data for prediction.
df_trans (DataFrame): Transformed input data.
scaler (object): Scaler object for inverse transformation.
Returns:
tuple: A tuple containing the lists of actual, predicted, and residual values, and the distances.
"""
pred = self.predict(df_new)
actual, resid = self.calculate_residuals(df_trans, pred)
pred = self.resize_prediction(pred, df_trans)
actual, pred = self.inverse_transform(scaler, pred, df_trans)
resid_pca = self.residual_pca(resid)
actual_list, pred_list, resid_list, resid_pca_list = self.update_lists(
actual, pred, resid, resid_pca
)
dist = self.calculate_distances(resid)
return actual_list, pred_list, resid_list, resid_pca_list, dist
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