Spaces:
Sleeping
Sleeping
File size: 8,327 Bytes
3b66598 fff2149 3b66598 fff2149 4a389dc 8e50b17 e8d4213 4a389dc e0c688d 4a389dc e8d4213 4a389dc e8d4213 0c3f62c 3b66598 0c3f62c 85d246e 8e50b17 3b66598 4a389dc e8d4213 0c3f62c 85d246e 0c3f62c 3b66598 0c3f62c e8d4213 3b66598 4a389dc 3b66598 0c3f62c e0c688d 4a389dc 3b66598 e8d4213 0c3f62c 3b66598 4a389dc e8d4213 4a389dc 3b66598 4a389dc e8d4213 4a389dc 3b66598 4a389dc e8d4213 3b66598 4a389dc 3b66598 fff2149 66977cd e8d4213 66977cd fff2149 4a389dc e8d4213 3b66598 0c3f62c 3b66598 0c3f62c 4a389dc 0c3f62c 4a389dc 8e50b17 0c3f62c 4a389dc 3b66598 85d246e 0c3f62c 85d246e 8e50b17 85d246e 0c3f62c 85d246e 8e50b17 85d246e 0c3f62c fff2149 3b66598 e8d4213 0c3f62c 3b66598 0c3f62c 3b66598 8e50b17 0c3f62c 85d246e 0c3f62c 85d246e 0c3f62c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
import numpy as np
from tensorflow.keras.models import load_model
import joblib
class RTUAnomalizer2:
"""
Class for performing anomaly detection on RTU (Roof Top Unit) data.
"""
def __init__(
self,
prediction_model_path=None,
clustering_model_paths=None,
pca_model_paths=None,
num_inputs=None,
num_outputs=None,
):
"""
Initialize the RTUAnomalizer object.
Args:
prediction_model_path (str): Path to the prediction model file.
clustering_model_paths (list): List of paths to the clustering model files.
num_inputs (int): Number of input features.
num_outputs (int): Number of output features.
"""
self.model = None
self.kmeans_models = []
self.pca_models = []
self.num_inputs = num_inputs
self.num_outputs = num_outputs
if (
prediction_model_path is not None
and clustering_model_paths is not None
and pca_model_paths is not None
):
self.load_models(
prediction_model_path, clustering_model_paths, pca_model_paths
)
(
self.actual_list,
self.pred_list,
self.resid_list,
self.resid_pca_list,
self.distance_list,
) = self.initialize_lists()
self.fault_1 = 0
self.fault_2 = 0
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] * 4] * size
initial_values2 = [[0] * 2] * size * 2
return (
initial_values.copy(),
initial_values.copy(),
initial_values.copy(),
initial_values1.copy(),
initial_values2.copy(),
)
def load_models(
self, prediction_model_path, clustering_model_paths, pca_model_paths
):
"""
Load the prediction and clustering models.
Args:
prediction_model_path (str): Path to the prediction model file.
clustering_model_paths (list): List of paths to the clustering model files.
"""
self.model = load_model(prediction_model_path)
for path in clustering_model_paths:
self.kmeans_models.append(joblib.load(path))
for path in pca_model_paths:
self.pca_models.append(joblib.load(path))
def predict(self, df_new):
"""
Make predictions using the prediction model.
Args:
df_new (DataFrame): Input data for prediction.
Returns:
array: Predicted values.
"""
return self.model.predict(df_new, verbose=0)
def calculate_residuals(self, df_trans, pred):
"""
Calculate the residuals between actual and predicted values.
Args:
df_trans (DataFrame): Transformed input data.
pred (array): Predicted values.
Returns:
tuple: A tuple containing the actual values and residuals.
"""
actual = df_trans[30, : self.num_outputs]
resid = actual - pred
return actual, resid
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):
"""
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.
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.actual_list.append(actual.flatten().tolist())
self.pred_list.append(pred.flatten().tolist())
self.resid_list.append(resid.flatten().tolist())
return self.actual_list, self.pred_list, self.resid_list
def calculate_distances(self, resid):
"""
Calculate the distances between residuals and cluster centers.
Args:
resid (array): Residual values.
Returns:
array: Array of distances.
"""
dist = []
resid_pcas = []
for i, model in enumerate(self.kmeans_models):
resid_pca = self.pca_models[i].transform(
resid[:, (i * 7) + 1 : (i * 7) + 8]
)
resid_pcas = resid_pcas + resid_pca.tolist()
dist.append(
np.linalg.norm(
resid_pca - model.cluster_centers_[0],
ord=2,
axis=1,
)
)
self.distance_list.pop(0)
self.distance_list.append(np.concatenate(dist).tolist())
resid_pcas = np.array(resid_pcas).flatten().tolist()
self.resid_pca_list.pop(0)
self.resid_pca_list.append(resid_pcas)
return np.array(dist)
def fault_windowing(self):
rtu_1_dist = np.array(self.distance_list).T[0] > 1.5 # rtu_3_threshold
rtu_1_dist = [int(x) for x in rtu_1_dist]
if sum(rtu_1_dist) > 0.8 * 60: # 80% of the 60 min window
self.fault_1 = 1
else:
self.fault_1 = 0
rtu_2_dist = np.array(self.distance_list).T[1] > 1 # rtu_4_threshold
rtu_2_dist = [int(x) for x in rtu_2_dist]
if sum(rtu_2_dist) > 0.8 * 60: # 80% of the 60 min window
self.fault_2 = 1
else:
self.fault_2 = 0
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)
actual_list, pred_list, resid_list = self.update_lists(actual, pred, resid)
dist = self.calculate_distances(resid)
self.fault_windowing()
return (
actual_list,
pred_list,
resid_list,
self.resid_pca_list,
dist,
np.array(self.distance_list[30:]).T[0] > 1.5, # rtu_3_threshold
np.array(self.distance_list[30:]).T[1] > 1, # rtu_4_threshold
self.fault_1,
self.fault_2,
)
|