Spaces:
Sleeping
Sleeping
File size: 6,326 Bytes
3b66598 fff2149 3b66598 fff2149 4a389dc 66977cd e8d4213 fff2149 3b66598 4a389dc e8d4213 4a389dc e8d4213 3b66598 e8d4213 3b66598 66977cd 3b66598 4a389dc e8d4213 66977cd 3b66598 e8d4213 3b66598 4a389dc 3b66598 4a389dc 3b66598 e8d4213 66977cd 3b66598 4a389dc e8d4213 4a389dc 3b66598 4a389dc e8d4213 4a389dc 3b66598 4a389dc e8d4213 3b66598 4a389dc 3b66598 fff2149 66977cd e8d4213 66977cd fff2149 4a389dc e8d4213 3b66598 4a389dc 3b66598 fff2149 3b66598 e8d4213 66977cd 3b66598 4a389dc 66977cd 3b66598 |
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 |
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.
"""
model = None
kmeans_models = []
def __init__(
self,
prediction_model_path=None,
clustering_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.num_inputs = num_inputs
self.num_outputs = num_outputs
if prediction_model_path is not None and clustering_model_paths is not None:
self.load_models(prediction_model_path, clustering_model_paths)
self.actual_list, self.pred_list, self.resid_list = self.initialize_lists()
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
return initial_values.copy(), initial_values.copy(), initial_values.copy()
def load_models(self, prediction_model_path, clustering_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))
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 = []
for i, model in enumerate(self.kmeans_models):
dist.append(
np.linalg.norm(
resid[:, (i * 7) + 1 : (i * 7) + 8] - model.cluster_centers_[0],
ord=2,
axis=1,
)
)
return np.array(dist)
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)
return actual_list, pred_list, resid_list, dist
|