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seq2point_test.py
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import os
import logging
import numpy as np
import keras
import pandas as pd
import tensorflow as tf
import time
from model_structure import create_model, load_model
from data_feeder import TestSlidingWindowGenerator
from appliance_data import appliance_data
import matplotlib.pyplot as plt
class Tester():
""" Used to test and evaluate a pre-trained seq2point model with or without pruning applied.
Parameters:
__appliance (string): The target appliance.
__pruning_algorithm (string): The pruning algorithm the model was trained with.
__network_type (string): The architecture of the model.
__crop (int): The maximum number of rows of data to evaluate the model with.
__batch_size (int): The number of rows per testing batch.
__window_size (int): The size of eaech sliding window
__window_offset (int): The offset of the inferred value from the sliding window.
__test_directory (string): The directory of the test file for the model.
"""
def __init__(self, appliance, pruning_algorithm, crop, batch_size, network_type):
self.__appliance = appliance
self.__pruning_algorithm = pruning_algorithm
self.__network_type = network_type
self.__crop = crop
self.__batch_size = batch_size
self.__window_size = 601
self.__window_offset = int(0.5 * self.__window_size - 1)
self.__test_directory = "./" + self.__appliance + "/" + self.__appliance + "_test_.csv"
self.__log_file = "./" + self.__appliance + "/saved_models/" + self.__appliance + "_" + self.__pruning_algorithm + "_" + self.__network_type + ".log"
logging.basicConfig(filename=self.__log_file,level=logging.INFO)
def test_model(self):
""" Tests a fully-trained model using a sliding window generator as an input. Measures inference time, gathers, and
plots evaluationg metrics. """
test_input, test_target = self.load_dataset(self.__test_directory)
model = create_model()
model = load_model(model, self.__network_type, self.__pruning_algorithm, self.__appliance)
test_generator = TestSlidingWindowGenerator(number_of_windows=100, inputs=test_input, targets=test_target, offset=self.__window_offset)
# Calculate the optimum steps per epoch.
steps_per_test_epoch = np.round(int(test_generator.total_size / self.__batch_size), decimals=0)
# Test the model.
start_time = time.time()
testing_history = model.predict(x=test_generator.load_dataset(), steps=steps_per_test_epoch, verbose=2)
end_time = time.time()
test_time = end_time - start_time
evaluation_metrics = model.evaluate(x=test_generator.load_dataset(), steps=steps_per_test_epoch)
self.log_results(model, test_time, evaluation_metrics)
self.plot_results(testing_history, test_input, test_target)
def load_dataset(self, directory):
"""Loads the testing dataset from the location specified by file_name.
Parameters:
directory (string): The location at which the dataset is stored, concatenated with the file name.
Returns:
test_input (numpy.array): The first n (crop) features of the test dataset.
test_target (numpy.array): The first n (crop) targets of the test dataset.
"""
data_frame = pd.read_csv(directory, nrows=self.__crop, skiprows=0, header=0)
test_input = np.round(np.array(data_frame.iloc[:, 0], float), 6)
test_target = np.round(np.array(data_frame.iloc[self.__window_offset: -self.__window_offset, 1], float), 6)
del data_frame
return test_input, test_target
def log_results(self, model, test_time, evaluation_metrics):
"""Logs the inference time, MAE and MSE of an evaluated model.
Parameters:
model (tf.keras.Model): The evaluated model.
test_time (float): The time taken by the model to infer all required values.
evaluation metrics (list): The MSE, MAE, and various compression ratios of the model.
"""
inference_log = "Inference Time: " + str(test_time)
logging.info(inference_log)
metric_string = "MSE: ", str(evaluation_metrics[0]), " MAE: ", str(evaluation_metrics[3])
logging.info(metric_string)
self.count_pruned_weights(model)
def count_pruned_weights(self, model):
""" Counts the total number of weights, pruned weights, and weights in convolutional
layers. Calculates the sparsity ratio of different layer types and logs these values.
Parameters:
model (tf.keras.Model): The evaluated model.
"""
num_total_zeros = 0
num_dense_zeros = 0
num_dense_weights = 0
num_conv_zeros = 0
num_conv_weights = 0
for layer in model.layers:
if np.shape(layer.get_weights())[0] != 0:
layer_weights = layer.get_weights()[0].flatten()
if "conv" in layer.name:
num_conv_weights += np.size(layer_weights)
num_conv_zeros += np.count_nonzero(layer_weights==0)
num_total_zeros += np.size(layer_weights)
else:
num_dense_weights += np.size(layer_weights)
num_dense_zeros += np.count_nonzero(layer_weights==0)
conv_zeros_string = "CONV. ZEROS: " + str(num_conv_zeros)
conv_weights_string = "CONV. WEIGHTS: " + str(num_conv_weights)
conv_sparsity_ratio = "CONV. RATIO: " + str(num_conv_zeros / num_conv_weights)
dense_weights_string = "DENSE WEIGHTS: " + str(num_dense_weights)
dense_zeros_string = "DENSE ZEROS: " + str(num_dense_zeros)
dense_sparsity_ratio = "DENSE RATIO: " + str(num_dense_zeros / num_dense_weights)
total_zeros_string = "TOTAL ZEROS: " + str(num_total_zeros)
total_weights_string = "TOTAL WEIGHTS: " + str(model.count_params())
total_sparsity_ratio = "TOTAL RATIO: " + str(num_total_zeros / model.count_params())
print("LOGGING PATH: ", self.__log_file)
logging.info(conv_zeros_string)
logging.info(conv_weights_string)
logging.info(conv_sparsity_ratio)
logging.info("")
logging.info(dense_zeros_string)
logging.info(dense_weights_string)
logging.info(dense_sparsity_ratio)
logging.info("")
logging.info(total_zeros_string)
logging.info(total_weights_string)
logging.info(total_sparsity_ratio)
def plot_results(self, testing_history, test_input, test_target):
""" Generates and saves a plot of the testing history of the model against the (actual)
aggregate energy values and the true appliance values.
Parameters:
testing_history (numpy.ndarray): The series of values inferred by the model.
test_input (numpy.ndarray): The aggregate energy data.
test_target (numpy.ndarray): The true energy values of the appliance.
"""
testing_history = ((testing_history * appliance_data[self.__appliance]["std"]) + appliance_data[self.__appliance]["mean"])
test_target = ((test_target * appliance_data[self.__appliance]["std"]) + appliance_data[self.__appliance]["mean"])
test_agg = (test_input.flatten() * 814) + 522
test_agg = test_agg[:testing_history.size]
# Can't have negative energy readings - set any results below 0 to 0.
test_target[test_target < 0] = 0
testing_history[testing_history < 0] = 0
test_input[test_input < 0] = 0
# Plot testing outcomes against ground truth.
plt.figure(1)
plt.plot(test_agg[self.__window_offset: -self.__window_offset], label="Aggregate")
plt.plot(test_target[:test_agg.size - (2 * self.__window_offset)], label="Ground Truth")
plt.plot(testing_history[:test_agg.size - (2 * self.__window_offset)], label="Predicted")
plt.title(self.__appliance + " " + self.__network_type + "(" + self.__pruning_algorithm + ")")
plt.ylabel("Power Value (Watts)")
plt.xlabel("Testing Window")
plt.legend()
file_path = "./" + self.__appliance + "/saved_models/" + self.__appliance + "_" + self.__pruning_algorithm + "_" + self.__network_type + "_test_figure.png"
plt.savefig(fname=file_path)
plt.show()