/
main_mtsc.py
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/
main_mtsc.py
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# Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I Webb
#
# MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
# https://arxiv.org/abs/2102.00457
import argparse
import os
import platform
import socket
import time
from datetime import datetime
import numba
import numpy as np
import pandas as pd
import psutil
import pytz
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sktime.datasets import load_from_tsfile_to_dataframe
from multirocket.multirocket_multivariate import MultiRocket
from utils.data_loader import process_ts_data
from utils.tools import create_directory
pd.set_option('display.max_columns', 500)
itr = 0
num_features = 10000
save = True
num_threads = 0
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--datapath", type=str, required=False, default="C:/Users/changt/workspace/Dataset/Multivariate2018_ts/")
parser.add_argument("-p", "--problem", type=str, required=False, default="UWaveGestureLibrary")
parser.add_argument("-i", "--iter", type=int, required=False, default=0)
parser.add_argument("-n", "--num_features", type=int, required=False, default=50000)
parser.add_argument("-t", "--num_threads", type=int, required=False, default=-1)
parser.add_argument("-s", "--save", type=bool, required=False, default=True)
parser.add_argument("-v", "--verbose", type=int, required=False, default=2)
arguments = parser.parse_args()
if __name__ == '__main__':
data_path = arguments.datapath
problem = arguments.problem
num_features = arguments.num_features
num_threads = arguments.num_threads
itr = arguments.iter
save = arguments.save
verbose = arguments.verbose
output_path = os.getcwd() + "/output/"
classifier_name = "MultiRocket_{}".format(num_features)
data_folder = data_path + problem + "/"
if os.path.exists(data_folder):
if num_threads > 0:
numba.set_num_threads(num_threads)
output_path = os.getcwd() + "/output/"
start = time.perf_counter()
output_dir = "{}/multirocket/resample_{}/{}/{}/".format(
output_path,
itr,
classifier_name,
problem
)
if save:
create_directory(output_dir)
print("=======================================================================")
print("Starting Experiments")
print("=======================================================================")
print("Data path: {}".format(data_path))
print("Output Dir: {}".format(output_dir))
print("Iteration: {}".format(itr))
print("Problem: {}".format(problem))
print("Number of Features: {}".format(num_features))
# set data folder
train_file = data_folder + problem + "_TRAIN.ts"
test_file = data_folder + problem + "_TEST.ts"
print("Loading data")
X_train, y_train = load_from_tsfile_to_dataframe(train_file)
X_test, y_test = load_from_tsfile_to_dataframe(test_file)
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_test = encoder.transform(y_test)
X_train = process_ts_data(X_train, normalise=False)
X_test = process_ts_data(X_test, normalise=False)
nb_classes = len(np.unique(np.concatenate((y_train, y_test), axis=0)))
classifier = MultiRocket(
num_features=num_features,
classifier="logistic",
verbose=verbose
)
yhat_train = classifier.fit(
X_train, y_train,
predict_on_train=False
)
if yhat_train is not None:
train_acc = accuracy_score(y_train, yhat_train)
else:
train_acc = -1
yhat_test = classifier.predict(X_test)
test_acc = accuracy_score(y_test, yhat_test)
# get cpu information
physical_cores = psutil.cpu_count(logical=False)
logical_cores = psutil.cpu_count(logical=True)
cpu_freq = psutil.cpu_freq()
max_freq = cpu_freq.max
min_freq = cpu_freq.min
memory = np.round(psutil.virtual_memory().total / 1e9)
df_metrics = pd.DataFrame(data=np.zeros((1, 21), dtype=np.float), index=[0],
columns=['timestamp', 'itr', 'classifier',
'num_features',
'dataset',
'train_acc', 'train_time',
'test_acc', 'test_time',
'generate_kernel_time',
'apply_kernel_on_train_time',
'apply_kernel_on_test_time',
'train_transform_time',
'test_transform_time',
'machine', 'processor',
'physical_cores',
"logical_cores",
'max_freq', 'min_freq', 'memory'])
df_metrics["timestamp"] = datetime.utcnow().replace(tzinfo=pytz.utc).strftime("%Y-%m-%d %H:%M:%S")
df_metrics["itr"] = itr
df_metrics["classifier"] = classifier_name
df_metrics["num_features"] = num_features
df_metrics["dataset"] = problem
df_metrics["train_acc"] = train_acc
df_metrics["train_time"] = classifier.train_duration
df_metrics["test_acc"] = test_acc
df_metrics["test_time"] = classifier.test_duration
df_metrics["generate_kernel_time"] = classifier.generate_kernel_duration
df_metrics["apply_kernel_on_train_time"] = classifier.apply_kernel_on_train_duration
df_metrics["apply_kernel_on_test_time"] = classifier.apply_kernel_on_test_duration
df_metrics["train_transform_time"] = classifier.train_transforms_duration
df_metrics["test_transform_time"] = classifier.test_transforms_duration
df_metrics["machine"] = socket.gethostname()
df_metrics["processor"] = platform.processor()
df_metrics["physical_cores"] = physical_cores
df_metrics["logical_cores"] = logical_cores
df_metrics["max_freq"] = max_freq
df_metrics["min_freq"] = min_freq
df_metrics["memory"] = memory
print(df_metrics)
if save:
df_metrics.to_csv(output_dir + 'results.csv', index=False)