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iforest_for_bpms.py
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iforest_for_bpms.py
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# coding: utf-8
'''
Created on May 4, 2018
Application of Isolation Forest algorithm for the diagnostics of BPM signal.
Original paper: Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation forest.” Data Mining, 2008. ICDM‘08. Eighth IEEE International Conference.
@author: Elena Fol
'''
from __future__ import print_function
import os
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import pandas
import argparse
from sklearn.ensemble import IsolationForest
from utils import logging_tools
from tfs_files import tfs_pandas
from model.accelerators import lhc
LOGGER = logging_tools.get_logger(__name__)
ARCS_CONT = 0.01
IRS_CONT = 0.01
FEATURES = "TUNE{0},NOISE_SCALED,AMP{0}"
FEATURES_WITH_NAME = "NAME,TUNE{0},NOISE_SCALED,AMP{0}"
PLANE = ("x", "y")
def get_bad_bpms(files, remove_bpms):
files_list = files.split(',')
files_x, files_y = separate_by_plane(files_list)
bad_dfs = {"x": [], "y": []}
bad_bpms_to_write = {"x": None, "y": None}
for files, plane in ((files_x, "x"), (files_y, "y")):
bad_dfs[plane].append(get_bad_bpms_from_measurement(files, plane))
bad_bpms_to_write["x"] = pandas.concat(bad_dfs["x"])
bad_bpms_to_write["y"] = pandas.concat(bad_dfs["y"])
write_bad_bpms(files_list[0], bad_bpms_to_write)
if remove_bpms:
remove_bpms_from_file(files_x, set(bad_bpms_to_write["x"].NAME), "x")
remove_bpms_from_file(files_y, set(bad_bpms_to_write["y"].NAME), "y")
def write_bad_bpms(first_file, bad_bpms_to_write):
meas_dir = os.path.abspath(os.path.join(first_file, os.pardir))
for plane in PLANE:
bad_bpms_summary_path = os.path.join(meas_dir, "bad_bpms_iforest_{}.tfs".format(plane))
tfs_pandas.write_tfs(bad_bpms_summary_path, bad_bpms_to_write[plane])
LOGGER.info("Bad BPMs summary from Isolation Forest written to: %s", meas_dir)
def get_bad_bpms_from_measurement(files, plane):
uplane = plane.upper()
bpm_tfs_data = _create_tfs_data(files, plane)
arc_bpm_data, ir_bpm_data = get_data_for_clustering(bpm_tfs_data, plane)
dataframes = []
for data_for_clustering, cont, title in ((arc_bpm_data, ARCS_CONT, "Arcs"),
(ir_bpm_data, IRS_CONT, "IRs")):
bad_bpms, good_bpms, all_bpms_scores, bad_bpms_scores =\
detect_anomalies(cont, data_for_clustering, uplane)
bpm_tfs_data, data_for_clustering, bad_bpms, good_bpms =\
[reassign_index(data)
for data in (bpm_tfs_data, data_for_clustering, bad_bpms, good_bpms)]
signif_feature = get_significant_features(bpm_tfs_data, data_for_clustering, bad_bpms, good_bpms, plane)
signif_feature.loc[:, "SCORE"] = bad_bpms_scores
dataframes.append(signif_feature)
if plot:
# plot_scores_threshold(all_bpms_scores, cont, title)
plot_bpms_3d(good_bpms, bad_bpms, uplane, title, bad_bpms_scores)
# plot_two_dim(good_bpms, bad_bpms,"TUNE", "NOISE_SCALED", uplane, title)
# plot_two_dim(good_bpms, bad_bpms, "TUNE", "AMP", uplane, title)
# plot_two_dim(good_bpms, bad_bpms, "AMP", "NOISE_SCALED", uplane, title)
return pandas.concat(dataframes)
def reassign_index(data):
data["NEW_INDEX"] = range(len(data.NAME))
return data.set_index("NEW_INDEX")
def get_significant_features(bpm_tfs_data, data_for_clustering, bad_bpms, good_bpms, plane):
features_df = pandas.DataFrame(index=bad_bpms.index)
columns = FEATURES.format(plane.upper()).split(",")
for index in bad_bpms.index:
max_dist = max([(abs(data_for_clustering.loc[index, col] -
good_bpms.loc[:, col].mean()), col)
for col in columns])
max_dist, sig_col = max_dist
features_df.loc[index, "NAME"] = bad_bpms.loc[index, "NAME"]
features_df.loc[index, "FEATURE"] = sig_col
features_df.loc[index, "VALUE"] = bpm_tfs_data.loc[index, sig_col]
features_df.loc[index, "AVG"] = np.mean(bpm_tfs_data.loc[good_bpms.index][sig_col])
return features_df
def detect_anomalies(contamination, data, uplane):
iforest = IsolationForest(n_estimators=100, max_samples='auto',
contamination=contamination, max_features=1.0,
bootstrap=False)
features = data[FEATURES.format(uplane).split(",")]
iforest.fit(features)
labels = iforest.predict(features)
bad_bpms = data.iloc[np.where(labels == -1)].copy()
good_bpms = data.iloc[np.where(labels != -1)].copy()
all_bpms_scores = iforest.decision_function(features)
bad_bpms_scores = iforest.decision_function(features.iloc[np.where(labels == -1)])
return bad_bpms, good_bpms, all_bpms_scores, bad_bpms_scores
def get_data_for_clustering(bpm_tfs_data, plane):
columns = FEATURES.format(plane.upper()).split(",")
arc_bpm_mask = lhc.Lhc.get_element_types_mask(bpm_tfs_data.NAME, types=["arc_bpm"])
ir_bpm_data_for_clustering = bpm_tfs_data.iloc[~arc_bpm_mask].copy()
arc_bpm_data_for_clustering = bpm_tfs_data.iloc[arc_bpm_mask].copy()
for col in columns:
ir_bpm_data_for_clustering.loc[:, col] = _normalize_parameter(ir_bpm_data_for_clustering.loc[:, col])
arc_bpm_data_for_clustering.loc[:, col] = _normalize_parameter(arc_bpm_data_for_clustering.loc[:, col])
return arc_bpm_data_for_clustering, ir_bpm_data_for_clustering
def _create_tfs_data(filepaths, plane):
bpm_data_rows = []
for filepath in filepaths:
bpm_tfs_file = tfs_pandas.read_tfs(filepath)
bpms_tfs_data = bpm_tfs_file[FEATURES_WITH_NAME.format(plane.upper()).split(",")]
bpm_data_rows.append(bpms_tfs_data)
return pandas.concat(bpm_data_rows)
def _normalize_parameter(column_data):
return (column_data - column_data.min()) / (column_data.max() - column_data.min())
def separate_by_plane(files_list):
files_x = []
files_y = []
for file_in in files_list:
if os.path.basename(file_in).endswith("linx"):
files_x.append(file_in)
elif os.path.basename(file_in).endswith("liny"):
files_y.append(file_in)
else:
print("Given file is not a measurement!")
return files_x, files_y
def remove_bpms_from_file(paths, bad_bpm_names, plane):
"""
Writes a backup of the original .lin files (e.g .linx --> .linx.notcleaned)
and removes the BPNs identified by iForest as bad.
:param paths: original lin files
:param bad_bpm_names: list of the names of bad BPMs identified by iForest
"""
for path in paths:
src_dir = os.path.abspath(os.path.join(path, os.pardir))
filename = os.path.basename(path)
new_filename = os.path.join(src_dir, filename + ".notcleaned")
os.rename(path, new_filename)
original_file_tfs = tfs_pandas.read_tfs(new_filename).set_index("NAME", drop=False)
original_file_tfs = original_file_tfs.loc[~original_file_tfs.index.isin(bad_bpm_names)]
pln_num = "1" if plane == "x" else "2"
original_file_tfs.headers["Q{}".format(pln_num)] =\
original_file_tfs["TUNE" + plane.upper()].mean()
original_file_tfs.headers["Q{}RMS".format(pln_num)] =\
np.std(original_file_tfs["TUNE" + plane.upper()])
tfs_pandas.write_tfs(path, original_file_tfs, original_file_tfs.headers)
def revert_forest_cleaning(files):
"""
Reverts the cleaning. The backup files are renamed back to the original names (e.g .linx.notcleaned --> .linx)
:param paths: list of files, where bad bpms identified by iForest are removed
"""
files_list = files.split(',')
for path in files_list:
src_dir = os.path.abspath(os.path.join(path, os.pardir))
filename = os.path.basename(path)
notcleaned_file = os.path.join(src_dir, filename + ".notcleaned")
original_file_tfs = tfs_pandas.read_tfs(notcleaned_file).set_index("NAME", drop=False)
os.remove(path)
lin_file = os.path.join(src_dir, notcleaned_file.replace(".notcleaned",""))
os.rename(notcleaned_file, lin_file)
tfs_pandas.write_tfs(lin_file, original_file_tfs)
def plot_scores_threshold(scores, cont, title):
threshold = stats.scoreatpercentile(scores,100*ARCS_CONT)
scores_bellow_threshold = []
scores_over_threshold = []
for score in scores:
if score < threshold:
scores_bellow_threshold.append(score)
else:
scores_over_threshold.append(score)
plt.hist(scores_bellow_threshold, bins=100, range=(-0.2,0.2), edgecolor='black', linewidth=2, histtype='bar', color='blue')
plt.hist(scores_over_threshold, bins=100, range=(-0.2,0.2), edgecolor='black', linewidth=2, histtype='bar', color='white')
plt.axvline(x=threshold, color='r', linestyle='-', linewidth=2, label='Learned Threshold')
plt.text(threshold, 0, str(threshold)[:-14], color='r', fontsize=35, verticalalignment='bottom', horizontalalignment='left')
plt.title(title, fontdict={'fontsize':35, 'verticalalignment':'baseline'})
plt.xlabel("Anomaly score", fontsize = 35)
plt.ylabel("Number of BPMs", fontsize = 35)
plt.xticks(fontsize = 35)
plt.yticks(fontsize = 35)
plt.legend(fontsize = 35)
plt.show()
def plot_bpms_3d(good_bpms, bad_bpms, plane, title, scores):
columns = FEATURES_WITH_NAME.format(plane.upper()).split(",")
fig = plt.figure()
ax = p3.Axes3D(fig)
ax.plot3D(good_bpms.loc[:, columns[1]], good_bpms.loc[:, columns[2]], good_bpms.loc[:, columns[3]], 'o', markerfacecolor="black",
markeredgecolor='black', markersize=15, label = "good")
ax.plot3D(bad_bpms.loc[:, columns[1]], bad_bpms.loc[:, columns[2]], bad_bpms.loc[:, columns[3]], '^', markerfacecolor="red",
markeredgecolor='black', markersize=20, label = "faulty")
# for index, score in zip(bad_bpms.index, scores):
# ax.text(bad_bpms.loc[index, columns[1]], bad_bpms.loc[index, columns[2]], bad_bpms.loc[index, columns[3]], bad_bpms.loc[index,"NAME"] + " {" + str(score)[:-10] + "}")
ax.set_xlabel('Tune', fontsize = 25, linespacing=3.2)
ax.set_ylabel('Amplitude', fontsize = 25, linespacing=3.2)
ax.set_zlabel('Noise', fontsize = 25, linespacing=3.2)
for axis in ('x', 'y', 'z'):
ax.tick_params(axis=axis, labelsize=25)
plt.legend(fontsize = 25, numpoints = 1)
plt.title(title, fontdict={'fontsize':25, 'verticalalignment':'baseline'})
plt.show()
def plot_two_dim(good, bad, col1, col2, plane, title):
label1 = "Tune"
label2 = "Noise"
if(col1=="AMP"):
label1 = "Amplitude"
col1 = col1 + plane
if(col2=="AMP"):
col2 = col2 + plane
label2 = "Amplitude"
plt.plot(
good.loc[:, col1],
good.loc[:, col2],
'o',
markerfacecolor="black",
markeredgecolor='black',
markersize=10,
label = "Good",
)
plt.plot(
bad.loc[:, col1],
bad.loc[:, col2],
'^',
markerfacecolor="red",
markeredgecolor='red',
markersize=10,
label = "Bad",
)
plt.xlabel(label1, fontsize = 25)
plt.ylabel(label2,fontsize = 25)
plt.xticks(fontsize = 25)
plt.yticks(fontsize = 25)
plt.legend(fontsize = 25, numpoints = 1)
plt.title(title)
plt.show()
def _parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--files",
dest="files", type=str,
)
parser.add_argument(
"--remove_bpms",
dest="remove_bpms",
action="store_true",
)
parser.add_argument(
"--revert",
dest="revert",
action="store_true",
)
parser.add_argument(
"--plot",
dest="plot",
action="store_true",
)
options = parser.parse_args()
return options.files, options.remove_bpms, options.revert, options.plot
if __name__ == '__main__':
_files, _remove_bpms, _revert, plot = _parse_args()
if(not _revert):
get_bad_bpms(_files, _remove_bpms)
else:
revert_forest_cleaning(_files)